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COST ACTION 734 IMPACT OF CLIMATE CHANGE AND VARIABILITY ON EUROPEAN AGRICULTURE www.cost734.eu [email protected] SURVEY OF AGROMETEOROLOGICAL PRACTICES AND APPLICATIONS IN EUROPE REGARDING CLIMATE CHANGE IMPACTS Edited by: Pavol Nejedlik and Simone Orlandini “ESF provide the COST Office through an EC contract” “COST is supported by the EU RTD Framework programme” ESSEM “Earth System Science and Environmental Management” 2008 ABOUT COST COST is an intergovernmental framework for European Cooperation in Science and Technology, funded by its member countries through the EU Framework Programme. The objective of COST is to coordinate, integrate and synthesize results from ongoing national research within and between COST member countries to add value to research investment. COST Actions aim to deliver scientific syntheses and analyses of best available practice to aid problem identification, risk assessment, public utilities and policy development. Acknowledgements: Francesca Natali and Valentina Di Stefano for the support in the editing of the book. Stefano Fallai for the photo in the cover and Maria Poli for the help in the editing of the logo. INDEX PREFACE Pavol Nejedlik and Simone Orlandini............................................................................1 1 CLIMATE CHANGES, IMPACTS ON AGRICULTURE, SUSTAINABILITY G. Maracchi, F. Rossi, L. Kaifez Bogataj 1.1 1.2 1.3 1.4 Expected impacts of climate change in Europe during this century ......................3 Increasing drought Risk with Global Warming in Europe.....................................6 Challenges, new approaches and research needs ...................................................7 References ............................................................................................................11 2. AGROCLIMATIC INDICES AND SIMULATION MODELS J. Eitzinger, S. Thaler, S. Orlandini, P. Nejedlik, V. Kazandjiev, V. Vucetic, T. H. Sivertsen, D.T. Mihailovic, B. Lalic, E. Tsiros, N. R. Dalezios A. Susnik, Christian K. C. Kersebaum, N. M. Holden, R. Matthews Abstract ...................................................................................................................15 2.1 Introduction..........................................................................................................15 2.2 Agroclimatic indices in use and research (Subgroup 1).......................................16 2.2.1 State of the Art...........................................................................................16 2.2.2 Present use of agrometeorological indices in Europe ................................20 2.2.3 Communicative value and limitations of the agrometeorological indices and simulation models ...............................................................................50 2.3 Application of process oriented models in Europe (Subgroup 2) .......................51 2.3.1 State of the Art...........................................................................................51 2.3.2 Present use of process oriented models in Europe.....................................53 2.4 Useful Outputs and main Limitations of Models for use in Climate Change Impact Research (Subgroup 3).............................................................................69 2.4.1 The problem of modelling natural phenomena ..........................................69 2.4.2 Present limitations on crop model applications Europe.............................72 2.5 Crop simulation models in combination with Remote Sensing and GIS (Subgroup 4).........................................................................................................75 2.5.1 State of the art ............................................................................................75 2.5.2 Spatial model applications in Europe.........................................................84 2.6 Conclusions ..........................................................................................................91 2.7 Acknowledgements ..............................................................................................92 2.8 References ............................................................................................................92 a 3. SUMMARIZING A QUESTIONNAIRE ON TRENDS OF AGROCLIMATIC INDICES AND SIMULATION MODEL OUTPUTS IN EUROPE V. Alexandrov, E. Mateescu, A. Mestre, M. Kepinska-Kasprzak, V. Di Stefano, N. Dalezios Abstract .................................................................................................................115 3.1 State of art ..........................................................................................................115 3.1.1 Observed climatic and agroclimatic trends..............................................115 3.1.2 Agroclimatic indices and crop models.....................................................117 3.1.3 Examples of previous case studies...........................................................121 3.2 Goal: a questionnaire..........................................................................................123 3.3 Summarizing the questionnaire..........................................................................124 3.3.1 Long-term meteorological and agrometeorological data .........................124 3.3.2 Numerical weather models, regional climate models, weather generators.................................................................................................131 3.3.3 Homogenization tests/procedures ............................................................139 3.3.4 Statistical methods for analyses of meteorological and simulation model output related time series ..............................................................144 3.3.5 Additional information listed within the questionnaire ...........................151 3.4 Concluding remarks ...........................................................................................152 3.5 Acknowledgments..............................................................................................153 3.6 References ..........................................................................................................153 4. SATELLITE SPECTRAL CLIMATIC AND BIOPHYSICAL DATA FOR WARNING PURPOSES FOR EUROPEAN AGRICULTURE L. Toulios, G. Stancalie, P. Struzik, M. Danson, J. Mika, D. Dunkel, E. Tsiros Abstract .................................................................................................................163 4.1 Introduction........................................................................................................164 4.1.1 The need of adaptation of agriculture to climate change .........................164 4.2 How the study on climate variability and change can benefit from space .........166 4.2.1 Making Sense of Satellite Data................................................................167 4.3 Satellite-based variables and models potential in monitoring of crop production ..........................................................................................................168 4.3.1 Climate and biophysical data records in responding to climate change impacts on agriculture .................................................................172 4.4 Satellite instruments for climate change management .......................................174 4.5 Status of satellite climatic and biophysical data for warning purpuses for agriculture in Europe..........................................................................................178 ........1 A summary of past experiences ..4 Mapping of climate change scenarios to the small scale: downscaling ....................................................................3 Detailed analysis of the data per country...............................................2......................................................................253 c ......................2 Satellite observations of processes at the Earth surface – selected applications ........... Mika Abstract .....223 5......................4..........................196 References ..........................................243 6......................................................................237 6......218 5.........213 5......3................................251 6... SATELLITE REMOTE SENSING AS A TOOL FOR MONITORING CLIMATE AND ITS IMPACT ON THE ENVIRONMENT – POSSIBILITIES AND LIMITATIONS P........................ E.................................................................................................2 Evolution of meteorological satellite system ............. Kaifez Bogataj............3................................................ M......................... Calanca................................6 References ..................................................3 Role of COST 734 ....................................................................................7 4......................................................................................................................200 5...8 4..................................... Danson......3 Lessons learned and outlook ............244 6...................205 5................................3 Dealing with uncertainties in climate change projections ..... J................196 Annex 1 ..................... C................ Halenka...246 6... Mika.......................................7 Appendix 1................................................................... G....................................................3..4 Difficulties in use of satellite data for climate observations .......... T..2. Toulios......................231 6.....................................228 5...........................................................................9 4................................5...................195 Acknowledgements .........3 Satellite climatology – possibilities..................................................................5............. Cloppet...............................1 Questionnaire processed results............................................................................ USE OF CLIMATE CHANGE SCENARIOS IN AGROMETEOROLOGICAL STUDIES: PAST EXPERIENCES AND FUTURE NEEDS P..........207 5...........205 5........ L................2 Type of data per country .................1 Introduction....5 Scenarios for extreme events ........250 6..... Struzik......... J.....2 Tools used in the preparation of climate change scenarios for impact studies ..............................248 6............................................1 Introduction...............................2.3.............2 Completed and ongoing projects at the European level.......237 6.........................6 4.....250 6...............................2. L...........179 4..3.2...........239 6.................187 Conclusions ............................ Domenikiotis Abstract ......................................1 Satellite observations of processes in atmosphere – selected examples .239 6........... Stancalie...214 5........................5 Conclusions .............5..182 4...................228 5....2 Climate scenarios ....................................1 Types of scenarios used in the past in agroclimatological studies.......................... . C....................4 Questionnaire .... LIST OF QUESTIONNAIRES .....................5 Vulnerabilities and climate impacts on crop ......304 7...1 Introduction.................1 Present climatic limitations and vulnerabilities ......7 Improving awareness and adaptation to climate change ..........................................................295 7..........300 7....... Rossi..............5 References ......7.................302 7......................6...............9 Conclusions ......................................2 Projections of climate change in Europe...........280 7....................................... Olesen.................8 Implications and perspectives .......... RISK ASSESSMENT AND FORESEEN IMPACTS ON AGRICULTURE J.................................................11 References ....267 7..................292 7.................................255 6................................5........282 7..... M.............................2 Dissemination and warning systems .... LIST OF CONTRIBUTORS.... P.......................................................... Seguin..258 7........................... Trnka... Micale Abstract ......................................................................................................................................................1 National impact assessments...................304 7...................... E..........................................................2........297 7..........270 7..........6 Additional references ....6 Adaptation to climate variability and climate change .........7.....................317 .............................................................300 7...2 Future adaptation responses.........................2..313 ANNEX 2.......304 7........268 7.6....4 Conclusions .........................................2 Climate change impacts ..........................................................3 Current European cropping patterns .................... Kozyra............. Skejvåg................... Kersebaum..................6.....269 7................. J.............................................................................................273 7...... adaptation strategies and awareness.................................................................................................................. Peltonen-Sainio..............5.......278 7........311 ANNEX 1................................................................1 Observed adaptation.................1 Observed climate change in Europe .10 Acknowledgements ........2 Observed and projected climate in Europe ...........................................................................................12 Annex I........................... B......................284 7.... O............................. A.................................... F............................................................................................................ F..........................301 7.....................................269 7..................255 6........... Based on these results.Joint Research Centre. suggestions. Time schedule of activity includes three main phases: • Planning.IPSCAGRIFISH UNIT .PREFACE Pavol Nejedlik and Simone Orlandini During 2006. 1 . building climate scenarios for the next few decades. The main objective of the Action is the evaluation of possible impacts from climate change and variability on agriculture and the assessment of critical thresholds for various European areas (COST 734 MoU. Each intersection point describes the evaluation of past. Four working groups. present and future trends of climate and thus the impacts on agriculture. establishment of WGs and inventory. reports and final publications. were created to address these aims: WG1 – Agroclimatic indices and simulation models WG2 – Evaluation of the current trends of agroclimatic indices and simulation model outputs describing agricultural impacts and hazard levels WG3 – Development and assessment of future regional and local scenarios of agroclimatic conditions WG4 – Risk assessment and foreseen impacts on agriculture The activity of WGs has been structured like a matrix. to assess hazard impacts on various European agricultural areas relating hazards to climatic conditions. the definition of harmonised criteria to evaluate the impacts of climate change and variability on agriculture.cost. www. the definition of warning systems guidelines. possible actions (specific recommendations.org).esf. • WGs activities to be concluded with emphasis on disseminations. warning systems) will be elaborated and proposed to the end-users. presenting on the rows the methods of analysis and on the columns the phenomena and the hazards.Word Meteorology Organization and Ispra. operational arrangements. Secondary objectives are: the collection and review of existing agroclimatic indices and simulation models. At present 28 countries join the Action with the collaboration of Agricultural Meteorology Division . COST Action 734 (CLIVAGRI-Impacts of Climate Change and Variability on European Agriculture) was launched thanks to the coordinated activity of 15 EU countries. with the integration of Remote Sensing sub working group. • Main scientific work to be conducted by each WG. depending on their needs. models. indices. Satellite Data Records Survey. including data. However the wide range of presented information.) to better plan the analysis and the adaptation of European agriculture to climate change and variability impacts. tools. For this aim.In this book the results of inventory phase are presented. methods. can represent an useful support for the workers of agricultural sectors (farmers. etc. but they are limited by the quantity of answering COST countries and by the quality of the answers. 2 . Risk Assessment and Foreseen Impacts on Agriculture) were disseminated among COST 734 countries and about 20 answers for each questionnaire were collected. Climate Change Scenarios. technicians. five questionnaires (titled: Agroclimatic Indices and Models. It has to be pointed out that the results of questionnaire analysis are not representative of the whole Europe. This book has to be considered as a general description of the activity performed in Europe in the field of climate change and variability impacts on agriculture. decision makers. Trends in Agroclimatic Indices and Model Outputs. agricultural yields. Federica Rossi. PESETA) have produced high-resolution maps representing the projected changes in climate variables. The very high air temperature and solar radiation resulted in a notable increase in the crops' water consumption. 2007). and projected impacts. CLIMATE CHANGES. Over all of Europe. In particular.. The estimate is that some 70. 2003).1. the main sectors hit by the extreme climate conditions were the green fodder supply. They illustrate what can be expected in Europe by the end of the century. such as mean temperature and precipitation. IMPACTS ON AGRICULTURE. resulted in an acute depletion of soil water and lowered crop yields.4°C.000 hectares of forest area (not including agricultural areas) were burned. together with the summer dry spell. so that the global mean temperature increases by about 3. 3 . This. Summer 2003 showed also the additional side effects. some projects (PRUDENCE. The observed changes are consistent with projections of impacts due to anthropogenic climate change. For instance the European heat wave in 2003 had major impacts on agricultural systems and society by decreasing the quantity and quality of the harvests. according to the IPCC scenario whereby no action is taken to reduce GHG emissions.1 Expected impacts of climate change in Europe during this century A wide ranging impacts of changes in current climate have been documented in Europe in the last decades.1).g. Potato and wine production were also seriously affected. Lucka Kaifez Bogataj 1. Finland. 1. Over the last few years the EU has financed several large research projects on regional climate modelling and impact assessment. SUSTAINABILITY Giampiero Maracchi. Spain. These maps are very useful for policy-making and awareness-raising purposes (Fig. particularly in Central and Southern Europe. which were felt in the next year such as problems of soil erosion and flooding. e. Denmark and Ireland. Austria. effects on winter sowing. France. The fall in cereal production in EU reached more than 23 million tonnes as compared to 2002. and the budding of trees (COPA COGECA. the livestock sector and forestry. The warming trend and spatially variable changes in rainfall have already affected managed ecosystems (Easterling et al. the arable sector. Italy.000 fires were recorded in Portugal. More than 26. Climate change will increase net primary productivity and total biomass in the North while reduced water availability is likely to decrease NPP and forest growth in Central Europe. Results are on impacts from the JRC-funded PESETA study (http://peseta.jrc.dk). some negative) and South (nearly all negative). For example: the extent of forests is expected to expand in the North and retreat in the South. among other. impacts may be unevenly distributed between the North (some positive. water availability.dmi. Therefore. Maps with projections of future changes in temperature and precipitation are based on DMI/PRUDENCE data (http://prudence. the capacity of Europe’s social systems to cope with climate change is high and is expected to continue rising. Adaptive capacity will vary between countries because of their different socioeconomic levels.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Figure 1.1: Simulated crop yield changes (%) by 2080s according to two different models (left) HadCM3.es). 2007) These maps are all based on IPCC SRES scenario A2. existing pressures on the already burdened natural environment of Europe may increase (Table 1. (right) ECHAM4 (European Commission. On the other hand. and accelerate tree mortality in the South. and processed by JRC within the PESETA study. climate regulation potential or biodiversity. 4 . Furthermore. Changes are projected for 2071-2100 relative to 19611990.1). Climate change in Europe is very likely to reduce ecosystem services such as soil fertility. N= negative. Sectors and systems Impact Floods Water Water availability resources Water stress Forest NPP Northward/inland shift of tree species Natural disturbances (e. drier conditions and rising temperatures in the Mediterranean region and parts of Eastern Europe may lead to lower yields. * to *: change in character through time. grasslands fire. 5 .1... especially in the steppe regions (Maracchi et al. insects) and Change of stability of forest shrublands ecosystems Drying/ transformation of wetlands Wetlands and aquatic Disturbance of drained ecosystems peatlands Suitable cropping area Agricultural land area Summer crops (maize. under the IPCC SRES A2 and B2 scenarios by 2050 even when the fertilising effect of increased CO2 is taken into account (Table 1.g. na = not applicable. 2007) Magnitude of impact: *. ***. Similar yield reductions have also been estimated for Eastern Europe. Climate-related increases in crop yields are mainly expected in Northern Europe. with increased variability in yield. Bindi and Moriondo (2005) showed a general reduction in yield of agricultural crops in the Mediterranean region. sustainability Table 1.. **. +8 to +25% by year 2050 and +10 to +30% by year 2080 (Olesen et al. Forest. N* N*** N*** P*to* N P*to* N N*** N** N** N* N** N** N*** N** N*** N** N** N* East N*** N** N** P* N** N** N*** N*** N*** N* N** N** P* N* N* N** na P** P** P*to* na N P*** P** P*to* N N* P** P* Under a changing climate. 2004).1: Some of the main expected impacts of climate change in Europe during the 21st Century (adapted from Alcamo et al..2). Climate change. impacts on agriculture. Type of impact: P= positive. sunflower) Agriculture Winter crops (winter wheat) and fisheries Irrigation needs Energy crops Livestock Marine fisheries Area North N** P** P** P*** P*** N* N** N** N*** P*** N** P*** P*** Atlantic N** P** P** P** P** N* N* N* na P** N** Central N*** N* P** P** N* N* N* N** P* N** P* P*to* N N** P* N** na Medit. For example wheat yield increase is projected to be +2 to +9% by year 2020. 8.4 -2.4 -14. Audsley et al.8 -10. Uncertainties in the projection of future precipitation complicate the estimates of future yield gains or losses.9 1. 2004. IPCC. N-E = Greece and Turkey. EEA. This is particularly true for Southern and SouthEastern Europe.8 0.9 -5.7 4. 2005) A2 Without CO2 B2 A2 With CO2 B2 C4 summer N-W 0.8 -9.2 4.4 Tubers N-W -10. 2005). 2005. projections are relatively robust.5 -0.4 N-W = Portugal. Spain.2: Changes of crop yields (%) for some Mediterranean regions by 2050 (modified from Bindi and Moriondo.0 –2. Under climate change conditions.4 N-E -18. depending on the scenarios in use and the model itself.9 -13.2 N-E -4. 2006.4 -4.9 N-E -22. Another example is sugar beet yield increase of 14-20% until the 2050s in England and Wales (Richter and Semenov.5 4. 2001&2007.. model results diverge to a great extent.0 7.5 -6.1 -7.6 -6.7 12.4 N-E -15.4 4.2 –4.0 -3. 2005).8 4. In addition.. In these areas.3 Cereals N-W -11.2 C3 summer N-W -21. Ewert et al.9 1. where water supply is less critical.5 -0.6 -8.8 3. an expected lowering of the 6 . Table 1. aggravating the competition with other sectors whose demand is also projected to increase.4 –12..Survey of agrometeorological practices and applications in Europe regarding climate change impacts 2006. France and Italy. Schröter et al.2). 2005). it is expected that irrigation water demand will further increase. where water will be a critical factor for agriculture in the future.3 N-E -6. Several recent studies highlight the challenges that result from changes in water availability and water quality (EEA. For Central and Northern Europe.2 Increasing drought Risk with Global Warming in Europe Increased drought risk associated with global warming and impacts on water resources are among the main concerns among agrometeorologists in Europe (Fig.6 Legumes N-W -24. 1.5 1.2 5. implying: there may be an opportunity to increase Europe’s share of world food production. as the continuously 7 . Europe faces less negative effects than most other parts of the world. sustainability groundwater table will make irrigation more expensive. efficient only if integration of climate change-related issues with other risk factors. such as sustainable development will be accomplished. intensification of rainfall. and with other policy domains. Might policies therefore be needed to support development in Southern Europe.1. having a major impact on irrigation management. and this process is still often underrated. Climate change. Taken together. from the global viewpoint. As the evaporative demand will increase due to higher temperatures. and more flooding in the Centre. Techno-industrial society is already fundamentally unsustainable (Rees. Overall European food and fiber production is not expected to be greatly altered by climate change. it is expected that capillary rise will increase the salinisation of soils. To sum up. Extreme weather events such as heat waves will impact on peak irrigation requirements. Adaptation action will be. has generated vulnerability. it may be necessary to increase food production in Europe in order to maintain global food security. North and mountains. if the challenge of climate change is greatest there? But in the global context. in turn might have to be limited to cash crops. which. Finite resources are decreasing or are being less utilizable. the above implies a South-to-North geographical shift of climate resources in Europe.3 Challenges. greater differences will arise between countries. However. The societal progress of civilization and technology attained so far. more “disbenefits” to the south of Europe. Furthermore it may aggravate current environmental problems (eg desertification in South. and. 2006). new approaches and research needs The trends in climate change and the likelihood of further changes occurring give urgency to determining potential impacts on farm activities and addressing adaptation measures. generated from the XIV Century scientific revolution.g. increased frequency of extremely hot days or seasons imply more benefits to the North. soil leaching in North). This may worsen current resource issues: e. 1.. increasing the difference in resource endowment between North and South of Europe. however. drier in the South. more water shortage and heat stress in South. impacts on agriculture. warmer in the North. such as climate variability and market risk. 2005) 8 . Figure 1.2: Ensemble mean soil moisture changes in Mediterrain between the periods 19611990 and 2070.2099 in spring and summer under the IPCC SRES A2 and B2 scenarios (PRUDENCE.Survey of agrometeorological practices and applications in Europe regarding climate change impacts increasing use of fossil fuels for productive purposes and for commercial exchanges at the basis of our development model. and agribusiness globalization is threatening the economic viability of traditional. 9 . enteric fermentation of ruminant animals. material extraction. nitrous oxides are emitted as a consequence of fertilization. and methane from rice paddocks. obtained through restorations of natural processes and local attitudes and tradition can be a very powerful mean. The reduction of pressures on land and resources. Despite of the advantages brought to developed Countries from this knowledge-based. A systematic effort to organize future arrangements and decision should start from now. for producing renewable biomass energy. most visible and impacting signal of the human enterprise on the environment is represented by climate changes. peculiarities. Multifunctional agriculture may produce economic and highvalue commodities.(Mc Bratney et al. Together with CO2. Precision farming will allow to use crop models and monitoring technologies (including GPS. animals.1. Fossil fuels consumption has changed the composition of the atmosphere and the natural cycle of Carbon. Although most adaptations are focused on reducing risk. Economic globalization. enhance crop quality. and nature conservation. reduce environment degradation. as well. land use is becoming a main problem: land is needed for food crops.. strictly linked to the emissions of greenhouse gases. technological model. Together with energy. transport. local. and appropriate land use may recover local vocationally and knowledge. 2005). including meteorological variables. to allow progressive changes of the models so far adopted avoiding abrupt. the energy and the material continuously extracted are returned to the ecosphere in degraded form. are released into the atmosphere and may cause hazards to man. remote sensing) to apply correct inputs. which long-term effects are still unknown. to pose additional challenges. Climate change issues contribute. The management of production is expected to trend towards micromanagement of each specific production site. fibres. as that linked to traditional no-food crops as. sustainability The first. eco and agro-systems. Potentially secondary pollutants. impacts on agriculture. Agriculture accounts for 52 and 84% of global anthropogenic methane and nitrous oxide emissions. open markets and liberalized trades are accelerating the rates of the human impacts. recreation. brusque transformations. minimize costs. and precision production (information intensive) are likely to characterize twenty-first century. refuse deposition. Climate change. a drastic revision of our concepts of economic model is impellent. for example. underground extractions. Emergence of ecological agriculture. great flows of resources and commodities. for urban-industrial uses. in order to lower impacts. as well as the societal processes generating vulnerability. allowing to contain fast and large growing pressures. for maintaining other ecosystem services. Moreover. there is a need to address local capacity to adapt. but also for leisure. adjusted to the ambient environment. A reduction of related energy consumption has to be looked for. savannas. populationregulating mechanisms and system resilience can lead to the redesign of agriculture at a landscape scale. but whether biofuels offer carbon savings depends on how they are produced. This would be possible if and when local and seasonal products consumption will be favoured and enhanced. and the environmental sustainability of agriculture. Biofuels are a potential low. The concept of Good Agricultural Practices has evolved in recent years as a result of the concerns and commitments of a wide range of stakeholders about food production and security. 1. awareness is increasing about the importance of ecological agriculture. nutrient cycling.3). Correct knowledge and interpretation of climate urgencies may address on-farm and post-production processes less sensitive and decrease vulnerability (ACCRETE project. biofuels made from waste biomass or from biomass grown on degraded and abandoned agricultural lands planted with perennials incur little or no carbon debt and can offer immediate and sustained GHG advantages (Fargione et al. of 1 kg of cherries from Argentina to Europe (12. The transport. Adequate land use and resource preservation policies must be coupled with appropriate energy saving actions. 2008). (Shennan.000 Kms) has been weighted to contribute to the release in atmosphere of about 16 Kgs of CO2. 2008).Survey of agrometeorological practices and applications in Europe regarding climate change impacts At the same time. as self-regulating pest management systems instead of pesticide applications. allowing agriculture itself to positively act to contrast climate change. The intensification of transports is one of the more preminent examples of deterimental effects of the globalization on the environment.carbon energy source. 45% of the global GHG production will be due to tranports (Fig. Previsions are that. In contrast. Converting rainforests. The ecological management of agroecosystems that addresses energy flows. At the same time. for example. 10 . in 2030. with the attemp to decrease the energy embedded into the food . or grasslands to produce food crop has been demonstrated to create a "biofuel carbon debt" by releasing 17 to 420 times more CO2 than the annual GHG reductions that these biofuels would provide by displacing fossil fuels. the adoption of environmentally sustainable practices in the field may positively reflect on climate. that shifts management practices to apply low-input paradigms. Strategies in agricultural activities to help farmers to better – and less riskilyproduce in a climate change scenarios constitute a potentially great value for agriculture. diverse crop or livestock instead of monocultures. and the impacts on climate changes. 2007). to minimize the »carbon foot-print« that some imported and air-freighted fresh fruit and vegetables may be making to the planet.. 1. Shvidenko. Moreno. Italian. 2007. makes productive use of the knowledge and skills of farmers.agrometeorology. 2008. Corobov. C. Giannakopoulos. Bindi.accrete. Czeck versions. so substituting human capital for costly external inputs. Climate change.org/ Alcamo J. B.1.E. impacts on agriculture. respect and revaluate traditions. as climate change mitigation (Pretty. R. Climate Change 2007: Impacts. Code of attitudes to prevent impacts between agriculture and climate change. J. Slovenian. available on-line at http://www. sustainability Figure 1. M. Nováky. E. Contribution of Working Group II to the 11 .N.eu/ and http://www. Devoy. climate. that includes environment.4 References ACCRET-E. J. R. resource and energy. Martin. This new vision minimizes the use of those non-renewable inputs that cause harm to the environment or to the health.. Olesen. food safety and other non-monetary benefits. carbon balance. Adaptation and Vulnerability. Romanian. Data from European Commission New approaches are hence in a holistic perspective. Outcomes will be positive for productivity. Greek. German. A.J. Europe.3: Sectorial emission trends and projections in the EU.M. 2008). Printed in English. Summary for Policymakers. Palutikof. Kirilenko. COPA COGECA.copa-cogeca.html 12 . G. issue 5867. Polaski. Impacts. M. O. J. J. Averyt.148-162. J. Impacts of present and future climate variability on agriculture and forestry in the temperate regions: Europe. 85 pp. Tilman. Brander. EEA Report No.)]. 2006.I. WWF. Maracchi G.-F. Adaptation and Vulnerability. 2008. Schmidhuber and F. Hill. 2007. and what not? Environ.P. Morales. UK. (1). T. and M. Trnka and V.E. M. IPCC. S. 2005.Adapting to climate change in Europe – options for EU action {SEC(2007) 849} Fargione J.Z. Qin. K. Precision Agriculture.L. USA. J. P. Parry.R.J. B. S.A. the European Economic and Social Committee and the Committee of the Regions . Sau. Mc Bratney A. Rubæk. Report of Working Group II of the Intergovernmental Panel on Climate Change.P.asp Easterling W.... C. EEA Signals 2004. Cambridge University Press.R. T. Hydrology and Water Resources. European Environmental Outlook.es/docs/Agriculture. 4/2005... A European Environmental Agency update on selected issues. P. Palutikov. 70(1). Moriondo. Pearn.H. 2006. Hanson.K. Alexandrov. Díaz-Ambrona. Quemada.be/en/dossiers. D..jrc.. M. Sirotenko. Smith. fibre and forest products. Chen. Cojocaru. A. C. EEA. M. C. Morton. Climate Change 2001. J. Adaptation and Vulnerability. PESETA. 2007. Green Paper from the Commission to the Council. Bouma. van der Linden and C. UK and New York. 2005b. Audsley E. J. Eds. Heidmann. T. the European Parliament. Cambridge University Press. http://www. Howden. 7-23. What can scenario modelling tell us about future European scale agricultural land use. Science. Cambridge. Bindi M. NY. Aggarwal. K. Fronzek. Ancev. Contribution of Working Group II to the Fourth Assessment Report of the IPCC. Canziani. EEA Report No 2/2004. S.F. and T. P. M. UK. 2004. 107 pp.E. J.M. 1235-1238. Climatic Change. 2005a. M. 319. Vulnerability and adaptation to climate change in Europe. Palutikof. 31 pp. P.F. Soussana. 6. 541-580. O. Hawthorne. M. 2001. K. Batima. Rounsevell. Bindi. J. Mínguez. EEA Technical report No 7/2005. Hickler. M. Whelan. B. M.E. S. Sykes. Climate change impacts in the Mediterranean resulting from a 2°C global temperature rise. Parry.. Climate Change 2007. Future directions of precision agriculture. Bindi. F. L. Koutsidou. 2004a. O. Olesen J. Impacts. Eds. 9.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Fourth Assessment Report of the IPCC. D.L. 117– 135. Food.D. 79 pp.N. van der Linden and C. Moriondo. Marquis. Sci. Climatic Change (in press). Section. Cambridge University Press. Cambridge. Miller (eds. 273-313. Tubiello.M. Cambridge. E. In: Climate Change 2007: The Physical Science Basis. M. Hanson. Holt. Manning. EEA. M. 2006. Impacts of Europe's changing climate: an indicator-based assessment. Tin. M. 2003. Contribution of Working Group I to the Fourth Assessment Report of the IPCC [Solomon. European Commission.J. T. M. (Projection of Economic impacts of climate change in Sectors of the European Union based on boTtom-up Analysis) http://peseta. Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Erda. EEA. 54–66. Simota. Ruiz-Ramos. Pol. P. 2004b. Land clearing and the biofuel carbon debt. IPCC. G. Giannakopoulos.L. P..E. Factsheets: Assessment of the impact of the heat wave and drought of the summer 2003 on agriculture and forestry. Canziani. M.. EEA. Impact of a 2 °C global temperature rise on the Mediterranean region: Agriculture analysis assessment. Eds.Tignor and H. T.. Carter. 2005. 2007.B. Semenov. Carter. Thonicke. G. 2008. 2005. S. Royal Soc.C. Royal Soc. House.T. W. Sabate. Globalization. impacts on agriculture. Leemans.T. principles and evidence. M. Arnell. Reginster.D. T. Ecosystem service supply and vulnerability to global change in Europe. Sykes. H. Glendining. Metzger. Smith. Rounsevell. Prentice. S.. 2006. Biol. Phyl. M. sustainability Pretty J. De La Vega-Leinert. Zaehle. S. I. W.I. 2005.R. Klein. I. Bondeau. Shennan C. B.1. Biol. Climate change.. Mitchell. Final report of DEFRA Project CC0368.C. ecological knowledge and agriculture.. Bugmann. 220-225 Richter G. Gracia. and M. Araujo. Zierl. (1492). 13 .. Phyl. 447-465. M. F. Thuiller. P. M.. R. 363. C.dk) Rees W. M. Science. M. T. 2008. Tuck. N. (1491). J. Cramer. Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects. B.J. Final Report (http://prudence. Erhard.A.W. trade and migration: undetermining sustainability. 717-739. Lindner. M. Lavorel. 310(5752): 1333-1337. S. S. K. Sci.dmi. Meyer. Agricultural sustainability: concepts. J. 59 (2). Biotic interactions. Smith. Sitch. Schröter D.B. Ewert. A. Ecological Economics. Sci. Re-Assessing Drought Risks for UK Crops using UKCIP02 Climate Change Scenarios. A.E.. J. 2005. Trans. PRUDENCE. B.J. B. Smith. Kankaanpää. R. 363. Trans. Survey of agrometeorological practices and applications in Europe regarding climate change impacts 14 . in connection with the development of computers. an analysis of the limitations for applications and an overview of spatial applications in combination with GIS and remote sensing in Europe. transpiration). Andreja Susnik. The aims of Working Group 1 of COST734 are a review and assessment of agroclimatic indices and simulation models relevant for various European agricultural activities. as well as growth and development are influenced by the patterns of each climatic variable and their combination diurnally.1 Introduction Agricultural activity is strongly related to climatic variables that are the main causes of the year to year variation of quantity and quality of production. Dragutin T. Simone Orlandini. Pavol Nejedlik. pest and disease warning models/algorithms. Much research was done worldwide in the field of model development. lack of knowledge on system responses or lack of calibration data. Emmanouil Tsiros. Tor H. Physiological processes (respiration. Valentin Kazandjiev. models can be used to simulate and analyse the complex interactions in the soil-plant-atmosphere system for example in the important field of climate change impacts on agricultural production. Visnja Vucetic. 2. AGROCLIMATIC INDICES AND SIMULATION MODELS Josef Eitzinger.2. Dalezios. For example. models for irrigation scheduling or agroclimatic indices can help farmers significantly in decisionmaking for crop management options and related farm technologies. In research. Sabina Thaler. Nicolas R. Sivertsen. many new software tools were developed to be used for agricultural research as well as for decision making. Christian Kersebaum. model improvements or model comparisons. Holden. All these modelled systems and their interactions include however many different kind of uncertainties and limitations. Also in Europe in many countries significant work was done in this area. Agroclimatic indices and simulation models 2. crop and whole farm system modelling. K. Branislava Lalic. The results of an europeanwide survey are presented in this study. models representation of reality. Mihailovic. seasonally and annually. Robin Matthews Abstract During the past decades. photosynthesis. 15 . such as trends in technology and human activities. It includes an overview of most used agrometeorological indices and process oriented models for operational and scientific applications. Nicholas M. monthly. The aims of WG1 of COST734 are a review and assessment of agroclimatic indices and simulation models relevant for various European agricultural activities. Concerning climatic impacts. dedicated to analyse specific questions of a survey of the COST734 countries: Subgroup 1 “Agroclimatic indices in use and research”. To determine the relationships between climatic conditions and agricultural systems. heat wave. crop protection. characteristics of production. fertilisation and other management tasks. the activity of Working Group 1 mainly addresses the following hazards. damage on production. seasonal shift (length of growing season. such as the number of frost days during the year.2.Survey of agrometeorological practices and applications in Europe regarding climate change impacts The direct consequence is differences in the success of cultivation. cultivation methods. 2. Subgroup 3 “Present applications and limitations of crop models in Europe”. At the same time this knowledge can be applied to manage the system with respect to irrigation. General descriptions can also be obtained. several indices and simulation models can be used. Subgroup 2 “Application of process oriented models in Europe”. frost. disease development and water balance). biological and physical relationships among the system components. budbreak).2 Agroclimatic indices in use and research (Subgroup 1) 2. relative to accumulation of materials concerning interrelated plant and weather observation. Their evaluation will be done according to the characteristics of selected crops in terms of seasonal development. flood. change in pest and disease.1 State of the Art At present. In this way information can be obtained concerning the chemical. hail. etc. WG 1 tasks were devided into 4 subgroups. Subgroup 4 “Crop simulation models in combination with Remote Sensing and GIS”. fire. drought. wind and snow. This includes an actual overview of agroclimatic indices and simulation models applied in research or in practice. and profit for the growers. harvesting. total precipitation. taking the ever-increasing efficiency of computer 16 . Main problems relevant for operational applications in Europe should be identified as well. growth. Simulation models and indices describe the effect of climate on a specific crop and a specific process (phenological development. The relationships with specific crop responses as well as an description of important model outputs and index thresholds relevant to evaluate the responses of crops to climate change and variability are an important aspect to be investigated. length of dry period and others. directly or indirectly arising from atmospheric conditions: rainfall. frost. snow cover. Their simple structure makes their application easier. so representing strong indicators for climate change studies. development. providing a look at the state of contemporary European research. Gain understanding and visualizing of the real present and possible future state of agroclimatic conditions could be chiefly useful to address the research and the political action in order to control the change of the natural balance of the ”soil-plant-atmosphere” system that for the time being it seems to be completely tempering. When the extent of measurements is limited. excess rain. yields. as well as its application for the purpose of the more efficient use of climate resources and in order to control the impact of climate change on agriculture. Agroclimatic indices and simulation models technology into consideration. quality aspects and others aspects. we considered these are the underlying reason for we dedicated a working group to analyses this topic and to prepare progress report. pest and disease of important plant and animal species in agriculture. 17 . seasonal shifts. phenology. and the analysis of their temporal trend can provide indications about the current and future impact of climate variability and change. It is deemed that the definition of climate indices is the point of departure of the study of the effect of the climate change. providing the end-users with very useful information to manage and plan agricultural activities. Quantification by physical methods is the basis of researching and understanding of processes that explain phenomena determining drought. The analysis has been finalized to assess which are the repercussions on the present and the futures climate because agroclimatic indexes are more and more used for the construction of climatic maps and classification. as matter of the fact that the climatic indexes are the synthesis of climate condition in a unit of time and space. agrometeorological indices are a very powerful tool to (semi-) empirically relate studied phenomena to such environmental observations. crop responses (incl. Basic of agrometeorological indexes information is presented in this chapter. At the same time they are frequently used at operational level by extension services. They can really provide exhaustive picture of the agroclimatic conditions of European agricultural areas.2. Considered processes are: drought. questions 1 and 3 of the text are dedicated to the just exploration of the agroclimatic indices largely used at the moment in the COST 734 countries. such as forest fire). heat stress. In particular. growth. multifactorial relationships are being developed which more fully expressed the complex impacts of climate conditions on plant productivity. but also to get useful information without the application of frequently too complex physical and biological simulation models. For this reason many agrometeorological indices. is able to give indications about the best distribution of measurement points in order to represent the spatial and temporal variability of meteorological variables by using the lower number of stations. For the calculation of the indices the following steps are required: -availability of the data: data have to be collected by specific meteorological station or by already existing network of stations. the quality of collected data needs to be controlled in order to avoid possible gaps in the series used for the calculations and to eliminate records affected by errors. but also the annual trends and the interannual variability can be considered in order to obtain information concerning the management of the systems and they planning.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Agrometeorological indices The simple climatic variables. in fact. are not able to fully describe the complex relations existing between climate and crops development and production. data need to be manually collected or automatically transmitted with a regular time step. Usually hourly or daily data are required. Agrometeorological indices provide required specific information about the stage and value of a particular parameter of weather. This information. easy to be collected and analysed.elaboration of results: the single values. -data control: because indices are the result of a combination of different meteorological data. were set up with different aims. . The use of Agrometeorological indices The development of farming technologies and management brings quite complicated managerial systems which require different inputs in order to optimize the decision process. On the other hand. derived from the combination of meteorological data. -calculation of the indices: automatic procedures can be set up for the indices calculation. -transmission of the data: depending on the indices. The obtainment of reliable information is obviously related to the quality of data and to the availability of a sufficient number of measurement points for the territory representation. General use of agrometeorological indices is in monitoring the development of the plants through their phenological stages. For this reason a preliminary study of the geotopographycal characteristics of the interested area represents an important point. Agrometeorological indices allow defining quantitatively the available resources for the different cultural needs. crops and/or reflect a certain relation in between and the impact of the weather on crop behaviour. 18 . it is particularly important that single data are controlled and eventually corrected before indices calculation in order to avoid the propagation of errors. The time scale is done by the character of the information and comprises following information: -evaluation of the past (pastcasting).2. Standard agrometeorological indices can be divided into categories according to their scope and character and the way of use. Computerized information systems require the agrometeorological data in a certain form in order to process it. -actual real time evaluation giving a very short prediction (nowcasting). Some of the agrometeorological indices are set specifically to a certain time scale evaluation (f. The decision making process requires full information about the situation in the past. in the present and the final decision is usually based on the forecast. In this cases weather data and further inputs over larger areas are required and remotely sensed data are widely employed as the input to calculate the indices. The spatial scale at which the indices are used and at which the models operate is particularly relevant. Meteorological data form the basis for agrometeorological indices calculation. soil and biological parameters together with the managerial practices taken at the particular time of the growing season. Agroclimatic indices and simulation models They are either used as a concrete single value/tool giving the information about a specific parameter entering a certain information agricultural system or as an input into the models. Agrometeorological indices calculation has to be based on in situ measurements and is site specific reflecting the conditions of the particular locality.forecast. Further to the farming practices the agrometeorological indices and models enter the evaluation of larger areas at the regional and country levels. Forecasting both the changes in the yields and in the quality production can support the planning of mitigation and adaptation activities. The main use of agrometeorological information is in the recent time in Europe concentrated on the farm – level applications. The basic agrometeorological data are related to the environmental conditions and comprise atmospheric. This data are of two categories: the data to calculate the indices and the data to calibrate and assess the models. This brings a force for further 19 . . The way of use is defined by the purpose of the evaluation which differs both in time and space. Big part of the agrometeorological indices is a part and form agrometeorological forecast. the indices describing snow cover concentrate on pastcastimg) but many of them are used in all time scales including pastcasting and forecasting (indices describing pest and diseases detection and prediction). Increasing recognition of climate change and its impacts leads to the development of long-term agrometeorological prediction/forecast. Agrometeorological forecast is applied at both national and regional levels influencing market planning and agricultural policy and insurance policy as well as in farming practices. ex. Croatia. products and has special issues for plant protection according to phenological stage and plant species. In Slovenia (SI) beside agrometeorological network at Meteorological Office another agrometeorological network is applied under the authority of the Ministry of Agriculture. Netherlands. These informations are provided on user’s request or published as results of research programs. Nevertheless.2. Norway. Hungary. Greece. there is also a special weather forecast provided by the National Meteorological Service and broadcasted by the National Television Network. Universities and private or public institutes provide weather and agrometeorological information. The changes of the climate also bring a significant impact on pests and diseases occurrence and further impact on risk management in crop growth. Slovakia. Except from the National Meteorological Service and the National Observatory of Athens. Romania. Not all answers were complete and some information was not available.2 Present use of agrometeorological indices in Europe A survey across Europe has been done in order to get the information on the present use of agrometeorological indices. Slovenia. Serbia. Following countries have answered the questionnaire: Austria. Private agrometeorological services are scattered and usually concentrate on some specific points of service like extreme weather warning service /in AT and NO/ or advisory services in case of plant protection against pests and diseases /in SK/. In some cases companies 20 . In Greece. The forecast also provides advisory information for new practices. Forestry and Food (at present the Phytosanitary Administration of the Republic of Slovenia) which has been since 1998 used for public plant protection service and to minor extend also for irrigation. Agricultural services act in some countries at national level but they run the mostly advisory services at the regional level. Italy. Both agrometeorological monitoring and service is mostly operated by the national state bodies. in 12 cases national meteorological and hydrometeorological institutes provide these services. the summary gives good review about the use of agrometeorological indices in Europe in the recent years. Spain and Switzerland. Germany. 2.Survey of agrometeorological practices and applications in Europe regarding climate change impacts development of agrometeorological indices and simulation models as some new phenomena have to be investigated and observed mainly regarding the impact of higher level of carbon dioxide on C3 crops by increasing photosynthesis and decreasing water use. Finland. Poland. Bulgaria. A complex information service for farmers including special weather forecast for farmers provided by a private company is organized in FI. France. Czech Republic. 1 and Fig. Further description is focused on the use of the agrometeorological indices both in operational practices and in the research.2 there are some agrometeorological indices describing mostly extreme weather events like hail and forest fire indices. The research activities regarding the development of the agrometeorological indices is clearly focused on the drought (Fig. Water balance represents the top parameter regarding the plant growth.1). The territorial distribution of the use of the indexes follows the territorial distribution of the natural phenomena while pest and diseases indexes are used equally through the whole Europe.2. That`s why the indices describing various components of the water balance are most frequently used at various levels. However.g. 2. Droughts and excess rain together with the pests and diseases occur as the most frequent detected phenomenon in operational agrometeorological practices (Fig. Generally. some descriptions include also the use of some process oriented models the interim results of which are used as the agrometeorological indices mainly to evaluate the plant development and water balance. 2. In many cases they cooperate with other national bodies providing them the data either free of charge or at the commercial base. Further to the natural phenomenos shown in Fig. In many other cases the standard climatic and agrometeorological indices are applied. 21 .2) and the attention paid to the development of other indices does not correspond with their practical use in operational practices. 2. Relatively little attention is paid to the operational monitoring of heat stress while the majority of responding countries notices the research activities in this field. Agroclimatic indices and simulation models selling chemicals or other materials and equipments to farmers include also some technical support and agrometeorological services and/or forecast as a part of their businesses. the agrometeorological indices are widely used in operational practices through Europe. 2. meteorological services as they run the meteorological networks so that they are the owners of the databases. General agrometeorological information issued at the national level is mostly produced by national bodies e. The drought indexes are in many cases parameterized reflecting the impact of local climate and the activities being given doth to the research and operational use correspond. 1: Operational use of agrometeorological indices in European countries 18 16 14 12 10 8 6 4 2 0 DROUGHT FROST SNOW COVER HEST STRESS EXCESS RAIN CROP RESP.Survey of agrometeorological practices and applications in Europe regarding climate change impacts 18 16 14 12 10 8 6 4 2 0 EXCESS RAIN CROP RESP. The indices are selected and the models are parameterized towards the regional and environmental conditions 22 . FROST P&D SNOW COVER DROUGHT HEAT STRESS OTHER OTHER Nb.2: Use of agrometeorological indices in research in European countries Operational use of agrometeorological indices The use of agrometeorological indices and models varies from country to country and there is no information about the frequency of use of the indices and models inside the particular country. Many of agrometeorological indices used operationally are regionally based. of countries Event Figure 2. of countries Event Figure 2. P&D Nb. Water balance components are used in various modifications in almost all countries in the extent from national level to a farm level. in some countries such as in France the operational agrometeorological service is run by the regional bodies and it is difficult to collect full information about the use of the particular indices in the region. IHS is calculated out of water balance components as follows: IHS = ∑ [VBi − VBp ] .adapted crop drought model used in Austrian Weather service and Index of Hydrometeorological Drought /IHS/ used in the practice of Czech Hydrometeorological Service and.1 and 2. These types of the indices are often regionally based as they have to use multiyear measured values of the particular parameters recorded or calculated for a certain locality.2. 2. The major part of the indices in use are rather complex and deal with water balance components and precipitation measures. The general problem of these indices is to include the physical and biological properties of the particular crop in order to reflect its sensitivity and limitations towards the lack of water supply during the vegetation period. A 10x10 km uniformed grid system is used for the production of all indices and parameters. However. There is a NPET.2. The main users are listed farmers and extension services.1). Agroclimatic indices and simulation models of the concrete region. Following the questionnaire the indices and some partial model outputs were divided into the categories according to the phenomenon they are dealing with as in Fig. The description of the indexes enables to give an overview of what the services provide but only a few concrete indices were listed. There was only one index bringing a certain forecast of drought listed. Drought Drought indices are constructed to quantify the lack of water during plant growth and development cycle. where VBi is actual and VBp is multiyear mean of i =1 n water balance. The major part of the indices is focused on pastcasting and some of them on nowcasting (Table 2. Both the indices based on water balance components and on the precipitation amount for a given period are produced mainly by 23 . That`s why the further description of the use of agrometeorological indices will be rather topic oriented than country oriented. VB represents a difference between precipitation and actual evapotranspiration and is parameterized by a coefficient depending on the season. Further problem of defining the drought is the time step used to calculate the particular indices. Some indexes in use define the meteorological drought which does not in all cases describe the real shortage of water for the crops. .) (e. apart from high precipitation past-casting (P) maps and the examination time-series of rainfall data in different regions for the investigation of the variation of precipitation during the second half of the 20th century. From the standard indices SPI. Dalezios 1988.. Loukas et al. Tsiros et al..2006. 2004.dmcsee. respectively (e. two other indices used in Greece are the Reconnaissance Drought Index (RDI) and satellite derived Vegetation Health Index. PHDI. Kanellou et al. Tsakiris et al. 2006). Percentiles and Precipitation for the region of southeastern Europe are products issued by Drought Management Center for South eastern Europe (DMCSEE) situated in Slovenia from 2006 and published on the web page http://www.org/.g. 2008. In Greece. 2002. Final data maps with two months delay are available after 20th day of the current month. In SI the irrigation model IRRFIB is used for daily calculation of crop water balance for different regions. 2005.Survey of agrometeorological practices and applications in Europe regarding climate change impacts national weather services as they run the meteorological networks at the regional and national level. for example. 2008).3). PDSI. Further to this parameter rainfall intensity is measured either by pluviographs or by weight rain gages providing on line signal. Z-index. representing meteorological-hydrological drought and agricultural drought. Some of the services provide special rainfall maps in their pastcasting identifying the areas with high precipitation and/or anomalies.2. It represents agricultural decision support tool which is running inside the Slovene Agrometeorological Information System (SAgMIS) package. In Greece. percent of normal precipitation and rainfall percentiles are in operational use. Excess rain Excess rain as a water related phenomenon is observed in all countries by simple measurements of daily sums of precipitation. for example. Some institutes use the partial outputs of the models like WOFOST to define the days with the lack of water for the crops /in NL and SK/.g. Tsakiris et al. Apart from the standard indices (SPI. They are updated once per month. an operational-research application of the non-hydrostatic model LMCOSMO of HNMS (Hellenic National Meteorological Service) has been used 24 . PDSI.1 are issued in the standard forecast of each meteorological service mainly at the regional scale. RAI etc. Major part of listed rainfall parameters in Table 2. Firstguess maps are available after the 5th day of the next month. Some preliminary maps of the SPI (Fig. several drought indices are used for the assessment and estimation of drought for research or operational (on user’s request) purposes. A threshold of the heat stress refers usually to the daily mean temperature over which a detectable reduction of growth or damages in plant begin. However. Institute of Environmental Research and Sustainable 25 .3: SPI for southeastern Europe issued for February 2008 Heat stress Heat stress is a complex function of the height of temperature. 2002). duration and rate of increase of the temperature. This model has very high resolution (grid distance of 2 km) and the forecasts of the parameters are calculated every 18 hours (Kotroni and Lagouvardos 2002). forecasts of surface temperature and wind speed over Attica and neighbouring areas are provided using the non-hydrostatic model MM5. it is difficult to use such information at the local/farm level. Heat index forecast is provided by Hungarian Meteorological Service which includes the forecast of daily average temperature above 25°C.2).2. Generally excess rain represents a damaging weather event and its characteristics are usually issued for general use stressing the regional differences. The model has been used for the simulation of severe thunderstorms (Avgoustopoulou et al. The heat stress prediction is naturally included in general weather forecast though there are very few services listed which provide special heat stress related indices (Table 2. The data are collected from stations of the Hellenic National Meteorological Service and the Ministry of Agriculture. The thresholds of the temperatures for the crops differ pretty much and they vary also according to the plant development stage. Agroclimatic indices and simulation models for forecasting excess rain events. The provider is the National Observatory of Athens. In Greece. Figure 2. The indices dealing with the snow cover are mostly focused on the postcasting which is done daily at different spatial scales from 10x10 km grids in Finland to the regional and national scale in the rest of countries (Table 2. This information is usually issued for concrete farmers and consultants. two German 26 . these indices in various forms are in wide use through Europe. The German Weather Service (DWD) provides a daily risk index for forest fire which combines several indices: a Swedish index (Angstöm). On the other hand a long duration under unfavourable conditions can bring rotting out of the plants below the snow cover.2 are mainly used by farmers and consultants and insurance companies. Frosts are frequently classified as either advective or radiative and this also defines their impact on the different type of crops. However. the detection and prediction on frost conditions considers it as the temperature below 0°C.3. A standard weather forecast includes the forecast of the frost or the possibility of ground frost occurrence. Frost events are both forecasted and monitored by the national Meteorological services in all countries. Specific events Further to the above listed indices some special agrometeorological indices in operational use are listed in Table 2. During radiative frosts occasions in many cases the frost line does not reach more than 1 – 2 m above ground so that only the crops close to the ground are affected by frost. only a few special indices in operational use focusing on the nowcasting and pastcasting were listed. Further to that three countries listed a pastcasting service at the national level providing different parameters of temperature.2). However. Snow cover The snow cover brings a valuable protection of plants against hard frosts during the winter. However. mainly in Mediterranean.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Development and the aim was to provide accurate forecasts in order to improve the weather forecast for the Olympic Games 2004. The use of forest fire/grass indices listed only three countries. Considering increasing occurrence of forest fire events more frequent use of these indices is expected. In some cases the water content of the snow cover is reported which brings the possibility to estimate the amount of the water being stored in the snow cover as a water source in the spring. Frost forecast is usually issued at the national level for general purposes while the special indices listed in Table 2. Frost The critical temperatures needed for damage to occur may vary depending on the duration that temperatures remain below freezing point. Crop response and pests and diseases monitoring There are not many services monitoring the response of the crops to the weather regarding the growth and phenological development (Table 2. The use of the data is mainly in pastcasting. wind speed and direction.html). Further to that either standard (WOFOST) or specific (IPHEN) models are used to simulate the development of different plants. Also. dynamic and microphysical characteristics of the potential hail producing clouds. Agroclimatic indices and simulation models indices (Baumgartner. potato.4. Regarding to hail events. M-68) and the Canadian forest fire warning system (FWI: Fire Weather Index. Instability Indices are calculated for Operational Hail Forecasting in Greece. index describing weather conditions for plant protection. A special set of parameters regarding the plant condition close to the harvest is provided by German Meteorological Service. the Greek National Hail Suppression Project (NHSP) weather modification program. Special weather forecast for farmers and complex growing season information is provided daily at the scale of 10x10 km by Finish Meteorological Service and a private company in Finland. pests and diseases and yields. 27 . temperature.de/Agrarwetter/Waldbrand_en. The objectives were to reduce hail damage and at the same time to examine and study the thermodynamic.4). These networks are run by the Meteorological Services and systematically monitor phenological development stages of selected plants and in several cases crop development including some pheno-metric parameters. an operational project has been carried out in Greece. relative humidity.agrowetter. FFMC: Fine Fuel Moisture Code) (http://www. Some services provide information about the workability of the soil with the regards to the depth of the frozen soil considering also the impact of frost on lumps of clay during the winter. Additionally. An exemplificative list of pest and diseases being monitored and forecasted by various indices and models is shown in Table 2. This information includes probability of rain and frost.2. recommendations are given for the sowing day of winter cereals. The German Weather Service provides up to 4 times a day actualized 7-day forecasts concerning the drying of hay and grain moisture of cereals and maize. rain amount. Operational phenological networks which comprise a sufficient number of stations work mainly in the region of Central Europe and in some countries in Balkan. On the other hand crop parameters including yields and the level of pest and diseases occurrence are widely simulated either by using special simulation models or by using partial outputs of crop growth models. In some cases some special parameters are monitored by remote sensing (greenness index). Remote sensing of phenological parameters is intensively used at the European scale by JRC Ispra within the MARS project. oats. sugar beets and maize for the upcoming 6 days. PL. I. SR HR P SPI WOFOST P P monthly daily Regional Regional ES. P Soil moisture content VHI P P. yearly daily. RO. SR. GR I Water table depth levels Usable water supply Palfai Aridity Index P Regional P P Weekly Year National National CZ HU. monthly Monthly from site specific to national 10x10 km to national National estimating drought affected regions estimating drought affected regions estimating drought affected crops and regions estimating drought affected crops and regions estimating drought affected areas estimating drought affected crops and regions estimating drought affected regions estimating drought affected regions water supply estimating drought affected regions estimating drought affected areas estimating monthly rainfall towards the normal precipitation deficit generating dry days CH CZ AT CZ. N daily. P daily National Water balance components N. yearly monthly Regional. SR GR PDSI P on users request Monthly National HR. PL.1: Reported operational use of agroclimatic indices including statistical models estimating water availability Type of use Time step Spatial realization Aim of use Country of use Used index/model Soil water content for top 10 cm Index of hydrometeorological drought NPET F 5 day Drought Regional N Weekly National N. monthly. SR. I. I. SR DE . SI. SK. weekly monthly. SI. weekly. National National AT. IT.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Table 2. F. SK 28 . SI. FI. SI. NO. GR NL. SI Precipitation totals and anomalies Rainfall percentile P weekly. 0 mm+ Daily forecast of 25. local nationaldistrict scale Regional National estimating affected regions estimating affected regions estimating affected regions estimating affected regions estimating affected regions? estimating affected regions All countries AT.0 mm+ precipitation Rainfall regime classes SPI maps F F F hourly daily F P monthly. DE P P if needed monthly National National HR. upper canopy and under plastic cover Temperature sum Temperature percentile F P Daily monthly. DE. SR. sixty and ninety days-daily calculation monthly decade Regional Regional. Agroclimatic indices and simulation models Excess rain Rainfall amount F hourly. (P) pastcasting Table 2. national RO I. SK CH GR Rainfall intensity 5 days probability forecast of 1. daily national. I. PL. SI HR 29 . of soil surface. regional. thirty.2: Reported operational use of temperature related indices Used index/model Type of use Time step Spatial realization Heat stress Regional national station network Aim of use Country of use Heat Index Maximum temp. HR Precipitation totals anomalies Palmer`s Z index P P Regional national station network estimating affected regions estimating affected regions AT. growing period days with heat stress heat stress indication for different crops and periods estimating affected regions estimating monthly air HU AT. SI.2. SI SR (F) forecasting. (N) nowcasting. I. (N) nowcasting. N N hourly P P Daily Nov-March National National estimation of regional frost risk frost occurrence DE RO P Maximum snow cover Snow depth Snow cover maps Snow depth and water content P P P P Daily Daily Daily Snow cover National National 10x10 Regional Daily National estimation of snow periods estimation affected areas Snow cover occurrence Snow cover occurrence estimation affected areas DE. HR. SR IT SK (F) forecasting. SI CZ F. SI. SI. SR P daily. SI A. FI. decade and monthly national station network Number of days with heat stress AT.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Heat Units Index /sum of daily maximum above 32°C/ Number of days with maximum daily air temperatures ≥ 30 o C and 35 oC Deviation of mean maximum air temperature from long-term average P National temperature towards normal days with heat stress RO P monthly national station network Number of days with heat stress AT. (P) pastcasting 30 . SR Frost Frost forecast and ground frost forecast ALADIN regional model forecasts Winter cereals hardiness modelling Start and end of glazed frost risk Frost Units Index F Daily 10x10 km to national district scale district scale estimating affected regions temperature fields maps winter resistance All countries CZ. SI HR. 31 .3: Operational use of other agroclimatic indices including statistical models Used index/model Hail Type of use F Time step Spatial realization national hail protection network National Aim of use Country of use GR. HR DE DE. SI. They require considerable simplification to be usable under operational conditions. They are mostly oriented towards monitoring regards of the impact of extreme factors and their forecasting. and the transfer of the technology brings practical obstacles in missing operational database data transfer or dissemination tools. Further to that the number of these indices is much bigger than the number of those being used in operational. P N P. Research activities are done in many cases by the institutes which do not run operational services. HR. Many of the indices were designed in a research context for use at the scale of a field. (P) pastcasting The use of agrometeorological indices and statistical models in research The set of indices used in research is relatively rich and reflects the needs of farming practice and advisory and monitoring services at various levels. SR daily hail appeareance Instability Indices for hail forecasting Weather forecasts for farmers Growing season information Gras fire risk index Forest fire index FROSTGAR Depth of frozen soil Duration of global radiation. SK AT.N P P P 10x10 km 10x10 km National National National National national station network various parameters various parameters grass fire risk forest fire risk soil workability soil workability calculation of potential evapotranspiration within the calculation of water balance FI. decade hail appeareance GR F F.2. (N) nowcasting. Potential evapotranspiration F On user’s request daily daily hourly daily daily daily Daily. SK. SR (F) forecasting. Agroclimatic indices and simulation models Table 2. GR FI DE DE. unregula r Daily By RS AT I. Potato beatle .Leptinotarsa decemlineata Botrytis cinerea Etc. P local to national Simulated Regional crop yields estimation estimation of phenological stages detecting pest and diseases occurrence estimating crop damages Pest and diseases detection and prediction All Many - - 10x10x km STICS F - regional? FALLZAHL /Falling number in wheat and rye/ Cereals grain moisture Water content grass/hay N Daily National N. CLIMEX F. >400 stations National. HR. SK All Pest and diseases occurrence Crop damages P Local to national P When occurs Daily Peronospora. Psila rosae.4: Operational monitoring of crop responses and pests and diseases detection Used Index/model Type of use N P P P. N Time step Spatial realization Observed National Regional Local to national National Aim of use/ notice Country of use Greeness index Phenomaps Crop yields Phenological stages unregula r Seasonal ly Daily. F N. P.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Table 2. DE. Delia brassicae. CZ. N. RO. F Daily Daily National. >400 stations direct effect of abiotic factors on pest fruit tree flowering prediction estimation of optimum harvest time estimation of optimal harvest estimation of optimal harvest FI F DE DE DE 32 . SI All AT. Plasmospora viticila. SI. CH. Phytophthora infestans Apple scab. P. 2. Many recent techniques of elaborating spatial information use neural network technology for the approximation and prediction of different 33 .4). More than one third of listed indices are dealing directly or indirectly with drought and its impacts (Fig. F F Daily Daily F F daily National. Core businesses focus on defining the agro-climatic characteristics of the particular regions and localities and estimating the extent and impact of various events as well as on the forecasting for farming management. Further to the drought the research was concentrated on the occurrence and forecast of temperature extremes. HU. oats. Some more research activities on agrometeorological indices not being mentioned were taken in the recent past mainly in elaborating forest fire tools and their prevention. >400 stations National >400 stations Regional 2x2km estimation of optimal harvest Estimation of optimal sowing date predicted pollen concentration phenological development of grapevine phenological development phenological development plasmospora.2. P Daily local. (N) nowcasting. (P) pastcasting The list of indices does not include any estimation of yields. However yield estimation is done more and more often by various crop simulation models (see next chapter) which are rather complex and require real time data of a number meteorological parameters from in situ measurement. No research effort of any agrometeorological indices or models dealing with irrigation systems was mentioned. Beyond their simplicity. regional NL. potato. blue mould on grape DE DE AT. sugar beet. P. and in the fact that data requirements are limited. CH. SK RO SK F Weekly Regional (F) forecasting. In the research of crop responses to the weather phenomenon dominate simulation methods over the observations those some research on making frequent camera pictures during the growing season of particular plant. Agroclimatic indices and simulation models Dry matter content of maize Sowing date winter cereals. Further to above mentioned items big research effort has been done when implementing web based techniques for data collection and dissemination of the information and mapping techniques using GIS instruments. maize Pollen information system IPHEN N. their main advantage is the fact that calculations can be done easily. SI I WOFOST Computed phenological dates Galati vitis N. Simulated crop response Drought Direct crop response Frost Excess rain Snow cover Heat stress Figure 2.6 and 2.Survey of agrometeorological practices and applications in Europe regarding climate change impacts agrometeorological characteristics at state scales in different climate zones based on reported daily weather data. The ability of 34 . New radar techniques as well as almost real time monitoring of precipitation in automated precipitation networks brings further possibility in monitoring the excess rain. No remotely sensed characteristics and parameters entering the indices were mentioned as this is a subject of another chapter.4: The distribution of the numbers of agrometeorological indices used in research according to their purpose Drought indices in Table 2. Further to the indices directly estimating the extent of drought and the content of soil water being available for the crops some models were used to estimate the effect of drought period on the crops by using complex model WOFOST and Autoregressive model DARMA. Extensive research bringing very detailed general statistical characteristics of excess rain at the regional level was done through the whole Europe. Country of elaboration/research use is not mentioned in this case as there is an intensive transfer of technologies in research as well as for practical use and in many cases the research is focused on detail parameterization of the particular index towards the local environmental conditions. 2.5 use a big set of standard parameters and characteristics dealing with water balance and soil water availability for the crops but some special indices towards concrete crops were produced /drought index for grape vine/.5.7 brings a certain review of the indices according to the event categories. Excess rain is monitored and studied mainly because of its destructive impacts leading frequently to the flash floods and bringing the soil erosion of high extent. Excess rain indices mostly use standard precipitation characteristics and no research of the excess rain towards a particular crop or the period of the development of some crops was mentioned. The list of indices in Table 2. A special system for winter cereals hardiness modelling mentioned as a research tool is also used in agrometeorological practice. Snow cover monitoring is done mostly by climatological services and no special indices associating snow cover with crop development were mentioned. RDI (Reclamation Drought Index). Heat index is used also in agrometeorological practice for its simplicity while Actinometric index is not mentioned in practical use.5: Water related agrometeorological indices including statistical models in research and development Used index/model Drought ET (Evapotranspiration) PET (Potential evapotranspiration) ET/PET. ground frost climatology at the mezzo meteorological scale plays a decisive role in frost protection planning. That is why many of the indices were investigated by statistical tools in order to detect the areas of frequent occurrence of frost. The active protection against the frost is much wider that is why big attention is paid to increase the quality of the forecast of frost. Next set of indices in Table 2. PDSI (Palmer Drought Severity Index). The absence of the snow cover is an important issue from this point of view as it brings black frosts during the severe winters which can cause big damages on crops being sowed in autumn. Frost climatology. SWSI (Surface Water Supply Index). there are only limited possibilities to protect the crops against the excess rain.6 deals with heat characteristics which bring the possible damage on the crops. However. RAI (Rainfall Anomaly Index) SPI (Standardized Precipitation Index). Monitoring of the water content of the snow cover brings good possibilities to estimate the water content in the soil after the winter. Agroclimatic indices and simulation models probability forecast of excess rain by NWP models has increased considerably. Aim of use Climatic characteristics 35 .2. CMI (Crop Moisture Index). All of these indices bring valuable information but further to the water regime management active protection against heat stress from the high temperatures is rather limited and concentrates to the genetics and breeding. Heat index brings a combination of the ambient temperature and relative humidity while Actinometric index describes the capability of radiation to produce a photochemical reaction. Further to the simple temperature characteristics being subjected to the statistical processing Heat index and Actinometric index were mentioned as research tools. Table 2. 50 cm height) Frequency distributions of the heat wave events Heat index Intensity Duration Frequency (IDF) curves Climatic characteristics Heat stress affected regions Estimating the frequency intensity of extreme events and 36 . 5 and 10-day total rainfall Daily totals over a certain threshold Generalized Extreme Value (GEV) distribution Intensity Duration Frequency (IDF) curves Snow cover Snow cover duration Snow depth Water content of snow cover Delineation of snow covered surface Estimating drought affected crops Estimating drought affected crops and regions and crop management at farm level Climatic characteristics Estimating affected regions Estimating the frequency of extreme events Estimation of snow periods Amount of snow Volume of water in snow cover Snow cover detection (Remotely sensed) Table 2. BMDI (Bhalme-Mooley Drought Index) Blaney-Criddle. de Martonne Index. Lange rain factor Precipitation totals Rainy spells of X days WOFOST DARMA (Discrete Autoregressive Moving Average model) Excess rain Rainfall amount Rainfall intensity Intensity (rain per rainday) Greatest 3.Survey of agrometeorological practices and applications in Europe regarding climate change impacts RDI (Reconnaissanse Drought Index). Heat Wave Duration.6: Water related agroclimatic indices including statistical models in research and development Heat stress Tmax 90th percentile. 90th Percentile Heat Wave Duration Temperature sum Days with Tmax > 35° Actinothermic index (10cm. Agro-hydrologic potential Index of hydrometeorological drought Drought index for grape vine Palfai Aridity Index Soil moisture content Usable water supply Hydrotermic coefficient. Agroclimatic indices and simulation models Frost Start and end of glazed frost risk ALADIN regional model forecasts Winter cereals hardiness modelling Duration of the frost free period Number of frost days . Phototemperature.Tmin < 0 degC Frost frequency in the Nordic/Baltic region First and last frost day -> frost free period Degree Days Frost days Estimation of regional frost risk Temperature fields maps Winter resistance Climatic characteristics Climatic characteristics Climatic characteristics Table 2. Nyctotemperature and Cumulative precipitation Agricultural weather index / yields of 5 main crops and on the records of 10 meteorological stations/ Dynamics of soil water contents at various depths Heat Units Index Frost Units Index Relative humidity-air temperature relations Aim of use Estimation of phenological stages Climatic characteristics Climatic characteristics Production estimation 37 . sums/ Duration of farming season Simulated crop responses Thermal Growing period (average daily temperature > 5 °C) Huglin-Index: temperature sum for site suitability for grape wine varieties Maturation index for vine Heat unit.7: Monitoring of crop responses and pests and diseases detection in research and development Used index/model Directly monitored crop responses Phenological stages cereals and other crops Start of the vegetation (thresholds of 5.2. Vapor pressure deficit. 10 and 15oC) Continental-scale estimation of climate change impacts Phenological responses to climate variability Simulated responses for assessing the impact of climate warming Infolding Index (beginning of growing season) WOFOST /temp. Photothermal unit. Gerstengarbe. 2005. M. A. Table 2. C. Klein Tank. G. Begert.. Böhm. (Analysis of drought impacts on agriculture in Austria in 2003) in StartClim2004: Analysen von Hitze und Trockenheit und deren Auswirkungen in Österreich Endbericht. E. P. J. Innsbruck. M. Koch. Ch. 2005.Res. G.3) and agrometeorological forecasts in Europe Austria Auer I. H. Jacobeit.Geophys. H.. Bergström.W.. Lipa. Petrakis. Müller W..1029/2006JD007103. Umweltbundesamt. Barriendos. P. Teil II. R. O. Yiou. Saladie. Änderung der Frosthäufigkeit in Österreich (Change of frost frequency in Austria). v. Verbund AHP Harlfinger O. Klimahandbuch der Österreichischen Bodenschätzung (Climatic handbook of the Austrian soil taxation). H. A. Koch. Maugeri.. Peter. Österreichische Nationalbank. Alexander. E. Fourth European Conference on Applied Climatology. 2002. 2007.-Beihefte zu den Jahrbüchern der ZAMG . Aguilar.V. Indices for daily temperature and precipitation extremes in Europe analysed for the period 1901-2000. Xoplaki. BMWA. Srnec. Auftraggeber: BMLFUW. 2000. Philipp.M. C. H. 12-15 November 2002. Auer. H. J. Auer I. 2002. pestdisease warning (see chapter 2. F. Brussels. Trnka. 1993. P. Reported country-related literature on operational used agroclimatic indices. 604-607. Trends of Spring Time Frost Events and Phenological Dates in Central Europe. F. Deliang. A. H. Der Förderungsdienst 48. Wien. Österle. T. Soja. Butler. 111. Tagungsbericht EMS/ECAM Moberg A. Selbstverlag der Alfred Wegener Stiftung.. ISBN 3-7030-0376-6. Suppan. Formayer. 1998. Drebs. E. Potzmann. Nekovar. Tueomenvirta. Almarza. Ungersböck. Founda. W. Alexandersson. Universitätsverlag Wagner.3. Klimakolloquium 20. COST725 establishing a European phenologial database for climatological applications: overview and first results. 25-29. R. BMBWK. D. BÖHM. doi:10.. D. Gruszcynski. Eitzinger. Endbericht von StartClim2004. H. März 2002. Caesar. L.C. Österreichische Hagelversicherung. Auer I. C.9 summarize reported country related literature and web links on the use and applications of agrometeorological indices in Europe. Brunet. Menzel. Vliet. J. Harlfinger O. M. Analyse der Auswirkungen der Trockenheit 2003 in der Landwirtschaft Österreichs . E. Croatian Meteorological Journal.M. 2003. S 77-80. ÖKLIM der digitale Klimaatlas Österreichs (The digital climatic atlas of Austria). Koukal.Klimatologie.Vergleich verschiedener Methoden. Lister. Werner. Kubu.. Schneider. Heft 9.8 and 2.D. Della-Marta. D22106. Korus. Klimatographie. Dittmann. P. Wanner.. Die klimatischen Bedingungen für den Qualitätsweinbau in Österreich (Climatic conditions for wine production in Austria). K. Jones. E.C. 7. J. M. E. Walther.. H. R. W. 2002. Universität für Bodenkultur. Wien.. Berlin Soja G. G. J.G. Tolasz. The Variability of Heat Waves and Dry Spells in the flat and mountainous Regions of Austria. Menzel.J. Sigro. A. Bulgaria Alexandrov V. Terra Nostra. Micela. I. A. M. J. Scheifinger H. R. Scheifinger. A. H. 2006. The Effect of Climate Variability and Change on Agroclimatic 38 . J. M.. ICAM/MAP 2005. Agroklimatische Kennzeichnung des zentralen Marchfeldes (Agroclimatic characteristics of the central Marchfeld region). Liderholm. Schriften der Alfred Wegener Stiftung 2003/6. Pandic. A.8. Koch E.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Table 2. A. Luterbacher. M. International Conference Climate Dynamics and the Global Change Persperctive. Petkova N. Italy (CD). 15–21. M. 2004.. Krakov.. (CD) 18 pp. 2006. Koleva. Agrometeorological Aspects of Drought in Bulgaria During Last Decade (1994-2003). 2005. Tomić.. Alexandrov.wiley. 53-66. UK: Ashgate Publishing Limited. Vučetić M. Rasprave–Papers.Risks and Challenges for the 21st Century”.). Vol. October. Kazandjiev V..M. E. Alexandrov V. (CD) 6 pp. Croatia Gajić-Čapka M. 2004. E. Husnjak. S.. Proceedings of the EWRA Symposium on “Water Resources Management. The snow regime in the Northern part of mount Velebit. Climate Variability and Change in Bulgaria during the 20th Century. Theoretical and Applied Climatology 79(3-4): 133-149. Croatian Meteorological Journal. 359–364. Silva Balcanica v. Drought severity index for Zagreb and its statistical forecast (in Croatian). 2004. J. V. Macedonia. 2000. Extraordinary meteorological and hydrological events in Croatia in 1996. M. Staneva (eds.. Issue 15. 107-115 Penzar B. 6. Turkey 2-4 September. Monitoring the 2003-2004 meteorological drought over Pannonian part of Croatia. Likso. Alexandrov. Proceeding on the 2nd European Conference on Applied Climatology.1002/joc.. 20. M. Climatol.. 1996. 26. Ek. Degree days in the Croatian lowlands. Ohrid 24-26 May 2006. Drought in Bulgaria: A Contemporary Analog for Climate Change. Vučetić V. F. Ohrid. 2213–2225. I. 5(2). 677–687 Mihajlović D. Raev and M. A case study on Utilization of Precipitation Indices in Bulgaria. B. 40. Phenological Development аs Indicator оf Meteorological Conditions. Vučetić. In: Knight. Alexandrov V. Vučetić. 1995. I.. Moisselin.M. Phenological Development Investigation of Common Locust (Robinia pseudoacacia L.. Dubuisson.. Moisselin. 6 pp. Temperature sums and climatic variations in the Parg region. Koleva.. J. 71–79. p. Gajić-Čapka M. 2004. Koleva. 2006. 331-342. Kazandjiev V. Climatic Characteristics of Drought in Bulgaria. C.. 1998. pp.. Alexandrov V. Duration of the Frostfree period during the Warm-half of the year in Bulgaria. Drought during the 20 Century. Slavov N. Koleva E. G. (in Croatian) 39 . Vučetić V. J. Izmir. T. 1–58. Short-term precipitation maxima in different precipitation climate zones of Croatia. Agroclimatic indices and simulation models Resources and Crop Productivity in Bulgaria. 13. Comparison of three mathematical models for estimation of 10-day drain discharge. Vučetić. V. Šimunić. Weather influence on weak crop of olives in Dalmatia during 1996. Vienna. V. 1931-2000.. Poland. 61-70. Snow Cover Variability and Change in Mountainous Regions of Bulgaria. Petošić. Int. 2006. International journal of Climatology. N.) Trees in Bulgaria. Hrvatski meteorološki časopis–Croatian meteorological journal. 17-20 October 1995. Austria. Aldershot. J. p. Alexandrov. D. Kazandjiev 2003. Meteorologische Zeitschrift 13(1): 19-23. Proceedings of the 3rd European Conference on Applied Climatology "Tools for the environment and man of the year 2000" Pisa. 23-26 May 2006. 1991. 2004. Slavov. 673–676.interscience. 11. Proceedings of the International conference on Water Observation and Information System for Decision Support (BALWOIS).. Slavov. Koleva. Proceedings of BALWOIS Conference-CD version.com. 1976. Bulgarian Journal of Meteorology and Hydrology 11 (3-4): 100-113 Slavov N. 2006.. E.2. (www. Theoretical and Applied Climatology. 2004. 1997.. N. 31. DOI: 10. V..1366) Pandžić K. 85. Schneider. 2000. Harrison. Hayes. Seminar: Drought evaluation and forecasting. Environmental 40 . Agricultural drought and spring barley yields in the Czech Republic. Potřeba závlah při predikované klimatické změně v České republice (Need for irrigation under the predicted climate change). Španjol.Nov. 251–261 (CD-rom). D. 2001. The phenological trends of the components of flood plain of South Moravia in the case of Ulmi-Fraxineta Caripin in the period from 1961-200. Plant. pp. Downing (eds. Mandić. Z. Climate Change and Agriculture in Europe: Assessment of Impacts and Adaptation. Granskog. D. Míková. and R... Z. R.chmi. Agricultural and Food Science in Finland 7: 161-179. 4. M. Climatic Change. 2006.cz/meteo/CBKS/sucho01 Litschamnn T. Spatial variability of heat waves in the Czech Republic and summer temperature pculiarity in southwest Bohemia. Palmerův index závažnosti sucha a jeho aplikace pro lokalitu Žabčice. Semerádová. Spitz P.. M.N. M. In: P. L. and M. A GCM simulation of heatwaves. Dubrovský. European Geosciences Union 2006. 2007. Bromand. Wild plants phenology. J. DIAS report Plant Production no. Butterfield and T.R... 2007. Voženílek. Valeriánová. Changes in components of the water balance in the Croatian lowlands. D. M. In proceedings from seminar held on 19. http://www. Meteorologické zprávy. Theoretical and Applied Climatology. Svoboda. 9. Atlas podnebí Česka (Climatic Atlas of Czechia) Finland Carter T. Rosavec. 1998.Czech Republic as a Case Study. Zaninović. V. Crop Production Conditions in the Northern European Region with Special Reference to Crop Protection. (Palmer drought severity index and its application of the Žabčice station). soil & Environment (in print) Tolasz R. Research Report No. Changes in the thermal growing season in Nordic countries during the past century and prospects for the future.. supplement 1... Danish Institute of Agricultural Sciences. Saarikko. Dubrovský. 2006. A. Pokorná. D.Zprávy. 2001. Žalud. M. Foulum. Hlavinka. Žalud.. A. Austria Trnka M. Rožnovský.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Vučetić M. Z. 2001. Semerádová.118-121. Wilhite. 2006.2006. Projection of Future Drought Conditions Using Drought Indices Applied to GCM-simulated Weather Series.R.. http://www.chmi. Kyselý. Meteorolog. Huth R. Jørgensen (Eds)..A. T. Ž.Nov. 46: 29-60.). Secular variations of monthly severity rating on the Croatian Adriatic coast during the forest fire season. April 2-7 2006 Vienna. 1995. 65. 2006. B. 2000. Kyselý J. Meteorologické zprávy: 59: 183-189 Mozny M. Carter T. D.E. Climatic conditions and constraints in the Nordic/Baltic region. Svoboda. Žalud. 111-117 Czech Republic Bauer Z. J. Brno. 2001. In proceedings from seminar held on 19. Vučetić.A. 2000. V. Wilhite. New Method for Assessment of the Drought Climatology . Monitoring of usable water supply in the surface soil layer under grassland. 2006. 16-47. 2001. Salonen. Gajić-Čapka. dry spells and their relationship to circulation.cz/meteo/CBKS/sucho01 Trnka M. K.. 59: 80-86 Dubrovský M. Semerádová. Denmark. Effects on spring wheat and spring barley in Finland. D. M. Part I. P. Forest Ecology and Management. M. M. R. Brno.E. Filip. 59. Možný. 234. Trnka. 59. Barčić. The Geological Society of America. L. 2006. Carter T. s. J. Hayes. Managing Drought and Water Scarcity in Vulnerable Environments: Creating a Roadmap for Change in the United States (18–20 September 2006). In: J. M. Shmakin.A. P. Available as pdf at: http://www. C.R. Saarikko R. Climate. Van den Hurk. The use of numerical weather forecast model predictions as a source of data for irrigation modelling. Available as pdf at: http://www. 109.2. 1996b. Climate Research 7: 243-252. Eur. France Boone A.. Fox. Assessing uncertainties in climate change impacts on resource potential for Europe based on projections from RCMs and GCMs.B. Noilhan. 115. Habets. Nouveau mode d'évaluation des possibilités héliothermique d'un milieu viticole. Clark. Carter. G. Jones. S. Huglin P. University of Oxford.. Nasonova. Estimating regional crop potential in Finland under a changing climate.R. 2005. Appl. 307318. J.A. Climatic Change. R.. E. Royal Institute of Technology.Hydrology.M. p. D.response to temperature and photoperiod under northern conditions. D. 247-252. T. 2002.de/struktur/institute/pfb/struktur/agrarmet/service/sr Chmielewski F. Facteurs explicatifs des critères de qualité de la recolte. Coupled heat and mass transfer model for soil-plantatmosphere systems. Bruns. J. 1117-1126 Huglin P. and T. A. A. LeMoigne... Niu. pp. Y. Carter. A. D. Agric. Venäläinen S. Jansson P. Mahanama. J. I. Meteorol.agrar.R. Res. Biologíe et ecologie de la vigne. Heikinheimo. Dirmeyer. B. Ed. 2004. Porgrès Agricole et Viticole (Montpellier). P.V. F. Gusev.. and R. París.E. Ledoux. Tanaka. Agrarmeteorologische Schriften 10 Humboldt University Berlin. S.huberlin. 2001. Hydrometeorological modeling of the Seine basin using the SAFRAN-ISBAMODCOU system. K. 81. The Rhone-Aggregation Land Surface Scheme Intercomparison Project: An Overview. Agric. Riou C. Phenological development in spring cereals . 2003. Noilhan. Physics and Chemistry of the Earth. 17. Noilhan. 1986. Carter. Vol.hu- 41 . Fortelius.M. A.. Agron. Haddeland.R. R.. and L. 321. Verant. 11. Stockholm. F. Payot Lausanne.. Carter T. Koster. G. D14105 Germany Chmielewski F. O. Pitman.M. 1996. K.hu-berlin.. E. Müller. and T.Y. S. Rötzer. Geophys. P. Agroclimatic indices and simulation models Change Unit. J. 2002. S. Verseghy. P. J. and T. J.A. Habets F. Klimawandel und Obstbau in Deutschland (KliO)– Projektspezifischer Zwischenbericht der Humboldt Universität zu Berlin. Blümel. Application au réseau modéles qualité en viticulture. Venäläinen A. Dept of Civil and Environmental Engineering. C. 1978. Meteorological data for agricultural applications. Agrarmeteorologische Schriften 8. J. Climate changes and trends in phenology of fruit trees and field crops in germany. K. Viterbo.E. Gomez. Saarikko R. Rousset F.agrar. 293 270-288.R. 79: 301-313. and Davis. J. Müller. Le Moigne. Lohmann. 5: 59-70. Henniges.agrar. Karlberg. 1961-2000. Morel. 357–371. E.. 2000. 1996a.1050. 12(4). Annual and spatial variability of the beginning of growing season in Europe in relation to air temperature changes. Acad. Humboldt University Berlin. 2007. 1998. 2004. 187-208. Available as pdf at: http://www. Fronzek S. Salo. For. Polcher.. Th. 1045 . 319-330. Estimating regional spring wheat development and suitability in Finland under climatic warming. 2007. Y. 27/23-24. Meteorol. Mitchell..de/struktur/institute/pfb/ struktur/agrarmet/forschung/fp/KliO_html Chmielewski F. and M. Habets. Saarikko. On the utility operational precipitation forecasts to served as input for streamflow forecasting. 1991. PIK Report 99.. 27. Dalezios. Climatol. 75-88. accepted.agrar. Proceedings of the International Symposium in Geographical Information Systems and Remote Sensing: Environmental Applications. Hydrological Sciences. Physics and Chemistry of the Earth. "Meteorology and Atmospheric Physics". Papamichail. Available as pdf at: http://www. Severity-duration-frequency analysis of droughts and wet periods in Greece. 87-100. The activities of the German Weather Service (DWD) in the field of agroclimatology. 45.G. Hupfer. E. T.K. pp. Gouvas.de/struktur/institute/pfb/struktur/agrarmet/service/sr Chmielewski F. Vol. Springer. (Eds. G. Dalezios N. 1991. Severity-duration-frequency analysis of droughts and wet periods in Greece. 23-24. K. Kropp J.affected areas from digital METEOSAT IR images. Potsdam. Müller. 751769. 61-67. 2003. Bampzelis. Humboldt University Berlin. Dalezios N.M. 45(5).R. Volos.hu-berlin.de/struktur/institute/pfb/struktur/agrarmet/service/sr Flörke M. Volume of Abstracts. 19p. Greece Dalezios N.M.R. L. Z. Blümel. E. Klimawandel und Obstbau in Deutschland (KliO)– Projektspezifischer Zwischenbericht der Humboldt Universität zu Berlin. Berlin. Spiliotopoulos. A. A. H.dwd. Liakopoulos. 24-26 May 2006. 751769. Y. Objective Assessment of Instability Indices for Operational Hail Forecasting in Greece. Available as pdf at: http://www. DWD Klimastatusbericht 2000. Matthäus. Features of frost.. F.D..R.Vol. D. Die mittleren klimatologischen Bedingungen in Deutschland (Teil IV). EU-MEDIN..) 2007. Kanellou. Supplement 1. Dalezios N.. Assessment of NDVI and Agrometeorological Indices for Major Crops in Central Greece. Annual Average Number of Days with Snow and Snowcover in Greece.agrar. Dalezios N. 2000. E. central Greece.R. On the Estimation of the Monthly. M. KLARA: Klimawandel – Auswirkungen – Risiken – Anpassung.de/struktur/institute/pfb /struktur/agrarmet/forschung/fp/KliO_html Chmielewski. Domenikiotis C. Appl. Meteorological applications 13. Greece. Theor. 8th Conference on Meteorology Climatology and Atmospheric Physics. C. pp. Phenology and Agriculture. 2007. Alcamo.de/de/FundE/Klima/KLIS/prod/KSB/ksb00/klimbed.. Assessment of global scale water stress indicators In: Lozán J.E. 7-9 November 2003. 45(5). Hamburg. 2004.. Papamanolis. and N. 2007.huberlin...) 2005. D. 2006. Hydrological Sciences. A. pp.. 1995. Extreme Temperatures over Europe: Expectations and Challenges for the 21st Century. 44. No. and E.. Classification of NOAA/AVHRR images for mapping of frost affected areas in Thessaly. Dalezios N. Loukas..P. Athens. Papazafiriou. Schneider. L. Agrarmeteorologische Schriften 12. 1025-1029. Liakopoulos.R. 25-31.Survey of agrometeorological practices and applications in Europe regarding climate change impacts berlin.R.M. Karacostas. Sakellariou.pdf Stock M. 2000. In: Integrating Natural Disasters and Risk Management. Henniges. Lavrediadou. Advances in Space Research. pp. J. 2002. Menzel. Schönwiese. 2007. and N.. H.R. M. Das Andauerverhalten der Schneedecke. L. http://www. (ed.S. Graßl. Drought assessment for the potential of precipitation enhancement in Northern Greece. Global change: Enough water for all? Wissenschaftliche Auswertungen und GEO-Verlag. 200-203 Friesland & Löpmeier. 42 . Loukas. A. Rachner M. 2000. Loukas. P. Vasiliades. Vasiliades. 2006. 15: 123-126. N. A. 25-28 September 2002. Budapest. A parlagfő pollen légköri koncentrációjának becslése meteorológiai tényezıkbıl. Bottai. Volume of Abstracts. szerk. RIAM 9: 116-117. 24-26 May 2006.. V. 2007. F. C. Proceedings of 6th Hellenic conference in Meteorology Climatology and Atmospheric Physics. Quaderno dei riassunti Convegno AIAM Vasto Caramanico 3-5/5/2005. Estimation of the Temporal Evolution of Drought Episodes in Greece with the use of the SPI Meteorological Drought Index.. Pavan. In: M. V. Amici. Morgillo. Universitätsdrucke Göttingen. Cacciamani. L.197-216 Szalai S. Karakostas T. 284pp. 31. 2005. Pasqui.Journal of the Science Food and Agriculture 84: (13) pp. Di Giuseppe. (editor) Szlávik Lajos. UK.S. Submitted to Int. 2007.. 2007. Mavrakis. Budapest Pék Z. Agroclimatic indices and simulation models Katsiabani K. L. Studio dei fenomeni siccitosi in emilia-romagna con il modello di bilancio idrico criteria (extended abstract). 2004. Sloboda [eds. (ISBN-13: 978-3938616-93-2) Pálfai I. 2007.2002. ISTE ltd. Drought sensitivity research in Hungary and influence of climate change on drought sensitivity. pp. 2006. 2004. kötet.179-188. Edited by Hartwig Dobesch.. R. Pavan V. European Meteorological Society 2005 Marletto V. and S. DEM based method for the determination of potential frost-risk territories. C. 2005.the use of GIS in Climatology and meteorology.. Tomozeiu R. (Drought studies with the Criteria water balance model). E. A. Cacciamani. K.. 42p. pp. 2007.. 2004. and S. Zinoni. Observed temperature changes in Emilia-Romagna: mean values and extremes. S. (Inland inundation and drought in Hungary) Hidrológiai tanulmányok. Kappas. Di Lorenzo. A 2003. Kleinn.. L. Ch. pp. B. G. Climate Research. C. 2006. M.2. Marletto V. Vízügyi Közlemények. ”Assessment of soil erosion estimate based on a high temporal resolution rainfall dataset". Bella.IF: 0. 217-225. Esposito. Spatial Interpolation for climate data . 43 . Climate change scenarios for surface temperature in Emilia-Romagna (Italy) obtained using statistical downscaling models. C. Tomozeiu. 2005. A. 1671-1674. The Evaluation of the Greek National Hail Suppression Project.871 Szalai S. Pierre Dumolard and Izabela Dyras. Busuioc. Theoretical and Applied Climatology. Nemeth. 8th Conference on Meteorology Climatology and Atmospheric Physics. (Estimation of atmospheric concentration of ragweed (Ambrosia elatior) Erdı és klíma V. and L. Vol 2.. (Drought in 2003). F. Athens. London.1007/s00704-0060275-z Tomozeiu R. Helyes. Szalai. pp. Botarelli. ISBN 978-1905209-70-5 Italy Ferrari R. 2004. Alessandrini. Évfolyam. Theocharatos.. C. EMS05-A00285.]: Global Change Issues in Developing and Emerging Countries. Alessandrini. Pavan.. Daily precipitation observations over Emilia-Romagna: mean values and extremes. Évi Aszály.(Hidraulic Engineering) LXXXVI. Zinoni. Botarelli.of Clim. évi 1-2 füzet. Quaderno dei riassunti Convegno AIAM Vasto/Caramanico 3-5/5/2005. 2007. (Drought monitoring in EmiliaRomagna). Hungary Bihari Z. Bella. A. Ioannina. pp. The effect of daily temperature on truss flowering rate of tomato. S. 75-82 Németh A. 614-620. Kartalis. RIAM 9: 32-33. Cacciamani. Belvizek és aszályok Magyarországon. M. M. Il monitoraggio della siccità in Emilia-Romagna (extended abstract).J. 393-400. DOI 10. Rossi. Wpływ niedoboru opadów na straty w produkcji ziemniaka i buraka. ARPA-Rivista ( climate change evidences from global to regional scale). 17.O.. Górska. CCXCI. Górski.(Calculation of first day of sowing using weather observations) Forskning og forsøk i landbruket 37: 295-301. 1997. Appendices Harstad M.. 27:1091-1101 Norway Årsvoll K.. Thesis of Master Degree. fenologia del territorio italiano. R. 57 pp.O. (A method for evaluation of crop yield losses due to the precipitation shortage) Rocz. Zmiany warunków agroklimatycznych i długość okresu wegetacyjnego w ostatnim stuleciu. Metodyka szacunku strat w produkcji roślinnej powodowanych deficytem opadów.E. Melior. 223-231. Antolini. Overvintring hos eng. Fenologisk utvikling hos timotei (Phenological development of timothy). Skjelvåg A. T. 81-87. 1994. CCXCI.. Doroszewski. K. InŜ. 59-78. Fenologisk utvikling hos timotei (Phenological development for timothy). 233-243. I. Perini L.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Cacciamani C. AR Pozn. Demidowicz G.Roma. Środ. NLH. V. Nauk Rol. Norsk landbruksforsknng – 1 215 –223. Marcinkowaska. Eng og beitedyrking III. Wpływ niedoboru opadów na straty w produkcji zbóŜ jarych w Polsce. Physics and Chemistry of the Earth. III.. Thesis of Master Degree. Doroszewski. F. Tibaldi. 57 pp. Środ. Evidenze di cambiamenti climatici dalla scala globale alla scala regionale. 1978. Post. (Effects of precipitation deficit on losses in the production of potatoes and sugar beets) Zesz. Campisi.og beitevekster (Cultivation of meadows and pastures. 1986. A. T.... Changes in agroclimatic conditions and in the length of vegetative period in the last century). Ås. Possibili scenari futuri sul territorio della regione Emilia-Romagna. A. UCEA . Melior. Risultati Conclusivi. G. Tomozeiu. (Effect of precipitation deficit on losses in the production of spring cereals in Poland) Rocz. 2002. Landbruksbokhandelen Ås ( written in Norwegian) Harstad M. AR Pozn.O.. ISBN 88-901472-6-1. G. 2006. Zinoni F. Første bermarksdag om våren ( First day of snowless ground in spring in Aust-Agder). Nauk Rol. G. CRA-UCEA Progetto di ricerca Climagri. et al. 1987. Doctorial Thesis( Dr. (impacts on yield). 1997. (w:) Długotrwałe przemiany krajobrazu Polski w wyniku 44 .. Characterisation of EmiliaRomagna region in relation with late frost risk.. Pavan. Post. and A. with special reference to snow mould fungi. S. Agricultural University of Norway. ( written in Norwegian) Skjelvåg A. Krakowiak. A. V. NLH. T. W. Overwinering of pasture species). Perini L. Górski T.. ISBN 88-901472-0-2 (Agroclimatic atlas).. Agric). Górski. Mågne Hårstad. Skjelvåg. Norway (Compillation of eight papers in scientific journals). Institutt for plantefag. T. 438. InŜ. Utrekning av første sådag ved vêrobservasjonar. 405. 1997. Deputat. 1997.O. 43-52. Appendices. 2004. Caratteristiche agrometeorologiche del territorio italiano e possibili scenari di cambiamento climatico. Studies on factors causing winter damage in Norwegian grasslands. 1986. T. Poland Demidowicz G. Zesz. Agrometeorological quantification of agricultural year in Poland. 2007. Baadshaug.. Demidowicz. Doroszewski A.. Institutt for plantefag. pedologia. Spoz-Pać. Marletto. 1996. (impacts on yield) Górski T. 2006. Górski. Demidowicz. 17. Atlante agroclimatico – agroclimatologia. Romania Mateescu E. 1999. 171-183. Mateescu. T.. ISBN 973-002675-9. 65-77. 2005. ISBN 973-8115-27-2. Agrometeorological monitoring system – decision-making instrument to prevent and mitigate the agricultural drought in Romania. (for assessing drought frequency). Atlas of climatic risk to crop cultivation in Poland. Central and Eastern European Workshop on Drought Mitigation. R. pg. I. Oprea. Wiej. Bucharest. Germany-Poland. 1 – 163. Geography of Vâlcea District – Theory and practice. 2004.. Poiana. CD-ROM. Povară R. XLI. Geographia Polonica.V. Proc. The frequency and areal of the agricultural drought in the south and south-eastern area of Romania. Woda Środ. 90-98. pg. Pescaru. AGRIS Publishing House – Agricultural Journals editorial office. 15-19 May 2005. 1990. 41-47. 2002. Workshop “Drought – Measures to mitigate the effects upon crops”.. Bąk. pg. R.. 21st ICID Conf. O. 2003. 2004. (spatial resolution and realization) Mager P. V. and B. Mateescu E.. Povară. Koźmiński C. Obsz. Turcu.. ISSN 1221-5339. IGBP-Global Change. Poznań (climatic water balance variability from April to October). 1999. Mateescu E. Puławy. and Kalbarczyk R. Oprişescu. Impact of the drought conditions upon the wheat and maize crops in the Caracalului Plain. Vol. M. ISBN 973-657-535-7. A.. Agricultural University of Szczecin. I. Frankfurt-Słubice. Assessment of the influence of the precipitation deficit in the autumn months upon the vegetation condition and the wheat crop in Vâlcea district. 2000. Adamiade. SITECH Publishing House.. Drought mapping in Poland using SPI. E. The hydric potential available for the winter wheat and maize crop in the Moldova Plateau. Kepińska-Kasprzak. 2003. Kuźnicka. Romania. D. Oprişescu. R. Identyfikacja okresów suszy atmosferycznej w okolicy Szczecina w latach 1963-2002. R. (for assessing drought frequency). Bucharest. Budapest-Felsogod. Study of the agrometeorological stress parameters and their impact upon the winter wheat during the earing-flowering-beans filling period – Scientific papers USAMV Bucharest.. Tuinea P.R. Łabęcki L. Kalbarczyk E. Vătămanu.. M. Drought phenomenon risk zonality on Romanian agricultural territory and its impact upon agricultural yields. Agroclimatic indices and simulation models zmian klimatu i uŜytkowania ziemi. 5 (14). Farad. Hungary. 12-15 April. Annual Scientific Papers Delivery Session NIMH. Changes in the intensity and frequency of occurrence of droughts in Poland (1891-1995). Górski. B. Oprişescu. Poiana Brasov. and Michalska B. Oprişescu. V. Offsetcolor Publishing House. Palade A. Mateescu E. 45 . Michalska. R. Mateescu. 2000. 2000. E. 2004. V. Climatic atlas of elements and phenomena hazards to agriculture in Poland. Alexandru. Marica.) 2001. N. 3-6 June 2002.V. Technical workshop on drought preparedness in the Balkans within the context of the UNCCD 25-26 October 2004. Craiova. Szczecin. Bucharest. Koźmiński C. 133-149. Râmnicu Vâlcea. (Identification of atmospheric drought periods in the vicinity of Szczecin). (eds. ISBN 973-86352-7-6. Volume III. 73. dedicated to the celebration of 75 years since the setting up of the Romanian Institute of Agronomical Researches (ICAR) 1927-2002. R. pg. Series: A. IUNG. Papers compendium. Tanislav.. 2003. 145-156.2. Lalić B. 1-2. Tomše.T. Belgrade.67-72. A. Extreme temperatures in Vojvodina during 1948-2003. Serbia. Lalic B. Mihailović D. I. 49-55. “Crop: Between soil and climate”. Mihailović. Proceedings. Seljak. 22-25 September 2004. Novi Sad. Heat index for extremely high temperatures during july 2000 in Novi Sad. reviews and analyses. Contemporary Agriculture.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Serbia Arsenić I. internal technical paper. Str. 2002. Malinović. Macedonia.T. 19-24 September 2004. S.T. In: Zbornik prac. Mihailović D. Guidelines for Preparing of regular Agrometeorological Bulletins. Novi Sad. 42. Using automatic meteorological stations for early pest warning and irrigation prediction. 54. International Conference on Sustainable Agriculture and European Integration Processes. 29. Lectures and Papers presented at the 4 th Slovenian Conference on Plant protection in Portorož. Agrometeorological modelling – powerful tool of modern agriculture.. Prediction of malting barley phenology based on air and soil temperature accumulation. 54. Proceedings. and Sušnik. 42. Radlović. Arsenić. D. Knapič V. Abstracts of Fifth Annual Meeting of the European Meteorological Society (EMS).. Indicators of desertification in Serbia.. 2007. 2005. Eco-conference 2004. Bratislava 1999. Novi Sad (Serbia). Lalić B. Škerlavaj. Proceedings. Conference on Water Observation and Information System for Decision Support. 1999. 19-24 September 2004. zv. 54. D.T... and Djorić E.T. September 12-16. BALWOIS 2006. B. In: Zbornik prac.T. March 4-5. 6th edition. Proceedings.. D. 312-317. 2005. 1997. Operativni agrometeorološki modeli za izračun vodne bilance kmetijskih tal (Comparison of three operational irrigation models).Eco-conference 2002. Nejedlik P. I. Estimating the quantitative parameters of chaos from filtered and non-filtered air temperature. Novi Sad. ISSN 1812-7053. pp. 2004. Mihailovic. Ohrid. 2007. 17-18 January. Agroclimatic condition of the Cereals growing within East Slovak Lowland Region. 1-2. 1-2..T. S. Scientific meeting organised by Matica Srpska –Health of people in Vojvodina. 69-75.T. Periodical of Institut for Field and Vegetable Crops. Kostadinov S. Serbia. 85-9l. 2002.. ( In Slovak).. Proceedings. 1999... Mihailović. 335-339 GPAB.. Pankovic. B.. K. 2001. 50. I. 2005. and Spasov P. Arsenić. An interactive software for climatological characterisation of Vojvodina province. Koči. Novi izzivi v poljedelstvu 2002. Novi Sad. Republic Hydrometeorological Service of Serbia. Analisys of Lange's rain factor for climatic characterisation of Vojvodina province. SHMU. 25-28 September 2002. Contemporary Agriculture. 63-71. SHMU. L. Proceedings. Utrecht (The Netherlands). Agroclimatic condition of the Cereals growing within East Slovak Lowland Region. Koči. Period.. 2005. 1999. ( In Slovak). Mihailović. D.. Contemporary Agriculture. 329-334. May 23-26. 19-24 September 2004. D. I. 46 . 1999. 164-169. Slovakia Nejedlik P. Slovenia Kajfež-Bogataj L. Beber. Bratislava 1999. zv. Lalić. V. Lalić. Lalić B. Mihailović D. 2006. D. International Conference on Sustainable Agriculture and European Integration Processes. International Conference on Sustainable Agriculture and European Integration Processes. G. Mihailović. M. 2004. Völksch. P. nitrogen. A. P. Thin Layer Formation and Evaluation. M. R.. Application in SW Slovenia. 2004. WMO CAgM/ET. In press. 169-184. 65–78. Lehning M. Brown.. Calanca.1029/2004WR003254. 35/3. 550– 563 Jasper K. J. Global warming and the summertime evapotranspiration regime of the Alpine region. VOL. Navodila za uporabo modela IRRFIB. 2006. Differential impacts of climate change on the hydrology of two alpine river basins. Jasper K. A physical SNOWPACK model for the Swiss Avalanche Warning Services. On-line irrigation support system in Slovenia./.B. I. Lehning M. 2111-2128.1016/j. Gyalistras. Schmid. Riedo M. A pasture simulation model for dry matter production.11. Part I: Numerical Model. podatki za uporabo v modelu.. and M. Calanca. Fuhrer. D. Technol. doi:10. 1998. Calanca P . Lehning. Sci. Processes. Wild. 47 . /Cold Reg. Oddelek za agronomijo: 147 str (in slovene). 2007. Sušnik A. available online. Climatic Change 79. Calanca P.2006. model IRRFIB. Climate Research. Ecological Modelling 105: 141–18 Torriani D..2. Vodni primanjkljaj v Sloveniji in možni vplivi podnebnih sprememb. 35/3. 123-145. Potential effects of changes in mean climate and climate variability on the yield of winter and spring crops in Switzerland. DOI 10. 2004. I./. Calanca. DINA Research report no. and Kurnik B. Matajc.001.gloplacha..L. 2006. water and energy. Meteorological Appl. A physical SNOWPACK model for Avalanche Warning Services. Agroclimatic indices and simulation models Kurnik. Roesch. 2006. I.. P.. Fuhrer. Magistrsko delo. Climate Research. Gustafsson. P.. Zappa. J. Journal of Hydrology 327. 40. Biotehniška fakulteta. Climate change and drought occurrence in the Alpine region: how severe are becoming the extremes? Global Planetary Change.. Sci. D.1007/s10584-006-9103-9.B. 2006. ALPINE3D: A detailed model of mountain surface processes and its application to snow hydrology. Part III: Meteorological Boundary Conditions. M. J.. Beniston. 2002. A. Agrometeorological support of fruit production... /Cold Reg. 2006a. M. Interannual variability of summer mean soil moisture conditions in Switzerland during the twentieth century: A look using a stochastic soil moisture model. Water Resources Research. /Hydrol. P. Jasper. 2002. doi:10. Bartelt. Fuhrer. 26. Nguyen. 2006. K. J. S. On-line agrometeorological application with decision support at the farm level. Kodrič. 113-129. Switzerland Bartelt P. COST 718: Meteorological application to agriculture. Technol. M. Fierz./ 20. W12502. Grub Rosset. C. Satyawali. 109: 113-121 Sušnik A. Sušnik A.A. B. (Supplement):8186(2006). Univerza v Ljubljani. Calanca P. and fluxes of carbon. Stähli. Fuhrer. Changes in summertime soil water patterns in complex terrain due to climatic change. T. fi/data/sade5.html portal.html chmi.hr Czech Republic chmi.ac.fr/meteopro/meteopro.fi/data/halla.de/Forschungsstelle/index.9.htm Germany agrar.at/phaenologie agrarwetter.asso.cz/meteo/CBKS/sbornik04/prispevky/MOZNY.fmi.cz Denmark eucablight.fmi.de/Agrarwetter/GFI_en.com/Wetter/Agrarwetter.org/EucaBlight.htm 48 .php srs.cz/meteo/om/inform/se.fi/data/lsumma.cz/meteo/CBKS/sucho01 chmi.pdf chmi.html chmi.fi/products/weather.3) and agrometeorological forecasts in Europe Austria zamg.agriculture. Reported country-related web links on operational used agroclimatic indices.fr/Situation-Hydrologique.fi/data/lumi.html pv.de/struktur/institute/pfb/struktur/agrarmet/forschung/fp/KliO_htm agrar.gif climate.htm agrowetter.hu-berlin.html chmi.com/expert/index.htm agrowetter.gif climate.at landwirt.ac.fr/srpv.gouv.at proplantexpert.asp planteinfo.cz/meteo/ov/aladin/results/index.de/Agrarwetter/fbidx.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Table 2.fmi.gif climate.fmi.cz/meteo/ok/oba/obs/o.fr ecologie.htm eaufrance.hu-berlin.dk Finland climate. pestdisease warning (see chapter 2.at zamg.gif ilmatieteenlaitos.fi/portal/page/portal/kasperit France acta.mtt.cz/meteo/ok/dpp.gouv.jsp Croatia meteo.de/struktur/institute/pfb/struktur/agrarmet/service/sr agrarmet.at hagel.html adcon. nl/nl/content/agri-monitor/pdf/Febr2007Oogstschadeverzekering.it/iphen Netherland lei.de/Agrarwetter/phyto.doc agrometeorologia.rlp.pdf topshare.sicilia.htm agrowetter.ro Slovakia galati.jrc.emr.html imgw.de/produkte/doku/Prognose/kornfeu_doku.html Romania agriplus.pl imgw.nl/cost725 Norway bioforsk.htm agrifish.it arpa.de/produkte/doku/Prognose/heutrocknung_doku.de/de/FundE/Klima/KLIS/prod/KSB/ksb00/klimbed.htm agrowetter.htm sias.html p7115.typo3server.no Poland agrometeo.htm?idlivello=120 arpa.emr.it/ia_siccita/index.sk/vitis shmu.it/marsstat/Crop_Yield_Forecasting/cgms.sk Slovenia dmcsee.regione.it ucea.2.pl/wl/internet/zz/pogoda/snieg.de unwetterzentrale.ro inmh.html imgw.bayern.htm agrowetter.de/internet/global/startpage.jrc.de/Agrarwetter/PNP_en.pl/wl/internet/zz/oddzialy/poznan/prognoza/prognoza.de/uwz/regen.it/marsstat/Crop_Yield_Forecasting/METAMP/04000002.it/wcm/ermesagricoltura/fitosanitario/home_sezioni/home_fitosanitario.info/index.org 49 .it/sim/?telerilevamento/innevamento ermesagricoltura.de/agm/ ISIP.jrc.wur.de/produkte/doku/Prognose/mais_modell.it/Public/CGMS/doc/GridWeather.pdf dwd.htm eumetsat. Agroclimatic indices and simulation models agrowetter.dlo.de/de/WundK/Warnungen/index.htm agrowetter.HTM agrifish.pl/wl/internet/zz/pogoda/opady.nsf/start/Home_Am?OpenDocument dwd.de lfl.php?475 Italy agrifish.html am. ch meteotest.ch 2.ch meteodat.html slf. the actual severity of that disease.3 Communicative value and limitations of the agrometeorological indices and simulation models During the eighties many agrometeorological simulation models and indices were set up and applied with the idea to automatically solve problems affecting important sectors of agriculture.ch slf.ch/web/en/services/agriculture/the_agricultural_industry_professional.ch meteoschweiz. In fact. In any case. as their name suggest. Anyway. for example.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Spain hispagua. In fact. from the irrigation to the pest and diseases control to the optimization of the yields. simulation models. empirical models usually have qualitative outputs that give an idea of what is happening in the field. On the other hand.es/portal/secciones/acm/aguas_continent_zonas_asoc/ons/ Switzerland agrometeo.htm mma.ch/swiss-snow/snowinfo.cedex.ch/web/en/services/product_overview/5dayforecasts.ch blw. after a period of study and scientific efforts.ch phytopre. mechanistic models are able to give quantitative information concerning. Moreover.html meteoswiss.ch/agroscope fusaprog.html meteoschweiz.es/documentacion/especiales/sequia/indicadores_sequia. agrometeorological indices and simulation models require meteorological data as input for calculation so the quality of these data result to be a crucial point. the use of indices and models can be reconsidered as an important tool for the support to decisions in agriculture. models can supply the end-users with different information depending on their structure. are a representation and a simplification of the different phenomena. data need to be 50 .admin. the experience was useful and in the last years. Agrometeorological indices.admin.ch slf. are not a precise description of a specific environment but represent a good indication of the territory characteristics.es mma. such as the potential risk for a specific disease. This approach was obviously fully unsuccessful and led to a lack of trust in these methods.ch/avalanche/avalanche-en.html sopra. Conversely. in fact. before any further process.2. 2. both qualitative and quantitative. In this way agrometeorological tools represent a valid support and a fundamental base for a correct management of a modern and efficient agricultural activity. However. 2003. with the aim to simulate the potential production. Penning de Vries. Three categories of variables can be recognized in dynamic crop simulation models: state.3.2. 2003). soil water content and can be measured at specific times. Sirotenko and Abashina. the Netherlands and the United States include APSIM models (Asseng et al. branching pattern as well as potential flowers/grain filling sites. 2. dry matter partitioning. which were especially applied in the new EU-members countries in the past. Here. amount of nitrogen in soil. but an overview is given by Sirotenko (1983) and Baker (2001). plant.3 Application of process oriented models in Europe (Subgroup 2) 2. models from the “School of De Wit” (Van Ittersum et al. needs to be analysed.5). Agroclimatic indices and simulation models checked both for the presence of possible gaps in the series and for the consistence of single data. The state variables are quantities like biomass. understood. Those aiming at crop-specific behaviour contain modules for phyllochron. dry matter production. or forcing functions. interpreted and eventually corrected before making any decision. Driving variables.1 State of the Art Process oriented crop simulation models have been studied for more than 50 years. 2001. The water and nutrition limited environment is added by models of soil water balance with transpiration by crop. and nitrogen transformations in soil with remobilization within plant. Crop simulation models are based on physical plant processes. Phenology. are the core processes of the model. 2000). photosynthesis. 1989) and the DSSAT family of crop models (Jones et al.. daily effects of change in growing environment on plant growth and development are simulated. As a consequence their output.. 1994). These models are not described in this report.g. Models of effects of weeds and pests are being developed and are available in the new generation of crop simulation models (Dadhwal 2003). The three most important “schools of development” from Australia. and their 51 . a mostly still unknown group of crop models in the western research community are the models developed in the former Sovjet Union (e. rate and driving variables (Fig. For these reasons indices and models should be used by trained users and the personal scientific knowledge and technical experience have to be taken in great consideration. describe the effect of the environment on the system at its limits. 5 presents a crop simulation model in computational iterations and the time step of the model. adapted from Delécolle et al. 1992. 2007).5: Simplified scheme of a crop simulation model (Dorigo et al. Figure 2. 52 . spatial data analysis techniques and geographical information system (GIS) can help to increase information of these outputs to a larger area (Guérif and Duke 2000). chemical and biological processes during crop growth (Dadhwal. on local scale (Fig 2. Dorigo et al.. nitrogen and water balance. need more or less various input data. 1992) Figure 2.. depending on their complexity. At each iteration. Crop simulation models. The model DSSAT (Decision Support System for Agrotechnology Transfer) for example. As crop simulation models produce a point output. Output is flowering and maturity data. needs information about weather. yield. soil. 2003). Their values depend on the state and driving variables according to rules. leaf area and harvest index etc.6). for example meteorological variables. 2007.Survey of agrometeorological practices and applications in Europe regarding climate change impacts values must be monitored continuously. These variables represent for example flow of material or biomass between state variables. Each state variable is linked with rate variables and describe their rate of change at a certain instant as a result of specific processes. vegetation state variables are updated and based on the input driving variables as well as the values of the state variables at the previous time step (Delécolle et al. which are based on knowledge of the physical. management and genetics. 6: Input.. Figure 2.11 and Figure 2.2 Present use of process oriented models in Europe As shown in Table 2. one count per crop and country) 53 . Alexandrov.2. 2000) the most frequently used process oriented crop models for research or operational applications in Europe are DSSAT (CERES models) (Jones et al.and output data of the crop simulation model DSSAT (Decision Support System for Agrotechnology Transfer) 2.g. WOFOST/SUCROS and STICS. WOFOST is the only model which is operationally integrated at the European level for the European crop yield prediction system. 2003). covering all countries (see also chapter of subgroup 4).3. however. with distinct differences between countries.7 as a result of the COST734 and literature survey (e. Agroclimatic indices and simulation models Figure 2.7: Reported crop model applications (operational and research.10 and 2. Survey of agrometeorological practices and applications in Europe regarding climate change impacts Table 2.10: Reported operational applications of process-oriented models in Europe – Summary 54 . 11: Reported scientific applications of process oriented models in Europe – Summary. 55 .2. Agroclimatic indices and simulation models Table 2. however. Most crop simulation models in Europe (Fig.. in specific often include the assessment of the dependence of growth.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Clearly it can be seen that research applications dominate and that only few models are already applied operationally at the beginning of the 21st century.g. Regionally. so the number of applications is often related to personal curricula and therefore often temporary. the assessment of yield response of crops by changes in production techniques such as fertilizing. plant protection etc. 2000). potatoes.8) are applied for annual crops. especially in research. Model applications. especially cereals and maize. The main application of the crop models is clearly in climate change impact research on agriculture (e.8: Reported crop model applications (operational and research. reflecting the economically most important crops in Europe. or WOFOST by Research Institutions in the Netherlands. Downing et al. oilseeds and others play an important role which results in specific model applications. Harrison et al. the assessments of crops development and related timing for crop management tasks. The applications however. Examples are the STICS model developed by researcher on INRA in France. cultivation. especially for operational applications. and early warnings or mitigation of damages from extreme meteorological phenomena and processes. development and yields of crops on limitations of soilwater regime. are often related to a relatively small group of researchers or single persons in the different European countries. irrigation. 1995. Often the number of national or European applications of the relevant models is related to established research institutions working on model developments. 2. whereas the operational applications have the focus on crop yield forecasting. sugar beet. Figure 2. Most operational applications are reported in the group of pest and disease models (see country reports below). one count per model and country) 56 . also permanent grassland. EPIC was especially used for determination of nitrate leaching and crop management assessments for various crops and crop rotations (Cepuder et al. 2001. or over a range of agrohydrological conditions. Several pest and disease models are used in Austria operationally. 2001. 2005b).. Eitzinger et al. 2002. provided and used by HBLFA Gumpenstein. Alexandrov et al.proplantexpert. Dynamic crop yield models are not yet used operationally in Austria.agrar-net. including also statisticalempirical approaches and pest-disease models . It has been applied as a tool for the analysis of yield risk and interannual yield variability.info. Müller et al. maize and soybean) and crop field water balance (Cajic et al. Stenitzer et al. 2002. in quantitative land evaluation. An overview of most important operationally applied pest and disease models used in Austria (and other countries) can be found at www. 2003..com/expert/index. The FAO-GRAM grassland yield model (Kromp-Kolb et al. vegetables and other crops (for more details see under “Germany”). of sowing strategies.. 2005a. 2000. 2006). by the Austrian agricultural extension service (http://www. The main users are farmers and agricultural extension services. such as regional assessments of crop yield 57 . Alexandrov and Eitzinger. Also SIMWASER (Stenitzer..zepp. 2000).. 2001. WOFOST (Eitzinger et al.jsp) applied e.. dynamic crop yield models such as from the DSSAT model group were used in Austria for assessing impacts of climate change and variability on crop yields (especially winter wheat and spring barley. In research. PERUN were used for similar tasks.at/). 2005) is a proecess oriented model for crop water balance combined with a statistical approach for grassland yield for detection of drought damages on a daily base and on field level scale and still in a testing phase. effects of climate change and critical periods for use of agricultural machinery..g. 2006. Agroclimatic indices and simulation models In the following all reported model applications (with the possibility to be incomplete) in European countries are described. Eitzinger et al.. Schmid et al. many of them within the decision support system PROPLANT (http://www. However. The model has also been used for predictive purposes..2. currently the AMBETI agrometeorological model (for crop water demand and canopy climate) is applied for operational daily (and hourly) forecasts through the Austrian Weather Service (carried out by German Weather Service).. including 26 models for cereals. 2007. Stenitzer and Murer. Alexandrov et al. of yield variability over soil types. In Bulgaria WOFOST has been used in many research studies and was adapted for three types of crops – winter wheat. 2004. orchards. 2003. maize and soybean for operational use (Kazandiev and Georgieva.. 2007). of relative importance of growth determining factors. 2004). 2003. Alexandrov and Eitzinger. de bary) where a mathematical forecasting model (Cvjetković et al. maximum leaf area index (LAI) and grain yield for the present and future climate. 2006): Fire blight (Erwinia amylovora) is used Billing's Integrated System (BIS. The input wheather series representing the change climate for the CERES-Maize model was created by the stochastic weather generator Met&Roll for different climate change scenarios during the 21st century. To determinate major changes in maize growth. 1998. 2006) is used for Strawberry Plantations. 1999.. Other applications are for Potato late blight (Phytophthora infestans mont. 1998. Fire blight (Erwiania amylovora) is predicted by a forecasting system using Internet (Cvjetković et al. 2006) is used which is based on "negative prognosis" (combination of PhytProg. de bary) (Šubić and Cvjetković. 2005) and CERES-Maize (Alexandrov and Slavov. development and yield in the important agricultural area over the period 1949-2004 and projection at the end of the 21st century. Alexandrov and Eitzinger.. 2001. 2001) and Gray mould (Botrytis cinerea) where the prognostic model BOTMAN (Miličević et al. 2000)... Phy models). water balance. 1999. Other models which were used for scientific applications are WEATHER-YIELD (Russian model) for simulation of yield and water balance at the regional and field based level and ROIMPEL (Audsley at al. Also multifactor statistical models were used for similar purposes.. biomass. For scientific applications CERES-Wheat (Alexandrov. NegFry and Pro. CERESMaize has been applied as a tool for the assessment of maize phenological stages. 2000. and regional yield forecasts. 2006) for regional and national assessments on yield under changing climate. 58 . 2000) were validated for the assessment of climate change impacts (especially with focus on drought) on yield. detection of adverse growing conditions by simulation-monitoring the agricultural season. 2006b). The Croatian Agricultural Extension Institute (CAEI) uses two types of agroclimatic stations: Agra and Methos for forecasting the occurrence and spreading of causative agents of plant diseases (Bičak. Alexandrov and Hoogenboom. In Croatia CERES-Maize is used to assess climate change impact on maize yield at the regional scale for scientific investigations in the frame of EUproject AGRIDEMA (Vucetic. 1997. 2006a.. Crop desease models are appleid follows: Operational use of process oriented models is done for for Tomato late blight (Phytophthora infestans mont. adaptation options. 2000. Alexandrov. estimation of benefits from irrigation and from fertilizer use. Hitrec et al. The linear trend anallysis and Mann-Kendall and Spearman rank statistics have been applied on the results representing the present climate. and regional and field based investigations ).Survey of agrometeorological practices and applications in Europe regarding climate change impacts potential in the form of maximum yield levels. Lecomte et al. 2002)) and for spring barley CERES-Barley (effect on yield. 1996). adaptation options (Žalud and Dubrovský. The Newhall simulation model (NSM) was used to determine the soil climate regimes (both hydric and thermic ones) for the present and changed climate. AGRICLIM is a new developed software for the assessment of agricultural production potential. Eitzinger et al. For Maize CERES-Maize was applied (effect on yield. (2006). 2003). Crop pest models as research tools were applied for the European Corn Borer.... including several agrometeorological algorithms and a phenological model (Trnka et al. For the Colorado Potato Beetle CLIMEX was applied to assess the effect of climate on the CPB climate niche (Trnka et al. WOFOST (effect on yield. Since 2006 a new operational pest and disease monitoring system (http://www. Thysen and Detlefsen..srs. STICS (effect on yield.. 1991. which includes for wheat the CERES-Wheat model (effect on yield. water balance (Dubrovský et al. water balance. water balance and phenology (Trnka et al. In Denmark a simlified grassland yield model on a daily basis (Søegaard et al. 2004). 2006). Downy mildew (Plasmopara viticola) is used Müller forecasting system. water balance and adaptation options (Trnka et al. 59 . 2006). 2006). 2005) and an irrigation scheduling programm (Plauborg and Olesen. Trnka et al... 2007)) and CLIMEX (effect of climate on the European corn borer climate niche (Trnka et al.. water balance. Trnka et al.. In the Czech Republic several research applications which crop models were carried out.g.combination of PhytProg and Smitht models). (2006) and Trnka et al. 2006). Eitzinger et al. 1966 . adaptation options (Trnka et al. Agroclimatic indices and simulation models 1998). 2004a. Apple scab (Venturia inaequalis) is used Mills-LaPlante model by temperature sums for predicting the beginning of spores flight (Creemers et al.. New version was developed and tested by Kapler et al.dk) are applied operationally.2... It is based on the set of degree day models and combination of the statistical and process based pest/disease models in combination with observed weather data.cz/srsmapa/) covers over 30 agricultural and horticulture species and almost 100 pests and dieseases. likelyhood of the shift from monovoltine to bivoltine populations in the Czech Republic (e.. 2004. Potato blight (Phytophthora infestans) is used mathematical forecasting model which is based on "negative prognosis" (Ullrich and Schrödter. 2003)). namely ECAMON (effect of climate change on development of ECB. 2006)via internet (www. 2004b.planteinfo. 2008).. The model SHOOTGRO was tested for wheat (Zalud et al. 2007).njf2007. Finland (Carter et al. sunflower. 2000). 1995. Delia brassicae and Psila rosae development have been forecasted based on simple models using effective heat accumulation as the input.. Europe (Porter et al. Olesen.. barley. Planned operational use of process-oriented models in Finland is reported for the development of fiber content in silage grass. Carter et al. For research applications CERES-Wheat is used for for yield estimation under changing climate and was tested for several sites and applied over a 10 km grid nationally in Finland (Carter et al. 60 . 1991)). In France an experimental operational monitoring is proposed from 2003 by the INRA unit Agroclim in Avignon. CropWatN for yield estimation over a 10 km Finnish grid for changing climatic conditions (Karvonen and Kleemola. 1996). 2004. for potato and barley yield and quality monitoring and for certain pest and diseases (https://portal. irrigated maize. 2005).. 1995)). 1996)) and European corn borer (Ostrinia nubilalis model. CLIMEX has been and will be used for predicting the direct effect of abiotic factors on establishment.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Models used for research studies are CLIMCROP for wheat. Other models used for research are AFRC-Wheat (calibration and performance testing for Finland (Laurila. Climex® is the main tool to be used for studies on climate change and predicting pest risks for 2020. grass. rapeseed.dk/).fr/veille_agroclimatique/)... 1996)). 1996)). Pest and disease models and warning systems have been published during the NJF congress 2007 in Denmark. oilseed rape simulating effects on yield..inra. 1996. A combination of CLIMEX and “habitat” models will be developed by Sini Olander at the University of Helsinki.avignon.. barley... 2050. Finland (Kaukoranta. (please see the NJF. permanent pasture) for 10 representative sites. 2080.org/EucaBlight.asp). pea. survival and development of pest populations. Research studies with pest and diseases models were carried out for potato blight (Phytophthora infestans model. Carter et al.fi/portal/page/portal/kasperit). Carter et al.eucablight. potato on effects on yield and water balance and to assess adaptation options in terms of timing of field operations (Olesen et al. columbia root-knot nematode (Meloidogyne chitwoodi model. 2000. Finland (Tiilikkala et al. DAISY was used for wheat. Saarikko. using the STICS model for a set of given soil and crop system parameters (http://www.home page http://www. maize. Potato late blight model is based on Nordic-Baltic collaboration and use of a special blight model published (http://www. 2000.mtt. water balance and nitrogen cycling in order to catch crops to reduce N leaching (Olesen et al. 1995. for the monitoring of 7 crops (wheat. potato cyst nematode (Globodera rostochiensis model. .agreste. maize (pyraausta nubilalis. Milvit.2. Seguin and García de Cortázar.gouv. Agroclimatic indices and simulation models For grassland the ISOP monitoring system is operated by Meteo-France for the Ministry of agriculture and fisheries (AGRESTE system. The advices are operationally diffused by the regional services of PV (SRPV). other for : eudemis. with the collaboration of research (INRA). More information is available at www. STICS is currently further improved for permanent grassland and vine (García de Cortázar Atauri. Ruget et al.g. 2004. 2006). rapeseed (sclerotinia).jsp). Milstop: Plasmapora viticola. together with irrigation bulletins and spring frost warnings.g.. Brisson et al. 2006) adapted for pastures on a daily time step and a 20km grid for assessment of general conditions for forage resources during the growing season and estimation of production losses at the end.fr/stics.zepp.com/expert/index.gouv. SCEES.. for example. 2004) and was also adapted for intercropping (Brisson et al. 1997) and SUCROS were used in some studies as well (e. the STICS model is widely used at INRA and other organizations for scientific investigations (Brisson.info. Yello: Puccinia striiformis). 2004. Presept : Septoria.. oidium). 2006). extension services (http://www. 2003. 2004. 1999).proplantexpert.htm). within the decision support system PROPLANT by e. Dürr et al. In research. a few privates firms also try to start a market with these forecasts. potato (phytophtora infestans).fr/srpv. ITV). Available models can be found for example at www. vine (EPI 89.agriculture. CROPSYST (Stockle et al. corresponding to the 21 administrative regions. sunflower (phomopsis). The main providers are Meteo-France. CTIFL. Pest and disease models for several crops are used..agriculture. Some agrometeorologica regional institutions are also disseminating this information. driven by the statistical service SCEES) in cooperation with INRA. INRA and the main user is MAP (ministry of agriculture and fisheries). For Wheat: the AMBETI soil-plant- 61 . apple (cydia pomonella.inra.avignon. Additionnally. 2006. and the Meteo-Pro software as an output of automated stations. for the ‘Service de la Protection des Végétaux’) and of professional institutes (Arvalis. conchylis. Top: Pseudocercosporella. Clean Arbo: venturia inequalis) and plums (grapholihta funebrana) and the service is available online (http://pv. More information can be obtained at http://www..fr/. Pest and disease algorithms and models for operational use have been built-up by the combined efforts of services from the Ministry of Agriculture (PV. In Germany several process oriented models are used already operationally. With this services several pest and disease models for several crops are applied including winter wheat (Spirouil : Puccinia triticina. sesamia). using the STICS model (Ruget. Brisson et al. 2006. 2006.htm). For potato: SYMPHYT1 and -3. WZU.agrarmet. 2006): effect on yield and water balance. 2005): effect on yield and water balance. SWIM (EPIC) (Krysanova et al..ISIP. For Maize: AMBETI is used for hourly and national wide prediction of water demand and canopy climate. http://www. field based.de. DAYCENT (Schaldach and Alcamo. regional assessment. 2007. 2006) is applied by the agrometeorological department of the German Weather Service (DWD.ISIP. which is also used as input for other models. 1999.(2006): effect on yield and water balance.de/Forschungsstelle/index.info/) is used for Puccinia recondite prediction of first occurrence and infection pressure and SIMSEPT (www. oilseed (Rape). For scientific purposes following process oriented models were used and applied for : Wheat: AGROSIM (Mirschel et al. LAUS (Friesland and Löpmeier.info/) is used for sugar beet to estimate begin of infection. SIMBLIGHT for estimation of Phytophthora infestans first occurrence and infection and SIMLEP a Population dynamic of potato beetle (Leptinotarsa decemlineata) is provided as well by (www.zepp. APSIM (Wessolek and Asseng. 2005)): effect on yield and carbon balance.de.de.. potato and sugar beet and many other crops (34 crops). 2005): effect on yield. (sub)field based and regional. 2005. Stehfest et al.info/). for services of farmers and advisors.ISIP.de. Pest and disease models for wheat are SIMCERC (www.de. FASSET (Berntsen et al. 1995. field based. infection density of Cercosporella beticola.de.zepp. regional.. a population model (number of aphids per ear and flag leaf) is used for cereal aphids in wheat (Sitobion avenae). www. http://www. 2005): effect on yield and water 62 .. CERCBET1 and -3 (www. AMBETI is also in use for grasslands. PUCREC (www.zepp. HERMES (Kersebaum et al. (Stock. N-uptake.ISIP. 2004.info/) for phenological development of winter cereals. Kersebaum 2007): effect on yield. SWIM (EPIC).ISIP.info/). Stock.de. http://www.de/Forschungsstelle/index. 2005): effect on yield and water balance. a decision support model for Sklerotinia sclerotiorum infection pressure for rape is provided by (www. nitrogen leaching.info/) for prediction of Septoria tritici and Septoria nodorum pressure. http://www. adaptation. Maize: HERMES (Herrmann et al.. SKLEROPRO. Friesland and Löpmeier. However. field based..zepp.zepp. http://www.htm). 2006. Gerstengabe et al. www. regional.Survey of agrometeorological practices and applications in Europe regarding climate change impacts atmosphere model (Braden. 2003. Nleaching.info/) for prediction of Pseudocercosporella herpotrichoides in winter wheat and winter rye and SIMONTO (www.ISIP. http://www.zepp.zepp. fertilization. it does not contain a dynamic crop growth module.ISIP.agrarmet. http://www. 2001. field based. Fodor. used in COST 718) was applied for apple scab (days with infection. For scientific and research purposes the following models are used such as the crop-growth simulation model for calculating biomass production potentials of sorghum within the “beaver” (Biomass Economic Appraisal & eValuation ExpeRt BEAVER) expert system environment (Danalatos. water and N-dynamics). regional. DAYCENT (Stehfest et al.2. regional.. 2007): effect on yield. WOFOST model for maize. 2005). Agrometeorological Simulation Using PERO Model for Grape Vine Downy Mildew (Dalezios et al. 2000). HERMES (Kersebaum.. SPASS and SUCROS and several methodological approaches (Priesack et al. In Ireland models have been used primarily for scientific investigation of the impact of climate change. WZU. Fodor and Kovacs. incorporating DSSAT models (Fodor et al. Sugar beet: AGROSIM (Mirschel et al. CERES-wheat and CERES-maize (Danalatos et al. 2007): effect on yield and carbon balance. Danalatos et al. 2002): effect on yield and water balance. Apple: ASCHORF (Wittich. regional. Grassland: DAYCENT (Stehfest et al.b. The phenological model of Hoppmann & Berkelmann-Löhnertz (2000) was applied to estimate the phenology of wine in Germany (FAG.. nitrogen leaching. 2006). Pedotransfer functions in predicting ground water recharge at regional scale (Kosmas et al. 2003. 2007). 1987).. 2004a. regional. 2007. Agroclimatic indices and simulation models balance.. adaptation. regional. incorporating basically the three crop models CERES. 1996). There is only one model used for routine 63 . (1994) investigated the change in the specific leaf area of maize grown under Mediterranean conditions. (sub) field based. Oilseed (Rape): DAYCENT (Stehfest et al. 1997)... organic and water balance analysis of various crops. 2007): effect on yield and carbon balance. Grape: N-VINO (Nendel and Kersebaum. 2005): effect on yield and carbon balance. 2005) was applied for nitrogen dynamics in vineyards (crop growth. 2001. 1998. CERES-Wheat and CERES-Maize are however often used for scientific investigations. In Greece there is still no operational use of crop models. infection severity). Recently a new software tool (4 M) was developed for Hungary.. 2007): effect on yield and carbon balance. DAYCENT (Stehfest et al. cotton and wheat (Danalatos and Sgouras. Lastly.. In Hungary there are still no crop models in operational use. The model system EXPERT-N is a modular simulation tool used for nutrient. 2003. 1998). 2005).. barely as a key feed and food grain using CERES-Barley (in DSSAT). Confalonieri and Bechini. Tubiello et al. and the Community Climate Change Consortium for Ireland (C4I) project will also be developing further applications for agricultural modeling (www. CRITERIA and WOFOST integrated water balance and crop growth model is used as an operational tool (Marletto et al.. ORYZA1 was tested for rice (Casanov et al. Holden and Brereton. Holden et al (2003) and Holden et al (2004). 1997. 2000). that little effort has been directed towards using models for operational purposes. cereals account for only 4. Anon. potato because of its traditional cultural position in Ireland using SUBSTOR (in DSSAT) and soybean as a very marginal exotic which could act as an indicator for the future using CROPGRO (in DSSAT). Current work being conducted at NUI-Maynooth is starting to use GIS analysis to investigate spatial aspects of climate change and pest / disease and development of energy crops. with the focus on nutrient and water use for potato and barley (using DSSAT. Mastrorilli et al.g. Initial efforts focused on assessing impacts on yield for key indicator crops: grass as the main agricultural crop using JTC grass model. 2005. P.RADA is an example of an operational application of 64 . Arable. Castrignano et al.. 1996). 2008a. Holden et al.. Confalonieri and Bocchi. 2006) and on dairy production systems (using the dynamic dairy system model Dairy_sim. 2003b).ie).. The Irish Environmental Protection Agency has funded work on quantifying the impact of climate change on Irish agriculture. 2001. Results from this work can be found in Holden and Brereton (2002. Important crop models applied in research are DSSAT models (CERES) (e. 2008). 2002) and CROPSYST (Donatelli et al. horticulture and vegetable crops represent such a small proportion of agricultural area and production in Ireland (e.. 2008).. 2000. Fitzgerald et al. 2008b. 2007).. 2003. 2003a. Stockle et al.. In Italy many research activities have been carried out in the last years on several aspects on the main crops.8% of gross output. 2003.Survey of agrometeorological practices and applications in Europe regarding climate change impacts management and that is the Johnstown Castle Grass Model (JTC grass model) (Brereton et al.C4I. based both on direct and simulated responses. 2004). Results on dairy system impact and adaptation are currently in press (Fitzgerald et al. which was specifically developed for the purpose. maize as a marginal crop with potential for introduction into Ireland using CERESMaize (in DSSAT).. CRITERIA is a model embedded within a geographical interface allowing to work with ArcGis shape files for soil maps and crop maps. More recently work has focused on impact and adaptation at the system level. This is used to estimate current and forecast grass growth and the results are published in the Irish Farmers’ Journal. 2005.g.. . rape seed. 1993. (Huygen. the successive WOFOST versions and their derivates have been used in many studies (e.. effects of climate change and critical periods for use of agricultural machinery. of yield variability over soil types. of differences among cultivars.2. 1993). SWAP (Kroes and van Dam. de Wit Wageningen School of Production Ecology are LINGRA (Bouman et al. grain maize. Wolf and van Diepen. 2002) for grass and clover grass leys are used for operational 65 . The dynamic crop models KONOR (Bleken. Wolf and Erickson. or over a range of agrohydrological conditions.g. WOFOST has been applied as a tool for the analysis of yield risk and interannual yield variability. 1995. soybean and rice. Nonhebel. Agroclimatic indices and simulation models grapevine downy mildew model integrated with radar data for the estimation of rainfall and leaf wetness... field beans. 2007) for soil carbon and nitrogen dynamics. durum wheat. 1993. sunflower.T. 1996) and LINTUS. 2003) is another well known soil-water-atmosphereplant model focused on sophisticated soil water balance dynamics and integrating the WOFOST crop growth module (often used for irrigation studies) and ANIMO (Kroes and Roelsma. The Australian model APSIM was tested for conditions in Netherlands (Asseng et al. Poels and Bijker (1993) developed the model TROPFOR to simulate growth and water use of tropical rainforest by adapting WOFOST and De Ruijter et al. potato. 1994) for winter wheat. of sowing strategies. estimation of maximum benefits from irrigation or from fertilizer use. 2000). 1992). The model has also been used for predictive purposes. of relative importance of growth determining factors. In addition to the mainstream of WOFOST versions several models have been elaborated on the basis of WOFOST (Van Ittersum et al. spring barley. winter barley. oats. Other models which originate from the C. In the Netherlands the operational use is focused on SUCROS and WOFOST (Supit et al. detection of adverse growing conditions by simulationmonitoring the agricultural season. field peas. A typical example is the SWACROP2 model formed by linking the WOFOST crop module to the SWATRE (soil water and transpirationrate model). 1994. Applications of WOFOST оver the last ten years. sugar beet. 2003). rye. and regional yield forecasts. in quantitative land evaluation. such as regional assessments of crop yield potential in the form of maximum yield levels. 2001) for wheat and ENGNOR (Baadshaug and Lantinga. Groot (1987) simulated the nitrogen dynamics in crops and soil. In Norway process-oriented models are used for the simulation of cereals growth and yield as a research tool (Hutchinson et al. Wolf. (1993) adapted WOFOST for simulating tulip growth. 1992). the water demands of crops. Based on decadal meteorological data from synoptic stations there are provided prognosis for: winter wheat. CROPWAT for assessment of water use and evapotranspiration was operationally used during 2002-2006 (Stancali and Marica. is used operationally (Górski and Spoz-Pać. forecasting yield and development. 2000. 2004.. In Romania crop models are operationally used for Wheat and Maize (Marica and Busuioc. Marica et al. potato.Survey of agrometeorological practices and applications in Europe regarding climate change impacts applications. Process-oriented models for scientific use are CERES-Wheat and Maize and the CROPWAT model for the assessment of evapotranspiration. time and intensity of infection). 2001.. regional. 2007). For pest and desease modelling the BAHUS model system (Mihailovic et al. 2003. 1996) and potato (Bloch et al. in-soil humidity deficits and for maize yield. In Serbia CROPSYST is in phase of experimental operational exploitation with data for six chosen locations in the most important agricultural areas. In the field of scientific investigations the model WOFOST was used for wheat (Faber et al. 2005). 1997a.. oats. (information based only on a web survey). field based). regional. time and intensity of infection) and for Grape (duration of incubation period. 2006). for apple (duration of incubation period. In Poland the prognostic statistical-empirical yield model IPO (regional level). Lalic et al. water balance. 2005a.. The CERES Wheat implementation is in progress.. 66 . rape. The prognosis are prepared for the Ministry of Agriculture and Rural Development. barley. ROIMPEL (DSSAT based) was used at the national level for climate change impact studies for several annual crops (Audsley et al. field based). An example of an applied disease model is NegFry. 2005b). sugar beet. the FAO method for daily ETP is operationally used from 2006-present. 2006). Also a Cereal Leaf Beetle . developed at the IUNG-PIB in Pulawy.. WOFOST is in use for studies of the potential production of barley in South-Eastern Norway (Hofmeister et al. adaptation options. 2007). time and intensity of infection). 2007) was developed and tested for Potatoes (duration of incubation period. 2001). spring wheat. Marica. a Phytophtora model for potatoe (Kapsa.Computing model (appearance and intensity of attack) was applied and tested (Pankoviić et al.. 2006).b) crops. (Lalic et al. Górski et al. CROPSYST (for maize) and PERUN (effect on yield. In Portugal CERES-Maize was adapted for a decision support system for maize (Braga. 2002. Scientific applications are reported for Wheat with SIRIUS (effect on yield. 1997). simulation and analysis of the growth and development of three maize hydrides from various maturation groups. 2005.. 1989. This model is used for wheat. 2001. Iglesias. 1995) was applied for several crops for assessement of soil water dynamics. Leaf wetness was calculated by the physical model DROPBEN shared in the frame of COST 718 activities.. 1999). For Spain no application of an process crop model is reported. http://www. In the poject PRADA PRADA (SI/IT) project leaf wetness model upgrade in regard to radiation data has been introduced by SI (Sušnik et al. CERES-Wheat and CERES-Maize were used for climate change impact assessment or irrigation studies in scientific studies (e. Iglesias et al. KajfežBogataj et al. 2002). such as in the Soil Science and Crop Research Institute. Iglesias and Mínguez. Model was incorporated into geographical information system of the Ministry of Agriculture for drought damage estimation maps elaboration. Some recent research activities have been performed on water shortage impacts on agricultural plants Model IRRFIB was used for water balance scheduling of different crops and fruit trees (Sušnik et al. potatoes and oilseed (rape) crops. 2000. however. SWAP was introduced for studies on water balance on 67 . field based applications). there is available a system of agroclimatic information for irrigation scheduling (SIAR. 2005).es/siar/) which operates on a daily time step. The aim is to provide representative agroclimatic data for Eto assessment and irrigation scheduling.sk). (Nejedlik and van Diepen. For the purpose of better grapevine downy mildew protection the simulation of pathogen life cycle based on agrometeorological variables the model PLASMO and PERO have been realised and validated in the area of Goriška (Orlandini et al. Guerena et al. (Siska and Samhuel.2. Scientific use of process oriented models are reported for wheat and maize with WOFOST (effect on yield and water balance. regional) and CERES-Maize (effect on yield plant development. Ministry of soil management (www. The model CERES – Maize was used in the study of climate change impacts on maize yield in Slovenia (Kajfež-Bogataj..vupu. 1996). 2007).g.. The numerical model GLOBAL (Majercak and Novak. providing national (in total 361 agroclimatic weather stations covering the agricultural areas all over Spain) wide information (the main provider is Ministry of Agriculture). maize. These data and information are also used for research purposes in agroclimatology and for pest and diseases control. regional). regional. Agroclimatic indices and simulation models The model WOFOST is in operational use for yield estimation and water balance monitoring in different governmental structures in Slovakia. 2000. 2006).mapa. 2007). DAISY (effect on yield and water balance... In Slovenia some research applications of plant disease models were used. 2006. sunflower. Farre et al. 1995.. .. Simpler models were also developed at the University of Nottingham to investigate dry matter accumulation and radiation use efficiency (Gillett et al. Bradley et al. fruit moth. 1994. 1995. and pear sucker are available (http://www. apple sawfly. developed originally for New Zealand conditions (Semenov and Porter. An example of disease model application in Spain is the evaluation of the different Alternaria prediction models on a potato crop (Iglesias et al.. 1998).. At Wye College. 2003). and to a lesser extent. resulting in the PARCHED-THIRST model (Young and Gowing. Torriani et al. 2000). millet. Other applications are the PhytoPRE model for a 2 days forecast for late blight (www. Clifford et al. 2005). drought-related reduction of potential yield. 1994. (Steenblock and Forrer.. maize and potato with the crop model CROPSYST (Stockle et al.. forecasts for downy mildew. the General Large-Area Model for Annual Crops (GLAM) has been developed to simulate growth of tropical crops 68 .sopra. fire blight and scab (www.phytopre. 2002. 2007) and PaSim for grassland (Riedo et al. In Switzerland scientific applications of crop models are reported for wheat. Steenblock et al. An agrometeorological model for the prediction of grape yield was developed in the framework of the project OLIWlN aimed at the prediction of crop productivity for grape and olive tree (Marin. At the University of Reading. including the PARCH model to simulate the growth and development of sorghum. the CERES-Wheat model was used to predict crop responses under six climate change scenarios for the years 2025 and 2050 (Ghaffari et al. 1996). Similarly. 2001). Azamali et al. and wheat yields (Richter and Semenov. 2005).agrometeo. The same group also developed a number of models for tropical crops (e..ch).b. 1994)..admin. 1999).ch).. 14-days forecast for risk of infection from Fusarium graminearum (www.. The PARCH model was developed further to investigate aspects of rainwater harvesting in Tanzania. Bannayan et al. 1999. 2003.. Lawless and Semenov. both the SUCROS model and the CERES-Wheat model were evaluated for their ability to forecast final grain yield and crop biomass for four sites in the United Kingdom (Bannayan & Crout.fusaprog.Survey of agrometeorological practices and applications in Europe regarding climate change impacts crops under field irrigation in the frame of EU-project AGRIDEMA (Utset et al. In United Kingdom the SIRIUS model.g.. at the University of Nottingham.ch/). maize. Jamieson et al. Musa-Steenblock and Forrer. 2002.ch). 2007). in semi-arid environments (Bradley and Crout. has been used to assess the effect of changing climate on maximum soil moisture deficit. apple moth. cherry fruit fly. 2002). 2005).. For pest and disease warning operational seasonal forecasts for the appearance of apple aphid. 1998a. 2007). the LADSS (Land Allocation Decision Support System) farm-scale integrated modelling framework consists of a core of biophysical simulation models overlaid by financial. Matthews et al. Matthews. Organic matter decomposition is simulated by a version of the CENTURY model (Parton et al. 1994). Agroclimatic indices and simulation models (Challinor et al. 2006). and the initial values of the parameters. and has been incorporated into the Met Office Surface Exchange Scheme (MOSES) of the HadAM3 General Circulation model (Osborne et al..g. Generally modelling in agro meteorology (and in modelling in related fields of biology) is scientifically based predictions to be used for tactical and strategic decisins in crop production. and also the different quantitative scenarios of the future climate of the earth are composed of very complicated scientifically based models based on the scientific principle. 2005. social and environmental accounting modules (Matthews et al. 2.. 1999). classification of phenomena of nature. 2005. 2004).. 1998). 2007).. 2007)..4 Useful Outputs and main Limitations of Models for use in Climate Change Impact Research (Subgroup 3) 2. The discussion in this paper is connected to possible strategic decisions for future crop production in Europe. 1988).. the People and Landscapes Model (PALM) is being used to investigate the interrelationships between socio-economic and biophysical processes (Matthews and Pilbeam.2. farm households) located on a landscape made up of a number of heterogeneous land units. while water and nitrogen dynamics are simulated by versions of the routines in the DSSAT crop models (Tsuji et al.. Also at Macaulay Institute. the basic hypotheses (laws of nature). PALM is an agent-based model operating at the level of a catchment. each of which contains routines to simulate its water balance and carbon and nitrogen dynamics over time. The outcome of the discussion is a documentation system for parameters (interpreted as quantitative entities attached to the 69 .1 The problem of modelling natural phenomena Numerical meteorological prognoses. In order to understand and discuss the temporal and spatial scope of modelling in meteorology and agro meteorology we ought to look at the scientific principle.4. In these papers is presented an interpretation of the scientific principle containing four levels. At the Macaulay Institute in Scotland. see (Sivertsen. 2005. the hypotheses derived from the laws of nature. 2006) and (Sivertsen and Gailis. The crop module is a version of the CROPSYST model developed at Washington State University (Stockle et al. which contains a number of decisionmaking entities (e. and for discussing the crop growth models as well. those connected to the different systems for making measurements. for discussing systems for making weather predictions. 2. Just by looking at the definitions and the units of the input parameters. Two types of parameters are presented. but the each model and sub-model may be analysed according to the scheme presented in Fig. Much information on the scope of any sub-system may be compiled by getting a review of the name.9. All the models mentioned are very complicated constructions. The general ideas connected to the scope of scientific modelling presented above are relevant for discussing the scenarios of future climate on global and regional European scale. An entity receives a user-defined phase that determines the behavior of the entity”. The testing is not interpreted as merely a testing of the hypotheses of physics. which represent the existence of some object in the system such as a philosopher. unit and measuring procedure for the measured parameters). but the idea is that the temporal and spatial scope of the system should be possible to discuss by using this interpretation. The events are represented as methods of an entity. The crop growth models discussed in this paper are all considered processoriented models. In addition. definition and unit of each parameter. physical and biological parameters. In the contribution from Subgroup 2 in this paper. Eliens (1995) describes process-oriented models in this manner: “With the process-oriented approach the components of the model consist of entities. classification of phenomena. the processes contained in process oriented crop growth models is discussed more specifically. We first identify the entities (or the types) in the model. In Fig. the laws of nature and the equational interpretation of these laws. the parameters used in the sub-processes and the output parameters of crop growth models one may discuss several elements of the spatial and temporal scope of the models. 70 . and then find out how initialization of the parameters are made (name.9 this interpretation is shown graphically. several of the parameters and equations may be derived by empirical or half-empirical methods.Survey of agrometeorological practices and applications in Europe regarding climate change impacts phenomena and the sub-phenomena). The parameters used may be meteorological. definition. A process-oriented model of nature consists of the three levels. The function operator calls these events based on the phase the entity is in. but the parameter of length of time should be contained in the model.” This description is in fact based on object oriented thinking connected to modelling natural phenomena quantitatively. and those being elements of the models of the phenomena of nature. 2. 9: An interpretation of the scientific principle as this is used in meteorology and agrometeorology 71 .2. Agroclimatic indices and simulation models Figure 2. 9). 2.12: Number of recommendations for the use of process-oriented models to determine consequences of drought.) Model CERES STICS WOFOST CropSyst PaSim AMBETI SWIM HERMES DAYCENT AGROSIM Weather Yield ROIMPEL APSIM Drought 5 2 4 1 1 1 1 1 1 1 1 1 Excess rain 8 Excess frost 1 1 1 Snow cover Seasonal shifts 8 1 1 Time step Daily Daily Daily Daily Daily and hourly Daily Daily Daily Daily Daily Daily Daily Daily 1 1 1 1 1 1 1 1 1 Recommended applications of crop models for assessing climate impacts as shown in Table 2. snow cover and crop responses (seasonal shifts etc. please name which inputs.4. In Table 2. excess rain. excess rain. Table 2. For input data. snow cover and crop responses. and the concept of a process oriented model is discussed connected to this interpretation. To help analysing the answers of the questionnaires two concepts were introduced. 72 .12 are the result of many years experience in different countries. frost.Survey of agrometeorological practices and applications in Europe regarding climate change impacts 2. an interpretation of the scientific principle (as shown in Fig. (b) Please indicate the main limitations in order to apply process-oriented models for operational use in your country.12 the answers on question (a) are summarized. Therefore the context of the actual application of the models is important to provide advice to societies in Europe. excess frost. Climate change is a very serious challenge for the agricultural crop production of Europe.2 Present limitations on crop model applications Europe The basis of the analysis was two questions of the survey to COST734 members: (a) Which model output of the named models are most useful in order detect the impact on climate change and variability regarding drought. Also the representation of historical temperature. biomass (STICS. for example. Specific crops are not explicitly mentioned for the other models.13). Roimpel. For the study of final biomass of the model STICS. (2008). Main named outputs for detecting seasonal shifts are phenology. where the parameter of interest is minimum air temperature. 73 . al (2003). soil water content (DAYCENT. For assessing impacts of excess rain named most useful outputs are water water stress.. 1993. SWIM. phenology and yield (CERES. soil temperature and phenology. For the models WOFOST and CERES see Faber et. Often.2. precipitation and solar radiation is important in order to be applied for climate change scenarios. In the paper of Gijsman et al. al (1996) and Eitzinger et. pedotransfer functions. yield (CERES. the duration of the snow cover. WOFOST. (2003) and Brisson (2004). For frost it was named biomass. water uptake. A weak quality of input data are a main source of uncertainty in simulated outputs (beyond the representation of significant natural processes in the model) such as the spatial representation of the weather and soil model input data or quality problems of data caused by measurement methods. which is also reflected by the survey (Table 2. because of its importance for soil water storage and availability for crops. HERMES). 2003). spatial data on field capacity or wilting point are not directly available and need to be assessed by e. oxygen shortage (WOFOST. biomass and yield. Especially soil input data need to be considered critically. WOFOST. The parameters characterizing snow cover are snow depth. HERMES and STICS). and CARBON and NO-emissions (DAYCENT). In several studies crop model sensitivities on these factors are compared and estimated (Nonhebel. Limitations of crop model applications are often related to availability and quality of model input data.g. al (2004). phenology. Agroclimatic indices and simulation models The crops considered in Table 2. and wheat and grassland connected to the French model STICS. (2002) it is shown that even the use of different pedotransfer functions can lead to significant deviations in simulated crop yields. Most useful model outputs named for assessing drought impacts are water stress. For drought response and performance of HERMES see Kersebaum et al. HERMES). and water equivalent of snow which are in same cases of interest for simulated soil water content.12 are wheat and maize connected to the model CERES. The weather input data for crop models and their spatial representation are very important in climate change impact studies (Tsvetsinskaya et al. see Brisson et al. Weather Yield. HERMES). HERMES) and nitrogen leaching (HERMES). see Brisson et. In climate change impact studies for large areas e. several authors apply interpolation methods on these weather parameters which should be chosen carefully (Trnka et al. which is of course often not possible or carried out under different soil conditions. FACE (Free Air Carbon Experiments) experiments show a much more complex picture and strong variability in the magnitude of this effect between cultivars and environments. which is difficult to simulate (Wolf et al. For a better spatial representation.) Low quality of soil data like parameter value describing texture. 2004). Also effects on crop yield quality are normally not considered in crop models..g. Table 2. Kartschall et al. Jamieson et al. the European scale often simplified models or empirical procedures within complex models are used. 1994.) Daily weather data not available in time Lack of quality of weather data Number 12 11 5 1 The answers in Table 2. a problem is the accurate simulation of the direct CO2-effect on biomass accumulation which is in many cases considered (if at all) as a fixed (positive) value in crop models. For climate change impact studies. 2000). soil physical indicators (too low spatial resolution for field applications etc.13 reflect also practical problems connected to socioeconomic conditions and local administration of data (data policy of data holding institutions) in Europe..13: Named limitations connected to the use of crop growth models in the survey Type of limitation Lack of availability of biological data for validation of models (long term yield of specific varieties etc. which has a strong feedback to soil water availability and use. as different crop models can have various sensitivities and levels of representation of certain soil-crop-atmosphere processes (Janssen. such as experimentally measured reduced content of protein in several crops. 2005). for example. Another similar problem is the still highly empirical simulated process of root growth.. It is shown that the representation of root growth can have a strong feedback on simulated soil water contents with soil depth (Eitzinger et al..Survey of agrometeorological practices and applications in Europe regarding climate change impacts Dubrovský et al. A significant source of uncertainty remains from the applied methods and models.. often only distinguishing between C3 and C4 crops. 1998). organic content. where these aspects are not considered at all due to lack of data and resources. 2002. Especially the weak availability of relevant biological or crop data for model calibration and the low quality and/or 74 .. The models should therefore be calibrated well in this aspect. 1995). slope. GIS is a computer-assisted system that acquires. Communication between GIS and model is with definite grid cells or polygons in input and output files. Heinemann et al. 1999). which is a specific countrywise problem of data policy often related to the costs of data. for example. to produce a database.) at spatial levels. 2000. etc. is an interpolation of model outputs. land use management and forecast (crop. In this context following terms should be explained: • Linking: Linking (Fig 2. storages. Linking is not able to utilize all possibilities of the system and suffers from limitation due to (a) dependence on formats of GIS and model. which are transferred in ASCII or biary format between GIS and model. Interfacing crop simulation models with a GIS helps to accomplish spatial and temporal analysis at the same time: regional-scale crop behaviour has a spatial dimension and models produce a temporal output. fire. 2.. GIS is developed to a powerful tool at the disposition of policy and decision makers (Maracchi et al. which contains model inputs as well as outputs. which is good news as the quality of the data (historical or of climate scenarios) is crucial for the use of crop models in order to get reliable results or to make strategic decisions based on simulation results. Only one comment was connected to the low quality of weather data. etc.1 State of the art Interfacing crop simulation models with a GIS Interfacing crop simulation models with geographic information system (GIS) increases the possibilities of applying these models for regional planning and policy analysis (Hartkamp et al. where simple linkage strategies are used..5 Crop simulation models in combination with Remote Sensing and GIS (Subgroup 4) 2.5. A simple approach. Agroclimatic indices and simulation models availabilty of physical soil data for modelling purposes is a main problem. weather. analyses and displays geographic data. Only 5 countries reported problems for the availability of daily weather data. (b) incompatibility of 75 . GIS visualize the results in spatial and hence their study by spatial analysis of model results (Dadhwal. 2002). 2003). overlay.2.. Due to the increasing pressure on land and water resources. information systems have become essential.10a) includes a GIS for spatially displaying model outputs. An advanced strategy is to use GIS functions like interpolation. Survey of agrometeorological practices and applications in Europe regarding climate change impacts • • operating environments and (c) not fully utilizing the capability of GIS (Dadhwal. 2003). 2003). An expertise. interface programmes. 1997). Figure 2.10: Organizational structure for (a) linking. Integrating: One system is incorporated in another one: a model is embedded in GIS or a GIS system is integrated in a modelling system.10b) involves processing data in a GIS and displaying model results: the model is configured with GIS and data are exchanged automatically.10c). It requires more complex programming and data management than simple linking (Dadhwal. 2. Combining: Combining (Fig. An example of combining is AEGIS (Agricultural and Environmental GIS) with ArcView (Engel et al. This accords automatic use of relational database and statistical packages (Fig 2. (b) combining and (c) integrating GIS and crop models (Hartkamp et al. This can be realized with facilities in GIS package of macro language.. 2003). effort and understanding of the two tools are necessary (Dadhwal.. 1999) 76 . libraries of user callable. Agroclimatic indices and simulation models One example of application is the CGMS (Crop Growth Monitoring System) of MARS (Monitoring Agriculture with Remote Sensing) (http://mars. for example drought. nutrient replacement. Examples of the combination of GIS and crop models in the field precision farming are given by Han et al. 1999). soil characteristics from the European soil map (King et al. 1995) and crop specific parameters (BoonsPrins et al. The project used two crop models (WOFOST and LINGRA) and Arc/Info for operational yield forecasting of main crops in the European Union (Supit. (1995) with interface between Arc/Info GIS and SIMPOTATO or Satti and Jacobs (2004) with GWRAPPS. The system-analytical part includes three main components: • Interpolation of meteorological data to a square grid (50x50 km) in real time • simulation of daily crop growth and • statistical evaluation of the results (historical yield statistics thought regression analysis in combination with a time-trend) The resulting regression equations per crop per region can be used to make actual yield forecast.. the link between GIS and crop simulation models is very vital.jrc. Precision farming is a way of agricultural production that takes into account the within-field variability. Databases of meteorological data. It is a technology where the application seeding. spraying.2. where GIS and crop models are used. weather stations 77 . CGMS produces on a 10 day and monthly basis three types of output on current cropping season: • maps of accumulated daily weather variables on 50x50 km grid to uncover any anomalies. which tightly couples ArcGIS with the Agricultural Field Scale Irrigation Requirements Simulation (AFSIRS) model. frost • maps of agricultural quality indicators based on comparison of simulated crop indicators with their long-term means maps and tables of yield forecasts (Dadhwal. The main intention of this project is to provide information on weather indicators and crop status during the growing season and to provide objective forecasts of crop yield for the EU member states early in the crop growth season (de Witt et al..it/). 2003). 2003) In precision farming. 1997). Another area. is in agro-ecological zoning. takes place to act on the local conditions of a given field (Neményi et al. etc. The district areas as polygons and model input parameters of soil.. 2005). Aggarwal (1993) utilized WTGROWS to simulate potential and water-limited wheat yields for 219 weather stations spread all over India... 1993) are available for the whole EU (Russell et al. Survey of agrometeorological practices and applications in Europe regarding climate change impacts as well as agro-ecological regions were saved in ARC/INFO GIS. The crop model outputs studied effects of future climatic changes on crop potential/productivity (Bacsi et al. To evaluate agricultural land use options for the state of Haryana. physiographic features. Aggarwal et al. It was demonstrated how GIS for land evaluation and the crop simulation model LINTUL-POTATO can be used together to assess possibilities for increasing crop production at regional or national scales. The different ways to combine a crop model with remote sensing observations (radiometric or satellite data) were at first described by Maas (1988) and further revised by Delécolle et al. A further example gives the study of Caldiz et al. Spectrally derived LAI (leaf area index) can be used as direct input to physiological crop model or as an independent check to model calculation for its re-initialization. • the adjustment of an initial condition to obtain a simulation in agreement with the remote sensing observations (‘re-initialization’ strategy). Remote sensing data integration into the models can be classified into 5 methods: • the direct use of a driving variable estimated from remote sensing data in the model. GIS as well as optimization techniques. Based on potential and rainfed productivity. Dadhwal. soil and agro climatic zones. less labour and material intensive methods are necessary (Dadhwal. while crop models give a continuous estimate of growth over time. (1998) used simulation modelling. Carter and Saariko. 2003). Already Wiegand et al. climate. The model outputs of potential and rainfed productivity were stored in GIS as polygon attribute data. Linking crop simulation models to Remote Sensing and GIS The main benefit of using remote sensed information is that it provides a quantification of the actual state of crop for large area (Dadhwal. 78 . The models for specific crops were linked to GIS layers of administrative boundaries. (2001). (1998). (1992) as well as by Moulin et al.. 2003). 2003). In this way. India. the districts were classified into ten yield zones represented as map (Dadhwal. where agro-ecological zones for potato production in Argentina were analyzed. 1991. (1982) proposed the use of remotely sensed information to improve crop model outputs. 1996. • the updating of a state variable of the model (for example LAI) from remote sensing data (‘forcing’ strategy). 2003). (1979) and Richardson et al. Italy and Germany for the period 19922000. Investigation area was Spain.. remote 79 .6 km² grid. Direct use of driving variable Driving variables of crop simulation models are weather input data. The results were evaluated with regard to the EnFK filter innovations and the relationship with yield statistics on administrative regions. Agroclimatic indices and simulation models • • the adjustment of model parameters to obtain a simulation in agreement with the remote sensing observations (‘reparameterization’/’re-calibration’ strategy). (1997) for example used METEOSAT based rainfall estimates to incorporate the current season’s rainfall data as input to CERES-Millet in Burkina Faso. As the crop model runs at a constant time step. 2003). This relationship may be applied to a case in which final yield is not known (Dadhwal. One reason is that these areas were irrigated and the model does not include irrigation. The model showed an over assessment of + 6. For grain maize the improvement is less evident because improved relationships could only be found for 56% of the regions. precipitation. Thornton et al. solar radiation. the corrective method. wind speed etc. The rainfall based on cold cloud duration as measured by thermal infrared radiometers on the METEOSAT satellite and was calculated every 10 days for a 7. With this new input provincial yields were simulated halfway the growing season and were within 15% of their final end-of-season values. a relationship between error in some intermediate variable as estimated from remotely sensed measurement and error in final yield.2. Maas (1988) calculated the ratio of daily absorbed PAR (Q) to integrated daily PAR (R) from radiometric NDVI and generated daily values of Q/R by linear interpolation between NDVI measurements. Forcing strategy The forcing strategy is to update at least one state variable in the model using remote sensing data. The assimilation of soil moisture estimates has clearly improved the relationship with crop yield statistics for winter wheat for the majority of regions (66%) where a relationship could be established. De Wit and van Diepen 2007 used an Ensemble Kalman filter (EnKF) for assimilating coarse resolution soil moisture estimates in the WOFOST crop model in order to compensate the effect of uncertainty in the rainfall.2% ground biomass at anthesis. France. such as maximum and minimum temperature. crop model simulations with the EnFK were used for winter wheat and grain maize. The result was used as driving variable in a simplified maize growth model. a) b) Fig.11 (Delécolle and Guérif. 2003). 1988).. Dates Wheat AFRCWHEAT SPOT/HRV LAIWDVI relation: daily interpolated LAI LAI-WDVI sensing derived LAI for forcing crop Evaluation of performance AGDM estimation improved Yield RMSE decreased Biomass at harvest Reference Maas 1988 Delécolle et al.11: (a) Simple schematic of a crop simulation model. The concepts of a simple crop simulation model and its modification for remote sensing derived LAI forcing is demonstrated in Fig. Table 2.based LAI forcing (Delécolle et al.14 (Dadhwal. (b) Modified structure of crop simulation model with remote sensing . 2. LAI is the most commonly updated state variable in the forcing strategy. 1988 Wheat SUCROS Bouman 1992. 1988) Some examples of forcing spectrally derived LAI in crop simulation models are summarized in Table 2. The common way is to fit an empirical curve to the estimated values from remote sensing observations and then carry out the interpolation according to the model time step. 2. The main objective is to minimize the difference between a 80 .Survey of agrometeorological practices and applications in Europe regarding climate change impacts sensing imagery is normally not produced at such high-resolution temporal steps. 1995 Re-initialization strategy The re-initialization method involves adjustment of initial condition of the state variables.14: Selected case studies on use of remote simulation models (Dadhwal. 2003) Crop Model LAI estimation & interfacing Maize Ground NDVI-LAI on obs. The Rotast 1. This was taken by CropScan equipment. Therefore. Re-calibration/re-parameterization strategy The model is formally adequate but requires re-calibration. The SUCROS simulation using standard values for emergence and early growth parameters did not accurately predict crop growth under these test conditions. The resulting model was calibrated under standard conditions and afterwards it was evaluated under test conditions to which the studied parameters of the SUCROS model were adjusted.0 model was used as it simulated daily interaction between climate. When re-initialization is used for only one observation produced results. Delécolle et al. then it is quite similar to updating (forcing). The observation at 51 days after emergence. which covers the electromagnetic spectrum between 460-810 nm in eight spectral bands. resulted in less than a 3% error using reinitialization (Maas. Agroclimatic indices and simulation models derived state variable or the radiometric signal and its simulation (Dadhwal. Maas (1988) modified the initial value of LAI (L0) in a simplified maize model at emergence based on the minimization of an error function between remotely sensed LAI values and simulated LAI values during the course of simulation. (1992) studied a re-calibration for rice crop with the GRAMI model. 81 . Guérif and Duke (1998) calibrated the SUCROS model on local scale with the SAIL reflectance model (Verhoef. This can be reached by minimizing the error between the remote sensing derived state variable (usually LAI) and its simulation by the model (Dadhwal. The results showed that improvement in simulated LAI profile by re-calibration depends largely on the number and timing of LAI observations. But the inversion of the combined model using a set of canopy reflectance measurements during crop establishment provided new parameter values that can be used to accurately estimate crop yield. Values of one to four parameters were re-calibrated between the simulated LAI profile and observed LAI values. 2003). emergence occurred later and the initial leaf area was smaller. For a number of times in the growing season. The stability of model estimates obtained through re-initialization increased as more observations were used. Better simulation results of variables could be accomplished with this method. soils and crops. 1988). The test conditions seedbed structure was coarser and the sowing depth was greater than expected. 1984) on emergence and early growth parameters. simulated values of LAI and canopy nitrogen contents were replaced with values estimated form remote sensing.2. which caused a 42% error using updating. 2003). Jongschaap (2006) tested various run-time calibration scenarios for replacing simulated values by remotely observed values to improve simulation results. 8 tha-1.8 and a root mean square error of 597 kg ha-1 which was 17 per cent of the observed mean yield (Dadhwal. The results were validated with yield observations.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Corrective approach Sehgal et al.6 to 4. The results were within 10 % of observed county yields.9 tha-1. management.1 to 4.81. 2005). 2007). The relationship of predicted grain yield and observed yield for the 22 farmers’ fields showed high correlation coefficient of 0. The main problem with models is often an oversimplified description of the natural system. The regression equation fitted on that day between simulated LAI and simulated grain yield showed saturating logarithmic nature with a R² value of 0. The predicted yields ranged between 2. are often not available or of low quality (see also chapter above) 82 . management practices and soil types occurring in the area. The matching range of simulated LAI on 27th Julian day was 0. The empirical biometric relation was applied to the LAI map of the wheat pixels and grain yield map of Alipur block was generated. Illinois.6 x 1. Added value by remote sensing data Crop simulation models are used to describe the impact of climatic conditions and management strategies at field scale and can be applied in a distributed model at regional scale. Data on boundary conditions. (2005) modified the crop yield model to incorporate biophysical parameters derived from MODIS 8-day imagery to access crop yields for McLean Country. The WTGROWS simulated grain yield for the combination of inputs showed a yield variability of 1. and therefore a low prediction performance (Dorigo et al. The crop simulations were conducted on a 1. 2003).. Biometric relation of grain yield and LAI was predicted from simulations by running the model for a combination of input resources.2.1 and 4. like soil..6 km² spatial resolution grid and the results integrated to the county level. inaccurate parameterization and uncertainty. (2001) used the corrective strategy for generating the wheat yield maps for farmers’ fields during 1998-99 in Alipur block (Delhi). USA. Crop model parameters were adjusted by minimizing the differences between MODIS derived LAI and crop model simulated LAI for the entire growing season. The remote sensing inputs (LAI estimates) were linked to the crop simulation model WTGROWS for yield mapping. Corn and soybean on a 71 x 52 km² area were tested. Afterwards this biometric relationship was applied to all the crop fields of the study area for which the LAI was computed from remote sensing data. Doraiswamy et al. during the 2000 growing season. These problems are mainly evident at regional scales where model input parameters have to be gathered from scattered point locations such as weather stations (de Wit et al. 2007).2.12: Schematic representation of different methods for the assimilation of remotely sensed model state variables in crop models: (a) ‘calibration’. 2. (b) ‘forcing’. agro-ecological zoning. • using simulation model to estimate impact of variation in a state variable (e. In future this linkage will be more important and improvements in sensor capabilities (spatial resolution..12b).12c) (Dadhwal. Agroclimatic indices and simulation models and model parameters have to be estimated from limited experimental data (Dorigo et al. crop suitability. The most promising method to estimate crop yield is combining crop growth models and remote sensing data. The different ways for integrating crop models with remote sensing data can be generally classified into following groups: • re-initializing or re-calibrating crop simulation models. adapted from Delécolle et al. 2.. 2003). yield gap analysis as well as in precision agriculture.12a). 2. and (c) ‘updating’ (Dorigo et al.g. hyper-spectral data) as well as retrieval of additional crop 83 . LAI) and final yield. 2007. and • direct use of remote sensing inputs as forcing variable (Fig. the model outputs of LAI match with remote sensing observations (Fig. Figure 2. using crop simulation models – remote sensing differences to modelling yield predictions (Fig. 1992) The linkage between crop simulation models and remote sensing has a number of applications in regional crop forecasting. The improved characterization of crop and its growing environment would offer additional ways to modulate crop simulation towards capturing the spatial and temporal dimensions of crop growth variability (Dadhwal. 2. These applications mostly include the use of GIS for visualization of model inputs and outputs. Several model applications were done not only for regions within nations but also on a larger scale including national scale and whole Europe. 84 .4) or actual data such as weather data for operational applications. leaf N and canopy water status can be expected. Also the spatial resolution of these applications varies.Survey of agrometeorological practices and applications in Europe regarding climate change impacts parameters like chlorophyll. In Europe the number of spatial applications of agrometeorological models vary from country to country. often limited by the availability of specific model input data. Thermal remote sensing can provide canopy temperatures and microwave data the soil moisture. for an improved assessment of spatial variable model input data or control parameters for verification of model outputs (such as Leaf area index). Spatial applications are further focused on the assessment of regional or larger area crop growth and yield information for monitoring issues or climate change impact assessments. Table 2. often for climate change impact studies. ● Crop and other agrometeorological model applications (operational or research) which were done beyond the national scale. often limited by the availability and costs of spatial variable model input data such as soil data (see chapter 2.5. combined for example in so called crop growth monitoring systems (CGMS). Spatial applications further often include the use of remote sensing data.2 Spatial model applications in Europe The following report contains an overview of the state of art and results of an European survey by questionnaires on following aspects of crop model applications in Europe: ● Crop and other agrometeorological model applications (operational or research) which were done with combination of GIS and/or Remote Sensing. 2003).15 (in 2 parts) gives an overview on the results of the COST734 survey for all European countries (including information from literature sources). A number of agrometeorological models are applied on a larger spatial scale than just on single sites at field level. Remote Sensing data (bold: models in operational use or planned for operational use) 85 .15: Survey results on European spatial agrometeorological model applications including GIS. Agroclimatic indices and simulation models Table 2.2. Survey of agrometeorological practices and applications in Europe regarding climate change impacts 86 . it/marsstat/). The best known example of an operational application is the MARS Crop Yield Forecasting System (MCYFS) for food security for Europe and other parts of the world (http://agrifish. that is a territorial information system developed by the regional government of Navarra region. in near real time (Rembold et al. an optimization of the weights given to the three system components is searched in order to minimize the prediction error. crop management) parameters.cra. A national example of spatial agroclimatic monitoring is SIGA (Servicio de Información Geográfico Agrario-Service of Agrarian Geographics Information) is an application running at the Ministry of Agriculture (Deputy Direction of annual crops) in Spain (Sanchez et al. thematic maps on agroclimatic variables and information about the plan of productive regionalisation of Spain for the application of the EC rules (EC-1251/1999) of the European Commission. but the operational application is still limited by the large amount of data to be processed with a high technical level for a final result only slightly improved.wallonie.jrc.be/en/). B-CGMS is based on the existing European harvest forecasting system but the databanks are supplemented and refined by Belgian physical (soil data) and technical (temperature sums. During all the growing period. The application (SIGCH. SAgMIS is internet 87 . 2005). Agroclimatic indices and simulation models Spatial agrometeorological model applications in Europe for operational use Only few applications of crop growth monitoring systems are already operational in Europe. The three results are introduced in a multi regression statistical model and the weight of each component is obtained by statistical fitting for each decade. The general item of remote sensing data assimilation in crop models has been the subject of mainly methodological research work these last years: they have allowed to elaborate practical solutions. All three approaches provide an independent estimation of the yield.2. Satellite data are used as an aid to arrive a quantitative estimate of production in B-CGMS wheres at the European CGMS uses it in for qualitative interpretation.. http://b-cgms. and the information provided by the 1 km² resolution imagery of NOAAAVHRR and SPOT-Vegetation.. the B-CGMS crop yield estimates are the integrated result of three independent procedures : a spatial agrometeorological model. 2006).GIS related to the management of annual crops) offers cartographic and alfanumerical information. (2001). MCYFS was adapted also for national CGMS at a finer grid scale of 1x1km² to 10x10km² (for defined zones below NUTS level) for Belgium (B-CGMS. There are also regional projects with similar characteristics like the SITNA. According to Tychon et al. which is providing quantitative crop statistics at EU (for a 50x50km² grid for NUTS units) and national levels. a trend function which copes with the long-term increases due to technological development. Recent availability of digital global databases of climatic parameters. Maps of water balance for different areas in Slovenia could be obtained for different time scales upon request (Sušnik and Kurnik. and it has led to this Global AEZ study. soils and landform. for specified management conditions and levels of inputs. however. 88 ...html or http://www.iiasa.at/Research/LUC/index. not all countries were involved in relevant studies. http://www. developed by the Food and Agriculture Organization of the United Nations (FAO) in collaboration with the International Institute for Applied Systems Analysis (IIASA). nutrients and physical support to plants. Further. The AEZ programs utilize the land resources inventory to assess all feasible agricultural land-use options and to quantify expected production of cropping activities relevant in a particular agro-ecological context. soil and terrain. which are basic for the supply of water. Two examples of global crop monitoring system should be mentioned which is the Global Water Satisfaction Index (GWSI) as a near real time monitoring system (Nieuwenhuis et al. the above described already operational systems are based on prior research studies.html) for estimating of potential and actual yields (focused on climate change impacts on crop production). and for expanding the geographical scope to temperate and boreal environments. The approach enables rational land-use planning on the basis of an inventory of land resources and an evaluation of their biophysical limitations and potentials for crop production. topography.Survey of agrometeorological practices and applications in Europe regarding climate change impacts based GIS information system managed by the Environmental Agency of the Republic of Slovenia which includes in situ information on crop water balance and irrigation forecast. 1998).iiasa. 2004). Similar tools have been developed for yield (or yield risk and crop status) prediction on the global scale during the past years. This effectively enabled global coverage for AEZ assessments of agricultural potentials. energy. and land cover has allowed for revisions and improvements in calculation procedures of AEZ crop suitability and land productivity potentials. mostly for climate change impact research studies. The term AEZ refers to the Agro-Ecological Zones system. 2006) operationally managed with MCYFS at JRC and the FAO-AEZ method (Agro Ecological Zoning Methodology. Almost all european countries are covered by relevant studies.ac. The characterization of land resources includes all relevant components of climate. Spatial agrometeorological model applications in Europe for research Most of spatial crop model applications in combination with GIS and remote sensing were carried out for research aims.at/Research/LUC/index.ac. (Fischer et al. radiation. Guerif and Duke. the interfaces are partial and not automated. Flowering dates for wheat crops can be estimated from timeseries of C-band radar data. Similar examples were done by other studies using different crop models. Very often NDVI data as a measure of the Leaf area index is used in combination with crop model applications to improve spatial yield estimates (see also prior chapter). especially emergence and early crop growth. 2006).g.2. The results demonstrate that the MeteoSat derived temperature and radiation products can used be as input in a mechanistic crop model. This enables scaling up point models to regional applications without an increase in (phenological) field observations on the ground. Di Bella et al. 2003). evapotranspiration) of a crop model (WOFOST) from observations of MeteoSat. This approach gave satisfactory results that are in agreement with regional yield statistics.. by simulated canopy reflectance. they use one particular set of data from one study site. Efforts exist but are not coordinated. Guerif and Duke for example coupled the radiative transfer model SAIL with SUCROS in order to derive certain sensitive parameters of the crop model to yield. Several research studies on spatial model applications including GIS and/or remote sensing data were carried out with the SUCROS model in the Netherlands and France (Bouman. 1992. Radar data (ENVISAT –ASAR) were also used for multi temporal information of above ground biomass and LAI in combination with CERES Wheat (Dente et al. 2005). e. (2004b. by Moriondo et al. Agroclimatic indices and simulation models An extensive overview of the scaling problem in crop model application (spatial and temporal) is given by Faivre et al. there is a crucial lack of operational and transferable tools adapted to this problem. they address one specific question. as radar signals are attenuated maximally at the flowering stage. 1998. Although there exist a number of applications of crop model spatialisation.. (2004). with the STICS crop model (Prevot et al. In another example de Wit and Van Diepen (2006) investigated the possibilities of deriving basic meteorological inputs (temperature.1995. In most examples. These studies are often specific to one application. eg. They conclude that different techniques have been used but it appears that there is a lack of analysis of the methods and strategies and of the requirements. (2002) by using SPOT satellite data and the ROTASK crop model. 2000. For 89 . Launay and Guerif. they consider one particular model. 2004c) used SPOT satellite for grassland biomass simulation by the STICS model. Jongschaap and Schouten (2005) used an approach combining optical and radar emote sensing data with point-based crop growth modelling by the crop model ROTASK. (2007) in combination with the crop model CROPSYST or Clevers et al. that is. which does not make the tool easily transferable to other researchers. However. the temporal variation of climatic inputs affected simulated crop growth more than spatial variation of soils. 2007). 2000). wheat and soybean was demonstrated by Basso et al.. deriving more precise pedotransfer functions.g. in another study was shown that spatial soil variation is often a main problem (Lagacherie et al. catching within-field variations for precision agriculture applications) for maize.Survey of agrometeorological practices and applications in Europe regarding climate change impacts water-limited production levels they concluded that the MeteoSat based simulations are unable to reproduce the drought stress which usually occurs under Mediteranean conditions. Upscaling methods in general often were applied within several EU-projects detecting climate change impacts on growth and yield of several crops. testing aggregation of soil types. This is a result of the fact that the MeteoSat based reference evapotranspiration is on average 30 percent smaller compared to the standard Penman reference evapotranspiration. 2000) and in Czech Republic (Trnka et al. Several possible improvements of the existing soil databases can be envisaged such as extending the range of systematic soil characterisation (e.. for example in ACELLERATES using the CERES based crop model ROIMPEL (Audsley et al. This case study demonstrated that a significant effort must be made for increasing the precision of yield estimates over vast areas. bulk density). by Harrison et al. 2006). 90 .g. by means of range of values by soil horizons). (2000) for phenological development to the European scale by using ARCWHEAT2. An example for rye yield modelling for a specific catchment area in Northern Germany is presented by Richter (1999). and enlarging the mapping scale in view to locate the STU. an example for smaller spatial scales (e.g. Therefore a considerable recalibration of the evapotranspiration related components of the WOFOST model would be necessary before using the MeteoSat based reference evapotranspiration in the model.. where weather generators are used for simulation of daily temperature values from monthly weather data. Lagacherie et al.g. Generally. (2007). For a known distribution of soil types in a catchment. providing more accurate descriptions of STUs (e. The CERES models were used in upscaling to national levels for climate change impact studies by aggregation of soil and climatic units for example in Finland (Saarikko.g. the area-weighted average of yield simulations based on mean soil properties equals the weighted mean of the individual simulated values. Methods of spatial input data aggregation were investigated in several papers (e. Upscaling methods of crop modelling without Remote Sensing data use were presented e. 2000).. Finally. agroclimatological indices can be considered as most important tools both for research and operational applications. Beside the effects of climate change on crop productivity. however. national) and temporal (nowcasting. expensive data management. pastcasting.6 Conclusions From the survey of Working Group 1 in the COST 734 participating countries in order to assess applications of agrometeorological indices and models in Europe it is clear that the relevant approaches and methods are of immense importance for research as well as for practical applications in European agriculture. monthly) makes the indices suitable for the application with historical climatic series. On the other hand new methods are being developed to overcome these problems partly such as by using GIS and integration of remote sensing data. avoiding the use weather generators to create the hourly data frequently required by complex mechanistic models. neglecting the necessarily finer spatial resolution to be of relevance for local practical recommendations for farmers. especially for the assessment of global and climate change impacts on agriculture. However. One of the main difficulties for the application of process oriented models in a high spatial resolution at the research level is often the lack of model input data (not available. The more complex approaches. Particularly the possibility of using a wide temporal time step (daily. in order to obtain correspondence tables.2. There are few cases (drought index.) scales. high costs. The results of the questionnaires elaboration pointed out their large use at European level for many purposes. a majority of these studies were carried out on a larger scale. However it seems to be clear the needs of a standardisation of their structure. In research. for example mostly named crop and management data for model validation and soil data. namely process oriented models are still very limited in operational applications (especially crop yield models). etc. grapevine quality index) where the different levels of indices are analysed taking into account thresholds describing the consequences of obtained values and the expected intervention needed to manage and to protect the agricultural systems from the main impacts. it is recommended that the modelling community should also have a 91 . Only very few examples exist for operational crop yield forecasting intergrating all these available tools. needed for the inter-comparison of the results and to improve the understanding of the results. Agroclimatic indices and simulation models 2. only to be used at the expert level. process oriented crop models play a very important role. weekly. which are the dominating studies till now.). etc. however. except the more simple models focused on irrigation scheduling or the already widely applied models for pest and disease management. spatial (regional. Due to their simple application. .8 References Aggarwal P. R. Dordrecht. Stara Lesna. etc. R. Bandhyopadhyay. soil carbon stocks. Proceedings of the XXth Conference of the Danube Countries on Hydrological Forecasting and Hydrological Bases of Water Management. (Eds. soil organic matter content and fertility or climate change itself trough trace gas emissions. (CD) 15 pp.T. H.W.H. Potential Climate Change Impact on Winter Wheat and Spring Barley in Austria. Alexandrov V. especially to the leader and co-leader of the action. and Eitzinger J. van Laar H.Survey of agrometeorological practices and applications in Europe regarding climate change impacts closer look on other aspects such as soil fertility and environmental issues like groundwater recharge and water quality. H. Roetter... This is not only required from the environmental view. R. Metselaar K.. pages 59-65. especially to all who responded to the questionnaires. The Slovak Republic. Alexandrov V.. 1993.K.K. Aggarwal P. N. Vol. in the future. Los Banos. Alexandrov V and Eitzinger J.7 Acknowledgements The authors thank all who contributed to this chapter. Therefore. erosion. In: Penning de Vries F. Kluwer Academic Publishers. T. Bratislava. Agricultural and Forest Meteorology.B. 2000.K. IRRI. Alexandrov V and Hoogenboom G. trace gas emissions. Luat N. 104(4): 315-327. Journal of Crop Improvement 13(1-2): 291-331. 92 .. (CD) 8 pp. pages 97-109. 1.K. Agro-ecological zoning using crop growth simulation models: characterization of wheat environments of India. 1998.S. Sehgal. SysNet Research Paper Series No.. The Impact of Climate Variability and Change on Crop Yield in Bulgaria. Slovakia. 2.. (Eds.. 2. V.V. 2.K. Joshi. 2001. An Approach for Irrigation Planning and Management of Maize Crop in North Bulgaria.): Exchange of methodologies in land use planning. Philippines. 2005. The Netherlands. Proceedings of the international conference on 150 Years of Meteorological Service in Central Europe.. We therefore also should try to combine our modelling of climate change impacts with ideas and experience of sustainable production. Hoanh C. In: Roetter R.): Systems Approaches for Agricultural Development. 2000.T. integrated modelling approaches are required which include the most relevant interactions in the soil-cropatmosphere system.. Rajput. S.g. Pathak. van lttersum M. Thanks to all other COST734 members for their support. Kalra. Exploring agricultural land use options for the State of Haryana: Biophysical modelling. Teng P. The Potential Effect of Climate Change and Elevated Air Carbon Dioxide on Agricultural Crop Production in Central and Southeastern Europe.. Part of the work on the study report of WG1 was directly supported by the research project DROSMON (Crop Drought Stress Monitoring by Remote Sensing) financed by the Austrian Science Fund (FWF). but also because some of these issues cause feedback effects on crop productivity. e.C. Kaur. and Eitzinger J. Choudhary. CAgM Report No. Oberforster. Kleschenko and Y. 372-389. Guerreiro. University Park. and Slavov N. Koutsidou.A. K. Global Change Biology 8 (4). Pearn. Economics and Planning Division. 2/2002 NLH Bacsi Z. Agroclimatic indices and simulation models Alexandrov V. Variations of Maize Yield Depending on Meteorological Conditions. Cajic. Eitzinger. E. and what not? Environmental Science and Policy 9(2): 148-162 Avgoustoglou. Sys. Bradley. 232-237.. Cojocaru. Switzerland.. A Grassland Crop Growth model for High Latitudes Documentation. 2008. Azamali S.. 35-42. 1997. Alexandrov. P. 37. Operational-Research Investigation of the model (LMCOSMO) at the Hellenic National Meteorological Service Proceedings of 6th Hellenic conference in Meteorology Climatology and Atmospheric Physics.1113. Italy. The Effect of Climate Variability and Change on Agroclimatic Resources and Crop Productivity in Bulgaria..H and Lantinga.. P: 37. ENGNOR. Gat. Anon. M. Impacts of future climate change on Hungarian crop production: An application of crop growth simulation models.54.D. Department of Agriculture and Food. J. Vulnerability and Adaptation of Agronomic Systems in Bulgaria. Perspectives in modelling resource capture by crops. Mishev (eds. J. Agric. E. Eitzinger.M. Simota. Alexandrov V. Agriculture and Climate Change. October. C. 2001. 1998. Knight. Beijing. Bioproductivity. 2000.M. Report no. Ioannina.E.K. Climatic Change 36: 135-149 Alexandrov V.. Z.. Baadshaug O.J. M. 95-96. Baker J. Alexandrov V. Audsley E.Alexandrov.. M. Florence. The Impacts of Climate Variability and Change on Agriculture in Bulgaria. V.G. G. 2003. Austria.. M. pp. 2000. V. Alexandrov V. Factsheet on Irish Agriculture.. Climate Change Impact on Water use of Maize in Bulgaria. 435-450. Geneva. 1991..A. Trnka. 2002. Plant Sciences 35(1): 11-17 (in Bulgarian). Potential impact of climate change on selected agricultural crops in north-eastern Austria. A.. 2002.1172. (CD) 13 pp. Proceedings of the international conference on 150 Years of Meteorological Service in Central Europe. Vulnerability of Agronomic Systems in Bulgaria.. R. Alexandrov V. Alexandrov V. 2528 September 2002. et al. Proceeding on the 2nd European Conference on Applied Climatology. 1999. Proceedings of the 2nd International Symposium on Modelling Cropping Systems. Climate Change Impacts on Agroecosystems in Selected Agricultural Regions in Austria.. Climate Research 12(2-3): 161-173. Volume 12.N. Proceedings of the International Symposium on Climate Change (ISCC). Performance and application of the APSIM Nwheat model in the Netherlands. van Keulen. Global Change and Bulgaria. Summarizing Crop Growth Simulation Models in Europe with Potential for Operational Assessment of Crop Status and Yield Prognosis. Rounsevell. pp. (CD) 6 pp. Vienna. WMO/TD-No..R. 2001. 119-214. Agronomy Journal. Report of the RA VI Working Group on Agricultural Meteorology. 2002. Slovakia. Government of Ireland. J. N. Z. Crout. Thorton. Pennsylvania. Oberforster. Introduction to Crop Modeling Papers from Eastern Europe and the Former Soviet Union.. Alexandrov V. 2002.B. Volume 93.. Stara Lesna. USA and Sofia. China. 2002. Ozalp.89. Asseng S. Stol. 2006. pp. 93 . 1998. P: 650.2. V. Resource Capture by Crops:125-148.). Dent. WMO/TD No. W. European Journal of Agronomy. H. 1994. M. R.. G. In: Staneva. Alexandrov V.. 177-198. pp. 2000. Hristov and D. 1998. T. In: Dunkel. What can scenario modelling tell us about future European scale land use. 2001. Vol 1. pp. Alexandrov V. de Koning. G. Crop modelling and remote sensing for yield prediction. KONOR.W.. L. D. J.A.. Simulating trends in crop yield and soil carbon in a long term experiment – effects of rising CO2.. Penning de Vries. In Quantitative Approaches in Systems Analysis. 1992. Physics and Chemistry of the Earth. May 2001. 1999. 2006. The model AMBETI – A detailed description of a soil-plant-atmosphere model. Demidowicz. Jones. Volume 30. 1997a. CABO-DLO.M.J. P: 82. (Growth and yield simulations of potato cultivated in Poland using WOFOTS – model calibbration).M. G. Weryfikacja modelu. Błoch Z. 235-245. A. J.. G. 2007. K.C. R. Spatial validation of crop models for precision agriculture. Wageningen University and Research Centre . Plant and Soil 287. Agron.E. R. Agricultural Systems. Oxford University Press.. Using VEGETATION satellite data and the crop model STICS-Prairie to estimate pasture production at the national level in France. Faber. Hoogenboom. Application of the CERES-Wheat model for within-season prediction of winter wheat yield in the United Kingdom... J. K. pp. 33 pp. G.. 97-112. Crop Specific Simulation Parameters for Yield Forecasting across the European Community. Frag. Petersen. J. Documentation. A.G. Primjena modela za prognozu važnih biljnih bolesti u Republici Hrvatskoj. B.A. F. 2 (54). Production Ecology and AB-DLO. magistarski rad.J.. Description of the growth model LINGRA as implemented in CGMS. Modelling the effect of the spatial distribution of agricultural practices on nitrogen fluxes in rural catchments. (Model application for forecasting the occurrence of more important plant diseases in Croatia).M. Analyzing the effects of climate variability on spatial pattern of yield in a maize–wheat–soybean rotation. Dąbrowska-Zielińska. A. M.D. 7. I. van Diepen.103pp. Volume 26. Basso B. Błoch Z. Ciołkosz. International Journal of Remote Sensing. Martin. J. Agronomy Journal 95(1):114-125.. Demidowicz. 96-101.T. Boken. European Journal of Agronomy. 2005. Berichte des Deutschen Wetterdienstes 195. Model. Basso B. Report No. P: 2565 – 2581. Agronomski fakultet. Pierce. Bochenek Z. Ruiz.. Number 2. 94 .W.91.M. Agric. Wageningen: European Commission. 1996. Schapendonk.P. N deposition and improved cultivation..R. C. Braga.. 2005. and Crout N. 11-22 Braden H.A. W. 137: 93-105. Kamasa. Netherlands J. F. L. W: Monitoring Agricultural Drought ...A.M. 2003.. van Kraalingen. J. 1993. Seguin.A Global Study. 143-161.. 2007.J. P. Bouman B. I. Frag. Beaujouan V. 2001. Simulation Reports No 32.H. Monitoring Agricultural Drought in Poland. Bertocco. N. 43. S. Ritchie. B. A stochastic modelling approach for real-time forecasting of winter wheat yield. 2001. A. Kalibracja modelu. Ecol. Agron. Zastosowanie modelu WOFOST do symulacji wzrostu i plonowania ziemniaków uprawianych w Polsce. Ruget.A. F.. Field Crops Research 62(1):85-95. 2006. 1995. Crout.M. Faber. Linking physical remote sensing models with crop growth simulation models applied for sugar beet. 2 (54). Sartori. 2005. Volume 68. Agricultural University of Norway. Olesen. Sci. Stol. A model for simulation of cereal growth. Volume 13. (Growth and yield simulations of potato cultivated in Poland using WOFOST – model verification). The Netherlands. M. Kamasa. Berntsen.J.M. 1995. E. Bouman B. Bičak L.. Boons-Prins E. Bleken. Vijendra. Durand. Bannayan M. 1992 Bouman. Faivre. Zastosowanie modelu WOFOST dosymulacji wzrostu i plonowania ziemniaków uprawianych w Polsce.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Bannayan. P: 3–9. B..C.T. M. Drupka.. 1997b. Offenbach. 2/2201. Zagreb. Bella C. 88-95.H.W. Spain. Wouters. Tropical Crops Research Unit. D. Zimmer. P. 309-332. Technical note no. D. 2007. Ripoche. Tychon. Switzerland: World Meteorological Organization Brisson N. M.G. UK. Joukainen. for Mediterranean conditions. Haverkort. 122 pp.R.P.A.J. Bradley R. Crout. Proceedings of the 3rd European Conference on Applied Climatology "Tools for the environment and man of the year 2000" Pisa. P.. Seguin. Struik. M. J.C. The PARCH Model for Predicting Arable Resource Capture in Hostile Environments: Users' Guide. 2001.A. An overview of the crop model STICS. 95 .G. Oxford. J.. Italy. Oger.CGMS Project: ESA International Symposium at Lleida.) silage production in Portugal. Launay. 1996. Leicestershire. Eur. A. ESA symposium Copenhagen Brisson N. Cardoso. J. European Journal of Agronomy.G. Henault. Sierra. N.E. Climatic Variability and Agriculture in Europe: An Integrated Assessment.. Gaspari. F.M. Agric.O.A. 1994. Burger. 2003. Climate Change. 79. S. Scott. B.. Y. A. Agronomie. Bosch. R. Saarikko. H. B.K. R. Carter T. D. 2000. R. 2000. 28.D. Agro-ecological zoning and potential yield of single or double cropping of potato in Argentina. Y. (CD) 6 pp. Dibet. R. Caldiz D.J. University of Oxford.P. Eitzinger.J. B. Volume 12. Spain. B. Dehem.E. European Journal of Agronomy(Article in press). Roche. Contribution of the STICS model outputs to the agronomic analysis of the 2003 drought in France. pp. A Web-Based Mapping Application To Link A Crop Modelling System (B. E. Monitoring Crop Growth at National or Regional Scale with an AgroMeteorological Model and Remote Sensing Data through a GIS Web. R. Debake. Butterfield and K. For. 2006. B. 1999b. Danielov. 1999. Downing. Bussière.J.CGMS Project: ESA International Symposium at Lleida. F. France. Ozier-Lafontaine. Oger. Gaudillere. Crozat. 1994.. Alexandrov. Sinoquet Tournebize. Goudriaan.A. Harrison.. 311-320. H. Justes. Coelho. P. Journal of Agricultural Science 122(1):161-162. Bertuzzi. B. 1999a. 24: 409-421 Brisson N. G. P. Cabidoche. and Crout N. Buffet D. C. Modelling crop growth in semi-arid environments. Agroclimatic indices and simulation models Bradley R.CGMS) And A GIS Database.R. Cajic V.18.. J. Geneva. 2004. Mary. P: 175–189. UK. Theoretical basis and parameterisation.P. Simulated Response of Soybean to Incremental Climate Change Scenarios at a Specified Location in Austria using CROPGRO Model. Agron.. Gary.. Testing the performance of ORYZA1. Cellier..R. C. Grain Legumes. Estimating regional crop potential in Finland under a changing climate. Bussiere. Dehemd. K. Evaluation of the STICS crop model within the EU INTERCROP project. Braga R.. S. Implementation of the Belgian Crop Growth Monitoring System with a Web-based mapping application linking a Crop Modelling System and GIS database.2. Chapter 16 in: T. A. Corre-Hellou... 2000. D. 2000. Meteorol.Based Mapping Application. Modelling climate change impacts on wheat and potato in Finland.M. Adaptation of the crop model STICS to intercropping. 301-313. Maraux. Azam-Ali. Sutton Bonington. Agricultural and Forest Meteorology 109 (4). P. H. EerensS.M. 1999. Buffet D. Crop model based decision support for maize (Zea mays L.197. Carter T. 289-312. F. J.GI&GIS Workshop. Environmental Change Unit. F.J. Brereton A.N. University of Nottingham.30 June 2000.H. 45: 10-12 Brisson N. J. Veroustraete. 21. 2004. Sinoquet. 2007.J. 6th EC. V.... Research Report No. F. D. Casanova D. P. H. Tychon. Agrometeorology of Grass and Grasslands for Middle Latitudes.. and Saariko R. 1996. Lonsdale (Eds). an explanatory model for rice growth simulation. S. Buffet D. Slingo.P. OGA.N. Proceedings of the Training Workshop 7 – 11 July. Halupecki.. 2002..J. Z.W. Mediterranean crop responses to water and soil salinity: Eco-physiological and agronomic analyses .. Greece. D. F. Remote Sensing of Environment. Agronomie. Agric.. 2004. Ruget 2001.. P. and Spanos S. 14. 1994. 2003.S. 2002. Cepuder P. Azam-Ali. Mod. International Symposium/ Workshop on Weather Modification. 2000. P: 315. Thailand. (Eds.. Šubić. Modelling of crop growth conditions and crop yield in Poland using AVHRR-based indices. N.R. In: Sivakumar M. A. K.E. 2002. Dalezios N. Dadhwal V. R. Bampzelis. de bary) on tomato). European Journal of Agronomy.W. 18: 195-204.N.. J. Black. M. B. S. of Wea. For.I.C.J.. The Palmer Drought Severity Index (PDSI) for rain enhancement in northern Greece. Volume 22. Alamirew. Gruszczynska. Evaluation of CropSyst for simulating the yield of flooded rice in northern Italy. Prévot . India. Crop growth and productivity monitoring and simulation using remote sensing and GIS. (Options Méditerranéennes : Série B. 2002. Cvjetković B. 2000. Volume 50.8.. Vol. Dalezios N. Physiologia Plantarum 110(1):78-88. 2005. Using SPOT data for calibrating a wheat growth model under Mediterranean conditions.V. Bouman.. Saha S. de bary) na rajčici (Forecasting methods for controlling late blight (Phytophthora infestans mont.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Castrignano A. Jongschaap. Roy P. Etudes et Recherches . Dehra Dun.P... R. Crout.-21.. Špoljarić 1999.. G. Hamdy..M. 36). M. Ltd. P: 687–694.2002. L. V. 263-290. Wheeler. Bruguier. 1995. Büker.326. 23. 6 tables. Katerji. A Framework For Monitoring Crop Growth By Combining Directional And Spectral Remote Sensing Information. Daoularis. Karam. Modeli prognoze za suzbijanje plamenjače (Phytophthora infestans mont.G. N. D. In: Srimuang Printing Co. V. 2004. Confalonieri R. J.): Satellite Remote Sensing and GIS Applications in Agricultural Meteorology. 2005.M. Vol. Osijek. drought and temperature on the water relations and gas exchange of groundnut (Arachis hypogaea) stands grown in controlled environment glasshouses. The occurence and control of fire blight in Croatia. Singleton-Jones. O. W. T.27 (1).K. Vonder. Meteorol. J.. King. L. Vol.. EPIC and GIS . Agrometeorological Simulation Using PERO Model for Grape Vine Downy Mildew in Greece. Craufurd. 124:99-120. Confalonieri R.): 17th World Congress of Soil Science.a tool to reduce nitrogen leaching.. Issue 2. 1109 – 1123. Volume 23..R. International Journal of Remote Sensing. C.F. Kowalik.Q. Desprats. C.J.C. F. Kogan. Matotan. Clevers J.. 2003. P. Issue 6.W. European Journal of Agronomy. Stronach. Bechini. M. 96 . Acta Horticulturae 489. 2000. Climate research.. Proceedings. A preliminary evaluation of the simulation model CropSyst for alfalfa.K.E. 71-77. Clevers J. p. Effects of elevated CO2.G.R. Dalezios N.R. van Leeuwen. Design and optimisation of a large-area process-based model for annual crops. Thailand. Bari : CIHEAM-IAMB. Grimes. Impact of local climate variability on crop model estimates in the south-east of France. Cvjetković B. I. November 1994. Thessaloniki. 1988.M.R. A. C. 2004. Challinor A. Operational Efficiency Assessment of Hail Suppression for Agriculture in Greece. S. P: 161-181 : 5 ill.M. Dabrowska-Zielinska K. P: 161-170. F. T.I. 24 ref. P: 223–237.A. 21-35. Volume 21.. 163. Harmsen. J. Courault D.F. (Ed. 2002. Bocchi. Aus der Schmitten. H. E. Summary (En). Ciolkosz. n. Zbornik sažetaka Postignnuća i perspektive hrvatskog poljodjelstva. Bangkok. Clifford S. Mastrorilli. 1621. 1-4 Aug.K. Bangkok. A modified version of ceres-maize model for predicting crop response to salinity stress. C. 16 pp.. 1994. A crop-growth simulation model for calculating biomass production potentials of sorghum within the “beaver” expert system environment. S. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Viovy. Netherlands. 125-127. G. Driessen.24. Danalatos N. M. Danalatos N. Rebella. M. Guérif. (Proceedings in press). Synergistic Use of ASAR AP Data and Crop Growth Model for Yield Map Estimates.. Proceedings of 2nd Panhellenic Conference in Soil Sciences. The change in the specific leaf area of maize grown under Mediterranean conditions. 2007. Ruget. De Wit A. Combal. Faivre. pp. Athens.. Introducing spectral data into a plant process model for improving its prediction ability. Boogaard. R. F.A. Ruget. 1988. Photogramm. Agronomie.Prairie to estimate pasture production at the national level in France. K. M. 145-161.L.C.. Guerif. Remote sensing capabilities to estimate pasture production in France. 2004b. Mattia. Brisson. 2004c. M. Using VEGETATION satellite data and the crop model STICS.671. Agronomie. Agroclimatic indices and simulation models Danalatos N. F. De Wit A. Aussols.J. Preceedings of the 4th International Colloquium Signatures Spectrales d’Objects en Teledetection.437-444. France. Tzortzios. F.Derived Meteorological Inputs.. A. P. Rebella 2004a. Physics and Chemistry of the earth. Maas.. 47. De Wit A.. van Diepen. Faivre. Final Partial Report of the E. 15-18 November 1998.. Baret. Delécolle R. 1997.. Project AIR3-CT93-0985: Biomass Economic Appraisal & eValuation ExpeRt (BEAVER). F. Perrier 2006. Agricultural University of Athens. Seguin. 38-56. B. Kosmas. 18-22 Jan.J. N.. 2006. R. Faivre. 5359-5372.30. 1992. Guerif. P: 667.A. 24: 397-407. Dept.. Engineering. 2006. International Journal of Remote Sensing. C. Danalatos N. WOFOST: A methodology for quantifying production potentials and its applicability in Greece. 1998. Yassoglou. Satalino. C. and Guérif M.2.W. B. Greek Publications. Geophysical Research Abstracts.W. N. Remote sensing and crop production models: present trends. St. B. Gervois. Agricultural and Forest Meteorology 14. C. ISPRS J. Di Bella C. ISPRS Commission VII Mid-term Symposium “ Remote Sensing: From Pixels to Processes”. Volume 8. 7th ICCTA – International Congress for Computer Technology in Agriculture. Ciais. Seguin. and Sgouras I. F. Seguin. Use of VEGETATION satellite data to improve pasture production simulated by STICS-Prairie in France. Di Bella C. Di Bella C. Dente L. Weiss.J. of Agric.G. Kosmas. R. P. S.3-9. and van Diepen C. 497-512.G.M. 811 May 2006. C. Rinaldi.G.. 1987. M. N. A simplified cropwaterbalance simulation model and its applicability for estimating the water-limited production of wheat under semi-arid conditions. B. De Noblet-Ducoudré N. Crop Growth Modelling And Crop Yield Forecasting Using Satellite. H. Delécolle R. 1988. 2005. Ruget. Seguin. Florence. Gerontides. Combal. B. 25. Italy. Weiss.. B.G. Spatial resolution of precipitation and radiation: The effect on regional crop yield forecasts. van Diepen. M. S. Agricultural and Forest Meteorology 135. 97 . 156-168. Remote Sens. Agronomie 14(1994): 1-8... Coupling the Soil-Vegetation-Atmosphere-Transfer Scheme ORCHIDEE to the agronomy model STICS to study the influence of croplands on the European carbon and water budgets. M.A..472. G. Semeradova.R. 2003. Stockle. Z. L. J. M. Prueger. Brazile. Butterfield. /ESA International Symposium "Modelling cropping systems". Calibration and evaluation of the WOFOST model for winter wheat. Sinclair. Wilkens 1997. C. M. Stern.J. J. J. Chemicals and Energy in the System Soil-Crop Canopy-Atmosphere' 16.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Donatelli M.G. Rinaldi. Zalud. van Diepen. Slovak Academy of Sciences). 165-193. Guérif. Eitzinger J.E. M. 2001. C. Remote Sensing of Environment 97. Sensitivity of CERES-Maize yields to statistical structure of daily weather series. D. Dubrovský. 1999.pdf 98 . S. G. Agricultural Water Management. Climate Change.E.. Błoch. M. (eds) 2000.. (Growth and yield simulations of winter wheat cultivated in Poland using WOFOST).11. Downing T. Hoogenboom. (Natural conditions for crop production in Poland and Europe according to CGMS simulation). Belgium: Commission of the European Union. 2004. Climatic Change 46. Climatic Variability and Agriculture in Europe. Singh. P. 8th International Poster day ' Transport of Water. E. A. Institute of Hydrology. ISBN 80-9684800-3.. Jones. Austrian Journal of Agricultural Research 52(4): 199-212. Ferré.. Comparison of CERES. M. Evaluation of CropSyst for Cropping Systems at two location of northern and southern Italy. An Integrated Assessment. D. A. 163-234. Z.A. Fragm. J.2000. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modelling. Faber A. A local simulation study on the impact of climate change on winter wheat production in north-eastern Austria. Hösch.C. Z. Z. Lleida (SP). Hollinger. Demidowicz. M. Zurita-Milla.E. Schaepman. 447. International Journal of Applied Earth Observation and Geoinformation 9. F. T. Akhmedov. 2007.a program for the application of crop simulation models across geographic areas. 192-202. M. 137-151 Faber A. Ecological Modelling 171 (3).de/medien/luft/inklim/dokumente/endberichte/weinbau. Dürr C.1997.... B. Žalud. Brussels.hlug. WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions. Agron. Trnka. C.171-172/. Zalud. die Sorteneignung sowie Mostgewicht und Säurestruktur der Trauben. 4. Trnka. Eitzinger J. Dubrovsky M. Application of MODIS derived parameters for regional crop yield assessment. Research Report No.. Puł. FAG-Forschungsanstalt Geisenheim. 2000. A. Harrison. Oberforster. June 1999. M. M. R. 2000. Dubrovský. 1997. 130. II Weryfikacja Modelu. 61. R. (CD version. A simulation study of the effect of soil water balance and water stress on winter wheat production under different climate change scenarios. V. M. P. Dubrovsky.A. Lonsdale. Štastná. Dubrovský. Dorigo W. Z.W. Kaczyński. M. Trnka. Oberfoster. Der Einfluss klimatischer Veränderungen auf die phänologische Entwicklung der Rebe. Žalud. Alexandrov..W.. European Journal of Agronomy. Eitzinger J. Žalud. AEGIS/WIN .A. Semerádová.. 89. F. Agronomy J. M. 2005. Środowiskowe uwarunkowania produkcji roślinnej w Polsce i Europie według symulacji CGSM.W. Pam. Ceotto. de Witt. Eitzinger J. Diepen. 223-246. Doraiswamy P. Volume 6. K. http://www. 2002. 21. 51-58. 445 pp. 2005. Engel T. Štastná. 1996. Study of crop establishment effects on subsequent growth using a crop growth model (SUCROS). INKLIM Final Report. Z. 919928. Contract ENV4-CT95-0154. Nieróbca. R. P: 35-45. Brochery. Symulacja wzrostu i plonowania pszenicy ozimej uprawianej w Polsce przy uŜyciu modelu wzrostu WOFOST. 3. M. Bratislava. G. Faci. M. Makowski. International Institute for Applied Systems Analysis.M. Supplement 1. Lasch.J. W. 2003. PhD thesis) Gerstengarbe F.M. Meteorological applications 13. PIK-Report. Thèse de doctorat de l’ENSA. 4M. 2003. Nauk. F.M. Lee. P: 205–217. J. European Journal of Agronomy 18. 2003.).Software Package For Modeling Cropping Systems. Górska.B. A. The activities of the German Weather Service (DWD) in the field of agroclimatology. ISSN 1436-0179 Ghaffari A. Fodor N. Agricultural and Forest Meteorology (reviewed) Fodor N. 313.J. Laxenburg. Volume 24. J. 2003.M. Analysis of maize growth for different irrigation strategies in northeastern Spain.J. A.und Landwirtschaft sowie die Ableitung erster Perspektiven. Leffelaar. Brereton. 2005.Wallach. 12: 225– 238. 2006. V. Scott. 1997. F. Fodor N. H. 2004. 1998.. P. 2002.. 2003. die Forst. Zesz. A. Volume 12.. Sensitivity of crop models to the inaccuracy of meteorological observations. Stokes. Crout. AEZWIN..B..J. European Journal of Agronomy 18 (1-2): 75-105. M. Fischer G. 2008b. K.J. Austria.. 2000. Wechsung. Spoz-Pać.W. Fitzgerald J. Nr. Wading through a swamp of complete confusion: how to choose a method for estimating soil water retention parameters for crop models. Spoz-Pać. Benoît. G. van Oijen. Gillett A. N. Geophysical Research Abstracts. M. 2004. T. Górski T. Brereton. 31-43. Grass and Forage Science (reviewed) Fitzgerald J. Deputat. No. Zesz.K. Gaspar. R. Suckow. Papy. Rol. 09186.. K. Report IR-98-051.G. Adaptation du modèle STICS à la vigne (Vitis vinifera L. Studie zur klimatischen Entwicklung im Land Brandenburg bis 2055 und deren Auswirkungen auf den Wasserhaushalt.A.B. M. H. Simulation of the influence of poor soil drainage on low-cost grass-based dairy production. Assessment of the adaptation potential of low-cost grass-based dairy systems to climate change in Ireland – the maximised production scenario. 1989.Marcinkowaska. I. Gijsman A. 2006. 4M – Software For Modelling And Analysing Cropping Systems. Climatic Change 55(4):509-533. 99-144. Friesland and Löpmeier. An interactive multiple-criteria analysis tool for land resources appraisal. AR Wrocław. G. M. D. W.2.F. N. Journal of Agricultural Science 136:369-381.C. Holden. Holden.. Cook.. Spatialising crop models. Agronomie. D. Nauk. Leenhardt. 2006. 61-67. Dedieu. Voltz. Post. Górski T. 99 . European Journal of Agronomy. N. Dairy system simulation for assessing regional climate variation effects on management Grass and Forage Science 60: 283296 Fitzgerald J.. F.M. 2006. Volume 5.. Werner. Jagtap. Journal of Universal Computer Science. 369. Brereton. Lahmer. Jones. Badeck. Stock. D.. Kovacs.. P: 1196-1207. 2001. Agroclimatic indices and simulation models Faivre R. Granat. S.S. Pokovai. An approach to modelling the effect of environmental and physiological factors upon biomass accumulation in winter wheat. 349pp (Adaptation of the STICS model to grapevine. 2008a. P: 389-393. F.. W. N. Probl. Demidowicz. Utilisation dans le cadre d’une étude du changement climatique à l’échelle de la France. R. (Empirical model of winter wheat yield in the function of meteorological parameters ). P.. Krysanova. Climate change and winter wheat management: A modelling scenario for south-eastern England.T. Holden. Sylvester-Bradley. G. G. 2002.J. 9. García de Cortázar Atauri I. 83. Empiryczny model plonowania pszenicy ozimej w funkcji czynników meteorologicznych.W. A method for forecasting sugar beet yield in Poland. F.C. Kovacs. G.. Use within a study of climate change at the scale of France. P.M. Hattermann. Farre I. Production of Land Resources maps for Barley for South East Norway with the plant production modell WOFOST. Arnoldussen.W.H. Sweeney 2003. Nitrogen fluxes in silage maize production: relationship between nitrogen content at silage maturity and nitrate concentration in soil leachate. Hummel. Kersebaum. UK. 2000.. Lahrouni. Fitzgerlad. J. vdf Hochschulverlag. Determination of spatial water requirements at county and regional levels using crop models and GIS An example for the State of Parana.L.M.R. O.E. Wexford. Hartkamp D. Luzern. ( Produksjonsgrunnlag for bygg på Østlandet basert på simuleringer med plantevekstmodellen WOFOST. Agricultural Water Management 52. and Duke C.W. 177-196. Guérif M. S. Agronomy Journal 93:237-249. Herrmann A.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Groot J. Butterfield.J. 21 . Cell size selection for site-specific crop management. Simulation of nitrogen dynamics in forage maize using the HERMES model. 2000. European Journal of Agronomy 9. Taube. 2002. M.H. S. J. T. 27: 1093-1117.B. Monitoring of irrigated wheat in a semi-arid climate using crop modelling and remote sensing data: Impact of satellite revisit time frequency. Proceedings of the 2nd Croatian Academic and Research Network (CARNet) Users Conference CUC 2000 (CD edition). University of Oxford.M. de Faria. E. Environmental Protection Agency.R. Climate Change: Refining the Impacts (provisional title). . Brereton. Ruiz-Ramos. Duchemin. 1987.. M.A. Simulation Reports CABO-TT 13. C.. A.I. Interfacing geographic information systems with agronomic modeling: A review. Agriculture. Holden N. 100 .R. Cvjetković.Assessment of Impacts and Adaptation. Hofmeister F.J. Trans. International Journal of Remote Sensing. Possible change in Irish climate and its impact on barley and potato yields. Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation. Ecosystems and Environment 81. Khabba. R.). P 33-79. ERTDI Report. 1998.R. P:167–186. T. Climate Change and Agriculture in Europe .C. G.. Volume 101. Hadria R. Assessment of climate change and agriculture in Spain using climate models. Zürich. 735-737. Han S. 411 pp... Croatia. NIJOS report 09/ 2001 8 in norwgian). Er-Raki. F. J. Taube. Wachendorf. 2008. 1995. G. Zagreb.. Johnstown Castle. 1999. Brereton. A.C. Cahn 1995. 761-772. (in press) Holden N. Bičak. Downing (eds. Porter. A. A. K..D. 57-69. Klakegg.. A. J. Wageningen. Dedieu.. Internet based Fire blight forecasting system.. Agricultural and Forest Meteorology 116: 181196.Nutrient Cycling in Agroecosystems 73 (1): 59-74. Harrison P. R. Agron. Herrmann A. Calibration of the SUCROS emergence and early growth module for sugar beet using optical remote sensing data assimilation.T. Hitrec I. Switzerland.In: Land Use Systems in Grassland Dominated Regions : Proceedings of the 20th General Meeting of the European Grassland Federation.A. Conde.. M. 19–26. Kersebaum. K. Guereña A. Heinemann A. 2000.. Scaling-up the AFRCWHEAT2 model to assess phenological development for wheat in Europe. 2004. J. G. In J. Diaz-Ambrona. Harrison P. September 24-26. Simulation of nitrogen balance in a system of winter wheatand soil. J. Hoogenboom. 2001. 2005. Fealy. F. Minguez. Halupecki. C. Sweeney (editor). B. Guérif M. WAU-TPE. White. B. L. M. 91.E. Agricultural and Forest Meteorology.24 June 2004:. Goering. Downing. J. ASAE 37 (1). 2001. Brazil. 2000.. R.B. Impact of Climate Change on Irish Agricultural Production Systems. and Duke C. Chehbouni 2006. Hoogenboom. 127-136. .L. CABO-DLO. 2000. Brereton. Climate change and the introduction of maize and soybean to Ireland. F. Dublin.. Agmet. Evaluation of the different Alternaria prediction models on a potato crop in A Limia (NW of Spain). Johnstown Castle. Holden N. Stol.. Jamieson P. Wageningen.A. & G.. B.E. pp 37-58 in: C. and Brereton A. 55: 23-44. Brereton 2006..M.D. Elsevier. “Perspectives for maize production in Spain under climate change”. P. 2003b. H.J. SUCROS2 and SWHEAT with measurements from wheat grown under drought.A. 2003a. Rodriguez-Rajo. Sweeney. Global Environmental Change-Human and Policy Dimensions 10:69-80. (ed) Climate Change and Agriculture: Analysis of Potential International Impacts.H. Mendez. Sweeney (editor): Climate Change Scenarios and Impacts (2000-LS-5.. p 259-273. J.. Janssen P.P. van Keulen.. M.I. Porter 2001.. M.2. (1998a): Sirius: a mechanistic model of wheat response to environmental variation. Goudrian. Semenov. Nix. Jones J. Holden N. Iglesias A. Pearson (ed. in: Rosenzweig C. A comparison of the models AFRCWHEAT2. S. Dordrecht. Fealy 2004. Climate constraints on cropping systems. The Impact of Climate Change on Irish Agriculture In: J. 1994. B. Predictability and nonlinear modeling in natural sciences and economics. Jamieson.F. Harper. 2002. 101 . J. In : Grasman. 2006. 1992.H.J.. F. Agricultural System 70.S.. 15. Iglesias A.R. 8: 161-179. 1992. and Mínguez M. Jones.H. 2007. An assessment of the potential impact of climate change on grass yield in Ireland over the next 100 years. In T.A.. Brooking and G.D. Francis. P 33-79.. WAU. Goodall (ed. J.1-M1) Final Report. Adaptation of water and nitrogen management of spring barley and potato as a response to possible climate change in Ireland. Pereira. Allen Jr. R. Rosenzweig.. SWACROP2. Agroclimatic indices and simulation models Holden N. and Brereton A. Department of Water Resources. Climate change and Irish agriculture.. Amsterdam. Collins (editors). Irish Journal of Agricultural and Food Research 41: 213-226. Keating. Iglesias A.) Field Crop Systems. Keane and J. Approaches to modular model development. I.M. Hutchinson M. Vol. Wexford.) Ecosystems of the World. Field Crops Research. Wisconsin. 23 (1): 27-34. Agricultural impacts of climate change in Spain: developing tools for a spatial analysis”..Kluwer.J. Agricultural Water Management 82: 297-317. 1995.J.2. H. Irish Journal of Agricultural and Food Research 42: 1-15 Holden N. Ritchie. L..M.R. 18 of D. 2000... J.W. ERTDI Report Series No. American Society of Agronomy. Prognosis of phenological stages of Vitis vinifera (cv. European Journal of Agronomy. A. Hollinger.. L.A.T. Huygen J. Sirius.M. User's guide. 421-443. Berkelmann – Löhnertz. Iglesias I. Assessing sensitivities and uncertainties in models: a critical evaluation. Hoppmann D. Madison.W. AEROBIOLOGIA. 2000. D.J. CERES-Wheat. Climate Weather and Irish Agriculture (2nd Edition).). 1998b. Holden N. Bulletin OEPP/EPPO (30) 121-126.M. A. Riesling) for optimizing pest management. Vol 13. J. CGE Hands-on Training Workshop on V&A Assessment. Use of DSSAT models for Climate Change impact ans assessment: calibration and validation. Porter. Environmental Protection Agency.W. J. McMahon. a quasi-two-dimensional crop growth & soil water flow simulation model. W. C. Jakarta 20-24 March 2006. 344-361. J. and Brereton A. J. van Straten (Eds.. DLO Winand Staring Centre. J.. C. J. Meteorological and Agrohydrological drought monitoring based on conventional and remotely sensed data.A. Str. Kaukoranta T.S. European Journal Of Agronomy. Porter. 67. Ioannina. Novi izzivi v poljedelstvu 2002. 2007.. Pinter. D.. Eitzinger. Hoogenboom. Vol 1. P: 481. Kapler P.489. W. Kmetijstvo. Ritchie. 335-369. P.E. Hunt. Semeradova. Grossman. Batchelor. Proceedings of 6th Hellenic conference in Meteorology Climatology and Atmospheric Physics. Impact of global warming on potato late blight: risk. P. (eds. Volume 25. Kajfež-Bogataj L. Št. Karakostas T. str. 7th EMS Annual Meeting / 8th ECAM. Kanellou E. A. Boote. 1996. Agronomy Sustainable Development. and Sušnik A. Kazadjiev V. Wall.E.. 2002. The DSSAT cropping system model.H.V. Wilkens. and Kleemola. Zbornik biotehniške fakultete universe v Ljubljani. Dubrovsky. 1995. D. Vienna. 11-18. 164-169. Agricultural and Food Science in Finland 5: 311-327.T.J.W. M.E. 25-28 September 2002.M. Nikolakakis. Carbon Dioxide Exchange and Yields under Ambient Atmosphere and Free-Air Carbon Dioxide Enrichment (FACE) Maricopa. Studying the Effects of Extreme Climatic Events on Wheet. 2006. J. D. 2005. R. 1995. C. Moteva.. Geophysical Research Abstract Volume 8. 2007.R. Jongschaap R. Kartschall T. ISSN 1029-7006. M. Volume 4. V. Wpływ warunków klimatycznych na zmienną presję infekcyjną Phytophora Infestans i jej szodliwość dla plantacji ziemniaka. J. J..L. European Geosciences Union General Assembly 2008. Vienna. XLVII Sesja Naukowa Instytutu Ochrony Roślin . Zalud. R. J. Wageningen. AZ. J.. Mozny. 2002. Predicting wheat production at regional scale by integration of remote sensing data with a simulation model. and Schouten L. Hunsaker. Domenikiotis. P. Newhall Model for Assessment of Agricultural Drought Event Probability under Present and Changed Climatic Conditions. Tsiros.T. 2003..E. Volume 18 (3-4). N. Austria. for Wheat. E. Operativni agrometeorološki modeli za izračun vodne balance kmetijskih tal (Comparison of three operational irrigation models)... CROPWATN: Production of water and nitrogen limited crop production.R. S. Trnka. Contribution to the Study of the Frost Days in Greece. M.M. 611-622. 621-629.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Jones J.. (The impact of climatic conditions on varied Phytophora Infestans pressure and its harmfulness at potato crops).. G. J. 2008. Poznań 15-16 2007. Modelling and Parameterization of the SoilPlant-Atmosphere System: A Comparison of Potato Growth Models.J. Proceedings from First Scietific and Practical 102 . Vol 2. Georgieva. EMS7/ECAM8 Abstracts. Kimball.H. pp. Zerva. Nastos. 13 – 18 April 2008 (accepted). 2002. pp. Georgieva 2005a. Kapsa J. G. Dalezios. L. Proceedings of 6th Hellenic conference in Meteorology Climatology and Atmospheric Physics.W. Gijsman. 25-28 September 2002. Pennas.J. P.. Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen. B. Ioannina. K. 316324. April 2-7 2006. Jongschaap R... EMS 2007-A-00107. In: EGU 2006. Garcia. 2007. Svoboda.I. L. The Netherlands. Kabat et al.. 2006. 97-98. Verification of WOFOST Model for Soil and Meteorological Conditions of Bulgaria.I. W. Karvonen T.. Journal of Biogeography.D.. Singh. Wageningen pers.. C. A. U. Agronomy 24. Kazandjiev V.). 1996. pp. Hösch.S. M. LaMorte. Agricultural issue. G. J. A Simulation of Phenology. 2005.. 91-99. Kantylis P. Blackwell Science Ltd. P: 235-265. Europ. Effects of climate warning on ceres-maize yield in Slovenia: sensitivity study. Growth.. M. Kajfež-Bogataj L. yield loss and control. In: B. Extremal meteorological conditions and production of autumn cereal crops in Bulgaria. Kotroni V. Agronomy 29: 1-12Kersebaum K. p.2. Operational use of agro-meteorological data and GIS to derive site specific nitrogen fertilizer recommendations based on the simulation of soil and crop growth processes. 223-238. and Roelsma J.. Longterm simulation of soil-crop interactions in semiarid southwestern Saskatchewan. Dordrecht. A. In: R. E. J. Lal (ed. W.. In: The Use of Pedotransfer in Soil Hydrology Research in Europe.. Alterra-Report 773. Wageningen. M. Howell (eds. 1999. Wendroth. Physics and Chemistry of the Earth 30 (1-3): 59-67 Kersebaum K. Green World Research. Yassoglou. 1996. Reuter. Kersebaum K. Boca Raton. Thomasson A. Campbell. R.4. N. Eur. 111-119.): Agricultural system models in field research and technology transfer. Model based evaluation of land use and management strategies in a nitrate polluted drinking water catchment in North-Germany.J. Seljak. 10-12/10/1996.C. O. Modelling crop growth and nitrogen dynamics for advisory purposes regarding spatial variability. H. A.A. Proceedings of Scientific Conference – 80 Years Soybean Institute.I. INRA Orleans (France). Springer. p. Kersebaum K.0. Wageningen.G. and Georgieva V.. (eds) 2003. L.): European Land Information System for Agro-environmental Monitoring. H. Kroes J. Hecker. Boca Raton. Tomše. Lectures and Papers presented at the 4 th Slovenian Conference on Plant protection in Portorož. 111 – 128. Jones R.. Lorenz. Wageningen (Academic Publishers).J. CRC Press. Wegehenkel. 2000..C. Drought in Bulgarian low regions during the 20th century. C. de Jong.. Kersebaum K.J.67-72. In: K. M. Schwarz. Lorenz. 244-253.A. 2007. R. Simulation of water and nitrogen flows on field scale: application of the SWAP-ANIMO model for the Müncheberg data set. Proceedings of the Second Workshop. Wendroth. The application of pedotransfer functions in predicting ground water recharge at regional scale. The EU map of soil water available for pants.. In: L. Mirschel.. Long term simulation of soil/crop interactions to estimate management zones and consequences for site specific nitrogen management considering water protection. 2007. Reference Manual SWAP version 3.G. Thomasson.. Wegehenkel (eds. [Hrsg. K. 2005b.. J. Beber. 39-52. 2005. 211 pp. Knapič V. March 4-5.C. Canada. Škerlavaj. Greece: Results of one year Operational Use. S.. 2002. N. J. Modelling nitrogen dynamics in soil–crop systems with HERMES. 106-115. V.A.J.In: Stafford.C. Alterra. 229 .. The Netherlands Kosmas C. Kersebaum. Theoretical and Applied Climatology Kroes J. Kazandjiev V. 2008. O. Pavlikeni. G. J. C.): Integrated Watershed Management in the Global Environment. Le Bas. I. Daroussin. Ntzanis. J. V. and Lagouvardos K.P. 2005. Yang. Ma & T. Kersebaum K.M.C.J. Using automatic meteorological stations for early pest warning and irrigation prediction. p. Ahuja. and Alexandrov V. K. van Dam. Sofia BAS. . King D. (Eds. C. Lorenz.. Lewis Publishers. Agroclimatic indices and simulation models Conference “Management in extraordinary conditions and defence population”. K. Jones 1995. Nutrient Cycling in Agroecosystems 77 (1). Reuter. Luxemburg. Zentner.. O. Danalatos. European Commission..]: Precision agriculture ´05: [peer-reviewed papers presented at the 5th European Conference on Precision Agriculture]: 795-802. Proceedings of 6th Hellenic conference in 103 . R. WOFOST Model calibration and Forecast of soybean (Glycine hispida (Moench) yields in Bulgaria. 1999. Fune Grid Weather Forecasts over Attica. Wurbs. Wendroth 2002. 131-141. Koleva E. In King D.): Modelling water and nutrient dynamics in soil-crop systems. A.C.C. H. Reuter. J.. 2007.252. I. K. Ioannina.Meteorol. 2005. Assessing lead-time for predicting wheat growth using a crop simulation model.. Kajfez Bogataj. Wassenaar. Ecosystems and Environment. Lalić B. Balaz.. Maracchi G. Volume 111. 2000. Gruszczynski. Mihailovic. Poschenrieder. Research and Development. Ljubljana (Slovenija).to Long-Range Predictions for Agriculture. GLOBAL . Environment research programmeCOST Action 718. Journal of Agriculture.. Mesoscale ecohydrological modeling to analyze regional effects of climate change. 1995. Ecosys. 2005b. Mihailovic. Maracchi. Mihailovic. Input Dara representativeness Problem in Plant Disease Forecasting Models. Laube. In Irrigation and Pest and Disease Models: Evaluation in Different Environments and Web-Based Applications.. Umwelt und Wasserwirtschaft. Use of remotely sensed information in agricultural crop growth models. Forschungsprojekt Nr.. Institute of Hydrology. 25-28 September 2002. Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Bojariu. L. Abstracts. Prediction of occurrence of plant diseases using the coupled atmosphere-land surface models.. September 15-19. M Barazutti. Application of climatic predictions and simulation models for the benefit of agriculture in Romania. Marica A.. Marica A. Modelling 41. M. 1999. Cirisan. C. W. A spatial approach using imprecise soil data for modelling crop yields over vast areas Source. 104 . Rome (Italy).. V. 2005. WMO Expert Team Meeting on Impact of Climate Change/Variability on Medium. Slovak Academy of Sciences. J. 2000. Trnka. 15-18 February 2005. 132-137. D. A. Ecol.. P: 302–313. J.T. Koci..R.. Science. Agric. R. Idojaras (In press). Majercak J..Survey of agrometeorological practices and applications in Europe regarding climate change impacts Meteorology Climatology and Atmospheric Physics. D. Kromp-Kolb H. Lagacherie P. and Novak V. Laurila H. Cazemier. 5-16. Brisbane. Pankovic.. H.. Gräfe. S. W. . Martin-Clouaire. 81/1. Rossi. A. Agric.J. Busuioc. 1995. European Commission. S.P: 321–339. Krysanova V. A.13. F. Modelling the effects of elevated CO2 and temperature on Swedish and German spring wheat varieties with CERES-Wheat and AFRC-Wheat crop models.T. p. 2005.. Semenov. 2005. Directorate General XII. 103. Lalic B. Becker. R. Bratislava.A. 1282.D.For. 1988. Boroneant. L. M. 2006. T. Vol. Lalic B.Research Report. Volume 135.. Radovanovic. Bestimmung der Auswirkungen von Trockenperioden im Grünland mittels Wachstumsmodellen und klimatologische Analyse österreichischer Daten (Estimation of impacts of drought periods on grassland by growth models and climatological analysis of Austrian data). F. Im Auftrag d. J. 259-271. Journal of Biogeography 22: 591–595. Journal of Agricultural and Forest Meteorology. Lawless C. pp 73-79. Vol 1. Formayer. 2007. Australia. Perarnaoud. Launay M. 4. pp. 2003. J. D. Applications of geographic information systems and remote sensing in agrometeorology. 2005. Applications of the CROPWAT model for irrigation planning and management in Romania. Wechsung.a numerical model for water movement in the soil root zone. Sixt European Conference on Applications of Meteorology. 119-136. and Env. Endbericht. D. Calibration of SIRIUS Model for Different NSsorts of Small Grains. Environmental Modeling & Assessment. Edited by G. and Guerif M.T. Eitzinger. 247-268. A. Abstracts of Sixth Annual Meeting of the European Meteorological Society (EMS). Orlandini. G. Bundesministeriums für Landund Forstwirtschaft. Balaz. September 4-8. Maas S. I. Kleschenko. 425-430. 2006. Nertan. Wheat growth simulation and yield prediction with seasonal forecasts and a numerical model. 2004. E. Electron. Romania. on Applied Agrometeorology and Agroclimatology. Busuioc. Tellus A. 1999. Andersen. Modelling 194(4):329-343. Matthews R. radar. A. (Agrometeorological models for the prediction of crop yield (grape)). No. Ventura. (ed. The Criteria project: integration of satellite. Agrónomos. Morgillo. 24-26 april 1996. Bucharest. Proceedings of International Symposium on Applied Agrometeorology and Agroclimatology. 711 Int. Agroclimatic indices and simulation models Marica A.. 1999.. Proc. Criscuolo. European Society for Agronomy. S. Katerji... Tomei. Implementation of Wofost in the framework of the Criteria geographical tool. F.134. Volume 147. Volume 19. WMO.. 2003. FAO & COST 718 Workshop on Climatic Analysis and Mapping for Agriculture. Tesis Doctoral.R.1-2. Polhill. 16-18 July 2001. G.B. 2005. A.R. B. M. COST 77. Mastrorilli M.. Ecol.G. C.. The People and Landscape Model (PALM): towards full integration of human decision-making and biophysical simulation models. and Pilbeam C. 23(1):9-26. L. G. Matthews R. European Journal of Agronomy. Agric. Implementation of a spatial decision support system for rural land use planning: integrating geographic information system and environmental models with search and optimisation algorithms.). Sibbald.B. J. A. Van Soetendael. 219220. Modelling the long-term sustainability of maize/millet cropping systems in the mid-hills of Nepal. Proc. Dec 9-12. N. 2005.J. A. Van Soetendael. M. N.6. S. E. Roach.M. Relationship between Growing Degree Days and phenological stages of cotton growth in Greece-A case study of Karditsa Perfecture.B. 2005a. Volos. Matthews K. Melbourne. Fontana. Marchesi. Modelling Cropping Systems. 14-17 June 2005.S. 1617-1623. Ceotto. ISBN 92-828-4137-5. Flueraru. P: 71–79. 105 .. Zinoni. Evaluation of downscaled DEMETER multimodel ensemble seasonal hindcasts in a northern Italy location by means of a model of wheat growth and soil water balance. U. Proceedings of MODSIM 2005 International Congress on Modelling and Simulation. 57(3). Migdakos E. and Gemtos T. Australia. L. Marica A. Application of GIbased procedures for soil moisture mapping and crop vegetation status monitoring in Romania. G.I... Fontana. Marletto V.. P: 125. 488-497 Marletto V. Grecia.T. Criscuolo.. 2001. 2007. Marletto V. Argent (Editors). 2005. D. Modelos agrometeorológicos de predicción del rendimiento de cosechas (la vid). Bologna. Stancalie. Marletto V. Symp. F. 79. 1998. and traditional agroclimatic data in a GIS-supported water balance modelling environment.B.. F. A. Using the CERES-Maize Model in a Semi-arid Mediterranean Environment. Integrating agent-based social models and biophysical models. 2003. of the 2nd Int. and Zinoni F.. In: EUR 18328. In: A. 24-26 April. Gilbert.B. 2005. 1998. 1998. Ecosystems & Environment 111(1-4):119-139. Craw.. pp. Zerger & R. Agriculture. Italy. Alexandru. Symp. 173-178. Romanian Journal of Meteorology. Florence.2. Matthews R. Nouna. Comput. Dalezios N. Agricultural and Forest Meteorology. Marín C. Volos. The potential impacts of climate change on the main components of water balance relating to maize crop. Validation of Three Revised Versions. Modelling and Simulation Society of Australia and New Zealand. Vol. 11. 6-9 November. K.D. B.T. Koči. S. Volume 26. D. J. 1998. . Eitzinger. 133-136 106 . 14th International Poster Day. Balaž.. Combining agricultural crop models and satellite observations: from field to regional scales. Environmental Modelling and Software.4. Bondeau. 2006. Rahmann. J.T.biometeorological system for messages on the occurrence of diseases in fruits and vines. In: TENHUNEN. P: 266-274..und Prognosesystem zur Bekämpfung der Kraut. Moriondo M.2006. Hrsg. B. 38. 14th International Poster Day.. Delević S. J. 330-332. Schultz. 2001. Verch.: J.): Systemtheorie und Modellierung von Ökosystemen.O. J. 2000. Volume 23. 691-696. Mirschel W. In: GNAUCK. I. Cvjetković.R. J. 2007. The main features of BAHUS . Koči. U. A. Assesing the Impact of Land Use Intensity and Climate Change on Ontogenesis. 2002: Spring frosts in the period of fruit flowering. Balaž. Greece. Bio-PhytoPRE – ein Warn. F.). Förkel. Maselli. Mijušković D.. Miličević T. J. European Journal of Agronomy. 329334. Proceedings of Annual Meeting of Review Scientific Works of Banatian University of Agricultural Sciences and Veterinary Medicine. I. Physika-Verlag (Unternehmen des Springer-Verlages) Heidelberg. New York. B. Chemicals and Energy in the Soil-Plant-Atmosphere System.. 136 – 156. I. 19 (6). Wenkel. Bratislava. Arsenić. Bindi. Moulin S. A.. LENZ. R. A simple model of regional wheat yield based on NDVI data. Delécolle. Hess und G. I.): Ecosystem Approaches toLandscape Management in Central Europe. Modulares dynamisches Wachstumsmodell für Zuckerrüben als integrativer Bestandteil von komplexen agrarökologischen Simulationsmodellen.. 2002. Predicting the occurrence of Erwinia amylovora using the biometeorological system BAHUS. Liebhard. G. Musa-Steenblock T. Eitzinger.. Ecological Studies. Arsenić. Schultz. 120-129. Kassel 1. 2002. ISBN: 8085754150. E. 2005. Radlović. In: A.Wenkel. Biometeorological system BAHUS for predicting the occurrence of plant diseases and ensuring their efficient control.135. Beiträge zur 8. Dynamic phenological model for winter rye and winter barley. Evaluation of alternative management measures in vegetable production systems by field measurements and EPIC simulations. M. I. Transport of Water. K. Kathmandu.. 2005. Eschelböck. 2002.and Forrer H. Springer-Verlag Berlin Heidelberg.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Mihailović D.T. April 2007. In: Ende der Nische. Biomass Production. Serbia. Nepal. Pommerening. 16. Mihailović D. D. 2001. HANTSCHEL (Eds.. Müller B. Mirschel W. R. 2006.und Knollenfäule im ökologischen Kartoffelanbau in der Schweiz. A. Proceedings of the 12th Congress of the Mediterranean Phytopathological Union. V. Issue 3.. Landschaftsokologie und Umweltforschung.. Chemicals and Energy in the Soil-Plant-Atmosphere System. 12-13 December. Mircov. P. 25. Application of BOTMAN Forecast Model in Integrated Botrytis Managment in Croatian Strawberry Plantations. Wissenschaftstagung Ökologischer Landbau. 299-313. and Yield of Northeast German Agrolandscapes. Rhodes Island. Lalić. Matejka (Eds. Vol. (UmweltWissenschaften). 147. Ivić. Mirschel W. (Hrsg.O. J. Radlović.. Perović T. H. K. 1021-1036. J. D. A. 45-51. Transport of Water.. Belgrade. B. International Workshop on Environmental Risk Assessment of Pesticides and Integrated Pesticide Management in Developing Countries. März 2005. Koči. Arsenić.. 7-11 October.. 18th International Conference on Carpathian Meteorology: Mountain influence on weather. Lalić. Timisoara (Romania). Lalić. European Journal of Agronomy. Franko. Umweltwissenschaften. F. Čelková. Schmid.. Mihailović D. D. Int. P: 123. I. Remote Sens. E. B.C. M. Dalla Marta. M. Sensitivity of field-scale winter wheat production in Denmark to climate variability and climate change. Kubiak. . R. 257-274. 2004. P.Jensen. Palutikof. European Geophysical Society. J. Kajfež-Bogataj L.R. J. Agriculture. Petersen. T. Quemada. Heidmann. Kersebaum. Petersen. 107 . C. 2004b.W de Wit. Wageningen Agricultural University. Rubæk. Minguez. van Diepen.E. J. Pecze. Maracchi G.D. P. Nieder. Comparison of scales of climate and soil data for aggregating simulated yields of winter wheat.A. 2006. 147-160.D. K. and Nieplova E. Olesen J. Effects of Data Resolution on Estimation of Regional Crop Yields in Western Slovakia with the WOFOST Crop Simulation Model. Computers and Electronics in Agriculture 40 (1). Holt. T. D. S. Nendel C. Kersebaum. Hickler. Publ. C. van Kraalingen. Morales. & Sykes..W.). 123-143.687. Number 2. G. 45-55. Preparation of MERA Database and the Test of the WOFOST Crop Growth Simulation Model. Journal of Crop Improvement 13. Nejedlik P.. March 1998. S. . Reuter. Olesen J. K. T. R. ISPRS Commission VII Mid-term Symposium “ Remote Sensing: From Pixels to Processes”. and Diepen C. Smith. Netherlands. T.I. Climate Research 15.A.A. Modelling nitrogen mineralisation from mature bio-waste compost applied to vineyard soils. Wageningen (Wageningen Acad. Irrigation and pest and disease models: evaluation in different environments and web-based applications. 2004a. Olesen J. .. Climate Research 15. Fiesland. R. 2005. Boogaard. T. Nonhebel S. Jensen. Nitrogen mineralization from mature bio-waste compost in vineyard soils: II. S. A. In: Proceedings from 2nd Slovak . 1998. P: 684. 221-238.L.J. Fronzek. 2007. COST Action 718 – Meteorological Applications for Agriculture: 127 – 139.Swiss Workshop. 1999. Nieder.E. K. 8-11 May 2006. Nendel C. Mesterhazi.. . G. Z.. Monitoring crop growth conditions using the global water satisfaction index and remote sensing. 2005. Jensen. Sau. Sušnik.. Olesen J. M. 1999. 221-238. T. Poster Session. Test of N-mineralization parameters in a long-term in situ incubation experiment. Olesen J. 2000. 1993.. ISSN 129-7006. Uncertainties in projected impacts of climate change on European agriculture and ecosystems based on scenarios from regional climate models. P. H. A. Ph. Climate change and CO2 effects on productivity of Danish agricultural systems.A. Sensitivity of field-scale winter wheat production in Denmark to climate variability and climate change. Thesis. Zurich/Payerne. Climatic Change 81.. Neményi M. The role of GIS and GPS in precision farming. S. 213-228. Z.C.E. Heidmann. T.C.. 2005.G.E. C. In: Geophysical Research Abstracts Volume1. F.. 2000. van. Rubæk. Effect of climate change on greenhouse gas emission from arable crop rotations.2.In: Controlling nitrogen flows and losses: 207-208. Description and testing of the model PERO for grapevine downy mildew predictions. Kersebaum. R. Bøcher.J.Journal of Plant Nutrition and Soil Science 168 (2): 219-227. Orlandini S. H.H. A. Stepan 2003. Olesen J. 2000. Agroclimatic indices and simulation models Nejedlik P.. Carter. Reuter. Kubiak. Aberystwyth SY23.K. Nieuwenhuis G. Ruiz-Ramos. Ecosystems and Environment 82. Børgesen... The importance of weather data in crop growth simulation models and assessment of climate change effects. Hansen. M.. Diaz-Ambrona.Ecological Modelling 177 (1-2): 1-15 Nendel C. (ur).E. A simple model approach to simulate nitrogen dynamics in vineyard soils. T. Nutrient Cycling in Agroecosystems 70. . Wegehenkel. Europ. Troufleau.A.11 /3-4. 2006. Department of Soil Science and Geology.M. Gayler. Abstracts of Sixth Annual Meeting of the European Meteorological Society (EMS). H.. Spatial Information Systems in Crop Monitoring Developing New Global Models and Sharing the Data. 2003. Delincé. WAU. Assimilating optical and radar data into the STICS crop model for wheat.J. (ur). 271 pp. Time Projection of Heatwave Events in Thessaloniki. Development and assessment of a coupled crop-climate model. Jansen. Maracchi G. Agricultural Systems 84(1):77-97.F. Richter G. Hecher J-M. p.H. Brisson 2004. M. and Bijker W. 8th Conference on Meteorology Climatology and Atmospheric Physics.. Kajfež-Bogataj L.T. J. A. Springer. D. T. Parry. H. S. 1988. 27-36. G.. P.. Dynamics of C.. Poels R. of Agronomy. D. For. ed. Richardson A. Volume 23. 6-10 November 2006.W.74p. Agric.H. and Semenov M. M. J. Hartmann. 1991. Penning de Vries F. J. 11-23. Wheeler. The potential effects of climatic change on agricultural insect pests. Th. Weiss. Computing Model for Cereal Leaf Beetle.J. COST Action 718 – meteorological Applications for agriculture: 140-159. 26. Future directions for advanced evapotranspiration modeling: assimilation of remote sensing data into crop simulation models and SVAT models. Chauki. T.R.. A. September 48. Global Change Biology 13:169-183. Karakostas.R.H. Brisson. Lalic.R. W. Pakalidou N.. Wageningen. Ljubljana (Slovenija).. 2006..M. Slingo. Mirschel. Mavrommatis. Porter J. K. Pankovic... Baret. Prévot L. Wiegand.O. V. F.. Burger.. 2006. Boogard. J. A.E.. 57: 221-240. Udvikling og validering af modellen MARKVAND til vandingsstyring i landbruget (Development and validation of the model MARKVAND for irrigation scheduling in agriculture). 2003. J.B. 2005. 1999. Plauborg. Meteorol. Chile. 108 .L. Parton W. Statens Planteavlsforsøg. Vol.. N. D. Volume of Abstracts.H. Simulation of ecophysiological processes of growth in several annual crops. M. Fiesland. F. C. a computer program to simulate growth and water use of tropical rain forests developed from the "WOFOST program. Stewart. Priesack E. Ottlé P.J.C. Osborne T. A. Challinor. 1989. Irrigation and Drainage Systems. A. Lawrence. P: 297–303. 1993.: Kersebaum.L. N. Irrigation and pest and disease models: evaluation in different environments and web-based applications.M.F. 24-26 May 2006. C.. Gerbermann 1982. J.. Nixon. 2007. Sušnik. Th.M. Cole.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Orlandini S..Bakema. Olesen. Meteorol.In : Modelling water and nutrient dynamics in soil-crop systems. B. Carter. H. Biogeochemistry 5:109-131. 2005. The impact of crop growth sub-model choice on simulated water and nitrogen balances. GSDI-9 Conference Proceedings.C Calvet.L.P.M ten Berge. Description and testing of the PLASMO model for the simulation of grapevine downy mildew.V. 239-253. H. N. Arkin. Journal of Agronomie.. Remotely sensed spectral indicators of sorghum development and their use in growth modelling. TROPFOR. Variability of winter rye grain yield in a glacial plain catchment modelling and observation. Makrogiannis.drought risks. T. A.W. Pudoc. Modelling impacts of climate change on wheat yields in England and Wales: assessing. H. Tidsskrift for Planteavls Specialserie S2113. Richter GM. Pankovic L. Agric. Proceedings.. 1991. Athens. Dalla Marta. P and S in grassland soils: a model. Rembold F.. 2007.. Rossing. pp. A pasture simulation model for dry matter production. and Software 21. and Porter J.12-23. Rosset. Climatic variability and the modelling of crop yields. M. Schröder.. (Implementation of a model for drought damage estimation in a GIS). 1995.A. Trnka. Model. 1993. Acta Horticulturae. Novak. 4. application spatialisée (Use of the ISOP system. A. Food and non-food crop management systems for EPIC modeling in EU25. J. Germany. 101 . 2006. 2000. Fourrages.ddb. 265-284.de/). Sirotenko O.A... in the proceedings of the Summer university on IT in agriculture and rural developement .2.. pp 23. plagas y enfermedades en los cultivo de pimiento del Campo de Cartagena (Murcia)”. Simulation Reports CABO-TT 33. Meteorology and Hydrology. nitrogen. Seguin B. Influence of global warming on agroclimatic resources and productivity of agriculture in Russia. A. 277-278. F. Mitteilungen der Gesellschaft für Pflanzenbauwissenschaften. P: 191206. Saarikko R. In: Ecological complexity and sustainability. use for estimating crop production. B. Journal of Ecological Modelling. 2007. S.112. Agroclimatic indices and simulation models Riedo M. Agroinformación. Volume 131. 3-4. ISBN 3-935380-16-X. Lacasa. 2000..J. Ecosystems and Hydrology. Remote Sensing for Agriculture. de. based on the STICS model. and García de Cortázar Atauri I. J. 2004. 2005. China. Changing P and N turnover in soils due to increasing air temperatures in Germany? A pilot study coupling the GIS-based modelling system WASMOD with REMO modelling IPCC scenarios. Schneider K. H. Wageningen. F. 2006. J. “Wasser und Pflanzenbau – Herausforderungen für zukünftige Produktionssysteme”. Formayer. Un sistema de información geográfica (SIG) para el manejo de enemigos naturales. Climate warning: consequences for viticulture and the notion of “terroirs” in Europe.R. Carbon and Nitrogen Fluxes with PROMET-V". 7-13 Ruijter F. Deunert. Schans. Balkovic. Europäische Kommission Brussels Europäische Union. Nos. p. Eitzinger. 1994. Stuermer. Du modèle STICS au système ISOP pour estimer la productivité fourragère. Fuhrer. Gidrometeoizdat. B.A... SPIE Vol.Jahrestagg..Agricultural and Forest Meteorology 73. water and energy. J. Adaptation à la prairie. and Mauser W. 27-29 Sept. 17. 186. 1998. Beijing. Abstracts of EcoSummit 2007. 48. 4171. Applying a site based crop model to estimate regional yields under current and changed climates.com (in spanish) Schaldach R.A. W. University of Hamburg. Wien. Proceedings of The Seventh International Symposium of Vineyard Physiology and Biotechnology. pp. 689: 61-70. WAU-TPE. Sánchez J. fort he assessment of forage production. 2006. and Abashina E.. Environ... Kurzfassungen d. 241-256 Ruget F. 1430-1446. 2005. Implementierung eines Bewertungsmodells für Trockenschäden in einem Geographischen Informationssystem (GIS). Alcamo. Mueller. Cánovas. Deabrecen (Hungary). Semenov M. Schmid E. 7 Ruget F. 21-22 august 2006. 21-25 juin Davis (USA). "Using Remote Sensing Data to Model Water. Introduction to the crop model STICS.. Granger. W. consumption and losses. Simulatie van opbrengstvorming bij tulp met WOFOST. M. 109 . J. Grub. 2006. Ecological Modelling 105: 141–183 Rinker A. CABO-DLO. 2000. No. (In Russian). (http://dnb. Adaptation to grassland and spatialized application). and fluxes of carbon. Deliverable for the European Non-food Agriculture (ENFA) project. S. Coupled simulation of regional land use change and soil carbon sequestration: A case study for the state of Hesse in Germany. Schaumberger A.H.. Stockle C. Soil Till Res 73: 43-56. vol. 110 . Meteorological Journal 1..H. edited by: G. D.. Mirschel. Simulation of global crop yields with the Ecosystem model DayCent. science. J.R. 2003.. Issue 3. Siverten T. Forecast of herbage production under continuous grazing. T. and Gailis J. Impact of soil compaction upon soil water balance and maize yield estimated by the SIMWASER model. H. John Wiley & Sons. Proceedings. 2002..a numerical model on soil water balance and plant growth. 2007. “Comparison of actual crop evapotranspiration simulated by the CROPWAT model with the estimated data from satellite images” in Irrigation and Pest and disease models: Evaluation in Different Environments and web-Based applications. 2006. Austria. In: Utilization of grazed grass in temperate animal systems. 2005. Volume 6362. COST Action 718.ch: the internet based decision support system (DSS) PhytoPRE+2000 to control late blight on potatoes in Switzerland..6 . rossi.H. Ojima. Thysen.H. DG XII. ’Quality considerations on meteorological parameters to be used for modelling UV-radiation’. Nielsen. Sivertsen T. Atmospheric Science Letters. No. The Internation Society of Optical Engineering. Murer. Stenitzer E. 27-36. 2003. 2007. CAgM Reprt No.. U. 2005. F. Priess. 2007. Documenting and Exchanging Meteorological Data”. PIK Report 99. Springer. In: Proceedings of the sixth Workshop of an European network for development of an integrated control strategy of potato late blight. Proceedings of satellite workshop of the XXth International Grassland Conference. September 2001. A-3252 Petzenkirchen. Vol. Orlandini. CropSyst. K.. Steenblock T. Søegaard K. Forrer. p 171-175. Hecher J-M. 2002. Alcamo 2007. S. 2004. 18: 289-307.: Kersebaum. J. Maracchi. 2005. ed. PPO-Special Report No. Scotland. J.. I..In : Modelling water and nutrient dynamics in soil-crop systems.O. Bratislava. 2005. Schwartengräber. p.. p. research and Development. m. Forrer. Performance of the model SIMWASER in two contrasting case studies on soil water movement.A.phytopre. 1983. Climate Change Impact on maize Yields in Conditions of Danubian Lowland. barazutti. W. Sivertsen T. 80-95. Schepers (eds). Donatelli. Stenitzer E. Wegehenkel. Stehfest E. European Commission.. R. P. Agrarforschung (9)05: 207214. Potsdam. E. J. Zenker.R. Idojaras.13. Šiška B. 183-192. Westerdijk and H. Stãncãlie G. Edinburgh.A.. evaluation. K. “Reflections on the Theme of Classifying. Remote Sensing of Clouds in the Atmosphere XI.. H. Stenitzer E..IKT-Reprt 5/2004... L. M.E. Geneva. H. Heistermann. Berntsen. Europ. 2005. a cropping system simulation model. C. p. Agron. and Marica A.H. Steenblock T. 2007. Environment Research Programme.. 218.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Sirotenko O. and Samuhel P. Stock M. M. Institut für Kulturtechnik und Bodenwasserhaushalt. III. Krautfäuleberatung via Internet. WMO.M. www. Development and applications of dynamic simulation models in agrometeorology. Global Biogeochemical Cycles (submitted).2-3 April-september 2007. M. Franko. Quarterly journal of the Hungarian Metterological Service. R. Nelson. Fried. Proceedings of SPIE 11-14 September 2006. Discussing a web-based system for administration.. 8. Diestel. SIMWASER . and correction of meteorological and biological datain perspective of actornetwork theory.. 26-30. KLARA: Klimawandel – Auswirkungen – Risiken – Anpassung. Kajfez-Bogataj. The Netherlands. On-line agrometeorological application with decision support at the farm level. Martin. Cabelguenne.. P: 89. J. 2006. M.A. Cvjetković. Omočenost lista. Debaeke. CropSyst. January 2000.O. a cropping systems simulation model. EPPO Bulletin 25: 419-435.2. 2001. Agencija RS za okolje. G. M..nl. 95-112. 2006a. J. and Detlefsen N.. Meteorological Appl. 2004.. Thaler S.. A. Urad za meteorologijo. P. AC. Met. 2003. Z.. Nelson. Dubrovský. Žalud. Trnka M.. Trnka M. European Journal of Agronomy.. Matajc. 21 str. Brock. Treemail Publishers.98. Agric. S. A. Kubu G. I. Zalud Z. Hooijer. Application in SW Slovenia. Vol 83/1-2. Eitzinger. Venäläinen. Joint Research Centre of the European Commission. System description of the WOFOST 6. WMO CAgM/ET.. Beniston. a cropping systems simulation model: water/nitrogen budgets and crop yield. Dubrovsky M. Van Diepen.. Estimating millet production for famine early warning: An application of crop simulation modelling using satellite and ground-based data in Burkina Faso. Agriculture and Forest Meteorology (submitted). Donatelli. Syst. Bowen. ISBN 90804443-5-9. P: 289. Agric. 1995. Študija teoretičnih osnov modela za izračun trajanja omočenosti lista.A. Calanca. Supit I. Stockle C. Heelsum. Tree Book 4. (Supplement):8186(2006). DINA Research report no. M. Expected changes of agroclimatic conditions in Central Europe. 269-276. Comparison of CropSyst performance for water management in southwestern France using submodels of different levels of complexity.0 crop simulation model implemented in CGMS. Oddelek za agronomijo: 147 str (in slovene). COST 718: Meteorological application to agriculture. EUR 15956 EN. Ecological Modeling (submitted) 111 . 2000. Potential effects of changes in mean climate and climate variability on the yield of winter and spring crops in Switzerland. 1994.and Ceglar. Agroclimatic indices and simulation models Stockle C. 2007. Schmid. 2003.O.. S. Luxembourg. M. 46(3):335-359. Farmer. On-line irrigation support system in Slovenia. 1994. Projektna faza 2:modul 2. 109: 113-121 Sušnik A. Heikinheimo.A.. Fuhrer. 1997.treemail. A. European Journal of Agronomy. Ljubljana 2007. Agric. 2008. Sušnik A. J.. Thornton PK. I. European Corn Borer Life Stage Model: Regional Estimates of Pest Development and Spatial Distribution under Present and Future Climate. (to be published) (in slovene). Oddelek za agrometeorologijo. J. B..307. Project INTERREG IIIA Italija – Slovenija. 1 2001. Kodrič. Sušnik A. 2. Thysen I.. Tiilikkala K. de bary) (Forcasting potato late blight (Phytophthora infestans mont. Semeradova. Stepanek P.S. Carter.. 2006. Volume 7. www. Sušnik A. Muška F. and Kurnik B.. 86. Balek J. de bary)). Semerádová. Brink. M. PW. Pest risk analysis of Meloidogyne chitwoodi for Finland..O. JE. Ravelo.. 2008. For. CropSyst. “Izvedba sistema za meritev okužbe Plasmopara viticola na teritorialnem nivoju (PRADA).. Online decision support for irrigation for farmers.. Supit I.. Water Manage.. (Eds. E. Vodni primanjkljaj v Sloveniji in možni vplivi podnebnih sprememb. Wilkens. Biotehniška fakulteta. Agrometeorological support of fruit production. Kocmánková. Climate Research. Stockle C. 69-72. WT. Campbell.).. Magistrsko delo. T. An exploratory study to improve predictive capacity of the crop growth monitoring system as applied by the European commission. In press. Univerza v Ljubljani. G.2. Glasilo biljne zaštite. Šubić M. Hlavinka P. Volume 1: Theory and Algorithms. P. 2007. C. Prognoza krumpirove plijesni (Phytophthora infestans mont. 1997. Volume 18.. Torriani D. R. Domenikiotis. Turkey. F. EWRA Symposium on water resources management: risks and challenges for the 21st century. Eitzinger.com). Trnka M. N. G. Dubrovský. A.. Journal of ecological modeling. 2004. Dordrecht.W. In. Dalezios.. Kocmankova. Tsakiris G. C. P: 179. Gao... M. 4th International Conference on Information and Communication Technologies in Bio and Earth Sciences. Athens. L. Trnka M. 18-20 September 2008. Dubrovský. 2006c.. 112 . P: 61–84. Domenikiotis. European Journal of Agronomy. European Corn Borer life stage model: Regional estimates of pest development and spatial distribution under present and future climate. Projections of uncertainties in climate change scenarios into expected winter wheat yields. Zalud. Z. E. P. 2008. Trnka M. M. Tsiros E. Mearns.. M. Effects of climate change and elevated CO2 on cropping systems: model predictions at two Italian locations.O. Trnka M. L. 18–20 September 2006. 1998. Tsvetsinskaya E. Publications.. Steinitzer. Tsuji G. 2005. 769-782.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Trnka M. Issues 1-2: 54-76. Žalud. Mozny. How to assess impact of climate change on soil climate by Newhall model . In 6th Annual Meeting of the EMS / 6th ECAC.. Central Europe as a case study area.K. Z.: Vulnerable Environments: Creating a Roadmap for Change in the United States.. D. Žalud.O. W. A GIS-based agro-ecological decision system based on gridded climatology. Pangalou. Spring barley production in the climate change hot spot – Czech Republic as a case study. Launay. 2006... Regional drought assessment based on the Reconnaissance Drought Index (RDI). Meteorological Applications 12: 57-68. 2000. F. Z. Volume 131.R. Identification of Water Limited Growth Environment Zones Using NOAA/AVHRR Data. 64. Semerádová. Ruget. M. M. Hoogenboom. Ewra. and rice production in the southeastern United States. (in press). B. M. 2007. 2006a. Volume 13. D.A. Quantification of uncertainties introduced by selected methods for daily global solar radiation estimation.W. M. Trnka M. J. M. 2007. T. Žalud. Semerádová. Dubrovsky. Establishing a drought index incorporating evapotranspiration. Vol. 2004a. Vangelis 2006.springer. (available online: www.: Managing Drought and Water Scarcity.O. Z. M. Rosenzweig. Tsiros E. Skjelvåg. C. 2004b.. Climate change impacts and adapation strategies in spring barley production in the Czech Republic. Longmont.E. P. pp. 2-4 September 2004. D.R. Svoboda. European Water 9/10. D. P. Z. HAICTA 2008. McDaniel. D. Stockle. Understanding Options for Agricultural Production.Y. Ljubljana. Dubrovský 2005. 3-11. 229-249 Trnka M. Downton 2003. 2005. M. Mavromatis.N. pp. J. The effect of spatial scale of climatic change scenarios on simulated maize. E. Žalud. winter wheat. Kluwer Academic Publishers. Tveito O. Spiliotopoulos.. E.. Aune. Climatic Change Vol. C. P. In. Dubrovský. Semeradova. In 6th Annual Meeting of the EMS / 6th ECAC. Dubrovsky. 77. M. Z.. E. and Vangelis H. C. Climate Change 60 (1-2): 37-71. Thornton. Kapler. Eitzinger. Application of crop model STICs in the Central European conditions – evaluation and climate change impact estimates. J. Tubiello F. H. Manage. 227-255 Tsakiris G. Hlavinka. Kanellou. Semeradova. 2006b. Hlavinka. 2006. Greece. volume 207. Žalud. Agricultural and Forest Meteorology. I. Dalezios. Water Resour. Colorado. Greece (submitted). Z. Bjørdal. Izmir. Eitzinger. Zalud.189. Use of NOAA/AVHRRbased vegetation condition index (VCI) and temperature condition index (TCI) for drought monitoring in Thessaly. Theoretical and Applied Climatology . Donatelli. M. Ljubljana. N. Muska. Prevot... 2003.. Wegehenkel M. H. Impact of climate change on the maize productivity in Croatia. AGRIDEMA). H.. Van Ittersum M. S. April 2002. (CD-Rom). 16. Volume 5.. H. Buffet. 32 pp http://www. Verhoef W. 1984..C. Nice. D.. The application of simple methods using remote sensing data for the regional validation of a semidistributed hydrological catchment model... P: 201. 2005a.org/opencms/export/sites/Agridema/Documentos/croatia_vucetic. J... R. Troufleau. 2006b. Utset J. M. Apple scab – potentials and limitations of operational infection forecasts in Germany. Brisson 2001.C. H.AGRIDEMA: An EU-Funded Effort to Promote the Use of Climate and Crop Simulation Models in Agricultural Decision-Making in “Climate Prediction and Agriculture“ :Advances and Challenges. Luxembourg 1998. Göttingen (Goltze). L. Slovenia. 1993. Bruguier. Kersebaum. 2001.J. Eerens. A.C.234. K. V. Specific Support Action Introducing tools for agricultural decision-making under climate change conditions by connecting users and tool-providers” (FP6-2003-Global-2 Proposal 003944. H.): COST 77. Volume 18. Bach. P. Wolf D.K.Physics and Chemistry of the Earth 30 (8-10): 575-587. The SAIL model. Vučetić V. Carbon Dioxide Effects on Plants: Uncertainties and Implications for Modeling Crop Response to Climate Change. 2006. Hansen. Kersebaum. F. Eur. Greece.. . pdf Wegehenkel M. D. 2001. 2003.In: Remote sensing & GIS for environmental studies: applications in geography: 189-196.agridema. EUR 18328 EN. Sivakumar and James Hansen Van der Keur P. Wegehenkel M. 2nd International Symposium: Modeling Cropping Systems – Florence.. vanKeulen. 711 Internat. H. N.. Edited by Mannava V. 2005b. In: Agricultural 113 . Forest Meteorol. 6EMS/6ECAC. Kropff . Trade-off between wheat yield and drainage under current and climate change conditions in Northeast Germany. K. K. EGS XXVII General Assembly. Baret. Chauki. Report of the Pilot Assessment Modelling of maize production and the impact of climate change on maize yields in Croatia.Eitzinger. Remote Sensing Data Assimilation by Model Inversion of Landsat TM Reflectance Images and Coupling to a Crop Growth Model.. Journal Agronomy 24. Agricultural and Forest Meteorology. 125-141. 333342. N. Olioso. On approaches and applications of the Wageningen crop models. Comm. Oger. Thomsen. 2003. A case study for the regional validation of a hydrological catchment model using remote sensing data. 79. Jochheim. Jochheim. Agroclimatic indices and simulation models Tychon B. L. Modification of DAISY SVAT model for potential use of remotely sensed data. 1998. Geophysical Research Abstracts. Kersebaum. Wittich K.. . Weiss M. Ljubljana. Environ. 24-26 April 1996. Coupling canopy functioning and radiative transfer models for remote sensing data assimilation.Alexandrov. Leffelaar. Agric. Goudriaan. The Belgian Crop Growth Monitoring System. 108: 113-128 Wessolek G and Asseng S.A. Symposium on Applied Agrometeorology and Agroclimatology. France. 106: 215–231...229-234. K.2. Schelde. In: N. Light scattering by leaf layers with application to canopy reflectance modelling. D. July 16-18. Italy.J. p.Bastiaans. 2002. Vučetić V. 2003.P. K. A Modelling System For Calculating Water Balance And Crop Growthat Different Spatial Scales Using Remote Sensing And GIS.2007. Eur. European Journal of Agronomy. and Erickson J. Verhoef W. 4–8 September 2006. Volos. 2001. Remote Sens. 2006a. A. Dehem.Dalezios (ed. and Van Diepen C.hlug.Wissenschaftliches Zentrum für Umweltsystemforschung. Modeling climate change impacts on maize growth and development.D. UK. Zalud Z. M. Effects of climate change on silage maize production potential in the European Community. 1995. E.A. Kempenaar. European Journal of Agronomy.0). (User Guide). The PARCHED-THIRST Model User Guide (v1.Eur. INKLIM Final Report.B.W.2. C. and Dubrovský M.. Department of Agricultural and Environmental Sciences. W. pp.S.pdf Young M. G. Luice Press. Climatic Change 29: 299-331. 2005.W. Kaiser and T. Wolf J. Newcastle-upon-Tyne. WZU . Effects of climate change on grain maize yield potential in the European Community.507. 2003. 2002. 33-60.. 153 .. 1996. Wolf J..de/medien/luft/inklim/dokumente/endberichte/landwirtschaft. Wolf J.. P: 495..178. USA. M.. Žalud Z. 281-292.-Agricultural and Forest Meteorology 71. Drennen. University of Newcastle upon Tyne. Evaluating SHOOTGRO 4. 114 .0 as a potential winter wheat management tool in the Czech Republic. http://www. 1994. Agron. J.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Dimensions of Global Climate Change / edited by H.A. Effects of climate change on wheat production potential in the European Community. Agriculture Ecosystems & Environment 93 (1-3): 227-247. 2002. St. Volume 19. van Oijen. Wolf J. Nos. and van Diepen C.. and Gowing J. Analysis of the experimental variability in wheat responses to elevated CO2 and temperature. Universität Kassel. 2003. 1993. Theoretical Applied Climatology. Wilhelm. Klimawandel und Landwirtschaft in Hessen: Mögliche Auswirkungen des Klimawandels auf landwirtschaftliche Erträge. McMaster. 72: 85-102.12. Malgorzata KepinskaKasprzak. etc. techniques and software. its temporal and spatial resolution. This figure is higher than the 2001 report’s 100-year estimate of 0. The warming trend throughout Europe is well established (+0.1.1 State of art 3. trends are higher in central and north-eastern Europe and in mountainous regions. 3. are listed and summarized. The major goal of this work is to summarize a questionnaire on trends in agroclimatic indices and crop model outputs in Europe.41°C/decade for the period 1979 to 2005. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . A special attention in this survey is paid on data homogenization tests. Elena Mateescu. 2001. updated from Jones and Moberg. weather generators. the answers based on the statistical methods for analyses of meteorological and simulation model output related time series. 2004). 115 . 2003). Valentina Di Stefano. This questionnaire was developed and disseminated by Working Group 2 of the COST 734 Action ‘Impacts of Climate Change and Variability on European Agriculture – CLIVAGRI”.90°C for 1901 to 2005. 3. Jones and Moberg. area coverage. Klein Tank. Nicolas Dalezios Abstract Some of the European systems and sectors have shown particular sensitivity to recent trends in temperature and precipitation. For the 1977 to 2000 period. 3. global and regional climate models. for respected European countries.3. Finally. with 11 of the last 12 years ranking among the 12 warmest years since modern records began around 1850..74°C over the past 100 years (Fig. SUMMARIZING A QUESTIONNAIRE ON TRENDS OF AGROCLIMATIC INDICES AND SIMULATION MODEL OUTPUTS IN EUROPE Vesselin Alexandrov. The first part of the survey is related to the availability of long-term historical meteorological and agrometeorological data. Antonio Mestre. However. The second part is dedicated on the various meteorological models applied in selected European countries – numerical weather models. while lower trends are found in the Mediterranean region (Böhm et al.1). 2003).6°C due to the recent series of extremely warm years.1 Observed climatic and agroclimatic trends The Fourth Assessment IPCC report (2007) concludes that the world’s average surface temperature has increased by around 0. the recent period shows a trend considerably higher than the mean trend (+0. Survey of agrometeorological practices and applications in Europe regarding climate change impacts . Figure 3.1: Annual anomalies of global land-surface air temperature, 1850 to 2005, relative to 1961-1990 mean (IPCC 4AR, 2007) Temperatures are increasing more in winter than summer (Jones and Moberg, 2003). An increase of daily temperature variability is observed during the period 1977 to 2000 due to an increase in warm extremes, rather than a decrease of cold extremes (Klein Tank et al., 2002; Klein Tank and Können, 2003). Precipitation trends are more spatially variable. Mean winter precipitation is increasing in most of Atlantic and northern Europe (Klein Tank et al., 2002). In the Mediterranean area, yearly precipitation trends are negative in the east, while they are non-significant in the west (Norrant and Douguédroit, 2006). An increase in mean precipitation per wet day is observed in most parts of the continent, even in some areas which are becoming drier (Frich et al., 2002; Klein Tank et al., 2002; Alexander et al., 2006). Some of the European systems and sectors have shown particular sensitivity to recent trends in temperature and (to a lesser extent) precipitation: • Upward shift of the tree line (Kullman, 2002; Camarero and Gutiérrez, 2004; Shiyatov et al., 2005; Walther et al., 2005). • Phenological changes (earlier onset of spring events and lengthening of the growing season); • increasing productivity and carbon sink during 1950 to 1999 of forests (in 30 countries) (Menzel et al., 2006; Nabuurs et al., 2003, Shvidenko and Nilsson, 2003; Boisvenue and Running, 2006). 116 3. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . • • • • • Change in high mountain vegetation types and new occurrence of alpine vegetation on high summits (Grabherr et al., 2001; Kullman, 2001; Pauli et al., 2001; Klanderud and Birks, 2003; Peñuelas and Boada, 2003; Petriccione, 2003; Sanz Elorza and Dana, 2003; Walther et al., 2005). Northern Europe: increased crop stress during hotter, drier summers; increased risk to crops from hail (Viner et al., 2006). Germany: Advance in the beginning of growing season for fruit trees (Menzel, 2003; Chmielewski et al., 2004). Britain, southern Scandinavia: increased area of silage maize - more favorable conditions due to warmer summer temperatures (Olesen and Bindi, 2004). France: Increases in growing season of grapevine; changes in wine quality (Jones and Davis, 2000; Duchene and Schneider, 2005). 3.1.2 Agroclimatic indices and crop models Climate plays a fundamental role in agriculture because of its direct and indirect influence on production. Each physical, chemical and biological process determining agricultural activity is regulated by specific climatic requirements, and any deviation from these patterns may exert a negative influence. European agriculture, mainly oriented to production of high quality food, may be more susceptible to meteorological hazard impacts because it is based on highly developed farming techniques (COST 734 MoU, 2006) To define agricultural responses to climate, studies can be based on the application of agroclimatic indices and simulation models. They can be used to describe the effect of climatic conditions on key agricultural aspects, including production, protection, fertilization, site selection, watering, etc. Agroclimatic indices Some of the above results were based on application of agroclimatic indices and crop models. An agroclimatic index is a measure or indicator of an aspect of the climate that has specific agricultural significance. A similar definition of an agroclimatic index is the following: an index relating some particular agricultural aspect or operation with one or more elements of the local climate. The agro-climatic indices are based on simple relationships of crop suitability or potential to climate. This type of empirically-derived coefficients is especially useful for broad-scale mapping of areas of potential impact. The indices are derived variables that are defined either by manipulating values of a meteorological variable into a different form or by combining variables with empirically-defined coefficients into a composite term. 117 Survey of agrometeorological practices and applications in Europe regarding climate change impacts . The most common derived variable to describe the thermal agro-climate is the Effective Temperature Sum (ETS), usually measured in growing degree days. It is calculated as the excess of temperature above a fixed datum (base temperature) over a period required for a specific phase of crop development. Growing degree-days above 5ºC (GDD): are usually computed by calculating the amount by which average daily mean temperature (Tmean) exceeds 5.0°C and summing these values from the time when Tmean first exceeds 5.0°C in spring until the last date of Tmean exceeded 5.0°C in fall Chapman and Brown 1978). Indices frequently used to measure moisture include Thornthwaite’s Precipitation Effectiveness Index, the Palmer Drought Index (Fig. 3.2), and the Relative Dryness Index (Palmer, 1965). Figure 3.2: Long-term variations of PDSI in 3 locations in Bulgaria (Kercheva, 2004) Other examples of an agroclimatic index are average length of growing season (period between average last and first freezing temperature dates), average total chill hours or chill units, average evapotranspiration, Agroclimatic Resource Index (ACRI), etc. Simple agro-climatic indices combined with geographical information systems have been used to provide an initial evaluation of the global agricultural climate change impacts (Leemans and Solomon, 1993; Fischer and van Velthuizen, 1996). When combined with a spatially comprehensive data base and a geographic information system (GIS), simple agro-climatic indices enable the mapping of altered crop potential for quite large areas at relatively low cost. Regional scenarios of seasonal 118 3. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . temperature and precipitation change for 32 world regions analyzed in the Fourth IPCC Assessment Report (2007) show the current variability of climate and the range of changes predicted by GCMs for 30-year time periods centered on 2025, 2055, and 2085. This background information is essential to interpret the potential impacts of climate change on crops and livestock production. Equally important background information is provided by agroclimatic indices. Agroclimatic indices are useful in conveying climate variability and change in terms that are meaningful to agriculture. They give a first approximation of the potential effects of climate change on agricultural production and should continue to be used (Sirotenko et al., 1995; Sirotenko and Abashina, 1998; Menzhulin, 1998). (See also chapter 2). Crop models Models, in general, are a mathematical representation of a real-world system (e.g. Mize and Cox, 1968; Hoogenboom, 2000). During the last decades the application of simulation and system analysis in agricultural research has increased considerably (e.g. Tsuji et al., 1998). Crop models, in general, integrate current knowledge from various disciplines, including meteorology, soil physics, soil chemistry, crop physiology, plant breeding, and agronomy, into a set of mathematical equations to predict growth, development and yield (e.g. Hoogenboom, 2000). Crop simulation models are increasingly being used in agriculture to estimate production potentials, design plant ideotypes, transfer agrotechnologies, assist strategic and tactical decisions (Fig. 3.3), forecast real time yields and establish research priorities (e.g. Bannayan and Crout, 1999; Penning de Vries and Teng, 1993; Uehera and Tsuji, 1993). Numerous crop growth and yield models have been developed for a wide range of purposes in the last decades (e.g. Casanova et al., 2000; Hoogenboom, 2000). These models range in complexity from the most sophisticated simulators of plant growth, primarily intended for research into plant physiological interactions, to multiple regression models using only a few monthly weather variables to forecast regional crop yields. One use of the crop models developed in recent years is to simulate the effects of cultural practices and climatic scenarios on crop growth and yield. However, their use for predicting yields over large areas is limited by the difficulty in obtaining information about local conditions or crop characteristics at any given point. Some crop or soil features may be considered to be constant for a group of genotypes in a given region, but others depend on changes in local conditions (e.g. Guerif and Duke, 1998). Testing over a range of environmental conditions is required to establish confidence in applying models (e.g. Goudriaan and Van Laar, 1994). In the future, models may be useful for improving the efficiency of agricultural systems and could be a tool for 119 Survey of agrometeorological practices and applications in Europe regarding climate change impacts . farmers trying to improve the profitability of their farms (e.g. Jacobson et al., 1995). Nevertheless, before this is possible, models must be calibrated and evaluated for each climatic region where they are intended for use in decision making (e.g. Sau et al. 1999). Crop simulation models permit the summary of scientific knowledge on the biological processes that regulate plant growth. They are generally built with an analytical purpose. Yet, these models are sometimes used as a predictive tool (e.g. Trousland-Kerdiles and Grondona, 1997). 650 Evapotranspiration [mm] 600 550 500 450 400 1920 1930 1940 1950 1960 1970 1980 1990 2000 b) 400 a) Irrigation [mm] 300 200 100 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 Figure 3.3: Variations of simulated irrigation and evapotranspiration during the crop-growing season of maize in Tifton, Georgia; CERES model (Alexandrov and Hoogenboom, 2001) Large area yield forecasting prior to harvest is of interest to government agencies, commodity firms and producers. Early information on yield and production volume may support these institutions in planning transport activities, marketing of agricultural products or planning food imports. Moreover, at world scale, agricultural market prices are affected by information on the supply or consumption of foodstuffs. Market price adjustments or change in agricultural supplies in one area of the world often causes price adjustments in other areas far distant (Supit and van der Goot, 2002). It is no longer necessary nowadays to demonstrate the usefulness of simulation models to explain and predict crop yields or changes in the environment at various scales of agricultural production (e.g. Boote et al., 1996). The value of exploring agronomic situations not tried experimentally is all the greater when the model can simulate several crops arranged in succession, and when as many cropping techniques and environmental limiting factors as possible are included (e.g. Cabelguenne et al., 1999). Crop models can also be used to generate input data for models for technical/economic optimisation, notably in the context of the analysis of European or national policies for competitiveness and environmental protection (e.g. Flichman, 1995; van 120 3. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . Ittersum and Rabbinge, 1997). In an economic context in which techniques and regulations are rapidly evolving, or where the objectives and limitations applied to cropping systems are also very diverse, long-term experiments cannot provide answers quickly enough for action to be taken. Models are called upon more and more to contribute to the formulation of innovative cropping systems. Clearly, the credibility of the conclusions from long-term exploratory simulations rests heavily on the reliability of the models, and especially on a good prediction of the yields of crops subjected to various water and thermal stresses (e.g. Cabelguenne et al., 1999). 3.1.3 Examples of previous case studies Wilby and Perry (2006) concluded there is significant evidence that regional variation in climate, particularly the rise of temperature, have already affected agricultural systems in Europe, increasing hazard impacts. Examples of observed changes include the lengthening of the growing season, latitudinal and altitudinal shifts of plant range, earlier flowering, outbreak of plant diseases, acceleration in breakdown of organic matter in soils, and emergence of insects. With respect to the latter for instance, between 1964 and 2004 in England, a 1°C increase in temperatures is associated with a 16-day shift earlier in the first appearance of the peach-potato aphid and a 6-day advance in peak flight time of the orange tip butterfly. More frequent precipitation and more humid conditions favour the spread of diseases. The highest intensity of rainfall reduces the infiltration of water in the soil, decreasing the net available soil water content. Finger (2007) analyzed trends in yield growth and yield variability of barley, maize, oats, rye, triticale and wheat in Switzerland from 1961 to 2006. In contrast to linear trends in crop yield growth for most European countries he found significant trends of slowing yield growth for cereal yields in Switzerland. This is caused by the introduction of direct payment schemes that foster environmentally friendly crop farming practices in general and extensive cereal farming in particular. The recently introduced reform of common agricultural policy in the European Union will foster higher shares of reduced input, i.e. extensive, farming. Thus, the saturation of cereal yield growth in Switzerland might indicate future development of crop yields in the European Union. Horváth et al. (2005) conducted a study with the aim to give a modern climatographical analysis on the varied hydrometeorological relations of the region, based on reliable observations of meteorological stations. The analysis includes statistical characteristics of the inter-annual variability, spatial and temporal correlation of the available soil moisture content and long-range changes, as well as their possible relation with climatic trends for greater 121 in three versions. the start of growing season (SGS). The results support the following conclusions. (2005) analyze the long-term (1901-2002) temporal trends in the agroclimate of Alberta. and also by determining climatically representative. with Thornthwaite’s plant-independent method and without homogenisation. the length of the growing season (LGS). and LGS. this expansion implies that the potential exists to grow crops and raise livestock in more regions of Alberta than was possible in the past.Survey of agrometeorological practices and applications in Europe regarding climate change impacts . 122 . and the increment is the largest in the north and the northwest of Alberta. 2) No significant long-term trends are found for the SGS. 1) The Alberta PCPN has increased 14% from 1901 to 2002. a later FFF. regions. Nine agroclimatic parameters are investigated: May-August precipitation (PCPN). growing degree-days (GDDs). To study the spatial correlations short PDSI sets of 17 stations were calculated for the period between 1951 and 1992. Monthly PDSI data series of five stations (Miskolc. and finally becomes large again in the southeast corner of the province. calculated according to the 1973-2002 normal. Shen et al. has extended to the north by about 200-300 km. The temporal trends in the agroclimatic parameters are analyzed by using linear regression. and by about 50-100 km. The above-mentioned aims are intended to be realised on the basis of the Palmer’s Drought Severity Index (PDSI) data series. and explore the spatial variations of the agroclimatic resources and the potential crop-growing area in Alberta. and corn heat units (CHUs). Kecskemét and Szeged) were determined for the 20th century. the end of the growing season (EGS). by performing objective regionalization based on soil moisture anomalies in the region. The significance tests of the trends are made by using Kendall's tau method. Debrecen. EGS. Nyíregyháza. and a longer FFP are obvious all over the province. when compared with the 1943-72 normal. 3) An earlier LSF. by publishing the characteristics and the calendar of the objective yeartypes. the length of the frost-free period (FFP). and the percentile analysis of precipitation attributes the increase to low-intensity events. The objective and results of our work is to help further the inter-disciplinary study of agro-ecological problems. then diminishes (or even becomes negative over two small areas) in central and southern Alberta. The annual total precipitation follows a similar increasing trend to that of the May-August precipitation. 4) The area with sufficient CHU for corn production. the date of the first fall frost (FFF). Canada. when compared with the 1913-32 normal. influenced by the soil moisture content. the date of the last spring frost (LSF). long dry and wet periods. models. 3. Spain. Italy. In order to help the achievement of the above key WG2 deliverables the following questionnaire was developed and disseminated among the national delegates of the COST 734 Action (Table 3. information. The following important notes were also disseminated together with the questionnaire: • Please consider ONLY data. Croatia. etc. France. Slovenia. Greece. Slovakia.4: Bulgaria.1). Romania. Finland Figure 3. Czech Republic. Germany. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . fill free to use your own style The European countries who have submitted the questionnaire are presented in Figure 3.3.4: European countries (in dark grey) submitted the questionnaire 123 . which might be useful for implementation of the WG2 tasks and achievement of the respective WG2 deliverables (detailed within the COST 734 MoU) • Please skip a question/point if you are not able to provide information or try to obtain it from other colleagues in your country • The example attached is mainly to assist you – it should not be assumed as a mandatory one.2 Goal: a questionnaire The major goal of this work was to summarize a questionnaire on trends in agroclimatic indices and crop model outputs. Serbia. methods. Switzerland. Hence. Norway. This questionnaire was developed and disseminated by Working Group 2 of the COST 734 Action ‘Impacts of Climate Change and Variability on European Agriculture – CLIVAGRI”(See Annex 2). Poland. leaf wetness duration ( 2m). However countries such as Bulgaria.1) An example from Norway Data set 1 . including maps.temporal resolution: hourly. Romania could provide mainly secondary data. Typical meteorological elements with long-term records in each European country are air temperature and precipitation as well as wind speed and direction. The spatial resolution of the meteorological data varies depending on the density of the respective weather networks and/or the interpolation techniques applied. (Table 3.area/country/region: agricultural districts in Norway .3.elements: air temperature ((2m. precipitation ( winter precipitation only available for 11 station). Most of the gridded weather data sets as well as data series from countries such as Germany. 3. etc. wind velocity ( 2m). such as the use of historical long-term meteorological data. various indices. maximum. remote sensing and re-analysis.additional information: The oldest series are from 1987.spatial resolution: site specific measurements of about 70 automated stations in summer and 50 stations in winter. daily and monthly averages .Survey of agrometeorological practices and applications in Europe regarding climate change impacts . air humidity. etc. average. solar radiation. France. relative humidity of the air) .3 Summarizing the questionnaire 3. Very few series are older than 1991.1 Long-term meteorological and agrometeorological data Long-term meteorological data The evaluation of climate impacts can be performed both on past and future data. The data temporal resolution covers different time slices mainly during the 20th century.availability for the WG2 tasks implementation: available on the internet . There are different ways to analyze past climate. minimum). Finland are generally available for the COST 734 applications. Both monthly and daily values of the relevant meteorological elements. 124 . global radiation(2m). in agricultural districts . Historical long-term meteorological data are available at the meteorological services and some other weather related institutions. Table 3. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe .3.1: Information on long-term meteorological data applied in each country 125 . 126 .Survey of agrometeorological practices and applications in Europe regarding climate change impacts . .3. Data set 2 .area/country/region: site specific measurements at 71’ meteorological stations in Norway mainland and islands in the Arctic .temporal resolution: annual and seasonal .area/country/region: Temperature for Norway. 1987a.air temperature 1931-60. snow thaw to agricultural communities . cloud cover (average clod cover. number of days with precipitation >=0.references: Skjelvåg.83. precipitation (2m.references: Nasjonalatlas for Norge Data set 4 . Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . snow cover .temporal resolution: monthly values . total precipitation. maximum daily precipitation. number of days with sunshine. relative humidity of the air (2m. snow thaw for Aust-Agder County in southernmost Norway . average minimum. number of days with minim temperature less than 0.1 mm).spatial resolution: Temperature in a 10 km grid.elements: Air temperature (2m. absolute minimum.b.elements: Long term precipitation variations (% change). average). . % of normal precipitation. Long term temperature variations ( % change) . snow thaw on map in publication .spatial resolution: site specific measurements at 71’ meteorological stations in Norway mainland and islands in the Arctic .availability for the WG2 tasks implementation: Temperature on map and data file. absolute maximum.additional information: It exists yearly reports of measurements since about 1870 from this Norwegian Meteorological institute.spatial resolution: map of the whole country . and snow thaw for the years 1957. 127 . number of days covered by clouds).temporal resolution: Temperature for the months April and July of the normal period 1931-60. average.area/country/region: country . degree-days base 5. .elements: gridded CRU data set . average maximum.availability for the WG2 tasks implementation: ( most of the data since 1996 are available on the internet on the web-page of Norwegian Meteorological institute) Data on paper available since about 1960 . degree-days base 17). Data set 3 . deviation from normal. Germany. the availability and use of agrometeorological data from weather stations or networks have become more and more important for the management and planning of agricultural activities and for the decision support system (Hoogenboom 2000). Croatia. Austria. 2001). In particular. Switzerland. Czech Republic. in some cases still on paper. geographic coordinates. The information on agrometeorological data available in selected COST 734 countries is presented in Table 3. rules and quality checks. going back in many cases to the 1950s and a few very long timeseries from single locations like bud burst of horse chestnut in Geneva since 1808 (Defila C. wind and precipitation heavily affect the characteristics of a given territory. evaluation of potential production. which can induce really wide differences over a short distance. prediction of the effect of climatic change and variability on crop growth and yield and definition of farm management techniques (Maracchi 2003). altitude above sea level. mostly on databanksystems. The observations should have been made following the same. Long-term agrometeorological data In recent years. Each country has its own database. distance from bottom of valleys.Survey of agrometeorological practices and applications in Europe regarding climate change impacts . Bulgaria. Phenological phases reflect among other things the environmental characteristics of the climate in the region where they occur. agrometeorological variables such as temperature. pheno-data are used for crop modeling. solar radiation.2 128 . environmental characterisation. These variables are then often used as input in many simulation models to investigate issues such as genotype improvement. Monitoring phenological phases is carried out in many European countries like Slovenia. phenological data are collected or have been collected in the past over several decades. and others. slope and aspect affect the spatial distribution of weather variables. relative humidity. similar. but it changes depending on the geomorphologic and topographical characteristics of the landscape. long series of phenological observations may be used for the detection of climate variability or climate change. Besides the scientific research in phenology that is now focused on climate warming and its impact on vegetation. pollen forecast and general information to the public via media and in schools. In most European countries. In fact. or at least comparable.. The trend of these variables is not constant over the territory. Slovakia. Consequently. 2: Information on long-term agrometeorological data 129 . Table 3.3. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . Survey of agrometeorological practices and applications in Europe regarding climate change impacts . 130 . Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . The reason for this differentiation is that the analysis and the initial conditions of LMCOSMO are based on the global model of German Meteorological Service. In a study implemented by Anadranistakis et al. In all statistical parameters the ECMWF model (resolution 0. The data is used as initial values for computer-based weather forecasts. (2006) the prognostic values of the wind force (up to 48 hours with time step of 6 hours) of the two numerical models are compared to the measurements of the wind force at 2 meters above sea level recorded from the National Marine Centre’s 5 buoys (for the time interval of 1 year). The numerical weather models and /or their related outputs used in Greece can be summarised as follow: . has varied with changes in data processing technology. Although the grid point data accumulated in the weather forecasts is very valuable information.5°) has better behaviour than the LM-COSMO model (resolution 0. long-term reanalysis data covering more than 10 years is required for an accurate understanding of a variety of climatological performed in Europe and the U. at regular grid points covering the whole of the earth (globe). which cover the Aegean Sea. The grid point data is prepared by analytical processing of meteorological data collected by a variety of observational means using advanced computational techniques.3 as well as the other answers in the questionnaire ECMWF. For this reason. and humidity. The accuracy of weather forecasts largely depends upon the accuracy of initial values.S since the 1990s and their results are utilized internationally as basic data for weather forecasts and studies on climate variability.3.3. and to obtain this accuracy the analytical processing technique used in meteorological observation has undergone constant improvements. 3. LM –COSMO and ALADIN are among the most applied numerical weather models in Europe.ECMWF and LM–COSMO. The reanalysis repeals the analytical processing of meteorological observations carried out in the past by application of the most advanced data processing technology available. regional climate models. An important element in the application of the numerical weather models is reanalysis data sets.0625°).2 Numerical weather models. its quality. such as its accuracy and characteristics. atmospheric temperature. In respect to reanalysis reproducing past weather – synoptic weather charts are based on meteorological elements including the wind velocity. According to the information in Table 3. weather generators Numerical weather models According the COST 734 Memorandum of Understanding numerical weather model outputs can be also used to obtain trends in agro-climatic indices and crop model outputs. 131 . motivated the integration of the nesting technique into the LM code. Modern computers have enhanced computational capabilities at low cost allowing thus the detailed description of topography in weather forecasting models through the use of high-resolution grids (Argiriou et al.lamma.it/sim/) • BOLAM by CNR Isac.sardegna. In Poland presently. two mesoscale weather prediction models ALADIN and COSMO are used.meteoliguria.LM. In the resulting version of the model (LM-nest) the numerical results from a coarse grid are used to provide boundary conditions for a fine grid embedded into the coarse grid for every integration time step.fisica. . when compared with measured weather parameters.htm) • DALAM (by Ucea – www. 2006).it) • LILAM (by Meteoliguria – http://www.it/) and Università di Genova (http://www. The following list of numerical local area models is adopted in Italy: • LAMI (by Servizio Meteorologico dell'Aeronautica) • LAMBO (by ARPA Emilia Romagna – SIM http://www. .http://www.arpa. 132 . Regional climate models are not used in Poland. based on surface measurements.emr.ucea.sar.it/ • atmosfera/bolam_avn.Survey of agrometeorological practices and applications in Europe regarding climate change impacts . managed by Servizio Agrometeorologico Regionale della Sardegna (http://www. present non-systematic discrepancies. several empirical methods have been proposed. Depending on research needs.toscana. the results of global models are adapted for estimation of expected regional conditions within the area of Poland. The need for very high resolution numerical weather prediction products either for direct operational use or to support meteorological products of broader meteorological significance. In order to cope with this problem. However even forecasts based even on 2-km horizontal resolution grid.rete.it/) A review on present state and perspectives of limited area models for agriculture in Italy was carried out by Buzzi (2002).unige.BOLAM. The algorithm of LM-nest is presented by Avgoustopoulou and Papageorgiou (2006) as well as results for characteristic weather situations over selected areas of Greece. through the use of artificial neural networks (ANNs).it) • RAMS (by LAMMA . In this paper presented the potential of applying such corrections to the near surface air temperature and wind speed predicted by the high resolution model BOLAM. by solving the model equations on a very fine horizontal grid. 12. elaborated at DWD and developed within COSMO (Consortium for Small Scale Modeling). Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . non-hydrostatic Lokal Model. • MM5. integrated twice a day (00 and 12 TU) for an anticipation of 72 hours • (horizontal spatial resolution of 20 km). and 18 TU) for an anticipation of 24 hours (horizontal resolution of 15 km).3.3: Numerical weather models in selected countries The forecasters in Romania use 4 numerical models: • ALADIN. 133 . integrated twice a day (00 and 12 TU) for an anticipation of 78 hours (horizontal spatial resolution of 10 km). Table 3. 06. • LM. integrated 4 times a day (00. • HRM. Climate models Italy applies the General Circulation Model PUMA (Portable University Model of Atmosphere) and Planet Simulator.availability for the WG2 tasks implementation: free from owner .de/plasim.references (incl. and then 16 study cases of severe rainfall were selected within the period 2002-2003. 2006) examines the potential to improve rain forecasting over the Greek Peninsula by making use of the atmospheric mesoscale model MM5.5 deg . has been implemented in the National Observatory of Athens (NOA). The DDC offers access to baseline and scenario data for representing the evolution 134 . The verification of NWFC-issued weather forecasts is made daily both for the whole country and for Bucharest.c) The regional climate models RegCM3. James (1994). Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC). the regional climate model PRECIS. PRECIS and MM5 are used in Europe.ro. The regional model RegCM3 is used in а study. initially the 3DVAR Data Assimilation system was setup. All the verification results are transmitted to mass-media and presented on-line in graphs on the NMA Internet site http://brutus. The Czech COST 734 partner provided information on IPCC DDC data and PRUDENCE outputs which are available on the internet. Fraderick et al. which runs operationally at NOA. For the application of the PRECIS model at NOA a horizontal analysis of 25 km was selected.uni-hamburg. which is the finest resolution used so far in the area as well as the complex land-sea distribution (Kotroni et al. web pages): www.area/country/region: globe . 2006).additional information: other free weather generators could be provided upon request .mi. For this purpose.b. A recent study (Oikonomou et al. University of Hamburg: . 2006). the dynamic core of which is based on the hydrostatic version of the mesoscale model MM5 while recently added a tracer model with six tracers for the study of aerosol transport and their feedbacks on climate. Both were developed in Germany.spatial resolution: 3.temporal resolution: monthly values . The performance of the results is examined by applying statistical methods and in comparison with independent precipitation measurements. (2005a. In order to investigate climate change and impacts in Greece as well as in the Eastern Mediterranean area.Survey of agrometeorological practices and applications in Europe regarding climate change impacts .inmh. Hoskins and Simmons (1975). Simulations performed with a grid resolution of 60*60 km over a greater European area as well as with a finer grid resolution 20*20 km over Greece using the NCEP meteorological fields as boundary conditions (Zanis et al. and application of baseline and scenario data is a crucial step in the analytical process. and other environmental conditions. and the general public.3. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . and by applying impact models and impact assessment methodologies to provide the link between the provision of climate information and its likely application to serve the needs of European society and economy. at different scales of analysis. The DDC is designed primarily for climate change researchers. of climatic. The data are provided by co-operating modelling and analysis centres. an intensified hydrological cycle or more vigorous atmospheric motions. A major limitation in previous studies of extremes has been the lack of: appropriate computational resolution obscures or precludes analysis of the events. using an array of climate models and impact models and expert judgment on their performance. long-term climate model integrations . and vulnerability involves a set of activities designed to identify the effects of climate variability and change. and in contrasting environmental and socio-economic contexts. adaptation. due to higher temperatures.drastically reduces their statistical significance. Most assessments of the impacts of future climate change are based on the results of impact models that rely on quantitative climatic and non-climatic data and scenarios. and vulnerability have evolved over the past decade. socio-economic. The IPCC DDC seeks to provide access to such data and scenarios and to offer guidance on their application. 135 . co-ordination between modelling groups . Analysis of climate impacts. Methods for analysis of impacts.limits the ability to compare different studies. and to examine possible adaptive responses. adaptation. The identification. governmental and non-governmental organizations. PRUDENCE is a European-scale investigation with the following objectives: • to address and reduce the above-mentioned deficiencies in projections. and a large array of methods and tools are now available for use in specific sectors. • to quantify our confidence and the uncertainties in predictions of future climate and its impacts. Climate change is expected to affect the frequency and magnitude of extreme weather events. These three issues are all thoroughly addressed in PRUDENCE. • to interpret these results in relation to European policies for adapting to or mitigating climate change. selection. to evaluate and communicate uncertainties. by using state-of-the-art high resolution climate models. by co-ordinating the project goals to address critical aspects of uncertainty. but materials contained on the site may also be of interest to educators. It also provides technical guidelines on the selection and use of different types of data and scenarios in research and assessment. The great diversity of the data required and the need to maintain consistency between different scenario elements can pose substantial challenges to researchers. maximum and minimum temperatures. 1996). Serbia. and others). A one-term Fourier series is used to model the seasonal variation in both temperature and solar radiation. solar radiation. The procedure used for generating solar radiation and temperature is based on the assumption that these are weakly stationary processes. Croatia. and ClimGen (Stöckle et al. 1998). which are held constant within each month but are varied from month to month. wind speed and some measurement of air water vapor (Acock and Acock. cropping management systems. hydrologic studies. 1996). insufficient in length. Spain) • CLIMGEN (applied in Germany) • LARS-WG (applied in Slovenia. Switzerland) • Met&Roll (applied in Czech Republic. Weather generators Long-term series of daily weather data are often required for the analysis of weather-impacted systems (e. 1997)... rainfall. The following weather generators were reported in the COST 734 questionnaire: • WGEN (applied in Bulgaria. and it is designed to preserve interdependence between variables as well as persistence and seasonal characteristics of each variable. four parameters are required for precipitation generation. 1990). records of such variables may be not available. 1984). Several computer programs have been developed that are capable of producing stochastically generated weather data from existing daily data. Weather generators are practical tools to bypass those problems (Johnson et al. CLIGEN (Arnold and Elliot.Survey of agrometeorological practices and applications in Europe regarding climate change impacts . Examples include WGEN (Richardson and Wright. and solar radiation. This approach can introduce inaccuracies in generating precipitation data. In some cases. incomplete. 1996).. 1984). USCLIMATE (Johnson et al. This model generates estimates of daily precipitation. The values of these parameters were determined from long records of data in 139 location in the United States.. environmental studies. The statistical properties of the generated data are expected to be similar to those of the actual data. CLIMAK (Danuso et al. etc. Weather generators are computer programs that use existing weather records to produce long series of synthetic daily climatic data. Weather variables required by many applications include precipitation.. WXGEN (Sharpley and Williams.) One model for weather generation that has been applied extensively in the United States is WGEN by Richarson and Wright (1984).g. or only summarized in monthly archives. thus limiting the application of this 136 . 1991). The coefficients of the Fourier term were determined throughout the locations tested and it was found that some of the coefficients were strongly location dependent (Richardson and Wright. maximum and minimum temperature. In WGEN. ClimGen. but with significant modifications and additions. and wind speed. ClimGen generates precipitation. is a weather generator that uses similar general principles than WGEN. alternative approaches allow users to estimate VPD and solar radiation from existing temperature records. for some locations. describes well the distribution of precipitation amounts.3. In some cases the cause of the differences was attributed to temporal and spatial smoothing that are inherent in WGEN. Monthly summaries of weather data cannot be used to generate daily data. This arbitrarily chosen functional form can lead to relatively poor fit to the data. Parameters required for WGEN are currently available only for the continental U. Semenov and Barrow. which can be used: to generate long weather time-series suitable for the assessment of agricultural and hydrological risk. Other features of ClimGen that are not available in WGEN include the generation of vapor pressure deficit (VPD) and wind speed. to serve as a computationally inexpensive tool to produce high resolution climate change scenarios incorporating changes in climate variability. to provide the means of extending the simulation of weather to unobserved locations. WGEN requires long records of daily weather data to estimate parameters. ClimGen uses quadratic spline functions chosen to ensure that the average of the daily values are continuous across month boundaries. there were differences in mean monthly precipitation and temperatures between actual and generated data. Richarson and Wright (1984) also reported that. LARS-WG has been used recently to develop high temporal (daily) and spatial (site) resolution climate change scenarios based on UKCIP02 and HadRM3 projections. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . all generation parameters are calculated for each site of interest while WGEN used fixed coefficients optimized from a large US weather data base. The Weibull distribution is easier to parameterize. solar radiation. 1991.S. model to areas where these coefficients are available. Semenov 137 . limiting its use to regions of the world where sufficient data are available. the first and most widely used weather generator in the US. The advantage is that ClimGen can be applied to any world location with enough information to parameterize the program. 1997. In addition. air humidity. LARS-WG is a model to simulate time-series of a suite of climate variables at a single site. daily maximum and minimum temperature.. and that the first derivative of the function is continuous across month boundaries. WGEN uses truncated Fourier series fits to produce daily values for monthly-calculated quantities of mean weather variables. derived from daily output from regional HadRM3 (Racsko et al. It uses a Weibull distribution to generate precipitation amounts instead of the Gamma distribution used by WGEN. In ClimGen. and can be simplified for applications to conditions with minimum data. Scenarios incorporate changes in climatic variability such as duration of dry and wet spells or temperature variability. . mouse-operated) or non-interactive mode called from a batch file using `initialisation files') • all actions are monitored in the report file (this file contains records of times and important parameters of all processes . Thus. SMOOTH). Met&Roll differs from WGEN and SIMMETEO by number of parameters estimated from the "learning" sample (observed series of the four weather characteristics): Met&Roll estimates means and standard deviations of SRAD. Optionally.it employs parametrisations). trivariate 1st order autoregressive model for SRAD. the whole report file or its blocks (if separated). 2007) Met&Roll (http://www. RAIN = precipitation amount. WGEN and SIMMETEO saves only monthly statistics and the daily ones are estimated by Fourier analysis just before the generation process. The detailed quality analysis includes comparison of Met&Roll with WGEN and SIMMETEO and it was found that the more detailed representation of the parameters of the generator's model (which is the case of the Met&Roll) improves reproduction of the structure of the weather series. In fact. TMIN = temperature minimum.cz/dub/impacts/met&roll. SIMMETEO does not calculate any standard deviation and does not distinguish wet and dry versions for SRAD.ufa. you may reconstruct all processes after viewing the report file. Martin Dubrovsky started his work in spring 1994 and it was inspired partly by weather generators included in the DSSAT software package (WGEN and SIMMETEO weather generators) and partly by paper of Wilks (1992). may be used as a regular initialization file.) • runs either in an interactive mode (managed by Menu bars and Option windows.. TMIN . gamma distribution for the precipitation amount. after quitting the Met&Roll. MODIFY. TMAX = temperature maximum. the model is the same as in WGEN and SIMMETEO: 1st order Markov chain for precipitation occurrence.Survey of agrometeorological practices and applications in Europe regarding climate change impacts . may be saved/loaded on/from the disk. GENERATE. et al. Main Met&Roll features are: • runs in a graphical mode (mouse-operated.ANALYZE. 1999..htm ) is a Czech weather generator which is designed to produce synthetic series of four daily climatic characteristics: SRAD = solar radiation. Semenov. 1998.cas. This feature may be also used to prepare the initialization file and run the Met&Roll in a non- 138 .. 2005. GENER. Met&Roll saves the annual courses of all above statistics in a day-by-day file. TMIN for both wet and dry days separately (WGEN included in the DSSAT does not distinguish wet and dry versions for TMIN. • the contents of the option panels (concerns only: ANALYZE. TMAX. TMAX. MODIFY). Semenov and Brooks. Lawless and Semenov. TMAX and TMIN). user-defined color palette. These factors include changes in: instruments. Some time ago.g. 1998. Auer and Boehm. However. Peterson et al. 1950). Szentimrey. can cause gradual biases in the data. station locations. development of reference time series. particularly change in the environment around the station. Vincent. Tuomenvirta and Heino. formulae used to calculate means and station environment (e. for example. Peterson et al. 3. two-phase regression.g. Craddock test.particularly climate variability and change analyses . 1998).g. use of metadata.g.3 Homogenization tests/procedures For long-term climate analyses . 1998.. 2002. 1997. 1999).3. Some changes cause sharp discontinuities while other changes. subjective and objective methods. rank order change point test. All these inhomogeneities can bias a time series and lead to misinterpretations of the studied climate (e. the climate data used must be homogeneous (e.g. side by side comparisons of instruments. multiple analysis of series for homogenization (e. Standard normal homogeneity test. most long-term climatological time series have been affected by a number of non-climatic factors that make these data unrepresentative of the actual climate variation occurring over time (e. the conditions for weather measurements were not standardized. Moisselin and Mestre.g. Easterling et al. According to the answers from the questionnaire the following homogenization tests/techniques/software are applied in European countries: 139 .. 1998). This effect can be different for different weather parameters. observing practices. however. Scholefield. breaks could be higher than trends). A homogeneous climate time series is defined as one where variations are caused only by variations in weather and climate (e. Nowadays the weather measurements are well defined by the World Meteorological Organization (WMO).g. 1994. Sneyers. 1996. Alexandersson and Moberg.g. 1999). interactive mode (the structure of the panel files is a same as a structure of the blocks in the initialization file). Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . The indirect methodologies consider use of single station data. 1997).g. 1996) There are several direct and indirect methodologies for homogeneity testing. Torok and Nicholls. Conrad and Pollak. many climatologists have noticed that many factors are likely to introduce homogeneity breaks into long-term climatological series (e. 1996). Peterson et al. It is important to remove the inhomogeneities or at least determine the possible error they may cause (e.3.to be accurate... Inhomogeneous data can be the reason of false conclusions that do not correspond to the real climate changes (e. statistical studies of instrument changes. Caussinus-Mestre technique. The available objective methods include: Potter's method. Unfortunately. The direct methodologies include. Furthermore. The Causinus-Mestre method. SMHT method uses T-test to analyse the shifts in mean and standard deviation of time series. For temperature also daily data are homogenized using harmonic functions on monthly corrections. Caussinus and Mestre.g. in Croatia. Craddock 1979) can be applied on yearly.Survey of agrometeorological practices and applications in Europe regarding climate change impacts . Craddock test is based on the analysis of von Neumann ratio. It is based on the premise that between two breaks. 1999. At each step. 2000). • • • • Standard Normal Homogeneity Test (e. Each single series is compared to others with the same climatic area by making series of ratio (e. in the Czech Republic. etc. Slovenia. Mestre.g. The Caussinus-Mestre method.g.g. 2002). Italy. precipitation and sunshine duration. seasonal or monthly time series of mean. Bulgaria.g. is now the standard detection part of the homogenization method used in Météo-France (e. in France. The knowledge of break positions can be a very interesting aspect for 140 .g. minimum and maximum temperature. leads to small improvements (e. 1999. Moisselin et al. These ratios or difference series are tested for discontinuities. (eg. The detected shifts should be approved in the history of the station and than they are adjusted. the break is attributed to the candidate station time series (e. If the series is homogenous with constant mean. For both methods homogenous reference time series is needed.) • ClimDex – software ( Italy) • MASH (Multiple Analysis of Series for Homogenisation).g.g. for air temperature). For detection purposes. Slovenia) Caussinus-Mestre technique (e. von Neumann ratio is close to 2.g. Slovakia. Spain. a triple step procedure. Mestre. Moisselin and Mestre. When a detected break remains constant throughout the set of comparisons of a candidate station with its neighbours. one or two more breaks are added to the previous selected hypothesis. 2002. Craddock and SNHT tests (e. Alexandersson 1986. with a double step procedure. simultaneously accounts for the detection of unknown number of multiple breaks and generating reference series.): Craddock test (e. which is closely related to the firstorder series correlation coefficient. 1998). for precipitation) and differences (e. a time series is homogeneous and these homogeneous sections can be used as reference series. Analytical studies (e. Mestre. otherwise it is smaller than 2. 2000. etc. in Hungary) Both. Bulgaria) AnClim – software (e... 1997. Peterson et al. the formulation described by Caussinus and Lyazrhi (1997) is used which allows the determination of a normal linear model with an unknown number of breaks and outliers. They formulated it as a problem of testing multiple hypotheses. in Austria.g. much more greedy in terms of computation time.g. 1999) show that this double step procedure gives better detection results than the single step procedure for up-and-down breaks. g. 2000). 2002. automatization LoadData – application for loading data from database (e. analysis. finding outliers. with the correlation between the stations. testing • Time series analysis (mainly cyclicity) • Filtering (smoothing) output data • Other tools: filling missing values. The weighted least squares allow correction of series with missing data. adjusting (transformation). statistical characteristics.g. LoadData are frequently used nowadays. according to their supposed quality. length of series.. statistical characteristics. The AnClim software: • Input format: TXT files. • Working with one series: graphs. outliers. The other one. ratios). some users. it allows unbiased estimations of the breaks affecting these series.g. elements. It provides the correction coefficient of a set of inhomogeneous series. periods) • Adjusting output (cross tables) 141 . The series within the same climatic area are considered as affected by the same climatic signal factor at each time. graphs.3. working with one station at a time • Menu is ordered in a sequence (steps) to be taken during data processing: viewing data. Given a set of inhomogeneous instrumental series. e. The above formulation is equivalent to an exact modelling of the relative homogeneity principle. creating reference series. is the break correction. Mestre. For many applications (such as climate change studies) it is the first half-part of the problem. filtering. Moisselin et al. settings for the software: use menu Options / Settings. and is currently the standard correction part of the homogenization method used at Météo-France (e. which can be estimated. in Italy. while the station factor remains constant between two breaks. Moisselin and Mestre. CLimDB. testing distribution. through weighted least-squares estimation of the parameters. testing • Working with two series: merging two series (using differences. described below. A two factors linear model is proposed for correction purposes (e. documentation can be found using menu Help / Documentation. Oracle) • Creating connection (using ODBC) • Specification what to download (stations. 2002) Software packages such as AnClim-Pro. number of missing values. outliers.g. using monthly data or seasonal and annual averages). The model is applied after break detection. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . It also allows the data weighting. This method does not require computation of regional reference series. • managing the software: Series Controler (form in the right bottom corner: info about period. for example. homogeneity testing (both absolute and relative homogeneity tests). Survey of agrometeorological practices and applications in Europe regarding climate change impacts . • Where the inhomogeneity detection using various tests. AnClim Running LoadData application from the AnClim • Download wizard – guides through all the steps • Transformation to files suitable for AnClim automatically ProClimDB software: • Used for processing whole datasets (all stations at a time) • There are two main input files in the software: Data file (dbf file with all stations data) and Data_info file (list of stations with their geography etc. then we can calculate statistical characteristics . • It is recommended to use several tests: e. correlations. Homogeneity testing in AnClim: after we export candidate and its reference series to TXT files (using ProClimDB). specify files. • After we decide inhomogeneites. Output to TXT files. • Further it is useful to run the tests for several types of reference series: based on correlations. then to go to metadata and verify them etc. Bivariate test. various reference series etc. ManWhitney-Pettit test (non-parametric test). This • 142 . Vincent test (two-phase linear regression). we can regard such cases as very probable to be inhomogeneous. Excel. SNHT (several modifications). distances. seasonal and monthly averages Results from homogeneity testing are put back to ProClimDB (imported to DBF file) and further processed. More specifically. using a graphical user interface.) • How to proceed: select a function. coincides. • Numbers of inhomogeneities detections per individual years or groups of years are calculated (summed). and in the end to fill missing values ClimDex is a Microsoft Excel program designed to assist researchers in the analysis of climate change and detection. their period etc. a lot of auxiliary output is created • First step of processing is getting information about all available stations. we can adjust them..g. ClimDex guides a user through a four-step analysis process. regional average (good for temperature but in case of precipitations we can get only one meaningful type of reference series) • Testing monthly. outliers. t-test (on differences). set options. we can use automatization in AnClim – running homogeneity tests for differences (ratios) between candidate and its reference series for a whole dataset. run the function for a whole dataset • User has full control over the processing all the time. A lot of tools for managing dataset is available as well. etc. the other step is importing geography. reference series. Dr. Pruchnicki.html). Presently. 1987). double-mass method. i.ataco. 3.g.e. Standard Normal Homogeneity Test developed by Alexandersson.e.cz/clidataweb/introduction/introduction.. In Spain for precipitation: the SNHT and Wald-Wolfowitz tests are applied to the monthly precipitation values in stations with more than 20 years of data from 1960 (approximately 1200 series). 2. (2004) deal with tests on homogeneity of temperature and time series in Greece.e. The access into the primary database is severely restricted and only homogenized and complete “so called technical series” are allowed to be used by researchers outside the CHMI. This process is conducted in different research centers. in which inhomogeneities occur.klimahom. but the volume of published homogenous measurement series is small. parametric tests. Dalezios et al.jsp) which is used for data quality control and is used for the administration of climatology stations and station observations. The emphasis is placed on the identification of inhomogeneities in temperature and precipitation time series as well as on the specification of certain years. Moreover in this study correction factors are identified to artificially homogenize the time series. KoŜuchowski.com/software/ AnClim. All certified data originate from a CLIDATA (http://www..: difference . For procedures of detection non-homogenous series we use the following methods: difference .: T-Student test. correlation method.quotient method. i. For temperature: Mann test (applied to the monthly temperature values in stations with more than 20 years of data from 1960) as well as Petit test (applied to the monthly temperature values in stations with long series). process consists of the following steps: 1. F-Snedecor test.g. Experts of CHMI (e.3. These data are homogenized before handover to the user based on the metadata and information from surrounding stations. In some cases. non-parametric tests. i. there is no coordinated program of climate data homogenization. Calculate Indices. Region Analysis Weather data in the Czech Republic that are certified for the use by various research teams are provided by the Czech Hydrometeorological Institute.quotient method. In case of non-homogeneity detected in observation series. This accomplished by employing various homogeneity tests to monthly data over 37 years (1951-1987) at 31 stations over Greece which has been 143 . the following methods are applied to remove it. etc. AnClim software package: http://www. WaldWolfowitz test (series test). Stepanek) conduct extensive research in the field of data homogenization and provide freely available software and know how (e. etc. 1990. Homogeneity Testing. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe .g. In Poland large variety of homogenization methods are generally accepted (e. Wilcoxon test. 4. isomer method.: Smirnow test. several methods are applied simultaneously for more accurate estimation of a given situation. Quality Control. g. Pnevmatikos et al. 1992). cluster analysis.. GWL). National weather data has been homogenized in the framework of the CLIMATE90 programme. assumes that the departures from the linear model (errors) are normally distributed. what is the likelihood that this data set could have occurred? The method attempts to find the linear model that maximises this likelihood.Survey of agrometeorological practices and applications in Europe regarding climate change impacts . STATISTICA software which needs a license. Czech Republic. classified in 5 regions using Factor analysis. Kolmogorov entropy and Kaplan-Yorke dimension. Slovenia. significance testing: confidence intervals for least squares. etc. Press et al. mostly using available FORTRAN routines (e. This method includes deriving low attractors in atmospheric data time series and calculations corresponding quantities as the Lyapunov exponent. Begert et al. ANCLIM (see above). regional circulation patterns (e. Croatia. given a linear model. Serbia applies time series analysis using quantitative parameters of chaos.g. particularly with the 4253H filter.4 Statistical methods for analyses of meteorological and simulation model output related time series Country examples For trend calculation the following statistics are applied in Bulgaria. 2005. and others. as well as the uncertainty in determining its significance. Switzerland: Fourier and spectral analysis.. Least squares linear regression is a maximum likelihood estimate i. trend calculation: seasonal or monthly decomposition (Census 1 or 2). like many statistical techniques. Visual Numerics. Several methods have been used in Switzerland (e. Non parametric correlation statistics are an attempt to overcome the limited resistance and robustness of the linear correlation coefficient. They used different methods and techniques that have been developed in the past. The methods are described in Aschwanden et al. 2006). 2006. Least squares linear regression.g. neural networks.. (1996).e. Spain. Recently. various techniques for assessment links with the agrometeorologically relevant events and e. 3. wavelet analysis packages.: least squares. Techniques that do not rely on such assumptions are termed robust.g. 2001. Slovenia uses correlation analysis.3. minimum absolute deviation. multiple regression analysis. It is also combining with filtering techniques for time series. Auer et al. the Mann-Kendall and Spearman rank statistics. for homogeneity adjustments of annual rainfall data of the 20th century in 36 Greek stations. Della-Marta. 144 . (2006) worked on homogeneity and quality control of rainfall time series. Italy. In the Czech Republic the statistical methods include standard statistical packages. Poland. 3. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . an increasing number of investigations have been carried out using the R language (R Development Core Team 2006.), which is becoming a standard. Of particular interest could be the studies on precipitation and the detection of extreme events in observed time series (Frei and Schär, 2001; Scherrer et al., 2006; Schmidli et al. 2002; Schmidli and Frei, 2006) Spain: Empirical Orthogonal Function and Principal Component. Composites. Statistical regression models. (e.g. Rodríguez-Puebla et al., 2007; Libiseller and Grimvall, 2002). Slovakia: GIS analysis and standard statistical methods are used. Poland: the basis for time series is generally adapted statistical methods. Calculations are performed by computer software of general use such as Excel, and more specialized such as STATISTICA, or original software written in specific research centers. Most applied methods (KoŜuchowski, 1990) are: presentation of measurement data with empirical formulae and estimation of their compatibility with tests such as χ2 or KolmogorowSmirnow test; spectral analysis of time-series data, harmonic analysis of timeseries data, filters, tests of statistical significance in phenomena changeability research, empirical orthogonal functions (EOF) In Italy, beyond the common statistical methods, indices for extremes (Klein Tank and Konnen, 2003) as in ECA&D (http://eca.knmi.nl) are applied. The most used statistical software is MATLAB. Some specific software for extremes is available from ECA&D (ClimDex). Greece: According to Anagnostopoulou et al. (2006) analysis of changes in extreme rainafall have used both parametric and non –parametric methods. The parametric methods usually involve fitting a suitable distribution to the data then analyzing changes in the distribution’s parameter. Non-parametric methods have utilized a large number of extreme rainfall indices. In the present study the Generalized Extreme Value (GEV) Distribution and the Pareto Distribution are applied and their results are analyzed. Daily precipitation data derived from 22 Greek stations evenly distributed in the Greek region have been used for the time period 1958-2000. The results derived from the analyses concern the threshold selection as well as the return period of extreme rainfall events in the study area. Skourkeas et al. (2006) focus on the estimation of mean maximum and minimum winter temperature over the Greek region, by applying a statistical downscaling method based on the CCA technique. Several test-hypothesis for the variance and the mean value were done between the observed and the estimated temperatures. The CCA approach is turned out to be a very useful multivariate technique for the construction of reliable linear models in downscaling methods. 145 Survey of agrometeorological practices and applications in Europe regarding climate change impacts . Trend calculation and significant testing The following text is an overview of linear regression methods for reference by members of the STARDEX project (Haylock, 2004; STARDEX, 2004). Least squares Least squares linear regression is a maximum likelihood estimate i.e. given a linear model, what is the likelihood that this data set could have occurred? The method attempts to find the linear model that maximises this likelihood. Suppose each data point yi has a measurement error that is independently random and normally distributed around the linear model with a standard deviation σi. The probability that our data occurred is the product of the probabilities at each point: For further information, see Wilks (1995) or Press et al. (1986). Maximizing this is equivalent to minimizing: If the standard deviation σi at each point is the same, then this is equivalent to minimizing: Solving this by finding a and b for which partial derivatives with respect to a and b are zero, gives the best fit parameters for the regression constant and coefficient (α and β): 146 3. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . Minimum absolute deviation Least squares linear regression, like many statistical techniques, assumes that the departures from the linear model (errors) are normally distributed. Techniques that do not rely on such assumptions are termed robust. Least squares regression is also sensitive to outliers. Although most of the errors may be normally distributed, a few points with large errors can have a large affect on the estimated parameters. Techniques that are not so sensitive to outliers are termed resistant. A more resistant method for linear trend analysis is to assume that the errors are distributed as a two-sided exponential. This distribution, with its larger tails, allows a higher probability of outliers: The solution to this needs to be found numerically. Example code can be found in Press et al. (1986). Three-group resistant line This method derives its resistance from the fact that one of the simplest resistant measures of a sample is the median. Data are divided into three groups depending on the rank of the x values. The left group contains the points with the lowest third of x values. In a time series this is equivalent to the first third of the series. Similarly, the middle and right groups contain points with the middle and highest third of ranked x values respectively. Next the x and y median values are determined for the three groups to give the points The slope of the line is taken as the gradient of the line through the medians of the left and right groups: The intercept of the line is calculated by finding the three lines with slope b0, then averaging their intercept: The three-group resistant line method usually requires iteration. After the first pass to find a0 and b0, the process can be repeated on the residuals to find a1 147 Survey of agrometeorological practices and applications in Europe regarding climate change impacts . and \ . The iterations are continued until the adjustment to the slope is sufficiently small in magnitude (at most 1%). The final slope and intercept is the sum of those from each iteration. Further information can be found in Hoaglin et al. (1983). Logistic Regression Linear regression has been generalized under the field of generalized linear modelling, of which logistic regression is a special case. This method utilizes the binomial distribution and can therefore be used to model counts of extreme events. Often in a series, the variance of the residuals (from the linear model) varies with the magnitude of the data. This goes against the assumptions of least squares regression, which assumes residuals to have constant variance, but is a natural element of the binomial distribution and logistic regression. Therefore data do not need to be normalized. The logistic regression model expresses the probability π of a success (e.g. an event above a particular threshold) as a function of time: Since the probability of a success is in the range [0,1], it needs to be transformed to the range (-∞, +∞) using a link function: Model fitting can be done using a maximum likelihood method. Further information about logistic regression, together with an example using extreme precipitation in Switzerland, can be found in Frei and Schär (2001). Confidence intervals for least squares Often the standard deviations σ. for the observations are not known. If we assume that the linear model does fit well and that all observations have the same standard deviation σ, the assumption that the residuals are normally distributed around the linear model implies that: 148 3. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . with N-2 appearing in the denominator because two parameters are estimated. From the above, it can be shown that the regression coefficient b will be normally distributed with variance: Since the variance of b is estimated, Student's t-distribution is used to define the multiplier t for the confidence limits for the regression coefficient: The assumption that the residuals are normally distributed can be tested with a quantile-quantile (Q-Q) plot of the residuals against the quantiles from a Gaussian distribution. For further information, see Wilks (1995) or Press et al. (1986). Linear Correlation The linear correlation coefficient (Pearson product-moment coefficient of linear correlation) is used widely to assess relationships between variables and has a close relationship to least squares regression. The correlation coefficient is defined by: the ratio of the covariance of x and y to the product of their standard deviations. In a least squares linear model, the variance of the predictand can be proportioned into the variance of the regression line and the variance of the predictand around the line: SST=SSR+SSE Sum of Squares Total = Sum of Squares Regression + Sum of Squares Error In a good linear relationship between the predictor and predictand, SSE will be much smaller than SSR i.e. the spread of points around the line will be much smaller than the variance of the line. This goodness of fit can be described by the coefficient of determination: 149 Survey of agrometeorological practices and applications in Europe regarding climate change impacts . = variance of predictand explained by the predictor It can be shown that the coefficient of determination is the same as the square of the correlation coefficient. The correlation coefficient can therefore be used to assess how well the linear model fits the data. Assessing the significance of a sample correlation is difficult, however, as there is no way to calculate its distribution for the null hypothesis (that the variables are not correlated). Most tables of significance use the approximation that, for a small number of points and normally distributed data, the following statistic is distributed for the null hypothesis like Student's t-distribution: The common basis of the correlation coefficient and least squares linear regression means that they share the same shortcomings such as limited resistance to outliers. See Wilks (1995) or Press et al. (1986) for further information. Spearman rank-order correlation coefficient Non parametric correlation statistics are an attempt to overcome the limited resistance and robustness of the linear correlation coefficient, as well as the uncertainty in determining its significance. If x and y data values are replaced by their rank, we are left with the set of points (i,j), i,j=1,N which are drawn from an accurately known distribution. Although we are ignoring some information in the data, this is far outweighed by the benefits of greater robustness and resistance. The Spearman rank-order correlation coefficient is just the correlation coefficient of these ranked data. Significance is tested as for the linear correlation coefficient using the last equation, but in this case the approximation does not depend on the distributions of the data. See Press et al. (1986) for further information. Kendall-Tau Kendall's Tau differs from the Spearman rank-order correlation in that it only uses the relative ordering of ranks when comparing points. It is calculated over all possible pairs of data points using the following: 150 Zwiers (1990) showed that. See Press et al. Therefore if the distribution of the residuals is not Gaussian. Resampling Resampling procedures are used extensively by climatologists and could be used to assess the significance of a linear trend. Kendall's tau is only concerned whether a rank is higher or lower than another.3. If enough random samples are generated. An important assumption in resampling is that observations are independent. Still. and can therefore be calculated by comparing the data themselves rather than their rank. Therefore no assumption needs be made about the sample distribution. for the case of assessing the significance of the difference in two sample means. A method has been proposed by Ebisuzaki (1997) whereby random samples are taken in the frequency domain (with random phase) to retain the serial correlation of the data in each sample. When data are limited to only a few discrete values.5 Additional information listed within the questionnaire There were just a few answers on the last question (for additional information if any) into the submitted questionnaires. (1986) for further information.3. bootstrapping could be used to test the significance of a least squares linear trend. discordant where they are the opposite. 3. then the least squares estimate is not valid. They were mainly related to the data problems. is that the maximum likelihood derivation of the least squares estimate for the linear trend assumed that data residuals about the line were normally distributed. given that this may not be the best trend estimate. however. sameX where the x values are the same and sameY where the y values are the same. The bootstrap method involves randomly resampling data (with replacement) to create new samples. T is approximately normally distributed with zero mean and variance: One advantage of Kendall's tau over the Spearman coefficient is the problem of assigning ranks when data are tied. Kendall's tau is a more suitable statistic. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . from which the distribution of the null hypothesis can be estimated. A problem. For example a representative from Spain pointed out “lack of data or poor quality data when the need is for data of high temporal and spatial 151 . the presence of serial correlation greatly affected the results. where concordant is the number of pairs where the relative ordering of x and y are the same. the significance of an observed linear trend can be assessed by where it appears in the distribution of trends from the random samples. Differences in the field.Survey of agrometeorological practices and applications in Europe regarding climate change impacts . although its measurement is not so spread across the continent as the collection of weather data. which are freely available on the internet. however. area coverage. In many cases it could be difficult to provide good quality gridded data of daily meteorological values for grids smaller than 40 km2”. soil moisture deficit. should include: Calibration and testing the performance of models to simulate the soil moisture dynamic in different climate conditions and soil depths. are observed in the terms of their temporal and spatial resolution. Quantification the effects of climatic variability on the main components of water balance (evapotranspiration. numerical weather models still represent short reanalysis data set. intervals and zones with high risk at the occurrence of extreme climatic events (droughts. including various indices. crop water requirement.4 Concluding remarks Obviously all European countries have historical long-term meteorological records. Analyze the spatial and temporal evolution of soil moisture dynamic over a long-term period in order to identify the. Some of the European institutions/countries are ready to provide primary weather data. while others at this stage are open to present mainly secondary data. GIS and modeling techniques coupled with decision support systems for agriculture.g. excess moisture. Development and improving agrometeorological applications through using remote sensing. for example in Romania. resolution.). The homogeneous series can be changed in the future. etc. trends. global and regional climate models as well as weather generators are more and more applied in Europe. maps. main research agrometeorological activities. CROPWAT model. effective rainfall) using e. especially availability for end users. hence the attention on the long term variations and trends of given agroclimatic indices should be focused on application of global and especially regional climate models as well as weather generators. As future steps. In a similar way the respond from Slovakia was: “pure quality of some agrometeorological data (soil moisture and agrotechnical data). The homogeneous series do not replace uncorrected (raw) data. Models such as numerical weather models. The raw data can be used for completing missing data or to improve 152 . There is no doubt that the long-term climate series should be homogeneous. The same conclusion could be directed to the agrometeorological data. some data are still stored on the paper sheets (part of soil moisture and phenological data)” 3. A possible solution of this problem is the application of gridded meteorological data sets. Inhomogeneous data sometimes can introduce wrong results and especially conclusions related to the issue of climate change. etc. However. M. New. Caesar. who replied to the e-mail requests for assistance and provided additional information in the field. Alexander L. J. for his help and useful suggestions. D05109.. and Acock M. Finally. Res.. The authors wish also to thank to the COST 734 chairman. J.3. those readers who consider the above information as insufficient.. G. Geophys. All this shows the existing of well organized capacity building as well as the opportunity to apply and develop know how in the field... D. However. as well as to any colleagues. A. a strategy for development of a common statistical approach analyzing time series from different European regions should be considered. Peterson. Prof.. Potential for using long-term field research data to develop and validate crop simulators. Gleason. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . These are often station specific studies that do not involve long-term trend analysis. Griffiths.L. 153 . 3.V. considerable work has been done on homogeneity testing and data adjustments and research will continue in this field. it is important to preserve the original data as well as the homogeneityadjusted versions. B. Peterson et al. Rusticucci. A. Finally. 6(6): 661-675. Also. Aguilar. 1998). A homogeneity test applied to precipitation data. During the last two decades. K. J. 83:56-61. J. Agron. There are even some analyses.5 Acknowledgments Special thanks are addressed to all colleagues. who submitted filled in questionnaires on trends of agroclimatic indices and simulation model outputs in Europe.C. J. M. Klein Tank. F.B. Brunet. where unadjusted data are preferred (e. Burn. 111.C. they may wish to refer to the filled in questionnaires. doi:10. Alexandersson A.g. B. Global observed changes in daily climate extremes of temperature and precipitation. Revadekar. T. Climatol. Vazquez Aguirre.. Tagipour. Taylor. M. M.1029/2005JD006290. D. the break detection or correction. L. Zhang. Zhai. 1986.G. It seems that various statistical methods and software are applied on time series in Europe.Orlandini. Rahimzadeh. J. M.6 References Acock B. 1991. M. Trewin. 3. E. the original data need to be preserved because new and better or improved approaches to homogeneity adjustments will probably be developed in the near future. the achievement of consistent and/or coherent results in this sphere in the European Union and beyond would be more real. Haylock. edited and posted on the COST 734 web page. X. warm thanks are directed to the COST office in Brussels for its financial support related to this survey. Collins. In this sense. Therefore. Stephenson. 2006. P. J. Rupa Kumar. Vincent. S. Dolinar. Int. Auer. A. Integration of the nesting technique into the LM model Proc. Athens. Zaninovic. Proc. O. Field Crops Research.. and Moberg A. Kveton. K.. Proc. D. Orlik. Lagouvardos. 1996. 2006. O. and Boehm R. Briffa. Arnold C. HISTALP-historical instrumental climatological surface time series of the Greater Alpine Region. Climatology. 1996. Maugeri.. May 24-26. 2001. and Elliot W. Lapin. V.. W. P. Correction of surface temperature and wind speed predictions of the BOLAM weather forecasting model using artificial neural networks. P.W.Atmospheric Physics. Change Biol. May 24-26. Andreadis. J. Müller-Westermeier.. K. Mestre. Glob. Nieplova. Jurkovic. 2006. CLIGEN Weather Generator Predictions of Seasonal Wet and Dry Spells in Uganda. Trans. A stochastic modelling approach for real time forecasting of winter wheat yield. Homogenization of Swedish temperature data.M. M. Nanni. Szalai.. P. L. C. V. A homogeneity test applied to precipitation data. Maugeri. Anagnostopoulou C. Ungersböck.Atmospheric Physics. 2006. Combined temperature-precipitation variations in Austria during the instrumental period. T. Aschwanden A. Schlegel.. Argiriou A. Climatol. W... T. Schoener. Szentimrey. K. Int J Climatol 17:25-34 Alexandrov V. Cegnar..Climatology . Boisvenue C. Avgoustopoulou E. Auer I. Climate Research . Data. M. Bochnicek. 1999. 8th Conference on Meteorology . Budapest: Hungarian Meteorological Service. W. Climate Variations and Agricultural Crop Production in Georgia. et al. K.Climatology . Auer I. G. M. 1986. M.. Potzmann. D. Alexandersson H. Climatol.A. J. A. Michailidou. Auer I. Theor Appl Climatol 49:161-174. Bereinigte Zeitreihen: Ergebnisse des Projekts Klima90..Atmospheric Physics. 2006. Böhm. Anadranistakis M. May 24-26. E. T. 2006. and Running S. Brunetti.. Maheras. 154 .Survey of agrometeorological practices and applications in Europe regarding climate change impacts . Moisselin. R. Tolika. Int. Begert M. Klimatologie der Schweiz. Regional temperature variability in the European Alps: 1760-1998 from homogenized instrumental time series. 62: 85-95. 2005. J. M. Schöner. Fragkouli. Kirchhofer. Proceedings of the first seminar for homogenization of surface climatological data.. May 24-26. Proc. USA. Begert. Nanni. 8th Conference on Meteorology .27:17-46. Alexandersson H. pp 83-96. 2001. 8th Conference on Meteorology Climatology . Matulla. 1997. 12: 862882. 1997. 1994. and Papageorgiou I. Majstorovic. Ioannou. M. Athens. C. S. 2006. Skourellos.J. and Crout N.Lipa.D. M. Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000. Extreme rainfall events over the Greek area: theory and application of the generalized extreme value (GEV) distribution and pareto distribution. 17(1): 33-43.J. Part: A homogeneity test for linear trends. Athens.25:65-80. and Boehm R. Kotroni. Stastny.. 4 Volumes available from the Swiss Federal Office of Meteorology and Climatology. Mamara. Bannayan M. 2006.. Boehm R. Impacts of climate change on natural forest productivity-evidence since the middle of the 20th century. A. Athens. Efthymiadis. T. 2006. T. N. Verification of prognostic fields of surface wind of numerical weather prediction models LM-COSMO & ECMWF. R.D. T. M. metadata and the question of homogeneity for the Austrian climatological network. J. Brunetti. P. 2006. Jones. W..Climatology – Atmospheric Physics. I. 21: 1779-1801. 2006.M. Int. Z. Int J Climatol 6:661-675. 8th Conference on Meteorology . and Hoogenboom G. of ASAE 39(3):969-972. M. Mercalli. Gajic-Capka. European Climate Assessment and Dataset (ECA&D). J. Danuso F. N. Budapest: Hungarian Meteorological Service.R. Evidence of slowing yield growth .the example of Swiss cereal yields. CLIMAK reference manual. Fischer G. Meteorol. 1996.. Conrad V. Cabelguenne M. Cambridge: Harvard University Press. Mueller.M. A. 1997. A method to estimate the statistical significance of a correlation when the data are serially correlated. an explanatory model for rice growth simulation. April 21. Tsidarakis. IAM.W.knmi. 2005.M... T.. sunflower.. 2003.. Phytophenolgical trends in Switzerland. EPIC phase. 63: 181-200. Canada Land Inventory Report No. Proc. Chmielewski F. and Lyazrhi F. and Schneider C. 2004.. Austria. H. Lands Directorate. 1994. Weather. Spanos. Bouniols... Dalezios N. A. pp 63-82 Chapman L. A method of homogenizing the extremes and mean of daily temperature measurements. 1996. Commission of European Communities. Laxenburg. 2004. DPVTA. Sust. 1950.J... Testing homogeneity of temperature and precipitation series in Greece. Volos... Camarero J. Agronomy Journal 88: 704-716. Casanova D.. Climatic Change. Ebisuzaki W. Choosing a linear model with a random number of change-points and outliers.. Debaeke. Caussinus H. et al. Environment Canada. Ann Inst Statist Math 49:761-775 Caussinus H. DG VI Agriculture. 1997. 459 pp Craddock J. Agric. 36 p. 34(9): 332-346. Grapevine and climatic changes: a glance at the situation in Alsace. http://www. Climate change and global agricultural potential project: A case study of Kenya. Easterling D. 155 . and Gutierrez E.J.. Karl. Boote K. Agron. Pace and pattern of recent treeline dynamics response of ecotones to climatic variability in the Spanish Pyrenees. 2007. Unpublished. Flichman G. 1996. S. Analysis of the Socio-Economic Impacts of Agricultural Reform in Certain European Regions: Competitiveness and Environmental Protection (Final Report for EU contract No.T. Int J. Jones. E.. 2000. soybean and winter wheat. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe .. Testing the performance of ORYZA1. T. P.B.19(17): 4179-4197 Duchene E. For. Bruns. 121: 69-78. 3. Clim. 1995. The Climates of Canada for Agriculture. Biometeorol 45: 203-207.nl/samenw/eca Finger R. and Pollak C. Methods in climatology. A. 96 pp. New mathematical tools and methodologies for relative homogeneity testing. of Climate. Revised 1978.1979. Potential uses and limitations of crop models. International Institute for Applied Systems Analysis. Proceedings of the first seminar for homogenization of surface climatological data. and Mestre O.. University of Udine.. 25: 93-99.D. 1997. 1999.M. Van Velthuizen. 1997. J Climate 9:1429-1434. 2006. and Clot B. taking account of developmental stages: validation on maize. J.C. 1961-2000. Montpellier. a version of the EPIC model simulating the effects of water and nitrogen stress on biomass and yield. International symposium / workshop on climatic variability and impact to agriculture. Pickering. Agricultural Systems 60: 175-196. 1994. On the development and use of homogenized climate data sets.M. 4706 A). Peterson. L. 10: 2147-2153. J. 2001.3. Climate changes and trends in phenology of fruit trees and field crops in Germany. sorghum. Goudriaan. 24 pp. Bosch. 1978. Italy. Defila C. Methods of comparing annual rainfall records for climatic purposes.. and Brown D.. European Journal of Agronomy 12: 175–189. France. Dev.. J.R.J. Della-Marta P. for Mediterranean conditions. Numerical simulation of a tornado producing thunderstorm: A case study Idıjárás. and Van Laar H. 14: 1568-1584.. F. 35: 153-177. 2005a. S. and environmental impacts of soybean and corn management. 2006.. Frei C.M.. 1995.Meteorol. Hawkins D. Detection probability of trends in rare events: theory and application to heavy precipitation in the alpine region. 2001. 2000. Contribution of agrometeorology to the simulation of crop production and its application. Geresdi. 14: 299–304. Joseph. A. Z.. Lunkeit. vol. Cambridge University Press. F. 1994. Decision support system to assess agronomic. E... ASAE Paper No. J. 110 3-4. F. Csirmaz. Lunkeit. Kirk... Metadata and their role in homogenization.. Soc. Detection probability of trends in rare events: theory and application of heavy precipitation in the Alpine region.. and Schär C. and Simmons A.J. Dordrecht. St. Hoskins B. Z. Lunkeit.W. Gottfried. 129-165. Jones. E. Kirk. Agric. 2002. H. F. Wiley. Kertész. J. Am. L. Welch. Idıjárás. Meteorol. 2005c. Climatic Research Unit.. Haylock. Hoaglin D. Mosteller. Z. 1994. P. Journal of Climate 7:1001-1013. and Schär C. Clim. Guerif М. Jansen. Jansen.. Mika. Frich P. 1994.W. Clim. J. Jacobson B. Heino R. The ARPEGE/ALADIN mesoscale numerical modeling system and its application at the Hungarian Meteorological Service. Budapest: Hungarian Meteorological Service. 2001.H. 19: 193-212. The Planet Simulator: Towards a user friendly model . 14: 305–314. E. universitatis szegediensis 38-39: 85-95. 203-227. Hoogenboom G. 101: 637—655. Tasks Veg. MI. H.Survey of agrometeorological practices and applications in Europe regarding climate change impacts . Kirk. Della-Marta. Fraderick K.M.. T.. 2006. Agricultural and Forest Meteorology. L. The Portable University Model of the Atmosphere (PUMA): Storm track dynamics and low frequency variability.W. Radnóti. Haylock M..N. 156 . vol. Meteorol. 2005b. Engineering.. Goudriaan J. Peterson.M. 2005. 952696. 279-297.J. A multi-layer spectral model and the semi-implicit method.C.Alexander. Horányi A.G. Horváth Á. Journal of Climate 14: 1568– 1584. The Planet Simulator: Green planet and desert world. Modeling Potential Crop Growth Processes: Textbook with Exercises. Tukey.V.. 2001... 1975. 1998. and Hanssen-Bauer I. Pauli. S. Fitting multiple change-points to data. submitted. Gleason. 17 James I. 103: 137-157. Lunksch.. 9: 127–136.K. Makra. Jansen. Tank. Horváth Sz. economic. Homogenizing long Norwegian precipitation series.. H. Res. Grabherr G. 2001. 110 3-4. p. Comput Statist Data Anal 37:323-341. Linear Regression Analysis for STARDEX. Quarterly Journal of the Royal Meteorological Society.. J. Observed coherent changes in climatic extremes during the second half of the twentieth century. Understanding robust and exploratory data analysis.J. Long term monitoring of mountain peaks in the Alps. 20th century variations of the soil moisture content in east Hungary acta climatologica et chorologica. M. 1983.. U. K. Introduction to circulating atmospheres. U. Kullmann. B. Fraderick K. H. I. L. 1997. G... Fraderick K. 2004. Lunksch. Kluwer Academic Publishers. European Journal of Agronomy. Frei C. Førland E. pp 5-8. and Duke C. Proceedings of the first seminar for homogenization of surface climatological data. M. Sci. Calibration of the SUCROS emergence and early growth module for sugar beet using optical remote sensing data assimilation. Climatology . Nordli. F. Clim. Rapid recent range-margin rise of tree and shrub species in the Swedish Scandes. pp.V. grape composition. Łódź. 2005. Peсuelas.L.H. Klein Tank A. 1990. Lykoudis. 214 pp.Vitic. 16: 206-223.. Kljuev N. Nauka. Menzel A. Sparks. Jones G. Briede. V. A. Ambio. 90: 68-77. In: Influence of Global Change in the Environment and Climate on Functioning of Economy of Russia..Atmospheric Physics. 116-143. Journal of Climate 16: 3665–3680.A. Russian J.. Aasa. Italy: Editrice L’Universo. Scheifinger. Plant phenological anomalies in Germany and their relation to air temperature and NAO. Lavielle M. Matalas N. 2003. M. Lawless C. Ecol. 1993.3. and Birks H. and Grimvall A. Johnson G. K. Z. and Moberg A.. Enol. Optimal segmentation of random processes. Zust. A. Am. Climatic projections in the eastern Mediterranean using the regional climatic model PRECIS. and Solomon A. Y. 2002 .. Russia and its Regions. 36: 69-75.Performance of Partial Mann Kendall Tests for Trend Detection in the Presence of Covariates.M. Hemispheric and large scale surface air temperature variations: an extensive revision and an update to 2001. 2003. Curnel. 1967.P.. pp. Moscow. Climate influences on Grapewine phenology. Libiseller C.J. and Davis R. A.. and Semenov M. C. May 24-26. Terent’ev. Filella.M.B.. Ahas. H. J. 2001. Kullman L. N. Kercheva M. European phenological response to climate change matches the warming pattern. S. Bissoli. 13: 1-6. A. Menzhulin G. Chmielewski. Ballard. Hanson.Florence. Athens. Koch. V. Environmetrics 13: 71-84 Maracchi G. Russia. R. Jones P.V..M. K. 30: 72-80.. Glob. J. Alm-Koeler. 2003 Meteorologia e climatologia applicate. The Holocene. S. IEEE Transactions on Signal Processing 46(5):365-1373. F. Information base for modeling components of soil water balance and assessment of agroecological risks. 2006.. Shiyatov.. Materials for climate history in period of instrumental observation. 8th Conference on Meteorology .W. Remišova.D. Braslavska. P. E. 2006. Dalh. 2002. (ed. J. A. J. Pirinen.N. 1998. Striz. KoŜuchowski K. F. 57: 243. 1946–99.. Lagouvardos.. 2000. 2005 Assessing lead-time for predicting wheat growth using a crop simulation model Agric Forest Meteorology 135:302–313. J. Defila.. Crepinsek.. Stochastic Weather Simulation: Overview and analysis of two commonly used models. Zach. 157 . 2006. Ш. 51: 249-261.I Holten. (in Russian). C. Fomin. Clim.. E. Impact of global warming of climate on agriculture in Russia. Moscow. Kotroni V. Res. M. Kundzewicz Z. VanVliet..G.B. Mestre. A. Y. Water Resources Research 3(4):937-945. Mathematical assessment of synthetic hydrology.P.L.E... Ecol. Donnelly.). Estrella. 3: 79-96. Journal of Applied Meteorology 35:1878-1896. Recent increases in species richness and shifts in altitudinal distributions of Norwegian mountain plants. Lalas. and Konnen G. Climatic Change.G. S. 20th century climate warming and tree-limit rise in the southern Scandes of Sweden. W. T. 48-73 (in Russian).. Jatczak.. 1996. France. Change Biol. O.M. Parry. Susnik. 2001.. A. M..V. Proc. Kullman L. Menzel A. 2004. Е. Leemans R. Mеge. and wine production and quality for Bordeaux. 2003. P. 2003. Trends in indices of daily temperature and precipitation extremes in Europe. Wielgolaski. Spatiotemporal dynamics of forest-tundra communities in the polar Urals. 12: 1969-1976. 1998. D. K. Cramer. S. The potential response and redistribution of crops under a doubled CO2 climate.C.... Hardegree. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . Wydawnictwo Uniwersytetu Łódzkiego. PhD thesis (in Bulgarian) Klanderud K. Moisselin J. I. digitization and homogenization of long-term data series.Atmospheric Physics.. 83: 89-106. 1: 36-46. 1993..H.C. Physics and Chemistry of the Earth vol. Jones. the Netherlands. Peterson T. Teukolsky. 2006.T. Int J Climatol 18:1493-1517.. L. 1999. High summits of the Alps in a changing climate. Dunkel. Plummer. and Teng P.C.A.. La Météorologie 38:45-56. Karl. Glob.. and Douguoudroit A.. Change Biol. Les changements climatiques en France au XXe siecle. Homogeneity and quality control of rainfall time series. O. May 24-26. D. Gullet.Climatology – Atmospheric Physics. Numerical Recipes in Fortran.. pp 15-26. and Boada M. Research. et al.G. Mestre. Gottfried. H. Mestre O. Szentimrey. Int J Climatol 14:671-679. Tuomenvirta. Proceedings of the second seminar for homogenization of surface climatological data.Adapted Behaviour and Shifting Species Ranges. S.T. S...R. Actes du Colloque "La Pérennisation et la Valorisation des données scientifiques et techniques" (Ensuring Long-Term Preservation and adding Value to scientific and technical data) organisé par le CNES du 5 au 7/11/2002. Thèse de doctorat de l'Université Paul Sabatier. J. B. Nicholls.Climatology . Auer. Agricultural impacts and adaptations to climate change in Europe. The oldest observation series on high mountain plant diversity in Europe. Petriccione B. 223-230. G.W.H. A global change-induced biome shift in the Montseny mountains (NE Spain).J. D. 2003. Mize J. 139-149. Toulouse. V. T. 1994. C. Cambridge Univ. Farm Policy Journal. Méthodes statistiques pour l'homogénéisation de longues séries climatiques.R. 2006. Peсuelas J. Schneider. Press W. Walther. and Bindi M. Prentice Hall. Mika J. Athens. 150: 545-562.“Fingerprints” of Climate Change . Flannery. 2001. Kotroni. M. 1968. P.Survey of agrometeorological practices and applications in Europe regarding climate change impacts . The Palmer Drought Severity Index (PDSI) as an indicator of soil moisture. and Cox J. The Art of Scientific Computing. 2006. Gall. Groisman. Easterling. 488-493.... M..A. Pauli H.M.. Moisselin J. I. N. Olesen J. Vincent. Th. Horváth. (eds.R. Numerical recipes: The art of scientific computing. Pálfai I.. Heino. Canellas. Cambridge University Press. and Easterling D. Torok.. Monthly and daily precipitation trends in theMediterranean. WCDMP 41/WMO-TD 962. May 24-26. Eds. Norwell. N. Systems approach to agricultural development. 2005. Cambridge Press W. Vetterling. Boehm.A.J.. 2006. Theor. and Mestre O. O. 8th Conference on Meteorology . Geneva: WMO. R. 2006. K. Salinger. P.S.2. M. Oikonomou E. 8th Conference on Meteorology . Toulouse: Université Paul Sabatier. S..H. Sz. Burga.. 30. Pnevmatikos J.. Creation of homogeneous composite climatological reference series. Hanssen-Bauer.E.-R. Essentials of simulation. 9: 131-140.P. L.fuzet (in Hungarian). Mestre O. Grabherr.. Homogeneity adjustments of In Situ atmospheric climate data: a review.. 1998.D. Peterson D. Step-by-step procedures for choosing a model with change points. D. Proc. 1992. Penning de Vries F. Etude des longues séries homogénéisées de données de température et de précipitations. 2003. Tzanakou. Forecasting extreme events over the eastern Mediterranean by use of 3DVAR assimilation of synoptic and satellite data. Massachusetts. Makra. Kluwer Academic Publisher. 1991.. Alexandersson. Acta Bot. Appl. 2nd Ed. H. C. Kornaros. Norrant C. Press. The drought of year 1990. Climatol.. 2002. NJ. 158 . 2000. Vizugyi Kozlemenyek 1991.. W. Englewood Cliffs. 2002. G. Athens. Forland. 2004.). 1986. E. Parker.M. Proc. R. Kluwer Academic Publishers. P.. G. Mestre. Short-term changes in key plant communities of Central Apennines (Italy). Z. Lagouvardos. Edwards. . and Brooks R. from 1901 to 2002.A.. 1998. Sanz Elorza M. Frei. E. A serial approach to local stochastic weather models... L. 159 . 1999. George Perry. Richardson. Temperature trends in Switzerland and Europe: implications for climate normals. C. Howard. L. USDA-ARS. and Barrow E. Int. H. M. K. http://www. Pruchnicki J. Mesoscale precipitation variability in the region of the European Alps during the 20th century. Yin. Field Crops Research.A. Semenov M.. 2006. K. C. Climate Research.W. Trends of heavy precipitation and wet and dry spells in Switzerland during the 20th century.. Temporal and spatial changes of the agroclimate in Alberta. 2003. US Department of Agriculture Technical Bulletin N° 1768.A. Int. Schmutz. Semenov M. Budapest: Hungarian Meteorological Service. Vienna. Schmidli J. Liniger. J.org. Geneva: WMO. Use of a stochastic weather generator in the development of climate change scenarios. 235 p. Proceedings of the second seminar for homogenization of surface climatological data.N. 235 p. C. 57 :27-41.J. The effects of climate variations on winter cereal production in Spain. Changes in the high mountain vegetation of the central Iberian Peninsula as a probable sign of global warming... Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . Sau F. Semenov M. Journal of Applied Meteorology 44(7):1090-1105.. Barrow. WCDMP 41/WMOTD 962. 1999. Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. 25: 753–771. Rodríguez-Puebla S. Model Documentation. Boote. J. 26: 565–580. PWN.A. Frías. T.W. L. 35:397-414. Climatol. L. M. Ann. 61: 273-291. Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. biodiversity and the urban environment: a critical review based on London. H.J. northwest Spain. Development of high-resolution UKCIP02-based climate change scenarios in the UK Agric Forest Meteorology 144:127–138. A synthesis of the impact of Russian forests on the global carbon budget for 1961-1998. Brooks. Climatol. Szeidl. Karl. S. Trans. Scholefield P. Shein K. and Williams J. 1999. EPIC-Erosion/Productivity Impact Calculator: 1. Metody opracowań klimatologicznych. Bot.A. A. pp. pp 195-202. UK Progress in Physical Geography.W.P. García-Casado. Schär 2002. 1997. The role of metadata in climate data homogeneity. 11:137-148.A.. Scherrer S... R Foundation for statistical computing.A.. 1984..D. 1982. Robert L. Proceedings of the first seminar for homogenization of surface climatological Data.M. Richardson C. of ASAE 25:735-739. 1990. Semenov. Warszawa. 22: 1049–1074.. and Wright D. 1991. 2006. Ecological Modelling. R Development Core Team. Int. Wanner. Shen S. The importance of the homogenization of climate data time series in the context of the world climate data and monitoring programme. M. C. Climatic Change. Racsko P.. Climatol.. 55B:391-415.. Ayuso.r-project. Dependence structure of daily temperature and solar radiation.. Schmidli J. 1999.M. and Dana E. Tellus. J. Ruiz-Nogueira. Cannon. and Nilsson S.3.A. 2006.S.. 30(1): 73-98. Shvidenko A. Richardson C. Climate Research 10:95-107. Climate change. WGEN: A model for generating daily weather variables. 2007. 1987. 92: 273-280. 2007.D.R. 2006. C. R. R. M.. 2005. pp 1-3. and Frei C. Canada. 2003. Chetner.R. Appenzeller.W.. Sharpley A. Climate Research.A. Evaluation and improvement of CROPGROsoybean model for a cool environment in Galicia. Semenov M. Wilby. 42-61. Kirchhofer. and soil fertility. June 2004.O.V. Maheras.. Konnen. Verification procedure for homogenized time series”.. May 24-26.. Kern-Hansen. 2006. R.. M. 2004.D. A.. Climatol. Szentimrey. and Abashina E. Sirotenko O. and Ihász I.G. G. Athens. and Koennen G. W. D. Razuvaev. Heino-Bessemoulin.Climatology – Atmospheric Physics. WCDMP 41/WMO-TD 962. 28 p.Survey of agrometeorological practices and applications in Europe regarding climate change impacts . E. 193-201. Sensitivity of Russian agriculture to changes in climate. M.. Maugeri.. Multiple analysis of series for homogenization (MASH). 2002.iwan-supit. L. Coelho.cistron. WCDMP 41/WMO-TD 962. T. P. T. A. R. chemical composition of the atmosphere. Aberfeld. Szalai.F. Bohm. P. 1999. Allen. Proceedings of the second seminar for homogenization of surface climatological data.3.. Bukantis. C. Anagnostopoulou. Sirotenko O. and Petrovic. 5th Congress of the European Society of Agronomy. Abashina. J. 1996. MullerWestermeier.A. 1998. 1998. Agroclimatic resources and physical-geographic zones of Russia under global warming. Sneyers R. A. Skourkeas F. Leitass. and Nelson. Proc. O.M.V. 1995.. Tank K..P. 2002.B. Cegnar. Kolyva-Machera. Meteorology and Hydrology. A. Diagnostic Extremes Indices Software. Model estimation of mean maximum and minimum temperatures in Greece. 4:107-114 (in Russian). Geneva: WMO.. V. pp 5-14.M. S. 3665-3680. R. Aust Meteor Mag 45:251-260.G.S. Washington State University. 2006..V.P.O.nl/~iwansupit/contents/ Szentimrey T. International Journal of Climatology 18: 1479-1491. J. The dynamical downscaling of ECMWF EPS products with the ALADIN mesoscale limited area model:preliminary evaluation 253-277. P. J. S. Ceylan. and Nicholls.N. User Information Version 3.. M. A. S. Nitra. Torok. Capaldo. Clim.. 92-103 (in Russian). WMO. VanEngelen. Tzanakou. R. Demaree. Biological Systems Engineering Department.V. Pavlova. S.. Budapest. P. Mietus. Geneva: WMO. Pullman.. Tank K.. Trends in indices of daily temperature and precipitation extremes in Europe.D. F. 8th Conference on Meteorology . pp 27-46.. E. Mares. Pachaliuk. Hungary.. Pashiardis. Szinell Cs. Alexander..A. Palsdottir. G.G. Steduto. 160 . WCDMP-No. Bussay. Stöckle C.. Szintai B. The Slovak Republic. L. and van der Goot (eds. Forland. Rubin. Tolika. V. Homogenizing time series of climatological observations: the search and adjustment for inhomogeneities principles of methodology and example of results. Meteorology and Hydrology. 22: 14411453.M. G. Updated System description of the WOFOST crop growth simulation model as implemented in the Crop Growth Monitoring System (CGMS) applied by the European Commission http://www. C. Stockle C. Moberg. Estimating daily and daytime mean VPD from daily maximum VPD. J. 1999. Proceedings of the Fourth Seminar for Homogenization and Quality Control in Climatological Databases. 3. ClimGen manual. Gocheva. N. 2006.) E. E. A historical annual temperature dataset for Australia. Fitzgerald. Hejkrlik. Int. Campbell. 16. pp. B.A. Drought Tendencies in Hungary. Mileta. Lуpez. Nieplova. T. A.. 1999. STARDEX. 2004. Supit I. Szentimrey T. „Multiple Analysis of Series for Homogenization (MASH). 56. Proceedings of the second seminar for homogenization of surface climatological data.. Dahlstrom. G. 6-10 October 2003.. 1998. Wijngaard. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment.. WA.1. R. C.. M.. A. K. 2003. 505-514. 1982. Geneva: WMO. Geophysica 32:61-75. Budapest: Hungarian Meteorological Service. Zwiers F.)..F.D. G. 14: 317-322. http://www. S. Repapis.. and Grondona M. Burga 2005. 2006.. K. Unpub. 160-176. and Rabbinge R. Statistical Methods in the Atmospheric Sciences. Walther G. Melas. 3: 1452-1461. and Heino R. 1998.vni. France. On the statistical analysis of series of observations. 8th Conference on Meteorology . 1995.R.com/products/imsl/. Regional climate model simulations for Greece with RegCM3 Proc. Kanakidou. Thornton. Tuomenvirta H. Kluwer Academic Publishers. Bulletin of American Meteorological Society 63:1309-1313. Viner D. 75-82. 1993.Atmospheric Physics.T. J. 1996. Fortran Subroutines for Mathematical Applications. and Tsuji G.. Summarizing a questionnaire on trends of agroclimatic indices and simulation model outputs in Europe . Athens. Veg. An Agroclimatic Resource Index for Canada and its use in Describing Agricultural Land Losses. 2001. Hoogenboom. 1997. eds. 1997. Tuomenvirta H..V. Teng.O... van Ittersum M. 2006.Climatology . Ecological Modelling. Review of the methodology of the Standard Normal Homogeneity Test (SNHT).. Williams G. In Systems approach to agricultural development. The IBSNAT project. A technique for the identification of inhomogeneities in Canadian temperature series. Proceedings of the INRA-KCW Workshop on Decision Support Systems. 1998. 1990. Technical Note 143/WMO 415. J.K. A. Wilks D.A. Understanding Options for Agricultural Production. May 24-26. 1975.M. Philandras. Ottawa Willmott C. Int.. Climatic changes in Finland – recent findings. 1990. Publ.. p. 16: 541-548. pp. Proceedings of the first seminar for homogenization of surface climatological data. Zanis P.C. Some comments on the evaluation of model performance. Clim. F. C. Tourism and its interactions with climate change.J.S. Vincent L. Academic Press.W. (Eds. Modelbased Decision Support in Agriculture. J. 161 .S. In: ten Berge.. Penning de Vries and P.. The effect of serial correlation on statistical inferences made with resampling procedures. Trouslard-Kerdiles V. Trends in upward shift of alpine plants. and Alexandersson H.Y. October 1997. P.. of Sustainable Tourism. Laon.. Tsuji G.. 399 pp. J Climate 11:1094-1104. M. Agriculture Canada. Explorative land use studies and their role in supporting regional decision making. Stein.. Kluwer Acad. Visual Numerics.. 2006..W. IMSL. A case of combined use of crop simulation models and general linear models. 99: 71-85. pp 35-46 Uehara G. Sci. the Netherlands. 1997. D. World Meteorological Organization (WMO).3. H. Rpt. Beissner.. Quantitative Approaches in Systems Analysis 15. 162 .Survey of agrometeorological practices and applications in Europe regarding climate change impacts . Mark Danson. The main variables that are collected in operational or experimental way are land surface temperature and NDVI. SATELLITE SPECTRAL CLIMATIC AND BIOPHYSICAL DATA FOR WARNING PURPOSES FOR EUROPEAN AGRICULTURE Leonidas Toulios. satellite data records. Some of the climate and biophysical variables essential for understanding and monitoring the climate system and the impact on agriculture can be efficiently observed from space since this technology enables their systematic. which must be addressed with global models and global data are needed as input to these models. series of observations over time that measures variables believed to be associated with climate variation and change. Zoltan Dunkel. To make these data useful for climate impact and warning studies. the contribution of Earth Observation (EO) data (satellite-derived data) for warning purposes in agriculture due to climate variability and change is discussed. Some examples of satellite images and satellite-derived products as referred in the questionnaire answers are also presented. Some of them are currently collecting satellite data for years and these data records could be useful for models for climate change impact studies. global and homogeneous measurement. were surveyed and collected among European countries. The analysis and the presentation in Tables of the data records which have been developed from operational satellite observations. but also on the national and local levels and the use of alert and warning systems must be based on such data. primarily for weather prediction. EO from space has a unique capacity to provide such global data sets continuously and consistently not only on this level. presents the status of satellite climatic and biophysical data for warning purposes for agriculture. based on a specific questionnaire.g. Climate and agriculture research is generally based on data collected for other purposes. Piotr Struzik.4. Among European countries there is a great unhomogeneity concerning climatic and biophysical data received from satellite sensors or collected as satellite-derived ready products. in Europe. it is usually necessary to analyze and process the basic observational raw data and integrate into models. e. Climate variability and change is a global issue. 163 . Evaluation of the agricultural impacts represents an important contribution for the assessment of vulnerability of agricultural systems to climate variability and change. Janos Mika. Satellite spectral climatic and biophysical data for warning purposes for european agriculture 4. Gheorghe Stancalie. In the frame of COST 734. Emmanouil Tsiros Abstract In this study. while conventional tillage and fertilizer use account for 70% of the nitrous oxides (FAO. forest fires. 2000).might become more frequent. Ciais et al. carbon dioxide. • Climate extremes . mainly deforestation and the burning of biomass. Climate change over the long-term. could affect agriculture in a number of ways . is produced by agricultural sources. • Climate variability might increase. 2000. Climate variability and change will add to these pressures and will make the challenges more difficult and costly.Survey of agrometeorological practices and applications in Europe regarding climate change impacts 4.. Agriculture is responsible for an estimated one third of global warming and climate change. On the other hand exceptionally wet years engender similar devastating effects on production. putting additional stress on fragile farming systems. Reliable seasonal forecasts of precipitation and temperature can have enormous positive economic impact for the global agricultural industry.. making planning of farm operations more difficult.1 Introduction 4. At the plant or field scale. threatening valuable coastal agricultural land. Most of the methane in the atmosphere comes from domestic ruminants. Climate change will impact agriculture by causing damage and gain at scales ranging from individual plants or animals to global trade networks. particularly in low-lying small islands. • The sea-level would rise. wetland rice cultivation and waste products. It is generally agreed that about 25% of the main greenhouse gas. The general consensus is that changes in temperature and precipitation normals will lead to adjustments in land and water regimes that will affect agricultural productivity.which are almost impossible to plan for . 164 . in particular global warming. The recent droughts in Europe caused billions of Euros in crop damage (Kogan.the majority of which would threaten food security for the world's most vulnerable people: • The overall predictability of weather and climate would decrease. 2005). 2001). Impacts and adaptation (agronomic and economic) are likely to extend to the farm and surrounding regional scales. (Sivakumar et al. climate change is likely to interact with rising CO2 concentrations and other environmental changes to affect crop and animal physiology.1 The need of adaptation of agriculture to climate change European agriculture will face many challenges over the coming years such as international competition and population decline with long-range economic impacts.1. Under a changing climate. The use of existing Community-supported information systems. Information and Communication Technologies (ICT) and their further developments will be a key instrument to support this adaptation process. The EU's research strategy places a strong emphasis on climate change. as well as threatening natural vegetation and fauna. e.though by nature more apparently dramatic. such as mangroves and tropical forests. Both climate variability and climate extremes may increase as a result of global warming.g. • Distribution and quantities of fish and seafoods could change dramatically. wreaking havoc in established national fishery activities. Updated synthesis reports on climate impacts. have less overall effect on agricultural production than chronic climate deficiencies. drought. and storms . forcing farmers to adapt. Long-term comprehensive and Europe-wide high-resolution datasets and models are needed. which in turn is one of the main factors behind food insecurity. adaptation and vulnerabilities should be produced based on the results from the Framework Programmes and national research. The natural variability of rainfall.violent and unusual events such as floods. The recent reforms of the Common Agricultural Policy (CAP) have been a first step towards a framework for the sustainable development of EU agriculture. information systems and networks should be improved. alert and warning systems must be encouraged and brought to full potential. • Climatic and agro-ecological zones would shift. the role of agriculture as provider of environmental and ecosystem services will further gain importance. both in terms of predictive capacity. Future adjustments of the CAP could provide opportunities to examine how to better integrate adaptation to climate change in agriculture support programmes and to promote good farming practices which are compatible with the new climate conditions and which contribute proactively to warning purposes in agriculture and to preserving and protecting the environment. temperature and other conditions is the main factor behind variability in agricultural production. modelling and adaptation strategies.4. Satellite spectral climatic and biophysical data for warning purposes for european agriculture Biological diversity would be reduced in some of the world's most fragile environments. • 165 . Access to existing climate. meteorological. Climate extremes . satellite and integrate data relevant for adaptation should also be improved. 2005). • The current imbalance of food production between cool and temperate regions and tropical and subtropical regions could worsen. Coordination between data centres. • Pests and vector-borne diseases would spread into areas where they were previously unknown (FAO. understanding and predicting climate variability and change is centred around providing the global scale observational data sets on the components of the climate system. and the interactions with the entire Earth system.Survey of agrometeorological practices and applications in Europe regarding climate change impacts enabling relevant. over large areas and at low cost (Fisher and Mustard. among others. radiation and vegetation health. in the frame of research actions. are collaborating to better understand seasonal variability of climate and apply that understanding to agricultural issues. Research methods. Earth observation has a unique capacity to provide such global data sets continuously and consistently not only on this level. but also on the national and local levels. their forcing. but includes developing and maintaining a modelling capability that allows for the effective use. include quantification and monitoring the state of the biosphere from satellites using remote sensing techniques. Space provides an ideal vantage point for the measurement of critical parameters for agricultural production. managing crisis situations and the assessment of possible impacts on agriculture. such as water availability. As new satellite measurements 166 . interpretation and application of the data. The ultimate objective is to enable predictions of change in climate on time scales ranging from seasonal to multi-decadal. The most important contributions that users of remote sensing data in impact studies of climatic change on agriculture. Climate change is a global issue.2 How the study on climate variability and change can benefit from space Substantial research is needed to achieve a better understanding of the dynamics of climate change affecting on farming conditions. Research organisations are collaborating to incorporate space-based measurements into models and systems used to monitor and forecast global and domestic agricultural production. which must be addressed with global models and global data are needed as input to these models. EU scientists. Remote sensing’ role in characterizing. 2007). can make to breaking down the barriers to the use of satellite – derived products is to provide very clear statements of their information requirements so that technology can develop to meet these requirements. 4. Earth observation from space provides satellite data that are necessary for the scientific community. flexible and speedy responses to the adaptation requirements for example for monitoring environmental changes. anticipating and assessing risks. Understanding these interactions goes beyond observations. the research community works to transition the observational capabilities to operational capabilities. soil moisture and other elements that play key roles in climate and agriculture are also studied. The Committee on EO satellites (CEOS) by the spatial agencies operates satellite that collect data from three domains. The European Space Agency (ESA) develops and operates EO satellites. 4. in collaboration with other space agencies and EO operators. Although almost all EO satellite systems were not specifically designed for climate monitoring.int/eo). 2004.org/pages/CEOSResponse_1010A. Accurate EO data is needed to describe climate and biophysical processes by improving the parameterizations of different elements. GCOS. but also takes an active part in ensuring that these data are effectively being used by institutional users (www. ESA thus contributes to the global efforts for providing needed data for checking the state of our Earth. Vegetation status. ESA does not only contribute to the provision of needed data.1 Making Sense of Satellite Data Space technology facilitates humanity and science with a global revolutionary view of the Earth through the acquisition of EO satellite data.esa.2. 2006) Algorithms to produce climate and biophysical parameters from raw satellite observations should go through selection processes or participate in intercomparison programmes to ensure performance reliability. Many of these satellite-derived products.ceos.4. difficulties in interpreting them and especially due to the lack of validations in operational activity. EO data should be able to detect changes of climate and biophysical elements that may be indicators of climate change. However. space agencies efforts have initiated a comprehensive climate data record that is forming the basis for better understanding the Earth’ climate system (GCOS. Satellites capture information over different spatial and temporal scales and assist in understanding natural climate processes and in detecting and explaining climate change. One area of study is clouds and aerosols. Satellite spectral climatic and biophysical data for warning purposes for european agriculture enable this capability. are occasionally or not at all used by potential beneficiaries due to a poor dissemination. which are monitoring the environment with many different instruments.pdf). particularly those on medium and large scales. oceanic and terrestrial. Within the quality management and 167 . and their role and effects on radiation. atmospheric. the so-called Essential Climate Variables (www. necessary to establish key Earth system parameters. org). ensuring continuity and reliability to access their data with minimal delay (www. global and homogeneous measurement. should pass a quality control before they are accepted in global databases for impact. by independent means. Validation is the process of assessing. 168 . 4. namely surface albedo (SA). and state variables of the radiative transfer problem.. Barnsley et al. A synergetic use of data from the satellite instruments may provide information on the two following categories of variables: variables controlling the radiative fluxes between the surface and the overlying atmosphere. Calibration and validation are activities that endeavor to ensure that remote sensing products are highly consistent and reproducible.Survey of agrometeorological practices and applications in Europe regarding climate change impacts systematic product evaluation context. 1999. Parameter datasets. Some of the variables essential for understanding and monitoring the climate system can be efficiently observed from space since this technology enables their systematic. obtained from satellite observations. 2002). namely the snow cover (SC) and biophysical parameters such as the leaf area index (LAI) and the fractional vegetation coverage (FVC).esa. and new satellite missions are launched.3 Satellite-based variables and models potential in monitoring of crop production The importance of systematic global observation for understanding climate change has been recognized by the global scientific community. Calibration is the process of quantitatively defining the system responses to known. Well-instrumented test sites and data sets for calibration should be supported. land surface temperature (LST) and soil moisture (SM). diagnostic or sensitivity studies. This is an evolving discipline that is becoming increasingly important as more long-term studies on global change are undertaken.ceos. ESA initiated several global-scale projects in order to transform satellite data into meaningful parameters that provide insight into climate change issues. the quality of the data products derived from the system outputs. The Committee on Earth Observation Satellites (CEOS) Program. controlled signal inputs. as well as a white charter on the “Data quality guide for satellite observations relevant to the GEOSS Program’s calibration and validation aspects” (www.int/eo). their calibration and validation is a major problem (Congalton and Green. to provide calibration information to supplement/substitute for on-board calibration. through the Working Group for Calibration and Validation established a strategy on calibrating/validating data provided by global observation satellites. particularly for land applications. 4. Soil-Adjusted Vegetation Index (SAVI). rainfall. 2004. digital elevation maps of the ice sheet surfaces. land surface temperature. or multiplied in order to yield a single value that indicates the amount or vigor of vegetation. 2007. 2001. A VI is a quantitative measure used to measure biomass or vegetative vigor. Vegetation indices have also been used for agricultural drought quantification and mapping. 2006.. Each of them was developed in order to evaluating vegetation characteristics and. VHI derived from a time series of NOAA/AVHRR ten-day images. 2008).. in some cases. Tsiros et al. 1994. Enhanced Vegetation Index (EVI).. divided. Bhuiyan et al. have been developed and tested for drought monitoring in several areas of the world with different environmental and economic resource (Kogan 1990. 2004). usually formed from combinations of several spectral bands. Domenikiotis et al. for 20 successive hydrological years (1981-’82 to 2000-’01) has been recently used for agroclimatic classification (Tsiros et al. Temperature Condition Index (TCI) (derived from CH4 data) and Vegetation Health Index (VHI) (a combination of VCI and TCI). correcting radiometric aberrations (e. Several vegetation indices were defined starting from the first simple ratio between infrared and red spectral channels (NDVI). Some example of vegetation products derived of satellite data are: • Vegetation Indices (VI). • Maximum greenness during the growing season. 1997. cloud cover. In particular Vegetation Condition Index (VCI) (an extension of NDVI). VI are the most simple approach to characterize vegetation parameters and for evidencing their spatial and temporal variation for phenologic and change detection studies. daily global albedo (the fraction of sunlight reflected back from the Earth). atmospheric distortion) and reducing the background effects in non-dense vegetated areas (Gitelson. 2004b. 1995. Houborg and Boegh. 2008). snow cover of both hemispheres. Lastly.g. Quattrochi and Luvall. Yao et al. Baret et al. This product represents the maximum value of the normalized difference vegetation index (NDVI) during the growing season. Satellite spectral climatic and biophysical data for warning purposes for european agriculture The most relevant variables that can be measured over land are: solar radiation. 2002.. 2000. Some of these variables are required as inputs to models designed to better understand and give an immediate view of climate change impact. whose values are added. 2005. fires and burnt areas. 2008. up to the more recent Soil and Atmosphere Resistant Vegetation Index (SARVI). as determined from the seasonal trajectory of the NDVI curve 169 .. 2004). LAI. vegetation indices. glaciers evolution and land cover (Huang. while RPAR is the ratio of the PAR absorbed by the green canopy only. between the two years in various regions (Ji and Peters. i.. Total greenness during the growing season. Aase et al.. Yang et al. +10°C. Gardner and Blad. The instantaneous green FPAR is integrated over the day with a weight equal to the cosine of the solar zenith angle to obtain the daily green FPAR presented in the map. Wiegand et al. 1996.gc.nrcan. 2001).. Fraction of Photosynthetically Active Radiation (FPAR). for the time during which surface temperature (obtained from satellite data) exceeded a temperature threshold (e. 2006).php). to APART. In the calculation of LAI from NDVI. This product is prepared using the NDVI and surface temperature data..ccrs.. 1992. Absorbed Photosynthetically Active Radiation (APAR). LAI is defined as half the total leaf area per unit ground surface area. 1986. different algorithms are used for different vegetation types (Holben et al. This product represents the area under the vegetation index (NDVI) curve for the growing season period. The images are able to show the amount and duration of chlorophyll in the two growing seasons and the differences..Survey of agrometeorological practices and applications in Europe regarding climate change impacts • • • • (www. The daily green FPAR can be used as a parameter to convert the daily-absorbed PAR to daily total incident PAR (Knyazikhin et al. Green FPAR refers to the fraction absorbed by green leaves only after the removal of the contribution of the supporting woody material to the PAR absorption.ca/optic/veg/index_e. 2004).. Li et al. litter. 1998. Errors in LAI estimation 170 .g. 1980. obtained from satellite measurements and corrected for atmospheric effects. correspond approximately to the average daily air temperature of +5°C). 1986. FPAR.e. Buermann et al. but includes the portion of PAR which is reflected by the soil/understory and absorbed by the canopy on the way back to space. and the fraction of PAR intercepted by the canopy.. soil. APART is the total PAR absorbed by all surface materials including canopy. Remote sensing of APAR has been achieved through estimation of downwelling PAR at the surface. FPAR is defined as the fraction of photosynthetically active radiation absorbed by a plant canopy. etc. PARd. Leaf area index (LAI). The units are 'NDVI days'. A new approach is proposed which defines APAR as the product of APART and RPAR. both positive and negative. It excludes the fraction of incident PAR reflected from the canopy and the fraction absorbed by the soil surface or the combination of forest floor and understory. APAR is the solar energy (400-700 nm) consumed by green canopy in the photosynthetic process. . reaching 5% percentage deviation between the predicted and the actual production. 2005b. Dabrowska-Zielinska et al.. remotely sensed metrics of vegetation activity have the following advantages: a unique vantage point. 2002. 2005a. DabrowskaZielinska et al. 2004. (2005b) used VCI for zoning areas of cotton production and developed empirical relationships between the VCI values and cotton production in Greece. Satellite spectral climatic and biophysical data for warning purposes for european agriculture are due to different factors such as the effect of vegetation canopy architecture. Bhuiyan et al. Vegetation indices derived from satellite data are widely applied in real-time heat and drought monitoring and is shown to provide quantitative estimation of duration and effect on vegetation and provide an indication of the final yield (Kogan 1990. and difficulties in cloud screening (Zhang et al. and a regular. 1996). 2005a.. 2006). and drought monitoring/assessment (Kogan. 1995. However.4.. White et al. thereby making them potentially better suited for crop monitoring and yield estimation than conventional weather data. Unfortunately. synoptic view. 2006). Vegetation indices derived from data from the Advanced Very High Resolution Radiometer (AVHRR) were also used for crop prediction. 2004. environmental monitoring.. Tsiros et al. at regional and country scale. Domenikiotis et al.. 2004a. In this sense. 1977. the satellite data are not always ideally suited for vegetation monitoring applications because of the lack of precise calibration. Crop monitoring and early yield assessment are important for agriculture planning and policy making at regional and national scales. 2004a. 171 . 2002.. Empirical relationships between the remotely sensed data and crop evolution and production estimates have been developed for monitoring and forecasting purposes since the early 1980s. most of these models are limited to specific regions/periods due to significant spatial and temporal variations of those variables. Domenikiotis et al. 1997). cost effectiveness. poor quality of geometric registration.. 2003). 2004b. 1990. Bhuiyan et al. Tsiros et al. 2005b. 2004b. In addition. which highlights the need for quantification of vegetation changes directly when monitoring climate impacts upon vegetation (Zhang et al. 2005). repetitive view of nearly the entire Earth’s surface. 2004a.. 1995. 1997. the limited network of stations and incomplete climate data make crop monitoring and yield assessment a daunting task (Kogan. 2004b. 2002.. Domenikiotis et al. Bouman et al. Furthermore... the meteorological data may miss important variability in vegetation production. Numerous crop growth simulation models are generated using crop state variables and climate variables at the crop/soil/atmosphere interfaces to get the pre-harvest information on crop yields (Monteith. GOES.noaa.Survey of agrometeorological practices and applications in Europe regarding climate change impacts 4. DMSP/SSM/I data).orbit.nesdis. DMSP SSM/I/ NOAAdata) (www. To integrate space research on global scale climate change. modeling. archived. The goal is to ensure that satellite data are processed.gov). it is needed to develop data bases with Climate and Biophysical Data Records from operational satellites. it is usually necessary to construct data records from data that span long time scales and sometimes from multiple data sources. Satellite data records are series of observations over time that measures variables believed to be associated with climate variation and change. Meteosat. sea ice cover. GOES.gov. GMS Visible IR imagery/NASA and NOAA data). clouds products (using POES/AVHRR.noaa. Scientists must characterize and quantify the sensor. vegetation indices NDVI and drought index (using POES/AVHRR data). 172 . GOES. diagnosing. water vapor (using DMSP SSM/I /NOAA data). GMS Visible imagery.3. understanding. Examples of climate data records which have been developed from operational satellite observations in USA are radiation budget (using POES and AVHRR /NOAA data). snow cover. atmospheric temperature (using POES/MSU /NOAA data). predicting. and assessing climate variation and change and possible impact on agriculture. clouds.gov. it is usually necessary to analyze and process the basic observational raw data and integrate into models. Meteosat. and precipitation ( using POES /AVHRR. www. Climate research is generally based on data collected for other purposes. has escalated significantly in the last decade. climate products like rainfall.nesdis. www. To make these data useful for climate studies. Meteosat and GMS Visible IR imagery. interannual.noaa. snow cover (using POES/AVHRR. to provide a framework for the use of data from existing and new instruments aboard satellites. and decadal to centennial) compared to the short-term changes that are monitored for weather forecasting.1 Climate and biophysical data records in responding to climate change impacts on agriculture The importance of understanding and predicting climate variation and change and avoid the impact on agriculture. primarily for weather prediction. These changes may be small and occur over long time periods (seasonal.ncdc. rain frequency. and distributed to users in a manner that is scientifically defensible for monitoring. Thus. spatial and temporal errors of these diverse and frequently large data sets in order to produce a sufficiently accurate time series for studying trends in climate variability and change. or in European or regional level. every 15 minutes. the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). climatology and the monitoring of planet Earth. including marine. for example. Satellite spectral climatic and biophysical data for warning purposes for european agriculture 4. thermodynamic and cloud physical parameters through dedicated spectral channels inherited from GOES and HIRS sounder instruments. dedicated high resolution imagery to monitor convective cloud evolution. The second satellite followed up in December 2005. fog and explosive development of small but intense depressions. heavy rain. enabling monitoring of rapidly evolving events. The main service provided by the METEOSAT system is the generation of images of the Earth. agricultural and aviation meteorology. high repeat rate of image data to observe rapid changes of clouds. which is useful for the weather forecaster in the swift recognition and prediction of dangerous weather phenomena such as thunderstorms. The main mission of MSG is the Imaging Mission and provides operational image data from the satellite's field of view. near real-time processing of SEVIRI raw imagery to geometrically and radiometrically corrected data. and the transmission of these images to the users in the shortest practical time. showing its cloud systems both by day and by night. plus atmospheric pseudo-sounding and thermal information. The first MSG satellite to be launched was METEOSAT-8. as well as. 2005) 173 . METEOSAT Second Generation (MSG) is the enhanced follow-on system to the previous generation of METEOSAT and includes a series of four geostationary meteorological satellites. The High Resolution Visible (HRV) channel has a resolution of 1 km. It provides particular support to now-casting applications through the following particular characteristics of the imaging mission: observation of air mass properties. with the emphasis on support to operational weather forecasting. However. MSG serves the needs of Nowcasting applications and Numerical Weather Prediction in addition to provision of important data for climate monitoring and research.4. the data are of use for all areas of this discipline. The full disc view allows frequent sampling. The MSG system has brought major improvements in these services through the 12 spectral bands of its radiometer. that will operate consecutively untill 2018. The data and services are mainly focused on the requirements of operational meteorology. SEVIRI delivers daylight images of the weather patterns with a resolution of 3 km.4 Satellite instruments for climate change management METEOSAT is the European contribution to the global observing system required for both meteorology and climatology. along with ground-based infrastructure. in 2002. which can lead to devastating windstorms (Lacaze and Bergès. either in-house or in collaboration with NASA (Shimabukuro et al. 1997). Many of these data are processed in response to specific requests from the scientific community who need long-time series climate records. regional. MSG satellites carry a comprehensive communications payload serving the needs of satellite operation. sea surface temperature. NOAA (www. Data and information from NOAA space-based and ground-based observing systems are used along with other climate-related NOAA and non-NOAA observing system data to construct long-term records regarding local. These functions include careful monitoring of observing system performance for long-term applications. NOAA scientists also produce a number of climate products. height and temperature. 2003). distributes. and the proper archival of and timely access to data and metadata.noaa. The NOAA operational satellite data program.Survey of agrometeorological practices and applications in Europe regarding climate change impacts EUMETSAT and the Satellite Application Facilities (SAFs) extract information from the processed SEVIRI data and turn it into products of particular use to meteorologists and climatologists. including the climate satellite products. data communication and user data dissemination. national. and archives data about climate. In addition to the SEVIRI. receives. aircraft and others in peril (www. and the Altimeters on board the TOPEX/Poseidon and Jason-1 satellites that can be used to monitor water levels in reservoirs around the globe. currently collects. the raw data and metadata are provided to external investigators such as those in academia or those involved in international projects. produces. NOAA maintains a comprehensive archive of climate-related data and information spanning the ice age to the space age. This package includes also the Search and Rescue transponder and relays distress signals from ships. 174 . In some cases.eumetsat. Besides these two instruments.gov) has statutory responsibility for long-term archiving of the environmental data and has recently integrated several data management functions. precipitation estimates and analyses of cloud coverage.. NOAA also operates the US's operational satellite observing system. Among other satellite systems used for climate studies are the Tropical Rainfall Measuring Mission (TRMM) that provides comprehensive data on precipitation at tropical and subtropical latitudes. the generation of authoritative long-term records from multiple observing platforms. the MSG satellites carry the Geostationary Earth Radiation Budget (GERB) instrument.int). which provides valuable data on reflected solar radiation and thermal radiation emitted by the Earth and atmosphere. which produce the climate data records. such as wind field diagrams.nesdis. and global climate variability and change (NOAA. 4. active scene measurements are recorded at equal intervals of 10 km (5 km for the 89 GHz channels) along the scan. Terra is the first EOS (Earth Observing System) platform and provides global data on the state of the atmosphere. Horizontally and vertically polarized radiations are measured separately at each frequency. the Measurements of Pollution in The Troposphere (MOPITT) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument. The five instruments onboard Terra include the Clouds and the Earth's Radiant Energy System (CERES). is at its daily minimum. The orbit is been adjusted so that it covers the complete Earth every 16 days. Satellite spectral climatic and biophysical data for warning purposes for european agriculture AMSR-E is the Advanced Microwave Scanning Radiometer for the Earth Observing System developed by the National Association of State Departments of Agriculture.The CERES instruments perform measurements of the Earth's "radiation budget. the spacecraft sub-satellite point travels 10 km. and particles in the atmosphere. TERRA (formerly EOS AM-1) is the flagship polar-orbiting satellite of NASA's Earth observing systems. .m.0 GHz). During a period of 1. 36.8. which obscures the land surface.9. The half cone angle at which the reflector is fixed is 47.65.MISR will measure the variation of surface and cloud properties. as well as their interactions with solar radiation and with one another. The critical components that affect the Earth's energy balance are the planet's surface. The AMSR-E is onboard the NASA AQUA satellite as a joint mission between Japan and the United States.9 to 89. 18. MISR will monitor monthly. . atmosphere. Because Terra emphasizes observations of terrestrial surface features.7. the Moderate-Resolution Imaging Spectroradiometer (MODIS). the Multi-angle Imaging SpectroRadiometer (MISR).5 seconds. aerosols and clouds. (ascending) and 1:30 a. land and oceans.4° which results in an Earth incidence angle (u) of 55. Even though the instantaneous field-of-view for each channel is different. The sensor has a Sun-synchronous track with equator crossings at 1:30 p. seasonal and longterm interactions between sunlight and these components of Earth's environment. Aqua was launched in May 2002 and is in a circular orbit of altitude 805km. Over a seven-minute period.0 GHz (6.m. points on the Earth within a 224 175 .5 and 89." the process that maintains a balance between the energy that reaches the Earth from the sun and the energy that goes from Earth back out to space. AMSR-E is a conically scanning total power passive microwave radiometer sensing microwave radiation (brightness temperatures) at 12 channels and 6 frequencies ranging from 6. with cameras pointed in nine simultaneous different viewing directions. 23.0°. (descending) local solar time. its orbit is designed to cross the equator at this time when cloud cover. 10. measures cloud properties.Survey of agrometeorological practices and applications in Europe regarding climate change impacts mile (360 kilometer) wide swath will be observed successively at all nine angles. Chen et al.The ASTER instrument.cr.gov/pub/imswelcome/)... the daily MODIS products are released 1–2 days after the image is taken. leaf area index). snow cover and evapotranspiration. oceans. clouds. cloud characteristics. For the 8-day or monthly composite products.The sophisticated Moderate Resolution Imaging Spectroradiometer (MODIS). provided by Japan's Ministry of International Trade and Industry. In general. land and ocean processes (including surface temperature of both the land and ocean).. . surface mineralogy. 2003). 2000). A valuable metric of vegetation production derived from this sensor are the satellite-based estimates of leaf area index (LAI). they can be downloaded from Earth Observing System data gateway (www. collects daily global data on vegetation condition.The MOPITT instrument. surface temperature. flown on both the Aqua and Terra satellites.. By examining the integrated CVII over the growing season. defined as the monthly contribution to anomalies in annual growth. a 1–2 weeks processing period is required. MODIS measures the atmosphere. The radiometric and geometric properties of MODIS provide a significantly improved basis for vegetation monitoring and yield predictions with remotely sensed data (Zhang et al. . motions and sinks of these gases can be determined. It views the entire surface (land. MOPITT is an infrared gas correlation radiometer and produces maps over the entire globe every 4-16 days. vegetation index. ocean color. etc) of the Earth every 1-2 days at a "moderate resolution" of onequarter to one kilometer. 176 . this LAIbased index can provide both fine-scale and aggregated information on vegetation productivity for various crop types. map carbon monoxide and methane concentrations at altitudes between 10 miles and the ground.g. this sensor seems well suited for near real-time crop monitoring (Doraiswamy et al. and snow cover. aerosols. Once the data have been archived. . 2006. Muchoney et al. Also. From these measurements the sources. 2006. global vegetation. The Climate-Variability Impact Index (CVII). The capabilities of MODIS present some exciting possibilities for improved and timely monitoring of crop production. provided by the Canadian Space Agency. temperature and moisture profiles.usgs. Leaf area responds rapidly to abiotic and biotic influences and the variability of LAI can integrate various conditions affecting plant growth and development. quantifies the percentage of the climatological production either gained or lost due to climatic variability during a given month. given the improved atmospheric correction and cloud screening and the high temporal and spatial resolution of the various MODIS vegetation products (e. education. is now operationally operated onboard SPOT 5 (http://vegetation. the most recent. and distribution. and global change research (Doraiswamy et al. geology. 2004). Geological Survey and the NASA to gather Earth resource data using a series of satellites.68 microns). filter and detect radiation from the Earth in a swath 185 km wide as it passes overhead. Every 1 to 2 days. and SW infrared (1. archiving. Landsat is the longest-running project for acquisition of moderate resolution imagery of the Earth from space. Terra uses the unique perspective from space to observe the Earth's continents.78-0. while the USGS is responsible for flight operations. NASA was responsible for developing and launching the spacecrafts. the Enhanced Thematic Mapper Plus (ETM+). Landsat systematically provides wellcalibrated. moderate resolution. processing and distribution are operational since April 1998. The Landsat 1 satellite was launched in 1972. Landsat 7 is a satellite designed for an Earth mapping orbit with a 16-day repeat cycle. The Landsat project is a joint initiative of the U. The ETM+ is designed to collect.4. regional planning. near infrared (0. at regional to global scales. The instrument and associated ground services for data archival.61-0.75 microns). The VEGETATION instrument is an imaging system in 4 spectral bands : blue (0. The instruments on the Landsat satellites have acquired millions of images.89 microns). red (0.S. multispectral. The first VEGETATION instrument is part of the SPOT 4 satellite and a second payload. surface temperature. mapping.cnes. substantially cloud-free.fr). These images form a unique resource for applications in agriculture. oceans and atmosphere with measurement accuracy and capability never before flown. forestry. digital images of the Earth's continental and coastal areas with global coverage on a seasonal basis. and management of all ground data reception.47 microns). maintenance. The red and near infrared are 177 . and surface topography for selected regions of the earth. VEGETATION 2. product generation. The LANDSAT satellite program provides operational data on agricultural production at the regional or local scale.58-1. processing.43-0. Hundreds of scientists are taken full advantage of Terra observations to address key scientific issues and their environmental policy impacts. Satellite spectral climatic and biophysical data for warning purposes for european agriculture soil properties. Landsat 7.. was launched in 1999. The VEGETATION Programme of the SPOT-4 and SPOT-5 Earth observation satellite is conceived to allow daily monitoring of terrestrial vegetation cover through remote sensing. Its payload is a single nadir-pointing instrument. The ETM+ provides for an eight-band multispectral scanning radiometer capable of providing high-resolution image information of the Earth's surface. Research indicates that many extreme year-to-year changes in local weather conditions are associated with changes in global-scale climatic phenomena. The range of agriculture applications include: crop monitoring. particularly wide (2 200 km on the ground) : this resolution is 1. continental and regional scales.1 .7 km on the sides of the field of view (101°).Remote Sensing.5 Status of satellite climatic and biophysical data for warning purpuses for agriculture. The contribution of the Working Group 2. in Europe The main objective of the Action 734 “Impacts of Climate Change and Variability on European Agriculture – CLIVAGRI” is the evaluation of possible impacts from climate change and variability on agriculture and the assessment of critical thresholds for various European areas. 4. The ongoing efforts to improve these models will lead to more accurate local weather and precipitation forecasts tailored to the needs of agricultural planning. VEGETATION uses telemetric optics giving a quasi constant spatial resolution through the field of view. The blue is design in this case to make atmospheric corrections. quick availability through internet.Survey of agrometeorological practices and applications in Europe regarding climate change impacts particularly well adapted to describe the vegetation photosynthesis activity. agricultural pest prediction and monitoring. improve decision support tools and optimise the actions of governments. crop production quantification. and strength of these climatic events. the VEGETATION system allows operational & near real-time applications. food aid agencies. at global. international financing &/or cooperation agencies towards food security. early warning systems to prevent food shortage. Data from these systems “feed” complex climate models developed to improve climate forecasts. while the SW infrared is a good detector for the ground and vegetation humidity. Thanks to its sensitivity to plant photosynthetic activity as well as to the vegetation and soils' moisture content. and still 1. duration.3 pixel). These NASA and ESA missions are helping the science community better understand the forces that drive global climate and weather. yields forecasting.15 km at nadir. Measurements of surface temperatures and biology are fed into models for predicting the timing. the systematic generation of 'ready-to-interpret' already corrected standard synthesis products. to the accomplishment of this main goal is the elaboration of a comprehensive study on the benefits of satellite remote sensing on climate change and variability 178 . its daily monitoring of the entire earth surface with an excellent geometric precision (local distortion better than 0. for the registration of the satellite data records existing in European countries. In parentheses the satellite systems and the instruments used for the data achievement are also presented. In order to describe the status of satellite climate and biophysical data that are used for warning purposes for agriculture. spectral and temporal resolution of satellite data for the climate change and variability impact study in agriculture. Among 25 countries signed the MoU of COST action 734. . ITALY (I). In ANNEX 1 a list of sample products of satellite images is presented. SLOVENIA (SL). FRANCE (F). More and more useful spectral information has been supplied to geoscientists from sophisticated space sensors with increasing detecting ability. CROATIA (CR). NORWAY (NR). SEVIRI on MSG. 18 countries answered to the questionnaire covering almost all parts of Europe. sensor development is the key to remote sensing techniques.1 the type of climatic and biophysical variable surveyed in each country is presented. .1 Questionnaire processed results In Table 4. 4. In this context the Table 4.Collection of spectral climate and biophysical data for several European regions. Satellite spectral climatic and biophysical data for warning purposes for european agriculture impact on agriculture. HUNGARY (HU). MODIS on TERRA platform. The research effort will be directed to the analysis of the role of satellite data in the suitable models and indices for assessing the impact of climate change and variability on European agriculture The key deliverables will be represented by: . This inventory was created through a questionnaire disseminated to the national delegates of COST 734 member countries. SWITZERLAND (CH) and UK. Because the quality of spectral information depends on its sensor characteristics. ROMANIA (RO). temporal. VEGETATION on SPOT. SPAIN (SP). in Europe. SLOVAKIA (SK). an initiative was started within COST 734 project.Determination of current trends of agroclimatic indices based on spectral data. GERMANY (DE). CZECH REPUBLIC (CZ). These countries are (in alphabetical order): AUSTRIA (A).Assessment of required spatial. GREECE (GR). spectral and 179 . BULGARIA (BG). .5.g.4.Determination of interannual variability of agroclimatic conditions based on spectral data.1 revels that the new generation of satellite sensors (e. AVHRR-3 on EPS/NOAA) have brought an upgraded level of remote-sensed information to the user community thanks to a much better spatial. SERBIA (SI). POLAND (PL). For example SEVIRI/METEOSAT data are extensively used to obtained climate and biophysical variable. condition index (VCI) (AVHRR/NOAA) Temp. Health Index (VHI) (AVHRR/NOAA) Cold Cloud Duration (CCD) (METEOSAT) Temperature Vegetation Dryness Index (AVHRR/NOAA) Surface temperature (AVHRR / NOAA) Evapotranspiration (AVHRR / NOAA) Degree days (AVHRR / NOAA) Cloud (snow) (AVHRR / NOAA) NDVI (AVHRR / NOAA) Surface Temperature (AVHRR / NOAA) Radiation budget (METEOSAT) Country A BG CZ FR DE GR (TVDI) HU 180 . namely within the scope of land surface processes and land-atmosphere interactions. condition index (TCI) (AVHRR/NOAA) Veget. The time resolution and global coverage provided by the new instruments.1: Type of climatic or biophysical data variables surveyed by country Variable Soil Moisture (METOP / EUMETSAT) Vegetation cover (TM / LANDSAT) NDVI (MODIS / TERRA) NDVI (MODIS / TERRA-AQUA) Surface temperature (MODIS / TERRA) Land cover (TM / LANDSAT) Snow cover (MODIS / TERRA) NDVI (AVHRR / NOAA) Surface temperature (AVHRR / NOAA) NDVI (VEGETATION / SPOT) LAI (MODIS / TERRA) NDVI (MODIS / TERRA) Land cover (TM / LANDSAT) Surface temperature (TM / LANDSAT) Evapotranspiration (TM / LANDSAT) NDVI (TM / LANDSAT) Land cover (ASTER / TERRA) Surface temperature (ASTER / TERRA) Evapotranspiration (ASTER / TERRA) NDVI (SEVIRI / METEOSAT) SAF products (METEO 5 / EUMETSAT) Land cover (SEVIRI / METEOSAT) NDVI (AVHRR / NOAA) Surface temperature (MODIS / TERRA) Veget. together with the extensive sampling in both the spectral and angular domains. Table 4.Survey of agrometeorological practices and applications in Europe regarding climate change impacts angular sampling of the radiative fields emerging from the surface of the Earth. paved the way for a broad spectrum of novel applications. TOVS / NOAA) Sea ice (AVHRR / NOAA) Sea wind (METOP) NDVI (AVHRR / NOAA) Cloud products (SEVIRI / METEOSAT) Rainfall (SEVIRI / METEOSAT) Solar radiation (MTP-HPI/ METEOSAT) Air stability (SEVIRI / METEOSAT) Storm detection (SEVIRI / METEOSAT) Temperature (TOVS / NOAA) Precipitation (TOVS / NOAA) Ozone content (TOVS / NOAA) SAF products (precipitation. soil moisture. Satellite spectral climatic and biophysical data for warning purposes for european agriculture NDVI (AVHRR / NOAA) NDVI (VEGETATION / SPOT) NDVI (MODIS / TERRA) Surface temperature (AVHRR / NOAA) Rainfall (MSG + MW) Rainfall (MSG + MW) Rainfall (GEO IR and LEO MW) Cloud products (METEOSAT) Cloud products (SEVIRI / METEOSAT Cloud products (AVHRR / NOAA) Precipitation (TOVS / NOAA) Air temperature (AVHRR. snow) (NOAA. AQUA) Cloud products (SEVIRI / METEOSAT) Precipitation (SEVIRI / METEOSAT) NDVI (SEVIRI / METEOSAT) Solar radiation (SEVIRI / METEOSAT) NDVI (VEGETATION / SPOT) MSAVI (VEGETATION / SPOT) Surface temperature (MODIS /AQUA-TERRA) Snow (MODIS / AQUA . METEOSAT. (SEVIRI / METEOSAT) Albedo (SEVIRI / METEOSAT) Solar radiation (SEVIRI / METEOSAT) Snow cover (SEVIRI / METEOSAT) NDVI (AVHRR / NOAA) I NO PL RO SK SL SP CH 181 .TERRA) Clouds (SEVIRI / METEOSAT) NDVI (MODIS / TERRA) Vegetation cover (MODIS / TERRA) Surface temperature (METEOSAT) Radiation (SEVIRI /METEOSAT) Albedo (SEVIRI / METEOSAT) Snow cover (SEVIRI / METEOSAT) Vegetation cover (SEVIRI / METEOSAT) NDVI (AVHRR / NOAA) Surface temp. TOVS / NOAA) Albedo (AVHRR.4. It is obvious that NDVI is the main biophysical variable that is recorded and used by most countries (12 countries).2 Type of data per country In Table 4. multi-sensor. and repeat cycles increase. On the other hand. These limitations may decrease in the future as spatial and spectral resolutions.2 the climatic and biophysical variables recorded at least in one country are presented. Among climatic variables the mostly used is surface temperature (11 countries). the diurnal and sub-diurnal sampling of thermal signatures by MSG. Such feature is particularly relevant over areas characterized by a high cloud occurrence as well as for semi-arid ecosystems having short vegetation cycles. however. is more limited at discrimination of multiple crops to this level of accuracy. the spatial characteristics of sensors mainly relate to events at the regional to continental scales. sea wind Biophysical variables NDVI MSAVI LAI VCI TCI TVDI Soil moisture Vegetation cover Land cover Evapotranspiration Degree days CCD 4. Remote sensing is also often unable to detect direct sources of crop damage. In a second series of the climatic variables are cloud products (6 countries). or GIS data is also used. In Table 4.3 the type of variable in order of the number of countries surveyed is presented. soil moisture. Remote sensing alone.5. afford solving the land surface temperature cycles. snow cover and radiation 182 . Availability of high temporal resolution from MSG are optimally suited to the measurement of environmental parameters that change rapidly in time as well as to those parameters where the signal change over time contains information about the parameter or the process of interest. Presently remote sensing is limited to mapping single crops to slightly higher than 90% accuracy when multi-date.Survey of agrometeorological practices and applications in Europe regarding climate change impacts MSG provides an image repeat cycle of 15 minutes offering new opportunities to detect short-term evolution of vegetation resources. snow) Air stability Storm detection Ozone content Sea ice. Table 4. However.2: Climatic and biophysical variables surveyed Climatic variables Surface temperature Precipitation Snow cover Solar radiation Albedo Cloud cover and other cloud products SAF products (precipitation. 4. Satellite spectral climatic and biophysical data for warning purposes for european agriculture (5 countries) land cover and precipitation (4 countries), SAF products (3 countries). Evapotranspiration and albedo follows, recorded for 2 countries and all the rest (Air-stability, Storm detection, Ozone content, VCI, TCI, VHI, TVDI, CCD, Soil moisture, MSAVI, LAI, Degree days, sea ice and sea wind) are recorded only in 1 country. The mentioned differences between different countries regarding the use of climate and biophysical variables can be explained by the fact that the high level products (like evaporation, soil moisture, storm detection, etc) require quite complex algorithms or schemes. The SAF products are not extensively used by many countries. In this respect, the 'Satellite Application Facility on Climate Monitoring' (CM-SAF), which started its operational activities in March 2007, will provide most valuable complex products in the near future. It has also to be mentioned that in many countries the assimilation of satellite data into crop growth simulation models is still in an experimental stage. Table 4.3: Type of variable in order of the number of countries surveyed VARIABLE NDVI Surface temperature Cloud products Snow cover Radiation Vegetation cover, land cover Precipitation SAF products Albedo Evapotranspiration Air- stability Storm detection Ozone content VCI, TCI, VHI, TVDI, CCD Soil moisture MSAVI LAI Degree days Sea wind and ice SUM OF COUNTRIES 12 (A, BG, F, DE, GR, HU, I, PL, RO, SL, SP, CH) 11 (BG, FR, DE, GR, HU, I, NO, PL, RO, SL, SP) 6 (I, NO, PL, RO, SK, SL) 5 (CZ, HU, RO, SL, SP) 5 (HU, PL, RO, SL, SP) 4 (A, BG, DE, SL) 4 (I, NO, PL, RO) 3 (DE, PL, SP) 2 (NO, SP) 2 (DE, GR) 1 (PL) 1 (PL) 1 (PL) 1 (GR) 1 (A) 1 (RO) 1 (DE) 1 (GR) 1 (NO) This table reveals that the most used satellite derived - biophysical variable is the NDVI. This vegetation index is still considered one of the most successful of many attempts to simply and quickly identify vegetated areas and their "condition" and it remains the most well-known and used index to detect live 183 Survey of agrometeorological practices and applications in Europe regarding climate change impacts green plant canopies in multispectral remote sensing data. In addition to the simplicity of the algorithm and its capacity to broadly distinguish vegetated areas from other surface types, the NDVI also has the advantage of compressing the size of the data to be manipulated by a factor 2 (or more), since it replaces the two spectral bands by a single new field. Nevertheless, it must be noticed that the NDVI has tended to be over-used in applications for which it was never designed. For example using the NDVI for quantitative assessments raises a number of issues that may seriously limit the actual usefulness of this index if they are not properly addressed. The NDVI should be used with great caution in any quantitative application that necessitates a given level of accuracy. All the perturbing factors (atmospheric soil effects, anisotropic effects and spectral effects) that could result in errors or uncertainties of that order of magnitude should be explicitly taken into account; this may require extensive processing based on ancillary data and other sources of information. More recent versions of NDVI datasets have attempted to account for these complicating factors through processing. The satellite-derived surface temperature (for land and sea), is also a broad used climate variable among the surveyed countries. Surface temperature is used in various agro-meteorological applications like: surface heat energy balance study, characterization of local climate in relation with topography and land use; mapping of low temperature for frost conditions or winter cold episodes, derivation of thermal sums (using surface temperature instead of air temperature) for monitoring crop growth and development conditions. Polar orbiting satellites in low orbit can provide much better spatial resolution and hence potentially more useful estimates of surface temperature than can other measurement methods. Table 4.3 shows that some variables like albedo, evapotranspiration, airstability, storm detection, ozone content, soil moisture, sea wind and ice are used by a much reduced number of countries. This can be explain by the fact that the procedures used to retrieve such variables are still in experimental phase and do not satisfy the users requirements related with accuracy, spatial or temporal scales etc. For example soil moisture is an important parameter for weather and climate prediction as well as for crop monitoring. Many efforts have been made for soil moisture estimation with space-borne sensors and insitu measurements. These approaches measure soil moisture at different spatial scales and each of them have certain advantages and limitations. Microwave remote sensing measurements can provide physical retrieval of soil moisture in low vegetation areas, but have poor spatial resolution. Optical/IR measurements can be used to retrieve soil moisture at high spatial resolution statistically, but limited to clear days. In spite of these results, presently soil moisture retrieval with satellites is still not operationally available. The new 184 4. Satellite spectral climatic and biophysical data for warning purposes for european agriculture generation of microwave remote sensing satellites (e.g. Terra SAR X) will provide soil moisture products in the near future. In Table 4.4 for each variable the type of the satellite instrument used is presented. This table reveals that SEVIRI/METEOSAT and AVHRR/NOAA are the most popular satellite sensors which provide climate and biophysical variables, among the surveyed countries. These two satellite systems are widely used by the European Meteorological Services, most countries having their own satellite reception systems. Table 4.4: Type of satellite / instrument per climate and biophysical product recorded Climate and biophysical product NDVI Surface temperature LAI MSAVI Cloud products Snow cover Radiation Vegetation / land cover Precipitation SAF products Air-stability Storm detection Ozone Evapotranspiration Soil moisture VCI TCI VHI TVDI Degree days CCD Albedo Sea ice Sea wind Type of satellite / instrument (MODIS / TERRA-AQUA, AVHRR / NOAA, VEGETATION / SPOT, TM / LANDSAT, SEVIRI / METEOSAT) (AVHRR / NOAA, TM / LANDSAT, ASTER / TERRA, MODIS / TERRA-AQUA, SEVIRI / METEOSAT) (MODIS / TERRA) (VEGETATION / SPOT) (SEVIRI / METEOSAT, NOAA / AVHRR) (MODIS / TERRA, SEVIRI / METEOSAT) (SEVIRI / METEOSAT) (TM-ETM / LANDSAT, ASTER /TERRA, SEVIRI / METEOSAT) (SEVIRI / METEOSAT, GEO / LEO satellites, TOVS/NOAA) (METEOSAT, NOAA, AQUA) SEVERI / METEOSAT) (SEVERI / METEOSAT) (TOVS / NOAA) (TM/LANDSAT,ASTER/TERRA, AVHRR/NOAA) (ASCAT / METOP) (AVHRR / NOAA) (AVHRR / NOAA) (AVHRR / NOAA) (AVHRR / NOAA) AVHRR / NOAA) (METEOSAT) (SEVIRI / METEOSAT, AVHRR / NOAA) (AVHRR / NOAA) (METOP) MODIS and ASTER onboard TERRA or AQUA platforms are preferred by a lot of the European countries due to easy accessibility via internet and because their improved spatial, temporal and spectral characteristics are appropriate for many agricultural applications. 185 Survey of agrometeorological practices and applications in Europe regarding climate change impacts In Table 4.5 the variables used operationally or/and experimentally are presented. NDVI, Surface temperature, Snow cover and Cloud products are used in both ways. The rest are used either operationally or experimentally. It is very interested that in countries with aerospace development there are no data records used operationally. Table 4.5: Operational or experimental use of data Operational NDVI (GR, I, PL, RO, SP) Surface temperature (GR, I, SP) Snow cover (SP) Cloud products (PL, RO, SK) Soil moisture (A) Precipitation (I, PL, RO) Solar radiation (PL, RO, SP) Air stability (PL) Storm detection (PL) Ozone content (PL) MSAVI (RO) Albedo (SP) Experimental NDVI (A, BG, FR, DE, GR, CH) Surface temperature (BG, FR, DE, GR, RO) Snow cover (CZ, RO) Cloud products (I) Vegetation cover (A) LAI (DE) Land cover (BG, DE) Evapotranspiration (DE, GR) SAF products (DE) VCI, VHI, TVDI (GR) Degree days (GR) CCD (GR) In Table 4.6 the variables that are used in models in each country are also presented. From this table it is obvious that only a few countries use spectral data in models. For example in Chech Republic MODIS/TERRA data are used for snow cover verification. In France NDVI and surface temperature calculated by AVHRR or SPOT/VEGETATION data are used experimentally in models for biomass, ETP and frost estimation. In Italy a few experimental data are used for rainfall and clouds estimation, and in Romania for LST evapotranspiration and snow cover. In Greece also all experimental data received are used in models. Table 4.6: The variables that are used in models in each country Variable / satellite system / country Snow cover NDVI Surface temperature NDVI NDVI, VCI, TCI, VHI, TVDI LST, ETP, DD CCD Precipitation Cloud products Surface temperature MODIS / TERRA AVHRR / NOAA AVHRR / NOAA VEGETATION / SPOT AVHRR/NOAA AVHRR/NOAA METEOSAT METEOSAT and GEO/LEO METEOSAT MODIS/TERRA (CZ) (FR) (FR) (FR) (GR) (GR) (GR) (I) (I) (RO) 186 4. Satellite spectral climatic and biophysical data for warning purposes for european agriculture A preliminary analysis shows that the countries involved in Cost 734 use very limited satellite data or derived products in models. 4.5.3 Detailed analysis of the data per country AUSTRIA : Biophysical and climate satellite data parameters are collected from three types of satellite systems: METOP-ASCAT satellite system, produced by EUMETSAT (for soil moisture, start 2008). LANDSAT satellite system, produced by NASA (for vegetation cover). MODIS satellite system, produced by NASA (for NDVI). Both in experimental use since 2005. Not yet assimilated to models. BULGARIA : Surface temperature and NDVI are collected from MODIS/TERRA and AQUA satellites experimentally since 2005. Land cover maps are produced in scale 1:100.000 occasionally from Landsat data. CROATIA : Satellite data records are not used in the Meteorological and Hydrological Service of Croatia for the agrometeorological purpose yet. Satellite data records are used only for the weather forecast with the satellite/instrument Meteosat 8/SEVIRI. CZECH REPUBLIC : Biophysical satellite data parameters are collected from one type of satellite system (MODIS Satellite system, produced by TERRA). The climate parameter is snow and it is covered experimentally since 2006, using a verification model. FINLAND : Climate satellite data parameters are collected from two types of satellite systems. AVHRR satellite system, produced by NOAA, and SEVIRI satellite system, produced by METEOSAT. From NOAA/AVHRR and SEVIRI/METEOSAT only cloud cover is collected operationally since 2000. 187 Survey of agrometeorological practices and applications in Europe regarding climate change impacts FRANCE : Climate and biophysical parameters are collected from two types of satellite systems. AVHRR satellite System, produced by NOAA and VEGETATION /SPOT satellite system, produced by VITO-Anvers. The collected data are NDVI and surface temperature, from AVHRR, and NDVI from SPOT/VEGETATION. AVHRR data were used experimentally during 1983-1998. Since 1998 0nly VEGETATION data are used as NDVI ready product for biomass estimation. The use of satellite data for agrometerological purposes has been an major item of research in France, especially at INRA with the support of CNES and the collaboration with CNRS, during about 20 years (from 1978 to 1998 approximately, mainly using Meteosat and NOAA-AVHRR). It has been proved that satellites were able to provide significant information for drought and frost mapping and to contribute to yield forecasting. The way for operational application has appeared however to be better in accordance with the European scale, and the expertise gained by the research team has been transferred to the team of MARS project in Ispra rather than at the national level. The mentioned satellite data (AVHRR, Meteosat, Envisat, GOES, Vegetation, MODIS), are received and processed in France by various institutions and laboratories, but are used for studies concerning the biosphere, and not strictly agriculture. Nothing appears to exist on an operational basis, and the research area in this field is still pursued in INRA, but on the more methodological aspect of assimilation into crop models, which is still limited to experimental studies. GERMANY : In Germany climate and biophysical satellite data parameters are collected from five types of satellite systems: MODIS satellite system, produced by TERRA, TM5 satellite System, produced by Landsat, ASTER satellite System, produced by TERRA, SEVIRI satellite system, produced by Meteosat, And Meteo 5 produced by Eumetsat. From MODIS, the parameters LAI and NDVI are collected experimentally since 2001. From LANDSAT, the parameters land cover, surface temperature, evapotranspiration and NDVI, are collected since 1992. 188 From MODIS. From METEOSAT satellite images are used for several products since 1995. Potential Evapotranspiration (ETp) is used for (P) and (N) monitoring. (P)=Past casting Model. Land Surface temperature and evapotranspiration are collected and calculated since 1981. Vegetation Condition Index (VCI). Before using LST as an input in ETp computation equation. GREECE : Satellite data parameters are collected from three types of satellite systems: AVHRR / NOAA. a good indication of the yearly production can be obtained ((F) model). BMVCI was calculated for the 1981-2001 time series and used in (F) models. Degree Days were collected only for 2004 and 2005. The potential evapotraspiration is calculated with the use of Blaney-Criddle method. MODIS / TERRA and METEOSAT. LST has been used in order to calculate ETp. NDVI has been extensively used for (P) and (N) monitoring and modeling. Finally SAF products are also collected from METEO 5/EUMETSAT since 2006. NDVI and land cover are collected since 2006. Satellite spectral climatic and biophysical data for warning purposes for european agriculture Using the ASTER instrument the parameters land cover. VCI and VHI have excellent ability to detect drought and to measure the time of its onset and its intensity. VHI. (N)=Now casting Model. surface temperature and evapotranspiration are collected since 2006. 189 . From SEVIRI. Surface temperature is used since 2004. CCD is experimentally used for precipitation (F) model and TVDI in (P) and (N) models for drought monitoring. Further development for production estimation is based on the VCI data which are proceeded with the Bhalme and Mooley Drought Index (BMDI) methodology. Temperature Condition Index (TCI). The first consists of 20 hydrological years (1981-2001) of ten-day composite raw images (for all channels) with 8x8 km spatial resolution and the second of NDVI and LST thematic maps (1x1 km) from 1998 until today. Two time series with different spatial resolution are used for the indices calculations. The droughtmonitoring VCI and VHI algorithm was developed for the 1981-2001 time series (8x8 km) and used for (P) and (N) modeling. From AVHRR the parameters NDVI. duration and impact on vegetation. Developing empirical relationships between the VCI values of the critical ten-day periods of the growing season and the yield. Specific comments for models: Abbreviations: (F)=Forecasting Model.4. LST has to be converted to air temperature using area specific models. The results are archived in full MSG resolution (3x3km in sub satellite point). the SAFNWC/MSG program package for a big Central European region is used. typically twice a day: one at night and on during the day. cloud type. and Shortwave and longwave incoming radiations at the surface. NDVI and albedo maps are archived. probability of precipitation. pressure. for the Charpathian Basin region with a spatial resolution of 2 km. NDVI and albedo have been calculated since 1996 and 1999 respectively. cloud top height. The images are processed every 15 minutes and archive the results (for example: the cloud mask. and choose the maximum value of 10-day periods. From the daily data. Land surface temperature (LST) is calculated (for cloud free area) using NOAA/AVHRR 4 and 5 channel data. The corresponding quality flags are also archived. An example of a 10-day composite image is shown in Fig 1. Cloud (snow) mask is computed using all channel data. albedo. VEGETATION/SPOT and TERRA/MODIS. adiation budget components were calculated from the METEOSAT first generation (using NOAA/AVHRR data to estimate the albedo) at 10 km resolution. from AVHRR/NOAA. from one operational NOAA satellite data.Survey of agrometeorological practices and applications in Europe regarding climate change impacts HUNGARY : From locally received NOAA/AVHRR data a subimage of 1024x1024 pixels containing Hungary is processed with programs developed in Hungary. 18) data. Data are transferred on a stereographic map. The SAFNWC/MSG program package is being developed by an international working group. typically once a day in the afternoon. Surface temperature. from AVHRR/NOAA. Atmospherically corrected NDVI and albedo are calculated (for cloud free area) using NOAA/AVHRR 1 and 2 channel data. in full resolution. 10-day composite NDVI. as well as daily LST maps are calculated. from NOAA 14. 190 . he daily data at original AVHRR resolution and the 10-day LST. ITALY : Climate and biophysical parameters are collected from several types of satellite systems: NDVI. 16. convective rain rate etc). Shortwave and longwave net radiation. in full resolution. o process the METEOSAT-8 (METEOSAT Second Generation) data. Using these data the following variables were calculated from 2004 until July 2006: Net radiation. temperature. 16 and presently from 18 satellite data. LST has been calculated since 1996 from one operational NOAA satellite (NOAA14. Fig 2 is an example. only daytime. convective rainfall rate. TOVS data. dew point temperature. From Ocean and Sea Ice SAF • Sea Surfach Temperature (SST). wind speed and direction. NORWAY : No satellite data is at the moment used for agro meteorological purposes by the Norwegian Institute for Agricultural and Environmental Research.LST – Land Surface Temperature. . These data the Norwegian Meteorological Institute get from different satellites. Agricultural crops are not included in the operational registrations. from MSG+MW satellites (produced by Italian National Met Service and LaMMA) and GEO IR and LEO MW satellites (produced by NASAGSFC) through the Global Precipitation Climatology Project (GPCP). 191 . precipitating clouds. .DSLF – Downwelling Longwave Radiation. And cloud products from MSG satellites (produced by ISAC-CNR). • Solar Shortwave Incoming Radiation at the ground (SSI). operationally.4. Calculation of the surface temperature of the tops of the clouds are provided for operational use every day. sea ice. geopotential height. Other products related to climate and agro are received but final product is still in experimental phase: From Land SAF: .DSSF – Downwelling Shortwave Radiation. NOAA/AVHRR. olar radiation at the surface is calculated with METEOSAT (MTP) HRI data. At the NORUT institution satellite data are used for getting information connected to the cover of natural vegetation. total precipitation and total ozone content are calculated with NOAA/TOVS data. total precipitation and storm detection. METEOSAT/SEVIRI data are used for cloud type and mask. Satellite spectral climatic and biophysical data for warning purposes for european agriculture Rainfall. air temperature and albedo are calculated locally using METEOSAT/SEVIRI. Cloud products. sea wind. but also some data they get directly from satellites. Some of the data they get on the Internet. At the Norwegian Meteorological Institute it is developed software using satellite data for calculating the surface temperature of the earth. The use of these data is mainly connected to weather forecasts of short temporal scale. POLAND : The main climate and biophysical parameters that are collected from satellite systems are: NDVI with NOAA/AVHRR data. arameters like distribution of temperature. air stability. From AQUA-TERRA/MODIS system. Sea Ice – extend. he data referred above are not yet assimilated into models. the National Meteorological Administration . running the NWCSAF software on a SUN system. soil Moisture products and snow products. Development phase of HSAF will be finished in 2010 and at this time or earlier presented products will be operational. NDVI and solar direct radiation. It actually receives stores and process all the HRIT and LRIT data dissemination formats. Precipitating Clouds.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Downwelling Long Wave Radiation at the ground (DLI). precipitating clouds. The National Meteorological Administration operationally receives MSG data and SAF products through EUMETCAST. In Romania. From SPOT 5/VEGETATION satellite system. Downwelling Long-wave radiation flux) are ready products received through EUMETCAST. Wind Speed and Direction over sea. Cloud Top Height & Temperature. Since 2004 Romania has become Cooperating States of EUMETSAT. The NWCSAF products (Cloud Mask. concentration. all 12 channels being available. respective a developed internally software. In the frame of H-SAF activities (Satellite Application Facility in support to Operational Hydrology and Water Management) new satellite products for hydrology are at the moment at experimental status. type. Convective Rainfall Rate. The LandSAF products (Downwelling Short-wave radiation flux. are calculated operationally. cloud top height & temperature. Cloud Type. From EUMETSAT MPEF (Central processing): .Multisensor Precipitation Estimates.Satellite Department benefits by the direct reception of digital High Resolution Imagery Transmissions data from METEOSAT SECOND GENERATION station which has been provided by the VCS-Engineering. the parameters: Land surface temperature and daily snow cover are experimentally calculated. decadal NDVI synthesis. Total Precipitable Water ) and NDVI product are locally obtained from the MSG real-time data. convective rainfall rate. ROMANIA : Biophysical and climate data for agrometeorological use are collected from three types of satellite systems: From MSG-1/SEVIRI satellite system (produced by EUMETSAT) the parameters: Cloud mask. cloud type. There is also interest for precipitation products. color composites and MSAVI synthesis are calculated operationally. MSG HRIT raw data is processed. • • • 192 . total precipitable water. and for SPOT VEGETATION . downwelling Surface Longwave Radiation Flux. SLOVAKIA : Only clouds are calculated operationally from SEVIRI/METEOSAT. a highly advanced ground based weather radar system and a long history of meteorological measurements through the UK Meteorological Office. downwelling short-wave radiation flux and snow cover are used recently as ready products from MSGSEVIRI satellite system. since 1993. the down-welling surface short-wave radiation flux. since 2006. These data. Satellite spectral climatic and biophysical data for warning purposes for european agriculture The time intervals of the data archives for MSG . snow Cover. Among climate variables. NHMI is or will be focal point for this kind of data. land temperature.4. since 2006 experimentally. produced by EUMETSAT (land-SAF).derived products from 2004 to present. albedo. I think that the main reason for this is that the UK has a very well developed and extensive network of meteorological stations. LandSAF in the frame of METEOSAT-MSG data are used for land Surface Temperature. and fractional Vegetation Cover calculation. UK : Researching the use of satellite data records for agrometeorology in the UK. the main conclusion is that there is really very little evidence of operational use of satellite data for this purpose. experimentally. from AVHRR/NOAA data. SLOVENIA : NDVI and vegetation cover are the biophysical parameters that are collected from MODIS instrument on board TERRA/AQUA. SPAIN : NDVI is used operationally as a ready product. SWITZERLAND : Only NDVI is used experimentally. Data are collected from AVHRR/NOAA by a local receiver. using a local receiver. SERBIA : The country is not member of EUMETSAT. surface albendo.derived products covers the period 2005 to present.. along with data from MSG are used for weather forcasting of course and weather data are highly accessible for agrometeological 193 . The same variables are calculated also from MODIS/TERRA and used operationally in EU since 2002.infoterra. SPOT-Vegetation and TERRA-MODIS (as indicated in the table) as well as LandSAF products of MSG-SEVIRI. SAVI. SAVI. These data are used by farmers to aid in-field crop management (http://www. MODIS data are only used experimentally in UK based applications to date. The link to JRC online portal (ImageServer). All data are provided by VITO.co.Survey of agrometeorological practices and applications in Europe regarding climate change impacts operational applications or for research. fAPAR and DMP are calculated also from SPOT/VEGETATION and used operationally in european scale since 1998. The variables NDVI. (www. which gives an overview of the data that are available is: http://cid.uk/applications_land_farmstar. Landsat TM and ETM. and SPOT HRVIR data are used for land cover mapping but this is only done overey 5-10 years and is of little relevance to agrometeorology.ac. The application of medium spatial resolution satellite data is more widespread for experimental agrometeorology. The data comprise the satellites NOAA-AVHRR. fAPAR. A second reason is the high frequency of cloud cover in the UK which prevents routine surface observations being obtained.The variables NDVI. 194 . The use of MSG. AVHRR data are received operationally at the Dundee Satellite Station and some operational data processing for coastal and marine applications is carried out.dundee.sat. but again the cloud cover problem is a major limitation for multi-temporal studies or for operational applications. and is certainly not operational.jrc. There are no examples of operational processing of AVHRR data for agricultural applications in the UK. AATSR or MODIS for land surface temperature measurements does not appear to have been a major research area for applications in the UK.it/idp/thematic-portals/mars-statimageserver/.uk). which are already contained in the table and therefore did not added again. DMP and TS are calculated from NOAA/AVHRR data and used operationally in EU scale since 1981. This system delivers field maps to farmers. JRC : In the questionnaire table are presented RS data that are currently available or processed in the framework of JRC activities.An example of an operational systems for agriculture is Farmstar which is a crop management tool from Infoterra. based on medium spatial reslution satellite data (primarly SPOT).This is a narrow range of examples but I believe that it does reflect the current picture of satellite applications for agrometeorology in the UK.php). and AVHRR. maize. 195 . Some of them are currently collecting satellite data for years and these data records could be useful for models for climate change impact studies. Additional information on crop stages will allow calibrating relationships with remote sensing seasonal characteristics as derived from satellite products time course. preventing from accessing the seasonality and associated phenology of the surfaces. most of the validation activities correspond to ‘one shot’ ground measurements. cotton. based on the specific questionnaires received from the COST 734 National delegates. Some efforts are currently made by the remote sensing community to provide such products from current medium spatial resolution satellite observations operationally available. MODIS. Among European countries there is a great unhomogeneity concerning climatic and biophysical data received from satellite sensors or collected as ready products.4. and others. This could also allow further exploitation of the measurements with regards to climate change impact and its simulation/estimation via crop models. The analysis. Satellite spectral climatic and biophysical data for warning purposes for european agriculture 4. although most of the information on vegetation functioning lies in its dynamics. This direct validation should be based on continuous ground measurements in order to access the phenology of the crops. sugar-beet. the products are currently poorly validated because of the lack of ground measurements. over a given continent such as Europe. shows a general interest in using satellite climate and biophysical data and products to better understand how climate affects crop growth and yield. It is needed to develop a network of sites across Europe to contribute to the validation of the satellite products specifically for agricultural areas. including VEGETATION. However. The validation should be based on the development of a network of ground measurements sites. The main variables that are collected in operational or experimental way are land surface temperature and NDVI. sunflower.6 Conclusions Increased availability of satellite sensor data is resulting in an increase in satellite sensor data and products use for warning purposes in agriculture due to climate variability and change. A meteorological station should be close to each site. In addition. edaphic and cultural practices observed over Europe. The main crops should be sampled and could include: wheat. Climatic and biophysical variables are very relevant for monitoring vegetation status and predict the possible impact on crops. to possibly link development stages with phenological models. This is even worth with regards to a given type of vegetation such as crops. It should be designed to sample the climatic. sunflower. and warning purposes for agriculture. rapeseed. M. Singh.A. T. Hagolle. van Keulen. O. 110 (3). MOD43 Validation: UK Activities. J. Barnsley M.8 References Aase J. F.pdf (accessed 2007-02-14). 52 (2). and if it is sufficient to produce reliable findings and to draw reasonable conclusions about climate change impacts on agriculture. M. J. 4.N. Zeng.L. Myneni. G. X. G.E. Kogan. Rabbinge. R. Weiss. In the next phase of our action (COST 734) it will be investigated how it is possible.S. using existing satellite data. Disney.M. M. Bouman B.P. H. CEOS/WGCV Land Product Validation Workshop on Albedo. 24..7 Acknowledgements COST 734 WG2. The development of quality satellite data records is key and that a program that focuses on the development. R. fAPAR and fCoverCYCLOPES global products derived from VEGETATION.. M. Boston University. pp 171-198. Baret F.P. P. LAI. pp 685-692. Muller. and distribution of satellite data records in Europe will be necessary to meet the needs of the science community. Millard. 14 (17). J. 1986.bu. R. Available at www-modis. Quaife1. J. International Journal of Applied Earth Observation and Geoinformation. Chen P. P. B. H. Buermann W. Journal of Climate. 2002. 4. J.H.1. The 'School of de Wit' Crop Growth Simulation Models: A pedigree and Historical Overview. Rebelo. Roujean. Lewis. P. Bhuiyan C. and long delivery times make it non operational at this time.. MODIS-NDVI and VEGETATION-NDVI Composite Data Using Agricultural 196 . M. L. Huc. retention.edu/brdf/barnsley. to produce long-satellite records and adapt observations designed for weather prediction to climate issues in an ad hoc way. J. Roberts. too coarse spatial resolutions. October 2002.. Geiger. F. Spectral Radiance Estimates of Leaf Area and Leaf Phytomass of Small Grains and Native Vegetation. Brown.G. the limitations of fixed spectral bands. Leroy. Dong. inadequate repeat cycles. pp 275-286. G. B. pp 289-302. Assessment of MODISEVI. Fedosejevs. 8. members acknowledge all COST 734 colleagues and all country associates for their help in collecting the data and answering the questionnaires. Evaluation of the Utility of Satellite-Based Vegetation Leaf Area Index Data for Climate Simulations. 2006. van Laar. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data.Y. 1996. pp 3536-3550. Bicheron. Shaw. Dickinson. Tiscareño-LóPez.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Although remote sensing from satellites has high potential for providing spatially variable data needed for precision agriculture. Thackrah. Agricultural Systems.. Berthelot. 2006.. Hobson. O.K. Samain.P. Fernando Niño. B. B. R. 2001. 2007.B. Miras. Remote Sensing of Environment. IEEE Transactions on Geoscience and Remote Sensing. Arnold. Spiliotopoulos. E. 23-26 May 2005. N. FAO. Greece. M. Prueger. 97. Tsiros. Domenikiotis C. 2007. and Mustard J. 2004a. Domenikiotis C. 2001. Akhmedov. pp 119-132. Hatfield.R. Ciais P. 197 . Tsiros.. N. Ciolkosz. Remotely Sensed Estimation οf Annual Cotton Production Under Different Environmental Conditions in Central Greece. Remote Sensing of Environment. Dalezios 2005a. Dalezios. Supplemental details to the satellite. 109 (3). and Green K. Greece. T. pp 1109-1123. Akhmedov. Satellite spectral climatic and biophysical data for warning purposes for european agriculture Measurements: An Example at Corn Fields in Western Mexico. Dalezios 2005b. Numbers 1-3 . Remote Sensing of Environment. N. 30.C. Domenikiotis C. Pests and Diseases on Food Security and Poverty Reduction. T. Spiliotopoulos. Kowalik. Greece. Domenikiotis C. Dabrowska-Zielinska K. M. Fisher J. 90 p. J. Application of NOAA/AVHRR VCI for drought monitoring in Thessaly. FAO. N.R. A. Issue 4. Kogan. Modelling of crop growth conditions and crop yield in Poland using AVHRR-based indices.. pp 183-193. F. S. J. 20. A. E. Zoning of cotton production areas based on NOAA/AVHRR images. et al. 1338).R. Evaluation of Spectral Reflectance Models To Estimate Corn Leaf Area While Minimizing the Influence of Soil Background Effects. E. 2004b. pp 192–202. GCOS-107 (WMO/TD No. 2005. M. B. Remote Sensing of Environment.. Domenikiotis C. Rome. Early Cotton Yield Assessment by The Use Of The NOAA/AVHRR Derived Drought Vegetation Condition Index In Greece.. Spiliotopoulos. Implementation plan for the Global Observing System for Climate in support of the UNFCCC. GCOS. Congalton R.J. 1219).I. B. Jackson. Stern. Remote Sensing of Environment Volume 92. A.. Rome. 69 – 82..N. International Journal of Remote Sensing. and MODIS data. Doraiswamy P.. Landsat.R. Special event background document for the 31st Session of the Committee on World Food Security. pp 261-273.. N. M. Prueger. 7-9 November 2003.G. Tsiros. Crop condition and yield simulations using Landsat and MODIS. 136 p. M. International Journal of Remote Sensing. pp 1663-1670. Washington D. W.. E. 25. pp 529-533. Tsiros. Sinclair. 2006. 1999. Dalezios. Satellite-based products for climate. Spiliotopoulos.4. International Journal of Remote Sensing 23.1007/s10661 – 005 – 9006 – 7. Application of MODIS derived parameters for regional crop yield assessment. pp 28072819. 1986. 2002. 25 (23). pp 45-52.R. Assessing and accuracy of remote sensed data: principles and practices. DOI 10.. Doraiswamy P. M. 2004. Gruszczynska. Physics and Chemistry of the Earth. Tsiros. Early cotton production assessment in Greece based on the combination of the drought vegetation condition index (VCI) and Bhalme and Mooley drought index (BMDI).F. pp 548-559.C. 1-5 July 2002.. 6th International Conference of Protection and Restoration of the Environment. Volume 119. pp 5373-5388. Volos. 2002. Lewis Publishers. Cross-scalar satellite phenology from ground. GCOS. Dalezios. Stern. 2006. Skiathos. 30 September 2004. Hollinger. Committee on Agriculture report "Climate variability and change". Gardner B. Impact of Climate Change. Spiliotopoulos. J.R.R. 2005.based component of the “Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC”. Nature. Report GCOS – 92 (WMO/TD No.C. E. 437. Environmental Monitoring and Assessment.. International Symposium in GIS and Remote Sensing: Environmental Applications. 2004.L. and Blad B.L. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. L.N. IEEE. Am.. Contribution of Remote Sensing to Drought Early Warning. (6-7).. 1998. Journal of Plant Physiology. World Meteorological Organization. The bidirectional effects of AVHRR measurements over northern regions. pp 651-656.. 6. 2000.drought. 281.J.. 2004. Martonchik. Geoscience and Remote Sensing Letters. 2005. Portugal. J. Ly. Soc.. Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation. 1990.. M. Borak. Geneva. Strahler. 2005. and Boegh E. pp 1949-1964. September 7th to 9th. Tucker.B.. pp 621636. Am.. 161 (2). 1. Kogan F. 2000. Holben B. J. Earth Observation Magazine. Germany. Proceedings of the International Conference: Remote Sensing and Geoinformation Processing in the Assessment and Monitoring of Land Degradation and Desertification: State of the Art and Operational Perspectives. IEEE Transactions on Geoscience and Remote Sensing. Morrow. LMU Munchen: Faculty of Biology (http://edoc. Hodges. Chi.. pp 32257-32275. C. J. Diner. D. Trans. Kogan F. Climate and the efficiency of crop production in Britain. Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance...html). C.. pp 1417-1434. Lisbon. A. pp 3-6.unl. Zheng.A.N.N. 1977. Res. September a. Li Z.N. Ser. Friedl. Bull.J. 198 . Huang S.ub. Meteorol. Int. Remote Sensing of Environment. 86–100. and Peters A. Switzerland (http://www.N. pp 1115-1138(24). 2004. 1994. Photogrammetric Engineering and Remote Sensing. Trier.. 112 (1).N. H. Operational space technology for global vegetation assessment.. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bull. Gopal... pp 18-21. NOAA plays leadership role in developing satellite technology for drought watch. Dissertation. 46. Kogan F. 1997.C. 103. N. 1996. Kogan F. S.de/3483). Fan..edu/monitor/EWS/EWS_WMO. J. Philos. pp 277-294. 2001. J.J. Myneni. Forecasting vegetation greenness with satellite and climate data. Geophys. S.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Gitelson A. J. X. International Journal of Remote Sensing.. pp 186-202. Contribution of Meteosat Second Generation (MSG) to drought early warning. WMO/Technical Document N° 1037. pp 655-668. Lond. R.N. pp. Remote Sens. 78. 21. The potential of multi-sensor satellite data for applications in environmental monitoring with special emphasis on land cover mapping. 1995. Remote sensing of weather impacts on vegetation. Global drought watch from space. Houborg R. 82 (9). Cihlar. R. Kogan F. Bulletin of the American Meteorological Society.unimuenchen. H. Moreau. In “Early Warning Systems for Drought Preparedness and Drought Management”. Ji L. Kogan F. desertification monitoring and fire detection. B. Monteith J. Spectral Assessment of Soybean Leaf Area and Leaf Biomass. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. Lacaze B. Proceedings of an Expert Group Meeting held September 5–7. 76. Muchoney D.. Meteorol. 2000. 2008. and Bergès J. Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America. 1980..V. Soc. pp 165-173.J. Soc. L. Runing. Knyazikhin Y. L. Yao Y. Cycles. B. 18-20 September 2008. E. Use of NOAA/AVHRRbased vegetation condition index (VCI) and temperature condition index (TCI) for drought monitoring in Thessaly. 1997. Rudorff. specifically outlines NOAA's roles and responsibilities for providing quality climate data. M.. Thornton. Identification of Water Limited Growth Environment Zones Using NOAA/AVHRR Data. Zhang P.E. Multisite Analyses of Spectral-Biophysical Data for Wheat. Thermal remote sensing in land surface processing 440 p.V. D20105 doi:10.informaworld. Huang. Comparison of seasonal and spatial variations of leaf area index and fraction of absorbed photosynthetically active radiation from Moderate Resolution Imaging Spectroradiometer (MODIS) and Common Land Model.F. 2004b. IEEE Transactions on Geoscience and Remote Sensing.. P. C. Kanellou. Quattrochi D. Gao. 2004. R. Schaaf. Lavoisier. 1997. Shimabukuro Y. C. Spiliotopoulos. 2008. Friedl. H.. Zhang X. A. Tsiros E. Aase. vol. 103 (2000). M. 4th International Conference on Information and Communication Technologies in Bio and Earth Sciences HAICTA 2008. Hatfield. Anderson.org/pages/CEOSResponse_1010A. 11.A. D. M. Global Biogeochem.J. Journal of Geophysical Research. Athens.. J. Analysis of Leaf Area Index and Fraction of PAR Absorbed by Vegetation Products from the Terra MODIS Sensor: 2000–2005. F. N.C.1029/2004JD004720. Q. LAI retrieval and uncertainty evaluations for typical row-planted crops at different growth stages. Zhang. Monitoring vegetation phenology using MODIS. B.v1818 (3).ceos. N.4. pp 1-21..L. pp 94-106.C. 109. R. Greece. 112 (1). A continental phenology model for monitoring vegetation responses to inter-annual climatic variability. Yu. Paris. D. Huang.. B. Hodges. 2004a. Agrometeorology and sustainable agriculture.L. Jackson. Shabanov. pp 471–475.D. Li.B.int/eo www. Zhang P. 2005. Greece (submitted). Dalezios. Tan.eumetsat.. R. Kanemasu. 2004. 109.C. Wiegand C. Tan. S. Agricultural and Forest Meteorology. Turkey. N.T. 132 (3-4). Wu. Huete. Carvalho. Myneni. Domenikiotis.esa. pp 344-358. Agricultural and Forest Meteorology. R.C. 2-4 September 2004. Myneni. J. Reed. pp 11-26. Izmir.A. Tan.com/smpp/title~content=t713722504~db=all~tab=issueslist~ branches=18 . C. White M. Running.F.T. B.H. Stroeve. Remote Sensing of Environment 42.C.A. B. Baier. 2006. www. Environ. X. http://www.. Anderson.. W. NOAA's Strategic Plan for FY2003-2008 and Beyond March. Tsiros E. 2003. 84. S. 2000. P.R. Friedl. pp 671-677. 1992.J. Liu. Remote Sens. W. and Luvall J.. Sivakumar M. Potential monitoring of crop production using a satellite-based Climate-Variability Impact Index.int 199 . V. pp 217-234. New Priorities for the 21st Century. Maas. Dalezios. pp 1829-1842. Barlow.W.K. R.E. pp 769-782. B. EWRA Symposium on water resources management: risks and challenges for the 21st century. A. Yang W. Satellite spectral climatic and biophysical data for warning purposes for european agriculture NOAA. Gommesb.org www. M. M. Climate related vegetation characteristics derived from MODIS LAI and NDVI. 2008. Strahler. NOAA-AVHRR data processing for the mapping of vegetation cover. Pinter Jr. D01103. 2003.K. P. J. 44 (7). B. J. J. Geophys..ceos. Zhang X. 2003.. Domenikiotis. E. Remote Sensing of Environment.. Res.. Lapitan.V.pdf www.R. Shaikh. International Journal of Remote Sensing. 2: The 2003 Heatwave at the European scale (July 2003/July 2002). 4.orbit.gov/pub/imswelcome/ http://vegetation. 4. NOAA/AVHRR 10-day composite image of 1-10 September 1997 200 .gov.VISIBLE EARTH) Fig.noaa. www. Hagolle) Fig. www.Survey of agrometeorological practices and applications in Europe regarding climate change impacts www. 2005 and 2006 in France.usgs.1: Summer droughts of 2003. processed by O. 4.noaa. NDVI deviations (1 August/mean 2002-2004) based on VEGETATION/SPOT 5 data (by CNES.9 Annex 1 Fig.nesdis.ncdc.3: Atmospherically corrected NDVI map of Hungary. Temperature map based on MODIS data (by NASA Εarth Observatory .gov www.nesdis.cr.noaa.cnes.gov.fr 4. 4. at 4:15 UTC: SEVIRI ch9 (IR 10. 15:30 UTC.4. at 4:27 UTC: AVHRR ch 124 (combined)). processed with SAFNWC. 4. Fig. provided only for operational use in agriculture 201 .4: Cloud Type of central Europe. available for subscribers at web-site serving farmers (FMI). 4. METEOSAT-8 data of 24-5-2006. provided only for operational use in agriculture Fig.6: Cloud cover of Europe (Meteosat. 4-7-2007. Satellite spectral climatic and biophysical data for warning purposes for european agriculture Fig.8µm)). 4-7-2007. available for subscribers at website serving farmers (FMI).5: Cloud cover image of NW Europe (NOAA. 16 days composite (Terra/MODIS data) Fig. Fig.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Fig.8: NDVI map of Italy. 4.7: NDVI map of Tuscany (ΝΟΑΑ/AVHRR data).9: NDVI map (SPOT/VEGETATION data) of Italy 202 . 4. 4. 2005. Satellite spectral climatic and biophysical data for warning purposes for european agriculture Summer NDVI Anomly 19822004 Summer Fig. Buermann et al. 2005. PNAS. Ciais et al. Science) 203 .4. A number of major droughts in mid-latitudes have contributed to the weakening of the growth rate of terrestrial carbon sinks in these regions (Angert et al.10: Drought Effects on the Mid-Latitude Carbon Sinks. PNAS. 2007. 4. based on NDVI. Survey of agrometeorological practices and applications in Europe regarding climate change impacts 204 . 5. For biosphere observations. limitations and challenges existing when satellite data are used for climate change analysis are discussed and summarized in the conclusions. Sensitivity of temperature retrievals by hyperspectral satellite sensors to changes of atmosphere and surface properties due to climate change are analysed. SATELLITE REMOTE SENSING AS A TOOL FOR MONITORING CLIMATE AND ITS IMPACT ON THE ENVIRONMENT – POSSIBILITIES AND LIMITATIONS Piotr Struzik. taking into account: satellite orbit drift. Gheorghe Stancalie. Examples of possibilities and results from long satellite data series are presented focusing on: atmospheric sounding. radiation budget. Christos Domenikiotis Abstract Application of satellite data available during last 47 years for detection of climate change and resulted changes to biosphere is presented. Availability of cloud free scenes in mid latitudes is discussed. Difficulties in the use of satellite data for climate observations are analysed. precipitation. Evaluation of the current trend of agroclimatic indices requires information about changes 205 . Leonidas Toulios. ozone. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations 5. Mark Danson. Necessary data stability and accuracy are discussed.5. cloud cover. different methods of post-launch sensors calibration. Benefits. For a better understanding of satellite sensor history. Janos Mika. Problems in proper calibration of data and their representativeness are presented as an example of surface temperature changes over period 19782002 determined from ground and space observations. which can be determined from satellite observations are listed. Essential climate variables. snow/ice cover.1 Introduction The main objective of COST 734 Action is the evaluation of the possible impacts arising from climate change and variability on agriculture. Changes in the biosphere are presented as examples of the evolution of vegetation indices and ocean colour over long term periods. variability of sensor spectral response in consecutive satellite missions. the limiting factor is cloudiness. the evolution of Earth remote sensing and meteorological satellite systems are described. sensors degradation. 1. Typical observations are located only on land and do not completely cover areas of oceans and seas which in fact cover the majority of Earth’s surface. Figure 5. Unfortunately typical ship and aircraft routes still do not cover the whole globe. North America).2 presents example of aircraft weather reports and ship reports for selected 2 week periods. Antarctica etc. use of satellite information is possible and must be more deeply analysed. The longest series of observations belong to stations which are located in the cities. seas and oceans. Climate analysis and monitoring of changes are routinely based on classical meteorological observations performed for more than two centuries. Still. Most frequent observations are available from northern hemisphere where ship traffic is heavier. Additional meteorological observations are provided from ship reports extending land network to the ocean area. 206 . Remote sensing. Amazon jungle. where anthropogenic influence due to urbanization and increasing population cannot be neglected and significantly perturbs the temporal course of temperature or other climate related variables.Survey of agrometeorological practices and applications in Europe regarding climate change impacts of climate and biosphere related variables. 5.1: Daily coverage of meteorological observations – ground stations and ship reports (Obasi. Figure 5. 2003) The need for extension of classical meteorological observations to the large areas resulted in inclusion of meteorological observations and measurements (manned and automatic) performed on ships and aircrafts. Ground observation networks are dense in developed countries (Europe. large areas of our globe are not covered with meteorological data. Taking into account rapid changes of mentioned variables during last 20-40 years.). due to spatial and temporal resolution of the sensors. much more sparse in poor or sparsely populated areas (Sahara. Typical daily coverage of observations made at meteorological stations and from ship reports are presented on Fig. can make possible analysis of large area phenomena including uninhabited areas. Space observations are dated from 1940s. was that of Boston USA in 1860.5. both spatial and temporal was significantly increased. 2001 (left) and ship weather reports 1-15 Apr. Data coverage. 5. Such a long period of observations allows for climate studies based on Earth observations from space. 2003) Development of satellite remote sensing allowed for completely new possibilities in weather observations and collection of long time-series of measured variables. To capture a view and share it with others was the next dream. followed by regular meteorological observations since the 1960s. Additionally. Sensors installed on meteorological satellites were designed for measurement and observation of typical meteorological variables.so the oldest surviving aerial photo. 2002 (right) coverage (Obasi. 5. using a tethered balloon over the Bievre Valley. The first aerial photo was taken by Gaspard-Félix Tournachon.3). 207 .aerialarts. Following technology development. satellite sensors make possible observations of land and sea surface and their features related to actual state of the surface – vegetation. taken by James Wallace Black. P. France. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations Figure 5. chlorophyll concentration. The era of remote observations of the Earth started in fact in the 19th century from the first photographs taken from balloon.htm).com/History/history. which are also a base for climate monitoring. Realisation of this dream was started in the 19 century. temperature. wind etc.2: Example of aircraft weather reports 1-15 Oct. better known as Nadar in 1858. the first photograph from an airplane was taken by L. also using a balloon.2 Evolution of meteorological satellite system To look at the Earth from above was a dream of mankind since for centuries. moisture. Bonvillain while flying in the Wright Brothers' craft in 1908 (http://www. Nadar’s aerial photos were lost . shown below (Fig. suspended matter. Of course more precise instruments are available in space starting from 1980s. the rocket-borne camera climbed straight up. years before the Sputnik satellite opened the space age.com) Figure 5. Snapping a new frame every second and a half.4: The first rocket-borne look at Earth from beyond the atmosphere (1946) 208 . The camera itself was smashed. black-and-white photos were taken from an altitude of 65 miles by a 35-millimeter motion picture camera riding on a V-2 missile launched from the White Sands Missile Range.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Figure 5.the first pictures of Earth as seen from space (Fig. a group of soldiers and scientists in the New Mexico desert saw something new and wonderful .3: World’s oldest surviving aerial photo On October 24.4). but the film. 5. The grainy.airspacemag. protected in a steel cassette. was unharmed (http://www. 1946. slamming into the ground at 500 feet per second. then fell back to Earth minutes later. 1960 (Fig. from altitudes as high as 100 miles. Figure 5.7). 01. Full Earth coverage from geostationary orbit was achieved in 1990 after the launch of a Russian satellite positioned over Indian Ocean. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations More than 1. ATS-1 constructed by USA (Fig. prototype spacecraft for the second generation of operational sun-synchronous meteorological spacecraft. were replaced by scanning radiometers operating also in infrared part of spectrum. The purpose of making such photos was navigation of space flights. Very quickly was found. rapid development of meteorological satellites has taken place. USA. 5.1960. 5. first IR (right) 209 . ITOS. Since that time. 14.6 and 5. 5.04. The first European geostationary meteorological satellite – METEOSAT-1 was placed on 0 deg position in 1977. Television images useless during night time.000 Earth pictures were returned from V-2s between 1946 and 1950. First prototype satellite with such an instrument onboard was ITOS-1 launched in 1970 (Fig.5: TIROS.5. USA.1970.02.5). first picture at all taken by a meteorological satellite (left). The weather related applications born in 1950s and finalised with launch of the first meteorological satellite on April 1st. Since that time regular observations of weather phenomena were started. The need for frequent observations for monitoring of meteorological processes in the atmosphere resulted with launch of the first meteorological geostationary satellite in 1966. that on the images mainly clouds are seen.4). 210 . 5. Benesch) The actual configuration of meteorological satellites is presented below (Fig.6: First years of meteorological satellites development Figure 5.9). Currently about 30 satellites are available for forecasters covering whole Earth including polar regions well observed by fleet of polar orbiting satellites of NOAA. focus European perspective (picture .courtesy W.7: Continuous development of global observing system for meteorology. METEOR and Feng Yun series with first European METOP-A.8 and 5.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Figure 5. 5. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations Figure 5.8: Actual status of meteorological satellite system Figure 5.9: Geostationary orbit coverage – examples of images 211 . They have determined geographical location.Survey of agrometeorological practices and applications in Europe regarding climate change impacts The development of satellite from the user requirements to the launch. That’s a reason of long term planning. Figure 5.10: Future evolution of meteorological satellites system Processes which take place in the atmosphere are four dimensional. Monitoring of them require frequent observations.g. Figure 5.11). in certain cases in range of minutes (e. 5.10). Below is presented evolution of repetition rate of meteorological satellites with an outlook for near future (Fig. convection).11: Evolution of repetition rate of meteorological satellites – past. altitude and time. At the moment perspective of 70 years of meteorological space observations is known (Fig. 5. takes 10-20 years. present and near future 212 . Essential climate variables in three domains atmosphere. Space observations provide valuable information in global.1: Essential Climate Variables which can be observed by satellite sensors Monitoring of climate require observations which are related to processes which are driving forces for possible changes. The longest series concern cloud observations but continuous improvement of instruments made possible also: air sounding. circulation and air-sea exchange.3 Satellite climatology – possibilities Meteorological satellites have been present in space since 47 years. Such a long period allows for climatological studies based on remotely sensed observations of the Earth and atmosphere. Especially important are satellite observations. • Chemistry of the middle and upper stratosphere.1. • Volcanic eruptions and their role in climate change. snow and polar ice caps observations and many other application. oceans and seas monitoring. • Changes in land use.5. • Oceanic productivity. land surface properties observations. which can be observed/measured by satellite instruments are listed below in Table 5. • Transformation of greenhouse gases in the lower atmosphere. 213 . including deforestation. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations 5. Table 5. continental and regional scale which help in better understand processes which are hardly detected by point ground measurements. including sources and sinks of stratospheric ozone. land cover and primary productivity. with emphasis on the carbon cycle. • Sea level variability and impacts of ice sheet volume. which help in key areas of uncertainty in understanding climate and Global Change. such as: • Earth’s radiation balance and the influence of clouds on radiation and the hydrologic cycle. ocean and land. wind speed and direction. But processes taking place in atmosphere concern not only cloudiness. • Next generation (TIROS N) was launched with an improved 20channel High Resolution Infrared Sounder (HIRS) accompanied by the 214 . Such measurements are possible with use of sounding instruments using both infrared channels and microwave measurements located at absorption bands of H2O.12: Where satellite sensors can be placed on this diagram? 5. Brief history presenting evolution of sounding instruments is listed below: ATMOSPHERE TEMPERATURE SOUNDING: • In 1969 Nimbus 3 carried the first of a new class of remote-sounding sensors. other trace gases. aboard the NOAA 2 In 1978.12).Survey of agrometeorological practices and applications in Europe regarding climate change impacts For analysis of long term processes related to climate. Satellite sensors measure: water wapour content and distribution in time and space. 5. the Space Infra-Red Sounder (SIRS A). Figure 5.1 Satellite observations of processes in atmosphere – selected examples. CO. CO2. ozone content. Satellite observations of the processes which take place in the atmosphere have the longest history. This paper is an attempt to answer this question. airmass circulation. clouds are continuously observed and analysed providing valuable climatological datasets. O3 and other gases of Earth atmosphere. The question is whether instruments placed onboard of meteorological satellites are well characterised in terms of those parameters (Fig. the Vertical Temperature Profile Radiometer (VTPR). Since the first image from space taken in 40s. tools with high stability and low uncertainty are required.3. • First operational sounder system in 1972. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations • • Microwave Sounder Unit (MSU) and Stratospheric Sounder Unit (SSU) forming the TIROS Operation Vertical Sounder (TOVS).4 µm) and 4 channels in the visible part of spectrum.5 µm spectrum). entire troposphere moisture and ozone content).13: Brightness temperatures changes of AIRS hyperspectral sensor due to atmospheric and surface anomalies related to climate change 215 . surface temperature. The importance of hyperspectral sounding for climate applications is described in Fig. low level tropospheric moisture. to the changes of typical parameters describing our environment (emissivity.13. Figure 5.15.7 – 15.6.5. Simulation of spectral response as a result of climate change is presented taking AQUA satellite AIRS sensor as an example. Changes in our environment related to global warming are well detected by hyperspectral sensors in different parts of spectrum depending on observed parameters. 2006 – first METOP satellite with hyperspectral interferometer IASI (8546 channels in 3. 5. where in the infrared part of spectrum is depicted sensitivity of remote sensed brightness temperature. In 2002 NASA launched Aqua satellite carrying the first hyperspectral spectrometer Atmospheric Infrared Sounder (AIRS) with 2378 channels in the infrared (3. 09°C decade–1 (University of Alabama Huntsville UAH. Christy et al. part of temperature increase is anthropogenic. As a result.06°C decade–1. The surface temperature trend is +0. causing increased UV radiation. ozone plays a critical role in absorbing ultraviolet (UV) radiation and preventing it from reaching Earth's surface. 2003). 2000): • In the stratosphere. Meteorological stations located outside of the cities are becoming inside the cities due to they grow.11 ± 0. Figure 5. infrastructure. Analysis based on satellite data suggest much lower (if really exist) trend of temperature increase.. especially in polar region. The linear trend through 2002 for the UAH T2 product is 0.14 presents comparison of mean temperature of our globe in period 1978-2002 retrieved from ground observations and in the frame of two satellite projects. 2003). Due to availability of satellite missions with temperature sounding measurements.. The amount of heat released is continuously growing due to industry. Ozone plays different role depending of altitude where exist (Committee on Earth Studies.09°C decade–1 (Remote Sensing System RSS. traffic etc. studies on temporal behaviour of temperature were done. Figure 5. especially for really long time-series suffers problem of urbanisation. The linear trend through 2002 for the RSS is 0.03 ± 0.14: Annual mean anomalies of global average temperature (1979-2002) for the lower troposphere from satellites (T2) and for the surface (SFC T) One of the most interesting problems related to human influence to the atmosphere is ozone depletion in stratosphere.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Ground measurements of temperature.20 ± 0. where 90 percent of atmospheric ozone resides. Mears et al. 216 . 5. and methyl bromide. Ozone depletion trend. ozone maintains the oxidizing power of the atmosphere by providing a source of the hydroxyl radical (OH−) in the presence of water vapor. ozone is a pernicious pollutant. Satellite sounding instruments measure total ozone content and ozone profile since the first instrument onboard NOAA satellites.16. ozone is a major greenhouse gas. including methane (CH4). causing inhomogeneous radiative forcing. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations In the upper and middle troposphere.5. • Figure 5. Oxidation by OH− is the main sink for a number of environmentally important gases. It is the principal contributor to smog over the United States. S to 50 deg N (Fig.15: Instrumets used for creation of Merged Ozone Dataset and total ozone temporal changes in period 1979. toxic to humans and vegetation. which was evident in 80-ties and 90-ties were recently stopped according to results presented on Figure 5. 2007) 217 . carbon monoxide (CO).15). • In the lower and middle troposphere.2006 (Gleason and Butler. Merge ozone dataset created with use of different space borne instruments presents temporal changes in most populated region 50 deg. hydrofluoro-carbons. • In surface air. For these reasons. Two factors influencing vegetation measurements from space are clearly seen: volcanic eruption and satellite data degradation for selected satellites. Parameters which can be obtained are: vegetation status. Likewise. Pinatubo eruption in June 1991 and El Chichon in March 1982 is also discernable. Examples of long series of vegetation indices anomalies (1981-2001) based on AVHRR/NOAA data. The type and distribution of vegetation native to a geographic region are diagnostics of the area’s climate. the impact of the Mt. The impact of satellite drift is clearly noticeable. 2006) 5.17. especially in the case of NOAA 11 and 14. radiation balance and many others.2 Satellite observations of processes at the Earth surface – selected applications Meteorological satellites were designed for observation of phenomena taking place in atmosphere but they provide also information on actual state of the surface allowing continuous monitoring of biosphere spatial and temporal changes.16: Total ozone trend in last 25 years as measured by satellite instruments (Yang et al.3. observing vegetation changes in the seasonal to inter-annual time frame and over long term is important to climate monitoring. This is because vegetation integrates the effects of precipitation and temperature over all time frames longer than a few days. for different climatic zones are presented below.. In addition the vegetation Leeds back into climate because of the plant species contribution to the surface energy and moisture balance and its impact on surface roughness and albedo.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Figure 5. JGR-Atm 111. snow/ice cover. Vegetation index monthly anomaly time series for period July 1981 to December 2000 is presented on Figure 5. 218 . D17309. 5.. Also maximal values of NDVI (Normalised Difference Vegetation Index) were grown during mentioned 13 years. resulting with longer vegetation season. Figure 5. Arrows indicate time of greatest volcano eruptions during last 20 years Long term monitoring of vegetation status make possible to detect temporal changes of beginning and finish of vegetation season.18: Temporal changes of beginning and finish of vegetation season based on satellite measured NDVI (Ohring et al. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations Figure 5.18. in different latitude zones. During the period 1981-1994. Example of such studies for latitudes above 45 deg is presented on Figure 5.17: Vegetation anomalies in period 1981-2001. 2004) 219 . both spring and autumn dates were shifted. . 2002. Time series of NDVI. VCI is a vegetation index adjusted for land climate. Hayes et al. This contribution fluctuates considerably depending mainly on climate.. 1995. allows derivation of the potential vegetation cover (according to soil conditions and environmental factors). soils. intersensor variation or simply noise in the channel data. air temperature). 2004). Higher accuracy assessment has been obtained based on the application of the Bhalme and Mooley Drought Index (BMDI) methodology on the Vegetation Condition Index (VCI) extracted by NOAA/AVHRR data (Domenikiotis et al. It has excellent ability to detect the existing anomaly conditions and to measure the time of their onset and intensity.. Kogan.. for 18 220 . the variability of NDVI values is related to the contribution of geographic resources to the amount of vegetation. The main question is whether is it greening trend? But other possibilities are: orbital drift.. 1987.. 1997) showing further potential of satellite-based data in climate impact assessment.. VCI separates the short-term weather signal in the NDVI data from the long-term ecological signal (Kogan. 1997). Examination of the temporal changes along the vegetation growth period as a function of the time-varying photosynthetic activity and according to the background environmental conditions (rainfall. mainly with respect to calibration and inter-calibration of sequential satellite instruments. Domenikiotis et al. These temporal differences could be an indicator of expected productivity which varies with the ecosystem potential and the specific conditions prevailing in a given site (for a given year or seasonal cycle) (Domenikiotis et al. 1996). Domenikiotis et al. For the purposes of estimating the impact of the weather on vegetation. 2002. 2004a). However. the nonweather effects have to be filtered out. duration and impact on vegetation.. The VCI algorithm was developed and tested in several areas of the world with different environmental and economic resource (Kogan.Survey of agrometeorological practices and applications in Europe regarding climate change impacts AVHRR observations suggest that the growing season increased between 1981 and 1994 by 10%. In many parts of the world empirical relationships between VCI and yield or production have been developed for early production/yield assessment (Dabrowska-Zielinska et al. Tsiros et al. Domenikiotis et al. 2004b). 2004a). 1993. derived from NOAA/AVHRR have also been extensively used in vegetation monitoring. ecology and weather conditions. A vegetation index that seems to be appropriate for incorporating these conditions is the Vegetation Condition Index (VCI). and topography of an area. vegetation type. White et al. crop yield assessment/forecasting. and hazard detection and mapping (Tucker and Choudhury. Studies have been carried out in several parts of the world (Subbiah. but questions remain. Benedetti and Rossini 1993. 1993. (2005) developed empirical relationships between the VCI values derived from a time series of NDVI ten-day Maximum Value Composite (MVC) images. Data records for long term studies begin in 60-ties and are continuous until present time. The record provides the longest. 2006). Bhuiyan et al. Satellite sensors used for snow and ice monitoring use visible. VHI derived from a time series of NOAA/AVHRR ten-day images. VHI have been used for the detection of agricultural drought (Tsiros et al. for 20 successive hydrological years has been recently used for agroclimatic classification (Tsiros et al. at regional and country scale.5.. The example of Northern Hemisphere snow cover anomalies from November 1996 to October 2003 calculated from NOAA snow maps and colour coded by season are presented on Fig. For the identification of vegetative stresses and their impact in vegetation health Kogan (2001) proposed the Vegetation Health Index (VHI) which represents overall vegetation health and used it for agricultural drought mapping. winter: blue. Figure 5. spring: green..19: 12-month running anomalies of hemispheric snow extent (left). 5.. 2008) and for monitoring agricultural drought (Kanellou et al. The predicted values showed 5% percentage deviation from the actual production.19. 2004. Changes in snow and ice cover represent potential changes in climate forcing due to the snow-albedo feedback mechanism. most consistent snow cover product available for documentating the state of the environment. monthly anomalies are colour coded by season: fall: orange. summer: red (Climate Data Records from Environmental Satellites: Interim Report. playing a major role in the spatial and temporal distribution of weather patterns. National Academies P) Oceans cover 70% of the earth surface.. chemical. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations successive years (1982–1999) and cotton production. 2008). infrared and microwave parts of spectrum. and biological processes are responsible for the 221 . For example. the production of natural resources and the large-scale transport and storage of greenhouse gases. marine physical. ocean colour.Survey of agrometeorological practices and applications in Europe regarding climate change impacts removal of carbon dioxide (CO2) from the atmosphere in certain regions and its release in other. Marine phytoplankton accounts for approximately half of the global annual primary production. To this end. and thus plays a significant role in the global carbon cycle. the use of satellite-derived oceanographic observations provides the means to characterize large-scale seasonal. Observed from satellites parameters are: sea surface temperature.e. and decadal spatial changes in physical and biological sea surface properties. 5. surface roughness (synthetic aperture radar). surface wind speed and direction (scatterometer). Figure 5. the balance between global atmospheric removal and release) and the rates of transport are a function of both ocean circulation and biological activity. In many respects ocean colour is the marine analogue to vegetation dynamics and land cover. These changes can then be statistically coupled to long term changes in atmospheric patterns to assess the sensitivity of oceanic processes to climate change. a proof-ofconcept sensor launched in 1978 onboard Nimbus 7. a measure of phytoplankton biomass (Fig.20: Chlorophyll concentration on global and regional scales based on MODIS (left) and SEAWifs satellite data (right) 222 . The global rates of marine CO2 sequestration (i. Satellite-based oceanographic observations provide unique regional and basin-scale datasets that can be used to test and improve our climate models and constrain error estimates in our forecasts. surface circulation patterns.21). The most widely used product derived from ocean color measurement is chlorophyll concentration (mg m–3). inter-annual. Ocean colour remote sensing has been a successful area of technological development since the Coastal Zone Color Scanner (CZCS).20 and 5. and it is motivated by similar concerns. consistent climate record from satellite observations alone is that satellites and instruments have a finite lifetime of a few years and have to be replaced. it is crucial for understanding climate processes and changes. when it comes to building satellite instruments. • difficulty of calibrating after launch (e.5. • intercalibration. Most important is proper calibration of satellite sensors during their entire time. • restrictions of spatial sampling and resolution. vicarious or onboard calibration). Although excellent absolute accuracy is not critical for trend detection. and their orbits are not stable.4 Difficulties in use of satellite data for climate observations Most of the operational satellites were created as weather rather than climate platforms. And. During creation of satellite based Climate Data Records. The difficulty arises because of the many known and unknown systematic uncertainties that are to be accounted for in the calibration of the instruments. However. it is not as necessary for determining long-term changes or trends as long as the data set has the required stability. • accounting for orbit drift and sensor degradation over time. 223 . • post-launch vicarious calibration. stability appears to be less difficult to achieve than accuracy. • temporal sampling...21: Chronology of Ocean Colour satellite Missions (Letelier et al. This can be done by: • pre-launch calibration. unique challenges appear: • the need to manage extremely large volumes of data. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations Figure 5. A chronic difficulty in creating a continuous. 2005) 5. long term absolute accuracy of satellite measurements was not a crucial issue. As a result. In the measurement of the climate variable it is vital for understanding climate processes and changes.g. • the need for significant computational resources for reprocessing. which aliases the diurnal cycle onto the record. spurious trends in the data can occur. ice caps.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Nominal calibration involves determining the calibration of a single sensor on a single platform. Figure 5. it is important to calibrate the sensor in orbit as well (Guenther et al. like deserts.22). as each sensor will have slightly different baselines even if they are built to the same specifications (Ohring et al. and while this is considered standard prelaunch practice. 2002). Byerly et al. MODIS/TERRA.. 224 . 5.23 presents spectral response functions of first 2 channels of AVHRR instrument used for long term studies of vegetation anomalies (NOAA 6-16. Goody and Haskins. 1998. Figure 5. used for vicarious calibration. to prevent drifting of the data over time due to orbital drift and drift in the observation time. 1997.22: Comparison of albedo measurements with and without vicarious calibration (Rao and Chen. or aircraft measurements (Fig. Differences between sensors of consecutive satellites require intercalibration of satellite data. SPOT and ADEOS satellites). balloon. 1995) These instruments should undergo vicarious calibration monitoring at regular intervals. dense tropical vegetation or even Moon used for SeaWifs postlaunch calibration. Different objects with stable albedo are used for calibration of visible sensors. Satellite-to-satellite cross-calibration involves adjusting several same-generation instruments to a common baseline. Vicarious calibration monitoring involves measuring a known target or comparing the satellite signal with simultaneous in situ. radiosonde. and this is particularly important for long term studies. The problem is with selection of the objects with stable properties.. 2004). Without proper post launch calibration. regardless of on-board nominal calibration. cloud height. Bruce Wielicki NASA Langley Research Center. 2004) Required accuracies and stabilities for climate variable data sets were listed in Appendix 1.23: Differences in spectral characteristics of NOAA AVHRR sensors (Latifovic et al.5. and sea ice measurements. cloud temperature. ocean colour. requirements for satellite instruments were computed. College Park. Stability requirements are being met. atmospheric temperature. ozone. or appear to be close to being met for solar irradiance. cloud cover. Among the problems which occur using different satellite platforms is satellite drift causing a change in the local time 225 . Bill Emery University of Colorado.. November 12-14. Raju Datla NIST).1). Roy Spencer NASA Marshall Space Flight Center. Taking values from Appendix 1 (Table 5. snow cover. 2002) NISTIR 7047. total column water vapour. edited by George Ohring NOAA/NESDIS (Consultant). Time series of climate variables have been constructed from those series. MD. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations Figure 5. Long term data sets have been assembled for many of these variables by stitching together observations from successive satellites and exploiting satellite overlap periods to account for systematic differences between successive instruments. Finally assessment which parameter measured by satellite data fit requirements is presented in Table 3 (state for 2002) (Source: Satellite Instrument Calibration for Measuring Global Climate Change (Report of a Workshop at the University of Maryland Inn and Conference Center. airborne or ground-based observation.Survey of agrometeorological practices and applications in Europe regarding climate change impacts of the observations during each satellite’s lifetime.. Cloud cover is a major constraint on optical remote sensing.24). whether it is space borne.24: Drift of orbital parameters of NOAA Unfortunately. for many climate variables. In some cases. and studies are needed to answer the question (question marks in the Appendix 1 Table 5. especially for the NOAA satellites (Fig.3). particularly in cloudy regions such as the United Kingdom presented as example (Armitage et al. we don’t know whether current systems are adequate. current-observing systems cannot meet both accuracies and stabilities (Appendix 1). 226 . Figure 5. 5. Ramirez et al. Cloud cover frequencies have a major effect on climatological applications of remote sensing. Clouds provide a major impediment to passive remote sensing at visible and infrared wavelengths. Total cloud cover prevents any observation of the ground from space borne sensors and can severely limit data collection from airborne sensors.. derived from the MODIS Cloud Mask SDS product at the 95% certainly level. 1994.5. 2001. 1990. The presence of scattered clouds can cause objects of interest to be obscured. Figure 5. 2007) 227 . across the UK in 2005 (Armitage et al. The impact of cloud cover on operational applications that require a time series of images can be significant (Kontoes and Stakenborg... 2007). particularly when regular repeat data collection is required.. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations 2007.25) clearly shows that some regions are continuously obscured by clouds (only a few cloud free days in year). 2002). Determination of any surface parameters or features with use of remote sensing is highly difficult. 5. The example (Fig. Fuller et al. Hollingsworth et al.. or can cast shadows which causes problems when processing images. Intrieri et al.25: Spatial distribution of cloud-free imagery frequencies for whole year and selected individual months. International Journal of Applied Earth Observation and Geoinformation. W. globallyintegrated climate products. Error estimates of version 5. Journal of Remote Sensing. Radiometric calibration. Kogan.R. 25.0 of MSU-AMSU bulk atmospheric temperatures.. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data.E.A. F. Christy J. Int. 2003. Visible/Infrared Imager/Radiometer Suite. Engel. T.M. Kogan. Rapid development of Earth observations resulted extremely huge volume of satellite data. Clement. Presence of meteorological and environmental satellites in space since the 1960s allows for real climatological studies. pp 2807-2819. Remote Sensing of Environment.B. E.. J. The case study of wheat yield estimate and forecast in Emilia Romagna. Braswell. 2002. Regarding future missions. Young. Ramirez. Kowalik.. 228 . W. Dalezios.. pp 311-326. Julian. Dorman. A. Examples of successful use of long time satellite data series were presented in this study together with description of limitations of this technology. M. Walker. Rev. S. J. 1. Modelling of crop growth conditions and crop yield in Poland using AVHRR-based indices. F. 8. Singh. Actually.. M.A.B.D. Domenikiotis C. Spiliotopoulos. and Rossini P.N. 2007. Gruszczynska. Algorithm Theoretical Basis Document. J. Oceanic Technol. D. R.. Norris. Version 5.5 Conclusions Satellite data offer an unprecedented potential for climate research provided that separate sensor/satellite data are integrated into high-quality.6 References Armitage R. J. 23.R. pp 613-629. new and more accurate sensors are envisaged. Spencer.. F. Journal of Remote Sensing. Ciolkosz. This requires overlapping periods of consecutive satellite missions. UK. Other problems concern data management (processing and reprocessing). pp 289-302. N.N.P. Comparison of AVHRR and MODIS Cloud Products for Estimating Cloud Cover Probabilities for the United Kingdom. not all climatic related variables can be traced with use of satellite sensors due to their not sufficient accuracy (Appendix 1). Benedetti R. 2006. Annual Conference 2007. pp 1109-1123. Also climate change influence on biosphere can be monitored with use of satellite data. Much improved post-launch calibration of satellite instruments. 1993.E. Proceedings of Remote Sensing and Photogrammetry Society. Bhuiyan C. F.P. Parker. On the use of NDVI profiles as a tool for Agricultural Statistics. R. Early Cotton Yield Assessment by The Use Of The NOAA/AVHRR Derived Drought Vegetation Condition Index In Greece. Int. 45. Tsiros. 5.W. Byerly W.Survey of agrometeorological practices and applications in Europe regarding climate change impacts 5.P. Raytheon ITSS. 20.W. Main issues are accuracy and stability of satellite measurements. 2002. J. Atmos. Newcastle. E. 2004a.Y. Danson. R. Miller. Ogunbadewa. Dabrowska-Zielinska K. and intercalibration of similar instruments flying on different satellites is highly required to achieve continuity of observations. W. Operational space technology for global vegetation assessment.M. 2001. Tsiros. European Geosciences Union General Assembly 2008. S. Int. pp 119-132. Spiliotopoulos. Kogan F. Vienna. pp 655-668. Int. Park. Goody R. pp 1357-1362. A. Calibration of radiances from space.J. Meteorological and Agrohydrological drought monitoring based on conventional and remotely sensed data. 17. 2001. Availability of cloudfree Landsat images for operational projects. and Stakenborg J. Zoning of cotton production areas based on NOAA/AVHRR images. Using NOAA AVHRR data to estimate maize production in the United States corn belt. pp 3189-3200.. 2002. J. Kogan F. 13-18 April 2008 (accepted). Int. 11(9). 1994. Greece. Greece. Bulletin of the American Meteorological Society. J. 25. Kontoes C.M. Tsiros.J. pp 1599-1608. 76. Int. Dalezios. Greece.R. Journal of Geophysical Research. Austria. 82.E. M... and Haskins R.M. 7-9 November 2003. Irish. Wallis. From EOS.M.. The analysis of cloud-cover figures over the countries of the European Community. Dalezios. Kanellou E. M. presentation at AMS 2007 Conference. Domenikiotis. J. and Butler J. pp 1663-1670. Intrieri J. P. Kogan F. 2005. Volos. Uttal. 107.. Science and Design. pp 5373-5388. Journal of Remote Sensing. C. Workshop on Strategies for Calibration and Validation of Global Change Measurements. Trishchenko. Journal of Remote Sensing. Int. Bulletin of the American Meteorological Society. Pouliot.. 15(6). 1998. Committee on Earth Studies. D. M. National Research Council. 2002. Khlopenkov. R.N. The National Academies Press. N. N. Cloud cover in Landsat observations of the Brazilian Amazon.R.. 125 p. 2007. Tsiros. Skiathos. Application of NOAA/AVHRR VCI for drought monitoring in Thessaly. Conference of Protection and Restoration of the Environment. NASA Reference Publication 1397. Hollingsworth B. pp 1-15. 1990. Dalezios.R. to NPOESS: The Satellite Climate Data Record. N. E. Tsiros. Dalezios. Domenikiotis C. pp 38553862. pp 621-636. Bulletin of the American Meteorological Society. Shupe. Fuller R.. Chen.5. S. Butler. 2004b. 1995. T. M. Ardanuy. G. through NPP. Guenther B.. 1-5 July 2002. Droughts of the late 1980s in the United States as derived from NOAA polar orbiting satellite data. N. Global drought watch from space. J. Generating Satellite Climate Data Record Over Canadian 229 . Chen. 78. Spiliotopoulos. Int. 2008.D.... Cihlar. Latifovic R. E. An annual cycle of Arctic cloud characteristics observed by radar and Lidar at SHEBA.B.. pp 754-758. B.. McCarty. Issues in the Integration of Research and Operational Satellite Systems for Climate Research: Part I. Climate 11. Domenikiotis C. E. 6th Int. Reichienbach. Groom.. 2004. Spiliotopoulos. C. Fernandes. The availability of Landsat TM images of Great Britain. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations Domenikiotis C. R. Journal of Remote Sensing. Symposium in GIS and Remote Sensing: Environmental Applications. Space Studies Board. B.. 1997. Journal of Remote Sensing. K.N.N. pp 1949-1964.R. Journal of Remote Sensing. E. Early cotton production assessment in Greece based on the combination of the drought vegetation condition index (VCI) and Bhalme and Mooley drought index (BMDI). 1996. 2000. Gleason J. L. Ungureanu. 22. 1997.L. and Decker W. Hayes M. E. Domenikiotis. prediction. Cunnold. November 12-14. of Stand.R.com/History/history. 2004. pp 157-181. C.. EWRA Symposium on water resources management: risks and challenges for the 21st century.J.R. 4th International Conference on Information and Communication Technologies in Bio and Earth Sciences. B.J.M. August 2005. Schabel. Russell. 1993. Howden. Turkey.J. Ramirez A. Davos. Md. A re-analysis of the MSU channel 2 tropospheric temperature record. Ohring G. D. In :Wilhite. Management and Planning Theory and Case.htm http://www. Subbiah A. Abbott. Emery. Natl. March 2006. R. Indian drought management from vulnerability to resilience.aerialarts. Domenikiotis. December 6-10..J.S. Drought in Australia. 2-4 September 2004.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Land Mass. 2003.M Zawodny. J. Spencer. F. 101 pp. Inter-satellite calibration linkages for the visible and near infrared channels of the AVHRR on the NOAA-7.. B. 2003. Miami. Wentz. Izmir. (Eds).G.airspacemag. Drought Assessment. 10th Int.R. Obasi G. Wielcki. M. J. R. Management and Planning Theory and Case.Y. Tsiros E. C. Yang E. NISTIR 7047. http://www. D. edited by G. 2002). 57p. Washington. 1993. 1995.M. 16. A. D. Tsiros E. Inst. Gaithersburg. Wielicki. S. N.P. MD. Kluwer Academic. Remote Sensing of Environment. R. Ocean Color Climate Data Records Workshop Report.J. management and policy. monitoring. pp 243-251. Salawitch. In :Wilhite. pp 769-782. R. Oltmans. pp 3650-3664.com 230 . Kluwer Academic. N. Satellite remote sensing of drought conditions. M. 116 p. Spencer. Int. Danson. Yebra. Oregon State University. 2004.P. and Technol. Strutton. Satellite Direct Readout Conference. B. Datla. Satellite Instrument Calibration for Measuring Global Climate Change (Report of a Workshop at the University of Maryland Inn and Conference Center. Greece. Use of NOAA/AVHRRbased vegetation condition index (VCI) and temperature condition index (TCI) for drought monitoring in Thessaly. P. 23. Newchurch. 111. E. C. 2004. pp.N. 2005. D. Letelier R. Mears C. Spiliotopoulos. Rao C. Athens.. Climate Data Records from Environmental Satellites. A. National Research Council (NRC).R. DC. and Choudhury B. M.O. J. 2004. HAICTA 2008. pp 1931-1942. Climate. and Chen J. Attribution of recovery 1 in lower-stratospheric ozone. Web-based model for analysis of time series remotely sensed data. M. 2007.. Florida.R. WMO. Tucker C.. 2004. Ogunbadewa. Drought Assessment.. 16 (22). Armitage. 1987. Symposium on Physical Measurements and Signatures in Remote Sensing. B. 1214th March 2007.M. F..A.. Dalezios. 213-237. White D. Kanellou. (Eds). Marston. Journal of Remote Sensing. M.. Boston. M. JGR-Atm. McCormick. Dalezios. Emery. Identification of Water Limited Growth Environment Zones Using NOAA/AVHRR Data. 18-20 September 2008.. National Academies Press. Satellite instrument calibration for measuring global climate change. The Role and Importance of WMO and the National Meteorological and Hydrological Services for Sustainable Development. Geneva.. College Park. Greece (submitted). Collins. Boston.C. R. R. Datla.. Ohring.P. 2006.. Rep. M. -9 and -11 spacecrafts. 2008. November 12-14. Bill Emery University of Colorado. Raju Datla NIST).5.7 Appendix 1 Required accuracies and stabilities for climate variable data sets were listed in a table below. Finally assessment which parameter measured by satellite data fit requirements is presented in Table 5. Column labeled signal indicates the type of climate signal used to determine the measurement requirements 231 . College Park. 2002) NISTIR 7047.2: Required accuracies and stabilities for climate variable data sets. MD. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations 5.4 (state for 2002) (Source: Satellite Instrument Calibration for Measuring Global Climate Change (Report of a Workshop at the University of Maryland Inn and Conference Center. Taking values from Table 5. edited by George Ohring NOAA/NESDIS (Consultant). Roy Spencer NASA Marshall Space Flight Center.2. requirements for satellite instruments were computed. Table 5. Bruce Wielicki NASA Langley Research Center. Survey of agrometeorological practices and applications in Europe regarding climate change impacts 232 . The instrument column indicates the type of instrument used to make the make the measurement 233 .2.3: Required accuracies and stabilities of satellite instruments to meet requirements of Table 5. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations Table 5.5. 4: Ability of current observing systems to meet accuracy and stability requirements 234 .Survey of agrometeorological practices and applications in Europe regarding climate change impacts Table 5. Satellite remote sensing as a tool for monitoring climate and its impact on the environmentpossibilities and limitations 235 .5. Survey of agrometeorological practices and applications in Europe regarding climate change impacts 236 . Parry et al. (iii) develop common tools for downscaling. or alternative futures that are meant to assist in climate change analysis (Nakicenovic and Swart. Use of climate change scenarios in agrometeorological studies: past experiences and future needs 6. Scenarios are neither predictions nor forecasts in a traditional sense. Lucka Kaifez Bogataj. (ii) systematically quantify uncertainties in climate change projections.. in particular the second (SAR. providing the basis for assessing the impact of climate change on human activities. 2001) and fourth assessment reports ( AR4.g. The survey clearly indicated that coordinated efforts are needed to: (i) provide a coherent view of climate change across all European countries. 2000). including agriculture (e. Houghton et al.. a survey was conducted among the signatory countries concerning the use of climate change scenarios in agrometeorological studies. This does not necessarily imply that climate change scenarios are unreliable: considerable efforts have indeed been produced during the last decade to improve our knowledge of the climate system and our capacity to model its dynamics. and. 2007). 6. 2007). This evolution is well documented in the reports issued by the Intergovernmental Panel on Climate Change (IPCC). Emmanuel Cloppet.. a point often neglected in impact studies. Advances are also evident in 237 . János Mika Abstract As a contribution to COST 734. Scenarios are inherently uncertain.1 Introduction Climate change scenarios are the backbones of impact studies.. The questionnaire was structured in such a way as to characterize the past experiences and identify the future needs. (iv) better account for the risks related to extreme events.6. Houghton et al. Solomon et al. rather they are images of the future. Tomáš Halenka. USE OF CLIMATE CHANGE SCENARIOS IN AGROMETEOROLOGICAL STUDIES: PAST EXPERIENCES AND FUTURE NEEDS Pierluigi Calanca. 1995) third (TAR. synthetic (usually obtained by specifying ad-hoc incremental annual. However. Statistical downscaling has the advantage of being computationally fast. the same cannot be said concerning radiation. at times. GCM or RCM results are usually downscaled with the help of high resolution atmospheric models (dynamical downscaling) and/or statistical models and stochastic weather generators (statistical downscaling).dk. 1 2 http://prudence. hindered by other kinds of difficulties.g. and application models. technical difficulties.dmi. on the other hand. this approach is much more demanding in terms of computational resources. air humidity and wind speed. From the point of view of the physical consistency of the results. and in relation to extreme events. accessed 20/01/2008 http://ensembles-eu. sometimes taking arbitrary. limited amount of resources available for the studies) rather than by the objectives of the study. such as PRUDENCE1 or ENSEMBLES2.com/. including institutional problems (e. In agrometeorological studies. dynamical downscaling is certainly the method of choice. while working with scenarios.g. by the fact that it has a clear focus it can provide a platform for coordinating national initiative. while statistical downscaling of temperature and precipitation has received much attention in the past. but tends to introduce additional uncertainties in the scenarios. setting the focus for new projects and pin point remaining weaknesses in the attempt to better understand climate change and its effects on European agriculture. accessed 20/01/2008 238 . lack of sufficient human resources). seasonal or monthly anomalies for the long-term mean temperature and precipitation) or analogue scenarios as a starting point for the analysis. Impact studies with a focus on agriculture have not exclusively relied on climate change scenarios simulated with global (GCM) or regional climate models (RCM). on the one hand. In earlier days the choice of scenarios was often dictated by practical considerations (e. For this reason. a coherent use of climate scenario to study the possible impacts of climate change agriculture was. Moreover.metoffice. Although COST 734 can not solve all of these problems. difficulties have often been encountered in relation to the disparity of scales between global and regional climate models. exchanging valuable information and experiences.Survey of agrometeorological practices and applications in Europe regarding climate change impacts the outcomes of research projects promoted within the 5th and 6th framework programmes of the European Union. Apart from the technical issues mentioned in the previous paragraphs. 2 Climate scenarios 6. 6. Time windows: 2010-2039.2. Those cited in the text have been chosen to provide an adequate coverage of the main topics. with IS92a emission pathway. NCAR DOE-PCM) with IS92a emission pathway • GCM scenarios from the IPCC-DDC (HadCM3. The results of the survey are summarized in Table 6. GCM scenarios from the IPCC-DDC (CCSR/NIES. CSIRO-Mk2b. HadCM2. • GCM scenarios from the IPCC DDC (ECHAM4. The full list of the references included in the questionnaire returned is provided separately. and A1B. • arbitrary & analogue scenarios Simulations with ECHAM5-MPI-OM and A2 emission pathway. NCAR DOE-PCM. CSIRO-Mk2. and this report provides an overview of the collected information. ECHAM4/OPYC3. rather than just going through each of questions. However. CGCM2. Use of climate change scenarios in agrometeorological studies: past experiences and future needs To better appreciate the current state of knowledge and research in the different countries participating in COST 734.1. A total of 16 answers were returned to WG3.1 Types of scenarios used in the past in agroclimatological studies In first instance. 2070-2099. dynamically downscaled to 10 km with RCMs. CSIRO-Mk2. the members were asked to report about the types of scenarios use in the past in agrometeorological studies.1: Summary of the scenarios used in impact studies during the last 10 years Country Austria Scenarios Scenarios from 2 GCMs (ECHAM4 and ECHAM5). ECHAM4/POYC3. CGCM1. GFDL-R30) • RCM scenarios from PRUDENCE Transient GCM simulations with ARPEGE and A2 and B2 emission pathways Simulations with ECHAM4 and ECHAM5. Table 6. Time window: 2041-2070. a questionnaire was distributed to the COST 734 members in spring 2007 to collect detailed information about the use of climate scenarios in agrometeorological studies conducted during the past 10 years. CGCM1. This is also reflected in the selection of references. GFDL-R15). and further statistically downscaled to 1 km. HadCM2. GFDL-R15. 2040-2069. A2 and B1 emission pathway Bulgaria Croatia Czech Republic Finland France Germany 239 . the text is developed in such a way as to give a more general perspective of the main themes. Downscaling of ECHAM4 and HadCM2 achieved with the MAGICC and SCENGEN.6. The 3 http://www. although this may reflect a bias in the coverage of the studies considered for the survey (e. and CGCM2 (A2 and B2 emission pathways) • PRUDENCE A2 and B2 scenarios • GCM outputs. PRECIS (A2 and B2 emission pathways).ipcc-data. on the background of the HadAM3P global runs. • RCM scenarios from the PRUDENCE project (in particular HIRHAM4) • arbitrary scenarios Scenarios provided directly by the Hadley Centre or the IPCC-DDC The majority of studies carried out in Europe considered scenarios simulated with GCMs or RCMs as a source of information. Later GFDL-R15 and ECHAM4 for time horizon 2025. GCM scenarios from GISS and CCCM (2 x CO2). A common source of scenarios was the database maintained by IPCC Data Distribution Centre (IPCC-DDC)3. global variables • GCM scenarios from the CMIP database • RCM scenarios from the STARDEX.org/. GCM scenarios from the IPCC-DDC (CSIRO-Mk2. Time windows: 2071-2100. statistically downscaled. RCM scenarios from the PRUDENCE project and from simulations carried out at the University of Castilla-La Mancha (UCM) • GCM scenarios from the IPCC-DDC (CCSR-NIES1. DOE-NCAR/PCM. and with reference to the A2 and B2 emissions pathways GCM scenarios from HADAM3H and ECHAM4. • Analogue scenarios used for time horizons 2010 and 2025. CGCM2. ECHAM4-OPYC3). HadCM3. DOE-PCM. The use of analogue or arbitrary scenarios appears to be restricted to a few countries.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Greece Hungary Italy Norway Poland Romania Slovenia Spain Switzerland United Kingdom Simulations with HadCM3 (provided through STARDEX). accessed 20/01/2008 240 . RegCM3 driven with NCEP/ERA-40.g. dynamically downscaled with HIRHAM • Earlier studies based on GCM scenarios from GISS and GFDL (2 x CO2). only the most recent publications). ECHAM4-OPYC3. CGCM1. used directly or scaled by MAGICC/SCENGEN • Empirica/statistical regression local vs. CSIRO. with statistical downscaling in the postprocessing. HadCM3). National Centre for Atmospheric Research (NCAR-DOE). B1 and B2 241 . respectively NCAR-PCM simulations for the A2. A2(b and c). the model used for the simulations and the SRES emission scenarios considered. the centre providing the data. A1T. GFDL by the Geophysical Fluid Dynamics Laboratory (USA) base on R30 simulations for the A2 and B2 emission scenarions. NCAR by the National Center for Atmospheric Research (USA) based on NCAR-CSM. German Climate Research Centre (ECHAM4). We include here some information concerning the Scenarios prepared for the SAR and TAR. National Institute for Environmental Studies (Japan) based on CCSR/NIES AGCM and CCSR OGCM simulations for the A1. More scenarios were adopted in preparing the TAR. B1 and B2 emission scenarios. respectively A1B.6. CSIRO by the Australian Commonwealth Scientific and Industrial Research Organization (Australia) based on CSIRO-Mk2b simulations for the A1. Use of climate change scenarios in agrometeorological studies: past experiences and future needs purpose of the IPCC-DDC is to “make available to the impacts community a set of recent GCM outputs that both reflect the state-of-the-art of model experiments and provide a representative range of results from different GCMs”. All of the SAR scenarios are based on the IS92a emission scenario. A2. A2. The full list of scenarios can be found on the web page of the IPCC-DDC. HCCPR by the Hadley Centre for Climate Prediction and Research (UK) based on the HadCM3 simulations for the A1FI. these were: MPIfM by Max-Planck Institute for Meteorology (Germany) based on ECHAM4/OPYC3 simulations for the A2 and B2 emissions scenarios. Denoting them through their acronyms. A1FI. For this reason criteria were defined to “identify a small number of GCM experiments whose results could be deposited at the IPCC DDC”. as these are those accessed in the past. Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO-Mk2). US Geophysical Fluid Dynamics Laboratory (GFDL-R15). Canadian Centre for Climate Modelling and Analysis (CGCM1). and were obtained from simulations by the following centres (name of the GCMs used in brackets): UK Hadley Centre for Climate Prediction and Research (HadCM2). B1 and B2(b) emission scenarios. Japanese Centre for Climate System Research (CCSR). and. CCCma by the Canadian Center for Climate Modeling and Analysis (Canada) based on CGCM2 simulations for the A2(b and c) and B2(b and c) emission scenarios. CCSR/NIES by the Center for Climate System Research. A2 and B2 emission scenarios. As stated by Christensen et al. RegCM. 2007). More than 30 experiments were conducted with respect to the A2 and B2 SRES greenhouse gases emission scenarios. REMO.gov/projects/cmip/index. PROMES and a stretched version of ARPEGE. and was initiated by the Joint Scientific Committee for the World Climate Research Programme5. CLM. HadRM3P. Among the specific objectives were the evaluation and intercomparison of the models in representing 1961-1990 observed climate in Europe.Survey of agrometeorological practices and applications in Europe regarding climate change impacts emission scenarios. Further details concerning the experimental setup are given in Christensen and Christensen (2007). (2007). 2007). 2010-2039.php.llnl. accessed 20/01/2008 http://wcrp. The relevant scenarios were generated during Phase II of CMIP. CMIP began in 1995. “the main objective of the PRUDENCE project was to provide high resolution climate change scenarios for Europe at the end of the twenty-first century by means of dynamical downscaling (regional climate modelling) of global climate simulations”.. the main focus if the World Climate Research Programme of the WMO).int/..wmo. 2007). and reviews of the results of the projects can be found in a series of papers published in a special issue of 4 5 http://www-pcmdi. accessed 20/01/2008 242 . 2050s. and the assessment of changes in the occurrence and incidence of extreme events (Christensen et al. Data are available for four different time windows: 19611990. the characterization of the uncertainties in projections attributable to model formulation and natural/internal climate variability. RACMO. A total of 11 RCMs were used to simulate climate scenarios at a spatial resolution of roughly 50 km x 50 km for the time windows 1961-1990 and 2071-2100 (Christensen and Christensen. CMIP is the analogue of the Atmospheric Model Intercomparison Project (AMIP) for global coupled ocean-atmosphere general circulation models. the intercomparison of climate projections for 2071-2100. As seen in Table 1 some investigations relied on scenarios obtained from RCM simulations. HadRM3H. CHRM. RCAO. Another source of GCM scenarios considered for agrometeorological impact studies has been the Coupled Model Intercomparison Project (CMIP)4. and 2080s). The project is one of the outcomes of CLIVAR (Climate Variability and Predictability. 2040-2069. and 2070-2099 (the latter three periods are referred to as 2020s. which started in 1997. in particular results of the RCM experiments carried out in the framework of PRUDENCE (Christensen et al. The models were: HIRHAM. There is still a justification in using analogue of arbitrary scenarios when the focus of the investigation is on the sensitivity of a target system or agrometeorological indices with respect to shifts in the climatic boundary conditions.2 Tools used in the preparation of climate change scenarios for impact studies At least in one case. Supplement 1.uk/projects/mice/.cgd. but the situation has changed in recent years due to the availability of other types of scenarios. Finally it is to be noted that the use of analogue or arbitrary scenarios was quite common in the 1990s.cgd. the derivation of regional climate scenarios was implemented with tools such as MAGICC9 (Model for the Assessment of Greenhouse-gas Induced Climate Change.html. 2004). Scenarios from both STARDEX as well as MICE have found their way into agrometeorological impact studies. accessed 24/01/2008 10 http://www.for the 2025s (2011-2040). 6. accessed 20/01/2008 9 http://www. accessed 20/01/2008 8 http://www. Thus in Alexandrov (2006) climate change scenarios for the Balkan Peninsula .html. see below) and SCENGEN10 (SCENario GENerator).cru. May 2007).uea.ac.uea.cru. Jasper et al.edu/cas/wigley/magicc/index.dmi. 81.uk/projects/stardex/.ac..2. All of the results are distributed through the project home page6. In view of their infrequent use.g. accessed 24/01/2008 243 .ucar. Arbitrary scenarios may also be helpful to put the results of more detailed analyses into a broader perspective (see e. 6 7 http://prudence.ucar. STARDEX (Statistical and Regional dynamical Downscaling of Extremes for European regions) was concerned with improving statistical downscaling methods for constructing scenarios of changes in the frequency and intensity of extreme events. Use of climate change scenarios in agrometeorological studies: past experiences and future needs Climatic Change.edu/cas/wigley/magicc/index. 2050s (2036-2065) and 2100s (2086-2115) were inferred from databases prepared with the help of MAGICC and SCENGEN.6. (Vol. STARDEX7 and MICE8 were run in a cluster with PRUDENCE. though not at the same rate as scenarios from PRUDENCE. accessed 20/01/2008 http://www. Two other European projects. while MICE (Modelling the Impact of Climate Extremes) made use of information from both dynamically and statistically downscaled methods to explore the potential impacts of extreme events in Europe.dk/. it is in order to briefly describe their main features. HCFCs. This software allows the user to determine changes in greenhouse-gas concentrations. a variable (temperature or precipitation). Pattern-scaling methods are employed to create the climate change fields at 5° resolution which can then be added to an observed 1961-90 baseline climate data set to obtain actual climate scenario values for the future time period in question”. to produce spatially-detailed information regarding future changes in temperature and precipitation. HFCs. climate and ice-melt models integrated into a single software package. together with results from a set of coupled Atmosphere/Ocean General Circulation Models (AOGCMs) and a detailed baseline climatology.g. and combining these with observed global and regional climate data sets. global-mean surface air temperature and sealevel resulting from anthropogenic emissions of carbon dioxide (CO2). reactive gases (CO. SCENGEN uses these results.3 Dealing with uncertainties in climate change projections Despite advances in our understanding of the processes governing the climate system. user-friendly interactive software suites that allow users to investigate future climate change and its uncertainties at both the global-mean and regional levels. NOx. As for SCENGEN it is noted that “scenarios for the world [are constructed] by exploiting the results from MAGICC and a set of AOGCM experiments. methane (CH4). Concerning MAGICC it is further stated that “MAGICC consists of a suite of coupled gas-cycle.Survey of agrometeorological practices and applications in Europe regarding climate change impacts According to the information provided on the home page. changes in their variability. MAGICC carries through calculations at the global-mean level using the same upwelling-diffusion climate model that has been and is employed by the IPCC. the halocarbons (e. The latest version gives the same globalmean warming and sea-level rise results as published in the IPCC Third Assessment Report (TAR). “MAGICC and SCENGEN are coupled. climate change scenarios remains highly uncertain. all GCM data have been interpolated onto a common 5° latitude/longitude grid. and a range of other statistics”. the capability to model the climate 244 . nitrous oxide (N2O). PFCs) and sulfur dioxide (SO2)“. and one or more of the AOGCMs in SCENGEN's library of model results. Uncertainties step in at several stages of development. Since the GCM experiments report results on different spatial grids. a month or season. A geographically-explicit climate change scenario is constructed by selecting a future time interval. VOCs). 6. including among others the specification of future emissions of greenhouse gases. SCENGEN contains a set of greenhouse gas-induced patterns of regional climate change obtained from different AOGCM experiments and also sulfate aerosol-induced patterns of regional climate change obtained from a series of sulfate aerosol experiments performed with the University of Illinois at Urbana-Champaign GCM.2. the task is less obvious.. Use of climate change scenarios in agrometeorological studies: past experiences and future needs sensitivity. 2007) and in the context of the PRUDENCE project (Déqué et al.. boundary uncertainty as a result of the fact that the regional models have been run under the constraint of the same global model. (2007) noticed that the majority of the RCMs exhibited a clear tendency to overestimate the inter-annual variability of the extra-tropical summer climate. as for the temperature. The assessment and representation of the uncertainties in global climate change projections have been systematically addressed in preparing the AR4 (Meehl et al. 2007). as for precipitation or for extreme events. (2007) pointed out that the role of boundary forcing was generally greater than the role of the model formulation. but estimated that the signal from the PRUDENCE ensemble was nevertheless significant.. in particular for temperature.6. 2007. but also appear in relation to the seasonal and inter-annual variability as well as in relation to the extreme events. However. Déqué et al. This is true for all of the relevant atmospheric state variables. As noted by Stainforth et al. (2005). model uncertainty due to the fact that the models use different techniques to discretize the equations and to represent sub-grid effects. the carbon cycle. A second point of concern when dealing with projections is the misrepresentation of the current climate by GCMs and RCMs.. (2007) in the context of the PRUDENCE project. 2003). It is also evident that the representation of atmospheric fields in mountain regions remains a challenge (Frei et al. Christensen et al. An evaluation of the simulations for 245 . but while in some cases tracing and solving the problem is straightforward. using the climatology prepared by the Climate Research Unit of the University of East Anglia as a reference (New et al. A systematic study of the differences among RCMs in representing climate variability has been conducted by Vidale et al. The result of their analysis was that the RCMs used in PRUDENCE were able to represent some of the observed west-east gradient in climate variability observed across the European continent. “the range of possibilities for future climate evolution needs to be taken into account when planning climate change mitigation and adaptation strategies”. Vidale et al. the ocean mixing and the aerosol forcing. An intercomparison of precipitation extremes as simulated by six different European regional climate models was undertaken by Frei et al. All RCMs had comparable model settings and were driven with boundary data from the same global climate model. Systematic errors in representing the current climate are not limited to the mean conditions. in other cases.. radiative uncertainty introduced by the fact that only but one (two) of the IPCC SRES emission scenarios was taken into account. 2000). Concerning the latter project. and. (2006). four sources of uncertainty were considered: sampling uncertainty in relation to the finite number of years simulated. (ii) statistical downscaling (including pattern scaling techniques). For this reason. such as solar radiation or the vapour pressure or the wind field. are clearly inadequate to address the possible effects of climate change on scales of the order of 1 to 10 km.Survey of agrometeorological practices and applications in Europe regarding climate change impacts present climate in the region of the European Alps indicated that RCMs are capable of representing spatial patterns in precipitation extremes not resolved by GCMs. have received considerably less attention than in the case of temperature and precipitation.2. Techniques used in agrometeorological studies conducted in European countries during the past 10 years are summarized in Table 6. Facing this situation. (2006) found that model biases were large in some cases. further downscaling of GCM and RCM scenarios has been a constant necessity in agrometeorological studies during the past 10 years. 246 . However. According to the survey. Frei et al. 6. In the context of model uncertainties it is also worth pointing out that the performance of GCMs and RCMs for other state variables of relevance in agrometeorology. Finally. in particular concerning the summer season. In one case. so-called downscaling techniques have been developed and applied to obtain scenarios at the desired resolution. Zaliwski et al 1999).2. (iii) generation of weather patterns from basic climate data by means of statistical techniques – stochastic weather generator. (iv) dynamical downscaling by atmospheric models. Currently it is therefore difficult to provide a measure of the reliability of climate projections in relation to these variables. for instance those issued by PRUDENCE. and. This is true irrespective of whether the climate scenarios were issued from GCM or RCM simulations.4 Mapping of climate change scenarios to the small scale: downscaling Scenarios with a spatial resolution of roughly 50 km x 50 km. four options have been considered across the studies reported: (i) combination of climate anomalies from GCM or RCM simulations with historical observations. even a resolution of 10 km is insufficient to adequately address the topographic control of the regional climate. use of a GIS was also mentioned (Zaliwski and Górski 1998. one of the evident results of the survey was that many impact studies suffer from the mismatch between the spatial and temporal scales of the scenarios and the scales implied by the application. In countries where mountains are a dominant component of the landscape. edu/mm5/.com/calpuff/calpuff1. Table 6. mesoscale models (such as RAMS11 or MM512) have been applied to obtain scenarios with a spatial resolution of the order of 1 to 10 km. • GCM output combined with historical data • Use of a stochastic weather generator • Dynamical downscaling with RCMs (REMO and CLM) • Statistical downscaling using WETTREG Bulgaria Croatia Czech Republic Finland France Germany 11 12 http://rams. accessed 24/01/2008 http://www. non-hydrostatic. Dynamical downscaling of GCM to 30 km and 10 km resolution using the non-hydrostatic model MM5 (PSU/NCAR). In other studies. Dynamical downscaling with RCMs. • GCM output combined with historical data • Use of a stochastic weather generator • Dynamical downscaling with a RCM (ALADIN) • Statistical downscaling based on CCA. One study made use of a diagnostic. Use of climate change scenarios in agrometeorological studies: past experiences and future needs With respect to dynamical downscaling. Thus. singular value decomposition (SVD).src. As pointed out by Huth (2002). accessed 24/01/2008 13 http://www.6. the choice of the model has been dictated by the specific needs. it is imperative that the different methods are evaluated. • GCM output combined with historical data • Use of a stochastic weather generator • Dynamical downscaling with RCMs. Dynamical downscaling using ARPEGE.ucar. the survey indicates a variety of approaches. relying on different statistical methods − canonical correlation analysis (CCA).mmm.atmos. further downscaling to 200 m resolution using the diagnostic model CALMET Mainly GCM output combined with historical observations. multiple linear regression (MLR). mass preserving atmospheric model (CALMET13) to generate scenarios with a spatial resolution of less than 1 km. accessed 24/01/2007 247 .edu/. Concerning statistical downscaling. the application of RCMs driven with boundary conditions from GCM simulations has been considered to obtain scenarios at scales between 20 and 50 km. or pattern scaling techniques − and different combinations of predictors and predictands.colostate. SVD or MLR • Statistical downscaling based on circulation patterns recognition.htm.2: Summary of the techniques used in the past for downscaling Country Austria Downscaling techniques used in the past Nested downscaling. far less has been done concerning for instance air humidity or solar radiation.. Clearly. Thus. PRECIS.. although advances have been achieved in the recent past (e. RAMS.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Greece Hungary Italy Norway Poland Romania Slovenia Spain Switzerland United Kingdom • Statistical downscaling based on circulation patterns recognition. while temperature and precipitation have been considered in the majority of studies. 2007). as for instance the analysis of humidity variables by Huth (2005). REMO. 1998. 2008): ALADIN. Weather generators examine the statistical structure of the observed weather and simulate synthetic sequences of weather data consistent with this structure. • Statistical downscaling of GCM output based on CCA • Dynamical downscaling using RegCM-50 and RegCM-25 Statistical downscaling • GCM output combined with historical data & use of stochastic weather generator • Dynamical downscaling with a RCM (PROMES) • Statistical downscaling • Use of a stochastic weather generator Different kinds of downscaling While a method can be adequate to approximate the temporal structure. another may be needed to represent the spatial structure. first projections with 25 km resolution in April. hemispherical observations • physical downscaling (since 2005. 2002. 2004). BOLAM. In doing so. statistical downscaling still needs to be explored systematically in terms of the range of predictands that are of potential interest for agrometeorology. Dubrovsky. 2008) Another tool used in agrometeorological studies for downscaling is stochastic weather generation.g. Dynamical downscaling using RCMs (COSMO-LM. care should be taken to make sure that the model adequately represents scales of variability (Dubrovsky et al. Hundecha and Bárdossy. RegClim • LARS WG and other (built-into impact models) weather generators geoagraphycal and historical analogy to obtain WG imput parameters Statistical downscaling by CCA • Nested downscaling using a RCM (HIRHAM) and statistical techniques • Statistical downscaling applied directly to GCM output Downscaling of GCM output exploiting GIS technology and time series analysis. 2008. POSEIDON) • Statistical downscaling from diurnal pressure patterns (objective macro-synoptic types and conditional autocorrelation) • regression between local vs. Information obtained from climate scenarios can be used to selectively modify the statistical structure and generate synthetic series consistent with the changes imposed in this way (e.g. There are nevertheless notable exceptions. Kysely. 248 . Additional efforts are also needed to improve the downscaling of extreme events. Busuioc et al. Semenov. Use of climate change scenarios in agrometeorological studies: past experiences and future needs One of the first studies to systematically examine the stochastic simulation of daily data is that reported by Richardson (1981).5 Scenarios for extreme events In agriculture. 1995 and 1996). Met&Roll15 by Dubrovsky. 2007).cas. The spatial interpolation of synthetic data has been examined by Semenov and Brooks (1999). 1991).rothamsted. extreme events can have catastrophic effects on production. the attention has also moved toward finding way to use weather generators to create regional scenarios.6. 6. however. (1998). A comparison of LARS-WG and WGEN can be found in Semenov et al. accessed 24/01/2008 249 .. In recent years. Wilks (1999). A summary of extreme events mentioned in the survey is given in Table 6.bbsrc. Weather generators are usually run at the local scale.ac.. accessed 24/01/2008 http://www. has considered the simultaneous generation of daily data. Probably at the exception of LARS-WG14 (Rackso et al.g. Extreme precipitation events.php. on the other hand. In this sense.uk/mas-models/larswg. based on an extended version of WGEN. heat waves and droughts are among the events more frequently addressed in a European context.3. it is not surprising that the question of how climate change might affect the occurrence of extreme events has been tackled in many investigations. the PRUDENCE scenarios suggest that by the end of the twenty first century countries in central Europe will experience the same number of hot days as they are currently experienced in southern Europe.htm#met&roll. a fact that has been ascribed to increases in temperature variability. and the results of that survey provide a good overview of what found in the majority of studies.2. Both approaches can give valuable support to studies where complex topography is a major constraint. 14 15 http://www.ufa. In practice. Further. it has been found that heavy winter precipitation increases in central and northern Europe but decreases in the south. Richardson’s (1981) model (WGEN) has been adopted as a basis for developing most of the follow-up products (e. This is also reflected in the analysis of the RCM simulations carried out in the framework of PRUDENCE (Beniston et al. while heavy summer precipitation increases in northeastern Europe and decreases in the south.cz/dub/dub. The intensity of extreme temperatures was found to increases more rapidly than the intensity of more moderate temperatures over the continental interior. 6. heavy rain.1 A summary of past experiences In the past. It appears. extreme precipitation events NA Risk of droughts has been suggested to increase in central Europe. As already mentioned.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Table 6. however. Problems identified by the participants were of variegated nature. but reference made to projects PRUDENCE.3: Types of extreme events addressed in past studies on climate change Country Austria Bulgaria Croatia Czech Republic Finland France Germany Greece Italy Norway Poland Romania Slovenia Spain Switzerland United Kingdom Type of extreme events considered in the past Hydrological extremes. wind storms and frost Droughts and dry spells. more specifically heat waves and droughts Hot days Not specified.cru. is that the precise definition of extreme events is central for understanding the results.3. accessed 24/01/2008 250 . a major difficulty in the application of climate scenarios was found in relation to the spatial representativeness of 16 http://www. that in the majority of cases. wind storm and hail NA Wind storms and heavy precipitation Extreme temperatures and precipitation. for the Mediterranean area.3 Lessons learned and outlook 6. STARDEX and ENSEMBLES Heat waves. the development and application of climate change scenarios have not been devoid of difficulties. A point sometimes overseen is impact studies.uea. STARDEX and ENSEMBLES Extreme precipitation events NA Extreme temperatures and rainfall Heat waves and droughts. little information was provided in the questionnaires. sea storm surges Not specified. For instance.ac. wind storm. hail NA (but analysis of extreme temperatures and precipitation events mentioned in the answer to Question 1) Heavy precipitation and floods. heavy rainfall and floods.uk/projects/stardex/deis/Core_Indices. but reference made to projects PRUDENCE.pdf. studies have adhered to the definitions adopted for STARDEX16 project. the PRUDENCE simulations indicate that droughts start earlier in the year and last longer. related for instance to the lack of clarity in the definition of variables. understanding of the questions. In the past. this often implied the impossibility to conduct specific studies and the necessity to resort to open access data and supplementary information. In many studies. Although nowadays storage capacity is not anymore an issue. it was noted that small or private institutions can still face problems in handling very large amounts of data.3. Use of climate change scenarios in agrometeorological studies: past experiences and future needs GCM output and the lack of generic but reliable procedures for downscaling the results. Rather it was pointed out that it may be difficult to find the appropriate combination of predictors and methods and that even for the optimal choice of predictors and procedures the results can be quite uncertain. a systematic analysis of uncertainties is possible at all stages of the analysis. In some countries. that the use of ensemble scenarios is to recommend. In this way. It was also mentioned that the procedure adopted to temporally downscale the output of monthly or seasonal scenarios should be chosen with care. one questionnaire mentioned the fact that impact studies may suffer from more fundamental problems. data needed for running the models or verifying the predictions were difficult to access. even though this may pose troubles in handling the data volume. several initiatives have already been completed or are currently in action to provide a better access to climate scenarios for the 21st century.6. The six key projects reported 251 .2 Completed and ongoing projects at the European level The need for more reliable scenarios of future climatic conditions in mitigation and adaptation studies implies continuous efforts to improve assumptions and methods adopted to infer scenarios. where the complexity of the topography on spatial scale often of the order of 1 km or less implies pronounced variability in the climatic fields. statistical downscaling was the only choice available for inferring climate scenarios at the regional to local scale. either for technical or institutional reasons A few countries complained about the lack of human and technical resources. Statistical downscaling was not criticized per se. At the European level. and the like (Sivertsen. Most of the participants emphasized. 2005a and 2005b) 6. This holds particularly true with respect to mountain regions. documentation of datasets. Finally. Of the three projects completed. as reflected specifically in the management of biological resources. CECILIA21. accessed 24/01/2008 22 http://www. the need to capture the effects of topographical and associated land-use features on the local climate in regions characterized by complex terrain. the investigation of adaptive responses to climate change of agroecosystems using the integrated models.com/.ac.Survey of agrometeorological practices and applications in Europe regarding climate change impacts in the survey are: ACCELERATES17. STARDEX19. PRUDENCE18.uea.cru. accessed 24/01/2008 http://prudence. most of the signatory countries of COST 734 were or are participating in one or more of these projects. decadal and longer timescales. The aim of the three ongoing projects. because its main goal was to examine the relationship between agricultural land use responses to environmental change drivers and environmental protection. 17 18 http://www.org/.4. the EC FP6 project ENSEMBLES has been initiated with two goals in mind: (i) develop an ensemble prediction system based on global and regional Earth System models. including agrometeorological investigations. is clearly to bridge the gap between the climate information provided by GCMs and that needed in impact studies. PRUDENCE and STARDEX have already been described in Section 2.dk/.clavier-eu. and. chemical. accessed 24/01/2008 19 http://www. Eventually. among others: the analysis of the impacts of future climate and socio-economic change on agroecosystems at the European and regional scales using the integrated models. CECILIA and CLAVIER. Two aspects stand in the foreground: the need to better quantify uncertainties in climate projections by providing probabilistic projections. accessed 24/01/2008 20 http://ensembles-eu. accessed 24/01/2008 21 http://www. As seen in Table 6. To address uncertainties in future climate at the seasonal. achieving these objectives will allow a more intensive use of scenarios in application studies.org/clavier/.geo. from Regional Analysis to The European Scale) differed from these other two projects. and CLAVIER22. ENSEMBLES20.dmi.ac.uk/projects/stardex/.be/accelerates/.1 and are not further treated in what follows. accessed 24/01/2008 252 . biological and human-related feedbacks in the Earth System.ucl. (ii) quantify and reduce uncertainty in the representation of physical. ACCELERATES (Assessing Climate Change Effects on Land use and Ecosystems. Specific objectives were. and.cecilia-eu. validated against observations and analyses. ENSEMBLES.metoffice. PR(UDENCE). 253 .e. only the emission scenario A1B will be considered for the experiments. Use of climate change scenarios in agrometeorological studies: past experiences and future needs Table 6. In view of the amount of resources necessary to carry out the simulations. ST(ARDEX). and the spatial resolution of the scenarios will be of 25 km (as compared to 50 km in the PRUDENCE scenarios). and CL(AVIER) Country Austria Bulgaria Croatia Cyprus Czech Republic Denmark Finland France Germany Greece Hungary Ireland Italy Luxembourg (The) Netherlands Norway Poland Portugal Romania Serbia Slovak Republic Slovenia Spain Switzerland United Kingdom AC • PR ST EN • CE • • CL • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Relying on dynamical downscaling.6. The spatial domain will be the whole of the European continent. ENSEMBLES will provide access to a set of transient runs covering the whole period of 1950-2050 (2100) from selected combinations of 14 RCMs and 6 GCMs. nesting of a RCM or a fine-scale limited area model within a GCM. i. EN(SEMBLES).4: Participation of signatory countries of COST734 in the following European projects with a focus on climate change: AC(CCELERATES). CE(CILIA). and assessments of their use in localization of model output for impact studies will be performed.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Based on key runs from ENSEMBLES. is not sufficient to properly resolve the spatial scales dominating the northern flanks of the Alps. the Carpathians. human and technical resources Bulgaria 254 .3 Role of COST 734 Besides advancing our understanding in the field of climate change. the participants in the survey offered quite a wide spectrum of possibilities (Table 6. A similar aim applies to CLAVIER. Asked for how they see the role of COST 734. as prescribed in the simulations carried out for the ENSEMBLES project. such as ENSEMBLES. have the merit of creating a network of partner institutions that can be exploited for the coordination of other activities. make uncertainty analysis a key research issue Needs for financial. The objective of CECILIA is to assess the local impacts of the A1B emission scenario in two time slices. touching such aspects as the necessity to provide common tools for the application of climate scenarios but also the possibility to overcome some of the financial and technical needs and the needs for human resources in some countries. the mid (2021-2050) and end (20712100) of the 21st century. regional climate simulations for selected areas in Central and Eastern Europe are run in the context of CECILIA. CLAVIER will also differ from CECILIA in that both the A2 and B1 emissions scenarios will be adopted. This will open opportunities for investigating the consequences of climate change for the occurrence of weather extremes in the regions under study. In addition.5). a spatial resolution of 10 km is adopted in CECILIA and CLAVIER for running the RCM simulations.3. CECILIA and CLAVIER. the Pyrenees. Slovakia. and smaller mountain chains and highlands in the Czech Republic. Table 6. Romania. statistical downscaling methods for verification of the regional model results will be developed and applied within CECILIA. Romania and Bulgaria. either in time slices or transient run experiments. but here the focus is clearly on a small region encompassing Hungary. Noting that even a resolution of 25 km. 6. projects at the European level. and Bulgaria.5: Needs for future studies Country Austria Requirements for future studies Foster collaboration between climate modelling and agricultural research communities. ranging from the coordination of research activities to the promotion of collaboration between scientists and stakeholders. NA 6. Improve climate models with respect to. In view of the changes in climatic conditions projected by global and regional climate models for the 255 . at least). comparison of the scenarios derived by different methods to assess the reliability of previously performed impact studies Foster the operational use and the quality assessment of climate scenarios across European countries Broaden the field of research. or devise techniques to better account for atmosphere/biosphere feedbacks. promote the use of and provide probabilistic scenarios NA Standardize the scenarios (create a database of reference scenarios) and the downscaling techniques Assess the suitability of particular climate scenarios. Moreover. differences among GCM or RCMs. including topics such as sustainability of the agricultural production and. need to more carefully address the issue of the extreme events. and appears to have already reached its maximum.g. Need to promote the use of probabilistic scenarios.4 Conclusions In Europe crop yields increased significantly during the second half of the 20th century. need to provide common downscaling tools. extreme events such as the heat wave that struck large portions of the continent during the summer of 2003 have clearly shown that the agricultural sector remains vulnerable to climate. Need for general support Need for common set of scenarios and downscaling tools Need for cooperation between neighbour countries. which lead e. to drought. observational data availability Availability of RCM model outputs (statistical parameters. performance of statistical downscaling. Use of climate change scenarios in agrometeorological studies: past experiences and future needs Croatia Czech Republic Finland France Germany Greece Hungary Italy Norway Poland Romania Slovenia Spain Switzerland United Kingdom Foster education and promote use of scenarios among stakeholders Overcome human and technical restrictions through concentration or coordination of the various efforts. need for common downscaling techniques. the pace at which productivity was raised started to decline in the 1990s. However.6. Adequate modelling or additional simulation of real autocorrelation in the anomalies in subsequent months/seasons. eventually the whole food chain. promote the use of probabilistic scenarios Uncertainties in the emissions scenarios. Rockel. I. Carter. M. B. D. D. Qin. Averyt. L.. United Kingdom and New York. V. 2006. Mearns. Sarr. Lüthi. Christensen. Advancing our understanding of the climate system and our ability to simulate the relevant processes. Christensen J. 2007. X.H. 2007. Stephenson. Tomozeiu. Amanatidis. Climatic Change 81:71–95. Goyette.L.)]. 1995. Jacob. Met&Roll: The weather generator for the crop growth model. is necessary to support policy makers and stakeholders in their effort to face climate change.H. Jylhä. Held. Schöll. Manning.. B. In: Regional Workshop on Climate variability and climate change vulnerability and 256 . Giorgi. Christensen. if not already..R. Palutikof. S. Menéndez. Statistical downscaling model based on canonical correlation analysis for winter estreme precipitation events in the EmiliaRomagna region. T. An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Future extreme events in European climate: an exploration of regional climate model projections. A summary of the PRUDENCE model projections of changes in European climate by the end of this century. The survey carried out among the signatory states of COST 734 had not the pretention of being exhaustive. K. Ferro. M. Tignor and H. A.. Chen. C. Rummukainen. Int. Climatic Change 81: 7–30. Whetton.B. Woth.P. D. Semmler.G. 2007. Z. van den Hurk. . K. 2008.. K. R. 189 pp. Climatic Change 81: 1-6. in press. Beniston M. USA. Kjellströmde. T. Chen. E. C. Miller (eds. W.K.. Rowell. Climate variability and change and their influence on ecosystems in Central and Southeastern Europe as well as Southeastern USA. Holt. D. Koffi. thus reducing the uncertainties in the projections. M. Rinke. B. NY.. Christensen J. R. A.B. Kolli. S. Halsnaes. Kwon. Busuioc A.-T.5 References Alexandrov V. M. D.B. C. 2007. Déqué M. 6. G. B. O. R. Regional Climate Projections. but nevertheless provided a useful overview of the present state of knowledge in a representative sample of European countries.B. Hewitson. 2007. Cacciamani. Climatic Change 81: 53–70 Dubrovsky M. J. including projections for changes in climate variability and occurrence and severity of extreme events. F. it is likely that adaptation to climate change. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon. Cambridge. Gao. J. J.H. R. Castro. A. M.T.. Cambridge University Press. In: Climate Change 2007: The Physical Science Basis. will soon become a key aspect of the agricultural policy in Europe. C. Christensen J. Laprise. A.. Climatol. Busuioc. T.A.Survey of agrometeorological practices and applications in Europe regarding climate change impacts coming decades. Evaluating the performance and utility of regional climate models: the PRUDENCE project. Räisänen. K. P. Frei. It helped identify problems in relation to the development and application of climate scenarios and some of the needs for the future work. DrSc thesis. Marquis. and Christensen O. R. J. Defining and implementing effective measures of adaptations will require quantitative knowledge of the possible evolution of the climatic conditions at the regional scale. Jones.. Magaña Rueda.H. Climatol. Met&Roll: the stochastic generator of daily weather series for the crop growth model. Climate. Cambridge. (Eds. Houghton J. D06105. X. United Kingdom and New York.). Chen. High-Frequency and Low-Frequency Variability in Stochastic Daily Weather Generator and Its Effect on Agricultural and Hydrologic Modelling. 2003. 2006. 2004. Climate Change 2007: Impacts. Johnson.H.1029/2005JD005965 Houghton J.L. Climate Change 1995: The Science of Climate Change.B. Stat. S. Gaye. 2007. J. J. Maskell. Vidale. P. Cambridge. Gregory. Weaver. I. Contribution of Working Group I to the Second Assessment of the Intergovernmental Panel on Climate Change. P. van der Linden. W. 111.6. Z. Jones. M. Miller (eds. Collins. Watterson. Cambridge University Press.F. R. AMS. 2008..U. Maskell. Z.A. Averyt. Climatol. Res. in press. van der Linden. Marquis. and Swart R.. J. J Climate 13: 2217–2238. J. A. Downscaling of humidity variables: A search for suitable predictors and predictands.G. Institute of Atmospheric Physics. doi:10.G. 243-250. VOL. P.C. Christensen. Dubrovsky M. Fuhrer. P.)]. Praha . Harris. doi:10. Int.A. 145-179. Prob. 1731-1742. K.1-2). A. D. 1995. Murphy.A. Tignor and H. Parry M.. New M. Callender.. J. Zalud. IPCC Special Report. Meehl G. A. 1996. D3. Hanson.L. C.J. Country Studies Program.C. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon. 49. Emissions Scenarios.J. Representing twentieth-century space-time climate variability.G. Jasper K. B.J. Calanca. Dai. M. Fukutome.T. J. 2001. D. Huth R. Noguer. Part II: Development of 1901–96 monthly grids of terrestrial surface climate. J.D. (Eds). T. Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps.T. Stocker. In: Climate Change 2007: The Physical Science Basis.. Knutti. 97-105. 2000.. Cambridge University Press. R. Cambridge University Press.. Z.. P. 4124. 2002. D. 26: 113–129. Griggs. Y. S. Estimating climate change impacts on crop yields with use of crop growth model and weather generator. Future change of precipitation extremes in Europe: Intercomparison of scenarios from regional climate models. JOURNAL OF GEOPHYSICAL RESEARCH.. (Eds.. P. Hundecha Y. D. Dubrovsky M. Palutikof. Contribution of Working Group II 257 . and Bárdossy A. Washington.M. M. 2005. A. Use of climate change scenarios in agrometeorological studies: past experiences and future needs adaptation (proceedings)..T.S.B. Kattenbergand. K. Manning. R.C. NO. Cambridge. Frei C. 15. Int. Climate change 2001: The Scientific Basis. In: Proc.L. Contribution of the Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. JOURNAL OF GEOPHYSICAL RESEARCH. A. 1998. 108. Noda. Meira Filho. 14th Conf.1029/2002JD002287 Frei C. Statistical downscaling of extremes of daily precipitation and temperature and construction of their future scenarios. Schmidli.. Hulme. Huth R. K. L. Clim. Nakicenovic N. Vidale. Differential impacts of climate change on the hydrology of two alpine river basins. Jones.M. Qin.. Zhao. 2000. Cambridge. Adaptation and Vulnerability. Climatic Change 63 (No. O. Buchtele. D. Schöll.). USA. J. Global Climate Projections.. Raper. J. C. Meteorological Bulletin.. Statistical downscaling of daily temperature in central Europe. Dubrovsky M. P. 2007. Déqué. Canziani.. M. S. 25. 2004. M.. Ding. Jacob. Friedlingstein. VOL. Cambridge University Press. Gyalistras. J. Kitoh. N. M. NY. 6 Additional references R1.W. C. Richardson C.. 2007. 2005b. D. Forest Meteor. Semenov M. 96: 85-101. Qin. J. 2007. Kettleborough.. Manning.. Wegmann. Physics and Chemistry of the Earth 30: 35-43. temperature and solar radiation a multiple sites in complex terrain. R. Frame. E.J. C.J. 2001. Christensen. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Faull.. Chen. Cambridge University Press. Martin. Lüthi. Siversten T. 1991. Smith. J. 1998. CD ROM. Semenov M. Development of high-resolution UKCIP02-based climate change scenarios in the UK Agric Forest Meteorology 144:127–138. Semenov. Aina... M. Murphy. Alexandrov V. Zaliwski A. Z.. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.B. 1981. LeRoy. Nature 433: 403-406.M.A. Cambridge. Science 293: 451-454. 1999. Lipski. R. Climate Change 2007: The Scientific Basis.. Stainforth D. M. Knight. vol. Discussing the scientific method and a documentation system of meteorological and biological parameters. Semenov M. Winiarski. Simultaneous stochastic simulation of daily precipitation.A.M. Thorpe. Jr. Averty. Richardson. Res. Solomon S. Szeidl.B.R. References cited in the answers to the questionnaire with respect to the type of scenarios Alexandrov V.A.L. Górski. Sexton. M. Climate Research. 27-41.H. 217224. A. 1999. E. N.. Wilks D. Cambridge University Press. (Eds) 2007. 2nd European Conference on Applied Climatology ECAC98. Interpretation of High Projections for Global-Mean Warming. temperature and solar radiation. Allen. M. 1999. 1998. Tignor. Climate Research 10:95-107. A. 19-23 Oct. D.H. A serial approach to local stochastic weather models. Implementation of a general documentation system for web-based administration and use of historical series of meteorological and biological data.L. Piani.M. S. Austria. R. 2005. Numerical Maps of Profit Probability for Maize Production in Poland. Schär. Zaliwski A. Agric. European summer climate variability in a heterogeneous multi-model ensemble. C. S. and Brooks R. A. T. 1997. Barrow. Stochastic simulation of daily precipitation.J.A. and Górski T. Ecological Modelling 57. L. Physics and Chemistry of the Earth 30: 217–222.M. 1999. L. Spicer. C. Vienna. 2005a. Water Resourc. T. Wigley T. Numerical Maps of Temperature Sums in Poland. T. Climatic Change 81: 209-232.. Collins.. EFITA/99 Conf. Bulgarian Journal of Meteorology and Hydrology 4(4): 205-211.J. H. M. Brooks. Cambridge. 1998. 17: 182-190.A. ZAMG. Siversten.S. GCM Climate Change Scenarios for Bulgaria.C. 258 .A. Vidale P.Survey of agrometeorological practices and applications in Europe regarding climate change impacts to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. M. Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. D. D. Bonn.W. 11:137-148. and Raper S..A. Racsko P. Wróblewska. R. Vulnerability and Adaptation of Agronomic Systems in Bulgaria. K. Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain.. Climate Research 12(2-3): 161-173..M. Marquis. 6. R.. (Economic consequences of climate changes in agriculture – preliminary appraisal). Bergant K. Adriana Marica.6. 2006. 455-472. Marinela Simota.of Clim. and G. Ekonomiczne konsekwencje zmian klimatu – ocean wstępna. 19-20 April 2007. Uncertainties in modelling of climate change impact in future: an example of onion thrips (Thrips tabaci Lindeman) in Slovenia. Bucuresti. Alexandrov V. 755-766. 244-255. Potential Climate Change Impacts on Selected Agricultural Crops in Northeast Austria. ECHAM4_OPYC_T42_22670_Scenario: Greenhouse Gas Experiment (GHG). Probl. Belda. University of Ljubljana. Alexandrov V. Gelybò 2007. DrSc thesis. T. pp. In: Staneva. in press.. Agricultural and Forest Meteorology. A. Systematic errors in the simulation of European climate (1961-2000) with RegCM3 driven by NCEP/NCAR reanalysis. Harasim. Ossolineum Wrocław. G. International Journal of Climatology 27(4). M.. 34(4).. and Eitzinger J. 2002. Eitzinger. 259 . A. ISBN 973-558125-6. Beijing. Department of Forestry and Renewable Forest Resources Slovenia. Corbus. Proceedings of the International Symposium on Climate Change (ISCC). M. pp. and J. 67-86 (only in Slovenian). D. Symposium: Climate changes: impact on forest and forestry (ed. Projections of climate change for Slovenia. 2000. S. C. 2006. 177-198. Proceedings of the international conference on 150 Years of Meteorological Service in Central Europe. Tecuci.. Črepinšek. University Park. P. L. R. Stanescu. China. The Impact of Climate Variability and Change on Crop Yield in Bulgaria. Serban. pg 1-23. 2000. Knight. Ed. Bergant K. J. 5. V. Kajfež Bogataj. 2007. The Potential Effect of Climate Change and Elevated Air Carbon Dioxide on Agricultural Crop Production in Central and Southeastern Europe..1-17. Bengtsson L. Pongràcz.. Bergant K. 104(4): 315-327. Demidowicz. Alexandrov V.dkrz. Alexandrescu. 1993. Hristov and D. available from: http://cera-www. Climate variability and change and their influence on ecosystems in Central and Southeastern Europe as well as Southeastern USA.. S. Pennsylvania.). Cuculeanu. 1-3. Bartholy J. C. Journal of Crop Improvement 13(1-2): 291-331.. Jurc M. Hoogenboom. Global Change Biology 8(4): 372389. Academia Romana-Comitetul National pentru Modificarile Globale ale Mediului.). Patrascanu.Al. Studia forestalia Slovenica. Use of climate change scenarios in agrometeorological studies: past experiences and future needs Alexandrov V. Trdan. T. 2003. 2003. 2007. Caijc. N.de.. Kajfež Bogataj. Int. Bis K. C. L. I. 232-237. Statistical downscaling model based on Canonical Correlation Analysis for winter extreme precipitation events in the EmiliaRomagna region . G. Biotechnical Faculty. Krasowicz. J. WMO/TD-No. Bioproductivity.. USA and Sofia. Žnidarčič. 194. Alexandrov V. Cacciamani 2007. 2005. Impact of climate change Developmental Dynamics of Thrips Tabaci (Thysanoptera: Thripidae): Can it be quantified? Environmental Entomology.1172. Slovakia. Busuioc A. Impactul Potential al Schimbarii Climei in Romania. Halenka. S. The Impacts of Climate Variability and Change on Agriculture in Bulgaria... 2001. 2001. Potential Climate Change Impact on Winter Wheat and Spring Barley in Austria. Eitzinger. 68. Geicu. Bergant K. (CD) 15 pp. V. Agriculture and Climate Change... Alexandrov V. A. P. 2005. Applied Ecology and Environmental Research. 130. Global Change and Bulgaria. Agrofizyki. Górski. Tuinea. 189 pp. Stara Lesna. Trdan. V. Busuioc A. Oberfoster. Tomozeiu. Alexandrov V. ARS DOCENDI. Simota. T. G. Regional climate change expected in Hungary for 2071-2100. Deputat. Z. M.. Mishev (eds. Ecological modelling. T. Wechsung. K. Geophys. Marica. P. PIK-Report. P. Papers on Global Change. J. Cuculeanu V. 153-160.. L. Stock. die Forstund Landwirtschaft sowie die Ableitung erster Perspektiven. held in San Jose. and Déqué M. V. and Piedelievre J. K. D. Nr. (Statistical distribution of the yields main cultivated crops in Poland according to actual climatic condition and two climate change scenarios). Climate change impacts on agricultural crops in Romania and adaptation options. 1994. Res. Christensen. 2003. F. R. Braun.. Hattermann. Climate Dynamics 20:327-339 Górski T. Déqué M. 2000. Deputat. 2007. F.. Costa Rica. ARPA-Rivista.G. Daily precipitation statistics in regional climate models: evaluation and intercomparison for the European Alps. Clim Dyn 10:249-266. The ARPEGE-IFS atmosphere model: a contribution to the French community climate modelling. Clim Dyn 14:173-189. In: Nobuo Mimura (eds. 2005. M. Studie zur klimatischen Entwicklung im Land Brandenburg bis 2055 und deren Auswirkungen auf den Wasserhaushalt.. C.. I. Marica. Dreveton. Cariolle. Planet. Global warming and the summertime evapotranspiration regime of the Alpine region. Planton.. 1493-1513. Jacob. W. Differential impacts of climate change on the hydrology of two alpine river basins. Evidenze di cambiamenti climatici dalla scala globale alla scala regionale. Górski T. Marquet. 2003. P. Stephenson.G. 57: 151-160. F. 647-666.). Lasch. Górska. Proceedings of the Conference on National Assessment Results of Climate Change. 139-156 Dudhia J. D... A. Déqué M.. Anthropogenic climate change over the Mediterranean region simulated by a global variable resolution model. Inter-Research ISSN 0936-577X. Calanca P. J.W. 1999. C. S. Raport IUNG.. Climatic Change 79. Simota. Change. Douville H. Rev.A. Clim Res 26: 113–129. 1993. Clim Dyn 11:321-339.. D. R. Wea. 1998..H.P. The impact of climate change on agricultural production in Poland. Badeck. Climate change impacts on agricultural crops in Romania and adaptation options. Spoz-Pać.. Kalvova. C. J. M. pp. R. 2000. 121.L. 1995. D. Tyteca.and Kuś J. 1998 (Printed by the Japan Environmental Agency and Overseas Environmental Cooperation Center). March 25-28. Ribaldi. W. Frei C. Déqué. S. vol. Suckow. The importance of vegetation feedbacks in doubled-CO2 time-slice experiments. Marcinkowska. J. I. Uncertainties in climate change scenarios for the Czech Republic. Pavan.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Cacciamani C. Roesch. A. Krysanova. pp. Rozkłady statystyczne plonów głównych roślin uprawnych dla stanu aktualnego i dwóch scenariuszy klimatycznych. 83. 65–78. Wild.C.. Jones. S.B.. Climate Research (CR SPECIAL 6.L. Simota. IUNG Report. Climate change and drought occurrence in the Alpine region: How severe are becoming the extremes? Glob. Jasper. Déqué M. Germany. 2007. F. Mon. 108: ACL 91-19 Gerstengarbe F. A. Possibili scenari futuri sul territorio della regione Emilia-Romagna. Lahmer. January 2000... Betts. P. Fuhrer. Werner. ISSN 1436-0179 Gibelin A. P. 12. M. Dubrovsky M. 260 . Jasper K. J. A nonhydrostatic version of the Penn State – NCAR mesoscale model: Validation and simulation of an atlantic cyclone and cold front. High-Resolution climate simulation over Europe. 2006. Jones. Simulation of climate change over Europe using a global variable resolution general circulation model. R Tomozeiu. Cuculeanu V. 1997. Climate Research 29. Gyalistras. T. 2002. V. Calanca P. Book Version). Vidale. 2004. Royer. Lafont. J Geophys Res 105:14841-14861. Nemesova.F. 2003. A. Kergoat. Calanca. S. 9: 79-90. 2004.L.C. Stevenson. Ingram. K. Kozyra J. 2004. Switzerland.M.A. M.M. Boer.. Jones. Improved global sea surface temperature analyses using Optimum Interpolation. Bergant K. Marica A. and A. M. and Smith T. Geneva. Kozyra J. Riedo M. Climate Dyn 16: 123-146. H.185-195. P. The impact of new parameterizations in the Hadley Centre model: HadAM3.6. Climate change projections for Finland during the 21st century.A. J.W. Gallani. Sexton. Pasture responses to elevated temperature and doubled CO2 concentration: assessing the spatial pattern across an alpine landscape. Gyalistras. Submitted to Int.173-181. Romania. Reynolds R. Wpływ zmian klimatu na uprawę roślin w Polsce (Impact of climate change on crop cultivation in Poland). J. Meehl G. Schöner. 2004. 1995. 2003. D. Pavan V. M. Covey. Roberts. Use of climate change scenarios in agrometeorological studies: past experiences and future needs Jasper K. Adriana.J. Clim Dyn. C.M. V. 2005.. 127-152.B. 24-26 January.M. Lowe. Roman. The coupled model intercomparison project (CMIP). Molnár K. Johnson.... Ed. 41-50. Mitchell. 2001. Seibersdorf..J. What might the climate of Slovenia look like in this centrury? Ujma.. 9. Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emission scenarios. Climate as a changing component of landscape: Recent evidences and projections for Hungary. B. J.J. A. 381-399. Climate Dyn 20: 583-612.6. Modelling maize responses to carbon dioxide doubling and climate changes. Kajfež Bogataj L.. Changes in summertime soil water patterns in complex terrain due to climatic change.. J. C. Tomozeiu.. Fuhrer.B. and Górski T. Wakayama City. R. Riedo M. Marica A. Latif.. Gobiet. 2004. R. 1. M. D.S. WMO. Reclip:more – Research for Climate Protection: Model Run Evaluation..J. Japan. G. 20. M. 1994. Fuhrer 2006. Gregory. ISSN 1354-1013.F. and Mika J. R. J. Boreal Environment Research. Volume1(Number 6). Bull Am Met Soc 81:313–318.E. 1998. Busuioc. Austrian Research Centers systems research. 261 . 2003. Daily precipitation observations over Emilia-Romagna: mean values and extremes. M. 2000. C. Loibl W. W. Polski Klub Ekologiczny. Calanca. w Klimat – Środowisko – Człowiek. ISSN 1223-1118. by The Asian Federation for Information Technology in Agriculture. H. Pope V. Tett. Fuhrer. Proceedings of the First Asian Conference for Information Technology in Agriculture (AFITA Conference).J. M. 1997.L. 94. D. A. 2007. Dorninger. 19.D. Ahrens. S. Stouffer. No. D. Gyalistras. P. Jahresbericht.H. Tanasescu.F. G. Zeitschrift für Geomorphologie 110. Wrocław. Miglietta F. 2000. Journal of Hydrology 327: 550– 563. Romanian Journal of Meteorology. W. Roman. Formayer. Okręg Dolnośląski. Tuomenvirta. Bucharest. Woodage.1-2. The potential impacts of climate change on the main components of water balance relating to maize crop..of Clim. Climatic conditions for millet cultivation in Poland.R. J. Vol.. Stephenson D. Net primary production and carbon stocks in differently managed grasslands: simulation of site-specific sensitivity to an increase in atmospheric CO2 and to climate change. Ruosteenoja. 2004. Jylhä K. The North Atlantic Oscillation in coupled climate models: a CMIP1 evaluation.. Cacciamani. Rowntree. CAgM Report No. and Pavan V. Di Lorenzo. J.B.A. Clim Res 17: 19–31. Cuculeanu. The expected effects of climate change on wheat development (IATA-Wheat phenological model). A. Stratton 2000. 34-35. Global Change Biology. D. Climate 7: 929-948. pp 407-415. 218-223 (only in Slovenian language). Johns T. Ecological Modelling 134 (2000) 207–227. pg.. Torriani D. S. in press. Busuioc 2007. Tuinea. D.B. Max Planck Institute for Meteorology (MPI). Kocmánková.. Climatic Change Vol. Beniston. Quadrelli. Patrascanu. Impactul schimbarii climei asupra ecosistemelor agricole. Trnka M. Potential effects of changes in mean climate and climate variability on the yield of winter and spring crops in Switzerland. 227-255. Beniston.. Observed temperature changes in Emilia-Romagna: mean values and extremes. M. Fuhrer. R. Trnka M. Cuculeanu. A statistical cloud scheme for use in an AGCM. Lips. A. F. Sevault.. M. 2006. V. Amman. Hamburg. Dubrovský. Theoretical and Applied Climatology .1007/s10113-0070039-z.) 2005. Chauvin. L. Part 1: Model Description.F. R. European Corn Borer Life Stage Model: Regional Estimates of Pest Development and Spatial 262 . Tompkins 2003. Clim Dyn 27: 401–420. Geophysicae 11:1095-1115.L. S.. Cuculeanu. S. 1997.F. 2006. Simon. Rascol. P. C. North Atlantic Oscillation response to transient greenhouse gas forcing and the impact on European winter climate: a CMIP2 multi-model assessment. M. Amici. Hagemann.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Ricard J. Pavan. .P. S. L. L. M. U. P.A. Bucuresti 2003. Simulation of climate changes during the 21-st century including stratospheric ozone. P.Al. I. 229-249. J Fuhrer. S. E. Žalud. DOI:10. U. A. V. 188 In V. Dubrovský. Douville. Coordonator: V. Planton. 217-225. Simota C. Cuculeanu & colab. Pavan. A.. Climate Research. E. D. 77. Kirchner. N.. Semerádová. Stock. Cuculeanu. Z. 1993. Ed. Regional assessment of climate change impacts on maize productivity and associated production risk in Swtzerland. V. Simota C. Royer J. M. evaluarea vulnerabilitatii si a masurilor de adaptare. (Environmental Protection Agency). Projections of uncertainties in climate change scenarios into expected winter wheat yields. Kornblueh. Torriani D. Giorgetta. Calanca. V.3. Roeckner E.. A. Marinela Simota. Muška Semerádová. Washington D. Adriana Marica. Theoretical and Applied Climatology. P. Cacciamani. The Atmospheric General Circulation Model ECHAM5. Cacciamani. M..1007/s00704-0060275-z. M. J. F.: National Country Study on Climate Change Impact in Romania. Calanca. M. Brokopf. KLARA: Klimawandel – Auswirkungen – Risiken – Anpassung.. I. Tecuci. Z. 64.L. Somot. Climate Research. Climate change impacts and adapation strategies in spring barley production in the Czech Republic. Schlese. and J. C. Junge. 2007. Z. 345 pp. (ed. Manzini. 1-230. Simota. G. DOI 10. Tyteca. 31. J. . F. Rhodin. Tomozeiu R. pg. Baeuml. V. S. and Adriana M. P. C. Final Raport. A. Géosciences 334 (3):147-154. Trnka M. E. Morgillo. Assessment of the potential effects of climate change on agriculture ecosystem and evaluation adaptation measures (by using DSSATv. Regional Environmental Change. M.. 2004a. Aristita Busuioc. M.. Déqué. PIK Report 99. Corbus. Collins. Cariolle. V. Pavan. Climate change scenarios for surface temperature in Emilia-Romagna (Italy) obtained using statistical downscaling models. pp. Potsdam. US-Country Study Program. Alexandrescu. pg 37-99. M. ARS DOCENDI. Ricard. Žalud. August 1997. Academia Romana-Comitetul National pentru Modificarile Globale ale Mediului. M. Esch.. Bonaventura. Ann. C. Report 349. Stanescu. A. In press. M. 2007. Stephenson D. Schulzweida..M. Tomozeiu R. Adriana.C. 2004b. Element 2: Vulnerability Assessment and Adaptation Options. ISBN 973-558-125-6.. Serban. A. In “Impactul Potential al Schimbarii Climei in Romania”. Schmid. Royer. USA. Žalud. Dubrovský. D. Salas y Melia. 2007. H. Terray. M.0). Valcke 2002. 2003. H. Vol. Geicu. D. M. Busuioc A. JOURNAL OF CLIMATE 1999.M. Wilks. October 19. R. Conway. 455-472.. and von Storch H. 72: 85-102. X. 2003. B. Appl. Agricultural and Forest Meteorology. Busuioc A.L. Wilby R. M.6 no 1-2. Theoretical Applied Climatology. Hellstrom. C. Chen.. F. Fuzzy-rule based downscaling of precipitation. H.L. Iss 1. Bogárdi. Bi. E. T. Halenka. 2006. 119-129.pact: Empirical-Statistical Downscaling in Climate Modelling. Geophys. I. Vol. I. 82. J. The Impact of Climate Variability and Change on Crop Yield in Bulgaria. Res. Effect of climate change on regional precipitation in lake Balaton watershed. and Dubrovský M. Iss 5. 2006. Int. F.L. References cited in the answers to the questionnaire with respect to the type of downscaling techniques Alexandrov V. 2006. Systematic errors in the simulation of european climate (1961-2000) with RegCM3 driven by NCEP/NCAR reanalysis. Hanssen-Bauer. 2000.. Wigley. D. 2004. 2001.1421.. 56A.. M.. Žalud Z.. Bogardi. 1995. DrSc thesis. doi:10..1002/joc. 2005 Benestad R. Temporal and spatial variability of precipitation in Sweden and its link with large-scale circulation. INTERNATIONAL JOURNAL OF CLIMATOLOGY 2001. X. 263 .6. Matyasovszky. and Eitzinger J. Number 42. 189 pp. 237-250.... Climate variability and change and their influence on ecosystems in Central and Southeastern Europe as well as Southeastern USA. Ionita. Theor.J.. D. Belda. Bardossy A. Climatol. Climatol..1029/2005GL023401. Busuioc A. Conditional stochastic model for generating daily precipitation time series.. 104(4): 315-327. 27. Statistical downscaling of general circulation model output: A comparison of methods. 1999. Performance of statistical downscaling models in GCM validation and regional climate change estimates: Application for Swedish precipitation. Alexandrov V. 1998. pp 258-272.E. Von Storch. Climate change scenarios for northern Europe from multi-model IPCC AR4 climate simulations. Giorgi. Ecological Modeling (will be published in July 07).E. Analysis of dynamical and statistical approaches for generating winter precipitation change scenarios over Romania. R2.D. Water resources research 34:2995-3008.. Vol 21. Verification of GCM-generated regional seasonal precipitation for current climate and of statistical downscaling estimates under changing climate conditions. Matyasovszky. 2001. no 3. D. J. 89-101.E. Bi.. Alexandrov V. 2005. Giorgi. I.E..S. Climate Research. Bartholy J. Appl. 2004. Chen. EOS. P. Tellus. 2005. J.. Main. Bergant K... 2002. I.. Use of climate change scenarios in agrometeorological studies: past experiences and future needs Distribution under Present and Future Climate. Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania. Vol 12. 2007. C. Benestad R. and Hoogenboom G. 24. Jones. Lett. Førland. The Potential Effect of Climate Change and Elevated Air Carbon Dioxide on Agricultural Crop Production in Central and Southeastern Europe. Hewitson. Theoretical and Applied Climatology. Busuioc A. Theor. pp 557-578.C. Clim. 86. T. Busuioc A. Hellstrom. 2005. Benestad R.. Climatol. Busuioc A. Benestad R. 10. 85. Romanian Journal of Meteorology vol. Tellus 53A. 32. Schnur. I. 2004. On Models for empirical-statistical downscaling of precipitation Int. Modeling climate change impacts on maize growth and development. Journal of Crop Improvement 13(1-2): 291-331. 348-367. Ionita. Tentative probabilistic temperature scenarios for northern Europe. Clim. Vol 48. 2005. Pam. Buchtele. Meteorology and Atmos. Model Agroklimatu Polski (A model of Poland’s agroclimate).WegCenter progress report. F. Schneider. A climate scenario for the Alpine region. 100-106. Climate Dynamics. J. 2007. Polcher. J. Rickli R. Górski T. In: Theor.B. Wea. Appl. Zalud. Gyalistras D. Gyalistras D. Hochschulverlag AG an der ETH. An uncertainty analysis of monthly temperature and precipitation scenarios for Switzerland. Clim Res 4:167–189. Davies HC. Phys.. 73. Schmutz C (eds) Klimawandel im Schweizer Alpenraum. Fischlin. Gaertner M. Wanner. Dudhia J. O.. A. A. 1993. Future Alpine climate. F. Gerstengarbe F. M. Luterbacher J. (Accepted. Prego.. Austria. Gobiet A. Gräfe. Simulationsergebnisse des regionalen Klimamodells STAR. GLOWA-ELBE I Integrierte Analyse der Auswirkungen des globalen Wandels auf Wasser.. Truhetz. 2003. O. Iss 4. 2002. In: Wechsung. of Graz. von Storch. Puł. A novel scheme to derive optimized circulation pattern classifications for downscaling and forecast purposes. and Zaliwski A. 1994. A. 95. L. C. J. Riegler. 145-179. 2005. Multi-year simulations with a high resolution regional climate model over the Iberian Peninsula: Current climate and 2xCO2 scenario.. Haugen. Climatic Change 63 (No. Davies... 2006. Wegener Center.W. Gyalistras D. 245-256.. Beniston. High-Frequency and Low-Frequency Variability in Stochastic Daily Weather Generator and Its Effect on Agricultural and Hydrologic Modelling. Jaeger JC (eds) Views from the Alps: regional perspectives on climate change. H. S.C.C. J.. Salvisberg E. H. Schär. TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY 1996..Tveito.. p 197–235. The impact of deforestation on the hydrological cycle in the western Mediterranean: an ensemble study with two regional climate models.. y Castro 2001. Changes in the winter precipitation in Romania and its relation to the large-scale circulation.. Imboden D. 121. Vol.. 1998. H.. 1996. Climate Dynamics) Enke W. 857-873. 264 . Gyalistras D. M. MIT Press. pp 538-552. Szeidl. J. Mon. P. Climatology and Meteorology Research Group. 242-250. C. Klimaszenarien für den Alpenraum und die Schweiz: Neuester Stand und Vergleich (Climate scenarios for the Alps and Switzerland: state of the art and comparison)..1-2). Christensen.E. Umwelt und Gesellschaft im Elbegebiet. Gallardo C.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Busuioc A. Climatol. Linking GCM-simulated climatic changes to ecosystem models: case studies of statistical downscaling in the Alps. 127. Gyalistras D. Mika. 1493-1513. Clim Res 25(1):55– 83. University of Bern. PIK-Report. In: Cebon P. 2000. ISSN 1436-0179. Temperature scenarios for Norway: from regional to local scale (Accepted.E. Dubrovsky M. Climatic Change) Engen-Skaugen T. reclip:more project year 3 . 2001. Deutschländer. Rev. A. Becker. Dobi-Wantuch I.A. 17.. No. 2004. Internal report.A. Refinement of dynamically downscaled precipitation and temperature scenarios. 82 (2005). 51–63. Gyalistras D. A nonhydrostatic version of the Penn State – NCAR mesoscale model: Validation and simulation of an atlantic cyclone and cold front. p 171–223.. Gallardo.A. Modelling wet and dry spells with mixture distributions. Prego. Gaertner. Engen-Skaugen T. Zürich. Quart J Roy Meteor Soc. Th. J. M. H. 2002. 2000. 130.A. Dahinden U. y Castro. Univ. and Werner P.. Arribas.. and Von Storch H. In: Wanner H. Development and validation of a highresolution monthly gridded temperature and precipitation data set for Switzerland (1951–2000).. Boston. Z. M. 1659-1682. P. Benestad.Response in extremes of daily precipitation and wind from a downscaled multi-model ensemble of anthropogenic global climate change scenarios (Submitted. L. J. Jylhä K. J. Jacob.E.. 2006. D.1. Belda. J. R. Simulation of extreme temperature events using general circulation models. Studia geoph. Regional analysis of an extended set of climate model simulations for the Nordic CE project. European Conference on Impacts of Climate Change on Renewable Energy Sources. I. D. Hahn. K. U.Førland. Mládek. Appl. Achberger.R. S. Bülow. Milliez 2003. 2006. Carter T. Climate Research. Räisänen. Z. Kysely. G. M. Vol. Keuler. Max-PlanckInstitut für Meteorologie. Rajkai. C.E. Á.. Dynamische und statistische Erstellung von hochaufgelösten Klimaszenarien (1/6°) als Basis für wasserwirtschaftliche Handlungsempfehlungen im KLIWA-Projekt B 1. Chen.. Molnár.. Huth. K. M. Physics and Chemistry of the Earth. 21-24. Changes in the composition of sand grasslands along a climatic gradient in Hungary and implications for climate change. Bartha. 2002. K. Tellus). and Iversen T. P. (accepted). 1731-1742 Huth R... Huth R. Førland. Sensitivity Studies with the Regional Climate Model REMO. R. Kotlarski S. 25. Kertész. 243-250. 24. statistickym downscalingem a stochastickym generatorem] Meteorological Bulletin 54. Meteorol. J. 2003. J. and Podzun R. J..Comparison of results from dynamical and empirical downscaling. (A) Vol. Climatol. Zemankova. 63.. Walter.6. G. Mika. Kalvova. R. M. 2007.E. Reykjavik. Atmos. Climatol. Mika. On the integrability of limited-area numerical weather prediction model ALADIN over extended time periods. 1999. 2007: Changes in frost and snow in Europe and Baltic Sea ice by the end of the 21st century.6. Tuomenvirta H. Temperature and precipitation scenarios for Norway . Dubrovsky. Chladova. Statistical Downscaling and Weather Generator: Higher-Order Statistical Moments. H. Phys. On the capability of RegCM to capture extremes in long term regional climate simulation – comparison with the observations for Czech Republic. Kyselý. Dubrovsky. Downscaling of humidity variables: A search for suitable predictors and predictands. K. 2001. Statistical downscaling of climate scenarios over Scandinavia.. Extended abstract. 125-145. Int. Huszár T. Böhm. D. 2005. Fronzek S. 5. im Auftrag des Arbeitskreis KLIWA – KLIWA Final ReportJylhä K. 255-268 Haugen J. Studia Geophysica et Geodaetica 47.J. [in Czech. Halenka. Hanssen-Bauer I. 2003. 86.J. O.2006. Janoušek. Hamburg. Phytocoenologia 30. Lóczy. Z. A. statistical downscaling and stochastic generator. Dobi-Wantuch..... Metelka.. 1997.. 2003. E..Tveito. A.1 Klimaszenarien. Fekete. Ruosteenoja. J.. Simulace extremnich teplotnich jevu globalnimi cirkulacnimi modely. Climate change and soil moisture: a way of simulation. 385-407. 15-27. 203-216 Jacob D. Kysely J. 5:119–125. Simulation of Surface Air Temperature by GCMs. M. Climate Research. 73-82. Theor. 29. 905-912. J.E. R. K. Statistical downscaling of daily temperature in central Europe. Sedlák. 2005. T. 2005.. geod. 15.. 863-873 Huth R. 25.-9. Iceland. Jacob D. Knoche. Rédei. K.Haugen. 2006. Huth R. M. Climate. M. Kovács-Láng E. Kröel-Dulay. L.. E. Advances in Geophysics. T. Hanssen-Bauer I. 119-129. 2005. Pokorná. Climatic Change.. I. S. and Ruosteenoja K. Regional Climate Model Simulations as Input for Hydrological Applications: Evaluation of Uncertainties. Kliegrová. Huthová. Demeterova. Tuomenvirta. Kertész. 47.. 265 . 2000. Use of climate change scenarios in agrometeorological studies: past experiences and future needs Halenka T. F. Academic Press. Racsko P. vol. Barrow.. Zaliwski A. Mesoscale precipitation variability in the region of the European Alps during the 20th century. Evaluation of a dynamic-diagnostic modeling approach to generate highly resolved wind fields in the Alpine region. J. 217224. C. Ecological Modelling 57. Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. H. Meteorol. Enke. S.E. Prog. Winiarski. Navarra (eds. Kirchengast.1. S. A User’s Guide for the CALMET Meteorological Model (Version 5). Gallardo.. Hagemann. Sanchez E. Rockel. L. E. Austria. Int. C.W. 22: 1049–1074. 1991. Skaugen T. Springer Verlag. Spekat A.. vol. Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods.R.. Zaliwski A. A serial approach to local stochastic weather models..L and Wigley T. Wróblewska. A.. Frei.L. Nordic Hydrology.. Downscaling general circulation model output: a review of methods and limitations. M.. Górski. 21.J. Von Storch H. L. R.M. E. Gaertner. Lenderink. 1999. E. A. A. Application of Statistical Techniques.L. Vienna. 227-258. Yamartino. C. 2006. P. Gobiet.Tar.M. van Meijgaard..J. 137-148.. P. Climatol.A. Effects of macro-circulation on local climatic conditions of plant development. Future climate extreme events in the Mediterranean simulated by a regional climate model: a first approach. 17: 35363551. 2nd European Conference on Applied Climatology ECAC98. A1B und A2 Final Report Umweltbundesamt. A. E. F. Z.. software manual. Kjellström. C.. Brooks. Frei. 2004. Vol. Climate Research 10. Szeidl. 27-41. Astrup.J.. 2003. 1995. 1995. 1997. 530-548. and Brooks R. J. 2005.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Mika J. 59. G. J. Wilby R. Earth Tech Inc. 1998. C. Semenov M. Semenov M. Climatol. y Castro. Kreienkamp. Phys. Graham. 266 .A. M. Semenov M. 18.). C. Hirschi. 95-107. T.S. R. Wanner.D. 2007. Numerical Maps of Profit Probability for Maize Production in Poland. Lipski.A. in press. Wilks S. Concorde. B. Schmutz. 19-23 Oct..A. Førland.15. H. Global and Planetary Change in press. 1998. Górski T. 2005. 2002.. Robe. International Geophysics Series. 26: 679–689. Scire J.. ZAMG. Schmidli J. 1998. 1999. G. Int. Fernau.. Statistical Methods in the Atmospheric Sciences. Physics and Chemistry of the Earth vol. Geogr.Vidale.J... 467pp. K. van den Hurk B. R. Neuentwicklung von regional hoch aufgelösten Wetterlagen für Deutschland und Bereitstellung regionaler Klimaszenarios auf der Basis von globalen Klimasimulationen mit dem Regionalisierungsmodell WETTREG auf der Basis von globalen Klimasimulationen mit ECHAM5/MPI-OM T63L31 2010 bis 2100 für die SRES Szenarios B1. Richardson. Schmidli J. Bonn. M. Molnár.. CD ROM. Truhetz H. M. Vol. E. Climate Research 11. Arribas. Roald. Scenarios of extreme daily precipitation for Norway under climate change. Soil control on runoff response to climate change in regional climate model simulations. A. W. 135-141243. Schär. Numerical Maps of Temperature Sums in Poland. Journal of Climate. EFITA/99 Conf. 2006.. 30. No. van Ulden. No. 2000. Spatial Patterns: EOFs and CCA. Comparison of the WGEN and LARS-WG stochastic weather generators in diverse climates. In: Analysis of Climate Variability. 1-13. von Storch and A. Arne Oddvar Skejvåg. Yields increased considerably during the period 1970 to 1990 in all countries due to improved technologies with the highest absolute increases in western and central Europe. 3) possible adaptation options as well as 4) adaptation observed so far. Europe was divided into 13 Environmental zones (EZ). Risk assessment and foreseen impacts on agriculture 7. The development in national grain yields for wheat in the period 1961 to 2006 for countries in Europe shows that yields in northern Europe are limited by cool temperatures. whereas yields in southern Europe are limited by high temperatures and low rainfall. In addition we also focused on the overall awareness and presence of warning and decision support systems. There were two types of questionnaires distributed to the COST 734 members and other experts: i) country based overview questionnaires and ii) climate region specific quantitative questionnaires. This has also resulted in a steadily increasing grain maize area in these countries. 267 . Pirjo Peltonen-Sainio. Jerzy Kozyra. The yield increases have levelled off considerably during the past 10-20 years. we had 16 complete national reports and 50 individual responses for specific EZ from 26 countries. Bernard Seguin. Kurt Christian Kersebaum. There is in recent years a tendency in many countries to lower yields and increased yield variability. RISK ASSESSMENT AND FORESEEN IMPACTS ON AGRICULTURE Jorgen Eivind Olesen. In total. In order to gather information on perceived risks and foreseen impacts of climate change on agriculture in Europe we designed a set of qualitative and quantitative questionnaires that were distributed to leading experts in 26 countries. even in recent years.7. 2) estimates of climate change impacts on the production of nine selected crops. Federica Rossi. Miroslav Trnka. The yields increases seem to be continuing in Belgium and Germany. A preliminary analysis shows that the yields in several European countries in recent years correlated well with the mean temperature during the main part of the growing season with observed yield reduction in warmer years. where wheat yield increases have been levelling off. Fabio Micale Abstract The studies on anthropogenic climate change performed in the last decade over Europe indicate consistent increases in projected temperature and different patterns of precipitation with widespread increases in northern Europe and rather small decreases over southern Europe. Grain yields in maize have been increasing over the period 1961-2006 in both central and southern Europe. The questionnaires provided both country and EZ specific information on the: 1) main vulnerabilities of crops and cropping systems under present climate. 2005). 1999. because a given change in temperature or rainfall have modest impact (Chloupek et al. 2004). However.Survey of agrometeorological practices and applications in Europe regarding climate change impacts The results show that farmers across Europe are currently adapting to climate change. and because the farmers have resources to adapt and compensate by changing management. Darwin and Kennedy.. agricultural reforms are expected to enhance the current process of structural adjustment leading to larger and fewer farms (Marsh. 1998). 2004). These differences are expected also to greatly influence the responsiveness to climatic change (Olesen and Bindi. In 2004 it accounted for 21% of global meat production and 20% of global cereal production. The productivity of European agriculture is generally high. in particular in terms of changing timing of cultivation and selecting other crop species and cultivars. 2005). On the other hand some of the low input farming systems currently located in marginal areas may be most severely affected by climate change (Reilly and Schimmelpfennig. The responses in the questionnaires show a surprisingly high proportion of negative expectations concerning the impacts of climate change on crops and crop production throughout Europe. The hydrological features in Europe are very diverse. infrastructure. in particular in western Europe. The proportion of fresh water abstraction used for agricultural purposes is only 4% in northern EU. but as high as 44% in southern EU and projected to increase to 53% by 2030 under baseline conditions (Flörke and Alcamo. This is not expected to greatly affect agricultural production in the short run (OECD. About 30% of abstracted fresh water in Europe is used for agricultural purposes. reduce environmental impacts and improve rural development. and average cereal yields in the EU countries are more than 60% higher than the world average. These systems may therefore respond favourably to a modest climatic warming (Olesen and Bindi. The EU Common Agricultural Policy has during the last decade been reformed to reduce overproduction. There is a large variation across the European continent in climatic conditions. soils. primarily irrigation (Flörke and Alcamo. even in the cool temperate north European countries. pressures and management approaches.1 Introduction Europe is one of the world's largest and most productive suppliers of food and fibre. Intensive farming systems in western Europe generally have a low sensitivity to climate change. 2000). About 80% of this production occurred in the EU25 countries. and there is also a large diversity in water uses. 268 . 2002). 7. land use.. 2002). 2005). political and economic conditions (Bouma et al. 2001). 2002).. 2004). trends are higher in central. Jones and Moberg.9 °C in annual mean temperature over the entire continent (Kjellström. primarily due to increase in warm extremes (Klein Tank and Können. Serbia and Romania. Temperatures are increasing more in winter than summer (EEA. which amounts to 0. An increase of temperature variability has been observed. the recent period shows a trend considerably higher than the mean trend (+0. The precipitation events over central Europe may therefore occur more frequently in the future (Pal et al. 2003). Klein Tank et al.. An increase in mean precipitation per wet day has been observed in most parts of the continent. diking and installation of reservoirs (Helms et al. 2006 or Beniston et al. e. 2002.1 Observed climate change in Europe Most of Europe has experienced increases in surface air temperature during 1901 to 2005. However.. 2007). 2004.g. Mean annual precipitation is increasing in most of Atlantic and northern Europe and decreasing along the Mediterranean (Klein Tank et al. Croatia. Recent results using high-resolution regional climate models have shown that global warming may be linked with a shift towards heavier intensive summertime precipitation over large parts of Europe (Christensen and Christensen. Heavy rainfall from storms crossing central Europe during early August triggered sequential flood waves that moved down the Vltava. particularly at the end of the growing season (e.2 Observed and projected climate in Europe 7. 2004. There are indications of changes in the rainfall pattern as indicated by the frequency of drought events during spring and early summer... 2002) and possibly by the agricultural land use in the river basins (van der Ploeg and Schweigert.. the results of studies based on the data collected over the last two centuries are not conclusive for western and Central 269 ..2. Jones and Moberg. For the past 25 years. and down the Danube river in Austria. 2002). Even though most model studies indicate a long-term tendency towards lower soil moisture.. 2004). the Czech Republic and Germany for three weeks during August 2002.. 2003). Risk assessment and foreseen impacts on agriculture 7. northeastern Europe and in mountainous regions. even in areas getting drier (Frich et al. 2003). Hungary. Seneviratne et al. while the lowest temperature trends are found in the Mediterranean region (Klein Tank. 2007). 2003). Slovakia. Labe and Elbe rivers in the Czech Republic and Germany. 2007). with particularly large increases in the Mediterranean region (Trenberth et al. There has been an increase in frequency of droughts in large parts of western and eastern Europe.4°C/decade for the period 1977-2001. Severe flooding affected parts of Austria.7. The severity of the floods was probably enhanced by human management of the river systems.g. Alcamo et al. . 2007) and associated with changes in ciruclation patterns.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Europe to be able to predict higher drought frequency or severity. The heat wave was associated with annual precipitation deficits up to 300 mm. but July was only slightly warmer than on average (1-3°C). Beniston and Diaz.. 2004). However. 7. Beniston and Diaz. several studies based on grided datasets (e.g. 2005). 2004. The main modelling uncertainties stem from the contrasting behaviour of different climate models in their simulation of global and regional climate change. 2004). 1998 and 2004). Pal et al. and this drought was a major contributor to the estimated reduction of 30% over Europe in gross primary production of terrestrial ecosystems (Ciais et al.g. The warm anomalies in June lasted throughout the entire month (increases in monthly mean up to 67°C). 2006 or Dai et al.. Meehl and Tebaldi. raising summer temperatures by 3 to 5 °C.. Other studies that were based on homogenized station series (e. 2004). Trnka et al. 2004). 2004. that describe drought in terms of soil moisture anomaly. it is consistent with a combined increase in mean temperature and temperature variability (Schär et al.. However. These shifts in intensity and frequency of drought in the region were shown to be driven by changes in near surfaces temperatures rather than changes in precipitation (e. 2008 or Szinel et al.2. 2004. biosphere and ocean.. van der Schrier et al.2 Projections of climate change in Europe Most of the recent global climate model (GCM) experiment results are based on coupled ocean-atmosphere models (AO-GCM). These uncertainties are largely a function of the relatively coarse resolution of the models and the different schemes employed to represent important processes in the atmosphere.g.. 2004. Maximum temperatures of 35 to 40 °C were repeatedly recorded in most southern and central European countries (André et al. This heat wave has been found to be extremely unlikely statistically under current climate (Schär and Jendritzky.. both during summer and over the entire year. As such the 2003 heat wave resembles simulations by regional climate models of summer temperatures in the latter part of the 21st century under the A2 scenario (Beniston. There has recently been an increased effort 270 . van der Schrier et al.. showed that in many regions of Europe did exhibit a severe decline (though in some cases not statistically significant) in the available soil water over the 20th century. 1998) indicated that the number of stations with statistically significant trends towards drier conditions (in terms of available soil moisture) prevail in Central Europe over those where either no trend at all or a tendency toward wetter conditions was noted. and the highest anomalies were reached between 1 and 13 August (+7 °C) (Fink et al. A severe heat wave over large parts of Europe in 2003 extended from June to mid-August. 2004). a b Figure 7.1). DJF (a) and summer. This has led to improved quality in projections of regional climate changes in Europe. A very large increase. 7.1). Risk assessment and foreseen impacts on agriculture in downscaling the coarse GCM results using regional climate models with spatial resolutions of 50 km or less (Christensen and Christensen. 2004). in summer temperatures. JJA (b) (Christensen and Christensen.1: Simulated changes in mean air temperature (°C) for the period 2071-2100 relative to 1961-1990 for the A2 scenario using the HIRHAM regional climate model for winter. is projected in the south-western parts of Europe (exceeds 6 °C in parts of France and the Iberian Peninsula) by the end of the 21st century under the A2 scenario (Fig. 2007) 271 . 2007). 2007. The warming is greatest over eastern Europe during winter and over western and southern Europe in June-July-August (Giorgi et al. 7.7.. The projections show marked seasonal and regional differences in the projected changes (Fig. Christensen et al. the mean annual precipitation increases in northern Europe and decreases farther south (Fig. whereas there is a substantial decrease in summer precipitation in southern and central Europe. DJF (a) and summer. a b Figure 7.2: Simulated changes in mean air precipitation (%) for the period 2071-2100 relative to 1961-1990 for the A2 scenario using the HIRHAM regional climate model for winter.2). 2007) 272 . JJA (b) (Christensen and Christensen. and to a lesser extent in northern Europe. There is a projected increase in winter precipitation in northern and central Europe. But the change in precipitation varies substantially from season to season and across regions.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Generally for all scenarios. 7. Hayes et al. The wheat yields in Germany and Greece seem to indicate an increased yield variability.Recent results also indicate that variability in temperature and rainfall may increase considerably over large parts of central Europe (Christensen and Christensen. which is negligible compared to the available genetic gains in yield potential. which mostly likely is also related to climate. 2003.. in particular in the Russian Federation and Ukraine. There seems still to be a small yield increase. and recent projections of climate change impacts support this hypothesis (e. By far the largest cropping areas are found in eastern Europe. if any. central and southern Europe.. Ciais et al. 2005). 7. This heat wave led to substantial reductions in primary productivity of terrestrial ecosystems and large and widespread reductions in farm income (Fink et al. Schär et al. There are also clear indications that increasing temperatures are causing grain yield reductions globally (Lobell and Field. potato and sugar beet. Indeed heat waves and droughts similar to the 2003 situation may become the norm in central and southern Europe by the end of the 21st century (Beniston and Diaz.1 and 7. 2007). Risk assessment and foreseen impacts on agriculture It is very likely that the frequency of drought spells and their severity will increase at least in some regions of Europe (the south and centre in particular).g. The cropping areas of eastern Europe are larger than the total of all other regions for wheat. The highest variation in yields within the regions used in Table 1 was obtained in North Europe. 2005. Yields in Greece have been declining. whereas yields in southern Europe are limited by high temperatures and low rainfall.. 2004). The yield increases have levelled off considerably during the past 10-20 years.2). but negative in Greece. during the past 10-20 years in Finland. 2004. The development in national grain yields for wheat in the period 1961 to 2006 is shown in Fig. where yields were considerably higher in UK. Ireland and Denmark than in the Baltic countries.7.3 for selected countries in northern. 2007). Yields in northern Europe are limited by cool temperatures. Calanca. 2004). Yields increased considerably during the period 1970 to 1990 due to improved technologies in all countries with the highest absolute increases in western and central Europe.3 Current European cropping patterns The highest yields of both cereal and tuber crops are obtained in West Europe and the lowest yields in South and East Europe (Table 7. 273 . 7. barley. Both effects may be climate related with increasing temperatures being beneficial in Finland. 0 3.0 10.2 8.1 42 55 Mean 5.2 43 68 France 6.4 7.8 6.6 16 31 Bulgaria 3.2 3.3 36 48 Latvia 3.7 11 42 Greece 2.4 29 49 United Kingdom 7.8 4.0 4.7 27 68 Mean 3.3 6.4 16 80 Bosnia + Herzegovina 2.2 39 57 Estonia 2.8 11.8 1.5 1.3 3.7 3.1 4.2 2.5 3.5 16 33 Albania 3.1 2.3 1.5 3.0 21 58 Italy 3.7 40 58 Luxembourg 6.5 1.1 2.7 6.5 15 26 Russian Federation 1.5 15 45 Ukraine 2.1 4.1 24 48 Moldova 2.1 2.8 11.4 2.7 6.4 2.7 9.9 21 44 Spain 2.1 1.1 5.8 0.5 2.5 9.6 12 37 Norway 4.0 24 57 Macedonia (FYROM) 2.5 8.7 26 Sweden 5.3 4.9 2.2 3.8 9 24 Poland 3.6 14 Finland 3.6 11 41 Slovenia 4.5 4.7 2.4 2.7 3.3 3.4 3.3 3.4 9.2 26 46 Austria 5.9 6.0 2.0 31 Netherlands 8.6 1.1 3.5 5.1 3.4 5.6 2.9 3.1 2.9 3.0 3.7 12 22 Mean 3.9 1.2 3.8 2.5 15 69 Serbia and Montenegro 3.8 23 49 Hungary 4.4 6.2 12 26 Slovakia 4.9 1.2 2.0 3.8 5.2 14 32 Portugal 1.6 43 61 Switzerland 5.8 2.0 3.1 6.0 5.4 2.4 2.6 2.4 3.8 4.4 5.0 2.6 2.5 37 72 Mean 6.9 1.6 2.1 15 21 Czech Republic 4.5 2.2 1.1: Mean national yields (Mg ha-1) of selected cereal.0 2.8 5.3 1.8 1.3 1.6 1.1 5.3 24 36 Ireland 8.2 2.9 2.7 13 37 Lithuania 3.7 2.6 9.4 41 76 Germany 7.9 4.1 3.9 1.7 1. tuber and root crops in Europe as average for the period 2001-2006 (FAOSTAT) Region North Europe Country Wheat Barley Rapeseed Maize Potato Sugarbeet Denmark 7.5 1.0 3.4 17 52 West Europe East Europe South Europe 274 .2 8.5 18 42 Romania 2.5 3.3 2.1 5.3 31 64 Belgium 8.7 4.4 38 66 Belarus 2.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Table 7.4 10 25 Croatia 4.6 6.5 8.8 1.0 1. tuber and root crops in Europe as average for the period 2001-2006 (FAOSTAT) Region North Europe Country Wheat Barley Rapeseed Maize Potato Sugar-beet Denmark 651 727 105 39 50 Estonia 74 135 44 16 Finland 190 548 78 29 29 Ireland 95 176 3 13 31 Latvia 177 135 44 50 14 Lithuania 349 339 90 84 24 Norway 76 164 8 14 Sweden 379 378 71 31 50 United Kingdom 1860 1042 525 149 157 Sum 3851 3644 968 0 425 355 Austria 285 203 45 174 22 44 Belgium 200 44 6 51 64 90 France 5106 1672 1161 1736 159 402 Germany 3046 2018 1293 427 283 428 Luxembourg 12 10 4 0 1 Netherlands 134 54 1 23 161 99 Switzerland 90 39 16 21 13 18 Sum 8873 4040 2526 2432 703 1081 Belarus 364 662 102 20 519 77 Bulgaria 1113 288 12 351 35 1 Czech Republic 814 509 288 83 40 72 Hungary 1135 337 113 1204 30 58 Moldova 351 107 3 495 39 43 Poland 2342 1081 503 326 777 292 Romania 2131 434 64 2862 277 34 Russian Federation 22995 9384 223 731 3125 800 Slovakia 375 210 100 146 23 32 Ukraine 5765 4297 110 1643 1553 731 Sum 37385 17309 1518 7860 6418 2140 Albania 88 1 0 49 10 1 Bosnia + Herzegovina 85 22 1 197 43 0 Croatia 203 52 14 375 46 27 Greece 814 99 2 226 44 41 Italy 2229 324 9 1134 74 206 Macedonia (FYROM) 104 48 1 33 13 2 Portugal 168 22 131 57 7 Serbia and Montenegro 640 118 3 1210 98 57 Slovenia 34 14 1 44 7 5 Spain 2190 3138 7 451 102 102 Sum 6558 3837 38 3850 495 448 West Europe East Europe South Europe 275 . Risk assessment and foreseen impacts on agriculture Table 7.7.2: Mean national area (1000 ha) of selected cereal. 3: National grain yield of wheat in northern. central and southern European countries for the period 1961 to 2006 (FAOSTAT database) 276 .Survey of agrometeorological practices and applications in Europe regarding climate change impacts 8 Grain yield (Mg ha ) -1 Norway Finland 6 4 2 8 Grain yield (Mg ha ) -1 UK Germany 6 4 2 8 Grain yield (Mg ha ) -1 Spain Greece 6 4 2 1960 1970 1980 Year 1990 2000 1960 1970 1980 Year 1990 2000 Figure 7. 4: National grain yield of wheat in northern. This has also resulted in a steadily increasing grain maize area in these countries. The yield of grain maize in France and Italy has not increased in recent years. The yield increases seem to be continuing in Belgium and Germany. where wheat yield increases have been levelling off. even in recent years. central and southern European countries for the period 1961 to 2006 (FAOSTAT database) 277 . Belgium 30 Maize area (%) Germany Area Yield 12 10 8 6 Grain yield (Mg ha ) Grain yield (Mg ha ) -1 -1 20 10 4 2 12 10 8 6 10 4 2 1970 1980 1990 2000 1970 1980 1990 2000 Year Year France 30 Maize area (%) Italy 20 Figure 7. Risk assessment and foreseen impacts on agriculture Grain yields in maize have been increasing over the period 1961-2006 in both central and southern Europe (Fig. this has impact on both maize yields and the area cropped with maize. 7.4). and since maize is predominantly an irrigated crop in these countries.7. which reduces the water available for irrigation. This is most likely due to warmer climate and a higher frequency of droughts. adaptation strategies and awareness 7.5: Environmental zones in Europe (Metzger et al.Survey of agrometeorological practices and applications in Europe regarding climate change impacts 7.5). Adaptation options 5.. and impacts and adaptation of crops and cropping systems to climate change was submitted to members of the COST 734 action.4 Questionnaire A questionnaire on aspects of climate vulnerability. Critical thresholds of climatic suitability and climate change 3. National impact assessments. The questionnaire asked for national based overviews of the current knowledge base within the following areas: 1. A more quantitative questionnaire was therefore developed and submitted to members of COST action 734 and a range of selected experts within agronomy and agrometeorology across Europe. Assessments and studies of climate change impacts 4. for some of the larger countries there were also considerations in the responses that impacts and adaptation would vary among climatic regions within countries. These experts were asked to fill in this questionnaire for separate environmental zones within each country according to the zoning defined by Metzger et al. (2005) (Fig. However. Observed adaptation 6. Dissemination of information and recommendations 8. 2005) 278 . 7. Warning systems These responses showed a wide range of communalities among responses from different countries. Main vulnerabilities of crops and cropping systems 2. Figure 7. 4: Scale used for scoring projected climate impacts by 2050 Score NA NI -2 -1 0 1 2 Explanation Not applicable (e.7. crop not grown) No information No problem Minor problem. major effects on regional production Table 7. occurs occasionally.5).g.3. 279 . 7. Table 7.6). small effects on regional production Major problem. although the number of responses varied among zones (Table 7.3: Scale used for scoring present limitations of crops to climatic conditions Score NA NI 0 1 2 3 4 5 Explanation Not applicable (e. occurs frequently. no detectable effects on regional production Small problem. small and rare effects on regional production Moderate problem.4 and 7. occurs rarely. More responses were received for the quantitative questionnaires. occurs sometimes. crop not grown) No information Large decrease Small decrease No change or no effect even if there is a change in the parameter Small increase Large increase Table 7.g. and this gave responses for all relevant European environmental zones. crop not grown) No information None Minor Moderate Large For the descriptive questionnaires responses were received for 16 countries in Europe. occurs almost every year.5: Scale used for scoring how much different adaptation options would contribute to change in cropping systems Score NA NI 0 1 2 3 Explanation Not applicable (e. moderate effects on regional production Large problem.g. Risk assessment and foreseen impacts on agriculture All of the questions involved some subjective assessments with a scoring that was based on the experts’ own evaluations (Tables 7. Atlantic Central PAN – Pannonian LUS – Lusitanian ANA – Anatolian MDM .Mediterranean Mountains MDN . 2002) or indirectly via effects on climate (e. Although the number of responses varied among zones (Table 7.g.Alpine South CON . temperature and rainfall affecting several aspects of ecosystem functioning (Olesen and Bindi. The exact responses depend on the sensitivity of the particular ecosystem and on the relative changes in the controlling factors.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Table 7.Continental ATC . The total number of responses was 50 from 26 countries and in most cases a whole team of experts took part in the questionnaire answering.6: Number of responses received within European environmental zones Zone ALN .5 Vulnerabilities and climate impacts on crop Biophysical processes of agroecosystems are strongly affected by environmental conditions.g.Mediterranean South Total Number of responses per zone 1 2 4 6 4 10 4 4 1 0 4 5 5 50 Number of countries/regions 1 2 4 5 4 9 4 4 1 0 4 4 4 46 Responses for the quantitative questionnaires regarded all relevant European environmental zones with the exception of the Anatolian zone (ANA) that can be found only in Turkey which was not included in the study. 2002) (Table 7..6) all of them were covered sufficiently and all zones with significant crop production being covered by at least 4 respondents.Mediterranenan North MDS .Atlantic North ALS . The list of all respondents and questionnaire co-authors is given in the Annex I. Where needed the respondents were contacted back to verify their responses (e. 7. 280 .Alpine North BOR – Boreal NEM – Nemoral ATN . The projected increase in greenhouse gases will affect agroecosystems either directly (primarily by increasing photosynthesis at higher CO2 (Kimball et al.7). when any inconsistency occurred between responses within one environmental zone) and the overall results were consulted with the respondents as well. However. 2000). Increasing temperature affects crops primarily via plant development. This may have the greatest effect in cooler regions. and may be beneficial for perennial crops or crops.. Harrison et al. e. 2006). recent estimates of the yield benefit from increasing CO2 are smaller than earlier ones (Ainsworth and Long. in particular when they are grown at the borders of their natural range. Risk assessment and foreseen impacts on agriculture Table 7.. Maracchi et al. Compared to temperate crops.7: Influence of CO2. In wheat. increased temperature reduces crop duration for many annual crops. With warming. sugar beets. an increase by 1 °C during grain fill reduces the length of this phase by 5%. 2005). and yield declines by a similar amount (Olesen et al. plants develop faster.. and the average annual increase of the next decades is marginal compared with what has been achieved through conventional crop management and breeding (Berntsen et al.. which do not reach their vegetative phase.g. 2005). sensitivity to warming may be even greater in tropical crops. the start of active growth is advanced. rainfall and wind on various components of the agroecosystem Component Plants Animals Water Soil Pests/diseases Weeds Influence of factor CO2 Dry matter growth Water use Fodder yield Soil moisture SOM turnover Quality of host biomass Competition Temperature Growth duration Growth and reproduction Irrigation demand Salinization SOM turnover Nutrient supply Generation time Earliness of attack Herbicide efficacy Rain/wind Dry matter growth Health Groundwater Wind. temperature. 2003). Such assessments are needed to properly identify needs for change in agricultural policy caused by climate change. 2002).7. However.g. relatively little work has been done to link these results across sectors to identify vulnerable regions and farming systems (Olesen and Bindi.and water erosion Disease transmission Many studies have assessed effects of climate change on agricultural productivity in Europe (e. and the potential growing season is extended. 281 . 2000. Increasing atmospheric CO2 concentration stimulates yield of C3 crops and to a lesser extent C4 crops (Fuhrer. Similar responses were found in Finland for spring barley. However. 1 Present climatic limitations and vulnerabilities The results of the questionnaire indicate that there are considerable differences between individual environmental zones as well as between crops that were examined in our study (Fig.6).5. d) grassland.6: Present limitation of crop production by climate factors for 5 selected crops over the individual European Environmental Zones: a) winter wheat. Some of the crops could not be evaluated in all zones as grain maize and grapevine are not grown in ALN and BOR and winter wheat is not present in ALN.Survey of agrometeorological practices and applications in Europe regarding climate change impacts 7. b) spring barley. e) grapevine 282 . 7. a b c d e Figure 7. c) grain maize. NEM. ATN and CON).7. MDN and MDS zones. Flooding and water stagnation in the field is considered as a persistent problem in ALN. where conditions are much drier. winter wheat and spring barley. Overwintering and damage caused to the crops during winter is considered a major problem in ALN zone in case of grasslands and in BOR zone for grasslands and winter wheat production. The damage caused by snow cover and overall winter conditions (including severe frosts) are also threats to grapevine production in ATN zone and less in ALS. However it is considered as a quite prominent risk even for all crops in PAN zone and particularly in Serbia. High frequency of rainy conditions complicates sowing and harvest across most of the north-western zones with the highest impacts being reported for ALN and BOR zones. ATN. Damage caused by late frost in the spring (or early frost during the fall) are seen as a limiting factor particularly in case of grain maize and grapevine in all cooler zones where these crops are grown (i. CON and PAN areas. On the other hand precipitation is not perceived as a limiting factor in the harvest or sowing timing in MDN and MDS. soil conditions (often on light and permeable soils) and environmental zones (mostly warmer and drier) where it is grown. Lower risk 283 . Grain maize and grapevine are not limited by this factor in LUS. barley and grassland production across most zones. LUS. The growing season duration is also a limiting factor for grassland and grapevine production in MDM zone. The hail damage seems to be a minor problem that occurs rarely and has no detectable effect on regional production in case of wheat. Also in BOR. MDN and MDS zones. ATN and ALS zones rain during key field operations seem to be important especially in case of harvesting cereals and hay. Local to regional scale damage has been reported also from NEM. whilst the effect on grassland is considered to be only marginal in these areas. CON and PAN zones in case of winter wheat. On the other hand respondents did not consider water stagnation or floods as limitations in ATC. ALS. Flooding and especially water stagnation at the field has been reported also from the PAN environmental zone for cereals. Out of all crops grapevine seems to be least endangered by this phenomena due to terrain (frequently on slopes). and in case of ALN zone risk for spring barley production is reported for some seasons. In case of grapevine the late frosts seem to be a limiting factor in a number of seasons in the PAN environmental zone. ATN and MDM zones. BOR. especially in case of grasslands.e. Risk assessment and foreseen impacts on agriculture The length of growing season considerably limits growth in BOR and ALN zone and also in ATN zone in case of grapevine. BOR. Carter et al. it should be noted that even under present climate conditions most of the respondents considered this factor as an important one even in zones that are not viewed as being threatened by this weather phenomena (e. However. LUS and MDM zones. However it is surprising that grassland or winter wheat production seems to be quite substantially limited not only within warm and dry zones (i. 2003). barley and maize in PAN and partly in the MDS zones. One of the most interesting findings in our survey has been the prominence of drought as a limiting factor.2 Climate change impacts Arable crops A climatic warming will expand the area of cereals cultivation (e. a rise in temperatures will lead to a small yield reduction. which often will be more than counterbalanced by the effect of increased CO2 on crop photosynthesis.. van Ittersum et al. 2002.. 1996). PAN. In case of grapevine the damage is considered to occur only rarely affecting production at the regional level only. For winter wheat.5. drought is particularly important during a critical period for yield determination (floret and thereby seed set).g. In case of these two crops the hail damage is seen as a moderate problem in case of ALS. MDN or MDS) but also in the mountains (MDM and ALS) as well as in the cool and relatively wet zones in the north (ALN. BOR or NEM). NEM or ALS). wheat and maize) northwards (Kenny et al. 1993. Such yield reductions have been estimated 284 .. Whilst drought rarely scores as the severely limiting factor it seems to be of concern across the all zones and all crops with exception of grapevine.g. The combination of both effects will for a moderate climate change lead to moderate to large yield increases in comparison with yields simulated for the present situation (Ghaffari et al. When the total ranking of the individual limiting factors is done across the zones and crops drought appears to be the single most significantly limiting factor.. In Finland drought interferes with grass crop establishment and also regrowth after the first cut in each season.e. For wheat. CON. Drier conditions and increasing temperatures in the Mediterranean region and parts of eastern Europe may lead to lower yields there and the adoption of new varieties and cultivation methods. On the other hand the pattern of perceived limitations caused by heat stress is much more erratic with reported effects mainly in case of wheat. 7.Survey of agrometeorological practices and applications in Europe regarding climate change impacts reported for small grain cereals and grassland is most likely given by their relatively high resistance to the damage compared to grain maize and especially grapevine. but winter types are far better able to escape from drought due to a deep root system compared to spring types that are extremely vulnerable. 2007)... while the largest reductions are expected around the Mediterranean and in the Southwest Balkans and in the South of European Russia (Olesen and Bindi. 2006)..... Climate change scenario studies performed using crop models show no consistent change in mean potato yield (Wolf and van Oijen. 2004). Olesen et al. where water supply is adequate. For sugar beet yield the increasing occurrence of summer droughts may severely increase yield variability (Jones et al. 2005). Perennial crops Many fruit trees are susceptible to spring frosts during flowering... Moreover.g. In southern Europe.. northern parts of Portugal and Spain) (Olesen et al.g. increased duration of the growing season will increase the yield potential for this crop in northern Europe. has shown a large response to rising atmospheric CO2 (Kimball et al... 2007). 1997). 2003). southern Sweden and Finland (Hildén et al. A climatic warming will advance both the date of the last spring frosts and the dates of flowering. On the other hand warming may reduce the growing season in some species and increase water requirements with consequences for yield. by 2050 energy crops show a northward expansion in potential cropping area. 2007). However. maize. Santos et al. 2005..g. yield is expected to strongly decrease in the most southern areas and increase in the northern or cooler areas (e. Some crops that currently grow mostly in southern Europe (e. sunflower and soybeans) (Audsley et al. Maracchi et al. there may very well be increased problems with pests and diseases (Salinari et al. 2003). particularly large decreases in yield are expected for spring-sown crops (e.g. 2002. including Ireland. Scotland. Potato. 2002). and the yield variability may increase.. especially in the steppe regions (Sirotenko et al.. 2006). Alcamo et al. Risk assessment and foreseen impacts on agriculture for eastern Europe. However.. on autumnsown crops (e. these results vary between SRES scenarios and climate models (Olesen et al. 2006). The projections for a range of SRES scenarios show a 30 to 50% increase in suitable area for grain maize production in Europe by the end of the 21st century.7. but a reduction in suitability in southern Europe (Schröter et al. maize. 2005. Climate-related increases in crop yields are only expected in northern Europe. sunflower and soybeans) will become more suitable further north or in higher altitude areas in the south (Audsley et al. and the risk of damage to flower buds caused by late frost are likely to remain largely unchanged (Rochette et al. 2002). Whilst. However. 285 . as well as other root and tuber crops. 2007. winter and spring wheat) the impact is more geographically variable. Additionally the risk of damage to fruit trees caused by early autumn frosts is likely to decrease. Survey of agrometeorological practices and applications in Europe regarding climate change impacts Grapevine is a woody perennial plant, which requires relatively high temperatures. A climatic warming will therefore expand the suitable areas northwards and eastwards (Jones et al., 2005). However, in the current production areas the yield variability (fruit production and quality) may be higher under global change than at present. Such an increase in yield variability would neither guarantee the quality of wine in good years nor meet the demand for wine in poor years, thus implying a higher economic risk for growers (Bindi et al., 1996). However, yields in grapevine may be strongly stimulated by increased CO2 concentration without causing negative repercussions on the quality of grapes and wine (Bindi et al., 2001). A climatic warming is also likely to lead to unsuitable conditions for currently economically important traditional varieties, at least at their current locations. Olive is a typical Mediterranean species that is particularly sensitive to low temperature and water shortage, thus both the northern and southern limits of cultivation are conditioned by the climate. The area suitable for olive production in the Mediterranean basin may increase with climate warming (Bindi et al., 1992), and several new fields are already established in some regions of Northern Italy, complementing traditional fruit tree assortment. Several perennial crops are candidates for bioenergy crops (Sims et al., 2006). This includes willow for coppice and reed canary grass and Miscanthus for solid biofuel crops to be used in providing biomass for fuel in combined heat and power plants (Clifton-Brown et al., 2004) or for use in second generation bioethanol production. The climatic suitability for many of these perennial bioenergy crops is projected to increase over most of Europe for the 21st century (Tuck et al., 2006). Grasslands Grasslands will differ in their response to climate change depending on their type (species, soil type, management). In general, intensively managed and nutrient-rich grasslands will respond positively to both the increase in CO2 concentration and to a temperature increase, given that water supply is sufficient (Thornley and Cannell, 1997). Nitrogen-poor and species-rich grasslands, which are often extensively managed, may respond differently to climate change and increase in CO2 concentration, and the short-term and long-term responses may be completely different (Cannel and Thornley, 1998). Climate change is likely to alter the community structure of grasslands (Buckland et al., 2001; Lüscher et al., 2004), in ways specific to their location and type, and these changes will often depend on complex interactions between soils, plants and animals. Management and species-richness of grasslands may increase their resilience to change (Duckworth et al., 2000). 286 7. Risk assessment and foreseen impacts on agriculture Fertile, early succession grasslands have been found to be more responsive to climate change than more mature and/or less fertile grasslands (Grime et al., 2000). In general, intensively managed and nutrient-rich grasslands will respond positively to both increased CO2 concentration and temperature, given that water and nutrient supply is sufficient (Lüscher et al., 2004). As a general rule, productivity of European grassland is expected to increase, where water supply is sufficient (Byrne and Jones, 2002; Kammann et al., 2005). On the other hand an increased frequency of summer droughts will severely affect grassland production in the affected areas. Weeds, pests and diseases The majority of the pest and disease problems are closely linked with their host crops. This makes major changes in plant protection problems less likely (Coakley et al., 1999). Conditions are more favourable for the proliferation of insect pests in warmer climates, because many insects can then complete a greater number of reproductive cycles (Bale et al., 2002). Warmer winter temperatures may also allow pests to overwinter in areas where they are now limited by cold, thus causing greater and earlier infestation during the following crop season. Insect pests are also affected directly by the CO2 effect through the amount and quality of the host biomass (Cannon, 1998). Climate warming will lead to earlier insect spring activity and proliferation of some pest species (Cocu et al., 2005). A similar situation may be seen for plant diseases leading to an increased demand for pesticide control (Salinari et al., 2006). Unlike pests and diseases, weeds are also directly influenced by changes in atmospheric CO2 concentration. Higher CO2 concentration will stimulate growth and water use efficiency in both C3 and C4 species (Ziska and Bunce, 1997). Differential effects of CO2 and climate changes on crops and weeds will alter the weed-crop competitive interactions, sometimes for the benefit of the crop and sometimes for the weeds. However, interaction with other biotic factors may also influence weed seed survival and thus weed population development (Leishman et al., 2000). Changes in climatic suitability will lead to invasion of weed, pest and diseases adapted to warmer climatic conditions (Baker et al., 2000). The speed at which such invasive species will occur depends on the change of climatic change, the dispersal rate of the species and on measures taken to combat non-indigenous species (Anderson et al., 2004). The dispersal rate of pests and diseases are most often so high that their geographical extent is determined by the range of climatic suitability (Baker et al., 2000). The Colorado beetle, the European cornborer, the Mediterranean fruit fly and karnal bunt are examples of pests 287 Survey of agrometeorological practices and applications in Europe regarding climate change impacts and diseases, which are expected to have a considerable northward expansion in Europe under climatic warming. Environmental impacts Environmental impacts of agriculture under a changing climate are becoming more and more important. In particular, the role of nitrate leaching on the quality of aquifers, rivers and estuaries is globally recognized (Galloway, 2004). A warming is expected to increase soil organic matter turnover provided sufficient water is available, and experiment have shown that increases in net N mineralisation rates may be considerably higher than the increases in soil respiration (Rustad et al., 2001). Projections made at European level for winter wheat showed for the 20712100 time-slice that decreases in N-leaching predominate over large parts of eastern Europe and some smaller areas in Spain, whereas increases occur in the UK and in smaller regions over many other parts of Europe (Olesen et al., 2007). This in combination with longer growing seasons for the aquatic ecosystems would likely lead to higher risk of algal blooms and increased growth of toxic cyanobacteria in lakes (Moss et al., 2003; Andersen et al., 2006). The climate change scenarios could also lead to increases in GHG emissions from agriculture. Increasing temperatures will speed decomposition where soil moisture allows (Davidson and Janssens, 2006), so direct climate impacts on cropland and grassland soils will tend to decrease SOC stocks for Europe as a whole (Smith et al., 2006). This effect is greatly reduced by increasing C inputs to the soil because of enhanced NPP, resulting from a combination of climate change and increased atmospheric CO2 concentration. However, decomposition becomes faster in regions where temperature increases greatly and soil moisture remains high enough to allow decomposition (e.g. North and East Europe), but does not become faster, where the soil becomes too dry, despite higher temperatures (southern France, Spain, and Italy) (Smith et al., 2006). Climatic impacts on the key crops within individual environmental zones Winter wheat The impacts of climate change on winter wheat are thought to be negative across most of the zones (Fig. 7.7a). Whilst higher temperatures are expected to enable using late maturing cultivars in BOR, NEM and ATN zones it will also mean shortening of growth in remaining zones with the ATC, LUS, MDM and MDN being most affected. However, the damage during winter and risk of frost damage are expected to be lower in most of the zones with the 288 7. Risk assessment and foreseen impacts on agriculture exception of ALS. Improved conditions for sowing and harvest are expected in NEM, ALS and LUS zones, whilst notable worsening of the conditions is expected only in MDM zone. a) b) c) d) e) Figure 7.7: Expected impacts of climate change on a range crop production limiting factors for 5 selected crops: a) winter wheat; b) spring barley; c) grain maize; d) grassland; e) grapevine. The scale used for scoring is presented in the Table 7.2 and colour-coding reflects positive effect (green) or negative effect (red). The gray colour represents area 289 Survey of agrometeorological practices and applications in Europe regarding climate change impacts The changes in the seasonal climate variability are considered to have neutral or negative effect on winter wheat production with the NEM and PAN regions being the most vulnerable. Not surprisingly risk of drought and heat stress are thought to increase over all zones except MDM. However, this increase is according to the used scale considered to be small with the exception of heat stress risk in PAN area where large increase is expected. The biggest threat for the northern and central European zones (BOR, NEM, ATN, ALS, CON and PAN) is thought to be higher risk from plant pathogens and pests, whilst in the southern zones this problem is considered to be only marginal. Higher intensity of weed occurrence or an introduction of new weed species has been picked up by most respondents with the exception of NEM and ALS zones. Risk from a soil erosion and nitrogen leaching is much higher in the zones where higher precipitation is expected (BOR, ATN or ALS) but also mentioned as a threat (however of lower importance) in the Mediterranean region (MDS and MDN). Spring barley As in the case of winter wheat the change in the evaluated parameters is expected to influence spring barley production mostly negatively (Fig. 7.7b). Increase of temperature is expected to prolong growth duration in the northern range of spring barley growing area (i.e. ALN, BOR and ATN) while a negative influence is expected especially in ALS, LUS, MDM and MDN. The frost risk is thought to decrease or remain the same in most of the zones. Improved conditions for harvest are expected in NEM, ALS and LUS and on the other hand in MDM and MDM zone a notable worsening of the harvest conditions is expected. The impact of changes in the seasonal variability is in general perceived as negative with the exception of Mediterranean area (MDM, MDN and MDS). The respondents expect the biggest changes for the PAN, NEM and ATN zones. Drought is perceived as a very prominent risk in most of the zones (except MDM) and spring barley production is thought to be more at risk compared to the winter wheat. Damage caused by the heat stress is also expected to rise in most of the zones, but with the cooler zones thought to be more vulnerable compared to the MDM, MDN or MDS. As it was in the case of wheat, changes in the hail risk do not show any significant pattern with the exception of pronounced risk increase in the PAN zone. On the other hand the expected damage caused by the phytopathogens and pests is significantly greater than in the case of winter wheat. Similar to this crop the negative impacts are thought to dominate in ALN, BOR, NEM, ATN, ALS, CON and ATC zones but in the remaining part of Europe no change or even improvement are expected. Higher occurrence of weeds or introduction of new weed species has been picked up by most respondents with the exception of 290 7. Risk assessment and foreseen impacts on agriculture NEM zone. Similar to the wheat case the soil erosion and nitrogen leaching are expected to increase under the changed climate with the exception of MDS. Grain maize The estimates of impact of climate change on the grain maize production indicate that this only C4 crop species among the range of assessed crops is expected to perform much better than the previous two C3 cereal crops (Fig 7.7a-c). Increase of temperatures is thought to positively influence length of growing season in BOR and ATN zone whilst shortening of growth duration is expected in CON, LUS, MDM and MDS zones. On the other hand across all the zones the decrease of the late frost risk is expected as well as increase in the number of suitable days for harvest (although in many cases only small one). While the impact of drought and heat stress in the colder zones (e.g. NEM, ATN or CON) are thought to be smaller compared to wheat or barley, grain maize seem to be more vulnerable to drought and heat stress in PAN, LUS or MDS zones compared with the present conditions. Despite the fact that the impact of climate change is expected to lead to increase of hail damage, pest and disease risk, increase of weed pressure, higher soil erosion and nitrogen leaching, the intensity of these changes are in most zones smaller compared with small grain cereals. Grassland Out of our sample of five crops, grasslands seem to be least affected by the climate change. In all zones growth duration is expected to increase especially in ALN, BOR, NEM, ATN and ALS. In the same time damage during winter and those caused by frosts is expected to decrease and the number of days suitable for harvest is thought to increase (but mostly slightly) in all zones except ALN. Only marginal negative impact is expected from hail occurrence, heat stress, soil erosion and nitrogen leaching and weed occurrence with a notable exception of ALN zone where some of these parameter are changing to the worse (Fig. 7.7d). However drought and changes of seasonal climate variability is expected to cause negative impact across all zones with quite significant effect on ALN, BOR, NEM, ALS, CON, PAN and LUS zones. Interestingly the magnitude of changes (both positive and negative) is thought to be highest in the northernmost zones (ALN, BOR and NEM) with only subtle changes expected to grassland production in Mediterranean (LUS, MDM, MDN and MDS). 291 Survey of agrometeorological practices and applications in Europe regarding climate change impacts Grapevine The changed climate conditions are expected to lead to decrease in winter and frost damage in the cooler ones of the wine-producing zones (ATN. policy. sowing dates and fertiliser and pesticide use (Ghaffari et al. etc.. for summer irrigated 292 . 2002). replacing winter with spring wheat) (Minguez et al. sowing the same cultivar earlier. where irrigation is largely practiced for this crop. In particular. 2002. 2001).g.. 2007). in southern Europe short-term adaptations may include changes in crop species (e. the changes are not expected to be large. 1993). They are autonomous in the sense that no other sectors (e.g. 2002. On the other hand increased temperatures will lead to an increased period of growth in the ATN zone.) are needed in their development and implementation. 2000. Tubiello et al.g. The soil erosion and nitrogen leaching is expected to rise. Significant increase of drought and heat stress was estimated by our respondents only for the ALS zone and partly for MDN area. Despite the fact that changes in all remaining parameters are expected to be negative across most of the zones. Alexandrov et al. however not significantly probably with the exception of the CON zone where quite pronounced increase of soil erosion is expected.. The agronomic strategies available include both short-term adjustments and long-term adaptations (Olesen and Bindi. The length of the growing season is expected to decrease in the LUS. MDM. Chen and McCarl.6 Adaptation to climate variability and climate change To avoid or at least reduce negative effects and exploit possible positive effects. or choosing cultivars with longer crop cycle... Number of days suitable for harvest also increases slightly in most of the zones with a decrease being reported only in MDM zone. research. CON or ATC) but increased frost risk is expected in the warmest areas (MDS and LUS). 7. Grapevine production is thought to suffer from the increased hail damage risk across the zones and higher risk of diseases and pest occurrence is expected especially in the ATN and ALS zones. changes in cultivars and sowing dates (e. Autonomous adaptations The short-term adjustments include efforts to optimise production without major system changes. MDN and slightly in the CON and MDS zones. for winter crops. Studies on the adaptation of farming systems to climate change need to consider all the agronomic decisions made at the farm level (Kaiser et al. several agronomic adaptation strategies for agriculture have been suggested. Examples of short-term adjustments are changes in varieties. .g. Sinclair and Muchow. 2001). earlier sowing for preventing yield reductions or reducing water demand) (Olesen et al. pasture or sorghum). etc. The use of early ripening fruit tree species may reduce the water consumption. This involves changes in land allocation and farming systems. wheat or maize) by crops with lower productivity but more stable yields (e. and a combination of traits may be needed to stabilise yield in poor years. 2006). as proper management practices may be applied to orchards to improve adaptation (Rossi. the effectiveness of such traits depend on whether there is simultaneous change in climatic variability. Other examples of long-term adaptations include breeding of crop varieties. Risk assessment and foreseen impacts on agriculture crops. grapevine. This involves changes of land use that result from the farmer's response to the differential response of crops to climate change..g. 1995. 2008). Changes in farming systems The farm is typically the entity at which adaptation to climate change and climatic variability must take place through introduction of new management methods and technologies. There are many plant traits that may be modified to better adapt varieties to increased temperature and reduced water supply (Sinclair and Muchow. and here adequate and region specific information on climate change and suitable species and varieties are critical for efficient adaptation..g. since the projections show a considerable reduction in total runoff (Lehner et al. 2005. In northern Europe new crops and varieties may be introduced only if improved varieties will be introduced to respond to specific characteristics of the growing seasons (e. 2007.. Kaukoranta and Hakala. Because of the complexities of processes. breeding of crop varieties. length of the day) (Hilden et al. new land management techniques. olive and fruit trees). Long lead times in crop substitution are present for the perennial crops (e. This means substitution of crops with high inter-annual yield variability (e.. 293 . new land management techniques to conserve water or increase irrigation use efficiencies. and more drastic changes in farming systems (including land abandonment). The changes in land allocation may also be used to stabilise production. without sacrificing yield in good years (Porter et al. 2008). PeltonenSainio et al.g. 2006). However.7. Increasing the supply of water for irrigation may not be a viable option in much of southern Europe. Crop substitution may be useful also for the conservation of soil moisture. 2001). Long-term adaptations The long-term adaptations refer to major structural changes to overcome adversity caused by climate change. Adaptation will have to deal with all of these issues. 2006). A different allocation of European agricultural land use seems to represent one of the major long-term adaptation strategies available.) for assessing the changes in crop yield and suitability (Schröter et al. For the A1FI and A2 scenarios both the quantity and the spatial distribution of crops will change. 2004). socio-economic. However. Fuhrer et al. studies on farming systems require a holistic approach (Rivington et al. 2006). 2006). and the links with water availability may be among the most important ones. The sensitivity to climate change of farming systems may depend on the degree of diversification.. or mineralisation of soil organic matter (Dueri et al. the substitution of food production by energy production through the widespread cultivation of bioenergy crops will affect land use (Tuck et al. 2006). Rounsevell et al. and this trend is unlikely to change in the immediate future (Bradshaw et al.. 2005). 1988. based on data from a large number of operations in Canadian prairie agriculture. The interpretation of four IPCC-SRES scenarios suggests that different types of adaptation of farming systems (intensification. marginal areas. crop resource use..) (Berry et al. Climate change will not only affect crop yield. starch crops. cereals and solid biofuel crops. the choice of energy crops in southern Europe may be 294 . including oilseed crops. etc. farms have recently become more specialized. Finally. (2005) estimate a decline of up to 50% in cropland and grassland areas under the A1FI and A2 scenarios. However.Survey of agrometeorological practices and applications in Europe regarding climate change impacts management and inter-relationships of land use within a farm. A similar situation is likely to take place in Europe.. whilst. etc. for the B1 and B2 scenarios the pressures toward declining agricultural areas should be counterbalanced by policy mechanisms that seek to limit crop productivity. Changes in farming systems may also play a fundamental role in the adaptation of European agriculture to climate change. affecting the need for improving irrigation efficiencies (Tavakkoli and Oweis.. Recent studies at European level have demonstrated the need to include changes in climate and non-climate factors (technological. Several temperate and Mediterranean crop species are suitable for various types of biofuels. 2006).. 2004) or the need for terracing (Wadsworth and Swetham. All climate change scenarios show a northward expansion of these species with northern Europe becoming more favourable for most species. although the trend to organic farming in some areas and the urbanization of some rural areas may restrict this development.. extensification and abandonment) may be appropriate for particular scenarios and areas (high latitude and altitude. but total farm-level production through effects on altered carbon and nitrogen flows resulting from changed crop and residue quality. 2006).1 Observed adaptation Observed adaptation based on quantitative questionnaires The quantitative questionnaire asked about ten specific adaptation responses and about five “new” crop species that were thought to be applicable through most of the environmental zones and would be picked up by respondents.8 shows. Figure 7.. minor to moderate changes in the cultivation timing were observed in all environmental zones during past decade. Taking into account potential impacts and adaptive capacity. As Fig..6. 7. whilst the B2 scenario seems to be least harmful for farmers’ livelihood (Metzger et al. Risk assessment and foreseen impacts on agriculture severely reduced in future. The A1FI and A2 scenarios anticipate greater vulnerability throughout. both due to increased temperatures and reduced rainfall. the vulnerability of agriculture based on “Farmer livelihood” (profit) have been analysed for EU15 (Metzger et al.8: Observed adaptation responses by the farmers as reported by respondents in individual environmental zones 295 . The results show the agricultural sector in the Mediterranean region as vulnerable under most climate change scenarios starting at different time slices. 2006).7. 7. depending on the scenario used. g. The introduction of new crops to the crop rotation has been also reported in the study with increase in the area of silage and grain maize being the most notable changes. especially in the most drought prone regions (PAN. MDN and MDS noted tendency to more grapevine production. The grain maize has also shown increase in the PAN zone. This is even more evident from a reported increase of interest in the cultivars that are able to cope better with drought and other weather extremes. where water resources are limited (LUS and MDS) we have seen quite marked drop in the area under irrigation in combination with change of crop structure. BOR. PAN. CON. In case of silage and grain maize ATN and CON areas seem to show the largest change. This response has been combined with the expansion of irrigated areas.g. Although the irrigation expansion seems to be an obvious response in very dry zones. ATC or PAN) compared to the Mediterranean (MDN or MDS). Despite the tendency to new cropping schemes farmers seem to be more interested in maintaining the present portfolio of crops that is documented by evident tendency to introduce new and more suitable cultivars of the presently grown crops across all zones.Survey of agrometeorological practices and applications in Europe regarding climate change impacts The most significant changes seem to be going on in the cooler zones (e. The respondents in NEM. The change of cultivation includes not only changes in the tillage practices but mainly shifts in the sowing dates (e. ATN. MDN and MDS) but partly also in other zones. BOR. As the drought has been spotted as one of the most pervasive crop growth limitations. in the southern zones the main reason seems to be higher tolerance of grapevine to drought compared with the field crops. there has been a wide spread effort to promote techniques that preserve soil water. Almost all zones show quite pronounced effort in introduction of cultivation that reduces soil erosion that might be an indication of higher frequency of more intense precipitation (as a lead cause of water erosion) but also the result of more frequent droughts as a prerequisite of wind erosion over the area. 296 . tendency to earlier sowing of spring crops). CON and PAN zones show increased interest in growing of sunflower as a response to changing climate conditions and this goes frequently in hand with introduction of soybeans to these zones. CON. Whilst in the cooler zones the main driver seems to be more favourable climate conditions enabling introduction of the crop. The changes in rainfall patterns are most likely behind the reported improvements in the field drainage systems but this measure seems to be the least notable. Especially in the warmest zones (LUS. MDN and MDS) we can see a tendency of farmers to reduce crops that are unsuitable for the changing climate conditions. 7. d) grassland. grassland and grapevine. c) grain maize. Risk assessment and foreseen impacts on agriculture 7.e. 7. ALN and BOR in case of wheat. maize.9). barley and grassland production and MDM and MDN in case of barley.2 Future adaptation responses Cultivation timing Changes in the timing of cultivation (including sowing and harvest) are expected to have minor to moderate effect for all five model crops evaluated by the respondents (Fig.9: Expected importance of adaptation measures under the expected climate conditions for individual crops: a) winter wheat. b) spring barley. e) grapevine 297 . a b c d e Figure 7. Interestingly the largest changes are expected on the opposite sides of climate gradient i.6. 298 . The expectations for drier zones are much lower and only minor shifts are expected as far as fertilization is concerned in MDN. Modification of the fertilization The expected shifts of fertilization patterns show an interesting north-south gradient with the northern most zones expecting moderate to major changes in the fertilization schemes both in field crops (wheat. CON and MDM zones expecting larger changes than remaining zones. barley and partly maize in the warm and dry zones (PAN. the zones that are expected to experience increase of precipitation (e. As the potential productivity of northern zones (ALN. The anticipation of large shifts in the cultivation timing in the northern zones is probably enhanced by the pronounced prolongation of growing season that will allow introduction of cultivars with longer growth period. MDN and partly MDS and CON). MDS and MDS zone that is associated with expected increase of water stress. Introduction of water-conserving tillage practices are assumed to be an important adaptation measure especially in case of the wheat. However. Tillage practices focused on soil water conservation are considered by respondents as likely adaptation response by grapevine farmers in the MDM. BOR. maize and grasslands. In grassland case the most notable change will occur in ALN and BOR zones when the length of growing season will increase leading to increase of potential productivity that will facilitated changes in the tillage practices. LUS. ATC and partly PAN in case of barley.Survey of agrometeorological practices and applications in Europe regarding climate change impacts In general. the expected increase of precipitation (in combination with more strict EU environmental regulation) will lead to higher risk of nitrogen leaching resulting in necessary modifications of fertilization schemes used in these zones. MDS. barley or maize) as well as perennials (grassland and partly also grapevine). MDM.g. NEM or ATN) is expected to be increased due to longer vegetation season it will also require adequate increase of available nutrients for the crops. significant changes of the sowing dates (and consequently of other field operations) are expected in order to avoid dry periods during summer and use as much of winter precipitation as possible. In case of grapevine the picture is more complex with ATN. Change in tillage practices Change of tillage practices is mostly focused on the soil water conservation and protecting the soil against soil erosion (both water and wind). BOR or ATN) put more stress on the erosion protection as soil water reserves are not expected to be replenished during winter months. barley and maize the changes in the crop protection schemes are expected to be major ones.e. barley. Obviously in case of grasslands the economic benefit of crop protection and monitoring is quite low (as are the risks) and thus the potential of these adaptation measure is considered to be small. CON and PAN zones. MDN and MDS) the importance of seasonal forecast is stressed by the increasing uncertainty in the rainfall amounts. The seasonal forecasting is expected to be important in case of field crops and partly also grapevine. The new cultivars are expected to be more important in case of spring barley and grain maize. Such a response could be explained when we realized that changed climate conditions will most likely cause spread of pests and diseases from warmer zones which threatens more zones in the north than those in the south. especially in the BOR. Fusarium sp.7. Overall the importance of some sort of monitoring system has been mentioned by respondents over all zones with the exception of LUS. However in case of wheat.). maize and partly grapevine. ATN. 299 . It is not surprising that within these zones also the introduction of pest and disease monitoring scheme has been emphasized as one of the key adaptation responses. NEM or PAN) compared to ATN or ATC zones whilst in case of Mediterranean zones (MDM. BOR. Risk assessment and foreseen impacts on agriculture Introduction of new cultivars The prospect of this adaptation response is obviously lowest in case of grassland (Fig 7. whilst in case of grasslands it is given minor priority probably for the same reasons as phytopathological monitoring. According to the respondents this measure is thought to be quite pronounced in case of NEM. NEM. those with higher continentality (e. Modification of crop protection and pest and disease monitoring The expected change in the crop protection technologies is one of the most prominent adaptation measures especially in the case of wheat. In addition. ALS. barley and maize the biggest effect of seasonal forecasting is expected in zones where relatively large inter-seasonal differences are more likely. Seasonal weather forecasting The changed climate conditions and according to some indications higher probability of unusual weather patterns lead most respondents to stress the role of seasonal forecasting as an adaptation tool.g. AKS. i. MDN and MDM zones. CON. For wheat. PAN.9d) whilst it is expected to be important in case of field crops and in some zones also for the grapevine.g. in BOR or ATN the expected higher precipitation might result in higher infestation pressure of some “native” diseases (e. CON. This explains why the respondents in the NEM.10: Reported level of climate change awareness among farmers.7 Improving awareness and adaptation to climate change 7. 7.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Crop insurance As the crop insurance is seen as a quite effective tool mitigating the effect of unsuitable weather conditions during growing season (or occurrence of severe weather events) the highest importance has been reported for the zones where the climate impact are expected to be mostly negative. whilst in ATC or LUS zone this option has not been seen as important.7. 7. adaptation strategies and awareness Fig. The expected benefit of the crop insurance is not seen as directly dependent on the value of the crop per unit of area as the expected importance of crop insurance for grassland is of the same magnitude as in the case of maize or grapevine with obvious regional differences. Figure 7. agriculture advisors and government officials in 26 countries and the status of agriculture adaptation strategy and education programs for farmers 300 .10 documents existence of top-town “gradient” in the collective knowledge about the imminent climate change impacts on agriculture with better level of understanding by the governmental offices and the lower one in case of extension service and individual farmers. ALS and especially PAN zones put large emphasis on this particular adaptation measure.1 National impact assessments. Only in one-third of the countries the most affected group (i. Surprisingly in case of the irrigation scheduling that has been always seen as one of the most efficient application of DSS. crop protection and fertilisation schemes seems to be quite well supported by existing DSS with more farm-based approach in case of fertilisation and regional approach in case of the crop protection. but only 3 countries reported an existing agriculture adaptation strategy whilst the rest of them either does not have any or it is in the process of preparation. 7. The survey showed quite large differences between individual countries (and crops) but in general the use of decision support systems (DSS) is lower than expected (Fig. It is even more surprising when we compared the use of DSS with the present climate limitation for crop production in individual zones. Although 75% of responding countries acknowledged activity aimed at increasing farmers´ awareness there seems to be quite long way to go before sufficient level of understanding is reached.7.e. Risk assessment and foreseen impacts on agriculture Despite the upsurge of the information flow from the various sources the level of awareness seems to be relatively low compared to the level of observed adaptation reported in the survey (Fig. in many countries where irrigation is used 301 . farmers are less inclined to demand such systems. On the other hand the drought or heat stress DSS benefit is less straight forward and whilst it might provide essential information to the decision makers.11). 7. It also seems that the policy makers in majority of the governments are not sufficiently informed about the risks associated with the climate change impacts for agriculture. farmers) are considered to have a good understanding to the consequences of climate change on their livelihoods. Even worse is the situation in case of heat-stress and weed management. Whilst drought seems to be a pervasive problem across all of the zones and is expected to get worse under the changed climate only half of the countries have some sort of DSS system and only one fifth of countries have some sort of nation-wide drought monitoring scheme. the results of the survey show rather mixed picture.2 Dissemination and warning systems The level of information dissemination and existence of warning systems shows that a lot needs to be done in improving resilience of farming systems across Europe. Whilst in some countries quite sophisticated DSS are applied (mostly in drier zones). 7.7. This could be a possible explanation of a large discrepancy between the claimed awareness among government officials as almost 2/3 of countries claim to have medium or good level of information about the possible impacts.8). On the other hand. One of the possible explanations of the present state is that the listed factors play an important role in the economy of every farm and direct benefits of DSS is well understood by the farmer as it could be well measured in terms of cost savings. selection of suitable crops or cultivars. for research and for development of the agricultural sector.Survey of agrometeorological practices and applications in Europe regarding climate change impacts for wheat. an increase in carbon sequestration in agricultural soils and the 302 . d) grapevine 7. and to strike a variable balance between economic.. Policy will have to support the adaptation of European agriculture to climate change by encouraging the flexibility of land use. In doing so. maize. b) grain maize.11: Present level of decision support system use for four key crops across 26 responding states: a) winter wheat. grasslands and grapevine production there is no DSS system in place. a b c d Figure 7. c) grassland. it is necessary to consider the multifunctional role of agriculture. Policy will also need to be concerned with agricultural strategies to mitigate climate change through a reduction in emissions of methane and nitrous oxide.8 Implications and perspectives The projected changes and the perceptions of impacts and adaptation as seen from the questionnaires have some implications for agricultural and environmental policy. environmental and economic functions in different European regions. crop production. This is in particular troubling when the drought and irrigation trends are considered. farming systems etc (Olesen et al. 2002). These include the effect on secondary factors of agricultural production (e. There is also a considerable need to better estimate the costs of various adaptation measures. pests and diseases).7. In other cases. weeds. This would be feasible utilising the main agricultural resources in agriculture (Olesen and Bindi. Risk assessment and foreseen impacts on agriculture growing of energy crops to substitute fossil energy use. and the interaction with the surrounding natural ecosystems. irrigation management. the effect of changes in frequency of isolated and extreme weather events on agricultural production. There is a considerable need for an increased focus on regional studies of impacts and adaptation to climate change in agriculture. economic. 1996).. 303 . taking into account the complexity of farm-level decision-making. because they would be ineffective if implemented as a reaction to climate change (Smith and Lenhart. some livestock production systems. diversities at different scales and regions (including the entire food chain). may be severely affected by climate change. including plant breeding (also using molecular techniques). The research on adaptation in agriculture has not yet provided a generalised knowledge on the adaptive capacity of agricultural systems across a range of climate and socioeconomic futures. the effect on the quality of crop and animal production. Such studies are now becoming more realistic with the arrival of detailed regional scale climate change scenarios (Christensen and Christensen. 2006a). However. and adaptation studies have to move from looking at potential adaptation to adoption. application of information and communication technology etc. current climate and cropping systems (Olesen et al. 2002). These studies should include assessments of the consequences on current efforts in agricultural policy for a sustainable agriculture that also preserves environmental and social values in the rural society.g. institutional and cultural barriers to change. the measures must be implemented in anticipation of climate change. In some cases such adaptation measures would make sense without considering climate change. The policies to support adaptation and mitigation to climate change will need to be linked closely to the development of agri-environmental schemes in the EU Common Agricultural Policy. and time-lags in responses and biophysical. It should also be noted that for obvious reasons most studies on climate change impacts have so far focused on crop production. especially those involving grazing systems or use of fresh fodder. Research will have to deal with some “unknown aspects” that due to their complexity have not yet been studied in detail. 2007). soils. and more studies on these systems are warranted. livestock feeding technologies. since effects and responses are likely to be regionally specific depending on interactions with soils. The adaptation to climate change has in particular to be factored in as part of the ongoing technological development in agriculture. because they help to address current climate variability. M. p. Larsen. P. Sci. 2006.11 References Ainsworth E. and adaptive capacity will affect in a different way the agricultural eco-systems across Europe. S. Trnka acknowledges support of KONTAKT OC187 project for supporting COST 734 activities in the Czech Republic. 7. both at the continental and regional levels. Jensen.Survey of agrometeorological practices and applications in Europe regarding climate change impacts 7.. M. Cajic. O.E. T.P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis. Moreno. R. Parry. Nováky.A.F. Climate-change impacts on hydrology and nutrients in a Danish lowland river basin. Hoffmann. cultivars and sowing dates) and long-term adaptations (e. agriculture in the Mediterranean region seems to be more vulnerable than in other European regions. Oberforster. Alexandrov V. J. Thus. C. changes in crop species. J.E. UK. Shvidenko. Palutikof.S. yield and production. Tot.g..g. B. E. Martin. 2007. Cambridge University Press. In M. In particular. Kronvang. 2007. 2005.E. Alcamo J. Eitzinger. adaptation strategies should be introduced to reduce negative effects and exploit possible positive effects of climate change. farming systems and institutions) should be considered. 304 .E.9 Conclusions The results of the studies on anthropogenic climate change performed in the last decade over Europe indicate consistent increases in projected temperature and different patterns of precipitation with widespread increases in northern Europe and rather small decreases over southern Europe. M. 8:372389. Corobov. V. 2002.). This calls for a considerable effort in research and development to deal with the changes. and Long S. Canziani. C. land allocation. Both short-term adjustments (e. crop suitability. Andersen H. Olesen. Giannakopoulos.L.N. Cambridge. C.C. M. 365:223–237. 165:351-372. 7. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. J.10 Acknowledgements We gratefully acknowledge all the experts contributing to the questionnaires. Rasmussen. the differences in climate exposure. Adaptation and Vulnerability. Potential impact of climate change on selected agricultural crops in north-eastern Austria. J. B. These changes in climate patterns are expected to affect all the components of the European agricultural ecosystems (e. Europe.P.J.J. New Phytol. Environ. canopy properties and plant production to rising CO2. E.K. sensitivity. However. A. etc. Global Change Biol. Hanson. 541-580. R. Climate Change 2007: Impacts. Bindi. water management. van der Linden. (Eds)..g. Devoy. Harrison. M. 2006. Miglietta.L. Lindroth.E. What can scenario modelling tell us about future European scale agricultural land use. Holt T. Geophys. B.R. Cunningham.. Risk assessment and foreseen impacts on agriculture Anderson P. N deposition and improved cultivation. Press. Future extreme events in European climate: an exploration of regional climate model projections.J. 1996. J. 1998. Brown. Global and planetary change. P. 1992. 38:301-309. Gozzini. N.H. 2006. Daszak. Miglietta. 2004. Semmler T. Clim.B. MacLeod. 19:535-544. Global Planet. R. Pol. Clim. The 2003 heat wave in Europe: A shape of things to come? An analysis based on Swiss climatological data and model simulations. D. Symrnioudis. Rounsevell. Modeling the impact of future climate scenarios on yield and yield variability of grapevine. T.F. C. Agric.A. Trnka. Ferrini. Bindi M. Beniston M. J. Morales. A. Bouma J.A. 81: 71-95.H. 31:L02202. Frei C. Geoscience 336:491-503. K.D. 2002. Grassland invasions: effects of manipulations of climate and management.J.H. Assessing the vulnerability of agricultural land use and species to climate change and the role of policy in facilitating adaptation. Whittaker.E. Patel. Ecosyst. A.J. Goyette S.. Global Change Biol. R. 2007.. B. L.. 57: 151-160.. K. C. Buse. Byrne C. Stephenson. Climatic change and the shift in the cultivated area of olive trees. Hodgson.. La vague de chaleur de l'été 2003 et sa prévision saisonnière. Appl. Good. 8:1-16. F. I. O. G.H. J. Bindi M. Pol. Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. P. J. 2001. Beniston M. Awmack. Mediter.C. S. Free air CO2 enrichment (FACE) of grapevine (Vitis vinifera L. Environ. Masters. Climate change and drought occurence in the Alpine region: Howe severe are becoming the extremes?. Cojocaru..P. Koutsidou. 2002. 67:103-119. P. 2004. Jones. E. The role of climatic mapping in predicting the potential distribution of non-indigenous pests under current and future climates. Bindi M. Harrington.F. 7:213-224. Audsley E. Petersen. DOI: 10. Simota. R. 2001. Bale J. Plant Soil 287:235-245. Planton.S.A. V. Berry P. Miglietta. J. Agric. Trends Ecol.D.G. M. climate change and agrotechnology drivers. and Jones M. C. Irish Acad. F. Koffi B.. Alexandrov. Ferro. Environ.. A. Change 44:73-81. and what not? Environ. Simulating trends in crop yield and soil carbon in a long-term experiment . V. Thompson. Olesen. Eur.M. I. Farrar. 102B:141-150. Halsnaes K. Switzerland.D.G. A. Fibbi.. 2004.A.K. Buckland S. Sci. community structure and species diversity of a semi-natural grassland in Ireland. Emerging infectious diseases of plants: pathogen pollution. Res. Epstein. Climatic Change. Ecosyst. K. F. 82:57-71.C.D. 22:41-44. R. F. Fibbi.C. C.. Let. 2000.T. Proc. Rogel. M. Change 67:119-141.M. Pearn. Effects of elevated CO2 and nitrogen fertilizer on biomass productivity. Rounsevell.E.. Woth K. Watt. Holly.R. G. Environ.A. Res. Batjes.. M. 2007.B. Palutikof J. P. Déqué. M. Cannon. E. Beniston M. Sansford.G.effects of rising CO2. 2004. J. Coulson. Grime. Smit. C. Schöll R. S. Walters. S. L. F. Baker R. J. André J.. and Diaz H. 2006. Bradshaw B. 14:145-155. 9:189-204.A. Jarvis. N.B.M.B. T.1007/s10584-006-9226-z Berntsen J. J.A. Butterfield. Farm-level adaptation to climatic variability and change: crop diversification in the Canadian Prairies. Christensen. Orlandini. 2004. Bezemer. Growth and quality of grape and wine in reponse to elevated CO2 concentrations.7. Agric. Evol. Principal land use changes anticipated in Europe. Agron. Ecol. J. 9:148-168.. J. G. Calanca P.. Hodkinson. Sci.C. J.M.. Jylhä K.): II. DOI: 305 . Harley. Varallyay. B.. The 2003 heat wave as an example of summers in a greenhouse climate? Observations and climate model simulations for Basel.K. Audsley. D. Christensen J. Malloch. Dai A. M. Rueda.. J.T. A.F. Kolli. G.F. In: Climate change 2007: The physical science basis. H. M. Biogeography 27:347-358. Tigor. N. Mearns. C. Granier. 847-940. C. Regional climate projections. Chloupek O. Grünwald. Qin. USA. 5:157-168. Cannon R. Assess. J. Nature 421:805-806. 2006. V.B. Global Variations in Droughts and Wet Spells: 19001995. and Janssens I. Annu. P. R. K. A. 2000. P. Jones. N. 1998..J. Matteucci. 10:509-518.G. and Kennedy D. Rinke.B. Nature 437:529-533. 4:785-796. A. Busuloc. Food Sci. N-poor ecosystems may respond more to elevated [CO2] than N-rich ones in the long term..M. S. Z.. W.B.. G.. Buchmann. Schweigert. J. D. 2005. B. Agric. in Europe. climate and land use influences on the phenology and numbers of the aphid. T. Schulze. Niemi. Q. Saarikko. F. with emphasis on non-indigenous species.1016/j. crop diversity. M.. p. Global Change Biol. and Thornley J. Keronen. T. R. G.C. Kown.Seufert. weather and fertilisation over 75 years in the Czech Republic in comparison to some European countries. and Christensen O. 37:399-426. Soussana. Carter T. R. Trenberth. A model analysis of grassland. Chen.1.. S. J.D. Christensen J. Nature 440:165-173.E. (eds)).001.Survey of agrometeorological practices and applications in Europe regarding climate change impacts 10. N.H. Laprise. DOI: 10. T. X. Heinesch.1029/98GL52511. A. Biogeography 32:615-632. Climate change and plant disease management.A.D. Sanz. Clim. J.R..J. Global Change Biol. Field Crops Res. 1998. Clim. Rambal. Chen. D. Davidsson E. 1996.G. Coakley S. A..M. Chakraborty. Change 50:475-487. Stampfl.L. M. Severe summertime flooding in Europe. M. UK and New York. Friedlingstein. 2004. Krinner.gloplacha. Valentini. 1999.M. M.M. Assessing the risks and uncertainties of regional crop potential under a changing climate in Finland.. 2007. and Christensen O. Rev. Global Change Biol. Geographical location. Modelling the potential effects of climate change on calcareous grasslands in Atlantic Europe. Knohl. 306 .A. Duckworth J. Carrara. 2001.C. Manca. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (S. H.K. P. Darwin R. DOI: 10. M.H.C. and McCarl B. Averyt. Bunce. The implications of predicted climate change for insect pests in the UK.J. Karl.. Miller. Räisänen. Trenberth. K. Ogée. B.. Papale. Scherm. 2007. Economic effects of CO2 fertilization of crops: transforming changes in yield into changes in supply.H. G. Chevallier. Dai A. J. Heid. Myzus persicae. adaptability and response to climate change. Manning. V. Ciais Ph.H.C. P... Loustau.11. Solomon. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Marquis. E. R. Miscanthus biomass production for energy in Europe and its potential contribution to decreasing fossil fuel carbon emissions. Finland 3:329-349. Cambridge University Press. Cannell M. L. 1998. Vesala.1175/JHM-386. 2004. 4:431-442.E.C.. K. A. A.. A. Change 81:7-30. Aubinet. Gao. Jones. Miglietta. 2003. Model. 85:167-190. Cambridge. Christensen J. Allard. Ourcival. A summary of PRUDENCE model projections of changes in European climate by the end of this century. Cocu N. D. Hrstkova. P. Whetton. Geophysical Research Letters 25 (17): 3367-3370. Environ.A. R. Clifton-Brown J.R.H.R. 2005. Viovy. I. Reichstein.A..B. A. 2000. An investigation of the relationship between pesticide usage and climate change. Menéndez. Phytopathol. Bernhofer.G. P. et al. Chen C. Taotao 2004. Friend.J. K. A Global Dataset of Palmer Drought Severity Index for 1870-2002: Relationship with Soil Moisture and Effects of Surface Warming. Sarr.2006. F. Hewitson. De Noblet. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. NY. R. K. Journal of Hydrometeorology 5: 1117-1130. Pilegaard. Yield and its stability. Jäger. Ecol. S. Hydrol.R. J. Tank. 2004.C. 93:191-214.K. Clim. G. J. The effects of climate change on land suitability of grain maize. Bindi. 267:94-114. ozone.. Kaiser H. Kimball.D. Dyn.J. Fink A. Calanca.. Anser. 97:1-20.org/meetings/ fm05/waisfm05. FINADAPT Working paper 5. G. M.. K. D. R. Helsinki. C. 2005.agu. M. R. Boyer. U. Pinto. Climate Res.F.F. Kaukoranta T. Climate change affects farm nitrogen loss . Brown. Ghaffari A.A. Eur.. 2008. 2003. Finnish Environment Institute Mimeographs 335.html. Askew. U. Hemispheric and large scale surface air temperature variations: an extensive revision and an update to 2001. J. Statistical analysis of the flood situation and assessment of the impact of diking measures along the Elbe (Labe) river. K. Agroecosystem responses to combinations of elevated CO2.W. Svoboda. E. In: AGU fall meeting. 2002.. Townsend. Green. potato and sugar beet in Finland. M. and Moberg A. Wilhite. Change 55:509-533. Kenny. E. G. Lee. 75:387-398. Jones.. Dentener. Jones P. I. Capone. J.M. Agron. San Francisco.H.J. K. D. Helms M.. J. Impacts of Europe's changing climate: An indicator-based assessment. EEA.H. A. Science 289:762-765. L.A. Grünhage. J. V. Cleveland.M. H.A. A.. Alexander. L. Bi. D. Semerádová.ì G. Žalud. C. D. poster – abstracts available: http://www.7.Synoptic diagnosis and impact. B. S. Leckebusch. 2004.. present. Trnka. Kielty. Ecosyst. P. Porter. M. Ihringer. Riha. 2002. Büchele. Brücher. Agric. Hayes M. Environ. Agriculture.. B. 6:351-365. Rossiter. Agric.J. Giorgi F. 2004.. 70:153-226.V. G. Ulbrich.. A. Holland. J.P. 23:839858. Nitrogen cycles: past. J. and Lethtonen H. 19:193-212. II: Climate Change scenarios (2071-2100). P. and global climate change. B. The response of two contrasting limestone grasslands to simulated climate change.P. 2004. D. 2005. Pal. Risk assessment and foreseen impacts on agriculture Dueri S. Econ. J. U. H. Vörösmarty. 2000. Change 73:319-343. Krüger. Sampath.. Wilks. Denmark. Climate change and global wine quality.G. Della-Marta. A. O. 16:206-223. Howarth. S. Weather 59:209-216. Thompson. A. Climate change and winter wheat management: A modelling scenario for South-Eastern England. M..P. D.C. P. 2005. 2005. Michaels. Copenhagen. Hilden M. Clim. The practice and process of adaptation in Finnish agriculture. Cook.J. Z. 1993.. F. Observed coherent changes in climatic extremes during the second half of the twentieth century. Kobayahsi.J. Food Sci. Merkel. Impact of spring warming on sowing times of cereal.P.E. Fuhrer J. Olesen.G. European summer heat waves and drought .M. Haylock. EEA Report No 2/2004. Am. Mean.. X. Parry.W.A.A Swiss case study with a dynamic farm model. Application of Drought Indices to Assess Drought Conditions in Changed Climate. Clim. Karl.A.P. interannual and trends in a regional climate change experiment over Europe.L. 2002. Janze.P. 2006.S.H. Grüters. Response of aboveground grassland biomass to moderate long-term CO2 enrichment.K. Clarke. 5-9 December 2005. Basic Appl. Seitzinger. Fuhrer. D. 2002.. White.A.L. Responses of agricultural crops to free-air CO2 307 . European Environment Agency. Agric. Storchmann. 1993. Grime J. Cooper. T.V.. J. J. The 2003. winter wheat and cauliflower in Europe.R. 107 pp. Galloway J. and future. Corker. A farm-level analysis of economic and agronomic impacts of gradual climate warming. M. S.D. Biogeochem. P. Hillier. in press. and Hakala K. 2003. Dubrovský.J. Kammann C. Syst.C. J. H.G. Peterson. M. Masters. N.G. Harrison. T.. Clim. 2:325-238. Gleason. Frich P. Diaz-Ambrona.E. The implications of Common Agricultural Policy reform for farmers in Europe. 2000.I..J.B. Mckee. Masters. Rubæk. C.A. Analysis of the 2003 CAP reform. Science 305:994-997. Sensitivity of field-scale winter wheat production in Denmark to climate variability and climate change. L. 2000. 308 . Acosta-Michlik. Sau. Clim. D. Clim.E. Jongman. C. T. Impacts of present and future climate variability on agriculture and forestry in the temperate regions: Europe. Global scale climate-crop yield relationships and the impacts of recent warming..W. P. Olesen J. Farm Pol. Minguez M. Adv. 2007. First-order impacts on winter and summer crops assessed with various high-resolution climate models in the Iberian peninsula. Geophys. Ruiz-Ramos.. Res. Biogeogr.. Bi. Alcamo. integrated analysis. I. Nösberger. and Tebaldi C. Change 75:273-299. Sykes.P. 31:L13202. Consistency of recent European summer precipitation trends and extremes with future regional climate projections. M. More intense. and Kaspar. H. Let. D. M. T. Wilson. J. Ambio 33:193198.H. B. Gill. Appl. T. C. J. 14:293-299. T. 16:3665-3680. S. G. 2004. Recent and future signatures of climate change in Europe. Sau. Jensen. Marsh J.E.D. M. Henrichs..W. 2006. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Schröter.G. 2004. Eaton. Environ. Harvey. Brown. Rounsevell. and Field C.R. Palutikov.. Lehner B. Smith. J. X.H. G. Fronzek. and longer lasting heat waves in the 21st Century. and Können G. Pal J. Maracchi G. 1):123-143. R. Change 70:117-135. F.S. F.1088/1748-9326/2/1/014002. H.Survey of agrometeorological practices and applications in Europe regarding climate change impacts enrichment. 2007. Quemada. Daepp. Petersen. K. Meehl G. Res. Bindi. Watkins. J. Olesen J.. J. D. 2:014002. T. V.K. Trends in indices of daily temperature and precipitation extremes in Europe. and Bindi M. Blum. Func. 2002. nutrient addition and fish on phytoplankton in shallow lake microcosms. 22:1441-1453. land use and policy.M. Leemans. J. Clim.G.A. Lüscher A. A. 2005.E. Díaz-Ambrona. Clim.J. Agron. I. Atkinson. doi: 10.Q. 2002.J. 21:379-398. Clim. How important is climate? Effects of warming. Olesen J. Heyes. O. Eur. Fertile temperate grassland under elevated atmospheric CO2-role of feed-back mechanisms and availability of growth resources.E. Change 81 (suppl. OECD publications.. Mücher.. Bunce. Holt.. Minguez.. Seed bank dynamics: the role of fungal pathogens and climate change.B. Int. Paris.H. Kjellström E. 16:239-262. M. J. J. 2007. T. 77:293-368.I.R. Agron. M. J. 40:782792.M. Metzger M. M. 2004. Ecosyst. 2004. Consequences of climate change for European agricultural productivity. Döll. A climatic stratification of Europe. J. Hatton.G. Clim. Lobell D. J. more frequent. U. F. Uncertainties in projected impacts of climate change on European agriculture and ecosystems based on scenarios from regional climate models. Clarke. R. M. Ecol.. et al. R. Heidmann. Eur. 2005. OECD. 2006. Leishman M. Ecol. Lett. M. Global Ecol. F. 2003. J. Morales. Ruiz-Ramos. Hartwig. Res. 2 (2):1-11. Giorgi. S. Hickler.. Quemada. 15:221-238. Moss B..H. 114:69– 85. Env. D. Agric. P. Carter.G. Clim. Change 81 (suppl 1):343-355.. M.. Collings. 2004. 2003. Klein Tank A. 14:549-563. Klein Tank A.. 2005.. Agron.B. Sirotenko. Estimating the impact of global change on flood and drought risks in Europe: a continental.. The vulnerability of ecosystem services to land use change.P. Metzger M.A. Rettori. Environ. Scand. R. Future scenarios of European agricultural land use. W. Reginster. Clim.. Rivington M.. F.R. Bondeau. F. (R. Seneviratne S.Dris (ed)).E. C.R. Can. J. 93:263-270. Modelling the effects of climatic change and genetic modification on nitrogen use by wheat. P. Sabaté. A. Schlamadinger. Schär C.R.B. Meyer. 2006. Lüthi. 2006. Semenov. Climate change in Portugal: Scenarios. Stöckle. Environ. S. Orchard-atmosphere exchange processes and sustainable management. Oecologica 126:543-562.B. Sirotenko O. Rossi F. V. Thonicke. Sims R. Zierl.7. Change 36:217-232. and Schimmelpfennig D. 2006. D. T. quality and biotechnology. Belanger. Projecting changes in cropland and grassland. Rounsevell. G.J. Hartley. Matthews. Plant Sci. Lisbon. G. Lüthi. M. Arnell. H. Araújo. 2001.N. Borby.A. Systems analysis of plant traits to increase grain yield on limited water supplies. Salinari F.I. Marion. D. M.. S. Hakala.. Y. 2006. R. Ewert. Impacts and Adaptation Measures. in press.C.L. Res.T. 6:193-201. Nature 432:559-560.J. Mitchell. CO2 and tropospheric ozone concentrations and soil fertility. Schröter D. 1997. Ecosystem service supply and vulnerability to global change in Europe. 2001.L. Climate change adaptation policy options.. Climate change and winter damage to fruit trees in eastern Canada. F.. Portugal.1038/nature05095. Cornelissen. G. C. and Lenhart S. J. Smith. Kankaapää. Tubiello. 107:117-135. 2006. B. Global Change Biol. Acta Agric. WFL Publ. Comparing regional risks in producing turnip rape and oilseed rape . K. Finland.. A meta-analysis of the response of soil respiration. Rustad L.A. 84:1113-1125.I. and aboveground plant growth to experimental ecosystem warming. Software 22:202-210.H. Taylor. 12:2054-2076. R. Metzger. J. L.D. Glendining. Clim. S. Clim. B. Gradiva. Ruosteenoja. Bellocchi. Forbes. 2005.C. S. M. Erhard. R. W. Miglietta. 12:1299-1307. Smith. 2008. 309 . net nitrogen mineralization. G. Frei.S.. Change 43:745-788. Smith J. Risk assessment and foreseen impacts on agriculture Peltonen-Sainio P. Land-atmosphere coupling and climate change in Europe. F. Rounsevell M. Ecosyst. Prentice. A. House. J. 1995. Thuiller. C. Mitchell. An integrated modelling approach to conduct multi-factorial analyses on the impacts of climate change on whole-farm systems. P. D. In Crops: growing. A. 2004. J. Ewert.. M. Liniger. C. The role of increasing temperature variability in European summer heatwaves. and Jendritzky G.. Global Change Biol. R. Smith. M. K. 2004. Tuck. I. Gurevitch.. C. J. N.L Campbell. Porter J. and Muchow R. C.B. Schär C. Vidale. (eds. A. Abashina. de la Vega-Leinert. Agricultural impact assessment. K. A. Lavorel. Leigh.D.E. Science 310:13331337.. Sinclair T.A.C. M. G. Reginster. Rossi. Leemans. H. Agron.. I. vulnerability.C. 2004..Impacts of climate change and breeding. I. Pavlova.. M.D.E. Carter. Spanna.O. C. and the scope for adaptation. M. K.A. M. Rosenzweig. Nature 427:332336. p..) 2002. Rochette P. Donatelli. M. Hastings. Nature. 1999.R. Santos F. DOI: 10. Lindner. 25-62.D. J. Smith. J.W. Climate change: Hot news from summer 2003. K. Agric.N. Zaehle. Litschi. Jauhiainen. Sensitivity of the Russian agriculture to changes in climate. Carter. Bugmann. K.J. T. Reilly J. 4:419-429. Häberli. Schär.. Gullino.H. J. Sitch.A. S. II. R. 456 pp. Moita. Gracia. 2005. V. SIAM project report. Downy mildew (Plasmopara viticola) epidemics on grapevine under climate change. A. Giosue. Buchan. M. Sykes. Bootsma. Mongrain.J. 443: 205-209.. Agron. B.V. Model.T. P. F. M. S. 1996. Leemans. M. T. Appenzeller. Castonguay. Klein.M. Cramer. Eur. Energy crops: current status and future prospects.. Reginster. Solomon. Efthymiadis. and Bunce J.L.. 30:83-197. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (S.1 van der Schrier G. Effects of climate change and elevated CO2 on cropping systems: model predictions at two Italian locations. B. Miller. 2006. 2006. Jones..R. Tigor.. M. 68:27-39. Asseng. 80:205-221. Thornley J.H.J. Model simulation of effects of changes in climate and atmospheric CO2 and O3 on tuber yield potential of potato (cv. D.. C. Projected changes in mineral soil carbon of European croplands and grasslands. D. P. P. Jones. (eds)). Marquis.1002/(SICI)1097-0088(199804)18:5 Tavakkoli A.Szentimrey. D. van der Ploeg R. F.M.D. Dubrovský. Averyt. and Cannell M. Ann.G. Stockle. M. M. Jones. Rosenzweig. van Ittersum. 1997. Naturwissenschaft 88:522-525. Tuck G. S. 1998. M. Ziska L. Wattenbach.R. International Journal of Climatology 27: 415-427. Biomass Bioener. I. M..S. International Journal of Climatology.M.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Smith J... H. Drought Tendencies in Hungary. 310 . Bussay. P. Agric. R. Global Change Biol. USA.. 2007. Changes in the Central European Soil Moisture Availability and circulation patterns in 1881-2005.. P.Y. Agric.A. A. 28: (In print).M. T. 54:199-208.I. P. 235-336. Zhai. Cambridge University Press. The role of supplemental irrigation and nitrogen in producing bread wheat in the highlands of Iran. Eur. UK and New York..D. and Swetnam R. Howden.H. Bintje) in the European Union. S.A. Rounsevell. Photosyn. Možný.1002/joc. Bot. Rahimzadeh. Agron. Easterling. C. Summer Moisture Variability across Europe. Elbe river flood peaks and postwar agricultural land use in East Germany. F. 65:225-236. Smith. Environ.1175/JCLI3734. Smith.. Montanarella. M. M.R. Chen. 1990-2080. K. Ambenje. Renwick. J. 19: 2818-2834. Tubiello F. temperature and precipitation. Osborn. S. P. M. DOI: 10. Agric.O. 2001. Res. 2003.K. DOI: 10. Water Manage.. Briffa.D. 2006. M. In: Climate change 2007: The physical science basis. and Oweis T. Zaehle. Ewert. van der Schrier G. 13:179-189. L.R. Ecosyst. Bojariu. 2006. and van Oijen M. Ecosyst. Trnka M. Observations: Surface and atmospheric climate change. Environ. 2007. European Alpine moisture variability 1800-2003. K. Trenberth K. M.E. D. R. P. Environ. A. 2000. Hiederer. Sensitivity of productivity and deep drainage of wheat cropping systems in a Mediterranean environment to changes in CO2. Journal of Climate. Wattenbach. R. Tank. Donatelli.B.. International Journal of Climatology 18: 1479-1491. 2008.A. NY. J. Glendining. Qin.K. J. 11:2141-2152. Szinell C.R.A. 1997. Kyselý.1411 Wadsworth R. The potential distribution of bioenergy crops in Europe under present and future climate. Manning. Ecosyst. DOI: 10. T. K. Soden. House. Rusticucci. Z. Modelling the impact of climate warming at the landscape scale: will bench terraces become economically and ecologically viable structures under changed climates? Agric. Parker. Temperate grassland responses to climate change: an analysis using the Hurley pasture model. Briffa. Influence of increasing carbon dioxide concentration on the photosynthetic and growth stimulation of selected C4 crops and weeds. p. M. Jones. 2003. J.J.J. and Schweigert P.N. 1988. 94:141-57... Cambridge.D. 97:255-273. Wolf J. Donnelly F. H. O. Kubu Affiliation of the lead author University of Applied Life Sciences in Vienna. Wenkel Leibniz-Centre for Agricultural Landscape Research Nagref Hungarian Met.gr dunkel. Country Austria Zone(s) CON.-O. Ingver P.Bartošová. Rötter J.Žalud Czech Rep. Lazauskas R.de [email protected] MTT Finland INRA Agrifood France Germany Greece Hungary Ireland Italy MDN. Orlandini. ALS. Service Trinity College CNR-IBIMET ckersebaum@zalf. L.seguin@avign on.12 ANNEX I Overview of the respondents of the survey that were essential in compiling of the report. ATC. CON NEM BOR [email protected] f. Olesen A.hr Bulgaria Croatia V. MDN CON Lead author J. Mariani. Kersebau m L. Vučetić M. K.C. M. MDM. ALS PAN CON.co m Denmark Estonia Finland ATN.lt reimund. Risk assessment and foreseen impacts on agriculture 7. Zoltan A. V.rotter@mtt. MDM. Jermuss S.Ingver@jpbi. Rossi W.Hlavinka.dk Anne.rossi@ibimet. Toulios Z. Thaler.fi bernard. G.Dubrovský.bg [email protected] c. Jones S. Eitzinger V.fi Jan. Seguin K.dhz.cnr. P.lv [email protected]@boku.ee Pirjo. PeltonenSainio B. O.inra. Facini.Formayer. Kazandjiev Contributors S. Di Stefano. Mirschel.hu donnelac@tcd. LUC ATN ATC CON MDS PAN ATC MDS. Z. MDN K.Olesen@agrsc i.Trnka L.z@met. Mendel University of Agriculture and Forestry Aarhus University mirek_trnka@yahoo. Meteorological and Hydrological Service Contact josef. Maggiore Latvia Lithuania Netherlands Netherlands NEM NEM ATC ATC A.E. Failla.Verhagen@wur. T.it M.at vkazandjiev@abv. Verhagen Zemkopibas Instituts Lithuanian Institute of Agriculture MTT Agrifood Finland WUR aivaram@inbox. Hakala JorgenE.nl 311 . a dmin. Kozyra E. Michajlovic. ATN. Iglesias H. sk UtsSuaAn@itacyl. Skjelvåg Norwegian University of Life Sciences State Research Institute. PAN ATN PAN. Pulawy [email protected]. Calanca J.ch Spain Spain Sweden MDN MDS NEM Switzerland CON ALS Agroscope ReckenholzTänikon.calanca@art. Nejedlík A.ns.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Norway Poland Romania Scotland Serbia ALN.elena@gmail . Rivington B.pl mateescu. A. Bratislava ITACYL UPM SLU Slovakia CON P. Siska Pavol.uk [email protected]@umb.es [email protected] Macaulay Institute D.se pierluigi.com m. Zurich 312 . Research Station ART.pulawy.ac. Utset Suastegui A. Lalic kuzyr@iung. BOR CON CON.no J.Eckersten@vpe .rivington@macaulay . CON. Eckerstee n P.es Henrik. Malesevic Faculty of Agriculture University of Novi Sad Slovak Hydrometeorological Instittue. M.ac. Takac B. Mateescu M. Graz. Helsinki Finnish Environment Institute SYKE. Universitaet fuer Bodenkultur. List of contributors ANNEX 1. Univ. Vienna Wegener Center. Vienna National Institute of Meteorology and Hydrology National Institute of Meteorology and Hydrology University of Zagreb Meteorological and Hydrological Service of Croatia Meteorological and Hydrological Service of Croatia Meteorological and Hydrological Service of Croatia Mendel University in Brno Mendel University in Brno Mendel University in Brno Mendel University in Brno Mendel University in Brno Institute of Atmospheric Physics.Annex 1. Prague Czech Hydrometeorological Institute Czech Hydrometeorological Institute Danish Institute for Agroecology Science. Vienna Universitaet fuer Bodenkultur. LIST OF CONTRIBUTORS Name Josef Eitzinger Heimo Truhetz Sabina Thaler Werner Schneider Wolfgang Wagner Valentin Kazandjiev Vesellin Alexandrov Darko Voncina Marko Vucetic Visnja Vucetic Cedo Brankovic Lenka Bartosova Miroslav Trnka Zdenek Zalud Petr Hlavinka Eva Kocmankova Martin Dubrovsky Tomas Halenka Jaroslava Kalvova Martin Mozny Petr Stepanek Jorgen Eivind Olesen Tim Carter Stefan Fronzek Affiliation Universitaet fuer Bodenkultur. Helsinki Denmark Finland Czech Republic Croatia Bulgaria Country Austria 313 . Vienna Technical University. Prague Charles University. Prague Charles University. Foulum Finnish Environment Institute SYKE. Volos National Agricultural Research Foundation Larissa Hungarian Meteorological Service Hungarian Meteorological Service Hungarian Meteorological Service Hungarian Meteorological Service University College. Bologna Ireland Italy Hungary Greece Germany France 314 . Toulouse INRA. Dublin University of Florence Joint Research Centre. Muencheberg Leibniz-Centre for Agricultural Landscape Research. Florence University of Florence IBIMET CNR. Ispra CNR IBIMET. Volos University of Thessaly. Muencheberg Leibniz-Centre for Agricultural Landscape Research. Volos University of Thessaly. Jokioinen MTT Agrifood Research Jokioinen Meteo France. Bologna CNR IBIMET. Muencheberg German Weather Service University of Thessaly. Jokioinen Finnish Meteorological Institute Agrifood Research.Survey of agrometeorological practices and applications in Europe regarding climate change impacts Pirjo Peltonen-Sainio Heikki Tuomenvirta Kari Tiilikkala Kaija Hakala Emmanuel Cloppet Bernard Seguin Kurt Christian Kersenbaum Wilfried Mirschel Karl-Otto Wenkel Martin Wegehenkel Hans Friesland Nicolas Dalezios Christos Domenikiotis Dimitrios Bampzelis Emmanouil Tsiros Efrossini Kanellou Leonidas Toulios Szabolcs Bella Zoltan Dunkel Janos Mika Maria Pustay Nicholas M. Volos University of Thessaly. Muencheberg Leibniz-Centre for Agricultural Landscape Research. Volos University of Thessaly. Holden Simone Orlandini Fabio Micale Federica Rossi Giampiero Maracchi Valentina di Stefano Osvaldo Facini Agrifood Research. Avignon Leibniz-Centre for Agricultural Landscape Research. Aas Institute of Meteorology and Water Management. Bologna CNR-IBIMET. Nitra Technical University of Zvolen Slovakia Serbia Portugal Romania Poland Norway Netherlands Reimund Paul Rotter Arne Oddvar Skejvåg Tor Haakon Sivertsen Malgorzata KepinskaKasprzak Jerzy Kozyra Piotr Struzik Ana Monteiro Gheorghe Stancalie Elena Mateescu Miroslav Malesevic Zoran Krajinovic Dragutin Mihailovic Branislava Lalic Pavol Nejedlik Jozef Takac Bernard Siska Jaroslav Skvarenina 315 . Krakov Institute of Soil Science and Plant Cultivation. Bologna Unità di Ricerca per la Climatologia e la Meteorologia applicate all'Agricoltura-CRA University of Milan ARPA Emilia Romagna. Wageningen Soil Science Centre. List of contributors Vincenzo Levizzani Domenico Vento Luigi Mariani Vittorio Marletto Fabio Maselli Alessandro Chiaudani Jan Verhagen ISAC CNR.Annex 1. ALTERRA. Padova ASgrosystem Research Plant Research International. Florence ARPA Veneto. Krakov Oporto University Romanian Meteorological Service Romanian Meteorological Service Institute for field and vegetable crops. Novi Sad Hydrometeorological Service of Serbia University of Novi Sad University of Novi Sad Slovak Hydrometeorological Institute Soil Science and Conservation research Institute Slovak Agricultural University. Wageningen University of Norway Norvegian Crop Research Institute. Pulawy Institute of Meteorology and Water Management. Zurich Telford University of Environmental Systems. Manchester Macaulay Institute Slovenia Spain Switzerland United Kingdom 316 .Survey of agrometeorological practices and applications in Europe regarding climate change impacts Lucka Kaifez Bogataj Andreja Susnik Antonio Mestre Barcelo Federico Sau Margarita Ruiz-Ramos Roser Botey Jose Luis Garcia-Merayo Ernesto Rodríguez Camino Pierluigi Calanca Mark Danson Robin Matthews University of Ljubljana Ministry of Environment and spatial Planning. Ljubljana Meteorological State Agency. Madrid Politechnic University of Madrid Politechnic University of Madrid Meteorological State Agency Meteorological State Agency Meteorological State Agency Environmental Protection and Climate Natural Sources. (e.g. water balance. bilateral. etc.. spatial resolution and realization etc. Please name if any model applications in your country (operational or research) were done with combination of GIS and/or Remote Sensing. for which aim they are used(A).). adaptation options. and name involved countries (or regions). Please name the agroclimatic indices (incl. water balance. Please name process-oriented models (crop models) which were used in your country as research tools in order to assess impacts of climate change and variability for (please indicate for which aim they were used (A) (e. List of contributors ANNEX 2. nowcasting(N) or pastcasting(P). 8. N or P. yield estimate etc. time step(T). irrigation scheduling. main providers (PR) and users(US) : 3. spatial realization and resolution(S). Any other aspects to be reported (optional) QUESTIONNAIRE 2 . Please name the agroclimatic indices (incl.g impacts on yield. for which aim they are used(A). Please indicate the main limitations in order to apply process-oriented models for operational use in your country. please name which inputs. 7.Annex 2. yield quality.Agroclimatic Indices and Models 1. statistical models) which were used in your country as research tools in order to assess impacts of climate change and variability for (please indicate for which aim they were used (A) (e. spatial resolution and realization. Please name process-oriented models (crop models) which are used in your country operationally for assessment of any impacts of weather and climate variability (please indicate F.Trends in Agroclimatic Indices and Model Outputs 317 . production technique. main providers (PR) and users (US) : 2.g impacts on yield. Please indicate which model outputs in which time step of the named process oriented models are by your expertise most useful in order detect the impact on climate change and variability regarding : 6. which time step(T). 9..).) : 5. yield quality. statistical models) which are used in your country operationally for (please indicate if for forecasting (F). 4. Please name if any model applications in your country (operational or research) were done beyond the national scale (e. production technique. regional). spatial realization and resolution(S).g. For input data. adaptation options. LIST OF QUESTIONNAIRES QUESTIONNAIRE 1 . Please name which downscaling techniques (spatial and temporal) were used in your country (again provide references) for assessment of 318 . Country 8. Any additional information/problems/questions related to the COST 734 activities and especially the WG2 ones QUESTIONNAIRE 2. Please specify any weather generators applied in agrometeorological studies and/or services in your country/region: 4. produced by 4. since 6. Which strengths and weaknesse of scenarios were found in your country? Who was the provider of scenarios (models)? 2. Please provide any information on the statistical methods for analyses of meteorological and simulation model output related time series in order to evaluate mean and variability patterns as well as to determine respective trends in agroclimatic indices and simulation model outputs 6. climate/biophysical product 2. operational/experimental 5. Area covered QUESTIONNAIRE 3 . Please provide any information on past and current meteorological as well as remote sensing data applied in your country/region: 2.1 .Satellite Data Records Survey The national members of COST 734 are kindly asked to fill in the survey related to WG2 Remote Sensing Subgroup task as below: 1. Please name which types of climate change scenarios were used so far (in the last 10 years) in your country for research or operational purposes (obligatory: provide references) and give short overview regarding the purpose of investigation. responsible company/other 7. Please name and shortly describe any homogenization tests/procedures applied to meteorological and agricultural related time series in your country/region: 5. Please indicate which numerical weather and regional climate models or their related outputs are used in your country/region: 3.Climate change scenarios 1.Survey of agrometeorological practices and applications in Europe regarding climate change impacts 1. Please list any possible (general/specific) constraints during the implementation of the WG2 tasks 7. satellite/instrument 3. if possible). 7. The thresholds may be provided for both changes in climatic variables. possible impacts of climate variability (please indicate time step and spatial resolution). 5. Assessments and studies of climate change impacts. if possible). Please provide information on assessments of climate change impacts on crops and cropping systems in your country. Which crops and cropping systems are most vulnerable? 2. and whether both CO2 and climate changes were considered. In your opinion how can WG3 of COST 734 help in meeting your needs? QUESTIONNAIRE 4 – Risk Assessment and Foreseen Impacts on Agriculture 1. Critical thresholds of climatic suitability and climate change. Please provide a description on the main vulnerabilities of crops and cropping systems in your country to current climatic variability as well as for projected climate change (provide references. This should include information on which types of scenario analyses were performed (sensitivity analyses.Annex 2. Please provide information on the timescale 319 . 4. Main vulnerabilities of crops and cropping systems. 6. Please provide information on critical thresholds of climatic suitability and climate change for crops and cropping systems in your country. agroclimatic indices and agroecosystem indicators. List of contributors 3. ENSEMBLES or similar projects? If so please give a description of your role and possible implementations (with references). Please name which Regional climate models (if any) were used in your country (again provide references) for assessment of possible impacts of climate variability on agriculture? Please name which Sea level rise scenarios (if any) were used in your country (again provide references) for assessment of possible impacts on agriculture? Please give information regarding any attemps in your country to analyse the risk of weather and climate extremes in future regional forecast scenarios (again provide references) Was your country in any way involved in EU PRUDENCE. The rationale behind the thresholds should be briefly described (provide references. GCM or RCM based analyses). 3. Are you familiar with high-resolution PRUDENCE climate change scenarios for 2071-2100 for Europe? Were they already used in any studies of impacts? Please identify critical research needs that address development and use of climate change scenarios in your country. References on studies of climate change impacts on agricultural and horticultural crops in your country should be included (if reports are available on the web. These assessments and studies often cover many sectors. who are working on climate change impacts and adaptation for crops and cropping systems. 8. the water sector). policy makers and other stakeholders in your country. which will also affect adaptation in agriculture. 9. Are there already visible implemented adaptation measures related to climate change in your country (provide references. adaptation strategies and awareness. 320 . Please give references on studies of adaptation of crop and cropping system management to climate change in your country (if reports are available on the web. Warning systems. plant breeders. Remember to consider also adaptation in related sectors (e. both in the short and long term. (projected year) of the scenarios applied in impact assessment. extension services. Which adaptation options within crops. Please provide a description of any warning systems in your country that is designed to cope with climate/weather (interannual) variability or otherwise could be used for re-ducing vulnerability to climatic change.g. Please provide names and ad-dresses (including e-mail) of persons in your country. Please provide information on dissemi-nation of information and recommendations related to climate change impacts and adaptation in agriculture to farmers. 6. What is the awareness and attitude to climate change among policy makers and stakeholders related to agriculture? Dissemination of information and recommendations. please provide the web address (url)). Observed adaptation. if possible)? National impact assessments. 7. Contact persons for climate change impacts and adaptation. and the answer should focus on the role of agriculture within this picture and identified relations with other sectors. 5. Adaptation options. cropping systems and crop man-agement would you consider the most appropriate for dealing with climate change in your country. please provide the web address (url)). Please provide information on the status and plans for national impact assessments and adaptation strategies in your country.Survey of agrometeorological practices and applications in Europe regarding climate change impacts 4. Firenze (Italia) nel mese di Luglio 2008 .Finito di stampare presso la Copisteria Sangallo .