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Trends in
Biosciences A Fortnightly International Journal Volume 7
Number 13
July, 2014
Online version available at www.trendsinbiosciencesjournal.com
Dheerpura
Society for Advancement of Science and Rural Development
Print : ISSN 0974-8 Online : ISSN 0976-2485 NAAS Rating : 2.7
Trends in
Biosciences A Fortnightly International Journal Volume 7
Number 13
July, 2014
Online version available at www.trendsinbiosciencesjournal.com SPECIAL OFFER Life Membership Journal Membership - Rs. 500 Benefit : Online access of Journal for lifetime alongwith certificate of membership Journal Membership with free authorship - Rs. 4000 Benefits : Authorship charges for publishing research papers in Trends in Biosciences Journal is nil (No authorship charges) for lifetime. Award of fellow of DSAS&RD Society
Dheerpura
Society for Advancement of Science and Rural Development
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Trends in Biosciences A Fortnightly International Scientific Journal www.trendsinbiosciencesjournal.com International Advisory Board Dr. A. Coomans, Ex-Professor, State University of Ghent, Belgium Dr. Randy Gaugler, Director, Centre for Vector Biology, Rutgers University, USA Dr. S.B. Sharma, Director, Plant Security, South Perth, Australia Dr. Zahoor Ahmad, Professor, Jubail Industrial College, Saudi Arabia
Advisory Board Dr. G.N. Qazi, Vice Chancellor, Jamia Hamdard University, New Delhi Dr. A.S. Ninawe, Advisor, Deptt. of Biotechnology, New Delhi Dr. I. Ahmad, Ex-Director, Department of Science & Technology, New Delhi Dr. N. Nadarajan, Director, Indian Institute of Pulses Research (IIPR), Kanpur Dr. Masood Ali, Ex-Director, Indian Institute of Pulses Research (IIPR), Kanpur Dr. H.S. Gaur, Vice-Chancellor, Sardar Vallabbhai Patel Agricultural University, Meerut
Editorial Board Editor in Chief : Dr. S.S. Ali, Emeritus Scientist, Indian Institute of Pulses Research (IIPR), Kanpur Dr. Erdogan Esref HAKKI, Department of Soil Science and Plant Nutrition, Selcuk University Konya Turkey Dr. S. K. Agarwal, Principal Lentil Breeder, ICARDA, Aleppo, Syria Dr. B.B. Singh, Assistant Director General Oilseed & Pulses, ICAR, New Delhi Dr. Absar Ahmad, Senior Scientist, National Chemical Laboratory, Pune Dr. N.P. Singh, Coordinator, AICRP Chickpea, IIPR, Kanpur Dr. Raman Kapoor, Head, Dept. of Biotechnology, Indian Sugarcane Research Institute, Lucknow Dr. S.K. Jain, Coordinator, AICRP Nematode, IARI, New Delhi Dr. Sanjeev Gupta, Coordinator, MULLaRP, IIPR, Kanpur Dr. Naimuddin, Sr. Scientist (Plant Pathology), IIPR, Kanpur Dr. Rashid Pervez, Sr. Scientist, Indian Institute of Spices Research, Khozicod, Kerala Dr. Badre Alam, Associate Prof. Gorakhpur University, U.P. Dr. Veena B Kushwaha, Associate Professor, Department of Zoology, DDU Gorakhpur University, Gorakhpur Dr. Savita Gangwar, Department of Plant Science, Faculty of Applied Science, M.J.P. Rohilkhand University, Bareilly Dr. Vijay Pratap Singh, Assistant Professor, Govt. R.P.S. Post Graduate College, Korea Dr. Durgesh KumarTripathi, Department of Botany, Banaras Hindu University, Varanasi Dr. Shamsa Arif (English Editor), Barkatullah University, Bhopal, M.P. Er. Sobia Ali, Genetic Asia Pvt. Ltd., New Delhi Dr. N.R. Panwar, Sr. Scientist (Soil), Division of Natural Resources and Environment, Central Arid Zone Research Institute, Jodhpur Dr. Shabbir Ashraf, Assoc. Professor, Dept. of Plant Protection, Faculty of Agril. Sciences, Aligarh Muslim University, Aligarh Dr. Anamika Pandey, Post Doctoral Research Scientist, Selcuk University, Turkey Dr. Mohd. Kamran Khan, Post Doctoral Research Scientist, Selcuk University, Turkey Dr. Rohini Karunakaran, Senior Lecturer, Unit of Biochemistry, Faculty of Medicine, AIMST University, Malaysia Dr.P.S.Srikumar, Associate Professor, Unit of Psychiatry, Faculty of Medicine, AIMST University, Malaysia
Dr Rohini Karunakaran, Senior Lecturer, Unit of Biochemistry, Faculty of Medicine, AIMST University, Malaysia Dr P.S. Srikumar, Associate Professor, Unit of Psychiatry, Faculty of Medicine, AIMST University, Malaysia Dr Ashwini V. Thul, Assistant Professor, Department of Agril Botany, Anand Niketan College of Agriculture, Maharashtra Dr M. N. Patond, Professor, Department of Animal Husbandry and Dairy Science, Anand Niketan College of Agriculture, Maharashtra Dr S. B. Thawari, Assistant Professor, Department of Agricultural Botany, Anand Niketan College of Agriculture, Maharashtra Dr. Devraj, Sr. Scientist, Indian Institute of Pulses Research, Kanpur Business Manager, Er. S. Osaid Ali, Biotechnology Research Foundation, Kanpur Trends in Biosciences abstracted in CABI Abstract, U.K. Subscription Rates for 2014: Version *Print /Number **Online
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List of Members Dr Atul Kumar Misra, Associate Professor, Zoology, DAV College, Kanpur, U. P. Dr Shabbir Ashraf, Assoc. Professor, Plant Protection Faculty of Agricultural Sciences, AMU, Aligarh, U. P. Dr Badre Alam Ansari, Asst Professor, Zebrafish Laboratory, Zoology, D. D. U. Gorakhpur University, Gorakhpur, U. P. Dr Farog Tayyab, Research Scholar, Medical Laboratory Technology, Faculty of Health Science, SHAITS, Allahabad, U. P. Dr Adesh Kumar, Professor. Plant Molecular Biology and Genetic Engineering, NDUAT Kumarganj Faizabad, U. P. Dr Chandresh Kumar Chandrakar, Research Scholar, Agronomy, KVK, Dhamtari, IGKVV, Raipur, Chhattisgarh Dr R. Sellammal, Research Scholar, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore Dr Jagdish Kishore, Assiatant Professor, Plant Pathology, CSA University of Agriculture Technology, Kanpur, U. P. Ms Syeda Huma, Student, Dr. Rafiq Zakaria Center for Higher Learning & Research Mr Chandan Singh Ahirwar, Student, Vegetable Science, GBPUA&T, Pantnagar, Uttarakhand Ms Gupta Bhavna, Student, Foods and Nutrition, Ethelind School of Home Science, SHAITS, Allahabad, U. P. Dr Karma Beer, Research Scholar, Horticulture, Institute of Agricultural Sciences, BHU, Varanasi, U. P. Dr Kishor K. Shende, Information Officer, Biotechnology and Bioinformatics Center, Barkatullah University, Bhopal, M. P. Mr Ashish Kumar Chandrakar, Rural Agriculture Extension Officer, Department of Agronomy, JNKVV, Jabalpur, M. P. Dr P N Verma, Research Scholar, Genetics and Plant Breeding, CSA University of Agriculture and Technology Kanpur, U. P. Dr Chinmayi Joshi, Research Scholar Mahyco Research Center, Dawalwadi, Jalna, Maharashtra Dr Mrs Sonia Kumari, Scientist, Dairy Microbiology, S. G . Institute of Dairy Technology, B. V. C, Jagdeopath, Patna, Bihar Mr Gourish Karanjalker, Student, College of Horticulture, PG Centre (UHS Bagalkot), GKVK Campus Bengaluru Dr Anita Mishra, Research Scholar, Biotechnology and Bioinformatics Center, Barkatullah University, Bhopal, M. P. Mr Murali, S, Student, Agril. Entomology, University of Agricultural Sciences, GKVK, Bangalore Dr Hema Swaminathan, Research Associate, Soil Science & Agril. Chemistry, Tamil Nadu Agricultural University, Coimbatore Dr Sellammal Raja, Research Scholar, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore Mr Samir Singh, Research Scholar, Plant Pathology, NDUA&Technology, Kumarganj, Faizabad, U. P. Dr Krishna Murari, Professor, Dairy Engineering, SGIDT, Bihar Agriculture University, Patna, Bihar Dr T. Sravan, Research Scholar, Genetics and Plant Breeding, Institute of Agricultural Sciences, BHU, Varanasi, U. P. Dr Ranvir Kumar, Asstt. Professor-Cum-Jr. Scientist, Agricultural Economics, B. P. S. Agricultural College, Purnea, Bihar Dr K. K. Chaturvedi, Scientist, Centre for Agricultural Bioinformatics (Cabin), IASRI, New Delhi Dr Raman A. Gami, Professor, Genetics and Plant Breeding, C. P. College of Agriculture, S. D. Agricultural University, Gujarat Dr Savita Gangwar, Research Associate, Plant Science, M. J. P. , Rohilkhand University, Bareilly, U. P. Dr Rajkumar Mishra, Territory Manager, Genetics & Plant Breeding, Allahabad School of Agriculture, SHIATS, Allahabad, U. P. Dr C. Prabha, Professor, Biochemistry, Kempe Gowda Institute of Medical Sciences Dr Pisal Rahhul Ramdas, Research Scholar, Agronomy, Navsari Agricultural University, Navsari, Gujarat Dr Sunil Suresh Patil, Professor, Genetics and Plant Breeding, College of Agriculture, Nashik Ms Kiran Tigga, Technical Assistant, Genetics and Plant Breeding, RMD College of Agri. & Research Station, Ambikapur, Chhattisgarh Mrs Smita Bala Rangare, Technical Assistant, Horticulture, College of Agriculture,Indira Gandhi Krishi Vishwavidhyalaya, Raipur (CG) Mr P Ashok Reddy, Research Scholar, Genetics and Plant Breeding,Allahabad School of Agriculture, SHIAT&S, Allahabad,, U. P. Dr Anjum N. Rizvi, Scientist, Zoological Survey of India, Dehradun, Uttarakhand
Mr Venkata R Prakash Reddy, Research Scholar, Genetics and Plant Breedings. V. Agricultural College Acharya NGRAU, Tirupati Dr Deepika Baranwal, Research Scholar, Department of Food and Nutrition, College of Homescience, MPUAT, Udaipur, Rajasthan Dr Sankaran K, Research Scholar, National Institute of Technology, Tiruchirappalli (Nit-T) Ms Shakti Chaudhary, Student, Food Science and Nutrition, Ethelind School of Home Science, SHAITS, Allahabad, U. P. Dr Mithu Mahmud, Research Scholar, Stamford University Bangladesh, Bangladesh Ms Latika Yadav, Research Scholar, Foods & Nutrition, College of Home Science, MPUAT, Udaipur, Rajasthan Dr Debosri Bhowmick, Research Scholar, Veterinary Surgery & Radiology, College of Veterinary Science & A H, NDVSU, Jabalpur, M. P. Dr Srinivasulu Ch, Lecturer, Zoology, SR&BGNR Govt. Degree College Khammam, Andhra Pradesh. Dr Sonal Shrivastava, Research Scholar, Veterinary Medicine, College of Veterinary Science & AH., NDVSU, Jabalpur, M. P. Mr Bhupendra Kumar Singh, Student, Genetics & Plant Breeding, NDUA&T, Faizabad, U. P. ) Ms Zareena Shaikh, Assistant Professor, Maulana Azad College, Aurangabad Mr Manoj Yadav, Research Scholar, Mycology and Plant Pathology, IAS, Banaras Hindu University (BHU),Varanasi, U. P. Dr Komma Renuka Devi, Student, Plant Physiology, ANGRAU, Hyderabad Dr Amit Alexander Charan, Asst. Prof., Molecular & Cellular Engg., Jacob School of Biotech. & Bioengineering, SHIATS, Allahabad, U. P. Mrs Bela Turkey Kaushal, Research Scholar, Applied Animal Sciences, SB&B Dr. BBA, Lucknow, U. P. Mr Amit Kumar Mukherjee, Asst. Prof., Food Technology, Haldia Institute of Technology, Haldia, West Bengal. Dr T. G. Nagaraja, Professor, Botany, The New College, Shivaji Pethkolhapur, Maharashtra Dr Hasansab Nadaf, Research Associate, Rars Bijapur, UAS Dharwad, Karnataka Mr Ajay Tiwari, Research Scholar, Genetics and Plant Breeding, College of Agriculture, IGKV Raipur (C. G. ) Dr Prashant Ankur Jain, Assistant Professor, Computational Biology& Bioinformatics, JSBB, SHIATS, Allahabad, U. P. Ms Laitonjam Ishwori, Lecturer, Department of Biotechnology, Kula Womens College, Nambol, Manipur Mr Savanta V. Raut, Associate Professor, Department of Microbiology, Bhavan's College, Munshinagar Andheri [W], Mumbai Mr Swami Rakesh Mohanlal, Student, Agricultural Biotechnology, B. A. College of Agriculture, AAU, Anand, Gujarat. Mr Vijay Sharma, Student, Genetics & Plant Breeding, Narendra Deva University of Agriculture & Technology, Faizabad, U. P. Ms Ankita Gautam, Research Scholar, Warner School of Food and Dairy Technology, SHIATS, Allahabad, U. P. Mr Dinker Singh, Research Scholar, Animal Husbandry & Dairying, Institute of Agricultural Sciences, BHU, Varanasi, U. P. Mr Swapanil Yadav, Lecturer, Biotechnology, Gandhi Faiz E Aam College, Shahjahanpur, U. P. Mr Vinay Kumar Singh, Research Scholar, Dairy Microbiology, SHIATS, Allahabad, U. P. Mr Amit Kumar Mukherjee, Assistant Professor, Food Technology, Haldia Institute of Technology, Haldia, West Bengal. Mr Pandya Mihirkumar Maheshbhai, Student, Plant Breeding & Genetics, Navsari Agricultural University, Navsari, Gujarat Ms Asmat Jahan, Lecturer, Biotechnology, Gandhi Faiz E Aam College, Shahjahanpur, U. P. Mr Mohsin Rahman, Lecturer, Biotechnology, Gandhi Faiz E Aam College, Shahjahanpur, U. P. Mr Vivek Kumar, Lecturer, Biotechnology, Gandhi Faiz E Aam College, Shahjahanpur, U. P. Ms Anchal Sharma, Lecturer, Biotechnology, Gandhi Faiz E Aam College, Shahjahanpur, U. P. Ms Farha Syed, Lecturer, P. G., Zoology, G. F. College, Shahjanhanpur, U. P. Dr Ashish Kumar Gupta, Principal, Subash Degree College, Kanpur, U. P. Mr Chaudhari Dhavalkumar Raghjibhai, Student, Genetics and Plant Breeding, N.M. College of Agriculture, NAU, Navsari, Gujarat Dr Sabina Kahnam, Lecturer, Dayanand Girls P. G. College, Kanpur, U. P. Dr Mehvash Ayeshah Hashmi, Head of Department, Dayanand Girls P. G. College, Kanpur, U. P. Dr Ashish Kumar Dwivedi, Post Doc Fellow, Indian Institute of Technology, Kanpur, U. P.
Mr Sujeet Kumar, Senior Research Fellow, Crop Improvement, Indian Institute of Pulses Research, Kanpur, U. P. Ms Shrasti Gupta, Research Assistant, B. I. F. C. (D. B. T. ), Dayanand Girls P. G. College, Kanpur, U. P. Dr Mohammad Shahid, Research Associate, Plant Pathology, CSAUA&T, Kanpur, U. P. Dr Mohd. Saeed, Senior Research Fellow, Bioscience, Integral University, Lucknow, U. P. Mr Chirag Mansukhbhai Bhaliya, Student, Plant Pathology, Junagadh Agriculture University, Junagadh. , Gujarat Ms Nisha Khatri, Research Scholar Botany, University of Delhi, Delhi Dr Anamika Pandey, Post Doctral Fellow, Selcuk University, Turkey Dr Mohd. Kamran Khan, Post Doctral Fellow, Selcuk University, Turkey Dr Anjali Srivastava, Associate Professor, Zoology, Dayanand Girls P. G. College, Kanpur, U. P. DrSunita Arya, Assistant Professor, Zoology, Dayanand Girls P. G. College, Kanpur, U. P. Dr Amita Srivastava, Lecturer, Zoology, Dayanand Girls P. G. College, Kanpur, U. P. Dr Rachana Singh, Lecturer, Zoology, Dayanand Girls P. G. College, Kanpur, U. P. Dr Seema Pandey, Lecturer, Zoology, Dayanand Girls P. G. College, Kanpur, U. P. Dr Vijay Pratap Singh, Assistant Professor, Govt. R. P. S. Post Graduate College, Korea, C. G. Dr Durgesh Kumar Tripathi, UGC-Kothari Post Doctoral Fellow (PDF), Botany, Center of Advance Study, BHU, Varanasi, U. P. Dr Veena B Kushwaha, Associate Professor, Zoology, DDU Gorakhpur University, Gorakhpur, U. P. Dr Ankush Haridas Raut, Assistant Professor, Agril. Entomology, Anand Niketan College of Agriculture, Maharashatra Mr R. B. Karthikkumar, Research Scholar, Spices and Plantation Crops, Horticultural College and Research Institute, TNAU, Coimbatore Dr P. Jansirani, Professor, Spices and Plantation Crops, Horticultural College and Research Institute, TNAU, Coimbatore Mr Sunil Pandey, Research Scholar, Medical Microbiology, Nobel College, Sinamangal Kathmandu Nepal Dr Akshay I Patel Assistant Professor, Vegetable Science, Aspee College of Horticulture and Forestry, NAU, Navsari, Gujrat Dr S Munawar Fazal, Lecturer, Botany, Sachchidanand Sinha College, Aurangabad, Bihar Dr Pallavi Mittal, Lecturer, I. T. S Paramedical College. , Muradnagar, U. P. Mr Nishitkumar Vasantkumar Soni, Student,Genetics and Plant Breeding, B. A. College of Agriculture, AAU, Anand, Gujarat Dr Mohammad Israil Ansari, Associate Professor, Amity Institute of Biotechnology, Amity University, Lucknow, U. P. Dr Eshita Pandey, Lecturer, Zoology, Dayanand Girls P. G. College, Kanpur, U. P. Dr Mohammad Ajaz Ul Islam, Assoc. Prof.-cum-Sr. Scientist, Faculty of Forestry, SEKUAS& T, Kashmir, Sopore, J&K Dr N. R. Panwar, Sr. Scientist, Division of Natural Resources and Environment, CAZRI, Jodhpur, Rajasthan Mr Yashlok Singh, Research Scholar, Genetics and Plant Breeding, NDUAT, Kumarganj, Faizabad, U. P. Dr Amrendra Pratap Singh, Research Associate, Department of Entomology, NDUAT, Faizabad, U. P. Dr Sumathi Smbanan, Research Associate, Seed Science and Technology, Tamil Nadu Agricultural University, Coimbatore Dr Khalil Khan, Subject Mattar Specialist, Soil Science, KVK, Jalon, Directorate of Extension, CSAU&T, Kanpur, U. P. Dr Erdogan E. Hakki, Associate Professor, Soil Sciences & Plant Nutrition, Selcuk University, Konya, Turkey
Dheerpura
Society for Advancement of Science and Rural Development (Reg. No. 01/01/01/16715/06)
The Dheerpura Society for Advancement of Science and Rural Development was founded on 28 July, 2006 with the following objectives
1.
To promote research and development in agriculture, life sciences through publishing journal, organizing seminars etc.
2.
To make people environmental conscious
3.
To work for human development in society
4.
To work for uplifting of rural masses and their development
Membership Membership to the society is open to all individuals / institutions interested in society’s objective by becoming ordinary life, institutional, corporate members against payment of membership fee. Membership fee
Indian (Rs.)
Foreign (US$)
Ordinary (Annual)
3,000
200
Life member
10,000
1,000
Institutional
15,000
1,500
Corporate member
20,000
2,000
Renewal of annual membership should be done in January each year; if the membership is not received by 15 February, the membership would stand cancelled. Membership fee should be drawn in favour of Dheerpura Society for Advancement of Science and Rural Development, State Bank of India, Kalyanpur branch (code 01962), A/c No. MSB31575856239, Kanpur on the following address. In case of out station cheque an extra amount of Rs. 50/- may be paid as clearance cheque. For e-banking add Rs. 25/-.
Dr S.S. Ali President H-1312, VIP Lane, Satyam Vihar, Awas Vikas No.1, Kalyanpur, Kanpur 208 018 (U.P.), India Ph. : 09919388690, 09696499966 Email:
[email protected],
[email protected]
Trends in Biosciences Volume 7
Number 13
July, 2014
CONTENTS MINI REVIEW 1. 2. 3. 4. 5.
Marker Assisted Selection in Hybrid Rice Breeding Nidhi Koshta, Sangeeta Tetwar and Pardeep Yadav Potential of Agronomy in Mitigating the Challenges of ‘Future Food Security’ S.K. Dubey, S.K. Tripathi, G. Pranuthi, Reshu Yadav, D. Maurya, Pankaj Upreti Extension Strategies for Diversification of Agriculture in Jammu Region of Jammu and Kashmir State Parveen Kumar, Mahender Singh, Brinder Singh Participation of Farm Women in Rural Dairy Enterprise- A Review S. J. Jadav and V. Durgga Rani Biopharming - Healing Foods, A Novel Concept Darshan. S, Seeja. G. and Gangadhara. K.
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RESEARCH PAPERS 6. 7. 8. 9. 10. 11. 12. 13. 14.
15. 16.
17. 18.
Genetic Variability in Sugarcane (Saccharum spp. Complex) P.P. Patil, D.U. Patel, S.C. Mali and V.A. Lodam Plant Diversity along Yamuna River in Delhi: A Status Anand Kumar Mishra, Shakoor Ahmed Mir and Maheshwar Prasad Sharma Influence of IPNM on the Economics of Cauliflower (Brassica oleracea var. botrytis L.) var. Snow Crown Adesh Kumar, Ramparsad, Prem Shanker Yadava, Gulab Chand Yadav and Bansh Narayan Singh Evaluation and Genetic Studies in Tomato Genotypes V. Premalakshmi, S. Ramesh Kumar and T. Arumugam Study on Seed Orientation for Production Quality Seedlings in Biofuel Tree Species N. Mariappan P. Srimathi, and L. Sundaramoorthi Effect of Foliar Application of Hormones and Nutrients on Yield of Ardusi (Adhatoda zeylanica) Tandel Madhuri, Krishnamurthy R., Pathak J.M., Chandorkar M.S. and Tandel D. H. Impact of Seed Treatment with Tree Leaf Extracts on Physiological Parameters of Radish S. Ramesh Kumar, J.Anitha, P.Karthika, B.Durgadevi, N. Mariappan and S. Krishnakumar and V. Rajendran Heterosis and Genetic Studies on Yield and its Component Traits In Rice (Oryza sativa L.). G. Sreenivas, C. Cheralu, K. Rukmini Devi and K. Gopala Krishna Murthy Study on Effect of Fertilizer and FYM Application on Black Gram (Vigna mungo L. Hepper) in Relation to Seed Yield and its Component Characters in Hilly Area of Nagaland Rita Nongthombam, S. Kigwe and Indrajit Yumnam Safety Evaluation of Diafenthiuron 50WP (NS) to Non Target Organisms J. Aravind and K. Samiayyan Base Line Toxicity of Lufenuron 5.4 EC against Diamondback Moth, Plutella xylostella L. for Resistance Monitoring K. Senguttuvan, S. Kuttalam and K. Gunasekaran Combining Ability Analysis for Grain Yield and Its Related Characters in Rice (Oryza sativa L.) Girish Chandra Tiwari and Narendra Kumar Jatav Study on Seed Physical Characteristics and Phytic Acid Content of Soybean Germplasm S. Abirami, A. Kalamani and T. Kalaimagal
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20. 21. 22. 23. 24. 25.
26. 27.
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34. 35.
36. 37. 38.
Influence of Pruning Severity on Phenology, Physiological Characters and Yield Attributes in Grape (Vitis vinifera L.) cv. Italia S. Senthilkumar and R.M. Vijayakumar Morphological Characterization of Soursop (Annona muricata L.) Germplasm in Tamil Nadu S. Manigandan and R.M. Vijayakumar Chlorophyll and Chlorophyll Fluorescence as Influenced by Combined Heat and Drought Stress in Rice A. R. Nirmal Kumar, C. Vijayalakshmi and D. Vijayalakshmi Production and Marketing of Goats Meat in U. P. Babu Singh, Birendra Kumar, Anil Kr. Sachan & O.P. Maurya Potato Contract Farming Reduces Risk and Improved Farmers Livelihood Security in District Kannauj , U.P. Babu Singh, Birendra Kumar ,Anil Kr. Sachan & O.P. Maurya Combustion Analysis of Coconut Husk as a Feedstock for Solar and Biomass Integrated Drying of Copra Saravanapriya, G., Kamaraj, S. and Mahendiran R. Combining Ability and Gene Action Studies for Seed Yield and Contributing Characters in Cowpea (Vigna unguiculata) A.K. Mukati, S.R. Patel, S.S. Patil1 and B.D. Jadhav Heterosis Studies for Seed Yield and Contributing Characters in Cowpea (Vigna unguiculata) S.R. Patel, A.K. Mukati, S.S. Patil and B.D. Jadhav Effect of Drought Stress on Growth Analytical Parameters and Yield of Red Gram (Cajanus cajan L.) Genotypes R. Nagajothi, J. Annie Sheeba, S. Vincent and U. Bangarusamy Effect of Seed Borne and Soil Borne Inoculum on Wilt Disease of Brinjal (Solanum melongena L.) Caused by Fusarium oxysporum f.sp. melonganae (Schlecht) Mutuo and Ishigami Narendra Kumar Jatav Effect of Fertigation, Micronutrients and Bacillus sp for Maximizing the Yield, Quality and Disease Management of Rose (Rosa hybrida var., Tajmahal) under Greenhouse Conditions V. Vasudevan and M. Kannan Influence of Azolla and Blue Green Algae in Residual Nitrogen and uptake in Different Rice Cultivation Systems N. Jeyapandiyan and A. Lakshmanan Influence of Solvent on Physical and Mechanical Properties of Chitosan Films Tanuja, P. and N. Varadharaju Effect of Fertigation Scheduling on Growth and Flowering in Cocoa (Theobroma cacao L.) Ravanachandar A. and P. Paramaguru Influence of Seed Rate and Fertilizer Levels on Dry Matter Distribution and Dry Matter Yield of Fodder Cowpea (cv. Swad) Bhavya, M. R., Palled, Y. B., Pushpalatha, Ullasa, M. Y., and Nagaraj, R. In Vitro Antioxidant and Antibacterial Studies of Fungal Pigment Extracts from Parambikulam Tiger Reserve C. Padmapriya, R. Murugesan and S. Gunasekaran Calibration and Validation of InfoCrop Model v.1.0 for Yield and Yield Attributing Characters of Kharif maize in Middle Gujarat Region D. Choudhary, H.R. Patel and V. Pandey Effect of Phytoextracts and Biocontrol Agents on Growth of Fusarium solani Causing Root Rot Of Brinjal Yogesh Kumar Sharma, L. F. Akbari and B.D.S. Nathawat Performance of Summer Mungbean as Affected by Variety and Date of Harvest S. K. Bhowaland and S. K. Bhowmik Standardization of Spacing, Bulb Size and Plant Growth Regulators for Maximizing Bulb Production in Tuberose (Polianthes tuberosa L.) cv. ‘Prajwal’ A. Palanisamy and S. Parthiban
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1507 1512 1516
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40. 41. 42. 43. 44. 45.
46.
47. 48. 49.
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51. 52.
Studies on Effect of Spacing, Bulb Size and Plant Growth Regulators on Growth, Flowering and Flower Yield of Tuberose (Polianthes tuberosa L.) cv. ‘Prajwal’ A. Palanisamy and S. Parthiban Field Evaluation of Spinetoram 12 SC against Etiella Zinckenella Treitschke on Pigeonpea A. Sanjeevi Kumar and N. Muthukrishnan Field Evaluation of Spinetoram 12 SC against Pod Borer Complex on Pigeonpea A. Sanjeevi Kumar and N. Muthukrishnan Biological Control of Tomato Root Pathogens with Fungal and Bacterial Antagonists Jayalakshmi K. and Lingaraju S. Biogas Purification System for Cassava Based Sago and Starch Industries M.M.C. Rajivgandhi, M. Singaravelu and S. Kamaraj Relationship of Root-knot Nematode, Meloidogyne incognita with Taxonomic Groupings of Host Plants Y.S. Rathore and S.S. Ali Effect of Bending and Plant Growth Regulators on Maximizing the Yield and Quality of Rose (Rosa hybrida Var., Tajmahal) under Greenhouse Conditions V. Vasudevan and M. Kannan Performance of Different Planting Geometry and Seedling Variation on Tiller Production and Yield of Rice (Oryza sativa L.) M.R. Nandhakumar and K. Velayudham Survey of Alternaria Leaf Blight of Cotton in Northern Karnataka Anil G. H. and Ashtaputre S. A. Chilli Wilt: A Complex Disease with Multitude of Pathogens Raghu, S. and Benagi, V.I. Sustainable Agriculture : Increasing Income, Employment and Food Security of Rural Farmers in District, Kanpur Nagar, U.P. Babu Singh, Anil Kr. Sachan, Birendra Kumar & O.P. Maurya Antixenosis Mechanism of Resistsnce to Brown Planthopper Nilaparvata lugens (Stal) in Selected Rice Genotypes Anita Sable, S. Suresh and S. Mohan Kumar Survival and Population Buildup of Brown Planthopper Nilaparvata lugens (Stal) on Selected Rice Genotypes Anita Sable, S. Suresh and S. Mohan Kumar Correlation and Path Analysis Studies in Ridge Gourd [Luffa acutangula (L.) Roxb.]* Anand Narasannavar, V. D. Gasti and Sheela Malghan
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Trends in Biosciences 7(13): 1357-1366, 2014
MINI REVIEW
Marker Assisted Selection in Hybrid Rice Breeding NIDHI KOSHTA, SANGEETA TETWAR AND PARDEEP YADAV Department of Genetics and Plant Breeding, Indira Gandhi Krishi Vishwavidyalaya, Raipur email :
[email protected]
ABSTRACT Hybrid rice was first come into market in 1970’s in China. A breeding system using three lines (A, B & R lines) was established by using a male sterile plant discovered from a wild rice species in 1970 by Prof. Longping Yuan. The hybrids provide many advantages in crop production by increasing yield for about 92% of total cereal production in world. In late 1980’s marker assisted selection (MAS) opens the new doors of technology and knowledge for rice breeding. In viewing the importance of hybrids, various markers have been developed for rice as a pivotal and for all other major crops of agriculture. The contribution MAS especially in hybrid rice breeding came forward while, reviewing the importance of markers in hybrid rice viz., genetic purity of parental line and hybrid seed lot, identification and transferring of eui gene for panicle exsertions, identification of fertility restoration gene, stigma exsertion gene, wide compatible gene, environment dependent traits and disease resistance in parental line of hybrid rice. Key words
Marker assisted selection, hybrid rice, markers, genes.
Rice is most important food crop and staple food for 40 per cent of world population. As a population goes on increasing it put pressure to increase rice production thus this become challenge for breeders to meet the growing demand of the population. Earlier production had been increased by principles of classical Mendalian genetics and conventional plant breeding methods. After conventional breeding, hybrid rice was first come into market in 1970’s in China. A breeding system using three lines (A, B & R lines) was established by using a male sterile plant discovered from a wild rice species in 1970 by Prof. Longping Yuan. The hybrids provide many advantages in crop production by increasing yield for about 92% of total cereal production in world. In late 1980’s the new technology and knowledge had been developed which opened new opportunities for rice breeding. The marker assisted selection (MAS) can be defined as “Selection of plant carrying genomic region that involve in the expression of interest through molecular marker” (Fig. 1). Marker assisted selection have the different role in hybrid rice breeding.
Genetic purity of parental line and hybrid seed lot: The traditional method of the morphology based Grow Out Test (GOT) usually followed to assess genetic purity has many limitations (Yashitola et al., 2002). Assays involving PCR-based DNA markers have been suggested for CMS purity assessments (Yashitola et al., 2004) as a replacement for GOT. Restriction fragment length polymorphism (RFLPs) that distinguish WA-CMS lines from their maintainers have been reported (Narayanan, et al., 1996; Sane, et al., 1997), but these are not ideally suited for rapid and large-scale screening since the process is laborious, time consuming, expensive, and hazardous. Random amplified polymorphic DNA (RAPD) markers that distinguish WA-CMS and maintainer lines of rice have been described (Sane, et al., 1997; Jena and Pandey, 1999; Ichii, et al., 2003), but their utilization in routine screening is not feasible due to their low reproducibility. A PCR marker based on a mitochondrial DNA sequence of rice capable of distinguishing WA-CMS lines from their maintainer lines was recently reported by Yashitola, et al., 2004 given (Fig 2). However, this marker amplifies a 386-bp band in the CMS line and no band in the maintainer line, which necessitates a multiplex PCR assay with another nuclear genome specific marker to serve as a positive control. Availability of complete sequence of rice mitochondrial genome in public domain (http://www.ncbi.nlm.nih.gov/ GenBank/index.html) has opened up new avenues for marker development. Rajendrakumar, et al., 2007 designed 35 oligonucleotide primer pairs that flank microsatellite repeats present within or upstream (500 bp) of mitochondrial genes responsible for cellular respiration and used them for screening a set of rice lines which included land races, cultivated varieties, and wild relatives of rice for polymorphism. Twelve out of the 35 primer pairs tested were polymorphic (Yonghong, et al.,2005), one of the polymorphic markers (RMT6) was found to amplify a (AT)6 repeat present upstream of the gene coding for the nad5 subunit. Since earlier studies had indicated a role for mitochondrial genes and their upstream sequences in CMS (Dai et al., 1978; Kubo and Kadowaki, 1997; Yashitola, et al., 2004) we examined the amplification of RMT6 marker among different types of CMS lines of rice and their maintainers. The marker was observed to be polymorphic
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P1
SUSCEPTIBLE
X
F1
P2
RESISTANT
F2
AFTER PCR AMPLIFICATION, GO FOR SELECTION OF ONLY RESISTANT LINES.
Fig 1. Systematic procedure of Marker Assisted selection (MAS)
between all WA-CMS lines and their maintainers amplifying two fragments in CMS line and one fragment in maintainer line. The amplicons were sequenced and primers were redesigned to obtain a clear amplification of two distinct fragments in CMS and maintainer lines. The marker ‘drrcms’ developed has a clear advantage over the marker ‘cms’ developed by Yashitola, et al., 2004 as it can distinguish WA-CMS lines from their cognate maintainers based on differential fragment sizes compared to the cms marker (Fig. 3), which requires multiplexing with another nuclear genome specific marker. Further, unlike the nuclear simple sequence repeats (SSRs) which sometimes show heterozygous amplification, the mitochondrial SSR marker ‘drrcms’ shows only a single
band and is easier to analyze in seed lots for detection of contaminants. Another notable feature of drrcms marker is its ability to distinguish CMS lines derived from O. nivara and O. rufipogon cytoplasmic background from their maintainers.The drrcms marker using the template DNA from a CMS line (IR58025A), its cognate isonuclear maintainer line (IR58025B), and the hybrid DRRH1. This marker could unambiguously distinguish CMS (or the hybrid) from maintainer line (Fig.4a and 4b). Since the hybrid also derived its cytoplasm from the CMS line, it also exhibited an amplification pattern similar to the CMS line. PCR was also performed with template DNA from 15 other cognate pairs of WA-CMS and maintainer lines and similar results were obtained with the marker showing clear polymorphism between CMS and maintainer lines (Fig. 5).
KOSHTA, et al., Marker Assisted Selection in Hybrid Rice Breeding
Fig 2.
PCR assay for distinguishing cytoplasmic male sterile (CMS) and maintainer lines of rice. PCR was performed with drrcms marker. M, 100-bp DNA ladder. Lanes 1 and 11, IR58025A and B; Lanes 2 and 12, IR69628A and B; Lanes 3 and 13, IR68888A and B; Lanes 4 and 14, IR68897A. and B; Lanes 5 and 15, CRMS31A and B; Lanes 6 and 16, DRR2A and B; Lanes 7 and 17, Pusa5A and B; Lanes 8 and 18, PMS10A and B; Lanes9 and 19, DMS3A and B; Lanes 10 and 20, DMS4A and B. (Rajendrakumar et al., 2007)
Identification and transferring of EUI gene for panicle exsertions: Among various cytoplasmic male sterility (CMS) sources available in rice, wild abortive (WA) type is the most widely exploited to develop hybrid cultivars. However, in most indica/indica hybrid developed have WA cytoplasm but incomplete panicle exsertion as a result 30-40% panicle enclosed, in flag leaf and spikelet are not available for cross pollination (Virmani, 1996). Incomplete exertion, known to be caused by reduced level of endogenous GA3 synthesis, is a major handicap in obtaining high seed yield in hybrid rice seed production plots. This problem is generally overcome by exogenous application of GA3 (50-100 g/ha in India and 200-250 g/ha in China) which in turn escalates the cost of hybrid seed (Honnaiah, 2003) and is also reported to have an adverse effect on seed quality (Yang, et al.,
Fig 3.
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2000). Hybrid rice breeders therefore, have been in constant search for a genetic alternative to GA3 application. Internode length of rice plant plays an important role in deciding the extent of panicle exertion.The first recessive rice internode elongation mutant was isolated from the Japanese rice cultivar Norin 8 by gamma ray treatment (Okuno and Kawai, 1978). The gene conditioning the elongated uppermost internode (eui) phenotype in the mutant 76:4512 has been localized in chromosome 5 through trisomic analysis and named as ‘eui-1’ (Librojo and Khush, 1986). Subsequent studies on inheritance of internode elongation trait also confirmed it to be controlled by single recessive gene (Gangashetti, et al., 2004; Ma, et al., 2004). Yang, et al., 1999 identified another recessive gene (eui-2) in a gamma rays irradiated maintainer line, Xinquing ZhaoB which was mapped on chromosome 10 using SSR markers (Yang, et al., 2000b; Yang et al., 2001). It has been
Single seedling assay for detecting hybrid seed purity. Polymorphism between CMS (IR58025A), hybrid (DRRH1) and restorer (IR40750) lines of rice at RM164 microsatellite locus (Lanes 2–4). DNA was isolated from single seedlings of the DRRH1 hybrid, PCR analysis was performed and genotype assessed (Lanes 5–13). Off types are in Lanes 7 and 12. Molecular weight marker (Lane 1) is a 1-kilobase DNA ladder. (Yashitola et al. 2002).
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Fig 4. Purity analysis of seed stock of popular CMS line IR58025A. Genomic DNA was isolated individually from 400 plants and PCR was performed using drrcms marker. Data from a representative set of 20 plants are shown. M, 100-bp DNA ladder; Lane A, CMS line; Lane B, maintainer line; Lanes 1–20, test samples. The CMS lines show a 130-bp fragment and maintainer line shows a 142-bp fragment. Asterisk indicates plants determined to be maintainer lines. (Rajendrakumar et al 2007).
reported that transfer of eui gene into CMS line can reduce or eliminate the use of GA3 in hybrid rice seed production. Later the mutant rice line is characterized by a near doubling of uppermost internode thus enhancing panicle exsertion and panicle length with almost no effect on other internode or plant characters. Due to its ability to cause complete panicle exsertion the EUI phenotype in CMS line controlled by recessive genes prove a very useful trait in hybrid seed production and hence was called a fourth genetic element after the three genetic tools viz. male sterile (A), maintainer (B) and restorer (R) lines (Rutger and Carnahan, 1981). So far two eui loci controlling the elongated internode have been identified in rice. Gangashetti, et al., 2004 identified a recessive gene controlling eui in the rice breeding line IR911591-3 and later Gangashetti, et al., 2006 tagged the gene by RAPD markers and mapped on chromosome 5 using a Sequence Tagged Site (STS) In view of the fact that the elongated uppermost internode gene is recessive in nature, hence its transfer is laborious and time consuming as one generation of selfing is required after every cycle of backcrossing. This problem could be overcome with the availability of molecular markers associated with this trait. PCR based markers such as simple sequence repeats (SSR), which exhibit co-dominant inheritance can be effectively used in marker based screening of eui gene and phenotype marker.A recessive ‘tall rice’ mutant that has an
elongated uppermost internode (eui) has been successfully applied to produce hybrid seeds by improving the heading performance of male sterile lines shown in (Fig.6) as a result of eliminating panicle enclosure (Yang, et al., 2005, Yang, 2005). The eui gene was isolated recently and characterized as a new cytochrome P450 monooxygenase, CYP714D1, which is involved in catalyzing 16a, 17-epoxidation of non13- hydroxylated gibberellins (GAs), leading to extremely high levels of GA4 and GA1 accumulating in the eui mutant plants (Yonghong, et al.,2005).
Identification of fertility restoration gene: Many crop species, including rice, sorghum, sunflower depend on CMS and its fertility restoration for hybrid breeding but in hybrid many traits like restoration test crossing and progeny testing for several generations is required but MAS replaces test crossing and progeny testing if markers closely linked to fertility restorability QTL. There is lack of restorer gene in cultivar. The breeding of restore line (WA-R) was eventually achieved by transferring restorer gene for WA to cultivar the list of restorer gene with linked markers is given in Table 1. The first CMS-WA line was developed and used in the hybrid rice production, a series of elite CMS lines have been developed by breeders in China. The wild abortive (WA) type cytoplasm derived from Oryza
Table 1. List of restorer gene with linked markers. Species
CMS
O.sativa
WA
MARKERS
Linked marker
Chromosome
Reference
RFLP
RG532,R173
1
Yao et al. 1997
RFLP
G403,C234
10
Tan et al. 1998
WA
RFLP
C1361,S11019
10L
Tan et al. 1998
WA
RFLP
R2309, RG257
10S
Tan et al. 1998
WA
R GENE Rf1
WA
Rf4
SSR
RM171, RM228
10L
Jing et al. 2001
WA
Rf6
SSR
RM244
10S
Jing et al. 2001
WA
Rf5
RFLP
RG374,RG394
1
Shen et al. 1998
KOSHTA, et al., Marker Assisted Selection in Hybrid Rice Breeding
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Table 2. Character of the three cytoplasmic male sterility (CMS) System of rice Source: Rajendrakumar, et al., 2007 CMS type
–
Morphology pollen
of
Pollen Stainability
Aborted type
Abortive stage
Fertility restorer alleles
CMS-WA
Irregular
No
Sporophytic
Uninucleate
1-2 pair (Rf3,Rf4),dominant
CMS-HL
Spherical
No
Gametophytic
Dinucleate
1 pair(Rf5,Rf6), dominant
CMS-BT
Spherical
Stainable
Gametophytic
Trinucleate
1 pair (Rf1), dominant
sativa f. spontanea (Yuan, 1993) .It is estimated that more than 60 types of CMS lines based on the origins of the cytoplasms including the WA- type, Dian1 type, Honglian type, Gambiaka type, K-type and Maxie type were developed in China from the 1970s to the mid-1980s. Although the CMS lines have different origins and abortive features or a restoration-maintenance relationship, it generally can be categorized into three types, namely, WA type (it includes most of the indica CMS lines currently commercialized in hybrid production such as, Aibai, Tenye, Indonesia rice type, K-type, Gambiaka-type, Dissi-type and Maxie CMS etc). , HL-type and BT-type (this CMS group also includes Dian-1 and Dian-3 CMS lines) named CMSBT, the maintainer line and the corresponding restorer line were developed from an interspecies cross of Chinsurah Boro II/Taichung native 65 (Shinjyo,1975) based on their inheritance, morphology of abortive pollens, and restoration maintenance relationships as shown in Table 2.
To understand the molecular mechanism of CMS in rice, Yaoguang Liu and his colleagues have shown that in BT-cytoplasm CMS rice, an abnormal mitochondrial open reading frame (orf79) that is co-transcribed with a duplicated copy of atp6 (B-atp6) encodes a cytotoxic peptide Akagi (1994) and confers gametophytic CMS (Yonghong, et al., 2005). Two fertility restorer genes, Rf1a and Rf1b, have been identified at the genetic locus Rf-1 as members of a multigene cluster encoding pentatricopeptide repeat (PPR) proteins. Restoration of male fertility occurs by silencing orf79 via Rf1a- or Rf1b mediated endonucleolytic cleavage or degradation of the dicistronic B-atp6 and orf79 mRNA. These findings have provided an interesting mechanistic link between CMS and its restoration and could be used to improve the efficacy of rice hybrid technology.
Identification of stigma exsertion gene: There are several phenotypic traits contributing to
Fig 5.
PCR amplification of DNA sequence specific to cytoplasm sterile (CMS) lines of rice.PCR was performed with RMT 6 mitochondrial microsatellite marker. Lane1. 100-bp; Lane 2, PCR-amplified product of CMS line (IR58025A); Lane 3the hybrid (DRRH1); and Lane $, maintainer line (IR58025B). An extra DNA band, which present only in hybrid and CMS line but absent in maintainer line, indicate by the arrow. (Rajender Kumar et al.2007).
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Fig 6.
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PCR amplification of a DNA sequence specific to cytoplasmic male sterile (CMS) lines of rice. PCR was performed with drrcms marker. Lane 1, 100-bp ladder; Lane 2, PCR-amplified product of a CMS line (IR58025A), Lane 3, the hybrid (DRR H1); and Lane 4, maintainer line (IR58025B). (Rajender Kumar et al.2007).
the hybrid seed production efficiency, for example, flowering behavior (days to heading or blooming time), number of pollens, pollen longevity and morphological traits in a floret such as size of the stigma and style, stigma exsertion or spikelet-opening angle (Virmani, 1994). Among them, stigma exsertion is especially emphasized as a component increasing the opportunity of pollination (Kato and Namai, 1987). Since continuous phenotypic variation is broadly observed, stigma exsertion in rice species is thought to be controlled by polygenes (Virmani and Athwal, 1973, 1974). Recent progress in the DNA marker technique has been providing genetic information about stigma exsertion applicable to actual breeding. For example, nine QTLs for the frequency of stigma exsertion were detected in the recombinant inbred lines (RILs) derived from the cross between a japonica variety, Asominori, and an indica variety-IR24 (Yamamoto, et al., 2003) and two QTLs for the rate of exserted stigma in the RILs derived from the cross between an indica variety, Pei-Kuh, and a wild riceW1944 (Oryza rufipogon Gri V.) (Uga, et al. 2003).
a. b. c.
Elongated uppermost internode mutant eui with a longer uppermost internode (left) and its wild type after heading (right) Semi-dwarf mutant sd1 with short stem by decreasing length of each internode (left) and wild-type plants (right). Bar 14.2 cm Elongated basal internode mutant ebi with special longer basal internode (left) and its wild type (right). Bar 14.5 cm (Zhu et al., 200 6)
Fig 7.
Co-segregation of STS, sAg01_1051 with EUI phenotype in F2 (IR58025A/ IR91-1591-3) population of rice P1: IR911591-3; P2: IR58025; E: EUI; non-EUI; Present; - : absent; M: 100 bp ladder (M.G. Gangashetti et al. 2010)
KOSHTA, et al., Marker Assisted Selection in Hybrid Rice Breeding
Fig 8.
Exploit yield QTL’s from wild species, Oryza rufipogon, yld1 & yld2, single QTL’s has increased yield by 18%.Introgress trait with high grain number per panicle.
However, it is still uncertain whether these QTLs effectively work in the genetic background of current candidates of the maternal parent in hybrid rice. To remove such a concern, Tanksley and Nelson, 1996 proposed a strategy of an advanced backcross QTL (AB-QTL), in which valuable QTLs are introgressed with the same timing as the QTL detection during the process of backcrossing using an elite cultivar as the recurrent parent the concept of the AB-QTL strategy to efficiently improve the stigma exsertion in elite japonica cultivar for hybrid seed production. According to (Kai et al., 2011) GS3 participate in stigma exsertion as well as seed length in rice. One major QTL for stigma exsertion, qES3 on chromosome 3 is also identified by (Mityata, et.al, 2007).
Identification of wide compatible gene: It is found that hybrids develop from indica-japonica show more heterosis but these crosses are partially or
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completely sterile. The strong hybrid vigor in the F1s between the (Oryza sativa ssp. indica) and japonica (O. sativa ssp. japonica) subspecies of the Asian cultivated rice (Oryza sativa L.) has attracted a large amount of research interest with the hope of developing hybrid rice by making use of such heterosis e.g. Oryza rufipogon, yld1 and yld2, single QTL’s has increased yield by 18%.Introgress trait with high grain number per panicle (gaint panicle of rice Fig. 7). A major difficulty encountered in the development of such inter-subspecies hybrids is the partial sterility that occurs frequently in indica-japonica crosses (Kato, et al., 1928). It has been shown that the fertility of indica-japonica hybrids varies widely from fully fertile to almost completely sterile, with the majority of the inter-subspecific hybrids showing significantly reduced fertility (Oka, 1988; Liu, et al. 1996). Thus, MAS help in identification of wide compatible genes which accelerates and facilitates the breeding process. One gene which regulate fertility and compatibility of indica-japonica hybrid in S5.This gene is located on chromosome 6 with linked marker RG 213 (Yanagihara et al., 1995)
Identification of environment dependent traits: It is necessary to identify environment dependent traits since in conventional method hybrid and its parent have to be planted in variety of environment to understand photosensitivity and temperature but once molecular marker associated with these traits minimizes the requirement of multi-environment location test and shorten breeding cycle. Marker assisted selection also predict the day to flowering from parental genotype (Table 3).
Identification of disease resistance in parental line of hybrid rice: Bacterial blight (BB) elicited by Xanthomonas oryzae pv.oryzae (Xoo) is still one of the most devastating diseases across the tropics and semi-tropics. Damages range from
Table 3. List of environment dependent traits with linked markers. Gene
Traits
Chromosome
Linked marker
Reference
Se1
Photoperiod sensitivity
6
RG64
Mackhill et al.,1993
Se2
Photoperiod sensitivity
6
A19
Maheswaran et al.,1995
Pms1
Photoperiod sensitivity male sterility
7
RG477
Zhang et al.,2001
Pms2
Photoperiod sensitivity male sterility
3
RG191
Zhang et al.,2001
Pms3
Photoperiod sensitivity male sterility
12
C751/RZ261
Li et al., 2001
Tms1
Thermo sensitivity male sterility
8
RZ562,RG978
Zhang et al.,2001
Tms3
Thermo sensitivity male sterility
6
OPAC3640
Subudhi et al., 1997
tms5
Thermo sensitivity male sterility
2
RM174,RM394
Jia et al., 2000
rtms
Thermo sensitivity reverse male sterility
10
RM239
Jia et al., 2001
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Fig. 9. Foreground selection using resistance gene linked PCR based markers for the four BB genes at BC1F1 stage. Gel photographs showing banding pattern of the markers used in the introgression in Mahsuri. P1 – Mahsur i (Susceptible), P2- IRBB60 (Resistant). H indicates Heterozygote. (Shanti et al. 2010)
6 to 60% in India, Japan and Indonesia. Historically, longterm cultivation of rice varieties carrying single resistance gene has resulted in a significant shift in pathogen-race frequency and consequent breakdown of resistance (Mew, et al., 1992). An example of this is failure of Xa4 which was incorporated widely in many high yielding varieties via. conventional breeding. Widespread cultivation of varieties carrying Xa4 has led to the predominance of Xoo races that can overcome resistance conferred by this gene (Khush, et al.,1989). More than 30 resistance genes have been identified and designated in a series from Xa1 to xa32 till now (Lin, et al., 1996; Nagato and Yoshimura, 1998; Zhang, et al., 1998; Khush and Angeles, 1999; Chen, et al., 2002; Lee, et al., 2003; Tan, et al., 2004; Xiang, et al., 2006).
Earlier efforts to generate three-gene pyramids in the backgrounds of rice cvs PR106 (Singh, et al., 2001), Pusa Basmati (Joseph, et al., 2003) and Samba Mahsuri by the collaborative work of the Directorate of Rice Research, Hyderabad, India (DRR). Pyramiding resistant genes in the restorer lines alone is not enough as the hybrid will have these genes in heterozygous condition, so that the level of resistance imparted will be reduced. The genes, xa5 and xa13 are recessive and therefore they have to be introgressed also in the female line. Currently, most of the BB resistance genes are available in partial restorer backgrounds, thus making it impossible to transfer them directly into the A lines. This necessitates first to transfer them to the maintainer background and, after stabilization; they can be transferred with ease to the male sterile
KOSHTA, et al., Marker Assisted Selection in Hybrid Rice Breeding
background. Once transferred to the maintainer and the CMS line base, transferring multiple resistance to any other CMS line will be easier without having to face the problem of fertility restoration This necessitates the introgression of BB resistant genes in the backgrounds of maintainers as well as restorers. Considering the importance of cv. Mahsuri in the Indian agriculture and the necessity to introduce BB resistance in it, Shanti, et al., 2010 have adopted markerassisted backcross breeding to introgress Xa4, xa5, xa13 and Xa21 (Fig. 8). Marker assisted selection (MAS) is particularly useful in the present breeding program, since resistance of xa5 and xa13 is manifested in recessive condition. In absence of markers, identifying backcross plants carrying these genes would be cumbersome due to masking effects. (Shanti et al., 2010) reported the pyramiding of four BB resistance genes Xa4, xa5, xa13 and Xa21 through marker-assisted selection (MAS) in the BB susceptible high yielding rice cv. Mahsuri and parental lines of hybrid rice, maintainers IR58025B and Pusa 6B and the restorer lines KMR3 and PRR78 simultaneously. The results show increased and wide spectrum resistance to the pathogen populations from different parts of India. This work is the first successful example of the use of molecular markers in foreground selection in conjunction with conventional breeding for the simultaneous introgression of genes of interest into multiple backgrounds.
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Accepted on 25-05-2014
Trends in Biosciences 7(13): 1367-1376, 2014
MINI REVIEW
Potential of Agronomy in Mitigating the Challenges of ‘Future Food Security’ S.K. DUBEY, S.K. TRIPATHI, G. PRANUTHI, RESHU YADAV, D. MAURYA, PANKAJ UPRETI Water Resource Development and Management Department, Indian Institute of Technology Roorkee-247667 (Uttarakhand) India email:
[email protected]
ABSTRACT Food security is the availability of food for all at every time. Now question is that our technology is sustainable enough? Have ability to meet the demand of burgeoning population without much affecting our natural economy? No doubt green revolution makes us self reliance, we have increased our food production many folds, but often a question is very common at what extent green revolution is sustainable? In modern era agriculture, industries and urbanization are highly exploitative in nature. Modern crop production technology has considerably raised output but the natural resource base is degraded and diminished gradually simultaneously the quality of environment sustaining human life is adversely affected. We have created threats for our natural resources (soil, water, forest) and biodiversity (crop, fishes, and other plant and animal genetic resources). We have to believe in the fact “Nature is for our need not for our greed”. The earth climate has been considered as till recently as a remarkably stable, renewable, taking care of all human misadventures and assaults on fragile biosphere. Even today if we reduce the anthropogenic activities the adverse effect of global warming will be managed. Now again question is, it is possible to combat the food demand with our traditional agriculture, without moving towards modern one? It never is possible, because our per capita demand growing and availability of land diminishing. Here each and every sector of management needs security. In that situation only agronomy has ability to answer these questions. The history of the world reveals that great civilizations (Nile, Harappa Mohan zodaro) flourished along the irrigation sources (river) and mis-management of these resources saw the extinction of these civilizations. Agronomist can take initiative to lead the challenge of Future Food Security (FFS) by increasing per capita income and make self reliance to the farmer through Integrated Intensive Farming Systems (IIFSs). Since meeting the need of present generation without eroding the ecological assets of the future generation is receiving top priority by food and environment managers. Key words
Food demand, agronomy, nutrient management, Integrated Farming System
Food and Agriculture Organization of the United Nations (FAO) stated that Future Food Security (FFS) will
be the global challenge in coming era. It is the physical and economic access to food every time for everyone. It can be achieved only through applying eco friendly, sustainable and corrective measure in use of natural resources. By which detrimental seasonal and annual insecurity can be minimized. Natural and man-made disasters can often be anticipated or even controlled, and suitable preventive measure can be taken with in time. Access to have a sufficient food by all people at all time can make possible. To sustain the FFS a comprehensive approach should be needed, to identify the available infrastructure and traditional practices in a given geographical area. That enables the demand of ever growing population by sustaining the productivity. The aim of this study to present a view to how the situation may develop in future and how that can be overcome from these constraints by applying agronomic advancement, in a changing world where required world GDP will be 2.5-time more from present one, and per capita income will be nearly twice.
Key drivers of FFS: Population pressure According to United Nations report the current world population of 7.2 billion is expected to increase by 1 billion over the next 12 years and attain the level of 9.6 billion by 2050 and 10.9 billion in the year 2100 (UN Report 2012). India is the second largest populated country after China having the total population of 1.237 billion in 2012 and increasing with the growth rate of 1.3 percent annually (India Census, 2012) and expected to be 1.69 billion in 2050 (Goswami, 2013).
Hunger Index As hunger and mall nutrition is the major problem facing by the vast majority, the one and only reason behind this is population explosion. According to recent estimates of FAO 12.3 percent of world population is hungry, out of 7.1 billion people approximately 0.87 billion globally suffering from hunger and malnutrrion or in other term one in every 8 person is affected. Huge population of this
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group ~0.852 billion belongs to developing countries which are near about 15% of total population in these countries (FAO, 2012). A very small population ~0.016 billion comes from developed nations (FAO, 2012). One very interesting figure pointed by Kaul and Singh (2002) nearly half of the world’s poor lives in South Asia out of which more than 50 percent belong to India only. According to Global Hunger Index (GHI) India’s rank are 67 th out of 122 countries in year 2010, the condition is same in 2012 (65th out of 79). Now as a country India has highest number of undernourished people in the world. Total 217 million populations (17.5%) out of 1.24 billion are suffering from hunger and malnutrition.
Land availability Out of total global land area (7.2 billion ha) 1.6 billion hectare land has put under cultivation. Remaining 5.7 billion ha land is not used for cultivation due to various constraints i.e. 2.8 billion covered with forest which is either protected area or non agriculture use. Remaining 2.9 billion ha 1.2 billion is comes under rain fed situation which is marginal in quality and 1.7 billion is covered with marshy area, hilly tracts, desert and mangroves there is very little chance of improvement. Global per capita availability of land has diminishing pattern and it is expected to decrease up to 0.181(ha per person) in 2050 from base year 2005 (0.242). The change is more pronounced in developing country it would be 0.405 in 2050 from 0.462 (2005) while slight change might be in developed nations it will become 0.139 in (2050) from 0.186 (2005). If the population trend continue up to 2050, to meet the demand of these population globally 70 million ha land needed. Most of it comes from developing countries of sub-Saharan Africa and Latin America. From above discussed figure it is clear there is no there is no land constraint to meet the demand in 2050. But that is not true. Condition will be different from expectation it’s not easily transformable in to cultivated one for example it’s very difficult to imagine the agriculture practices in hilly tract of Himalayan range, great plains of china etc. India has very small pocket of land (328 million ha) only 2.5% of the global area for feeding of its huge population ~ 17.2% of world.
Changing climate According to recent report of Intergovernmental Panel on Climate Change (IPCC) global warming is real and is in increasing mode after1950s. Impact is visible with increasing sea level, decreasing ice core, it is due to increase in temperature through anthropogenic gases. First time John Tyndall in 1861 proposed that atmospheric CO2 will affect the earth atmosphere. Now the several researchers have reported by studying the evidence of geological and paleoclimatic situation that the current CO2 concentration is
higher at any time since 20 million years (Tripati, 2009). Due to anthropogenic activities total 350 billion metric ton of carbon (equivalent to 1285 billion metric ton of CO2) being emitted since 1959 out of this about 55 percent absorbed b Ocean and land in form of “Blue and Green carbon” and remaining quantity is in the atmosphere (Ballantyne et al. 2012). CO2 concentration is in rising trend and has touch level of 400 ppm in November 2013. If the present trend is continue concentration of green house gas will be rise up to 56 Gt of CO2 equevalent in 2020. In fact, since 50 years the sum of solar and volcanic effect have cooling trend if we remove the impact of anthropogenic gases (Wigley and Santer 2012). Global average temperature shows the warming of 0.85 o C from preindustrial period (1880) to 2012. It is due to the observed increase in anthropogenic greenhouse gas concentrations” (Solomon, 2007). Global Ocean warming indicates that warming near the surface up to 75 meter is 0.11 oC increase from the period 1971-2010. Average ice loss around the world increases continuously and now it is 226 Gt per year over the period 1971-2009. Number and frequency of drought in world is pronounced more after the mid of 20th century (Li et al. 2009). This condition might be associated with Russian Heat wave or decreasing trend of rainfall in southern hemisphere. Simulation study shows that global crop production especially maize and wheat has negative trend by 4 and 5.5 percent respectively after 1980 (Lobell et al. 2011). Climatic model based study shows that the temperature during winter in India might be increase by 2oC that can reduce the production of most staple food wheat to many folds (Lobell et al. 2012).
Need of FFS (Demand and Supply) Understanding of demand and supply is necessary for future planning as well as for policy concern. Study conducted across the world revealed that our food requirement will be 50 times more than present one in 2030. That mean we have to increase the productivity of our major crops by 40 %, it requires ~10 % more area up to 2030 (Godfray et al., 2010). To determine the demand and supply at global and regional scale a very specific and clear assessment was made by Alexandratos and Bruinsma (2012). Summary of their assessment report is presented in table 1 indicates the requirement of food grains, sugar, pulses, oils, Meat and Milk (kg/capita/year) from the period 1970 to 2006 and from 2006-2050. In 1970 our global food grain requirement is 144 kg/capita/year which has increase 9.7 percent in 2006 and if the trend is continue our food demand will be 160 kg/capita/year which is 1.3 percent more from the base period (2006). Beside food grain per capita requirement of total cereals (including food grain), Root and tubers, sugar, pulses oil, meat and milk content will be 5.1, 13.2, 13.6, 14.8, 33.3, 25.6 and 19.3
DUBEY, et al., Potential of Agronomy in Mitigating the Challenges of ‘Future Food Security’
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Table 1. World: food consumption, Past (1970) Present (2006- base year) and Future (2050) Food Requirement (Kg / person / year) Time
Cereals*
All Cereals Roots and tubers
Sugar crops (Raw sugar eq.)
Pulses
Oils
Meat
Milk
World 1970
144
304
84
22
7.6
7
26
76
2006
158
314
68
22
6.1
12
39
83
2050
160
330
77
25
7
16
49
99
Developing Countries 1970
140
193
79
15
9.3
4.9
11
29
2006
155
242
66
19
7
10.1
28
52
2050
158
262
78
24
7.7
15.4
42
76
Developed Countries 1970
155
571
96
41
3.6
11
63
189
2006
167
591
77
34
2.9
19
80
202
2050
166
695
72
33
3.1
21
91
222
*Wheat and Rice only
percent respectively in 2050. The condition will be dangerous in developing countries their per capita requirement of food grain, total cereals (including food grain), Root and tubers, sugar, pulses, oil, meat and milk content in 2050 will be 1.9, 8.3, 18.2, 26.3, 10.0, 52.5, 50.0 and 46.2 percent higher than present (2006). While the condition of developed nation will be quite good, reason is there less population growth and good productivity. In developed nations per capita requirement of food grain, Root and tubers as well as sugar have decreasing trend because of their self sufficiency and increasing number of diabetic person and change in food habit (Basu et al. 2013). In case of total cereal, pulses, oil, meat and milk the requirement will be 17.6, 6.9, 10.5, 13.8 and 9.9 percent more from base period (2006). Increment in total cereal requirement will be highest because it might be required for energy industries for ethanol and alcoholic beverage production (Mejia and Malaga, 2009, Anonymous, 2013). Global population will be 39 percent more (table 2) in 2050 as compared to base period (2006) for that figure we need to increase our energy (kcal/person/ day) by 11 percent which required our total production (including, cereals, tuber, pulses, oil seed), 60 percent more as compare to 2005. For the fulfillment of this required production we need
(Data Source: FAO, 2012)
to increase our cereal and meet production 46 and 76 percent respectively. The condition is more adverse in developing country as compare to developed nation. In developing country our population and daily energy supply (kcal/person/day)in 2050 will be 47 and 15 percent more respectively as compare to 2005, the figure is very less in developed countries i.e. 7 and 4 percent only. For the fulfillment of this requirement we need to increase total food, cereal and meat production 77, 56 and 113 percent respectively in developing country while 24, 32, and 27 percent respectively in developed country.
Options to mitigate the FFS: By increasing yield per unit area To fulfill the demand of 2050 population level under changing climate is one of the greatest challenges for researchers and policymakers. Yet there is no further improvement is possible in developed nations because there
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land area got saturated, increasing production per unit area is only option. There is chance of further improvement in developing countries especially in South and East Asia. We have to increase our crop yield more than 75 percent in different part of world condition is more dangerous in East and South Asia because there increasing population pressure.
By increasing Cultivated Area: Currently only 12 percent of global surface area has put under crop production including cropped area, garden and orchard. Still many African and Asian countries have scope for area expansion. About 28 percent of global area is prime quality (A class) land and that can be utilized in future for meeting the demand of our growing population. The chances are seems to be more in developing nations especially East Asian countries.
Cropping Intensity Beside expansion in cultivated area food security can be achieved by increasing production per unit area that is only possible through intensive agriculture. Cropping intensity is governs with the concept of taking number of crop at a time in the fixed piece of land. Cropping intensity from the period 1961 to 2005 shows the diminishing pattern for the East Asian countries. To meet our future demand global crop intensity need to increase, and is especially important for East and South Asian countries. Increasing land area and cropping intensity will not be very easy step to get the desired productivity of crop. Future condition will be different from expectation it’s not easily transformable in to cultivated land because only that land is vacant till now those having some constraints beside its good in quality. For example the land is so far from locality or village, difficulties in transportation, endemics of disease and pest, land covered with permanent forest, or under government authority. To make productive such land we need intensive tillage practices, deforestation, use of certain chemical and other product to cure their problems it means. During that operations emission of anthropogenic gases will be more which not allow our carbon credit. In such a tremendous situation only sustainable practices can play an important role to achieve our desired productivity. Despite the multiple useful features we can’t take risk to adopt the sustainable practices suddenly by changing our modern technology. Connor (2008) claimed that organic agriculture has not ability to feed our world in coming decades. Yet there is about 25 percent yield gap may possible between organic and modern agriculture therefore before planning or diverting the agriculture system we need to assess the relative performance at regional and global scale (de-Ponti, et al. 2012). Organic agriculture will take shape but it takes time (TOI, 2013) sudden change may dropped
the production level which is not good for developing country like India. Organic farming is sustainable agronomic practice that has ability to get the desired production without affecting our natural resource, but sudden diversion may slow down our economy. Under such a condition need to intensify our agriculture practice by using integrated agronomic approach by combining both organic and conventional agriculture (Patil, et al. 2014).
Agronomy: A way to sustaining the productivity Agronomy being the oldest discipline of agricultural science, the history of agronomic research is wellconnected with history itself. An Agronomist is a scientist who studies the ways and means of producing food for man and feed for animals, and therefore, agronomy forms the field of study upon which the future welfare of a hungry human race depends. It is the division of agricultural science embracing preparation of soil in accordance with crop demands, enriching the soil with organic matter and plant nutrients, choice of crops and varieties to fit the climate, crop rotations and intercropping, appropriate time of sowing, moisture regulation, drainage, and weed management, harvesting and processing to an extent. Some basic principles of agronomy that governs the productivity of food grains by reducing the impact of climate change are given here under:
Agro climate zone Agro climatic situation is the most important aspect that determines the productivity in given geographical boundary. Strategic approaches through appropriate agronomic researches have gives out number of cropping pattern and crop suitability under different eco climate. Most important aspect has been in terms of the selection of suitable crops, varieties and cropping systems. Multiple criteria’s being used to define a set of agroclimmatic zone in different part of world, more detailed about global zonation schemes is given by (van Wart et al. 2013) .
Selection of crop and their varieties The choice of suitable crops and intensive cropping approach depends on number of factors like, soil type, texure, pH, topography; water availability etc. i.e. in sole cropping pattern is suitable for shallow soils (available moisture of 100 mm) to double cropping in deep soils. Some time many long duration varieties being used in dry land situation that feel moisture stress at the time of grain filling. Some time it has observed that the length of succeeding crop is more, even next crop is not fitted in sequence in that case intercropping is the best approach of increasing production per unit area also minimize the risk of crop failure. As per the availability of natural resources the crop planning should be made. The suitability of crop
DUBEY, et al., Potential of Agronomy in Mitigating the Challenges of ‘Future Food Security’
and variety varies from region to region, state to state and even district to district as described by Ardeshna, and Shiyani , (2011) and Ramphul, (2012) for Gujrat and Haryana state of India.
Cropping sequence Crop rotation and crorpping sequence is one of the most important aspects that govern the productivity of food grain in given environment by reducing the infestation of soil born diseases, insect pest and flora community. There is great chance of improvement in the food production in West Africa, East Africa, South Asia and Austraila (FBIGR 2010) only need to fallow the correct package of practices, crop rotation, and farming system as described by (Pearson, 1995).
Seeding technology Seeding technology started from the tillage to the emergence of seed. Seeding technology alone has capability to increase production without enlarging the area only need to fallow the appropriate technology (Timber press, 2013). Tillage is major aspect that governs the establishment of seedling and final plant population. Some studies conducted in India shows that the conventional practice of puddled transplanting (specially in Rice- Wheat cropping system) could be replaced with no-tillage-based crop establishment, it not only gives similar production also reduces the amount of water, energy and labor, which are becoming increasingly scarce and expensive (Lav et al., 2007 and Sharaswat et al. 2010) . Selection of good quality seed is equally important in improving the production of food grain. Good quality seed have ability to increases the yield by 15-20 per cent. A certified good quality seed have ability to boost the production and increase the local income of farmers which support the economy of emerging nations (ICRISAT 2013). Taking the importance of quality seed in productivity a new concept came in existence “Seed village”. Sowing time is the non monetary input which affects the production of crop. Timely sowing/planting is possible only when our farming community are ‘seed secure’, when they have access to adequate quantities of quality seed. Each and every crop requires certain fixed amount of water and energy to germinate. It has been reported that in Indian continent delay in the sowing of wheat each day after 15th November reduces about 1 percent yield per hectare (Randhawa, 1981). Sowing depth: Research efforts by Agronomists revealed a rule of thumb for the planting depth of a crop is to be taken as 4 to 5-times the average seed diameter. After opening of the land (ploughing) soil evaporation rate get increased
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and just after sowing moisture of the top soil depleted quickly and seeds which are placed at lesser depths are at risk of germination. It can be minimized by deeper placement of seed but it has negative effects on crop establishment and finally on total production. In case of proper moisture supply seed of plant should not be sown below the 5 cm. In wheat crop depth of sowing is determined by the length of coleoptiles it should not be placed 4-5 cm deeper in Mexican Wheat. Spacing: Optimum plant spacing determines the total plant population which is responsible for the final production. A field research was conducted during 2005 to 2007 at USA using maize as a test crop revealed that the narrow spacing (45cm x 30 cm) increases the biomass per unit area but highest yield was attain at the spacing of 60 cm row to row and 45 cm plant to plant (Coulter, 2008). Standard row spacing of wheat crop under Indian situation is 22.5 cm. Sowing Direction is also one of the most important aspects that have to keep in mind before the planting of seed. Interception of solar radiation is directly proportional to the amount of biomass produced. So planting of crop should in such a way that has minimum shedding effect on each other thus it is advised that row crop always be planted in North –South direction that allow more solar radiation interception and produced more yield.
Nutrient Management Each crop has its own pattern of nutrient uptake. Besides N, P, K, crop also needs nutrients like Ca, S, Zn, Fe, Cu Mn, B and Mo. In some cases elements like Si and Cl are also essential for imparting special characters like pest and disease resistance. Though they are needed in micro level, their requirement is most essential for growth and full expression of their genetic potential. In recent times, the yield potential of crop varieties has already reached a plateau owing to several constraints. Micronutrients are required for normal growth, development and yield of crops. For example, cotton needs Zn for the normal pollen production. Boron application to groundnut reduced the occurrence of ill filled pods and enhances the yield. Pulses need molybdenum for better nodulation. Manganese application to gingerly increase its oil content a seed yield. Copper deficiency in citrus causes die-back disease. Besides the application of needed dose of N,P and K fertilizers, Zn application increased the yields by over 300 kg/ha in rice, 800 kg/ha in maize, 500 kg/ha in soybean and 200 kg/ha in groundnut. For example, excess application of Zinc will result in decreased uptake of Copper and Manganese leading to deficiency of Copper and Manganese. Sometimes under
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certain situation like strongly acidic soils, toxicities of certain micronutrients is likely to occur due to their excess availability. Further, the toxicities created by imbalance of applied nutrients or improper dose / concentration of applied nutrients has been encountered occasionally. Therefore, there is an absolute need for a thorough knowledge on the deficiency and the toxicity symptoms of each nutrient. It is also necessary to have a complete understanding and through expertise in the subject so as to distinguish the deficiency and toxicity symptoms of various nutrient elements from one another. Nutritional balance: Quantity of nutrient applied in form of fertilizer after estimating the loss of nutrient removed by crop, percolation, seepage, volatilization, denitrification etc. Nutrient Removal is the amount of nutrients removed in plant material harvested from the field.
NR ( Kg/ha) Ydm ( Kg / ha)
Concentration (%) 100 %
Where- NR= Nutrient removed from the field (Kg/ ha), Ydm= Dry matter production (Kg/ha)
Nutrient Use Efficiency (NUE) Efficiency is defined as the amount of product produced per unit of resource used. This means nutritional efficiency is the amount of dry matter produced per unit of nutrient applied or absorbed. NUE may be classified into three groups and called them agronomic efficiency, physiological efficiency, and apparent recovery efficiency. The agronomic efficiency is defined as the economic production obtained per unit of nutrient applied. Sometimes, agronomic efficiency is also called economic efficiency. If the efficiency is determined under greenhouse conditions, the agronomic efficiency may be expressed in mg mg-1. Physiological efficiency is defined as the biological production obtained per unit of nutrient absorbed. Sometimes, it is also known as biological efficiency or efficiency ratio. The apparent recovery efficiency is defined as the quantity of nutrient absorbed per unit of nutrient applied. Under field conditions, the economic part of the plant is the best criteria to calculate the nutrient efficiency, and agronomic efficiency is the most appropriate to express the nutrient efficiency. Under greenhouse conditions, the best way to express the nutrient use efficiency may be either the agronomic efficiency or the physiological efficiency. There are some important tips given below towards sustainable efficiency of nutrient management, to meet the FFS- Use integrated nutrient management system, apply balance dose of fertilizer, select suitable source of fertilizer
(organic or inorganic), method of fertilizer application should be based on crop and soil condition, know the nutrient status of soil, quantify the nutrient removed by crop and other factor, maintenance of soil pH, add legume in crop rotation, timely application, use of control release/incubated fertilizer, placement at appropriate depth, follow experimental findings, use suitable mathematical models i.e. crop nutrient response tool and fertilizer chooser to quantify the dose.
Water management The objective of irrigation is to provide suitable moisture environment to crops to obtain optimum yields on long term basis by keeping good soil health with maximum economy of irrigation water. Thus, an efficient irrigation management would not only include intake, conveyance, regulation, measurement and application of water to farms for crop water use in appropriate quantities and at right time to increase production but also include timely and effective drainage of excess water from irrigation and rain. Irrigation should be profitable and in times of crop need and in proper amount. The excess or under irrigation may damage lands and crops.
Irrigation Scheduling: Proper irrigation management demands the application of water at the time of actual need of the crop with just enough water to wet the effective root zone soil. An ideal irrigation schedule must answer the three basic questions, i.e.
How much water need by crop? Water is needed mainly to meet the demands of evaporation (E), transpiration (T) and the metabolic needs of the plants together termed as consumptive use (CU). Since it is difficult to determine E and T separately, these are estimated together as evapo-transpiration (ET). Water requirement (WR) includes losses during the application of irrigation water in the field (percolation, seepage and runoff) and water required for special operations such as land preparation, transplantation, leaching, etc. Crop water requirement depends on some factors like crop factors such as varieties, growth stage, duration, plant population and growing season; soil factors such as texture, structure, depth and topography; climatic factors such as rainfall, temperature, relative humidity, wind velocity; and crop management practices like tillage, fertilization and weeding.
On what stage irrigation is required? The optimum irrigation schedules for most of the crops have been developed and critical stages of irrigation identified in arable crops. Greater irrigation depth (more than 5-6 cm) generally results in more water and nutrient losses and poor yield and water use efficiency. Irrigation
DUBEY, et al., Potential of Agronomy in Mitigating the Challenges of ‘Future Food Security’
Table 2. Moisture sensitive stages of major crops (Reddy and Reddy 2010) S. No.
Crops
Critical Stages
1
Rice
Panicle initiation, flowering
2
Wheat
Crown root initiation, jointing, milking
3
Maize
Silking, tasseling
4
Sugarcane
Formative stage
5
Soybean
Blooming and seed formation stage
management for different intercrops and crop sequences, optimum crop plan for a given water supply, soil-plantwater relation, crop modelling, water production functions, crop coefficients and other refinements have been made in the area of irrigation management of field crops.
What method of irrigation is suitable? Irrigation can be applied to crops by adopting any of the several methods. Broadly, methods of irrigations are grouped as surface, sub-surface, overhead or sprinkler and drip or trickle irrigation. The main factors determining the choice of a particular method include surface topography, soil characteristics, crop to be grown, irrigation source, depth of water application, labour requirement, level of technology, layout and management. In general, the proper irrigation method should not only provide high level of water application efficiency but should also ensure its economic viability, sustained productivity and wide adaptability to prevalent features of farm. The general guidelines for selection of appropriate irrigation method are described in detail by Brouwer et al (1988).
Weed Management: To the farmer, the economic aspect of weeds growing among the crop plants is of primary importance. Weeds growing in croplands are, like the crop plants themselves, merely trying to perpetuate themselves and make a living from the soil and the air. Unfortunately for the interests of the farmer, weeds usually make their living at the expense of the crops. Since both weeds and crops are plants, they have basically the same requirements for normal growth and development. They require and compete for an adequate supply of the same nutrients, moisture, light, heat energy (temperature), CO., and the growing space. They have been recognized as the potential pests and various methods have been devised from time to time to combat them right from shifting cultivation in pre-historic period hand pulling 10000 BC to the chemical weed control after invention of 2,4-D in 1941 by Andrew H. Cobb and John P. H. Reade.
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Integrated Weed Management practices: Manual methods some time called as mechanical method of weed control. It consist of- Pulling, chaining, burning, flooding etc. Cultural methods of weed control involve the modification in package of practices in crop production. It consist of- crop rotation, stale seed bed preparation, intercultural operation, deep ploughing, closer spacing, line sowing, pudling, mulching Biological methods involve utilization of natural living organisms (bio agents) such as insects, pathogens and competitive plants to limit weed infestation. The objective of biological control is not eradication, but reduction and regulation of weed population below economic threshold level. BioWeed: Herbicide is a certified organic input which can be adopted for year round weed management. Bioherbicides: Some bacteria and fungi applied as biological control agents do not survive from year to year. These organisms must be applied on an annual basis. Some important bioherbicide given below-
Table 3. A list of pathogens registered or approved worldwide as bioherbicides. Pathogen
Weed
Product name
Alternaria destruens
Cuscuta spp.
Smolder
Chondrostereum purpureum
Broad-leaved trees
Chontrol, MycoTech,
Colletotrichum gloeosporioides
Malva pusilla
BioMal
C. gloeosporioides sp. aeschynomene
Aeschynomene virginica
Collego
Cylindrobasidium leave
Acacia spp.
Stumpout
Phytophthora palmivora
Morrenia odorata
DeVine
Puccinia canaliculata
Cyperus esculentus
Dr. BioSedge
Puccinia thlaspeos
Isatis tinctoria
Woad 006489
Xanthomonas campestris
Poa annua
Camperico
(Charudattan, 2012)
Chemical method is widely accepted and feasible method of weed control. It depends upon the-Nature of weed, weed environment, selectivity of herbicide. Yet the method is most suitable, cost effective but it creates environmental hazards thus the minimum application of chemical should be include in integrated system of weed control.
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Use of crop Modeling Crop simulation modeling has emerged as a concept, which is used to study the interactive response of various growth factors on crop yields. A crop simulation model may be perceived as a black box in which a minimum data set relating to crop, soil, weather, etc. is fed. The model utilises this input data set in calculating various growth processes using established quantitative relationships and gives the required information regarding the daily growth and development of the crop, water and nutrient balance information and the simulated final yield of the crop.
Strengthening of field Experimentation Modern agronomic research is tending to incorporate slate-of-art technologies with emphasis on design and analysis of experiments, long term projects, and regional operations including fanner participatory research apart from the basic research. Modern computer has significantly contributed in this direction because of the large capabilities it has to analyse and store appreciable amount of information. With the help of computers we can design the experiments and analyse the data with maximum number of aspects to obtain a lot of information from a single experiment. Earlier a field experiment was planned in which only a few aspects of investigations could be extrapolated. Due to this difficulty in analyzing the multifactorial and voluminous data the computers have aided in such situations. Various inputs in field crops that need to be optimised, computers are able to fit the multiple regression equations of yield and yield response. Computer simulation of crop growth and yield has emerged as a valuable tool for enhancing our understanding of their ecophysiology of crops. Such high-tech support in agronomic experimentation has immensely contributed in productive results and has provided a strong foundation in obtaining sustainable food production. After reviewing the number of literature finally reach at the point of concluding remarks. It is clear that our environment is continuously changing. It cannot be stopped only the pace can be reduced. Food security is threatened by increasing day by day food prices and decreasing per capita income. It can be insure by increasing the income of farmers without much deteriorating the environment. Booming crop production in sustainable manner is the key to food security. Sustainability is needed because it is only option that can provide low cost technology, clean environment and nutritious food without harmful residual effect. Our scientist community thinking on that topic but still they cannot gave solid, sufficient and reliable option. Adoption of sustainable practices takes some time, it is not
possible to divert suddenly from modern to traditional one. There will be huge back drop in production. Because it cannot meet our future challenge due to low yield, lack of timely and effective management of weed, insect and disease. We have to produce more to tackle the challenge of FFS but we have to do it differently. We have to adopt IIFSs and need to link our agriculture with livestock, agro forestry and other sectors. Agronomy has, is, and would be the anchor subject in making the holistic appraisal and realisation of potential and sustainable crop production by intelligent application of its basic elements including tillage, plant population dynamics, plant growth and developmental analysis and those of nutrient, water and weed management. It is not out of place to mention that contribution to food security for ever-growing population of human being and livestock depends on the efficient production technologies generated by Agronomists for different agro-ecosystems.
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ICRISAT 2013. Seeds for change: a certified seed project in Malawi is boosting local incomes and supporting emerging national agricultural policy. Case study- Policy response p 1-4. Kaul, J.N., Singh, Harmeet 2002. Role of agronomy in food security. In: Recent advances in Agronomy by Singh, G., Kolar, J.S., Sekhon, H.S. Indian Society of Agronomy, IARI, New Delhi, India. Lav, B., Ladha, J.K., Gupta, R.K., Singh, S., Tirol-Padre, A., Saharawat, Y.S., Gathala, M., Pathak, H. 2007. Saving of Water and Labor in a Rice–Wheat System with No-Tillage and Direct Seeding Technologies. Agronomy Journal 99: 1288–1296. doi:10.2134/agronj2006.0227. Li, Y., Ye, W., Wang, M., and Yan, X. 2009. Climate change and drought: a risk assessment of crop-yield impacts. Climate research, 39(1): 31. Li,C., Zhang,Z., Guo, L., Cai, M., Cao,C.(2013) Emissions of CH4 and CO2 from double rice cropping systems under varying tillage and seeding methods, Atmospheric Environment 80:438-444. Lobell, D. B., Schlenker, W., and Costa-Roberts, J. 2011. Climate trends and global crop production since 1980. Science, 333(6042): 616-620. Lobell, D. B., Sibley, A., and Ortiz-Monasterio, J. I. 2012. Extreme
Saharawat, Y.S. Singh, B., Malik, R.K., Ladha, J. K., Gathala, M., Jat, M.L., Kumar, V. 2010 Evaluation of alternative tillage and crop establishment methods in a rice–wheat rotation in North Western IGP. Field Crops Research, 116 (3) 260–267. Seufert, V., Ramankutty, N., & Foley, J. A. 2012. Comparing the yields of organic and conventional agriculture. Nature, 485(7397), 229-232. Singh, R.P., Reddy, G.S. 1988. Identifying crops and cropping systems with greater production stability in water-deficit environments. In: Drought Research Priorities for the Dryland Tropics. (eds. F.R. Bidinger and C. Johansen) ICRISAT, Patancheru. 77-85. Solomon, S. 2007. Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC (Vol. 4). Cambridge University Press. Timber Press, 2013 Sowing seed to increase production, without enlarging the garden.(http://www.timberpress.com/blog/2013/05/ sowing-seed-to-increase-production-without-enlarging-thegarden/) Times of India, 2013, http://articles.timesofindia.indiatimes.com/201303-22/patna/37935915_1_darveshpura-nalanda-rice-cultivation TNAU 2013 Seed Village- (http://agritech.tnau.ac.in/seed_certification/ seed_tech_Seed%20Village.html) Tripati, A. K., Roberts, C. D., and Eagle, R. A. 2009. Coupling of
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CO2 and ice sheet stability over major climate transitions of the last 20 million years. Science, 326(5958), 1394-1397. UN- DESAPD 2013. World Population Prospects: The 2012 Revision, Key Findings and Advance Tables. Working Paper No. ESA/P/ WP.227, pp. 1. van Wart, J., van Bussel, L. G., Wolf, J., Licker, R., Grassini, P., Nelson, A., ... & Cassman, K. G. 2013. Use of agro-climatic zones to upscale simulated crop yield potential. Field crops research, 143, 44-55. Wigley, T. M., and Santer, B. D. 2013. A probabilistic quantification of the anthropogenic component of twentieth century global
warming. Climate dynamics, 40(5-6): 1087-1102.
Zhang, F., Cui, Z., Chen, X., Ju, X., Shen, J. Chen, Q. Liu, X., Zhang, W., Mi, G., Fan, M., Jiang, R. 2012. Integrated Nutrient Management for Food Security and Environmental Quality in China. Advances in Agronomy 116: 1–40. Zhang, X., Chena, S., Sun, H., Ren, T., Wang, Y. 2010. Effects of winter wheat row spacing on evapotranpsiration, grain yield and water use efficiency Agricultural Water Management 97( 8):1126–1132. Received on 01-03-2014
Accepted on 08.03.2014
Trends in Biosciences 7(13): 1377-1380, 2014
MINI REVIEW
Extension Strategies for Diversification of Agriculture in Jammu Region of Jammu and Kashmir State PARVEEN KUMAR, MAHENDER SINGH, BRINDER SINGH *NICRA-AICRPDA, Dry Land Research Substation, SKUAST-J email :
[email protected]
The state of Jammu and Kashmir is predominantly an agrarian one. More than 65 per cent of the population in the state is dependant on agriculture and allied sectors. Small and marginal farmers constitute 85 per cent of the total. The average size of the holdings in the state is 0.66 ha. There exists a huge diversity in agro climate at the micro and macro levels. The NARP classifies the state into four distinct agro climatic zones based on their distance above mean sea level. These zones are subtropical (up to 800 m AMSL), Intermediate (800 m to 1500 m AMSL), temperate (1500 m to 2500 m AMSL) and the cold arid zone (more than 2500 m AMSL). About 75 per cent of the area of the state is rainfed. Agriculture sector continues to remain the important sector for socio economic development of the peoples in the state. Despite abundance of natural resources and varied agro climate the state faces massive deficit in food (40%), Oilseeds (70%) and vegetables (30%) (Anonymous, 2013). The Jammu division of the state comprises of ten districts that have varied agro-climatic zones ranging from subtropical to intermediate to temperate. Districts like Rajouri, Kathua and Udhampur are largely rainfed with as much as ninety five percent of the area in district Udhampur being cultivated under rainfed conditions. The number of total agricultural land holdings recorded in the division is 6, 08, 456 with an area of 5, 53,410 ha and a net sown area of 3, 92,910 ha. The average landholding size thus becomes 0.65 ha (Raina, 2014) in this division. With serious limitation of area expansion and decline in per capita availability of land, the non remunerativeness of the traditional vocation, the diversification of agriculture towards non food grains and high value cash crops including fruits and vegetables, compatible with the comparative advantage of the region is suggested as a viable solution. Besides there is also a growing concern about the viability of small farm agriculture in the state as the small landholders have dominated the agriculture. Diversification has the potential of income generation, employment generation, and poverty alleviation. According to a study, in the state of Jammu and Kashmir, the scope to raise output through diversification is highest in the country as one per cent
shift in area from food grains to non food grains entails more than 3 per cent growth rate in crop output (Bazaz and Haq, 2013). The viability of small farms can also be improved through diversification of agriculture with high value crops and other enterprises like vegetables, sericulture, floriculture, goatry etc. Against this background, an endaveour has been made to understand the agriculture status in Jammu region as well as to suggest suitable extension strategies for diversification of agriculture in Jammu region of state of Jammu and Kashmir. Table 1 contains information about the land resources in the state of Jammu and Kashmir. Of the total reported area of 24.16 lakh hectares, the forest constitute 6.58 lakh ha, non agricultural land (2.91 lakh ha), barren and uncultivable land (2.11 lakh ha), pastures and other grazing land (1.05 lakh ha), land under miscellaneous trees (0.63 ha), fallow land (0.74 lakh ha), Cultivable waste land (1.05 lakh ha) and the net sown area constitutes 9.09 lakh ha.
Table 1. Land Resources in Jammu and Kashmir S. No. 1 2 3 4 5 6 7 8 9
Particulars Forest Non-Agri Lands Barren & Un-cultivable lands Pastures & other grazing land Land under Misc. trees Cultivable Waste Land Fallow land Net Sown Area Total Reported Area
Area (Lakh ha.) 6.58 2.91 2.11 1.05 0.63 1.05 0.74 9.09 24.16
As is evident from table 2 the maximum area in the state is under maize (3.19 lakh ha) followed by Wheat (2.62 lakh ha), Paddy (2.58 lakh ha). This also reflects that maize, wheat and Paddy are the three important staple food crops of the state. The maize is grown largely as a rainfed and wheat both as a rainfed as well as irrigated crop in the state. The area under pulses is pulses, vegetables, flowers and oilseed is comparatively less as compared to the three staple crops grown in the state.
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Table 4. District wise fish caught in Jammu region (2011-12)
Table 2. Area under various agricultural crops in Jammu and Kashmir State S. No 1 2 3 4 5 6 7 8
Crops Paddy Maize Wheat Pulses Vegetables Flowers Other cereals & millets Oilseeds
Area (Lakh ha.) 2.58 3.19 2.62 0.30 0.51 0.00250 0.38 0.62
District Jammu Samba Udhampur Reasi Doda Kishtwar Ramban Kathua Rajouri Poonch Total
GAS Gross Area Sown; NAS Net Area Sown; CI Cropping Intensity: The figures in table 3 depict the area under different crops in different districts of Jammu region. If we see the total area under different crops in different districts we find that Maximum area is under wheat crop (273058 ha) followed by maize (210361 ha), Rice (123320 ha), Fodder (27670 ha), Pulses (20217 ha) and oilseeds (16383 ha). The overall cropping intensity of the region stood at 180 which is less that for the individual districts like Samba with highest cropping intensity of 211, followed by Kathua (210), Jammu (209) and highest than the districts like Udhampur (165), Reasi (147), Doda (128), Kishtwar 129), Ramban (136), Rajouri (163) and Poonch (166).
No. of fishermen registered 1212 209 594 306 366 219 129 594 566 426 4621
Fish caught (in qtls) 7520 2700 4580 2415 1520 1570 1161 4720 4810 3500 34496
coming from district Jammu (7520 qtls) followed by Rajouri (4810 qtls.), Kathua (4720 qtls.), Udhampur (4580 qtls.), Samba (2700 qtls), Reasi (2415 qtls.), Kishtwar (1570 qtls.), Doda (1520 qtls.) and least Ramban (1161 qtls.)
Table 5. Livestock status (in lakhs) in different district of Jammu region
The data in table 4 reveals the district wise fish caught in the region. The number of fishermen varies from as low as 129 in district Ramban to as high as 1212 in district Jammu. Similarly in district of Samba the number of fisherman registered are 209, Udhampur (594), Reasi (306), Doda (366), Kishtwar (219), Kathua (594), Rajouri (566) and Poonch (426). The total production of all the districts comes to be 34496 quintals with the highest production
District
Cattle
Buffaloes
Goat
Sheep
Jammu
2.080
1.615
1.54
1.59
Samba
0.953
0.516
2.095
2.795
Udhampur
2.548
1.438
1.686
2.559
Reasi
1.715
1.142
1.822
3.135
Doda
2.303
0.253
1.198
3.633
Kishtwar
1.751
0.932
0.772
2.225
Ramban
2.003
0.460
0.865
1.937
Kathua
2.370
0.850
2.095
2.795
Rajouri
1.950
1.660
1.686
2.559
Poonch
2.369
2.025
1.588
3.945
Table 3. Area under different crops (in ha) in different districts of Jammu region District
Rice
Maize
Wheat
Barley
Pulses
Oilseeds
Fodder
GAS
NAS
CI
Jammu
50716
11275
80558
293
5505
1465
9533
169444
81192
209
Samba
11236
3219
29011
724
3163
2405
4755
60262
28464
211
Udhampur
8649
36188
28135
593
1889
2051
285
80259
48508
165
Reasi
1721
23540
11353
233
454
387
432
38783
26346
147
Doda
1868
25281
3701
2551
2593
1518
419
38606
30107
128
Kishtwar
943
11692
2579
2181
1368
83
591
22750
17544
129
Ramban
1261
16912
3997
1910
-
782
-
54937
18242
136
Kathua
27573
21305
55982
1822
4814
5808
7907
128055
61010
210
Rajouri
15684
36967
42196
-
376
1193
1195
98459
53638
163
Poonch
3669
23712
14946
-
55
691
2553
45970
27565
166
Total
123320
210361
273058
10307
20217
16383
27670
707525
392616
180
KUMAR, et al., Extension Strategies for Diversification of Agriculture in Jammu Region of Jammu and Kashmir State
Table 5 represents the quantitative status of the livestock in the Jammu region. District Udhampur has the largest number of the livestock (2.548 lakhs) followed by Kathua (2.370 lakh), Poonch (2.369 lakhs), Doda (2.303 lakhs), Jammu (2.080 lakhs). District Poonch Samba has the lowest number of livestock population (0.953 lakhs). Among the population of sheep district Poonch ranks first with 3.945 lakhs, Doda (3.633 lakhs), Reasi (3.135 lakhs) and the least in Jammu (1.59 lakhs). Similarly in case of goat the highest population is in district Kathua and Samba (2.095 lakhs) and the least in district Kishtwar (0.772 lakhs).
Table 6. Area under horticulture in different districts of Jammu region Area (ha) District
Fresh fruits
Dry Fruits
Jammu
11847.90
0.00
Samba
7696.90
0.00
Udhampur
6267.50
4303.85
Reasi
4913.60
2522.90
Doda
7951.49
6505.05
Kishtwar
3582.00
4466.00
Ramban
4811.27
4501.12
Kathua
11730.83
4151.08
Rajouri
10462.51
4491.00
Poonch Total
8397.60
8192.00
77, 661.00
39, 133.00
As is clear from table 6, the area under horticulture crops (fresh fruits) in Jammu region is highest in district Jammu (11,847.90 ha) followed by district Kathua (11,730 ha), Rajouri (10,462 ha), Poonch (8,397 ha) and the lowest in Kishtwar (3,582 ha). When we see the area under dry fruits in the Jammu region, what is amazing is that district Jammu which has the highest area under fresh fruits has none under dry fruits. Similar is the fate of district Samba. The maximum area under dry fruits is in Poonch (8,192 ha), followed by Doda (6,505.05 ha), Ramban (4,501 ha) and Rajouri 94,491 ha). On the whole 1, 16,794 ha of area is under horticulture crops in the region of which 77,661 ha is under fresh fruits and 39,133 ha is under dry fruits.
Strategies for Diversification: Increasing the cropping intensity: There exist a great inter district variation in the cropping intensity. While it stands as low as 128 and 129 for districts like Doda and Kishtwar, it is as high as 209 and 210 and 211 for districts like Jammu Kathua and Samba. The cropping intensity of such districts can be brought at par with the districts that have a cropping intensity of more than 200. This can be
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increased by utilizing the fallow period in between kharif and Rabi seasons, lands, proper crop management and by growing short duration crop varieties that mature earlier thus giving more time for growing other crops. Bringing more area under pulses: A careful analysis of the tables mentioned above reveal that in the state area under pulses is just 0.30 lakh hectares as against 2.58 lakh ha under Paddy, 3.19 lakh ha under maize and 2.62 lakh ha under wheat. Similar is the condition at regional level. In the Jammu region area under pulses is only 20,217 ha as against 1, 23, 320 ha under rice, 2, 10, 361 under maize and 2, 73, 038 under wheat. Thus it can be inferred that both at the state as well as the regional level, the area under pulses is very meager. This is despite the fact that pulses are low input use crops. These can be grown under rainfed conditions under which most of the area of the region falls. Increasing Seed replacement rate: For enhancing productivity and production the seed replacement rate must be increased. In the state of Jammu and Kashmir the seed replacement rate which earlier used to be less than 20 percent is now more than the national average for wheat and oats and in case of rice it has crossed 25 percent (Mir, G.H., 2103). Similarly for other crops the seed replacement rate should be increased atleast up to the national level. Utilizing waste lands: In Jammu and Kashmir the cultivable waste land is 1.05 lakh hectares. This waste land can be put to use by cultivating crops suitable for these lands. These waste lands provide conducive agro-climatic conditions for growth of aromatic & medicinal plants, and Jatropha cultivation on waste land and rainfed areas of Jammu region Sericulture: Presently about 27.000 families are associated with this enterprise in Jammu region. In Jammu region it is flourishing in the rainfed areas of Rajouri, Udhampur and Kathua districts. Mulberry tree can be grown in the rainfed regions to cater to the needs of silkworm rearers. In Kathua district it is a subsidiary occupation and more than 4000 families and especially women are associated with it. Kathua district with sixteen mulberry nurseries and an annual production of 5 lakh plants ranks first in production of mulberry plants in the Jammu province. Sericulture can be practiced on a large scale by involving more and more families in this enterprise. As an enterprise, it can contribute a lot in boosting rural economy in the largely rainfed and unreached areas and particularly for the empowerment of women farmers. The only need is to develop region specific sericulture technologies suitable to the agro climatic conditions of the area concerned and to make the people aware of the various schemes launched by the government and skill up gradation of the farmers through regular trainings and awareness campaigns.
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Alternate land use systems: Alternate land use system is basically a diversification strategy. An alternate land use system involving horticulture is very economical. Over 70% of NSA is under food crops. A little over 13% is under fruits. The favorable weather helps in the production of many kinds of fruits. It is budding industries of the state that earns large revenue. Almost 45% economic returns in agriculture sector are accounted for by horticulture produce. 5 lakh families comprising of 30 lakh people are involved in horticulture trade. The need is to create necessary infrastructure for these horticulture sector as the produce is mostly perishable. Floriculture is also another sector whose potential can be tapped. Presently 245.70 hectare of land in Jammu division and 330 hectares of area in Kashmir division is under commercial floriculture. The Floriculture department has earned revenue of Rs 3.39 crore in Kashmir and Rs 1.09 crore in Jammu till December last from its gardens and other assets (Anonymous, 2013). Flower cultivation can be promoted among farming community through different schemes like Rashtriya Krishi Vikas Yojana, National Horticulture Mission for Himalayan and North Eastern States. Goatry: Called as the poor men cow, goat rearing is also another economic enterprise in the hilly areas of state. Almost every family rears goats. The state of Jammu and Kashmir is ideally suited for rearing of sheep and goats owing to its favourable agro-climatic conditions, rich alpine pastures and host of other natural endowments. Sheep and goat rearing is the core activity of rural masses in the State and plays a vital role in socio- economic upliftment of weaker sections of the society viz; Gujjars, Bakerwals, Chopans, Gaddies and Changpas. Attempts should be made to provide farmers with cross bred goats which have higher yields and more lactation periods.
Fishery: It is another enterprise which can serve as an alternate source of income for the farming families. As against 6, 08, 456 number of land holdings, the number of registered fishermen in Jammu division are only 4621. Various programmes are launched both at the national as well as state level to promote fish farming in the state. The only need is to make the farmers aware of these programmes so that they can be benefitted. The promotion of fish farming in dry land areas by constructing artificial ponds will also lead to the water harvesting and its recycling. This water could also be used for irrigation purposes.
ACKNOWLEDGEMENT The authors place on record the cooperation of the officials of Directorate of Economics and Statistics, Jammu, Government of Jammu and Kashmir for providing the relevant secondary data regarding agriculture, livestock, fisheries, horticulture.
LITERATURE CITED Anonymous. 2013 a. Peerzada reviews functioning of Floriculture. Cited at http//scoopnews.in /det.aspx?q. Accessed on Feb. 18, 2014. Anonymous. 2013 b. Cited at http//www.jkapd.nic.in. Accessed on Feb. 21, 2014. Mir, G. H. 2014. Apple, Cherry, Mango brought under crop insurance. Daily Excelsior, Feb. 20. Pp: 6 Naseer Hussain Bazaz and Imtiyaz ul Haq. 2013. Crop Diversification in Jammu and Kashmir: Pace, Pattern and Determinants. OSR Journal of Humanities and Social Science, 11(5): 1-7 Raina, J. C. 2013. Fast Changing scenario of farming in Jammu division, Daily Excelsior Singh, Harbans. 2014. Impact of climate change on food grains, Daily Excelsior, January 23 Received on 06-05-2014
Accepted on 26-05-2014
Trends in Biosciences 7(13): 1381-1384, 2014
MINI REVIEW
Participation of Farm Women in Rural Dairy Enterprise- A Review S. J. JADAV¹ AND V. DURGGA RANI² Department of Veterinary and Animal Husbandry Extension Education1, 2 Vanbandhu College of Veterinary Science and Animal Husbandry. Navsari Agricultural University, Navsari 396 450 email:
[email protected]
ABSTRACT Dairying is one of the important enterprise, which supports the rural households by providing gainful employment and steady income. Most of the activities related to rural dairy enterprise are carried out by farm women. They were mostly involved in cleaning of animal shed & utensils, milking, care of animals & newborn calf and compost making. Under feeding management women involved actively in feeding, watering, collection of fodder, storage of concentrates, feeding the young calf and soaking of concentrates. Under breeding taking animals for pregnancy diagnosis, care during pregnancy and arranging bedding materials during parturition were performed by farm women. They also took care of healthcare activities like care of sick animals, diagnosis of common diseases and vaccination, etc. Farm women were comparatively less involved in record keeping, banking, processing and marketing activities related to rural dairy enterprise. Key Words Farm women, Participation, Rural dairy enterprise
Dairy enterprise is the major component of farming system as well as the rural economy as they provide employment and financial support to the rural families not only for land holders but also to landless families. In India, most of the activities related to dairy farming are done by women. The nature and extent of women’s involvement in livestock farming vary widely among different ecological sub zones, farming systems, castes, classes and socioeconomic status of families (Swaminathan, 1985). It is established beyond doubt that women always participated in dairy and animal husbandry activities in addition to their daily household chores (Belurkar, et al., 2003). About 75 million women as against 15 million men engage in dairying in India (Thakur, and Chander, 2006). Livestock farming plays a significant role in accelerating the rural economical growth in developing countries like India. Many of the important tasks in animal husbandry activities are performed by women besides fulfilling their responsibilities as home makers (Randhawa, and Chandra, 1993). Most of the dairy farming activities like bringing fodder from the field, chaffing the fodder, preparing feed for animals, watering,
protection of animals from ticks and lice, cleaning of animals and sheds, preparing of dung cakes, milking and ghee-making are performed by farm women. Women play crucial and significant role in livestock rearing, but their contribution in livestock rearing has not been given the due place they deserve. They always remain invisible workers (Chayal, et al., 2009).
Participation of women in different dairy farm activities: Women performed various livestock activities independently such as feeding of animals, care of livestock, cattle shed and excreta management, retention of produce for household consumption and processing of produce (Gupta, and Godawat, 2011). Involvement of women belonging to large farmer’s families in milking and preparation of milk products was maximum where as in land less farmer’s families, women were equally involved in all the operations and in small and marginal farmer’s families performed almost all the operations related to animal husbandry and dairying (Kumar, and Prajapati, 2011).
Participation of women in management activities: In India, majority of dairy farm women participated in care of newborn calf, milking, cleaning of animal shed, cleaning of utensils, weaning and management of calf, preparation of cow dung cakes and construction of animal sheds but their participation was least in maintenance of farm records. Rangnekar, 1992 claimed that livestock management has always been perceived as the traditional responsibility of women. Involvement of farm women in care of newborn calf and cleaning of utensils and shed was 100 percent (Uma Sah, et al., 2006, Chayal, et al., 2009 and Lahoti, et al., 2012). The majority of the farm women were involved in compost making, milking of animals and were involved in weaning and management of calf (Lahoti, et al., 2012). Women performed activities like Milking (90 per cent), cleaning of animal sheds (89.16 per cent) and disposal of cow dung or preparation of cow dung cakes (86.66 per cent) (Rathod, et al., 2011). The farm women to the tune of 75.83 per cent were involved in the construction of animal sheds (Toppo, et al., 2004
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Table 1. Participation of farm women in management activities Sr. No. 1 2 3 4 5
Farm activities Care the animal during pregnancy Care the newborn calf Attending the animal during parturition Cleaning the animal shed Milking the animal
Only by herself 11.50 65.00 34.50 54.50 90.50
Jointly
By other members
18.50 16.00 39.50 33.50 4.50
Not in practice
34.00 5.50 20.50 12.00 0.00
36.00 13.50 5.50 0.00 5.00
(Hai, et al., 2011)
and Rathod, et al., 2011). Participation of women in the maintenance of animal shed and maintenance of farm records was observed to be low (Chayal, et al., 2009 and Lahoti, et al., 2012). Farm women were mostly participated in dairy management activities presented in table 1.
Participation of women in feeding and watering of animals: Most of the work related to feeding and watering of animals was the sole responsibility of the women folk. They were responsible for the tasks like feeding, watering, collection of fodder, storage of concentrates, feeding young calf, soaking of concentrates, taking the animals for grazing, chaffing and storage of fodder. Most of the farm women were involved in the storage of concentrates, feeding young calf, watering the livestock, offering the concentrate mixture and soaking of concentrates (Chayal, et al., 2009 and Lahoti, et al., 2012). Under feeding domain, the farm women’s involvement was high in feeding, watering and collection of fodder (Hai, et al., 2011). In dairy husbandry 86.66 per cent of farm women were involved in the feeding of animals followed by watering of animals (85 per cent). 82.5 per cent farm women were involved in taking the animals for grazing. 80.83 per cent of women were involved in activities like fodder collection while 75 per cent women performed chaffing of fodder for animals. The women also looked after storage of feed and fodder (77.5 per cent) in
the form of hay making. The act of preparing feed i.e. mixing of concentrates with roughages or fodder was performed by 67.5 per cent of farm women (Rathod, et al., 2011).
Participation of women in Breeding of animals: Farm women participated in various activities related to breeding like taking animals for pregnancy diagnosis, care during pregnancy, arranging bedding materials during parturition and heat detection but they were less involved in taking the animal for natural service or artificial insemination. Under breeding domain, observing the animals for heat detection was carried out by the majority of the women either by herself or jointly with other members of the family, but taking the animal for natural service or artificial insemination is entirely done by male members of the family (Hai, et al., 2011). The majority of the (80 Per cent) women was involved in care during pregnancy however their involvement was less involved during parturition, castration of male calves and carrying animal for A.I./Service centre (Lahoti, et al., 2012). In taking animals for pregnancy diagnosis, 90.83 per cent of farm women were actively involved 78.33 per cent of farm women took animals for Artificial Insemination while 69.16 per cent of farm women took the animal for natural service (Singh, 2003 and Rathod, et al., 2011). In breeding practices, 73.33 per cent farm women called the
Table 2. Participation of women in financial and marketing activities Sr. No.
Particulars
Extent of Participation Regular
Recurrent
Occasional
Rare
Never
A
Taking loan for
1
Purchase of animals
16.7%
4.2%
6.7%
12.5%
60%
2
Purchase of feed
0.0%
1.7%
18.3%
15%
65%
B
Marketing of Milk 10%
3.3%
11.7%
8.3%
66.7%
61.7%
10%
14.2%
4.2%
10%
1
House hold sale of milk
2
Selling of milk through cooperative society
(Toppo, et al., 2004)
JADAV and RANI, Participation of Farm Women in Rural Dairy Enterprise- A Review
veterinarians during dystocia while 67.5 per cent farm women arranged bedding materials during parturition (Tripathi, and Bhanja, 2000). About 90 per cent of the farm women participated in breeding of milch animals. A nearly equal number of the women (91.66 per cent) and (90.50 per cent) participated in care at the time of calving and care of newborn calf, where as 88.34 per cent were involved in selection of method of breeding (Upadhyay, and Desai, 2011). Low participation of rural dairy women in taking animals for A.I., natural service and selection of sire for natural service was due to their physical stature and restrictions prevailing in the society (Singh, 2003). In breeding practices, Majority of women were involved either in the actual doing or in a supervisory role (Narmatha, et al., 2009). But Mishra, et al. (2008) found that breeding of animals is mainly done by men.
Participation of women in healthcare of animals: Farm women were actively involved in healthcare of animals like care of sick animals, diagnosis of common diseases, vaccination, etc. Women were actively involved in the diagnosis of common diseases and care at household level, care of sick animals, disposal of infected litter material, grooming, cleaning and bathing buffalos/cows, deworming and vaccination. Where as less participation in disposal of carcass was noticed (Lahoti, et al., 2012). Farm women were engaged in grooming and brooming of animals, treatment of animals from veterinary doctors and vaccinating animals (Upadhyay, and Desai, 2011). Chayal, et al., 2009 found that 92.50 per cent women were involved in disposal of carcass. Narmatha, et al., (2009) reported that more than 90 per cent of women actually involved themselves in taking care of sick animals and 74 per cent were involved in taking the animals in hospitals. Women were involved in taking animals for health treatment (82.5%) and vaccination or medication (79.16 %) (Tripathi, and Bhanja, 2000).
Participation of women in processing and marketing: Participation of women in processing and marketing activities was not much appreciated. The majority of the farm women performed processing of milk and preparation of milk products. Involvement of farm women in marketing activities was poor. Only 33.33 per cent were involved in purchasing of feeds and concentrates, followed by marketing of milk and milk products (28.00 per cent), purchasing of equipments (20.00 per cent), involvement in dairy co-operatives (16.00 per cent), maintenance of accounts and financial records (8.00 per cent), purchase and sell of animals (6.66 per cent) and involvement in the banking process (4.00 per cent) (Raju, et al., 1999, Chayal, et al., 2009 and Lahoti, et al., 2012). With respect to
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marketing, women were more involved in marketing of milk than marketing of animals. About 50 per cent farm women involved themselves in milk marketing while their involvement in purchase of live animal or animal feeds was never observed as an independent activity (Hai, et al., 2011). Similarly, Mishra, et al., 2008 reported that 100 per cent men’s involvement in sale and purchase of animals. Women were participated in financial and marketing activities presented in table 2.
Participation activities:
of
women
in
miscellaneous
The majority of the farm women were ignorant about record maintenance and hence, only 52.5 per cent of farm women maintained records in the form of a small book or a piece of paper (Yadav, et al., 2005). The farm women perceived the activities of procuring and repaying of loans/ credits as the responsibility of men and hence only 4552.5 per cent of women were involved in this activity. The reason behind this might be lack of information about existing financial services, complicated procedure of accessing loans, poor repayment capacity, consequences of not paying the loan/ credit, high interest rates and insecurity, etc. created a troublesome situation for the women to get a credit / loan (Jain, and Singhal, 2012). Dairy farming is the backbone of rural economic in our country. It is either main or subsidiary occupation in villages. In addition to their household activities women actively involve in dairy farming. Most of the activities related to care and management of dairy animals are performed by farm women even though their efforts remain unacknowledged. Their involvement is minimum in economic activities like procurement of loan for purchasing animals, processing and marketing of milk and milk products. This might be due to the lack of awareness and exposure to outside world. These farm women should be appreciated for their efforts and should be provided with support and awareness needed to perform various economic activities.
LITERATURE CITED Belurkar, G.M., Wakle, P.K. and Gholve, M.A. 2003. A study on decision making pattern and participation of farm women in animal husbandry and dairying enterprsise. Maha. J. Ext. Edu. 22 (2): 81-85. Chayal, K., Daaka, B.L. and Suwalka, R.L. 2009. Analysis of role performed by farm women in dairy farming. Indian. J. Dairy. Sci. 62: 491-494. Gupta, M. and Godawat, A. 2011. Access and Control of Rural Women over Livestock Resources. Asian J. Ext. Edu. 29: 57-61. Hai, A., Akand, A.H., Shanaz, S. and Bulbul, K.H. 2011. Contribution of farm women towards dairy enterprise in Ganderbal district of Kashmir Valley. J. Dairying, Foods & H.S. 30 (2): 140 -146.
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Jain, P. and Singhal, A. 2012. Participation of rural women in livestock management activities in Bhilwara district. Raj. J. Extn. Edu. 20: 190-193.
Singh, S.P. 2003. Role performance of rural women in dairy management practices in Haryana. Indian J. Dairy Sci. 56 (2): 100-106.
Kumar, S. and Prajapati, C.R. 2011. Quantificatsdsion of Involvement of farm women in animal husbandry and dairy based farming system. J. of Community Mobilization and Sustainable Dev. 6: 190-193.
Swaminathan, M.S. 1985. Imparting of rural women user perspective to agricultural research and development. IRRI Phillippines.
Lahoti, S.R., Chole, S.R. and Rathi, N.S. 2012. Role of women in dairy farming. Indian J. Dairy Sci. 65 (5): 442-446. Mishra, Sharma, S. Vasudevan, P. Bhatt, R.K. and Pandey, S. Singh, M. Meena, B.S. and Pandey, S. N. 2008. Gender participation and role of women in livestock management practices in Bundelkhan region of central India. Intern. J. Rural Studies. 15 (1): 3-7. Narmatha, N. Uma, V. Arun, L. and Geetha, R. 2009. Level of participation of women in livestock farming activities. Tamilnadu J. Vet. Sci. 5 (1): 4-8. Raju, L.D., Nataraju, M.S. and Niranjan, M. 1999. Women in animal production an ex-post-facto analysis. Agricultural Ext. Rev. 11 (3): 3-8. Randhawa, A. and S. Chandra. 1993. Changing role of home science scientists in transferring farm technologies to farm women in agriculture, Paper presented at national seminar on ‘Women in agriculture- development issues’ at NAARM. Rangnekar, S. D. 1992. Involvement of women and children in goat keeping in some villages of Gujarat and Rajasthan. 5th International Conference on Goats, Dehli, India. Abstracts of Contributory Papers, 1. Rathod, P.K., Nikam, T.R., Sariput, L., Vajreshwari, S. and Amit, H. 2011. Participation of rural women in dairy farming in Karnataka. Indian Res. J. Ext. Edu. 11 (2): 31-35.
Thakur, D. and Chandar, M. 2006. Gender based differential access to information among Livestock owners and it’s impact on house hold milk production in Kangra district of Himachal Pradesh. Indian. J. Dairy. Sci. 59 (6): 401-404. Toppo, A., Trivedinand, M.S. and Patel, A. 2004. Participation of farm women in dairy occupation. Gujarat. J. Ext.Edu. 15 (2):1521. Tripathi, H. and Bhanja. 2000. Women’s role in small holder production. Proceedings of the International conference on Small holder production system in developing countries held in Thrissur. pp. 550-556. Tripathi, H. and Bhanja. 2000. Women’s role in small holder production. Proceedings of the International conference on Small holder production system in developing countries held in Thrissur. pp. 550-556. Uma sah., Shantanu, K. and Fulzele, R.M. 2006. Perceived needs of dairy farmers and farm women related to improved dairy farming in India - an overview. Agric. Rev. 23 (1): 65 – 70. Upadhyay, S. and Desai, C.P. 2011. Participation of farm women in animal husbandry in Anand District of Gujarat. J. Community Mobilization and Sustainable Development. 6 (2): 117-121. Yadav, J.P., Sharma, K. and Saini, H. 2005. Role performance of farm women in animal husbandry practices. Paper presented in 3rd National Extension Education Congress on “Revitalization of Extension System in New Economic Order”, held at National Dairy Research Institute, Karnal: April 27-29 .pp.111-112. Received on 09-05-2014
Accepted on 22-05-2014
Trends in Biosciences 7(13): 1385-1387, 2014
MINI REVIEW
Biopharming - Healing Foods, A Novel Concept DARSHAN. S, SEEJA. G AND GANGADHARA. K Department of Plant Breeding and Genetics, KAU, College of Agriculture, Vellayani, Kerala email:
[email protected]
ABSTRACT ‘Biopharming’ is the production of metabolites valuable to medicine or industry in plants traditionally used in an agricultural setting. It promises more plentiful and cheaper supplies of pharmaceutical drugs, including vaccines for infectious diseases and therapeutic proteins for treatment of conditions such as cancer and heart disease. The bioreactors containing cell cultures that are required for large-scale production are extremely expensive and the increasing demand for therapeutics prompted researchers to seek new ways of producing large quantities of affordable and safe drugs. Biopharming offers commercially viable alternatives for allopathic drugs as value added foods. Key words
Biopharming, Pharmaceuticals, Plant-made pharmaceuticals.
‘Biopharming’ is defined as the production of metabolites valuable to medicine or industry in plants traditionally used in an agricultural setting. Crop plants produce large amounts of biomass at low cost and require limited facilities. Molecular farming / biopharming represents a novel source of molecular medicines, such as plasma proteins, enzymes, growth factors, vaccines and recombinant antibodies, whose medical applications are understood at molecular level (Raskin, 2002). The manufacture of pharmaceutical products in plants has been among the promised benefits of plant genetic engineering for nearly 20 years. This application of biotechnology, sometimes known as “bio-pharming”, “pharming”, or “molecular farming,” has now moved beyond the realm of speculation into the experimental testing phase in fields, greenhouses and clinical trials (Fischer et al. 2000.). This technology hinges on the genetic transformability of plants, which was ûrst demonstrated in the 1980s (Bevan, et al., 1983). Bio-pharming promises more plentiful and cheaper supplies of pharmaceutical drugs, including vaccines for infectious diseases and therapeutic proteins for treatment of conditions such as cancer and heart disease (Kamenovora, et al., 2005). “Plant-made pharmaceuticals” (PMPs) are produced by genetically engineering plants to produce specific compounds, generally proteins, which are extracted and purified after harvest. Although PMP technology offers
potential health and economic benefits, all observers agree that it must be strictly regulated to prevent pharmaceuticals from entering the food supply and to avoid unintended effects on the human beings, flora, fauna and the environment (Giddings, 2001). The following information, presented in question and answer format, covers basic information on the production, regulation, risks and benefits of PMPs.
What crops are being pharmaceutical production?
considered
for
The most common PMP crops that have been grown in field trials are corn, tobacco, and rice. Other crops being investigated include alfalfa, potato, safflower, soybean, sugarcane and tomato (Floss, et al., 2010).
What are the benefits pharmaceuticals (PMP’s)?
of
plant-made
PMPs can be produced at a significantly reduced cost compared to current production methods. Therefore, the technology has the potential to benefit medical patients by providing a cheaper source of vaccines and other medicines. Producing pharmaceuticals in plants is more flexible than current methods, because production can be more easily scaled up or down depending on demand.
·
Plants can be engineered to produce proteins of greater complexity than is possible with micro-organisms, and to produce proteins that cannot be produced in mammalian cell cultures.
A limited number of growers and communities will likely benefit economically from this new agricultural enterprise. The number of acres required to produce a year’s worth of a given pharmaceutical will likely be quite small compared to crop acreage for food and feed use.
Historically, plants have been a primary source for medicinal products for many centuries. Many of the therapeutically active compounds in plants have been identified in the past century. Advances in molecular biology have resulted in the ability to produce some drugs by recombining the gene for the desired product within the genetic material of bacteria. A vast quantity of the transgenic
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Trends in Biosciences 7 (13), 2014
bacteria is then grown and the desired protein purified from it. This recombinant technology, such as is used in the production of insulin, is molecular farming in its simplest form (Horn, et al. 2010).
Why only the plants? 1.
Plants are the cheapest, most abundant source of protein on the planet.
2.
Plant provide low upstream production costs: low cost
3.
Plants, as eukaryotes, can express and process most prokaryotic and eukaryotic proteins: stability
4.
Plant provide scalable production capacity and flexibility: easy scale up
5.
Plant seed permit stockpiling of inexpensive inventory: good storage Plants are free from animal and human pathogens: safety
6.
7.
Using plants as factories to produce therapeutic proteins also enables researchers ability to develop novel and complex molecular forms that could not otherwise be produced in mammalian cell cultures. (Kant, et al. 2011)
Since 1982, 95 therapeutic proteins expressed in microbial or animal cell cultures have been licensed for production in microbial or animal cell cultures (Battaglino et al. 1991). The bioreactors containing cell cultures that are required for large-scale production are extremely expensive and increasing demand for therapeutics prompted researchers to seek new ways of producing large quantities of affordable and safe drugs. That search led them to explore the potential of plants. President Emeritus of the Boyce Thompson Institute for Plant Research at Cornell University, Arntzen is working on making safer vaccines for viruses which kill millions in the developing world. He discusses his work developing edible vaccines (inside GM bananas, tomatoes, or potatoes). Dr. Arntzen is
Fig. 1. Process of Plant molecular farming.
DARSHAN, et al., Biopharming - Healing Foods, A Novel Concept
internationally recognized for his work on the development of transgenic plants to yield oral vaccines that meet the needs of developing nations where infectious diseases are a major cause of infant mortality (Langridge, 2000) A broad range of plant species have been used for PM farming including alfalfa, arabidopsis, banana, barley, carrot, false flax, flax, lettuce, maize, pea, peanut, pigeonpea, potato, rice, rape, safflower, spinach, soybean, sugar beet, sugar cane, tobacco, tomato, wheat, white clover, and white mustard (Twyman, et al. 2005, Twyman, 2004). The human intrinsic factor intended to be used as a food supplement for patients suffering from vitamin B12 deficiency is produced from Arabidopsis grown in greenhouses. According to their self-portrayal – as the first company world-wide, Cobento received a permit for commercial production of the human intrinsic factor from Arabidopsis in greenhouses from the national authorities and it also received authorisation of its use as a food supplement in Poland. BASF Company is developing a range of products to be used as food supplements, food additives or feed additives including omega-3 and omega-6 fatty acids produced in rape (Brassica napus) as well as carotenoids, vitamins and amino acids produced in soybean and other crops. Field trials for these products have been conducted outside the EU. Only omega-3 and omega-6 fatty acids and carotenoids are intended to be extracted and used as supplements whereas the other GM crops would be directly processed giving rise to biofortified food. Monsanto is developing a GM soybean producing omega-3 fatty acid. Increased pharmaceutical demands, as well as advances in gene identification following the completion of the Human Genome Project, have led to an interest in plants as expression systems for therapeutic products. Plants have advantages compared with traditional systems for molecular farming of pharmaceutical proteins. Plants might one day surpass other production systems because of the economic and safety benefits and ultimately, it should be possible to make pharmaceuticals available to everyone who needs them, at a cost that they can afford. For the biotech and drug industry, biopharming offers economic
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and health benefits. A combination of strong and adaptable regulatory oversight with technological solutions are required if the goals of realizing the full potentiality of plant molecular farming are to be met. Despite numerous benefits to this approach, however, the concerns that have been raised must be adequately addressed. Although molecular farming offers an exciting alternative for pharmaceutical production, industry and government must proceed cautiously in this area in order to gain public acceptance.
LITERATUR CITED Bevan, M.W., Flavell, R.B., and Chilton, M.D. 1983. A chimaeric antibiotic resistance gene as a selectable marker for plant cell transformation. Nature, 304:184–7. Battaglino, R.A., Huergo, M., Pilosof, A.M.R., and Bartholoma. G. 1991. Culture requirement for the production of protease by Aspergillus oryzae in solid state fermentation. Applied Microbial Botechnology, 35:292–296. Floss, D.M., Sack, M., Arcalis, E., Stadlmann, J., Quendler, H., and Rademacher, T. 2010. Inûuence of elastin-like peptide fusions on the quantity and quality of a tobacco derived human immuno deûciency virus-neutralizing antibody. Plant Biotechnology Journal, 7: 899–913. Fischer, R., and Emans, N. 2000. Molecular farming of pharmaceutical proteins. Transgenic Research, 9:279-299. Giddings, G. 2001. Transgenic plants as Protein factories. Current opinion in Biotechnology, 12:450-4 Horn, M.E., Woodard, S.L., and Howard, J.A. 2010. Plant molecular farming: systems and products. Plant Cell Reports, 22:711–20. Kamenarova, K., Nabil, A., Kostadin, G.,and Atanas, A., 2005. Molecular farming in plants: An approach of agricultural biotechnology. Journal of Cell and Molecular Biology, 4: 77-86. Kant, A., Suchetha, Reddy., Shankraiah, M.M., Venkatesh, J.S., and Nagesh, C. 2011. Plant made pharmaceuticals (PMPs) - A protein factor: An overview. Newsletter, Pharmacology online, 1: 196209. Langridge, W.H.R. 2000. Edible vaccines. Scietific American, 283:4853. Raskin, 2002 . Plants and human health in the twenty-first century. Trends in Biotechnology, 20:522–531. Twyman, R.M., Stoger, E., Schillberg, S., Christou, P., and Fischer, R. 2003. Molecular farming in plants: host systems and expression technology. Trends in Biotechnology, 21:570-578. Twyman, R.M.2005. The transgenic plant market in the pharmaceutical industry. Expert Opinion on Emerging Drugs, 10:185–218. Received on 14-05-2014
Accepted on 24-05-2014
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Trends in Biosciences 7 (13), 2014
Genetic Variability in Sugarcane (Saccharum spp. Complex) P.P. PATIL, D.U. PATEL1, S.C. MALI1 AND V.A. LODAM Department of Genetics and Plant Breeding,N.M. College of Agriculture Navsari Agricultural University, Navsari-396 450 (Gujarat) 1 Main Sugarcane Research Station, NAU, Navsari 396 450 email:
[email protected]
ABSTRACT Thirty sugarcane genotypes were evaluated for cane yield, sugar yield and related morphological characters. Genotypes significantly differed for all the 16 characters indicating sufficient variability in the experimental material. The characters germination % at 45 days followed by tillers at 120 days and shoots at 240 days showed high GCV and PCV. High estimates of heritability along with high genetic advance (% of mean) were observed for tillers at 120 days (000/ha) followed by germination % at 45 days, internodes/stalk at 360 days, shoots at 240 days (000/ha) and stalk height at 360 days. Therefore, selection will be effective for these characters. Key words
Sugarcane, genetic variability, heritability, genetic advance
Sugarcane is an important commercial crop cultivated in tropical and sub tropical region. The progress in development of more productive sugarcane has been slowed down in recent past in India and major sugarcane growing countries. Improvement in yield in crops largely depends on the extent of genetic variability, proportion of heritability components of variability is a basic requirement for initiating any selection programme. The information available on the inheritance is scanty and adoption of mendelian principles is difficult. Thus present study was carried out in sugarcane to assess the variability for yield components, cane and sugar yield and heritability and genetic advance.
MATERIALS AND METHODS The experimental material consisted of 30 genotypes of sugarcane obtained from the germplasm maintained at Main Sugarcane Research Station, Navsari Agricultural University, Navsari and grown during 2010-2011 in randomized block design (RBD) with three replications. The gross plot size for each genotype was consisted of five rows each of six meter length with row to row spacing of 90 cm and the net plot was consisted of middle 3 rows each of 5 meter length with row to row spacing of 90 cm (excluding 0.5 m ring line at both ends of the plot). The two budded setts of sugarcane were planted in rows keeping 12 buds per meter row length. The crop was raised under irrigated conditions following all the recommended package of practices and fertilizer application (250 kg N + 125 kg
P2O 5 + 125 kg K 2O per ha.). The observations were recorded on yield components and quality traits viz., germination % at 45 days, tillers at 120 days (000/ha), shoots at 240 days (000/ha), stalk height at 360 days (cm), stalk diameter at 360 days (cm), internodes/stalk at 360 days, stalk weight at 360 days (kg), number of millable canes/ha (NMC) at 360 days (000/ha), cane yield at harvest (t/ha), juice brix % at 360 days, sucrose % juice at 360 days, juice purity % at 360 days, CCS % at 360 days, Fibre % cane at 360 days, pol % cane at 360 days and sugar yield at 360 days (t/ha). The analysis of variance was carried out following the procedure of Panse and Sukhatme, 1978. The genotypic and phenotypic variations, phenotypic and genotypic coefficient of variability, heritability in broad sense and genetic advance as per cent of mean were determined as per the standard procedure.
RESULTS AND DISCUSSION The analysis of variance indicating the genotypic differences were highly significant for all the characters indicating considerable amount of genetic variability among the genotypes tested in the present study (Table 1). This indicated an ample scope of exploitation of the characters under study. Similar results were reported by Kumar, et al., 2004, Patel, et al., 2006, Rahman, et al., 2008, Rahman and Bhuiyan, 2009, Anbanandan and Saravanan, 2010 and Tyagi, et al., 2011. The wide range of phenotypic variation was observed for stalk height at 360 days (225.70 to 318.30) followed by shoots at 240 days (118.47 to 216.22) and tillers at 120 days (141.57 to 240.03) (Table 2). The highest values of genotypic and phenotypic variance were found in tillers at 120 days (683.17 and 800.07) followed by stalk height at 360 days (442.21 and 654.437), shoots at 240 days (383.88 and 539.817) and germination % at 45 days (80.29 and 101.715). Similar findings were reported by Hapase and Hapase, 1990 and Verma, et al., 1999 and Khan, et al., 1991 found variability of higher magnitude for number of shoots per plot, NMC and cane yield. Also Rahman, et al., 2008, Kumar, et al., 2010, Anbanandan and Saravanan, 2010 and Pawar, et al., 2011 found similar results for most of the cane yield and its contributing traits. The moderate to low values of ó2g and ó2p were found
PATIL, et al., Genetic Variability in Sugarcane (Saccharum spp. Complex)
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Table 1. Analysis of variance showing mean square for sixteen characters in Sugarcane Source
d.f.
Germination % at 45 days
Tillers at 120 days (000/ha)
Shoots at 240 days (000/ha)
Stalk height (cm) at 360 days
Stalk diameter (cm) at 360 days
Internodes /stalk at 360 days
Replication
2
31.185
10.952
34.865
3.344
0.005
0.377
Genotype
29
262.304**
2166.431**
1307.593**
1538.875**
0.0739**
17.978**
Error
58
21.420
116.898
155.928
212.218
0.0155
2.035
S.Em +
2.672
6.242
7.209
8.410
0.072
0.8237
C.V. %
7.64
5.60
7.98
5.43
4.96
5.85 Sucrose % juice at 360 days
Source
Replication Genotype Error S.Em + C.V. % Source Replication Genotype Error S.Em + C.V. %
d.f.
2 29 58
d.f. 2 29 58
Stalk weight (kg) at 360 days
NMC at harvest (000/ha)
Cane yield (t/ha)
0.011 0.024** 0.004 0.0384 5.60
10.267 231.075** 66.321 4.701 6.67
57.524 274.075** 71.595 4.885 6.57
Juice brix % at 360 days 0.601 2.945** 0.663 0.470 3.85
Juice purity % at 360 days 2.722 3.966** 1.516 0.710 1.32
CCS %
Fibre % cane
Pol % cane
CCS (t/ha)
0.049 1.120** 0.342 0.337 4.21
0.105 0.970** 0.208 0.263 3.09
0.122 1.246** 0.376 0.354 4.15
1.064 8.069** 1.909 0.797 7.74
for cane yield (67.493 and 139.089), NMC at harvest (54.91 and 121.240), internodes per stalk at 360 days (5.314 and 7.350), CCS t/ha (2.054 and 3.963) and rest of the traits. Such type of results were reported by Doule and Balasundaram, 1997, Kadian, et al., 1997, Verma, et al., 1999 and Rahman and Bhuiyan, 2009. A perusal of the estimates of environmental component of variance in relation to their genotypic counterpart revealed that the estimates of ó2g were higher than ó2e for most of the characters. The higher magnitude of genotypic variance suggested little influence of environments in the expression of genetic variability. The estimates of genotypic and phenotypic coefficient of variation were high for germination % at 45 days (14.80 and 16.66) followed by tillers at 120 days (13.54 and 14.66) and shoots at 240 days (12.52 and 14.85). The values of genotypic and phenotypic coefficient of variation were low for juice purity % (0.97 and 1.65). Coefficient of variations for rest of the characters was moderate to low. The genotypic coefficient of variation for majority of traits were quite close to the estimates of phenotypic coefficient of variation. This indicated that these traits were least affected by the environment. Hapase and Hapase, 1990 obtained highest GCV and PCV for germination per cent, total and millable height of cane, moderate for number of internodes and low for brix per cent and purity per cent. Also similar
0.169 2.250** 0.619 0.454 4.01
results were found by Doule and Balasundaram, 1997 and Hapase and Repale, 2004. In present study, higher estimate of heritability was observed for tillers at 120 days (85.39 %) followed by germination % at 45 days (78.94 %), internodes/stalk at 360 days (72.31%), shoots at 240 days (71.11%) and stalk height at 360 days (67.57%). Low heritability was observed for juice purity % (35.02%) followed by CCS% (43.11%) and pol % cane (43.55%). Heritability for rest of the characters was observed moderate to high. Genetic advance as per cent of mean was as high as (27.09) for germination % at 45 days followed by (25.78) for tillers at 120 days, shoots at 240 days (21.75), internodes per stalk (16.56) at 360 days, stalk height at 360 days (13.28), CCS (t/ha) (11.91) and stalk weight at 360 days (10.95). For rest of the characters it was moderate to low. Shift in the gene frequency towards superior side under selection pressure is termed as genetic advance and is generally expressed as percentage of mean (genetic gain). Johnson, et al., 1955 found it more useful to estimate heritability values together with genetic advance in predicting the ultimate choice of the best genotypes by selection. However, high genetic gain along with high heritability showed most effective condition for selection.
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Table 2. General mean, phenotypic range, variance components, genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), heritability and genetic advance (% of mean ) of 30 sugarcane genotypes Characters/ Parameters
Germination % at 45 days
Genotypic General Range Mean (Phenotypic) variance
Phenotypic Environment variance al variance
GCV (%)
PCV (%)
H2 (b) G.A. as (%) % of mean
60.53
46.07-79.96
80.29
101.715
21.420
14.80
16.66
78.94
27.09
192.96
141.57240.03
683.17
800.076
116.898
13.54
14.66
85.39
25.78
156.47
118.47216.22
383.88
539.817
155.929
12.52
14.85
71.11
21.75
268.06
225.70318.30
442.21
654.437
212.218
7.84
9.54
67.57
13.28
Stalk diameter (cm) at 360 days
2.51
2.22-2.85
0.019
0.035
0.016
5.54
7.44
55.52
8.51
Internodes /stalk at 360 days
24.38
19.45-29.44
5.314
7.350
2.035
9.45
11.12
72.31
16.56
Stalk weight (kg) at 360 days
1.18
1.03-1.38
0.007
0.011
0.004
6.86
8.86
59.98
10.95
121.91
107.13139.19
54.91
121.240
66.322
6.07
9.03
45.30
8.43
128.71
103.25142.53
67.493
139.089
71.595
6.38
9.16
48.53
9.16
Tillers at 120 days (000/ha) Shoots at 240 days (000/ha) Stalk height (cm) at 360 days
NMC at harvest (000/ha) Cane yield (t/ha) Juice brix % at 360 days
21.15
19.19-22.85
0.763
1.424
0.663
4.12
5.64
53.42
6.21
Sucrose % juice at 360 days
19.61
17.83-21.11
0.544
1.163
0.619
3.76
5.50
46.74
5.30
Juice purity % at 360 days
92.71
89.47-94.73
0.817
2.333
1.516
0.97
1.65
35.02
1.19
CCS %
13.86
12.51-14.90
0.259
0.601
0.342
3.67
5.59
43.11
4.97
Fibre % cane
14.77
13.05-15.66
0.254
0.462
0.209
3.40
4.60
54.85
5.20
Pol % cane
14.75
13.63-15.94
0.290
0.667
0.376
3.65
5.54
43.55
4.97
CCS (t/ha)
17.85
14.05-21.24
2.054
3.963
1.909
8.02
11.15
51.82
11.91
High heritability coupled with high genetic advance was observed for the characters tillers at 120 days (000/ ha) followed by germination % at 45 days, internodes/stalk at 360 days, shoots at 240 days (000/ha) and stalk height at 360 days. This indicated that these characters are governed by additive gene action and selection for these characters will be useful in choice of best genotype. Hapse and Repale, 2004 reported high heritability coupled with high genetic advance for number of tillers at 120 days, germination percentage, total height and millable height of cane. Kumar, et al., 2004 observed same trend for number of tillers at 240 days, cane height and number of internodes/ stalk. Similar results were obtained by Rahman, et al., 2008, Rahman and Bhuiyan, 2009, Kumar, et al., 2010 and Pawar, et al., 2011 for the traits like stalk height and other yield contributing characters. From the above results and discussion it can be concluded that high heritability coupled with high genetic advance was observed for the characters tillers at 120 days (000/ha) followed by germination % at 45 days, internodes/ stalk at 360 days, shoots at 240 days (000/ha) and stalk height at 360 days. This indicated that these characters are
governed by additive gene action and selection for these characters will be useful in choice of best genotype.
ACKNOWLEDGEMENTS The authors wish to thank, Honorable Vice Chancellor, Directorate of Research & Dean PG Studies, Registrar, Main Sugarcane Research Station, Library and Agricultural Statistics Departments of N.M. College of Agriculture, N.A.U., Navsari 396 450 for providing necessary facilities during the course of studies and investigation.
LITERATURE CITED Anbanandan, V. and Saravanan, K. 2010. Genetic variability in interspecific and intergeneric progenies in sugarcane. Plant Archives, 10 (2): 627-632. Doule, R.B. and Balasundaram, N. 1997. Variability, heritability and genetic advance for yield and quality attributes in sugarcane. Indian Sugar, 47 (7): 499-502. Hapase, R.S. and Hapase, D.G. 1990. Genetic variability studies in late maturing sugarcane varieties. Bhartiya Sugar, 15 (10): 13-16. Hapase, R. S. and Repale J.M. 2004. Variability studies of some quantitative and qualitative characters in sugarcane varieties. Indian Sugar, LIV (3): 205-210.
PATIL, et al., Genetic Variability in Sugarcane (Saccharum spp. Complex) Johnson, H.W., Robinson, H.F. and Comstock, R.E. 1955. Genotypic and phenotypic correlation in soyabeans and their implication in selection. Agron. J., 47: 477-483. Kadian, S.P., Chander Kishor and Sabharval, P.S. 1997. Genetic variability and heritability in sugarcane. Indian Sugar, 46 (12): 937-975. Khan, M.I., Chattergee, A. and Bargale, M. 1991. Genetic variability and character association analysis for yield and its attributes in sugarcane. Co-operative Sugar, 23 (1): 29-33. Kumar, K., Singh, P.K. and Singh, J.R.P. 2004. Genetic variability and character association in sub tropical clones of sugarcane (Saccharum complex hybrid ). Indian Sugar , LIV (3): 189-198. Kumar, N., Singh, T. and Kumar, V. 2010. A study on genetic parameters, repeatability and predictability in plant and ratoon crops of sugarcane (Saccharum officinarum L.). Indian Sugar, LX (2): 23-27. Panse, V.G. and Sukhatme, P.V. 1978. “Statistical methods for Agricultural Workers”. ICAR, New Delhi. Patel, K.C., Patel, A.I., Mali, S.C., Patel, D.U. and Vashi R.D. 2006.
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Variability, correlation and path analysis in sugarcane (Saccharum spp.). Crop Res., 32 (2):213-218. Pawar, D.S., Hapse, R.S., Repale, J.M. and Deshmukh, R.B. 2011. Studies on genetic variability, heritability and genetic advance in different sugarcane genotypes. Proc. of 10th Joint Conv. of STAI and DSTA. pp. 206-215. Rahman, M.M. and Bhuiyan, M.S.R. 2009. Variability, heritability and genetic advanced for cane yield and its components in some indigenous and exotic promising clones of sugarcane (Saccharum officinarum L.). Indian Sugar, LIX (2): 35-41. Rahman, M.M., Podder, B.P., Rahim, M.A. and Karim, K.M.R. 2008. Estimates of genotypic and phenotypic variation, heritability and genetic advance under some characters of sugarcane clones. Indian Sugar, LVIII (4): 31-36. Tyagi, V.K., Sharma, S. and Bhardwaj, S.B. 2011. A study on the nature and magnitude of variations in different traits in sugarcane. Electronic J. Plant Breeding, 2(3):334- 341. Verma, P.S., Pal, S, and Karma, N.K. 1999. Genetic variability and correlation studies in sugarcane. Indian Sugar, 49 (2): 125-128. Received on 02-05-2014
Accepted on 10-05-2014
1392 in Biosciences 7(13): 1392-1400, 2014 Trends
Trends in Biosciences 7 (13), 2014
Plant Diversity along Yamuna River in Delhi: A Status ANAND KUMAR MISHRA, SHAKOOR AHMED MIR AND MAHESHWAR PRASAD SHARMA Department of Botany, Faculty of Science, Jamia Hamdard, New Delhi-62 email :
[email protected]
ABSTRACT The Yamuna River is one of the most sacred rivers of Indogangetic plains in India. The Delhi segment of Holy River extends for over 48 km stretch from Palla village to Okhla barrage. In the present work, an extensive survey of various wild growing plants along Yamuna bank was carried out with a view to record the species which are medicinally important. Data on their past and present distribution, relative abundance, rarity, habit and habitat was recorded. It has been observed that many plants like Boerhavia chinensis L., Capsella bursapastoris (L.) Medik., Heliotropium indicum L., Hemigraphis hirta T. Anderson etc are struggling for their survival due to pollution and shrinkage in species specific habitat. Key words
Yamuna River, Delhi, Alien species, Pollution
Flora of Delhi was explored by J K Maheshwari in 1950 and published in 1963. In 1950s, when Maheshwari explored the flora of Delhi, two third of total area was rural. The situation has completely changed now. Last six decades, Delhi has experienced unprecedented deterioration of environment and loss of biodiversity. Demography and land use pattern of Delhi has changed completely since the flora of Delhi was published 60 years ago. Yamuna plains of Delhi are not escape from various developmental actives along their sides such as Delhi metro, various flyover and agricultural actives. There is left little of nature and landscape that has not been altered by the action and activities of human beings in Delhi. Many species are facing extremely unfavorable situation for their survival in such changed conditions. A number of plant species reported in the flora of Delhi might have already vanished or may at the verge of local extinction due to deterioration of various habitats. Yamuna is life line for flora of Delhi. It played very important role in floristic diversity of Delhi when Maheshwari explored the flora of Delhi. The river Yamuna, a major tributary of river Ganges, originates from the Yamunotri glacier near Banderpoonch peaks in the Mussourie range of the lower Himalayas at an elevation of about 6387 meters above mean sea level in district Uttarkashi (Uttranchal) (Dillion, et al., 2013). The Yamuna River enters Delhi at village Palla and leaves at Okhla barrage, travelling a total distance of 48 km within Delhi. The twenty-two kilometers stretch extending from
Wazirabad to Okhla is perhaps the most threatened riverine ecosystem in the world because of the immense anthropogenic pressures on this riparian habitat. Based on the water quality, the entire Yamuna river stretches may be segregated into five distinguished stretches i.e. Himalayan stretch, upper stretch, Delhi stretch, mixed stretch and diluted stretch (Water Quality Status of Yamuna River, 2006). During post Master Plan period of Delhi (1962 onwards), several developments have been taken place river Yamuna, which include Power Houses, Sanitary Landfills sites, STPs, fly ash ponds, Construction of barrages/ bridges and roads, together with encroachments by Jhuggi Jhomri (JJ) clusters, unauthorized colonies, religious structures etc. Although river Yamuna’s is a major source of water for Delhi, with indiscriminate and unplanned developments this has virtually become a drain and seasonal nalla. Various proposals had been framed earlier, but due to lack of integrated approach, i.e. non-integration of projects/ plans of water supply, sewerage (Delhi Jal Board), Slum and Jhuggi-Jhompri (JJ) Rehabilitation, Flood Control and Drainage (Irrigation and Flood Deptt.), pollution abatement, etc. there had been no discernible improvement in the sustainability of the bed. (Jain, 2009). Due to high density population growth and rapid industrialization today Yamuna is one of the most polluted rivers in India, especially around New Delhi, the capital of India, which dumps about 58% of its treated or partially treated waste into the river (Dhillon, et al., 2013).
Study Area Delhi is located in northern India between the latitudes of 28°-24’-17" and 28°-53’-00" North and longitudes of 76°-50’-24" and 77°-20’-37" East. Delhi shares borders with the States of Uttar Pradesh and Haryana. Delhi has an area of 1,483 sq. Kms. Its maximum length is 51.90 Km. and greatest width is 48.48 Km. (Economic Survey of Delhi, 2001-02). Physically the natural capital territory of Delhi can be divided into three segments, the Ridge, the Yamuna flood plains and Plains. The Yamuna flood plains are low lying and sandy areas. This area is also known as Khadar and made of recurrent floods (Alam, et al. 2009). The River Yamuna enters National Capital Territory of Delhi near village Palla (North east Delhi) and leaves Jaitpur village (South Delhi) (Naithani, et al., 2007). The total length of
MISHRA, et al., Plant Diversity along Yamuna River in Delhi: A Status
Yamuna river from Palla Village to Okhla barrage is 48 Km. and width ranges from 1.5- 3.0 Km. Delhi contributes 0.4 percentage of total catchment area of Yamuna river. (Water Quality Status of Yamuna River, 2006) The river floodplain occupies about 97 sq. km in Delhi region. Flow of River Yamuna within Delhi is regulated at three barrages, viz., Wazirabad, ITO and Okhla Barrage. Delhi’s share of Yamuna River’s total water is 4.6%. The 22 km stretch of Yamuna in Delhi from Wazirabad Barrage to Okhla Barrage polluted stretch within City. Twenty two drains are falling into Yamuna in Delhi in which 18 major drains fall directly into river and 4 through Agra and Gurgaon canal. There are three Power plants situated near Yamuna Bank Rajghat, Indraspatha ,Bardapur respectively (Fig. 1)
MATERIALS AND METHODS A survey was undertaken in Delhi region during 201012 to collect species of wild growing plants. During the field survery, observation were made. Plants Collected during survey were identified with the help of Flora of Delhi (Maheshwari, 1963) and National Institute of Science Communication and Information Resources (NISCAIR). Updated nomenclature of plant species were checked (www.theplantlist.org). Standard procedures (Singh and Subramanium, 2008) were followed for preparation of herbarium and the voucher specimens are housed in the
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Department of Botany, Jamia Hamdard New Delhi. During this exercise few species were reported to be new records for the flora of Delhi. The collected data of plants incorporated in table with their plant name, families, phenology, status and location (Table 1).
RESULT AND DISCUSSION The survey resulted in collection of 405 species from Delhi of which 152 species collected from Yamuna bank. The number of plant species found on Yamuna basin belongs to 56 families. Among them 95 are herbs, 25 are shrubs, 20 trees and 12 climbers (Fig. 2). The most commonly dominant families were Asteraceae(12 Sp.), Poaceae (9 Sp.), Fabaceae and Amranthaceae (8Sp. each ) Convolvulaceae and Moraceae (6 Sp.), Euphorbiaceae, Malvaceae and Solanaceae (5 Sp. each ), Menispermaceae, Meliaceae and Rhamaceae (2 Sp. each) (Fig. 3). Out of 152 plant species, 69 species (45.39%) are common; 53 species (34.86%) occasional; and 30 species (19.73 %) are rare (Fig. 4) There are some common species reported along the the Yamuna bank Viz., Alternanthera paronychioides A. St-Hil., Alternanthera philoxeroides (Mart.) Griseb., Alternanthera sessilis (L.) R. Br. ex DC., Cannabis sativa L., Chrysopogon zizanioides (L.) Roberty, Coronopus
Fig. 1. Map of Study Area
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Fig. 2. Vegetation along Yamuna River
Fig. 4. Status of plant species along Yamuna River
didymus L., Croton bonplandianum Baill., Cyperus rotundus L., Dactyloctenium aegyptium (L.) Willd., Desmostachya bipinnata (L.) Stapf., Dysphania ambrosioides (L.) Mosyakin & Clemants,, Eichhornia crassipes (Mart.) Solms, Eucalyptus tereticornis Sm., Ficus religiosa L., Fimbristylis dichotoma (L.) Vahl, Gnaphalium indicum L., Heliotropium curassavium var. obovatum DC., Ipomoea obscura (L.) Ker Gawl, Mazus japonicus (Thunb.) Kuntze, Melia azedarach L., Oxalis corniculata L., Panicum antidotale Retz., Phyla nodiflora (L.) Greene Parthenium hysterophorus L., Polygonum plebeium R. Br. Ranunculus sceleratus L., Rumex dentatus L., Saccharum bengalense Retz., Saccharum spontaneum L., Sesbania sesban (L.) Merr., Solanum americanum Mill., Sonchus oleraceus L., Tamarix dioica Roxb., Trianthema portulacastrum L., Typha domingensis Pers., Verbascum chinense (L.) Santapau,,Veronica anagallis-aquatica L. along with rare species Alhagi marorum Medik., Boerhavia chinensis (L.) Rottb. Capsella bursa-pastoris (L.) Medik, Hemigraphis hirta T. Anderson, Monocharia hastate (L).Solms, and
Potentilla supina L. is threathern species. There are some new plant species reported from Yamuna bank such as Chromolaena odorata L., Heliotropium indicum L. Pistia stratiotes L. Pogostemon benghalensis (Burm. f.) O. Kuntze Rev. Gen. et Sp., Saussurea heteromalla (D.Don) Hand.Mazz.
Fig. 3. Dominant Families of along Yamuna River
Maheshwari reported 52 species along Yamuna river side only, out of 52, 50 species are common; one species are abundant and rare in both categories respectively. We collect 152 species totally along Yamuna plains, 35 species only on Yamuna river bank, 19 species are common; 14 species occasionally; one species is rare as well as one species is threathern which were reported by Maheshwari. Many species are come from agricultural practices, planted species, escape species from other habitats and alien species. During when Maheshwari explored flora of Delhi along Yamuna river, There were some commonly found species along the Yamuna river bank Viz., Hemarthria compressa (L.f.) R.Br., Hydrilla verticellata (L.f.) Royle. Juncus bufonius L., Pentanema indicum (L.) Ling. Pentanema vestita (Wall. ex DC.) Ling, Potamogeton nodosus Poir., Trapa natans var. bispinosa (Roxb.) Makino, Utricularia aurea Lour. Utricularia seellaris var. inflexa (Forssk.) C. B. clarke Lour., Vallisneria spiralis L., Zannichellia palustris L. but our great efforts we could not found these species in and along Yamuna. Beside this, floristic diversity in and along Yamuna river is badly effected by urbanization, destruction of habitats, invasive species and water pollution. The population of Delhi had been 1.25 million in 1951 which has been increased 2o.oo million in 2011along with growth of industries from 15,000 (1951) to 1.30 lakh (2001). The no. of vehicles had been 18.0 lakh in (1980) which has been increased 57.0 lakh (2007) as well as the road network of Delhi 32,000 Km2 (1980) to 14,000 km2 in 1980 (State environment report of Delhi, 2010). Due to
MISHRA, et al., Plant Diversity along Yamuna River in Delhi: A Status
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Table 1. List of Medicinal Plants of Along Yamuna River Status
S. No.
Plant name
Family
1
Abutlion indicum (L.) Sweet.
Malvaceae
C
C
Aug. - Sept. Kilokri
2
Acacia nilotica var. indica (Benth.) A .F.Hill
Mimosaceae
C
C
Aug. - Oct.
Usmanpur, Yamuna Vihar
3
Achyranthes aspera L.
Amaranthaceae
C
C
Aug. - Sept.
Nangli Razapur, Bhalopur Khadar
Past Present
Flowering
Location
4
Ageratum conyzoides (L.) L.
Asteraceae
C
O
Aug. - Sept. Wazirabad,
5
Ailanthus excelsa Roxb.
Simaroubaceae
C
C
Jan.- Mar.
6
Alhagi marorum Medik.
Fabaceae
C
R
Mar. - Apr. Palla
7
Alocasia macrorrhiza (L.) G. Don
Araceae
-
R
-
Sonia vihar
8
Alternanthera paronychioides A.St-Hil.
Amaranthaceae
-
C
Aug .- Nov.
Yaumna metro bridge, Palla, Okhla barrage
9
Alternanthera philoxeroides (Mart.) Griseb.
Amaranthaceae
-
C
Nov.- Jan.
Okhla barrage, Agra canal Geeta colony bridge
Wazirabad, ISBT bridge
10
Alternanthera sessilis (L.) R. Br. ex DC.
Amaranthaceae
C
C
Aug. - Oct.
11
Amaranthus viridis L.
Amaranthaceae
C
C
Whole year Okhla barrage, Bhalopur Khadar
12
Ammannia baccifera L.
Lythraceae
C
O
Sept. - Oct. Rajghat
13
Ammannia multiflora Roxb.
Lythraceae
-
R
Oct. - Nov.
14
Ammi majus L.
Lamiaceae
C
O
Aug.- Sept. Bhalopur Khadar
15
Argemone mexicana L.
Papaveraceae
C
C
Oct.-Nov.
Bhalopur Khadar
16
Azadirachta indica A. Juss.
Meliaceae
C
O
Apr. - May
Yamuna Vihar, Old Delhi railway bridge
17
Bacopa monnieri (L.) Wettst.
Plantaginaceae
C
O
Feb. - Mar.
Khirzabad
18
Basella ruba L.
Basellaceae
C
O
Aug. - Oct.
New Delhi railway bridge
19
Boerhavia diffusa L.
Nyctaginaceae
A
O
Aug. - Sept. Kyabar by pass
20
Boerhavia chinensis (L.) Rottb.
Nyctaginaceae
C
R
Sept. - Oct. Palla
21
Bombax ceiba L.
Malvaceae
-
O
Mar. - Apr. Rajghat
Palla
22
Calendula officinalis L.
Asteraceae
-
O
Dec. - Jan.
23
Calotropsis procera (Aiton) Dryand.
Apocynaceae
C
O
Sept. - Nov. Samash Pur Jagir
24
Cannabis sativa L.
Cannabaceae
C
C
25
Capsella bursa-pastoris (L.) Medik
Brassicaceae
-
R
Jan. - Feb.
Okhla barrage
26
Carissa carandas L.
Apocynaceae
C
R
Feb. - Mar.
Old Delhi railway bridge
27
Cassia fistula L.
Fabaceae
C
O
Apr. - Aug. New Delhi railway bridge
28
Catharanthus roseus (L.) G.Don
Apocynaceae
C
O
Whole year Delhi Transco limited
29
Chenopodium album L.
Amaranthaceae
C
O
Sept. - Dec. Nangli Razapur, Sonia Vihar
30
Chromolaena odorata L. R. M. King & H.Rob.
Asteraceae
-
R
Sept. - Oct. Wazirabad barrage
31
Chrysopogon zizanioides (L.) Roberty
Poaceae
-
C
Aug. - Sept. Samras Pur Jagir, Sonia vihar
32
Cichorium intybus L.
Asteraceae
C
O
Jan. - Feb.
Old Delhi railway bridge
33
Cirsium arvense (L.) Scop.
Asteraceae
C
O
Feb. - Mar.
Yamuna bus depot.
34
Cleome viscosa L.
Capparaceae
C
O
Aug. - Nov. Wazirabad barrage, Kilokri
Bhalopur Khadar New Delhi railway bridge
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Trends in Biosciences 7 (13), 2014
Status
S. No.
Plant name
Family
35
Cocculus hirsutus (L.) W.Theob.
Menispermaceae
C
O
Aug. - Oct. New Delhi railway bridge
36
Cocinia indica (L.)Voigt.
Cucurbitaceae
C
C
Aug. - Oct. Nangli Razapur
37
Colocasia esculenta (L.) Schott.
Araceae
C
R
-
38
Commelina benghalensis L.
Commelinaceae
C
O
Aug. - Sept. Okhla barrage
39
Convolvulus pluricularis Choisy
Convolvulaceae
C
R
Mar. - May Chilla Khadar
40
Coronopus didymus L.
Brassicaceae
C
C
Jan. - Feb.
Wazirabad barrage, Chilla Khadar
41
Cressa critica L.
Convolvulaceae
C
O
May - Jun.
Wazirabad, Madanpur Khadar
42
Croton bonplandianum Baill.
Euphorbiaceae
A
C
Aug. - Oct. Kilokri, Palla, Okhla barrage
43
Cucumis sativus L.
Cucubitaceae
C
C
Oct. - Dec.
Pultoon Pull
44
Cuscuta reflexa Roxb.
Convolvulaceae
C
R
Oct. - Dec.
Geeta colony bridge
45
Cyanthillium cinereum (L.) H.Rob.
Asteraceae
C
C
Aug. - Nov. Wazirabad, Madanpur Khadar
46
Cynodon dactylon (L.) Pers.
Poaceae
C
C
Whole year New Delhi railway bridge
47
Cyperus difformis L.
Cyperaceae
C
O
Sept. - Oct. Bhalopur Khadar
48
Cyperus rotundus L.
Cyperaceae
C
C
Aug. - Oct. Rajghat, Bhalopur Khadar
49
Dactyloctenium aegyptium (L.) Willd.
Poaceae
C
C
Aug. - Oct.
50
Dalbergia sissoo DC.
Fabaceae
C
O
Feb. - Mar. New Delhi railway bridge
51
Datura innoxia Mill.
Solanaceae
C
C
Sept. - Oct. Bhalopur Khadar
52
Desmostachya bipinnata (L.) Stapf.
Poaceae
C
C
Oct. - Nov. Indrapras, Wazirabad
53
Dicliptera paniculata (Forssk.) I. Darbysh
Acanthaceae
C
C
Aug. - Sept. Okhla barrage, Chilla Khadar
54
Dipteracanthus prostratus (Poir.) Nee
Acanthaceae
A
O
Aug. - Sept. Okhla barrage
55
Dysphania ambrosioides (L.) Mosyakin & Amaranthaceae Clemants
A
C
Aug. - Sept.
56
Eclipta prostrata (L.) L.
Euphorbiaceae
C
O
Aug. - Oct. Okhla barrage
57
Ehretia leavis Roxb.
Boraginaceae
C
R
Jun. - Sept. New Delhi railway bridge
58
Eichhornia crassipes (Mart.) Solms
Pontederiaceae
C
C
Whole year
Khyber Pass, Magazine road, Okhla barrage, Khirzabad
59
Eleusine indica (L.) Gaertn.
Poaceae
C
O
Aug. - Oct.
Palla, Wazirabad, Samash Pur Jagir,
60
Eucalyptus tereticornis Sm.
Myrtaceae
C
C
Sept. - Oct. New Delhi railway bridge
61
Euphorbia hirta L.
Euphorbiaceae
C
R
Aug. – Oct. Rajghat
62
Ficus benghalensis L.
Moraceae
C
O
Whole year Yamuna Vihar, Rajght
63
Ficus palmata Forrsk.
Moraceae
C
R
Jul. - Sept.
Past Present
Flowering
Location
Kyabar by pass
Wazirabad barrage, Samash Pur Jagir
Chilla Khadar, Nangli Razapur, Khyber Pass
Khyber Pass
64
Ficus racemosa L.
Moraceae
C
O
Delhi Transco Limited, Aug. - Dec. New Delhi railway bridge
65
Ficus religiosa L.
Moraceae
C
C
Whole year ISBT Bridge, Usmanpur
66
Ficus virens Aiton
Moraceae
C
O
Sept. - Jan. Usmanpur, Sonia Vihar
67
Fimbristylis dichotoma (L.) Vahl
Cyperaceae
C
C
Oct. - Nov. Bhalopur Khadar
68
Gnaphalium indicum L.
Asteraceae
C
C
Feb. - Mar.
Old Delhi railway bridge, Geeta colony Bridge
MISHRA, et al., Plant Diversity along Yamuna River in Delhi: A Status
S. Plant name No.
Family
Status Past Present
1397
Flowering
Location Sonia Vihar, Wazirabad, Chilla Khadar, Jahangirpuri,
69
Heliotropium curassavium var. obovatum Boraginaceae DC.
-
C
Aug. - Dec.
70
Heliotropium indicum L.
Boraginaceae
-
C
Mar. - Apr. Shaheen bagh
71
Hemigraphis hirta T. Anderson
Acanthaceae
C
R
Oct. - Nov. Khyber Pass
72
Holoptelea integrifolia Planch
Ulamaceae
-
R
Feb. - Mar. New Delhi railway bridge
73
Ipomoea batatas (L.) Poir.
Convolvulaceae
C
C
Mar. - Apr. Rajghat
74
Ipomoea carnea Jacq.
Convolvulaceae
C
R
Sept. - Oct. Sweeper Colony Drain
75
Ipomoea obscura (L.) Ker Gawl
Convolvulaceae
C
C
Aug. - Oct. New Delhi railway bridge
76
Ipomoea aquatica Forssk.
Convolvulaceae
C
O
Sept.- Nov.
77
Jatropha curcas L.
Euphorbiaceae
C
O
Mar. - Apr. New Delhi railway bridge
78
Lantana × aculeata L.
Verbenaceae
C
C
Whole year Garhi Mandu forest
79
Launaea nudicaulis (L.) Hook.f.
Asteraceae
C
O
Jan. - Mar.
80
Lippia alba (Mill.) N.E.Br. ex Britton & P.Wilson
Verbenacae
-
R
Oct. - Nov. Palla
81
Ludwigia adscendens (L.) H.Hara
Onagraceae
C
C
Aug. - Oct. Rajghat
82
Ludwigia octovalvis (Jacq.) P.H.Raven
Onagraceae
-
O
Aug. - Oct. Khyber Pass Drain
83
Ludwigia perennis L.
Onagraceae
R
R
Aug. - Oct. Burari Chowk
84
Malvastrum coromandelianum (L.) Garcke Malvaceae
C
R
Aug. - Oct. Nizamuddin Bridge
85
Mangifera indica L.
Anacardiaceae
C
O
Apr. - May Usmanpur
86
Mazus japonicus (Thunb.) Kuntze
Scrophulariaceae
C
C
Jan. – Mar. Shubhpur , Wazirabad barrage
87
Melia azedarach L.
Meliaceae
C
C
Mar. - Apr. New Delhi railway bridge
88
Melilotus indicus (L.) All.
Fabaceae
C
O
Jan. - Mar.
89
Monocharia hastate (L).Solms
Pontederiaceae
-
R
Oct. - Nov. Gopal pur
90
Moringa oleifera Lam.
Moringaceae
C
C
Jan. - Mar.
91
Morus alba L.
Moraceae
C
C
Mar. - Apr. New Delhi railway bridge
92
Musa paradisiaca L.
Musaceae
C
O
Oct. - Nov.
93
Nerium indicum Mill.
Apocynaceae
C
O
Whole year Yamuna vihar
94
Oenanthe javanica (Blume) DC
Apiaceae
C
O
Feb. - Mar. Palla, Majnu ka Tila
95
Operculina turpethum (L.) Silva Manso
Convolvulaceae
C
R
Aug. - Oct. Palla
96
Oxalis corniculata L.
Oxalidaceae
C
C
Whole year Nizamuddin bridge
97
Oxystelma secamone K. Schum
Asclepiadaceae
C
R
Aug. - Sept.
98
Panicum antidotale Retz.
Poaceae
-
C
Aug. - Oct. New Delhi railway bridge
99
Parthenium hysterophorus L.
Asteraceae
-
C
Whole year Bhalopur Khadar, Rajghat
100 Pergularia daemia (Forsk.) Chiov.
Asclepiadaceae
A
O
Aug. - Oct. ITO Barrage, Rajghat
101 Persicaria glabra (Willd.) M.Gómez
Polygonaceae
C
C
Feb. - Mar. Khureji Khas Khadar
102 Phoenix sylvestris (L.) Roxb.
Arecaceae
-
O
Sept. - Oct. Old Delhi railway bridge
Wazirabad barrage, Sri nanaksar Pusha
Madanpur Khadar,
Bhalopur Khadar, Chilla Khadar Rajghat Bhalopur Khadar, Chilla Saroda Khadar
Wazirabad barrage, Yamuna Vihar
1398
S. No.
Trends in Biosciences 7 (13), 2014
Plant name
Family
Status Past Present
Flowering
Location
103 Phragmites australis (Cav.) Trin. ex Steud Poaceae
C
C
Wazirabad barrage, Okhla Oct. - Nov. barrage, New Delhi railway bridge
104 Phyla nodiflora (L.) Greene
Verbenaceae
C
C
Sept. - Nov.
105 Phyllanthus fraternus G. L. Webster
Phyllanthaceae
C
O
Sept. - Oct. Kudesia ghat, Oklla barrage
106 Physalis angulata L.
Solanaceae
C
C
Sept.- Oct.
107 Pistia stratiotes L.
Araceae
-
C
Mar. - Apr. Shaheen bagh, Okhla barrage
108 Pithocellobium dulci (Roxb.) Benth.
Fabaceae
-
C
Nov. - Jan.
109 Pogostemon benghalensis (Burm.f.) Kuntze Lamiaceae
-
R
Sept. - Oct. Palla
110 Polygonum plebeium R. Br.
Polygonaceae
C
C
Oct. - Nov.
111 Portulaca oleracea L.
Portulacaceae
C
C
Aug. - Oct. Rajghat
112 Potentilla supina L.
Rosaceae
C
Th.
Oct. - Nov. Bhalopur Khadar
113 Pterospermum acerifolium (L.) Willd.
Sterculiaceae
-
O
Aug. - Oct. Delhi Transco limited
114 Pulicaria crispa Sch. Bip.
Asteraceae
C
R
Nov. - Dec. Samras Pur Jagir
115 Ranunculus sceleratus L.
Ranunculaceae
C
C
Feb. - Mar.
Bhalopur Khadar, Chilla Saroda Khadar
116 Ricinus communis L.
Euphorbiaceae
C
C
Dec. - Mar.
Samras Pur Jagir, Khureji Khas, New Delhi railway bridge
117 Ruellia suffruticosa Roxb.
Acanthaceae
-
R
Aug. - Sept. Okhla barrage
118 Rumex dentatus L.
Amaranthaceae
-
C
Feb. - Mar. Sonia vihar, Madanpur Khadar
119 Saccharum bengalense Retz.
Poaceae
-
C
Wazirabad barrage, Kudesiaghat, Sept. - Oct. Okhla barrage, New Delhi railway bridge
120 Saccharum spontaneum L.
Poaceae
C
C
Sept. - Oct. Nangli Razapur, ITO barrage
Asteraceae
-
R
Sept. - Oct. Palla
122 Scoparia dulcis L.
Plantaginaceae
-
C
Oct. - Nov. ITO barrage, Getta colony bridge
123 Senna occidentalis (L.) Link.
Fabaceae
C
C
Apr. - Jun.
124 Sesamum indicum L.
Pedaliaceae
C
O
Aug. - Oct. New Delhi railway bridge
Fabaceae
C
O
Aug. - Oct. Samras Pur Jagir, Nangli Razapur
Fabaceae
C
C
Aug. - Oct. Rajghat
C
O
Aug.- Oct.
121
125
Saussurea heteromala (D.) Don. HandMazz.
Sesbania bispinosa (Jacq.) Fawcett & Rendle
126 Sesbania sesban (L.) Merr. 127
Sida alnifolia var. obovata (Wall. ex Mast.) Malvaceae S.Y. Hu
Wazirabad barrage, Chilla Khadar Khadar Samras Pur Jagir, Chilla Saroda Khadar New Delhi railway bridge, Garhi Mandu forest Geeta colony Bridge, Chilla Khadar, Chilla Saroda Khadar
Wazirabad barrage
Chilla Saroda Khadar, Madanpur Khadar
128 Solanum americanum Mill.
Solanaceae
C
C
Aug. - Oct. Chilla Saroda Khadar
129 Solanum virginianum L.
Solanaceae
C
O
Jan. - Feb.
Sonia vihar, Samras Pur Jagir
130 Sonchus asper (L.) Hill
Asteraceae
C
O
Jan. - Mar.
Bhalopur Khadar
131 Sonchus oleraceus L.
Asteraceae
C
C
Jan. - Mar.
Sonia vihar, Wazirabad barrage
MISHRA, et al., Plant Diversity along Yamuna River in Delhi: A Status
Status
1399
S. No.
Plant name
Family
132
Spilanthes acmella (L.) L.
Asteraceae
C
R
133
Suaeda fruticosa Forssk. ex J.F.Gmel
Chenopodiaceae
A
C
Nov. - Jan.
134
Syzygium cumini (L.) Skeels
Myrtaceae
C
O
Mar. - Apr. New Delhi railway bridge
135
Tagetes patula L.
Asteraceae
-
C
Nov. - Jan.
136
Tamarix dioica Roxb.
Tamaricaceae
C
C
Aug. - Oct. Kudesia ghat
137
Terminalia arjuna (Roxb. ex. DC) Wt. & Arn.
Combretaceae
C
O
Aug. - Oct. New Delhi railway bridge
138
Tinospora cordifolia (Willd.) Miers
Menispermaceae
-
O
Aug. - Oct. New Delhi railway bridge
139
Trianthema portulacastrum L.
Aizoaceae
C
C
Jul. - Sept.
140
Tribulus terrestris L.
Zygophyllaceae
C
C
Aug. - Oct. Samras Pur Jagir
141
Trichosanthes cucumerina L.
Cucurbitaceae
C
O
Aug. - Oct. New Delhi railway bridge
142
Tridex procumbens (L.) L.
Asteraceae
C
C
Aug. -Nov.
143
Triumfetta rhomboidea Jacq.
Tiliaceae
C
O
Aug. - Oct. New Delhi railway bridge
144
Typha domingensis Pers
Typhaceae
C
C
Aug. - Oct. Khirzabad, Okhla barrage
145
Urena lobata L.
Malvaceae
C
O
Feb. - Mar. New Delhi railway bridge
146
Verbascum thapsus L.
Scrophulariaceae
-
R
Aug. - Oct. Palla
147
Verbascum chinense (L.) Santapau
Scrophulariaceae
C
C
Aug. - Oct. Geeta colony bridge
148
Veronica anagallis-aquatica L.
Scrophulariaceae
C
C
Feb. - Apr.
Nangli Razapur, Bhalopur Khadar, Chilla Saroda Khadar
149
Withania somnifera (L.) Dunal
Solanaceae
C
O
Aug. -Oct.
Bhalopur Khadar
150
Xanthium chinense Mill.
Asteraceae
C
R
Aug.- Oct.
Bhalopur Khadar
151
Ziziphus jujuba Mill.
Rhamaceae
C
C
Sept. - Nov. Garhi Mandu forest
152
Ziziphus nummularia (Burm. f.) Wight & Arn
Rhamaceae
C
O
Aug.-Oct.
Past Present
Flowering Feb. - Mar.
Location Palla Jagatpur, Khirzabad Khirzabad
Rajghat, Samras Pur Jagir
New Delhi railway bridge
Chilla Saroda Khadar
Abbreviations : Status: A- Abundant, C- Common, O- Occasionally, R- Rare, Th.- Threatened
rapidly increasing population of Delhi coupled with increasing number of industries in capital of India, there is an immense pressure for conversion of these Yamuna flood plains for other various developmental activities. Floodplain of this holy river from Wazirabad to Okhla region has been largely converted into agricultural use resulting into the reduction of sub-soil moisture content. Pesticides are being used extensively for growing vegetables, which are great health hazards. Birds, fishes and plant species found in Yamuna wetlands are losing their natural habitats. The flow of water in Yamuna river in Delhi region is regulated by three major barrage Wazirabad, ITO and OKhla Barrage. Various construction activities is happened on Yamuna plains viz., Commonwealth Games Village 0.59 km2, Geeta Colony Bridge 0.49 km2, Swaminarayan Akshardham Mandir 0.40 km2, Yamuna Depot 0.36 km2, Delhi Transco Limited and DMRC S.P Depot, IT Park, Quarters 0.16 km2, Clover leafs bridge 0.14 km 2, Theme Park 0.10 km 2 respectively (Impact Assessment of Bridges
and Barrages on River Yamuna (Wazirabad to Okhla Section, 2009) Alien species is a major problem for flora of Delhi along Yamuna river bank, these species come intentionally or unintentionally outside from their natural habitats and become major threaten to native flora of Yamuna bank for food and space. These alien species have luxuriant growth and suppress the growth of other native species. This results in a loss of native floral diversity of the Delhi along the Yamuna river. These alien invasive plants are becoming a major concern, during past two decades such as Alternanthera paronychioides A.St. Hil., Alternanthera philoxeroides (Mart.) Griseb., Ageratum conyzoides L., Eichhornia crassipes (Mart.) Solms, Ipomoea cairica (L.) Sweet, Saccharum spontaneum L. Pers. Tridax procumbens (L.) L. Typha domingensis Pers. The entire stretch of Yamuna River from origin to confluence with Ganga is used for various human activities. The results of these activities are the generation of wastewater. The various
1400
Trends in Biosciences 7 (13), 2014
sources of pollution are Immersion of Idols, Dumping of Garbage and Dead bodies, Agricultural Pollution, Industrial Pollution. The domestic pollution is the major source of pollution in Yamuna river. About 85% of the total pollution in the river is caused by the domestic sources (Water Quality Status of Yamuna River, 2006). BOD level in the Yamuna from its origin till Palla has been observed generally between in the range of 1-3 mg/l with annual average not exceeding 3 mg/l. However, the BOD concentration rises beyond the desired standard i.e. from 3.0 mg/l to 6.0 mg/ l between Palla to Okhla barrage. There has been significant increase in the BOD level downstream Wazirabad barrage at Delhi immediately after the biggest drain, i.e. Najafgarh Drain joins river Yamuna. The other reasons for this may be due to human/ animal activities in the river e.g. washing, defecation etc. At Agra Canal, the Bio-chemical Oxygen Demand (BOD) ranged between 5 to 23 mg/l. with annual average of 13 mg/l. (Abhay, 2012) All these factors are responsible for depleting of native floristic diversity in and along Yamuna bank in Delhi region. Therefore efforts should be made to conserve these valuable plant species. Unsustainable use of land resources has serious negative effect on the flora of Yamuna plains. Proper care should be taken for their conservation by exsitu, in-situ conservation and multiplication of rare, threaten plants through modern techniques. It is also important to develop Biodiversity Park and declaration of reserve forests of along Yamuna river bank in Delhi which will help in the conservation of such species. Where rare plant exists, there is no recreational access permitted.
ACKNOWLEDGEMENT Authors are thankful to H. B. Singh, Chief Scientist, The National Institute of Science Communication and Information Resources for plant identification process. Authors are also thankful to UGC for financial assistance during the study.
LITERATURE CITED Abhay, R.K. 2011. Status of Water Quality and Pollution Aspect in Delhi, International Journal of Social Science Tomorrow, 5: 1-10 Alam, M., Rais, S. and Aslam, M. 2009. Hydro-chemical Survey of Groundwater of Delhi, India, e-journal of Chemistry, 6(2): 426436. CPCB, 2006. Water Quality Status of Yamuna River (1999 – 2005), Central Pollution Control Board, Ministry Of Environment & Forests, Assessment and Development of River Basin Series: ADSORBS/41/2006-07 Dhillon, M. K., George, M.P and Mishra, S. 2013. Water quality of River Yamuna – Delhi stretch, International Journal of Environmental Sciences, 3(3): 1416-1423 Economic Survey of Delhi. 2001-2002. Delhi Economic Survey, Govt. of India. Impact Assessment of Bridges and Barrages on River Yamuna, 2009. Environics Trust, New Delhi and Peace Institute Charitable Trust, Delhi pp.1-134. Jain, A.K. 2009. River Pollution (Regeneration and Cleaning) A. P. H. Publishing Corporation 4435-36/7, Ansari Road, Daryaganj New Delhi-11002, pp.1-314. Maheshwari J.K. 1962. The Flora of Delhi. C.S.I.R., New Delhi, pp.1-947. Maheshwari J.K. 1965. Illustrations of the Flora of Delhi. C.S.I.R., New Delhi, pp. 1- 282. Naithani, H.B. Negi, S.S. Pal, M. Chandra, S. and Khanduri,V.P. 2007. Vegetational survey and inventorisation species in the ridge forest of Delhi .Forest Research institute ICFRE New Forests, Dehradun, pp. 48 Singh, H.B., and Subramanium, B. 2008, Field manual on herbarium techniques, (NISCAIR, New Delhi) pp. 297. State of Environment Report for Delhi, 2010. Department of Environment and Forests Government of NCT of Delhi Level 6, C Wing Delhi Secretariat I P Estate New Delhi-110002. Water Quality Status Of Yamuna River, Central Pollution Control Board (Ministry of Environment & Forests, Govt. of India) Parivesh Bhawan, East Arjun Nagar, Delhi – 110032, pp.1-136. http://www.theplantlist.org Received on 06-05-2014
Accepted on 16-05-2014
Trends in Biosciences 7(13): 1401-1406, 2014
Influence of IPNM on the Economics of Cauliflower (Brassica oleracea var. botrytis L.) var. Snow Crown *ADESH KUMAR, RAMPARSAD, PREM SHANKER YADAVA, GULAB CHAND YADAV AND BANSH NARAYAN SINGH Department of Vegetable Science, Narendra Deva University of Agriculture and Technology, Narendra Nagar, Kumarganj, Faizabad (U.P.) India *e-mail:
[email protected]
ABSTRACT The present experiment was carried out with cauliflower var. Snow Crown in randomized block design with three replications. The experiment comprised of eleven different treatment combinations of five different sources of nutrients including organic, inorganic and biofertilizers alone in combinations were applied following the proper procedure as per the treatments. The experimental findings revealed that the treatment T11 (Half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) showed better response to plant growth and its attributes and quality. However, maximum yield 269.33 q/ha and 267.02 q/ha were obtained with the application of half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha followed by T 9 (Half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha). On the basis of economic analysis, maximum cost: benefit ratio (3.18 and 3.05) was recorded with the application of half dose of NPK/ha + FYM @ 15 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha (T7) during 2009-10 and 2010-11, respectively and this treatment combination was found most beneficial and feasible for cultivation of cauliflower. Key words
IPNM, Bio-fertilizers, Cauliflower, Yield, Economics.
Cauliflower (Brassica oleracea var. botrytis L.) belongs to family cruciferae and most popular Cole crop among the winter vegetables. It is grown for its highly suppressed ‘prefloral fleshy apical meristem’ branches called “curd.” Cauliflower was introduced in India in 1822 by Jemson at Saharanpur (U.P.) during the period of EastIndia Company. After originating in Cyprus, the cauliflower got established around Mediterranean region, particularly in Italy. India is the second largest producer of cauliflower in the world after China followed by USA, Spain, Italy and France. In India, cultivation of cauliflower is done over an area of about 321000 hectare with a production of 57,97,000 metric tonnes and their productivity is 18.1 metric tonnes per hectare. The highest production of the crop in the
country are West Bengal (1.70 milliom tonnes), followed by Bihar (1.0 million tonnes) and Orissa (0.60 million tonnes) and highest productivity is in Maharashtra 25.30 metric tonnes) and West Bengal (25.10 metric tonnes) followed Uttar Pradesh (20.40 metric tonnes). Cauliflower shares about 4.1% of the total area under vegetables and 4.0% of the total production. However, it is at 4th rank in productivity after tapioca, cabbage and potato (Anon., 2008). It is one of the most important and a popular vegetable crop being grown round the year throughout the country for its white and tender curd. The use of chemical fertilizers contributes a lot in fulfilling the nutrient demand of cauliflower but irregular, excessive and imbalanced application of nutrients deteriorates the physico-chemical properties of the soil and quality of the cauliflower curd. Organic/inorganic fertilizers alone does not supply the major nutrient element in required quantity hence the integrated plant nutrient management (IPNM) in cauliflower is one of the best method of supplying nutrients to the plants to maintain the soil fertility to sustain the agriculture productivity and to improve the profitability. It has been reportedly confirmed that continuous sole use and imbalanced use of mineral fertilizers leads to decrease in nutrient uptake efficiency of plants resulting in either yield stagnation or decrease in yield (Samant et al., 1992; Rubeiz et al., 1993 and Rajanna, et al., 1994). Therefore, there is need to increase the productivity of cabbage per unit area by integration of judicious and balanced use of mineral fertilizers with organic manures. The integrated supply and use of plant nutrients from the chemical fertilizers and organic manures have been proved to produced higher crop yields than when each applied alone. This increase in crop productivity results from their combined effect the synergistic effect, improve chemical, physical and biological properties of the soil. Manures and fertilizers are the kingpins of improved technology contributing about 50-60% increase in productivity of vegetable in India irrespective of soil and agro-ecological zone. But without an integrated supply and use of plant nutrient from chemical fertilizers and organic sources, increased production is not possible. The economics of
1402
Trends in Biosciences 7 (13), 2014
Table1. Effect of integrated nutrient management options on economics of the crop (Rs./ha) of cauliflower
Symbols
Yield (q/ha)
Treatments
Gross income (Rs.)
Cost of cultivation (Rs.)
Net return (Rs.)
Cost : Benefit ratio
200910
201011
200910
201011
200910
201011
200910
201011
200910
201011
T1
Recommended dose of NPK/ha (150 kg:100 kg:80 kg)
186.12
184.11
148896
147288
42426
43326
106470
103962
1:2.51
1:2.40
T2
Half dose of NPK/ha + FYM @ 15 tonnes/ha
209.71
206.04
167768
164832
45363
46263
122405
118562
1:2.70
1:2.56
T3
Half dose of NPK/ha + Azospirillium @ 5 kg/ha
201.34
198.87
161072
159096
40338
41263
120734
117833
1:2.99
1:2.86
T4
Half dose of NPK/ha + FYM @ 15 tonnes/ha + Azospirillium @ 5 kg/ha
226.11
223.21
180888
178568
45838
46763
135050
131805
1:2.95
1:2.82
T5
Half dose of NPK/ha + VAM @ 5 kg/ha.
193.36
190.83
154688
152664
40338
41263
114350
111401
1:2.83
1:2.70
T6
Half dose of NPK/ha + FYM @ 15 tonnes/ha + VAM @ 5 kg/ha.
219.33
217.18
175464
173744
45838
46763
129626
126981
1:2.86
1:2.72
T7
Half dose of NPK/ha + FYM @ 15 tonnes/ha + Azospirillium @ 5 kg/ha + VAM @ 5 kg/ha.
236.25
233.95
189000
187160
46313
47263
142687
139897
1:3.08
1:2.96
T8
Half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha
241.85
240.40
193480
192320
52863
53763
140617
138557
1:2.66
1:2.58
T9
Half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha
262.29
260.47
209832
208376
53338
54263
156494
154113
1:2.93
1:2.84
T10
Half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + VAM @ 5 kg/ha
252.24
250.68
201792
200544
53338
54263
148454
146281
1:2.78
1:2.70
T11
Half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha + VAM @ 5 kg/ha
269.33
267.02
215464
213616
53813
54763
161651
160853
1:3.00
1:2.94
any experiment is directly affected by different inputs which were used viz. cost of chemical fertilizers, FYM, Vermicompost and biofertilizers for present experiment. So, for comparing the different sources of nutrition in response of the benefit:cost ratio the present experiment was planned and undertaken.
MATERIALS AND METHODS The present investigation entitled “Influence of IPNM on the economics of cauliflower (Brassica oleracea var. botrytis L.) var. Snow Crown” was carried out at the Main Experimental Station, department of Vegetable science, N.D.U.A.&T. Kumarganj Faizabad (U.P.) during winter season of 2009-10 and 2010-11. The experiment comprised
5 different sources of nutrients including organic, inorganic, and biofertilizers i.e. (1) recommended dose of NPK/ha (150 kg:100 kg:80 kg), (2) ½ NPK/ha + FYM @ 15 tonnes/ha, (3) ½ NPK/ha + Azospirillum @ 5 kg/ha, (4) ½ NPK/ha + FYM @ 15 tonnes/ha + Azospirillium @ 5 kg/ha, (5) ½ NPK/ha + VAM @ 5 kg/ha, (6) ½ NPK/ha + FYM @ 15 tonnes/ha + VAM @ 5 kg/ha, (7) ½ NPK/ha + FYM @ 15 tonnes/ha + Azospirillum @ 5 kg/ha + VAM @ 5 kg/ha (8) ½ NPK/ha + Vermicompost @ 2.5 tonnes/ ha, (9) ½ NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillum @ 5 kg/ha, (10) ½ NPK/ha + Vermicompost @ 2.5 tonnes/ha + VAM @ 5 kg/ha and (11) ½ NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillum @ 5 kg/ha + VAM @ 5 kg/ha applied on cauliflower variety Snow
KUMAR, et al., Influence of IPNM on the Economics of Cauliflower (Brassica oleracea var. botrytis L.) var. Snow Crown
1403
Table 2a. Details of different commodities and cost operation during the period of investigation (2009-10 and 2010-11) S. No.
Particulars
Rate (Rs.)
Quantity/time
2009-10
Total cost (Rs.) 2010-11
2009-10
2010-11
COMMON COST 1.
Field preparation:
a
Pre planting irrigation by tube well
1 irrigation
300/ha
300/ha
300.00
300.00
b
Labour for irrigation
2 labour
100/ labour/day
100/ labour/day
200.00
200.00
c
Ploughing by disk harrow
1 ploughing
800/ha
900/ha
800.00
900.00
d
Ploughing by cultivator
2 ploughing
700/ha
800/ha
1400.00
1600.00
e
Planking
2 planking
400/ha
500/ha
800.00
1000.00
2.
Nursery raising
a
Seed
500g
11000/kg
11500/kg
5500.00
5750.00
b
Labour for nursery raising
10 labour
100/ labour/day
100/ labour/day
1000.00
1000.00
3.
Layout and transplanting
a
Layout preparation
25 labour
100/ labour/day
100/ labour/day
2500.00
2500.00
b
Labour for uprooting of seedling
4 labour
100/ labour/day
100/ labour/day
400.00
400.00
40 labour
100/ labour/day
100/ labour/day
4000.00
4000.00
c
Labour for transplanting
4.
Cultural practices:
a
Irrigation by tube well
5 irrigations
300/ha
300/ha
1500.00
1500.00
b
Labour for irrigation (3 labour per irrigation)
15 labour
100/ labour/day
100/ labour/day
1500.00
1500.00
c
Weeding
2 weeding
Labour for weeding
40 labour
100/ labour/day
100/ labour/day
4000.00
4000.00
1275.00
1350.00
825.00
900.00
5.
Plant protection
a
Spray of Dithane-45
3 kg
425/kg
450/kg
b
Spraying of Rogor
1.5litre
550/litre
600/litre
c
Labour for spraying
8 labour
100/ labour/day
100/ labour/day
800.00
800.00
6.
Harvesting
40 labour
100/ labour/day
100/ labour/day
4000.00
4000.00
7.
Rental value of land
For six months
1000.00
1000.00
8.
Transportation and marketing charges
3500.00
3500.00
9.
Miscellaneous charges Total common cost
Crown. The soil of the experimental field was sandy loam, pH 7.70, organic carbon (0.30), available N (122.75 kg), available P (21.30 kg) and available K (173.10 kg). The treatments laid out in RBD (randomized block design) and replicated thrice. The treated seeds were first sown in the solarized nursery bed on 15 October during both the years. After seed sowing the seeds were first covered with compost mixture, i.e., soil: sand: compost in the ratio of 1:1:2. Thereafter the bed was covered with the help of paddy straws. Water was sprayed as per need regularly during morning and evening with the help of rose can to keep the bed moist. The seedling became ready to transplant in 25 days after seed sowing. Organic manures were applied 15 days prior to the transplanting in the main field. Whereas the chemical fertilizers ½ dose of nitrogen (75 kg/ha) in
2000.00
2000.00
37300.00
38200.00
form of urea and full dose of phosphorus and potassium (as single super phosphate and muriate of potash) were applied at the time of final field preparation as per treatments. Rest half dose of nitrogen was applied as top dressing in two equal splits at 25 and 45 days after transplanting. Various observations related to the growth, yield and quality of the cauliflower were recorded at periodic growth intervals. Five plants from the centre of each plot were selected for recording the observations to avoid the border effects.
RESULTS AND DISCUSSION The acceptance of any agricultural recommendation is mainly depending on its benefit: cost ratio. It was, therefore, desirable to work out the cost of cultivation (Rs./ ha), gross income (Rs./ha) and net profit (Rs./ha) under
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Trends in Biosciences 7 (13), 2014
Table 2b. Details of different commodities and cost operation during the period of investigation (2009-10 and 2010-11) S. No.
Particulars
Rate (Rs.)
Quantity/time
2009-10
Total cost (Rs.) 2010-11
2009-10
2010-11
VARIABLE COST 1.
Fertilizers
a
Urea
241.00kg
5.54/kg
5.54/kg
1335.00
1335.00
b
Di Ammonium Phosphate (DAP)
217.00kg
11.18/kg
11.18/kg
2426.00
2426.00
c
Murrate of Potash
133.00kg
5.00/kg
5.00/kg
665.00
665.00
Labour for application of fertilizers
7 labour
100/ labour/day
100/ labour/day
700.00
700.00
2.
Manures
a
Farm Yard Manure (FYM)
15.00 tonnes/ha
300/ton
300/ton
4500.00
4500.00
Labour for application of FYM
10 labour
100/ labour/day
100/ labour/day
1000.00
1000.00
Vermicompost
2.50 tonnes/ha
5000/ton
5000/ton
12500.00
12500.00
Labour for application of Vermicompost
5 labour
100/ labour/day
100/ labour/day
500.00
500.00
b 3.
Biofertilizers
a
Azospirilliun
5.00kg/ha
55/kg
60/kg
275.00
300.00
Labour for application of Azospirilliun
2 labour
100/ labour/day
100/ labour/day
200.00
200.00
Vesicular Arbascular Mycorrhiza (VAM)
5.00kg/ha
55/kg
60/kg
275.00
300.00
Labour for application of VAM
2 labour
100/ labour/day
100/ labour/day
200.00
200.00
b
the various treatments in both the years of experiment. The values of both the years had been calculated and presented in Table-1. The cost of cultivation for one hectare cauliflower crop, and variable costs of used different treatments has been presented in detail in Table-2a and 2b.
Cost of cultivation (Rs./ha): It is evident from the value presented in Table-1 that the highest cost of cultivation (Rs.53813/ha and Rs.54763/ ha) was calculated under T11 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) followed by the treatment T9 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha) and T10 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) with an expenditure of Rs.53338/ha and Rs.54263/ha were equal in during the years 2009-10 and 2010-11, respectively. The minimum cost of cultivation Rs.40338/ha and Rs.41263/ ha was calculated for T 3 (half dose of NPK/ha + Azospirillium @ 5 kg/ha) and T5 (half dose of NPK/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) during the years 2009-10 and 2010-11, respectively.
Gross income (Rs./ha): The maximum gross income of Rs.215464/ha and Rs.213616/ha was calculated for T11 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/
ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) followed by the treatment T9 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha) with Rs.209832/ha and Rs.208376/ha respectively in both the years. The minimum gross income of Rs.148896/ha and Rs.147288/ha was work out with T1 (recommended dose of NPK/ha -150kg :100kg :80kg) during the years 2009-10 and 2010-11, respectively.
Net profit (Rs./ha): The net profit (Rs/ha) was calculated by subtracting the cost of cultivation from the gross income. It was found that maximum net profit (Rs.161651/ha and Rs.160853/ ha) with the application of T11 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) followed by net return of Rs.156494/ha and Rs.154113/ha was obtained in case of T9 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha). This finding is supported by Singh and Singh (2005) who found the highest net return (Rs. 53965/ha) in cauliflower with combined use of Azospirillum + 100% recommended dose of NPK @ 120:60:60 kg/ha. The minimum gross income of Rs.106470/ha and Rs.103962/ha was work out for T1 (recommended dose of NPK/ha -150kg :100kg :80kg) during the years 2009-10 and 2010-11, respectively. Acharya and Mondal (2010) and Wani et al. (2010) findings
KUMAR, et al., Influence of IPNM on the Economics of Cauliflower (Brassica oleracea var. botrytis L.) var. Snow Crown
also supported the above findings. Kannaujia et al. (2010) also found the highest net return (Rs. 77,932) of radish by the application of 50 % NPK (80:60:60 kg/ha) + 50 % Vermicompost @ 5 tonnes/ha + Biofertilizers.
Cost: Benefit ratio: The maximum cost: benefit ratio (1:3.08 and 1:2.96) was recorded in the treatment combination T7 (half dose of NPK/ha + FYM @ 15 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) followed by T11 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) with 1:3.00 and 1:2.94. In support of above findings Narayanamma et al. (2005) recorded the highest cost: benefit (1:2.96) in cauliflower with the application of VAM + 100% recommended dose of NPK @ 180:60:60 kg/ha and Singh and Singh (2005) recorded highest cost: benefit ratio (1:2.23) in cauliflower with combined use of Azospirillum + 100% recommended dose of NPK @ 120:60:60 kg/ha. The minimum cost: benefit ratio (1:2.51 and 1:2.40) in the treatment combination T1 (recommended dose of NPK/ha -150kg :100kg :80kg) during the years 2009-10 and 2010-11, respectively. It is clearly indicated that treatment T7 (half dose of NPK/ha + FYM @ 15 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) was found better than T11 in terms of cost: benefit ratio and also farmer’s point of view. Incorporation of organic manures with chemical fertilizers is not beneficial only for soil health but even it is helpful in fetching highest net returns. Acharya and Mondal, 2010 highest cost: benefit ratio (1:2.92) of cauliflower through the use of 75% recommended dose of fertilizer along with 25% N through FYM. Wani, et al., 2010 recorded highest cost: benefit ratio (1:3.59) of cauliflower with the of 50% recommended dose of NPK combined with poultry manure @ 3 tonnes/ha.
Economics of various treatments: The cost of cultivation was directly associated with different inputs viz. cost of chemical fertilizers, FYM, Vermicompost and biofertilizers. Gross income was found directly related with the yield of curd under different treatment. In the present investigation, the treatment T11 come out to be the best in all aspects followed by T9 and the best effective treatment was T10. In terms of cost of cultivation T11 followed by T9, T10 and T1 was the least expensive. This was due the high cost involved of Vermicompost (Rs.53813/q and Rs.54763/q). But when we calculate the cost benefit: ratio of all the treatments, T7 (1:3.08 and 1:2.96) was the most rewarding. But if the selling price of the produce increases slightly, which is volatile and keeps on changing on daily basis, the treatment T11 may come to be most benefiting. From the above facts
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I may be inferred that the treatment T9 treatment (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) and treatment of T11 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) were the most beneficial treatment may be followed in commercial cauliflower cultivation on large scale. The yield of curd was significantly increased by the use of various sources of integrated nutrient management. The maximum yield of curd was recorded with the application of T11 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) followed by T9 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha) consistently during both the years 2009-10 and 2010-11, respectively. An effort was made to estimate the cost of cultivation and cost: benefit ratio with use of each treatment separately and some interesting results were obtained. In the present experiments although the total returns from T11 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) and T9 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha) were higher, but expenditure incurred in their use was also high. This resulted in comparatively poor cost: benefit ratio in case of the above two treatments. In case of selling price of the produced increased slightly the cost: benefit ratio would also be among the highest. On overall basis, however, the treatment of T11 (half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) and T7 (half dose of NPK/ha + FYM @ 15 tonnes/ha + Azospirillium @ 5 kg/ ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha) were most remunerative in this experiment. Application of half dose of NPK/ha + FYM @ 15 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha (T7) gave maximum cost: benefit ratio (3.18 and 3.05) during 2009-10 and 2010-11, respectively and this treatment along with the application half dose of NPK/ha + Vermicompost @ 2.5 tonnes/ha + Azospirillium @ 5 kg/ha + Vesicular Arbascular Mycorrhiza @ 5 kg/ha (T11) was found to be most remunerative.
ACKNOWLEDGEMENT The authors are thankful to the HOD, Department of Vegetable Science, NDUA&T, Kumarganj, Faizabad (U.P.) for providing seed material and facilities for conducting research.
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Trends in Biosciences 7 (13), 2014
LITERATURE CITED
production. Commun. Soil Sci. Pl. Anal.24:1583-89.
Acharya, D. and Mondal, S.S. 2010. Effect of integrated nutrient management on the growth, productivity and quality of cabbage. Indian J. Agron., 55 (1): 1-5.
Samant, P.K.S., Sahu, S.K. and Singh, D.N. 1992. Studies on balanced fertilizer use for cabbage on acid clay loam soils of Orissa. Orissa J. Agric. Res. 5: 45-49.
Anon. (2008). National Horticulture Board, Data Base.
Singh, V.N. and Singh, S.S. 2005. Effect of inorganic and biofertilizers on production of cauliflower (Brassica oleracea var. botrytis L.). Veg. Sci., 32 (2): 146-149.
Kanaujia, S.P.S.; Singh, V.B. and Singh, A.K. 2010. INM for quality production of radish (Raphanus sativus L.) in alfisol. J. Soils and Crops, 20 (1): 1-9. Narayanamma, M.; Chiranjeevi, C.H.; Reddy, I.P. and Ahmed, S.R. 2005. Integrated nutrient management in cauliflower (Brassica oleracea var. botrytis L.) Veg. Sci., 32 (1): 62-64. Rajanna, K.M., Mathad, J.C. and Patil, V.S. 1994. Effect of different levels of NPK on yield of cabbage. South Indian Hort. 42 (3): 152-54. Rubeiz, I.G., Sabra, A.S., Al-Assir, I.A. and Farrava, M.T. 1993. Layer and broiler poultry manures N fertilizer sources for cabbage
Wani, A.J.; Mubarak, T. and Bhatt, J.A. 2010. Effect of integrated nutrient management on curd yield, quality and nutrient uptake of cauliflower (Brassica oleracea var. botrytis L.) cv. Snowball16 under temperate Kashmir conditions. Crop Research (Hisar), 40 (1/3): 109-112. Wani, A.J.; Mubarak, T. and Rather, G.H. 2010. Effect of organic and inorganic nutrient sources on growth and curd yield of cauliflower (Brassica oleracea var. botrytis L.) cv. Snowball-16. Environment and Ecology, 28 (3): 1660-1662. Received on 06-05-2014
Accepted on 16-05-2014
Trends in Biosciences 7(13): 1407-1410, 2014
Evaluation and Genetic Studies in Tomato Genotypes V. PREMALAKSHMI1, S. RAMESH KUMAR2 AND T. ARUMUGAM3 1
Department of Horticulture, Agricultural College and Research Institute, TNAU, Madurai-625 104, Tamil Nadu. 2 Department of Horticulture, Vanavarayar Institute of Agriculture, Manakkadavu, Pollachi-642 103, TNAU, Tamil Nadu. 3 Dean (i/c), Imayam Institute of Agriculture and Technology, Kannanur, Thuraiyur, TNAU, Tamil Nadu. email :
[email protected] ABSTRACT The present investigation was conducted with fourteen genotypes of tomato to find out the variability, correlation and path coefficient effects to identifying the desirable combiners. The accessions revealed wide variability for all characters evaluated. Phenotypic variances were higher than their respective genotypic variances thus revealing the role of environmental factors. High estimates of heritability, genetic advance and genotypic coefficient of variation for the traits of average fruit weight, number of fruits/plant and number of branches were controlled by additive gene action indicating the possibility of selection to improve these characters. Correlation and path co-effiecnt analysis revealed that number of fruits per plant not only had positively significant association with fruit yield per plant but also have positively high direct effect on fruit yield per plant regarded as the main determinants of fruit yield per plant. Key words
Character association, genetic parameters, yield, selection
Tomato (Solanum lycopersicum L.) is one of the most widely cultivated and important vegetable crops in in the world (Varela, et al., 2003). It has high nutritional and antioxidant properties. Tomato is a distinctive vegetable crop, which is very responsive to genetic improvement due to its high degree of homogeneousness (Pradeepkumar, et al., 2001). A number of cultivars were hosted to India for genetic improvement work of tomato. The present trend in crop improvement programmes is the development of hybrid cultivars to boost the productivity and profitability of farmers. Genetic variability among the parents is a prerequisite to develop new cultivar and select better segregants for various economic characters (Tanuja, et al., 2012). Proper and systematic evaluation of genetic resources is essential to understand and estimate the genetic variability, heritability and genetic advance (Pradeepkumar, et al., 2001). Knowledge on the association of component traits with fruit yield may greatly help in making selection more precise and accurate. An estimate of genotypic and
phenotypic correlation coefficients gives a measure of genotypic association since it is an inherited relationship between the traits (Ramesh Kumar, 2011). Though correlation analysis indicates the association pattern of the component traits with fruit yield, they simply represent the overall influence of a particular trait on fruit yield rather than providing cause and effect relationship. With this aim, tomato cultivars and lines of diverse origin were evaluated for yield and other characters.
MATERIALS AND METHODS The experimental material consisting of 14 genotypes of tomato collected from different regions were evaluated during 2010-12 in the Department of Horticulture, Agricultural College and Research Institute, TNAU, Madurai. The crop was grown in randomized block design with three replications at spacing of 60 x 60 cm. The other cultural practices were carried out according to TNAU crop production guide, 2005. Observations regarding, plant height (cm), number of branches, number of fruits/plant, average fruit weight and fruit yield/plant were recorded from 5 sampled plants in each replication for each genotype. The statistical analysis was done according to the methods for genetic coefficients of variation for heritability in broad sense and Johnson, et al., 1955 for genetic advance. Correlation coefficients were calculated by the method described by Al-jibouri, et al., 1958 and path coefficients worked out according to Wright, 1921.
RESULTS AND DISCUSSION The ANOVA and mean performance of different genotypes are presented in the Table 1 and 2. Highly significant varietal differences were recorded for all the characters except fruit yield per plant revealed by analysis of variance. Mean values of all the characters showed wide variations for the plant height (77.20 -156.0 cm), number of branches (6.80-20.0), number of fruits per plant (28.50150.20), average fruit weight (15.60-65.20g) and fruit yield per plant (1.13-2.39 kg). Arka Asish tomato was the highest yielding variety with a yield of 2.39 kg/plant followed by EC 531804 and EC 617048 with a yield of 2.34 and 2.25kg/ plant, respectively. The medium sized fruits (36.27g) and
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Trends in Biosciences 7 (13), 2014
Table 1. Analysis of variance for different characters Source Replication Genotypes Error
df
Plant height (cm)
Number of branches
Number of fruits per plant
Average fruit weight (g)
Fruit yield per plant (kg)
2 13 26
5.76 868.20* 18.77
0.11 46.08* 0.04
5.5882 3060.08* 2.98
5.92 463.66* 0.66
0.003 0.52 0.001
The magnitude of variability in respect to five different characters among the genotypes measured in terms of range, genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV) along with heritability, genetic advance and genetic advance (in per cent of mean) are presented Table 3. In general, phenotypic coefficients of variation were higher than genotypic coefficients of variation demonstrating that the genotypic effect is reduced under the influence of given environment. Nevertheless, a close correspondence between GCV and PCV in respect of all the characters showed that environment has very little influence on the expression of the characters under study. The magnitude of heritability (broad sense) for most of the characters were quite high indicating that the genotypes under study have a great scope for the selection based on these characters. High values of GCV and heritability estimates appended with better genetic gains also exposed role of additive gene effects regulating the
highest number of fruits (66.66) in Arka Asish were responsible for high yield. Though, the genotype EC 531804 having small sized fruits (15.60g), the number of fruits (150.20) were very high that will responsible for high yield. The characters showing high range of variation of were indicate good scope for improvement. A wide range of yield/ plant and yield/ha (1.57 to 4.25 kg/plant and 27.91 to 75.55 t/ha, respectively) was also reported in cherry tomato lines by Prema, et al., 2011. Some of the genotypes produced very tall plants. Among the genotypes, EC 29933 was the tallest in height (156.0 cm) followed by EC 683185 (148.0 cm) and EC 683186 (140.0 cm). Genotypes EC 631369, EC 617048 and Arka Vikas were found determinate in growth habit. The genotype EC 531804 had the highest number of 20.0 branches/plant while Arka Vikas had the lowest number (6.80). The highest number of fruits/plant was recorded in EC 531804 whereas the lowest number of fruits/plant was recorded in EC 631369 (28.50).
Table 2. Mean performance of tomato genotypes S.No
Genotypes
Plant height (cm)
Number of branches
Number of fruits per plant
Average fruit weight (g)
Fruit yield per plant (kg)
1.
Arka Vikas
89.00*
6.80
35.15
32.00
1.13
2.
Arka Ashish
112.00*
8.50
66.00*
36.27
2.39*
3.
Arka Ahuti
119.60*
8.60
47.00
31.90
1.50
4.
EC 683185
148.00
16.60*
44.80
35.60
1.59
5.
EC 683186
140.00
13.00*
45.00
40.60*
1.80*
6.
EC 29933
156.00
13.20*
84.00*
25.06
2.10*
7.
EC 521074
137.80
13.20*
40.70
31.25
1.27
8.
EC 531804
155.00
12.20*
150.20*
15.60
2.34*
9.
EC 615056
125.00*
20.00*
32.20
65.20*
2.01*
10.
EC 617046
135.00
12.80*
31.00
45.00*
1.39
11.
EC 617048
89.80*
8.60
45.00
50.00*
2.25*
12.
EC 620536
117.00*
8.00
47.00
30.83
1.45
13.
EC 620555
137.00
8.00
32.00
46.87*
1.50
14.
EC 631369
77.20*
7.00
28.50
50.00*
1.43
Mean
124.17
11.18
52.04
38.30
1.73
SEd
3.53
0.17
1.41
0.66
0.03
CD
7.27
0.36
2.89
1.37
0.07
CV %
19.37
33.78
59.14
31.28
23.39
*Significant at 5 % level
PREMALAKSHMI, et al., Evaluation and Genetic Studies in Tomato Genotypes
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Table 3. Estimates of mean, range, genotypic and phenotypic variances, genotypic and phenotypic coefficient of variations, heritability and genetic advance in tomato Character Plant height (cm) Number of branches
a
Range
Mean
PCVa
GCV
ECV
h2
GA as % of mean
77.20-156.0
124.17
20.29
19.99
3.48
0.97
40.57
6.80-20.0
11.18
35.09
35.04
1.93
0.99
72.07
Number of fruit per plant
28.50-150.20
52.04
61.43
61.34
3.31
0.99
126.18
Average fruit weight (g)
15.60-65.20
38.30
32.50
32.43
2.13
0.99
66.67
Fruit yield per plant (kg)
1.13-2.39
1.73
24.42
24.29
2.54
0.98
49.76
PCV = phenotypic coefficient of variation; GCV = genotypic coefficient of variation, h2 = heritability and GA = genetic advance.
inheritance of such traits (Narayan et al., 1996). Plant height showed a moderate, GCV and PCV but higher heritability estimate. Similar findings have been reported by (Pradeepkumar, et al., 2001).
amount of genetic variability along with heritability and genetic advance. This reveals that there is a greater scope for improving these characters by simple phenotypic selection.
High heritability with high genetic advance was noticed for number of branches/plant, number of fruits/ plant and average fruit weight which indicates that the selection among the genotypes can bring about significant improvement in the fruit yield and its component characters. Further high heritability coupled with high expected genetic advance indicated the involvement of additive genetic variance, therefore selection may be effective. The remaining traits plant height and fruit yield/plant were recorded high heritability with moderate genetic gain suggesting that which indicates the presence of non-additive gene action for these traits and therefore, these traits could not be improved through simple selection.
Genotypic, phenotypic correlation and path coefficient analysis for all pairs of five characteristics are presented in Table 4 and 5. Positive association between fruit yield and plant height is in support of the findings of (Monamodi, et al., 2013). Genotypic correlation was higher than the phenotypic correlation coefficients indicating that there is strong association between two characters genetically. Number of fruits per plant expressed a significant positive correlation with fruit yield per plant, while negatively but significantly correlated with average average fruit weight. The negative correlation of fruit number with average fruit weight means that if there are more fruits in a truss, the tomato average fruit weight will tend to be smaller as fruits will compete for space for attachment in a truss as well as for the nutrients. Plant height showed significant positive correlation with number of branches/plant, whereas negative relationship with average fruit weight. Another antagonistic relationship occurred between average fruit weight and fruit yield per plant. The inter correlation between number of branches and number of fruits per plant was found to be positive and non-significant.
These findings are in accordance with the results of Singh and Narayan, 2004 in tomato. Burton, 1952 suggested that the GCV together with high heritability and genetic advance would give the best picture on the extent of advance expected from selection. From the foregoing discussion on variability analysis, it could be concluded that the characters for number of branches/plant, number of fruits/plant and average fruit weight recorded high
Table 4. Genotypic and phenotypic correlation for different characters Characters Plant height (cm) Number of branches Number of fruits per plant Average fruit weight (g) *Significant at 1 % level
G P G P G P G P
Number of branches 0.611* 0.598
Number of fruits per plant 0.479 0.471 0.078 0.077
Average fruit weight (g) -0.415 -0.407 0.223 0.220 -0.703* -0.699*
Fruit yield per plant (kg) 0.210 0.225 0.221 0.629* 0.621* -0.047 -0.042
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Table 5. Path co-efficients for different characterss Characters Plant height (cm) Number of branches Number of fruits per plant Average fruit weight (g)
Plant height (cm) 0.0004 0.0002 0.0002 -0.0001
Number of branches -0.0295 -0.0484 -0.0037 -0.0107
Number of fruits per plant 0.5748 0.0935 1.2004 -0.8439
Average fruit weight (g) -0.3355 0.1800 -0.5683 0.8083
Fruit yield per plant (kg) 0.210 0.225 0.629* -0.047
Residual effect: 0.542
Path coefficient analysis provides an effective means of partitioning direct and indirect causes of association; it permits a critical look to recognize the specific forces acting to produce a given correlation and measures the relative importance of each causal factor. Such an analysis was carried out in the present study. Number of fruits per plant had maximum direct effect followed by average fruit weight indicating that these are the real independent characters and have maximum contribution towards increase in fruit yield. Number of branches showed Negative direct effect on yield per plant. The residual effect determines how best the causal factor accounts for the variability of the dependent factor i.e. fruit yield per plant in this study. In the present investigation, it was interesting to note that the residual effect was only 0.542 which reveals that variability in fruit yield per plant has been explained by five characters included in the study. The characters taken up for path analysis in the present study appear to be appropriate Similar findings were also obtained by (Tanuja, et al., 2012). From the present findings it is suggested that number of fruits per plant be considered when selecting for yield components, especially that this trait have previously been identified to have high variability coupled with high heritability and genetic advance. Conversely, outcomes of this study derived from only fourteen genotypes and further work need to be done with more genotypes to confirm these results.
LITERATURE CITED Al-Jibouri, H.A., Miller, P.A. and Robinson, H.F. 1958. Genotypic and environmental variances and co-variances in an upland cotton
cross of interspecific origin. Agronomy Journal 50(10): 633-636. Burton, G.W. 1952. Quantitative inheritance in grasses. Proc. 6th Int. Grassld. Congr. 1:277-283. Johnson, W.W,, Robinson, H.F. and Comstock, R.E. 1955. Genotypic and phenotypic correlation in soybeans and their implications in selection. Agron. J., 47:477-482. Monamodi, E.L., Lungu, D. M. and Fite, G.L. 2013. Analysis of fruit yield and its components in determinate tomato (Lycopersicon lycopersci) using correlation and path coefficient. Bots. J. Agric. Appl. Sci., 9 (1): 29-40. Narayan, R., Singh, S.P., Sharma, D.K. and Rastogi, K.B. 1996. Genetic variability and selection parameters in bottle gourd. Indian J. Hort., 53: 53-58. Pradeepkumar, T., Dijee Bastian., Joy, M., Radhakrishnan, N.V. and Aipe, K. C. Genetic variation in tomato for yield and resistance to bacterial wilt. Journal of Tropical Agriculture 39: 157- 158. Prema, G., Indiresh, K.K. and Santosha, H.M. 2011. Evaluation of cherry tomato (Solanum lycopersicum var. cerasiforme) genotypes for growth, yield and quality traits. Asian J. Hort. 6(1): 181-184. Ramesh Kumar, S. 2011. Genetic improvement of local types of brinjal (Solanum melongena). Ph.D., (Hort.) Thesis, Agricultural College and Research Institute, TNAU, Madurai. Singh, A.K., and Narayan Rai. 2004. Variability studies in tomato under cold arid condition of Ladakh. Horti J 17(1): 67-72. Tanuja, B., K. Sharma and Thakur, K.S. 2012. Genetic diversity and path analysis in tomato (Solanum lycopersicum L.). Vegetable Science 39 (2): 221-223. Varela, A.M., Seif, A. and Lohr, B. 2003. A guide to IPM in tomato production in Eastern andSouthern Africa. CTA/ICIPE/GTZ. Wright S. 1921. Correlation and causation. Journal of Agricultural Research 20:557- 585. Received on 08-05-2014
. .
Accepted on 25-05-2014
Trends in Biosciences 7(13): 1411-1415, 2014
Study on Seed Orientation for Production Quality Seedlings in Biofuel Tree Species N. MARIAPPAN1* P.SRIMATHI2, AND L. SUNDARAMOORTHI1 1
Vanavarayar Institute of Agriculture, Manakkadavu, Pollachi-642103 Department of Seed Science and Technology, Tamil Nadu Agricultural University, Coimbatore-641 003 email:
[email protected] 2
ABSTRACT Studies were conducted at the Department of Seed Science and Technology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu in the major biofuel crops viz., Jatropha (Jatropha curcas), pungam (Pongamia pinnata), simarouba (Simarouba glauca), neem (Azadirachta indica) and mahua (Madhuca longifolia) to standardize the orientation of seed at sowing under germination room condition for expression of their maximum germination with seedling quality characters. The results revealed that orienting the seeds as radicle down wards for Jatropha curcas and Madhuca longifolia and orienting seeds as flat for Pongamia pinnata, Azadirachta indica and Madhuca longifolia resulted in production of higher percentage of normal seedlings (Germination) with better seedling quality characters viz., shoot length, root length, fresh weight, dry weight and vigour index. Key words
Biofuel crops, orientation, normal seedlings
Biofuel crops gained more importance in this century, due to their ecofriendly nature and the depleting levels of fossil fuel. All oil seeds are capable of giving biofuel, but due to other economic uses and wider adaptability, a few crops are focused for research on biofuels as an alternative source of automobile fuel (Nigam and Singh, 2011). Among the biofuel crops, Jatropha, pongamia, simarouba, neems, and mahua, which are perennial in nature and non-edible oil trees, are preferable for production of biofuel with economic feasibility (Kumar and Sharma, 2011). All these biofuel crops propagated through seeds and seedlings are being used for raising plantation. These crops possess bigger size seeds, indirectly requiring specific sowing methodology for production of good quality transplantable elite seedlings. Seed orientation is one such technique where the seeds are sown in respect to the genetic structural component viz., embryo and storage tissue (Pandey and Sinha, 2004). Sivasamy, 1991, Masilamani, 1996 and Gunasekar, 1997 with their research on, pungam, teak and rubber respectively reported that the seed orientation at sowing had effective influence on production of elite seedlings at nursery and also under laboratory conditions (Gurunathan, et al., 2009). The newly introduced biofuel crops require standard
technique for testing their quality, which would be helpful in trade of seed / seedlings. Hence, studies were formulated with five different biofuel crops viz., Jatropha curcas, Pongamia pinnata, Simarouba glauca, Azadirachta indica and Madhuca longifolia on identification of a suitable sowing technique at germination room condition (25± 2oC and 95±3% RH) and this would assist in obtaining reproducible and reliable results from seed testing laboratories.
MATERIALS AND METHODS Seed Sources: Bulk seeds of Jatropha (Jatropha curcas), Pungam (Pongamia pinnata), Simarouba (Simarouba glauca), Neem (Azardirachta indica) and Mahua (Madhuca longifolia) were collected respectively from Mettupalayam (11.3000°N 76.9500°E), Bhavanisagar (11°282 83 N 77°72 223 E), Kumulur (10.89 o N 78.83 o E), Pattukkottai (10.43°N 79.32°E) and Pollachi (10.67°N 77.02°E) in different places of Tamil Nadu (India).
Experimentation and experiment materials: Collected seeds of above mentioned species were homogenized based on weight using specific gravity grading. The seeds were sown in germination trays filled with sand media in four replicates of 100 seeds each in three different orientations (method of sowing based on radicle position) viz., radicle upwards, radicle downwards and flat (Fig.1). The seeds sown were kept in the germination room maintained at 25±20C and 95 ± 2% RH. After the germination period of 16, 25, 23, 23 and 41 days respectively for Jatropha, Pungam, Simarouba, Neem and Mahua, the seedlings were evaluated for the seed and seedling quality characters. Observations were made on germination, based on normal seedlings (ISTA, 1999) percentage and the seedling vigour evaluations on days to first germination, abnormal seedlings (%), speed of germination (Maguire,1962), root length (cm), shoot length (cm) (Tanwar and Singh,1985) and fresh weight and dry matter production of 10 seedling-1 (g). The vigour index values were computed as per Abdul - Baki and Anderson, 1973 adopting the formula, Vigour index = germination % x total seedling length in cm.
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Statistical analysis: The data gathered were analysed as per Panse and Sukhatme (1967) at 0.05% probability for understanding the significance of variance.
RESULTS AND DISCUSSION Highly significant results were obtained for all the species, for all the evaluated characters viz., days to first germination, germination (%), abnormal seedlings (%), speed of germination, shoot length (cm), root length (cm), fresh weight and dry weight of 10 seedlings-1 (g) and vigour index. The species variation could be attributed to their the genetic variation that expressed their uniqueness in time of emergence, germination expression and seedling growth rate especially, the days to first germination which vary within the species due their specific genetic identity (Sivasamy, 1991 and Soeda, et al., 2005). The orientations also expressed highly significant variations for the mentioned seed and seedling quality characters. The interaction effect between the species and orientations were also highly significant for all the characters except for root length, shoot length and fresh weight of 10 seedlings. The days to first germination vary among the species from 5 to 22 days which indicated Jatropha curcas seeds were early to germinate (6 days) while Madhuca longifolia took the longest duration of 23 days. The other biofuel test crops took 15 to 16 days. The variation due to orientation, for days to first emergence, was only 1 to 2 days for radicle down, radicle up and flat, except for Madhuca longifolia and Simarouba glauca, where it was observed to be 4 days (Fig.2). But the final germination percentage, based on normal seedlings (ISTA,1999) vary widely with orientation, in Jatropha curcas and Madhuca longifolia, the seeds sown as radicle downwards recorded the maximum germination of 85 and 65 per cent respectively and was followed by orientating the seed as flat at the time of sowing. The seed sown as radicle upwards recorded the lowest germination which was 10 and 20% lesser than the seeds orientated as flat and radicle downwards in Jatropha curcas while it was 8 and 23 per cent respectively with flat and radicle downwards sowing in Mahuca longifolia. In the rest of the three species (Pongamia pinnata, Simarouba glauca and Azadirachta indica) the seeds sown as flat recorded the maximum germination of 98, 90 and 95 per cent respectively with flat orientation which was 6, 10 and 3 per cent higher than radicle downwards orientation (Fig.3) and 18, 10 and 8 per cent respectively with seeds oriented as radicle upwards. The germination results were in supportive of the abnormal seedling percentage, which correlates well with reduction in germination of seeds, (Fig.4) irrespective of species positioned as radicle upwards and resulted in production of more of abnormal seedlings
which was 15 and 14 per cent higher than seeds sown as radicle downwards and flat. The day to first emergence was also earlier (Fig.2) with seeds sown as radical downwards. Thapliyal, 1979 reported that as per the cholordywent theory the germination and seedling growth of seeds placed as micropylar (radicle) end upwards were poorer, since the auxin produced were liberated in the root tip and accumulated preferentially in the lower half and moves basipetally into the elongation zone. This polarized lateral movement of auxin was considered to be dependent on metabolic energy. According to the theory (a) there is inherent auxin in the lower side of elongating zone and (b) there is inherent difference in the response of stem and root to auxins. So ,the change in position of seed in relation to gravitational force disturbs the polar flow of auxin from tip to the zone of elongation as the concentration of auxin increase more on the lower than on the upper side of horizontally placed organs. The growth rate increases with the increased accumulation of auxin and as a result, a shoot will grow away showing negative geotropic property and a root because of inherent differences in response will grow towards the lower side showing positive geotropic property. Negative geotropism shows accelerating influence of auxin on the growth of shoots while positive geotropism shows retarding influence of auxin on those roots (Pandey and Sinha, 2004). Geotropism is the movement caused in response to gravitational stimulus. Positive geotropism is observed in the primary roots of many plants and negative geotropism in their shoots. In germinating maize seeds, placed in any direction, positive geotropic habit of the roots cannot be changed. The secondary branches of roots and stems would be diageotropic (growing at right angle), plagiogeotropic (angle other than 90 o) or apogeotrpic (negative). Tertiary roots and their further branches are not sensitive to gravitational stimulus and are called ageotropic. Benne-Clark, et al., 1959 also suggested that the inversion of seedling might bring about abnormal chemical or prebiotic changes and the manifestations of these changes bring abnormal morphological development in the seedlings. Hence, this might be reasonable to assume that the vigorous seeds produced by upright seedlings by virtue of their inability to maintain functional metabolic integrity and subcellular co-ordination event, under adverse conditions by affecting curvatures the seeds oriented as radical downwards recorded the lower germination and higher abnormal seedlings. The proper directional growth of plumule and radicle, require additional hormones as well as extra energy for better seedling survival and emergence. Vigour of seed is normally expressed through the seedling quality characters. The speed of germination
MARIAPPAN, et al., Study on Seed Orientation for Production Quality Seedlings in Biofuel Tree Species
Influence of orientation of sowing on seed and seedling quality characters of some biofuel plants
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Trends in Biosciences 7 (13), 2014
observed with crops were in line with germination percentage, wherein Jatropha curcas and Madhuca latifolia, seeds sown as radicle downwards recorded speedy germination while in other three crops flat sowing recorded the highest speed of germination (Fig.5). The seedling characters measured in terms of root length, shoot length and fresh weight also exposed significant variation due to orientation, genetic variation and their interaction (Fig. 6 and 7). However, the dry weight of seedlings irrespective of the crops were higher in orienting the seed as radicle downwards at the time of sowing, while the other two orientations viz., radicle downwards and flat were on par in production of higher seedlings biomass (Fig.8). The vigour index, which is the total potential of the seed observed a negative influence in expression of physiological seed quality characters on orienting the seed as radicle downwards, while the other two orientations were on par with one another though the genotypic difference was wider due the size of the seed genuine to its nature. The Jatropha curcas and Madhuca longifolia, which exerted higher seedling length and breadth ratio proved themselves expressive of physiological characters on sowing as radicle downwards (Fig.9). But the crops which have lower length and breadth ratio viz., Pongamia pinnata, Simarouba glauca and Azardirachta indica proved itself superior with flat sowing. Orientation or the placement of seed is known to influence seed germination and seedling growth especially in crops with larger sized seeds, the perennial tree crops, whereas in small seeded crops like wheat, rice and barley, the seed sowing as vertical inverted position with embryo ends upward gave the best germination per cent and seedling vigour (Bagoury, 1973). The researchers working with species with larger size seeds also expressed wider variability on seed quality expression with seed orientation. Krishnasamy, 1992 observed that in cucurbitaceous vegetables, emergence occurred early and percentage of emergence as well as seedling vigour were higher when the seeds were sown in vertical orientation (micropylar end upwards), while Gunasekaran, 1997 convinced that horizontal orientation with micropylar end of seed facing sideways proved superior to vertical and inverted position in Clove (Syzygium aromaticum), Nutmeg (Myristica fragrans) and Rubber (Hevea brasiliensis). Germination behaviour was hypogeal in nutmeg and rubber and epigeal in clove. In spite of such variation horizontal orientation performed uniformly better in all these plantation species. Douglas-fir (Pseudotsuga menziesii) seeds oriented scaleside-up on a moist surface germinated more rapidly than seeds placed scale-side-down as per Sorensen and Campbell, 1981 who also reported that, When seeds were germinated under laboratory conditions, the response to orientation varied among seeds of different geographic origin
and most of the geographic variation in germination rate occurred when seeds were oriented as scale-side-up. Parameswari, et al., 1999. l also indicated that for elite seedling production the tamarind seeds should be sown in upright position with micropylar end downwards at nursery. The next position was found to be upright position with micropylar end pointing sideways and was followed by horizontal. The poorest orientation at upright position with micropylar and pointing upwards. Gurunathan, et al., 2006, in Jatropha also revealed that radical downward and flat orientation improved the production of normal seedling and aid in expression of higher seed germination with better seedling vigour characteristics. According to Prasad, et al., 2003 emergence was poorer in case of Bauhinia vahlii seeds when sown in flat position and was the lowest when seeds were sown in a horizontal position (keeping the radicle end at 90° in respect to soil surface), while in case of Bauhinia racemosa emergence was poorest for seeds sown in an inverted and horizontal position and was lowest when they were sown in the flat position. Seedling survivals, mean germination time, germination index, shoot and root growth also followed the same trend. Seed coat adherence to cotyledons in polyploidy seeds is another problem in seedling emergence. In watermelon, characterized for polyploid seed, Jaskani, et al., 2006 expressed that that the seed planting orientation, affected both seedling emergence rate and seed coat adherence. Seed positioned with radicle up reduced the seed coat adherence to cotyledons, while seed orientation with the radicle end up decreased seed coat adherence. Nettles, 1971 researched on vegetables seeds, also noted that if the seed depth was less than 2 cm, with radical up seed orientation, the hypocotyl emerged first instead of epicotyl from the media. However, fewer seed coats adhered to cotyledons when seeds were positioned with radicle up. For all the five species (Jatropha, pungam, simarouba, neem and mahua), the orientation of the seed that radicle up position may be increased the seed coat adherence to cotyledons that resultant, lower germination percentage compare to other orientation. Thus the present study with five different biofuel crops revealed that orienting the seeds as radicle downwards for Jatropha curcas and Madhuca longifolia and orienting seeds as flat for Pongamia pinnata, Simarouba glauca and Azardirachta indica resulted in production of higher percentage of normal seedlings (germination) with better seedling quality characters viz., shoot and root length, fresh weight, dry weight and vigour index.
ACKNOWLEDGEMENT The authors are grateful to National Oilseeds and Vegetable Oils Development (NOVOD) Board for funding this scheme.
MARIAPPAN, et al., Study on Seed Orientation for Production Quality Seedlings in Biofuel Tree Species
LITERATURE CITED Abdul – Baki, A.A., Anderson, J.D. 1973. Vigour determination in soybean seed by multiple criteria. Crop Science. 13: 630 – 633. Bagoury, O.H.E.L. 1973. The effect of the orientation of some cereal grains in the seedbed on seedling growth. Seed Science and Technology. 1:759-766. Bennet C, Younis, T.S.A.F, Esnauff, R. 1959. Geotropic behaviour of roots. J.Expt.Bot., 10:69 - 86. Gunasekaran, M. 1997. Seed technological studies in Clove (Syzygium aromaticum L.), Nutmeg (Myristica fragrans. Houtt) and Rubber (Hevea brasiliensis muell - Arg) Ph.D. Thesis. Tamil Nadu Agricultural University, Coimbatore - 3.
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Maynard D. 1989. Triploid watermelon seed orientation affects seed coat adherence on emerged cotyledons. Hort. Science. 24: 603604. Nettles, V.F. 1971. Vegetable seedling uniformity studies. Proc. Fla. State Hort. Soc. 84: 99-103. Nigama, P.S., Singh A. 2011. Production of liquid biofuels from renewable resources. Progress in Energy and Combustion Science. 37(1): 52–68. Pandey, S.N., Sinha, B.K. 2004. Plant physiology. Vikas Publishing House Pvt Ltd. pp. 479-481. Panse VG, Sukhatme, P.V. 1967. Statistical methods for agricultural workers. ICAR, New Delhi. pp. 610.
Gurunathan, N., Srimathi, P., Paramathma, M. 2009. Influence of size polymorphism on seed and seedling quality of Jatropha curcas. Madras Agric. Journal, 96 (1-6): 62-66.
Parameswari, K. 1999. Seed technological studies in tamarind (Tamarindus indica Linn.) M.Sc. (Ag.) Thesis, Tamil Nadu Agricultural University, Coimbatore.
Gurunathan, N. 2006. Studies on seed management techniques in Jatropha curcas. M.Sc. (Ag.) Thesis, Agricultural University, Coimbatore.
Prasad, P., Nautiyal, A.R. 2003. Effect of orientation of seed placement in soil on seedling emergence in two Bauhinia species Bauhinia vahlii Wight et. Arn. and Bauhinia racemosa Lam. International Seed Testing Association. Seed Science and Technology. 31(2): 497-503(7).
ISTA. 1999. International Rules for Seed Testing. Seed Sci. & Technol. (Supplement Rules). 27: 25-30 Jaskani, M.J., Kwon, S.W., Kim, D.H., Abbas, H. 2006. Seed treatments and orientation affects germination and seedling emergence in tetraploid watermelon. Pakistan J. Botany. 38(1): 89-98. Krishnasamy, V. 1992. Effect of orientation of seed placement in soil on seedling emergence in some cucurbitaceous vegetables. Seed Research. 20(2): 70-73.
Sharma, M.M., Purohit, S. 1980. Seedling survival and seed germination under laboratory condition in Shorea robusta. Seed Science and Technology. 8(2): 283-287. Sivasamy, M. 1991. Net production effect, seed production spectrum and quality polymorphism as influenced by ecology in Azadirachta indica and Ailanthus excelsa. Ph.D. Thesis, Tamil Nadu Agricultural University, Coimbatore.
Lal, P., Karnataka, D.C. 1996. Effects of orientation of seed sowing and soil mixture on germination behaviour of Querus lencorichorea. Indian Forester. 119 (20): 122–125.
Soeda, Y., Konings, M.C.J.M., Vorst, O., Houwelingen, A.M.M.L. van, Stoopen, G.M., Maliepaard, C.A., Kodde, J., Bino, R.J., Groot, S.P.C., and Geest, A.H.M. van der. 2005. Gene Expression Programs during Brassica oleracea Seed Maturation, Osmopriming, and Germination Are Indicators of Progression of the Germination Process and the Stress Tolerance Level. Plant Physiology. 137: 354–368.
Maguire JD. 1962. Speed of germination – Aid in selection and evaluation of seedling emergence and vigour. Crop. Sci., 2: 176177.
Sorensen, F.C., Campbell, R.K. 1981. Germination rate of douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] seeds affected by their orientation. Annals of Botany. 47: 467-471.
Maguire, J.D. 1962. Speed of germination aid in selection and evaluation for seedling emergence and vigor. Crop Science. 2: 176-177.
Thapliyal, R.C. 1979. Influence of seed orientation on seedling emergence in chir plant (Pinus roxburgii sarg.). Indian J. Exp. Biol., 17: 119-120.
Kumar, A., Sharma, S. 2011. Potential non-edible oil resources as biodiesel feedstock: An Indian perspective. Renewable and Sustainable Energy Reviews. 15(4): 1791–1800.
Masilamani, P. 1996. Seed technological studies in Teak (Tectona grandis Linn) Ph.D. Thesis. Tamil Nadu Agricultural University, Coimbatore.
Tunwar, N.S., Singh, S.V. 1985. Minimum seed certification standards. Central Seed Committee. Department of Agriculture and Cooperation, Govt. of India, New Delhi. Received on 10-05-2014
Accepted on 25-05-2014
1416 in Biosciences 7(13): 1416-1417, 2014 Trends
Trends in Biosciences 7 (13), 2014
Effect of Foliar Application of Hormones and Nutrients on Yield of Ardusi (Adhatoda zeylanica) TANDEL MADHURI, KRISHNAMURTHY R., PATHAK J.M., CHANDORKAR M.S. AND TANDEL D. H.* Department of Plant Pathology, N.M. College of Agriculture, Navsari Agricultural University ,Navsari * email :
[email protected]
ABSTRACT Adhatoda zeylanica plant leaves were used which were given the foliar application of hormones and nutrients. Plant height and no. of branches/plant were higher in treatment T5 – 0.2% urea + 100ppm BA and T4 – 0.2% urea + 100ppm kinetin at 36 month of age as compared to other treatments. While No. of leaves/plant was observed higher in treatment T2 – 0.1% urea + 100ppm kinetin at 36 month of age as compared to other treatments. Fresh leaf yield/plant (g) was observed higher in treatment T4 at 36 month of age as compared to other treatments. While Dry leaf yield/plant was observed higher in treatment T2 at 36 month of age as compared to other treatments. The percentage yield was found to be higher in treatment T2 as compared to the other treatments in both January and February sampling. Minimum percentage yield was found in treatment T1 – Water spray. Keywords
Adhatoda zeylanica, Foliar spray, Urea, Kinetin
With the global increase in the demand for plant derived medicine, there is a need to ensure the quality of the herbal drugs. Plants are a complex mixture of a variety of chemical constituents that can vary considerably depending on genetic and environmental factors, method of cultivation, time of collection, post-harvest processing etc. This inherent variability in the chemistry may adversely affect the efficacy of medicinal plants. Hence it is a pre-requisite to ensure that the plants used for therapy or for research purposes are of a quality that gives the desired efficacy and that the quality is maintained at each re-collection of the plant materials. To meet this requirement it is essential to establish qualitative and quantitative chemoprofiles of the samples. Urea, when properly applied, results in crop yield increases equal to other forms of nitrogen. Urea is highly water soluble. Research data indicate that urea should contain no more than 0.25% biuret for use in foliar sprays. Plant hormones are signal molecules produced within the plant, and occur in extremely low concentrations. Benzyl adenine or BA is a first-generation synthetic cytokinin that elicits plant growth and development responses, setting blossoms and stimulating fruit richness by stimulating cell division. It is an inhibitor of respiratory kinase in plants and increases post-harvest life of green vegetables. Kinetin is a kind of cytokinin, a class of plant hormone that promotes cell
division. The main objective of this work was to study foliar application of Kinetin, BA and Urea on yield of Adhatoda zeylanica.
MATERIALS AND METHODS Plant Material Collection : This trial was laid-out in 2009 with 5 treatments and 4 replication each in a RBD design with spacing 1 x 1m. The 5 treatments were labeled along with first as control (Water spray T-1), 0.1% urea + 100 ppm kinetin (T-2), 0.1 % urea + 100 ppm BA (T-3), 0.2 % Urea + 100 ppm kinetin (T-4) and 0.2 % urea + 100 pm BA (T-5). All necessary supplements as FYM and dose of NPK Mix (20:10:10) were given at due stage of trial and irrigated at the 15 days interval. Here we harvested the plants at the age of 36 months. The market product named “VASA Churna” also used for comparision. It is in the form of fine green powder of Vasaka leaves. In this study it is considered as given “unknown” treatment.
Percentage yield : The percentage yield was calculated from the product that was obtained after evaporation. Weight of the product obtained after evaporation Percentage yield = —————————————x100 Weight of the powdered sample taken initially
RESULTS AND DISCUSSION Field growth data : No.
Characters
Age
T1
T2
T3
th
36 78.35 80.35 80.15 month
T4
T5
82
83.05
24.4
27.45
1.
Plant height (cm)
2.
No. of 36th 22.95 24.3 Branches/plant month
3.
No. of Leaves/plant
36th 124.95 146.1 121.45 128.55 month 126.9
4.
Fresh leaf yield/plant (g)
36th 246.4 260.2 256.25 294.7 252.75 month
5.
Dry leaf yield/plant (g)
36th 112.15 132 117.6 113.5 116.15 month
23.8
MADHURI, et al., Effect of Foliar Application of Hormones and Nutrients on Yield of Ardusi (Adhatoda zeylanica)
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Figure: Graphical Comparison Of Percentage Yield
As the field growth data shows that the growth parameters viz. plant height and no. of branches/plant were higher in treatment T5 and T4 at 36 month of age as compared to other treatments. While No.of leaves/plant was observed higher in treatment T2 at 36 month of age as compared to other treatments. Fresh leaf yield/plant (g) was observed higher in treatment T4 at 36 month of age as compared to other treatments. While Dry leaf yield/plant was observed higher in treatment T2 at 36 month of age as compared to other treatments. The percentage yield was found to be higher in treatment T2 as compared to the other treatments in both January and February sampling. Minimum percentage yield was found in treatment T1.
Percentage yield : The study revealed that Adhatoda zeylanica has been widely used for its pharmacological activities and regarded as Universal Panacea in Ayurvedic medicines and finds its position as a versatile plant having a wide spectrum of medicinal activities. It is concluded that the foliar application of hormones and nutrients increases the yield. Hormone Kinetin is more efficient than Benzyl adenine to increase yield. As the concentration of nutrient Urea increased from 0.01 % to 0.02 %, it lowers the yield. Received on 08-05-2014
Accepted on 18-05-2014
1418 Trends in Biosciences 7(13): 1418-1421, 2014
Trends in Biosciences 7 (13)s, 2014
Impact of Seed Treatment with Tree Leaf Extracts on Physiological Parameters of Radish S. RAMESH KUMAR, J.ANITHA, P.KARTHIKA, B.DURGADEVI, N. MARIAPPAN AND S. KRISHNAKUMAR AND V. RAJENDRAN 1
Vanavarayar Institute of Agriculture, Manakkadavu, Pollachi-642 103, TNAU, Tamil Nadu. email:
[email protected]
ABSRTACT Lab experiment was conducted with an objective of studying the influence of tree leaf extracts of selected trees on seed attributes and identifying the best leaf extract that ensure high quality seeds in radish. The experiment was carried out from September 2013 to January 2014 in a completely randomized design replicated thrice. The experiment consisted of 7 treatments i.e. different green tree leaf extracts @ 5 % in the form of seed treatment and control using distilled water. The results indicated that there were significant differences in seed attributes due to the application of tree leaf extracts. Seed quality parameters were found to be high in seeds treated with Jatropha curcas in radish followed by T4 (Annona squamosa) gave higher mean values for all the parameters studied in radish. Hence, it is recommended that seed treatment with T5 fresh leaf extract of Jatropha curcas can be exploited to get good quality seeds/seedlings of vegetables. If Jatropha curcas leaf extract is not available leaf extract of Annona squamosa and Morinda tinctoria may be given as seed treatment. Key words
Organic seed teratment, tropical trees, seed quality
Vegetables have a special significance in human diet by supplying protective nutrients and tone up energy and vigour of man. Radish (Raphanus sativus L.) is an ancient and important root crop grown during winter in india order to meet the domestic needs. Seeds are the basic input for the crop cultivation. Pre-sowing seed invigoration treatments are numbered many by the researchers (Sundaralingam, et al.,2001) and all are claimed to have invigorative effect at field for enhancing the yield of crop to a tune of 10-15 per cent. Some of the widely pronounced pre-sowing seed management techniques are seed fortification with growth regulators and nutrients (Venkataraman, et al., 1978) and botanicals (Jegathambal, 1996 ). Adoption of any of these techniques for a particular crop required standardization work as the responses of the seed to the pre-sowing treatment vary with concentration
and duration of the treatment. Uses of different chemicals are costly and may cause natural hazard, whereas botanicals are less costly, easily available to the farmers, safe to handle and they can prepare easily. Comparative study of botanicals helps to choose the suitable one for storing the seeds. The studies were carried out to determine the effects of six botanicals viz., Cassia fistula, Cassia auriculata, Annona squamosa, Dalbergia oliveri, Morinda tinctoria and Jatropha curcas on the seed quality of radish.
MATERIALS AND METHODS The present investigation was carried out to screen the radish seeds against different leaf extracts to understand the physiological parameters viz., germination per cent, shoot length (cm), root length (cm) and vigour index under laboratory. Seeds were allowed to germinate in Petri dishes. Petri dishes were sterilized using 70 % ethanol and finally washed with distilled water. The surface sterilized (70 % ethanol) 20 seeds were placed in each Petri dish. The seeds were allowed to germinate. Distilled water was used for maintaining the control. The leaf extracts for each tree species were prepared by grinding fresh leaves and distilled water at 1:1 proposition and the extract was filtered and this served as stock solution. From the stock solution, five per cent solution was made for soaking of seeds for duration of 12 hours. The seeds were subjected to germination test with three replications. The germinability was recorded on the fifteenth day after sowing (DAS) and number of seeds germinated was expressed as per cent. On 7 th DAS, seedlings from each replication were carefully removed at random. Length of shoot was measured from the collar region to the tip of the longest leaf and expressed as cm. Root length of the seedlings was measured from the base of the stem to the tip of the longest root and expressed as cm. The vigour index of the seedlings was calculated using the following formula proposed by Abdul-Baki and Anderson (1973) and expressed as per cent. The data for different parameters were compiled and subjected to statistical analysis following a computer using Microsoft Excel Programme 1997.
KUMAR, et al., Impact of Seed Treatment with Tree Leaf Extracts on Physiological Parameters of Radish
1419
Table 1. Response of radish to seed treatment with different tree leaf extracts S. No
Treatments
Germination%
Shoot length (cm)
Root length (cm)
Vigour index
1
Control
65
3.8
5.5
604.50
2
Cassia fistula
75
5.1
6.2
907.50
3
Cassia auriculata
70
6.6*
7.1
959.00
4
Annona squamosa
97*
7.1*
8.7*
1532.66*
5
Dalbergia oliveri
75
5.0
6.0
825.00
6
Morinda tinctoria
80*
6.8*
6.5
1064.00*
7
Jatropha curcas
85*
4.3
9.1*
1139.00*
Mean
78.14
5.53
7.01
1004.52
SD
9.75
1.21
1.28
268.40
SEd
1.39
0.17
0.18
38.34
CD
2.66
0.37
0.03
74.01
*Significant at 5% level
RESULTS AND DISCUSSION Germination %: The interaction between radish seeds and treatments were found to be significant for seed germination. The treatment with T4 (Annona squamosa) has given higher germination percentage of 97% followed by T7 (Jatropha curcas) 85% and T6 (Morinda tinctoria) 80%. The germination percentages of other treatments were on par. The results expressed that seed fortification with Jatropha curcas leaf extract at 5 per cent found to maximize the germination percentage which was followed by Morinda tinctoria and Annona squamosa leaf extract and control had lowest germination. The increase in germination by leaf extracts is due to these botanicals contain some of the micro nutrients which are conducive for seed invigoration as reported by Sasthri, 2010. The leaf extracts also contains gibberellins like substances and micronutrients the zinc, which have synergistically activated to form the indole acetic acid (IAA).
Shoot length: The shoot length differed significantly from 65 to
97% due to different sources of leaf extracts. Among the treatments, T4 (Annona squamosa) recorded the longest shoot length of 7.1 cm followed by T6 (Jatropha curcas) and T3 (Cassia auriculata). The range of shoot length varied from 3.8 -7.1cm. The increased shoot length due to seed treatment with tree leaf extracts may be attributed to cell wall extension and increased metabolic activities at low water potential, as in matripriming (Afzal, et al., 2002). Kavitha, et al., 2012 stated that the leaf extracts of V. negundo caused significant changes in the germination percentage.
Root length: The data on root length was significant between varieties of different vegetables. The treatments T7 (Jatropha curcas) and T4 (Annona squamosa) recorded significant mean values of 9.1 and 8.7 cm, respectively. Whereas, the lowest values were obtained by T5, T6 and T2. The present study discovered that root length was maximum in the treatment of seed soaking with Jatropha curcas. The presence of phenols in the leaf extracts would have promoted the root length. Similar observations were recorded by Singh and Attrey, 2002 in beet leaf.
Table 2. Correlation matrix among different parameters of radish Characters
Germination% Shoot length (cm) Root length (cm) * Significant at 5% level
Pearson’s correlation co-efficient (r) Shoot length (cm)
Root length (cm)
Vigour index
0.49
0.80
0.96*
0.28
0.67 0.83
1420
Trends in Biosciences 7 (13)s, 2014
Fig 1. Relationship between shoot length (cm) and germination (%) of radish
Vigour index: The treatment with T4 (Annona squamosa) followed by T7 (Jatropha curcas) recorded significantly greater values for vigour index of 1532.66 and 1139.00, respectively. The range of vigour index was between 604.50 and 1532.66. In respect of vigour index maximum were obtained in Jatropha curcas and minimized in control. The botanicals also have insecticidal property as that of can maintains the viability and vigour of seed for long period.
Correlation and regression co-efficient analysis: Simple regression coefficient analysis between different seed attributes are shown in figures 1 to 3, while the correlation co-efficients are shown in table 2. A positive correlation was observed between germination percentage,
Fig 2. Relationship between root length (cm) and germination (%) of radish
shoot length, root length and vigour index in radish. There was also significant and positive relationship between germination percentage with vigour index in radish (0.96*). The relationship between shoot length and root length were found to be very weak since it recorded low r values. It was found that, the association between shoot length with vigour index was strong and positive. Similarly, very strong and significant positive relationship was observed between root length and vigour index in radish. Khatun, et al., 2009 also found positive and significant correlation of germination percentage with root plus shoot length andvigour and root plus shoot length with vigour. Based on the above discussion, it is concluded that seed treatment increased significantly its attributes. In nutshell, based on the present seed treatment study among the six seed treatment practices Jatropha curcas tree leaf extract @ 5% may be recommended for improving the seed quality characters in radish.
LITERATURE CITED Sundaralingam, K., P. Srimathi and K. Vanangamudi. 2001. Pre-sowing seed management. In :Recent trends and participatory approaches on quality seed production.(eds: K. Vanangamudi, A. Bharathi, P. Natesan, R. Jerlin, T. M. Thiyagarajan and S.Kannaiyan) Dept. Seed Science and Technology, Tamil Nadu Agricultural University, Coimbatore. Venkataraman, S., W. W. Manuel, P. Parthasarathy and R. Vaithialingam. 1978. Germination of seeds in chemical medium. Aduthurai Reptr., 1(8) : 87-88.
Fig 3.
Relationship between vigour index and germination (%) of radish
Jegathambal, R. 1996. Pre-sowing seed treatment to augment productivity of sorghum cv. CO26 under rainfed agriculture. Ph.D.
KUMAR, et al., Impact of Seed Treatment with Tree Leaf Extracts on Physiological Parameters of Radish Thesis, Tamil Nadu Agricultural University, Coimbatore. Abdul-Baki., A.A. and J.D. Anderson. 1973. Viability and leaching of sugars form germinating barley. Crop Sci., 1: 31-34. Sasthri, G. and P. Srimathi. 2010. Effect of organic and inorganic seed priming treatment on production of quality seed in cowpea. Green Farming, 1(4): 366-368. Afzal, I., S.M.A. Basra, N. Ahmad, M.A. Cheema, E.A. Warraich and A. Khaliq. 2002. Effect of priming and growth regulator treatment on emergence seedling growth of hybrid maize. Int. J. Agric. Biol., 4: 303-306. Kavitha, M., S. Natarajan and B. Senthamizhselvi. 2005. Effect of
1421
organics on growth and yield of onion (OASIS). Proceedings Seminar on Organic Agriculture Peninsular India – Promotion. Tamil Nadu Agricultural University, Coimbatore, pp.61-69. Singh, N. and D. P. Attrey. 2002. Studies on round the year organic production of beet leaf in trenches (underground green house) in cold desert high altitude conditions of Ladakh. In: International Seabuckthorn Association meeting, Berlin (Germany), sep.1418. Abst pp. 30. A. Khatun, G. Kabir, M. A. H. Bhuiyan and D. Khanam. 2009. Effect of preserved seeds using different botanicals on seed quality of lentil. Bangladesh J. Agril. Res. 36(3) : 381-387. Received on 08-05-2014
Accepted on 25-05-2014
1422 Trends in Biosciences 7(13): 1422-1428, 2014
Trends in Biosciences 7 (13), 2014
Heterosis and Genetic Studies on Yield and its Component Traits In Rice (Oryza sativa L.). G. SREENIVAS1, C. CHERALU2, K. RUKMINI DEVI3 AND K. GOPALA KRISHNA MURTHY4 1,4
Department of Genetics and Plant Breeding, Agricultural College, Aswaraopet, ANGRAU. Regional Agricultural Research Station, Warangal, ANGRAU. email:
[email protected] 2,3
ABSTRACT The nature and magnitude of heterosis and combining ability was studied in 24 F1 rice hybrids involving four lines and six testers using line × tester analysis. Warangal Samba was best combiner for grain yield, among the testers Ramappa, Jagtial Samba and Kavya were judged as better parents for yield. Crosses viz., Samba Mahsuri x Jagtial Samba, Akshayadhan x JGL-11727, Surekha x Ramappa, Samba Mahsuri x Ramappa, Warangal Samba x Jagtial Samba, Warangal Samba x Ramappa and Akshayadhan x Early Samba having high sca values and per se performance for grain yield per plant, also recorded a high degree of relative heterosis. The maximum expression of (79.21 per cent), heterobeltiosis (70.59 per cent), and standard heterosis (88.76 per cent) for observed in cross Samba Mahsuri x Jagtial Samba. The crosses exhibiting good heterotic expression in F1 are likely to give better segregants in later generations were additive gene effects were high.
Rice (Oryza sativa L.) is the world’s second most important cereal crop and staple food for more than 60% of the global population. In India, rice cultivation is very closely inter woven with livelihood and culture of millions of people and gained the status of major export commodity in the last ten years. . To meet the food demand of the growing population and to achieve food security in the country, the present production levels need to be increased by 2 million tons every year. It is estimated that 130 million tons of rice is required to feed the increasing population by 2025. As there is no scope to increase cultivable area, the only alternative is to improve the genetic yield potential of this crop. Of the various approaches contemplated to break the existing yield barriers in rice, hybrid rice technology offers an opportunity to boost the yield of rice under fragile conditions as hybrid rice varieties have a yield advantage of 15-20% over the conventional high yielding varieties (Virmani, et al., 1996). Breeding strategies for developing hybrids with high yield potential and better grain quality require the expected level of heterosis and combining ability. Combining ability analysis helps in identification of parents with high general combining ability (GCA) effects and cross combinations with high specific combining ability effects (SCA) for commercial exploitation of heterosis and isolation of pure lines among the progenies of the heterotic
hybrids. The Line X Tester design proposed by Kempthorne, 1957 is one of the effective method of estimation of gca and sca which enables effective screening of large number of parental lines.
MATERIALS AND METHODS The material for the present investigation was derived from four lines viz., Surekha(L1), Warangal Samba(L2), Akshayadhan(L3) and Samba Mahsuri(L4) and testers viz., Cotton Dora Sannalu(T1), JGL-11727(T2), Ramappa(T3), Kavya(T4), Jagtial Samba(T5) and Early Samba(T6) at Regional Agricultural Research Station, Warangal during rabi 2010-11. The 24 F1s along with parents were evaluated in kharif 2011. All the parents and crosses were planted in RBD with two replications. During the course of investigation recommended agronomic practices were followed to obtain a healthy crop. Observations were recorded on yield contributing characters. The data was recorded on five plants selected randomly from middle of each row in each replication. The relative heterosis (H1), heterobeltiosis (H 2) and standard heterosis (H 3) were estimated. For computing standard heterosis, Samba Mahsuri was used as a standard check. To test the significance of heterosis following formula given by Arunachalam (1976) were used.
RESULTS AND DISCUSSION The analysis of variance revealed highly significant differences among the genotypes for most of the characters. The analysis revealed significant difference among the parents and crosses for all characters except for number of productive tillers per plant. The lines and testers were found to be significant for most of characters. Variation among parent’s vs crosses was significant for most of yield contributing characters which indicate the presence of substantial heterosis in crosses (Table 1). In the present study variance due to gca was higher than sca as evidenced by the ratio being greater than one, for days to 50 per cent flowering, plant height, panicle length, number of grains per panicle, test weight, indicating predominance of additive gene action (Table 1) therefore, pedigree breeding may be useful for these characters. The results were in agreement with the findings of Ganasekaran,
SREENIVAS, et al., Heterosis and Genetic Studies on Yield and its Component Traits In Rice (Oryza sativa L.).
1423
Table 1. Analysis of variance of combining ability for yield and yield components characters in rice Source of variations
df
Days to 50% flowering
Plant height (cm)
No. of productive tillers plant-1
Panicle length (cm)
Number of grains per panicle
Test weight (g)
Grain yield plant-1(g)
Replicates
1
0.01
32.34
2.88
1.38*
201.30*
0.02**
0.94
Treatments
33
58.69**
393.03**
1.63*
12.33**
3808.3**
48.10**
37.45**
Parents
9
64.24**
417.32**
1.05
9.05**
3903.4**
50.48**
14.60**
Crosses
23
44.91**
330.41**
1.86*
10.68**
3318.0**
46.71**
33.30**
Parents vs Crosses
1
325.83**
1614.4**
1.68
79.93**
14227.7**
58.78**
338.56**
Lines
3
5.46
2217.05**
3.74
70.74**
14878.7**
270.34**
27.39
Testers
5
177.27**
135.58**
1.62
5.02**
4100.9**
26.48*
58.38
Lines x Testers
15
8.68**
18.03
1.57
0.55
744.9**
8.73**
26.12**
Error
33
1.56
10.35
0.88
0.31
39.1
0.01
2.44
σ GCA
8.98**
116.5**
0.18*
3.75**
945.0**
14.84**
4.04 *
σ2SCA
3.56**
3.83
0.34
0.11
352.9**
4.35**
11.83**
2.52
30.37
0.52
31.40
2.6
3.40
0.34
2
σ2GCA/ σ2 SCA
* Significant at p= 0.05, ** Significant at p= 0.01.
et al., 2006 and Dalvi and Patel, 2009 for days to 50 per cent flowering; Sharma et al., 2007 and Raju, et al., 2006 for plant height, panicle length, number of grains per panicle and test weight.
therefore, heterosis breeding is a better choice for exploiting these characters (Table 1). The results were in conformity with findings of Ganasekaran, et al., 2006 and Dalvi and Patel, 2009.
The characters like number of productive tillers per plant, grain yield per plant, variance due to sca was higher than gca as evidenced by the ratio being less than one suggesting significant role of non-additive gene action
Modern rice ideotype envisages rice plants with short to medium duration, moderate tillering ability and more number of grains per panicle with better test weight to improve the yield levels. Hence, study of yield components
Table 2. Estimates of general combining ability effects for yield and yield component characters in rice Days to 50% flowering
Plant height (cm)
No. of productive tillers plant-1
Panicle length (cm)
Number of grains per panicle
Test weight (g)
Grain yield plant-1(g)
Surekha
-0.47
-4.88**
-0.56*
-0.840**
-32.56**
5.10**
2.14**
Warangal Samba
0.77*
2.35 *
-0.06
-0.09
33.10**
-3.75**
1.29 **
Akshayadhan
-0.64
17.41**
-0.14
3.34**
-28.22**
2.96**
0.74
Samba Mahsuri
0.35
-14.88**
0.77**
-2.40**
27.68**
-4.32**
0.098
SE ±
0.36
0.92
0.27
0.16
1.80
0.03
0.45
-8.89**
-1.51
0.104
0.32
-28.39**
3.00**
-3.07**
Parent Lines
Testers Cotton Dora Sannalu JGL 11727
3.22**
1.66
0.104
0.86**
6.35**
-0.12 **
0.13
Ramappa
-1.14 *
2.43*
0.604
0.29
12.10**
0.15**
2.07**
Kavya
2.72**
3.83**
-0.771 *
-0.11
29.60**
0.23**
1.64**
0.60
1.18
-0.146
0.11
5.97 *
-2.65**
2.74**
3.47**
-7.59**
0.104
-1.47**
-25.64**
-0.62**
-3.52**
0.44
1.13
0.33
0.19
2.21
0.04
0.55
Jagtial Samba Early Samba SE ±
* Significant at p= 0.05, ** Significant at p= 0.01.
1424
Trends in Biosciences 7 (13), 2014
Table 3. Estimates of specific combining ability effects for yield and yield component characters in rice Days to 50% flowering
Plant height (cm)
No. of productive tillers plant-1
Panicle length (cm)
Number of grains per panicle
Test weight (g)
Grain yield plant-1(g)
Surekha x Cotton Dora Sannalu
-1.52
-1.49
0.06
-0.27
6.81
-1.06 **
0.26
Surekha x JGL 11727
-0.14
-1.12
0.56
0.74
13.56 **
-0.28 **
-0.79
Surekha x Ramappa
3.22 **
1.31
-0.43
0.35
-0.18
-2.13**
3.31 **
Surekha x Kavya
0.85
3.61
0.43
-0.08
-28.68 **
0.37**
1.24
Surekha x Jagtial Samba
-1.02
-1.24
-1.18
-0.86 *
0.43
0.39**
-3.05 *
Surekha x Early Samba
-1.39
-1.06
0.56
0.12
8.06
2.71**
-0.98
Warangal Samba x Cotton Dora Sannalu
0.72
-1.28
0.56
-0.16
-12.35 *
1.26**
0.02
Warangal Samba x JGL 11727
-0.39
1.63
-0.93
0.04
-4.60
-0.55**
-0.88
-3.02 **
4.36
1.06
0.46
34.64 **
-0.74**
2.37 *
Warangal Samba x Kavya
2.10 *
-1.83
-0.56
-0.22
16.64 **
-0.64**
-1.69
Warangal Samba x Jagtial Samba
0.72
2.11
0.31
0.39
-16.72 **
2.14**
1.10
Warangal Samba x Early Samba
-0.14
-5.00 *
-0.43
-0.51
-17.60 **
-1.46**
-0.92
3.14 **
2.71
0.64
-0.05
15.97 **
-0.48**
-1.22
Akshayadhan x JGL 11727
1.02
-1.62
0.14
-0.84 *
-13.77 **
1.80**
5.26**
Akshayadhan x Ramappa
0.39
-2.39
-0.85
-0.33
-20.52 **
2.81**
-5.37**
Akshayadhan x Kavya
-2.97 **
0.41
0.02
0.28
-2.02
0.65**
1.85
Akshayadhan x Jagtial Samba
-1.35
-2.24
-0.60
0.65
-0.39
-4.78**
-4.54**
Akshayadhan x Early Samba
-0.22
3.13
0.64
0.29
20.72**
-0.01
4.02 **
-2.35 *
0.06
-1.27
0.49
-10.43 *
0.28**
0.92
Samba Mahsuri x JGL 11727
-0.47
1.12
0.22
0.05
4.81
-0.96**
-3.58 **
Samba Mahsuri x Ramappa
-0.60
-3.29
0.22
-0.48
-13.93 **
0.06
-0.32
Samba Mahsuri x Kavya
0.02
-2.19
0.10
0.03
14.06 **
-0.38**
-1.39
Samba Mahsuri x Jagtial Samba
1.64
1.36
1.47*
-0.19
16.68**
2.23**
6.50 **
Samba Mahsuri x Early Samba
1.77
2.93
-0.77
0.09
-11.18 *
-1.23**
-2.12
SE ±
0.88
2.27
0.66
0.39
4.42
0.08
1.10
Parent
Warangal Samba x Ramappa
Akshayadhan x Cotton Dora Sannalu
Samba Mahsuri x Cotton Dora Sannalu
* Significant at p= 0.05, ** Significant at p= 0.01.
and the gene action controlling these traits is of paramount importance to manipulate the genetic architecture of rice. Character wise estimation of GCA effects of lines revealed the line Warangal Samba can be used as prospective parent for improving grain yield and contributing character like number of grains per panicle (Table 2). Samba Mahsuri to be a good combiner for plant height, number of
productive tillers per plant and number of grains per panicle, Akshayadhan is also a good combiner for panicle length and test weight. Among the testers Ramappa, Jagtial Samba and Kavya were judged as better parents for yield and yield component characters. This implied that favourable genes for grain yield were present in Ramappa, Jagtial Samba and Kavya (Table 2). The favourable genes for earliness
SREENIVAS, et al., Heterosis and Genetic Studies on Yield and its Component Traits In Rice (Oryza sativa L.).
1425
Table 4. Estimates of Heterosis (H 1), Heterobeltiosis (H 2 ) and Standard heterosis (H 3 ) for for yield and yield component characters in rice Days to 50% flowering
Plant height (cm)
Cross Surekha x Cotton Dora Sannalu Surekha x JGL 11727 Surekha x Ramappa Surekha x Kavya Surekha x Jagtial Samba Surekha x Early Samba Warangal Samba x Cotton Dora Sannalu Warangal Samba x JGL 11727 Warangal Samba x Ramappa Warangal Samba x Kavya Warangal Samba x Jagtial Samba Warangal Samba x Early Samba Akshayadhan x Cotton Dora Sannalu Akshayadhan x JGL 11727 Akshayadhan x Ramappa Akshayadhan x Kavya Akshayadhan x Jagtial Samba Akshayadhan x Early Samba Samba Mahsuri x Cotton Dora Sannalu Samba Mahsuri x JGL 11727 Samba Mahsuri x Ramappa Samba Mahsuri x Kavya Samba Mahsuri x Jagtial Samba Samba Mahsuri x Early Samba SE ±
No. of productive tillers plant-1
Panicle length (cm)
H1
H2
H3
H1
H2
H3
H1
H2
H3
H1
H2
H3
-11.35 **
-16.00 **
-24.66 **
6.68 *
3.56
27.72 **
12.00
0.00
0.00
12.07 ** 8.35 **
26.50 **
-3.94 ** -5.34 **
-12.56 **
3.36
0.17
31.67 **
11.11
-6.25
7.14
10.15 ** 5.71 *
34.25 **
-2.77 *
-3.50 *
-13.45 **
10.24 **
9.64 **
35.22 **
12.00
0.00
0.00
8.03 **
5.28 *
29.50 **
-2.49 *
-2.97 *
-12.11 **
9.81 **
6.81 *
39.33 **
8.33
0.00
-7.14
4.81 *
2.45
25.25 **
-3.34 ** -6.00 **
-15.70 **
7.33 **
6.22 *
31.00 **
-12.00
-21.43
-21.43
1.55
-1.61
22.50 **
-3.50 **
-3.50 *
-13.45 **
6.07 *
-1.53
21.44 **
15.38
0.00
7.14
6.82 **
2.36
19.50 **
-8.14 **
-13.37 **
-21.52 **
5.25 *
-4.45
36.00 **
14.29
14.29
14.29
14.19 ** 8.96 **
30.75 **
-3.43 ** -4.37 **
-11.66 **
4.30
0.31
42.78 **
-13.33
-18.75
-7.14
8.91 **
5.91 *
34.50 **
-8.27 ** -9.41 **
-17.94 **
10.97 **
3.04
46.67 **
28.57 *
28.57 *
28.57* 10.08 ** 8.74 **
33.75 **
4.91 *
28.25 **
-0.50
-0.50
-9.87 **
3.63
-0.70
41.33 **
-11.11
-14.29
-14.29
-0.77
-3.96 **
-13.00 **
8.53 **
0.31
42.78 **
7.14
7.14
7.14
8.38 ** 6.43 **
32.50 **
-1.49
-1.98
-11.21 **
0.90
-12.10 **
25.11 **
-3.45
-6.67
0.00
5.73 *
20.00 **
-20.63 **
16.37 **
2.06
57.17 **
6.67
0.00
14.29
21.22 ** 9.19 **
48.50 **
-4.37 **
-11.66 **
9.23 **
1.23
55.89 **
-6.25
-6.25
7.14
11.98 ** 8.27 **
47.25 **
-4.83 ** -5.08 **
-16.14 **
12.96 **
1.23
55.89 **
-6.67
-12.50
0.00
13.51 ** 8.09 **
47.00 **
-5.53 ** -6.93 **
-15.70 **
12.89 **
4.26
60.56 **
-10.34
-18.75
-7.14
14.62 ** 8.82 **
48.00 **
-2.86 * -4.59 **
-16.14 **
12.58 **
0.43
54.67 **
-13.33
-18.75
-7.14
15.93 **
11.03 **
51.00 **
-5.60 ** -9.69 ** -1.99
5.88 **
0.00
-1.52
-2.50
-12.56 **
16.22 **
-2.02
50.89 **
3.23
0.00
14.29
16.26 **
3.86
41.25 **
-16.42 **
-24.66 **
-24.66 **
9.51 **
1.91
18.33 **
0.00
0.00
0.00
17.22 **
12.39 **
22.50 **
-8.62 **
-12.11 **
-12.11 **
6.34 *
-6.38 *
23.06 **
13.33
6.25
21.43
8.37 **
-3.15
23.00 **
-10.95 **
-16.14 **
-16.14 **
7.21 *
-2.46
19.00 **
28.57 *
28.57 *
28.57*
5.38 *
-4.47
17.50 **
-7.76 **
-12.11 **
-12.11 **
5.69 *
-6.64 *
21.78 **
11.11
7.14
7.14
6.19 **
-3.48
18.00 **
-5.34 **
-12.56 **
-12.56 **
11.22 **
1.66
22.78 **
35.71 **
35.71 *
35.71*
5.12 *
-5.22 *
18.00 **
11.62 **
8.62 *
14.78 **
3.45
0.00
7.14
7.73**
4.21
11.50 **
2.78
3.21
3.21
0.81
0.93
0.93
0.05
0.05
0.05
-4.96 ** -9.87 ** -9.87 ** 1.08
1.24
* Significant at p= 0.05, ** Significant at p= 0.01.
1.24
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Trends in Biosciences 7 (13), 2014
Table 4(Cont.) Estimates of Heterosis (H1), Heterobeltiosis (H2 ) and Standard heterosis (H3) for yield and yield component characters in rice Cross
Number of grains per panicle
Grain yield plant-1 (g)
Test weight (g)
H1
H2
H3
H1
H2
H3
H1
H2
H3
Surekha x Cotton Dora Sannalu
15.77 **
12.40 *
-7.94
8.88 **
5.77 **
100.00 **
1.15
-2.22
4.14
Surekha x JGL 11727
32.04 **
21.50 **
18.41 **
14.39 **
-3.30 **
82.85 **
15.16
12.86
16.86
Surekha x Ramappa
13.69 **
-3.51
13.33 **
11.94 **
-9.36 **
71.40 **
64.86 **
53.57**
52.66 **
4.69
-12.30 **
6.35
21.98 **
0.64
90.30 **
21.99 **
8.88
37.87 **
1.47
-18.40 **
9.84 *
13.05 **
-10.42 **
69.39 **
13.24
7.49
18.93
15.95 **
15.50 **
-5.40
44.65 **
6.35 **
101.09 **
4.61
-5.36
-5.92
0.66
-26.06 **
21.59 **
4.01 **
-14.55 **
52.35 **
3.10
-6.94
23.08 *
Warangal Samba x JGL 11727
13.45 **
-9.65 **
48.57 **
-5.16 **
-10.95 **
16.31 **
15.93 *
3.36
36.69 **
Warangal Samba x Ramappa
25.68 **
7.72 **
77.14 **
0.88
-0.19
16.93 **
53.60 **
26.62**
67.46 **
Warangal Samba x Kavya
23.78 **
7.53 **
76.83 **
-0.46
-3.80 **
18.24 **
8.80
6.49
40.83 **
Warangal Samba x Jagtial Samba
-5.94 *
-14.48 **
40.63 **
4.34 **
2.48 **
17.51 **
34.96 **
23.94**
63.91 **
Warangal Samba x Early Samba
-2.33
-27.03 **
20.00 **
4.10 **
-7.57 **
5.98 **
7.93
-13.20
14.79
Akshayadhan x Cotton Dora Sannalu
22.39 **
15.27 **
0.63
3.90 **
2.09 **
88.58 **
9.20
5.56
12.43
Akshayadhan x JGL 11727
12.37 **
6.51
3.81
15.75 **
-1.20 *
82.49 **
67.35 **
64.00**
69.82 **
0.78
-12.16 **
3.17
27.17 **
3.91 **
91.94 **
27.80 **
19.05
18.34
20.85 **
3.93
26.03 **
14.92 **
-4.31 **
76.76 **
40.31 **
25.23**
58.58 **
1.00
-16.75 **
12.06 **
-21.45 **
-37.21 **
15.98 **
21.13 *
14.97
27.22 **
25.05 **
20.73 **
5.40
21.00 **
-10.37 **
65.56 **
56.58 **
41.67**
40.83 **
Samba Mahsuri x Cotton Dora Sannalu
34.77 **
19.37 **
19.37 **
1.39 *
-20.87 **
41.08 **
17.48 *
13.89
21.30 *
Samba Mahsuri x JGL 11727
53.05 **
51.11 **
51.11 **
-5.33 **
-16.42 **
9.16 **
11.63
9.71
13.61
Samba Mahsuri x Ramappa
31.39 **
21.62 **
42.86 **
9.31 **
1.31
18.68 **
55.41 **
44.38**
44.38 **
Samba Mahsuri x Kavya
55.24 **
41.62 **
71.75 **
4.06 **
-5.64 **
15.98 **
19.58 *
7.01
35.50 **
Samba Mahsuri x Jagtial Samba
35.05 **
17.69 **
58.41 **
8.28 **
3.10 **
14.01 **
79.21 **
70.59**
88.76 **
Samba Mahsuri x Early Samba
33.10 **
20.63 **
20.63 **
9.52**
3.47 **
3.47 **
11.48
0.59
0.59
5.41
6.25
6.25
0.09
0.11
0.11
1.35
1.56
1.56
Surekha x Kavya Surekha x Jagtial Samba Surekha x Early Samba Warangal Samba x Cotton Dora Sannalu
Akshayadhan x Ramappa Akshayadhan x Kavya Akshayadhan x Jagtial Samba Akshayadhan x Early Samba
SE ±
* Significant at p= 0.05, ** Significant at p= 0.01.
SREENIVAS, et al., Heterosis and Genetic Studies on Yield and its Component Traits In Rice (Oryza sativa L.).
were present in Ramappa and Cotton Dora Sannalu and those for dwarfness were present in Early Samba. The cross combination showing desirable sca effects for days to 50 per cent flowering are Warangal Samba x Ramappa, Akshayadhan x Kavya and Samba Mahsuri x Cotton Dora Sannalu. For plant height negative estimates of sca are desirable and the promising specific combiner was Warangal Samba x Early Samba. For number of productive tillers per plant Samba Mahsuri x Jagtial Samba exhibited high sca effects (Table 3). The promising specific combiners for no. of grains per panicle were Warangal Samba x Ramappa followed by Samba Mahsuri x Jagtial Samba. Akshayadhan x Ramappa, Akshayadhan x Kavya, Samba Mahsuri x Jagtial Samba and Warangal Samba x Jagtial Samba showed high sca effects for test weight. High sca effects results mostly from the dominance and interaction effects existed between the hybridizing parents. Out of 24 crosses only five crosses Samba Mahsuri x Jagtial Samba, Akshayadhan x JGL 11727, Akshayadhan x Early Samba, Surekha x Ramappa and Warangal Samba x Ramappa exhibited significantly positive sca values for grain yield per plant (Table 3). It is evident that cross combinations, which expressed high sca for grain yield, have invariable positive effects for one or more yield related traits also. Among the crosses high sca values and per se performance for grain yield per plant was registered by Samba Mahsuri x Jagtial Samba which also showed high sca values for other yield contributing characters (Table 3). The cross Samba Mahsuri x Jagtial Samba involved medium x high general combiner for yield, so it can be used for obtaining superior recombinants in advance generations. The cross Akshayadhan x Early Samba involved low x low general combiner for yield and therefore may be used for heterosis breeding.
Evaluation of heterosis: The heterosis in negative direction was considered to be desirable for days to 50 per cent flowering, since early parent is treated as better parent for comparison. Heterosis of desirable nature was recorded in 19 crosses (Table 4). Standard heterosis with Samba Mahsuri varied between 9.87 to -24.66 per cent. Heterosis for earliness was also reported Pandya and Tripathi, 2006 and Eradasappa, et al., 2007. In case of plant height, negative heterosis is preferred to evolve dwarf and semi dwarf varieties for general cultivation. Only three crosses viz., Warangal Samba x Early Samba (-12.10 per cent), Samba Mahsuri x Kavya (6.64 per cent) and Samba Mahsuri x JGL-11727 (6.38 per cent) recoded significant negative heterobeltiotic effects (Table 4).
1427
Among the yield components productive tillers per plant is considered as most important parameter. In the present investigation only three crosses viz., Samba Mahsuri x Jagtial Samba (35.71 per cent), Samba Mahsuri x Ramappa (28.57 per cent) and Warangal Samba x Ramappa (28.57 per cent recorded a significant heterosis, heterobeltiosis and standard heterosis of varying degrees (Table 4). The number filled of grains per panicle is an important yield contributing factor so far crosses are concerned. Samba Mahsuri crosses exhibited significant heterosis, heterobeltiosis and standard heterosis of varying degrees in desirable direction. Eradasappa et al., 2007 and Narasimman, et al., 2007 reported superiority of crosses over parents. The genotype with greater panicle length and having more number of primary and secondary branches with spikelets is desirable. Six crosses recorded more than 40 per cent standard heterosis. The crosses of Akshayadhan exhibited significant heterosis, heterobeltiosis and standard heterosis of varying degrees in desirable direction. Yield is the ultimate result, which is dependent on its components. Significantly positive heterosis was recorded in 13 crosses for grain yield. Heterobeltiosis was significant and positive in eight crosses. To have the practical value standard heterosis is a must and it was observed in 15 crosses. . Crosses viz., Samba Mahsuri x Jagtial Samba, Akshayadhan x JGL 11727, Surekha x Ramappa, Samba Mahsuri x Ramappa, Warangal Samba x Jagtial Samba, Warangal Samba x Ramappa and Akshayadhan x Early Samba performed exceedingly well for grain yield (Table 4). Pandya and Tripathi, 2006, Eradasappa, et al., 2007 and Panwar and Mashiat Ali, 2010 reported positive heterosis for this trait. The magnitude of heterosis varied from trait to trait, and cross to cross and none of the cross combination recorded significant heterosis for all the traits simultaneously. These results are in agreement with the findings of Jelodar and Bagheri, 2010 and Eradasappa, et al., 2007. The maximum expression of (79.21 per cent), heterobeltiosis (70.59 per cent) and standard heterosis (88.76 per cent) for observed in cross Samba Mahsuri x Jagtial Samba which was due to cumulative effect of highly significant and desirable heterosis for quantitative characters (Table 4). The next best cross for most of the quantitative character was Warangal Samba x Ramappa. It showed significant heterosis for characters like earliness, number of productive tillers per plant, panicle length and number of grains per panicle. The crosses Akshayadhan x JGL 11727, Surekha x Ramappa has high heterotic values for grain yield per plant but other characters were not in desired direction.
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Trends in Biosciences 7 (13), 2014
LITERATURE CITED Arunachalam, V. 1976. Evaluation of diallel cross by graphical and combining ability methods. Indian Journal of Genetics and Plant Breeding. 36: 358-366. Dalvi, V.V and Patel, D.U. 2009. Combining ability analysis for yield in hybrid rice. Oryza. 46 (3): 97-102. Eradasappa, E., Ganapathy, K.N., Satish, R.G., Shanthala, J and Nadarajan, N. 2007. Heterosis studies for yield and yield components using CMS lines in rice (Oryza sativa). Crop Research. 34(1, 2 & 3): 152-155. Ganasekaran, M., Vivekanandan, P and Muthuramu, S. 2006. Combining ability and heterosis for yield and grain quality in two line rice. Indian Journal of Genetics. 66 (1): 6-9. Jelodar, N.B and Bagheri, N. 2010. Heterosis and combining ability analysis for yield and related-yield traits in hybrid rice. Int J Biol. 2(2):222-227. Kempthorne, O. 1957. An introduction to genetics statistics. John wiley and Sons Inc New York.
Narasimman, R., Thirugnanakumar, S., Eswarn, R., Praveen, C., Sampath Kumar and Anandan, A. 2007. Combining ability and heterosis for grain yield and its component characters in rice (Oryza sativa L.). Crop Improvement. 34(1): 16-18. Pandya, R and Tripathi,. R.S. 2006. Heterosis breeding in rice. Oryza. 43(2): 87-93. Panwar, L.L and Mashiat Ali. 2010. Heterosis and inbreeding depression for yield and kernel characters in scented rice. Oryza. 47 (3): 179-187. Raju, Ch. S., Rao, M.V.B and Sudarshanam, A. 2006. Heterosis and genetic studies on yield and associated physiological traits in rice (Oryza sativa L.). Oryza. 43(4): 264-273. Sharma, M. K., Sharma, A. K., Agarwal, R.K and Richharia, A.K. 2007. Combining ability and gene action for yield and quality characters in Ahu rices of Assam. Indian Journal of Genetics and Plant breeding. 67(3): 278-280. Virmani, S.S. 1996. Hybrid rice. Adv Agron. 57:328-462. Received on 09-05-2014
Accepted on 14-05-2014
Trends in Biosciences 7(13): 1429-1433, 2014
Study on Effect of Fertilizer and FYM Application on Black Gram (Vigna mungo L. Hepper) in Relation to Seed Yield and its Component Characters in Hilly Area of Nagaland RITA NONGTHOMBAM1*, S. KIGWE2 AND INDRAJIT YUMNAM3 1
ICAR Research Complex for NEH Region, Arunachal Pradesh Centre, Basar-791101 School of Agricultural Sciences and Rural Development, Nagaland University 3 College of Agriculture, CAU, Imphal, Manipur email : *
[email protected] 2
ABSTRACT The experiment was conducted at Medziphema, foothills of Nagaland to study the performance and yield of fourteen genotypes of black gram (Vigna Mungo L. Hepper) on two different treatments – one with fertilizer @ 20:40:20 NPK Kg/ha, the other with FYM @ 20 tons/ha and without any treatment i.e., control. The experimental design used was RBD. The plot size was 1.5 x 2 m2 with a spacing of 30 cm row to row 10 cm plant to plant. Data on eight characters were under study. Based on the analysis of variance it was revealed that significant differences were existed of all the characters among the genotypes. Further, significant differences due to environment (linear) indicated that the performance of the genotypes could be predicted. Analysis of variance (mean squares) for different characters in three different environments (Table-II) showed significant differences in E3 (FYM treated soil). Environmental means for different characters in three different environments (Table-III), indicated that soil treated with FYM @20 tons/ha was found to be the best in terms of seed yield and other yield attributing characters under the foot hill conditions of Medziphema, Nagaland. Key words
Vigna mungo, fertilizer, FYM, Control, yield etc.
Black gram is one of the main pulse crops that have been the mainstay of Indian agriculture, enabling the land to turn out reasonable quantities of food grain when, over the several decades, a major part of the area under cultivation has hardly received any manures or fertilizers. Black gram is generally included in crop rotation in most of the areas and has helped to keep the soil alive and productive. In addition, it also serves as excellent forage and grain concentrates as the feed of the large cattle population of the country, and some of them are excellent green manure crops adding the much needed humus and the major plant nutrients to soil. It also plays an important role in sustaining soil fertility by improving soil physical properties and fixing atmospheric nitrogen. Being a drought resistant crop, it is suitable for dry land farming and predominantly used as an intercrop with other crops.
Black gram is hardly cultivated due to low yield in the north eastern states of India particularly in Nagaland. Therefore the present experiment was taken up to study the effect of fertilizer and FYM application in relation to yield and its attributing characters.
MATERIALS AND METHODS The present investigation comprises three soil environments viz., E-1 (control- without any treatment), E-2 (Soil treated with fertilizer only @ 20:40:20 NPK Kg/ ha) and E-3 (Soil treated with FYM only@20 tons/ha) which were created to study the performance of black gram in relation to seed yield and its attributing characters. The present investigation was carried out in Medziphema. It is located at the foothills of Pauna range in the Dimapur District of Nagaland. The soil texture of Medziphema is Sandy Clay loam. Fourteen genotypes of black gram namely, RBU 1012, NDU3-4, Uttara, PantU 19, KU 323, PantU 35, PantU 31, KU 99-22, KOBG 653, SB 27-3, Type 9, IPU021, NDU5-3 and NDU 99-2 were used under study. The experiment was carried out in randomized block design with three replications during kharif 2009 Medziphema, Nagaland. Each genotype was grown in a plot size of 1.5 x 2 m2 consisting of 5 rows each with a spacing of 30 cm row to row and 10 cm plant to plant. Other recommended packages of practices for raising a good crop of black gram were followed. The parameters were taken from eight characters. viz., days to 50% flowering, days to 80% maturity, primary branches per plant, clusters per plant, pods per cluster, pods per plant, 100 seed weight (g), and seed yield per plant(g). Ten randomly chosen plants in each replication were taken to record the observations and the observations on the character days to 50% flowering and days to 80% maturity were recorded on the plot basis of visual observation. The performance of seed yield was estimated using the model proposed by Eberhart and Russell (1966).
RESULTS AND DISCUSSION Analysis of variance (Table 1) revealed significant differences of all the characters among the genotypes (G)
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Trends in Biosciences 7 (13), 2014
Table 1. Analysis of variance (mean squares) for different characters in black gram (Eberhart and Russell, 1966). Source of variation
d.f.
50% flowering
Days to 80% maturity
Primary branches
Cluster per plant
Pods per cluster
Pods per plant
100 seed weight
Seed yield per plant
Genotypes
13
76.29**
92.24**
0.57**
22.27**
0.12**
44.97**
0.16**
11.83**
Env. +(G x E)
70
63.02**
223.83**
0.56**
25.46**
0.18**
67.25**
0.19**
7.94**
Env. (linear)
1
3258.82**
14520.21**
22.14**
1386.88**
7.64**
2979.42**
7.50**
340.12**
G x E (linear)
13
39.98**
40.73**
0.41**
10.76**
0.07*
49.22**
0.11NS
11.23**
Pooled deviation
56
11.29
NS
0.06*
19.42
NS
0.08*
1.24 NS
RBU1012
4
NDU3-4
4
Uttara
4
NS
11.04*
0.21
2.98 NS
2.95 NS
0.42**
3.66 NS
0.07 NS
1.07 NS
0.07 NS
0.4 NS
0.79 NS
20.26 **
0.21 NS
8.48**
0.11**
25.61*
0.11*
3.57*
0.05
NS
0.19
NS
0.05
NS
17.39
0.07
NS
2.54
NS
0.02
NS
24.34*
0.10*
2.25 NS
54.59**
4.21
NS NS
4.56
NS
NS
0.02
NS
1.08 NS
PantU 19
4
48.42**
5.08
KU323
4
4.14 NS
10.89 NS
0.20 NS
5.27 NS
0.05 NS
24.52*
0.03 NS
1.18 NS
PantU 35
4
5.89 NS
6.97 NS
0.23 NS
11.60**
0.05 NS
49.95**
0.08 NS
1.83 NS
PantU 31
4
8.03 NS
34.48**
0.02 NS
8.72**
0.10**
28.63*
0.19**
1.83 NS
KU 99-22
4
7.62
NS
NS
NS
NS
NS
0.11 NS
KOBG 653
4
4.68 NS
1.22 NS
0.26 NS
5.21 NS
0.12**
33.96**
0.21**
1.54 NS
SB 27-3
4
4.53 NS
2.79 NS
0.09 NS
4.19 NS
0.09**
27.09*
0.04 NS
0.82 NS
Type 9
4
9.12 NS
54.57**
0.66**
1.96 NS
0.02 NS
3.63 NS
0.13**
0.33 NS
IPU02-1
4
2.52 NS
2.78 NS
0.33*
1.07 NS
0.08*
3.08 NS
0.03 NS
0.19 NS
NDU5-3
4
2.44
NS
NS
NS
0.18**
0.20 NS
NDU99-2
4
2.39 NS
1.59 NS
0.10 NS
9.06**
0.14**
19.52 NS
0.06 NS
1.80 NS
Pooled error
156
11.29
7.59
0.18
3.33
0.04
13.68
0.05
1.96
6.15
0.69
NS
0.12
0.17
NS
1.73
0.21
NS
0.06
0.01
NS
11.95
1.26
NS
0.02
*, ** Significant at 5% and 1% levels respectively NS=Not Significant
under study. Further, significant differences due to environment (linear) indicated that the performance of the genotypes could be predicted. The G x E (linear) was found to be significant in all the characters under study except 100 seed weight and pooled deviation mean sum of squares were found significant for days to 80% maturity, pods per cluster and 100 seed weight and indicating the presence of both predictable and non-predictable components. The importance of both linear and non-linear sensitivity for the expression of the traits was thus evident. Significant linear component against pool deviation for the said characters showed that the major component for differences in stability was due to linear regression and the performance can be predicted with some reliance under different environments. The significant effects of G x E (linear) interaction against pooled deviation were observed in all the characters except 100 seed weight. Analysis of variance (mean squares) for different characters in three different environments TableII showed significant differences in E3 (FYM treated soil). As per Table-III, considering early flowering of the genotypes, soil environment (E3) showed the earliest
flowering as compared with the other two soil environments and also showed maximum numbers of cluster per plant, pods per plant, 100 seed weight and seed yield per plant. Likewise, soil environment (E2) showed the shortest duration and also the maximum number of primary branches per plant and maximum number of pods per plant. Amany, 2007 and Caliskan, et al. 2008 reported similar results in chickpea and soybean, respectively. However, soil environment (E3) performed the best as regards to yield and its component characters similar results were also observed by Sharma, et. al., 2008 and Futie Xie, et al., 2011. The mean performance and environmental index of the investigated black gram genotypes in each environment are presented in Table-III. The environmental index presented in Table-III was the deviation of each environment from the grand mean of all environments. The environmental indices for soil environmental conditions (E1)under study recorded significantly the highest number of Cluster per plant, Pods per cluster, Pods per plant, 100 seed weight and Seed yield per plant which were reflected in the highest positive environmental indices. Similarly, soil
NONGTHOMBAM, et al., Study on Effect of Fertilizer and FYM Application on Black Gram (Vigna mungo L. Hepper)
1431
Table 2. Analysis of variance (mean squares) for different characters in three different environments d.f.
50% flowering
Days to 80% maturity
Primary branches
Pods per cluster
Pods per plant
Seeds per pod
E-1 Replication Genotypes Error
2 13 26
5.16** 13.56** 1.57
4.35** 16.56** 1.99
3.79** 1.04 NS 0.92
0.14 NS 0.16 NS 0.08
2.00 NS 11.68 NS 7.78
0.23 NS 0.48 NS 0.29
1.44** 0.31 NS 0.18
0.45 0.99 0.54
E-2 Replication Genotypes Error
2 13 26
0.21NS 16.95** 0.39
36.85** 6.04 NS 3.16
2.44** 0.92 NS 0.61
0.73** 0.33* 0.12
360.82** 51.05 NS 30.86
0.39 NS 0.32 NS 0.21
1.08** 0.24* 0.09
49.74** 4.25 4.07
E-3 Replication Genotypes Error
2 13 26
68.02 NS 75.56 NS 71.02
1.78 NS 22.38** 7.14
0.18 NS 0.10 NS 0.22
1.10** 0.21* 0.08
192.59** 34.05* 14.20
0.24 NS 0.31 NS 0.38
0.04 NS 0.14** 0.03
19.35** 5.49* 1.94
Source of variation
100 seed Seed yield weight per plant
*, ** Significant at 5% and 1% level respectively NS= Not Significant
environment (E2) was significantly higher corresponded to the positive environmental indices compared to those negative of other two environmental indices i.e, E1 and E3 for the character Days to 80% maturity. The value of environmental indices for the characters Days to 50% flowering and Primary branches were found to be highest in soil environment E3.Significant differences were observed between the environments as the value of C.D (E) at 5% was less than the difference between the highest
and the lowest mean value over the environments for all the characters under study. Among the different soil environments created viz., E1- without any treatment, E2- treatment with fertilizer and E3-treatment with FYM, the best soil environment for yield, 100 seed weight, pods per plant and cluster per plant was found to be FYM treated soil (E3). Soil environment E3 also showed the earliest in term of days to 50%
Table 3(1): Environmental means for different characters in three different environments Sl. No
Genotype s
Days to 50% flowering
Days to 80% maturity
Primary branches
E1
E2
E3
Mean
E1
E2
E3
Mean
E1
E2
E3
Mean
1.
RBU1012
46.00
45.33
45.00
45.44
73.33
68.67
74.33
72.11
0.80
1.53
2.07
1.47
2.
NDU 3-4
43.33
41.67
41.33
42.11
71.33
68.33
70.33
70.00
1.87
2.00
1.87
1.91
3.
Uttara
41.00
39.33
57.00
45.78
68.00
66.67
67.00
67.22
1.80
1.80
1.47
1.69
4.
PantU 19
41.00
41.00
39.33
40.44
69.67
68.33
72.00
70.00
1.73
2.80
1.73
2.09
5.
KU323
44.00
41.00
39.33
41.44
68.00
68.00
68.33
68.11
1.27
2.27
2.00
1.85
6.
PantU 35
43.00
44.00
39.33
42.11
70.33
69.33
72.00
70.55
2.07
3.53
2.47
2.69
7.
PantU 31
39.00
39.33
37.00
38.44
69.67
64.67
67.33
67.22
1.53
2.00
1.87
1.80
8.
KU 99-22
41.67
39.00
37.00
39.22
68.00
66.67
67.00
67.22
1.80
1.93
2.00
1.91
9.
KOBG 653
44.33
44.00
41.33
43.22
74.00
70.33
72.00
72.11
2.40
2.47
2.00
2.29
10. SB 27-3
42.67
39.00
39.33
40.33
68.33
66.67
67.33
67.44
1.53
1.40
1.60
1.51
11. Type 9
47.00
45.00
40.67
44.22
74.33
68.33
73.67
72.11
3.13
2.20
1.80
2.38
12. IPU02-1
41.00
39.67
39.00
39.89
68.00
66.67
68.00
67.56
1.87
2.33
1.93
2.048
13. NDU5-3
44.00
39.33
39.00
40.79
69.00
68.33
68.33
68.55
1.13
2.80
2.13
2.02
40.33
68.00
67.00
66.33
67.11
1.07
2.00
1.53
1.54
41.70
70.00
67.71
69.57
69.09
1.71
2.22
1.89
1.94
-0.91
1.38
-0.48
-0.23
-0.28
0.05
14. NDU99-2 Mean Env. Index C.D. (E) at 5%
42.67
39.33
39.00
42.90
41.21
40.98
-1.2
0.49
0.72 2.46
2.02
0.27
1432
Trends in Biosciences 7 (13), 2014
Table 3 (2). Environmental means for different characters in three different environments Sl. No.
Genotypes
Cluster per plant
Pods per cluster
Pods per plant
E1
E2
E3
Mean
E1
E2
E3
Mean
E1
E2
E3
Mean
1.
RBU1012
2.53
7.33
11.47
7.11
1.87
2.40
2.33
2.20
8.07
17.53
19.87
15.16
2.
NDU3-4
3.93
9.27
12.40
8.53
2.07
2.53
2.73
2.44
8.40
13.93
24.33
15.55
3.
Uttara
3.07
5.73
7.93
5.58
2.20
2.73
2.47
2.47
8.40
16.33
16.27
13.67
4.
PantU 19
3.20
7.53
8.73
6.49
2.00
2.13
2.00
2.04
7.60
14.53
15.60
12.58
5.
KU323
2.87
7.13
8.73
6.24
1.87
2.47
2.40
2.25
6.13
17.60
16.67
13.47
6.
PantU 35
4.27
13.87
14.07
10.74
1.60
2.73
1.93
2.09
6.00
24.60
20.73
17.11
7.
PantU 31
2.87
7.33
7.73
5.98
2.13
2.67
2.20
2.33
8.73
21.47
15.47
15.22
8.
KU 99-22
3.40
5.93
6.13
5.15
2.27
2.33
2.13
2.24
10.60
10.20
11.20
10.67
9.
KOBG 653
3.67
7.47
7.87
6.34
1.80
2.00
2.53
2.11
6.40
10.40
18.13
11.64
10.
SB 27-3
4.93
5.47
5.80
5.40
2.53
2.20
2.27
2.33
12.33
10.60
14.93
12.62
11.
Type 9
4.13
6.47
8.80
6.47
1.80
2.33
1.80
1.98
10.27
18.07
14.93
14.42
12.
IPU02-1
3.73
5.33
6.87
5.31
2.07
2.80
2.13
2.33
10.33
15.53
12.73
12.86
13.
NDU5-3
2.80
7.00
7.47
5.76
1.93
2.40
2.07
2.13
6.87
18.33
17.27
14.16
14.
NDU99-2
2.80
5.33
6.93
5.02
1.87
3.33
1.87
2.36
6.13
17.53
14.33
12.66
Mean
3.44
7.23
8.64
6.44
2.00
2.50
2.20
2.23
8.30
16.19
16.60
13.70
Env. Index
3.00
-0.79
-2.2
0.23
-0.27
0.03
5.4
-2.49
-2.9
C.D. (E) at 5%
1.32
0.13
flowering. Regarding days to 80% maturity, pods per cluster and number of primary branch, soil treated with fertilizer (E2) was found to mature early with maximum number of primary branches. From the above data it could
2.71
be concluded that soil treated with FYM@20 tons/hawas found to be the best in terms of seed yield and other yield attributing characters under the foot hill conditions of Medziphema, Nagaland.
Table 3 (3). Environmental means for different characters in three different environments Sl. No.
Genotypes
1.
RBU1012
100 seed weight E1
E2
E3
4.53
5.07
5.26
Seed yield per plant Mean 4.95
E1
E2
E3
1.23
5.58
6.38
Mean 4.40
2.
NDU3-4
4.58
4.49
4.83
4.63
2.34
4.56
8.67
5.19
3.
Uttara
3.63
4.10
4.66
4.13
1.63
4.66
5.35
3.88
4.
PantU 19
4.06
4.61
4.80
4.49
2.09
4.97
5.14
4.07
5.
KU323
4.38
4.69
4.90
4.66
1.76
5.73
5.79
4.43
6.
PantU 35
3.93
4.31
4.70
4.31
1.38
7.15
7.02
5.18
7.
PantU 31
3.97
4.13
4.46
4.19
1.99
6.64
4.49
4.37
8.
KU 99-22
4.52
4.27
4.67
4.49
2.78
3.01
3.44
3.08
9.
KOBG 653
4.43
4.30
4.83
4.52
1.68
3.30
5.64
3.54
10.
SB 27-3
4.20
3.91
4.33
4.15
3.04
3.59
4.31
3.65 4.27
11.
Type 9
4.04
4.32
4.50
4.29
2.71
5.44
4.65
12.
IPU02-1
4.23
4.23
4.72
4.39
2.61
4.61
4.03
3.75
13.
NDU5-3
3.92
4.32
4.78
4.34
1.71
5.78
5.29
4.26
14.
NDU99-2
3.57
4.20
4.58
4.12
1.47
5.37
4.42
3.75
Mean
4.14
4.35
4.71
4.40
2.03
5.03
5.33
4.13
Env. Index
0.26
0.05
-0.31
2.1
-0.9
-1.2
C.D. (E) at 5%
0.15
1.03
NONGTHOMBAM, et al., Study on Effect of Fertilizer and FYM Application on Black Gram (Vigna mungo L. Hepper)
LITERATURE CITED Amany, A.B. 2007. Effect of plant density and urea foliar application on yield and yield components of chickpea (Cicer arietinum L.).Res. J. Agric. Biol. Sci., 3 (4): 220-223. Caliskan, S., Ozkaya, I., Caliskan, M.E., Arslan, M. 2008. The effect of nitrogen and iron fertilization on growth, yield and fertilizer use efficiency of soybean in Mediterranean type soil. Field Crops Res., 108: 126-132.
1433
Sharma, D.K., Prasad, K., and Yadav, S.S. 2008. Effect of integrated nutrient management on the performance of dwarf scented rice (Oryza sativa L.) grown in rice-wheat sequence.Internat. J. agric. Sci., 4 (2): 663-666. Eberhart, S.A. and Russell, W.A. 1966. Stability parameter for comparing varieties.Crop Sci. 6: 36-40. Futi Xie, St. Martin S.K., Zhang, H and Wang, H. 2011. Improvement of soybean yield and lodging in relation to morphological traits.Crop Res. 41 (1, 2, & 3): 64-74 (2011). Received on 10-05-2014
Accepted on 20-05-2014
1434 Trends in Biosciences 7(13): 1434-1439, 2014
Trends in Biosciences 7 (13), 2014
Safety Evaluation of Diafenthiuron 50WP (NS) to Non Target Organisms J. ARAVIND* AND K. SAMIAYYAN Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore -641003, Tamil Nadu, India email :
[email protected]
ABSTRACT Laboratory investigations were carried out to evaluate its acute toxicity to C. punctiferalis, safety to non target insects like honey bees viz., Apis dorsata F., Apis cerana indica F., Apis florea F. and Trigona iridipennis Smith. Diafenthiruon 50 WP (NS) at its highest dose of 1600 and 800 g a.i. ha-1 was found slightly harmful to A. dorsata, A. cerana indica and A. florea by recording more than 70 per cent mortality and it was moderately toxic to T. iridipennis, where the mortality is above 80 per cent. Meanwhile 400 g a.i. ha-1 was found safer to A. dorsata (46.67%) and A. cerana indica (39.27%) at 48hours after treatment, while it was slightly harmful to A. florea (63.33%) and T. iridipennis (60.00%). Key words
A. dorsata, A. cerana indica and A. florea, Trigona iridipennis, Diafenthiuron50wp
Diafenthiuron has been reported to be effective against sucking pests like Bemisia tabaci (Gennadius) and Amrasca biguttula biguttula (Ishida) in brinjal (Patel, et al., 2006), Trialeurodes vaporariarum (Westwood) (Javed and Matthews, 2002), Myzus persicae (Sulzer) and Frankliniella schultzei (Tyrbom) in tomato (Scarpellani, 2000), Scirtothrips dorsalis Hood and Oligonychus coffeae (Nietner) in tea (UPASI, 2005), diamondback moth, Plutella xylostella L. (Ishaaya, et al., 1993; Lingappa, et al., 2004) and all sucking pests in cotton (Kranthi, et al., 2004). Also, diafenthiuron was reported to be safe to parasitoids viz., Cotesia plutellae Kurdjumov and predators viz., predatory stink bug, Podisus nigrispinus (Dallas) (Torres, et al., 2002), Chrysoperla carnea (Stephens) (Preetha, et al., 2009) and Menochilus sexmaculatus (Fabricius) (Stanley, 2007). New formulations and new sources of existing molecules are likely to hold superiority in terms of higher toxicity, pest suppression, safety to natural enemies and non target organisms, reduced spray dosages and rounds of spray and the benefits accrued in terms of savings in labour and time. And, in the event of change in the source material of diafenthiuron, it is mandatory to generate data on the safety to the natural enemies as per the guidelines of Central Insecticides Board (CIB).
by dry film contact toxicity method (Rajathi et al., 2006). Indian bees were collected from Apiary, stingless bees from staff quarters, Tamil Nadu Agricultural University, little bees from mango tree at P. N. Pudur and rock bees from an apartment at Kavundanpalayam, Coimbatore. Plastic containers (10 x 8 cm) with perforations were used for the experiment to allow adequate aeration for the bees. Filter paper discs were wetted with 1 ml of different concentrations of insecticides used for field study as detailed in table given below which was replicated thrice and allowed to dry by hanging in a string. Shade dried filter paper was placed in the bioassay container and honeybees viz., A. cerana indica, T. iridipennis, A. florea and A. dorsata were released at the rate of 10 per container. Honey bees were kept in refrigerator prior to test for 4 to 6 minutes in order to calm them which helped in their easy transfer. After exposure for 1 h, honey bees were transferred to a perforated polythene bag (20 X 30 cm) and cotton swabbed with 40 per cent sucrose solution tied in a twine was provided as feed for the honeybees. The mortality of bees was observed after 12, 24 and 48 hours of treatment and per cent mortality worked out (Plate 1). Corrections were carried out for mortality in the control, by using Abbot’s formula (1925). Treatments T1 T2 T3 T4 T5 T6 T7
-
Diafenthiuron 50WP(NS) Diafenthiuron 50WP(NS) Diafenthiuron 50WP(NS) StandardDiafenthiuron 50WP(ES) Standard Quinalphos 25EC Standard Quinalphos 25EC Untreated check (water spray)
(%) Dosage 0.04 0.08 0.16 0.08 0.06 0.12 -
NS – New Source ES – Existing Source
RESULTS AND DISCUSSION The results of laboratory experiments conducted to assess the diafenthiuron 50 WP (NS) against safety to nontarget organisms viz., honey bees are presented below.
Honey bees :
MATERIALS AND METHODS
Rock bees – Apis dorsata F.:
The effect of diafenthiuron 50 WP (NS) on A. dorsata, A. cerana indica, A. florea and T. iridipennis was assessed
The toxicity of diafenthiuron 50 WP (NS) to A. dorsata was studied in the laboratory and the results are
ARAVIND and SAMIAYYAN, Safety Evaluation of Diafenthiuron 50WP (NS) to Non Target Organisms
1435
Fig. 1. Honey bees exposed to diafenthiuron treated filter paper in containers
presented in Table 1. Diafenthiuron 50 WP (NS) at lower dosage of 400 g a.i. ha-1 were found less toxic to the rock bees. They recorded the least mortality rate of 23.33 and 46.67 per cent respectively at 24 HAT and 48 HAT. Diafenthiuron 50 WP (NS) 800 g a.i. ha-1 recorded 63.33 per cent mortality a day after exposure and the mortality was very high at diafenthiuron 50 WP (NS) @ 1600 g a.i. ha-1 recorded 80.00 per cent mortality. Diafenthiuron 50 WP (ES) 800 g a.i. ha-1 recorded least mortality of 40.0% at 48 HAT. Diafenthiuron 50 WP (NS) @ 1600 g a.i. ha-1 recorded highest mortality of 86.67 per cent at 48HAT. The standard checks, quinalphos 25 EC 600g a.i. ha-1 and quinalphos 25 EC 1200 g a.i. ha-1 were highly toxic and recorded mortality of 86.67 and 90.00 per cent, respectively at 24HAT(Table 1).
Indian bees – Apis cerana indica F.: Worker honey bees of A. cerana indica were tested for their safety against diafenthiuron 50 WP (NS) and also against standard checks quinalphos 25EC in the laboratory. Diafenthiuron50WP (NS)1600 and 800 ga.i.ha -1caused 63.33 and 86.67 per cent mortality at 24 HAT, while quinalphos 25 EC 600g a.i.ha-1and 1200 g a.i. ha-1 registered a maximum of 83.33 and 96.67 per cent mortality. Diafenthiuron50WP (NS) 1600 g a.i. ha -1 at 48 HAT
recorded 90.00 per cent mortality and it was on par with quinalphos 25 EC 600g a.i. ha-1 (90.00%) and diafenthiuron 50WP (NS) 800 g a.i. ha-1 (83.33%). Diafenthiuron 50 WP (NS) at 400 g a.i. ha-1 and diafenthiuron 50 WP (ES) at 800 g a.i. ha-1 recorded less than 50 per cent mortality at 48 HAT and were found to be moderately toxic. Lower doses of diafenthiuron 50 WP (NS) 400 g a.i. ha-1 recorded a mortality per cent of 30.00 and 43.33 and the corrected mortality per cent was 27.58 and 39.27 per cent, respectively at 24 HAT (Table 2).
Little bees – Apis florea F.: Contact toxicity of diafenthiuron 50 WP (NS) to little bees assessed by filter paper disc bioassay in the laboratory revealed that diafenthiuron 50 WP (NS) caused 63.33, 80.00 and 93.33 per cent mortality in the doses 400, 800 and 1600 g a.i. ha -1 respectively at 48 HAT respectively. Diafenthiuron 50 WP (NS) at 800 g a.i. ha-1 recorded 60.00 per cent mortality 24 HAT where as diafenthiuron 50 WP (NS) at 1600 g a.i. ha-1 registered a maximum of 80.00%. Higher dose of diafenthiuron (NS) 800 g a.i. ha-1 registered 80.00 per cent mortality at 48HAT and was found to be highly toxic. Both the standard checks viz., quinalphos 25 EC 600 g a.i. ha-1 and quinalphos 25 EC 1200 g a.i. ha-1 were found to be extremely toxic causing
1436
Trends in Biosciences 7 (13), 2014
Table 1. Toxicity of diafenthiuron 50 WP (NS) to rock bees - Apis dorsata F 12 HAT
24 HAT
48 HAT
Treatments
Dose (ga.i. h1 )
Per cent mortality
Corrected mortality (%)
Per cent mortality
Corrected mortality (%)
Per cent mortality
Corrected mortality (%)
Diafenthiuron 50WP
400
13.33 (21.41)c
13.33
23.33 (28.88)b
20.68
46.67 (43.09)b
42.85
Diafenthiuron 50WP
800
46.67 (43.09)d
46.67
63.33 (52.73)c
62.06
80.00 (63.43)c
78.57
Diafenthiuron 50WP
1600
60.00 (50.77)
60.00
80.00 (63.43)d
79.31
86.67 (68.59)d
82.75
Standard Diafenthiuron 50WP
800
6.67 (14.97)b
6.67
20.00 (26.57)b
69.30
40.00 (39.23)b
34.47
T5 Quinalphos25EC
600
63.33 (52.73)de
63.33
86.67 (68.59)e
69.79
93.33 (75.03)e
92.50
T6 – Quinalphos25EC
1200
73.33 (58.91)f
73.33
90.00 (71.57)f
89.67
100.00 (90.00)f
1.00
T7- Untreated check
-
0.00 (0.00)a
0.00
3.33 (10.51)a
-
6.67 (14.97)a
-
Mean of three observations HAT – Hours after treatment In a column means followed by a common letter are not significantly different at P = 0.05 by LSD Figures in parentheses are arcsine P transformed values
Table 2. Toxicity of diafenthiuron 50 WP (NS) to indian bees - Apis cerana indica F.
12 HAT Treatments
Diafenthiuron 50WP Diafenthiuron 50WP Diafenthiuron 50WP Standard Diafenthiuron 50WP Quinalphos25EC Quinalphos25EC
Dose (ga.i. h-1)
48 HAT
Per cent mortality
Corrected mortality (%)
Per cent mortality
Corrected mortality (%)
Per cent mortality
Corrected mortality (%)
400
16.67 (24.10)b
16.67
30.00 (33.21)b
27.58
43.33 (41.17)c
39.27
800
46.67 (43.09)c
46.67
63.33 (52.73)c
62.06
83.33 (65.90)d
82.13
1600
60.00 (50.77)d
60.00
86.66 (68.58)de
86.20
90.00 (71.57)e
89.28
800
13.33 (21.41)b
13.33
26.67 (31.09)b
24.14
40.00 (39.23)b
35.71
600
63.33 (52.73)d
63.33
83.30 (65.88)d
82.72
90.00 (71.57)e
89.28
1200
73.33 (58.91)de
73.33
96.67 (79.49)f
96.55
100.00 (90.00)f
1.00
Untreated check
24 HAT
0.00 (0.00)a
3.33 (10.51)a
Mean of three observations HAT – Hours after treatment In a column means followed by a common letter are not significantly different at P = 0.05 by LSD Figures in parentheses are arcsine P transformed values
6.67 (14.97)a
ARAVIND and SAMIAYYAN, Safety Evaluation of Diafenthiuron 50WP (NS) to Non Target Organisms
1437
Table 3. Toxicity of diafenthiuron 50 WP (NS) to little bees - Apis florea F
12 HAT
24 HAT
48 HAT
Treatments
Dose (ga.i. h1 )
Per cent mortality
Corrected mortality (%)
Per cent mortality
Corrected mortality (%)
Per cent mortality
Corrected mortality (%)
Diafenthiuron 50WP
400
26.67 (31.09)c
26.67
50.00 (45.00)bc
48.27
63.33 (52.73)c
60.70
Diafenthiuron 50WP
800
43.33 (41.17)d
43.33
60.00 (50.77)c
58.62
80.00 (63.43)d
78.57
Diafenthiuron 50WP
1600
63.33 (52.73)
63.33
80.00 (63.43)e
79.31
93.33 (75.03)f
92.85
Standard Diafenthiuron 50WP
800
23.33 (28.88)b
23.33
43.33 (41.17)b
41.37
53.33 (46.91)b
49.99
Quinalphos25EC
600
63.33 (52.73)e
63.33
73.33 (58.91)d
72.41
83.33 (65.90)de
82.13
Quinalphos25EC
1200
76.67 (61.12)f
76.67
90.00 (71.57)f
89.65
100.00 (90.00)g
1.00
-
0.00 (0.00)a
Untreated check
3.33 (10.51)a
6.67 (14.97)a
Mean of three observations HAT – Hours after treatment In a column means followed by a common letter are not significantly different at P = 0.05 by LSD Figures in parentheses are arcsine P transformed values
mortality of 83.33 and 100.00 per cent, respectively at 48HAT. (Table 3).
Stingless Bees - Trigona iridipennis: The results on the influence of diafenthiuron to stingless bees revealed that diafenthiuron was toxic. At diafenthiuron 50 WP@ 800 g a.i. ha-1 (86.66%) and 1600 g a.i. ha-1(93.33%) more than eighty per cent mortality was observed which proved it as extremely toxic. Diafenthiuron 50 WP even at the recommended dose of 400 g a.i. ha-1 and 800 g a.i. ha-1 caused 60.00 and 53.33 per cent mortality in both the cases of NS and ES. Quinalphos 25 EC 600 g a.i. ha-1 and Quinalphos 25 EC 1200 g a.i. ha-1 proved to be highly toxic recording a per cent mortality rate of 93.33 and 100 per cent, respectively (Table 4). Diafenthiuron50WP (NS) 400 g a.i. ha-1 and 800 g a.i. ha-1 recorded 40.00 and 53.30 per cent mortality a day after exposure (24HAT), respectively (Table 4). The effect of insecticides on a particular natural enemy involves numerous biotic and abiotic factors. Therefore, it would be regrettable to exclude toxic compounds without looking for their specific uses. Selection of a suitable insecticide in an IPM program not only depends on its toxicity level to beneficial insects but also on its efficacy against the target pest, its weathering and persistency. As extensive use of synthetic chemicals result in the destruction
of non target organisms directly or where they are transported and enter into the niche of the organisms. Laboratory studies conducted to test the safety of diafenthiuron 50 WP (NS) against honey bees revealed that diafenthiuron 50WP (NS) at the highest dose of 1600 g a.i. ha -1 was slighty harmful to three bee species viz.,Apis cerana indica Fabricius, Apis florea F. and Apis dorsata F. which recorded 82.75, 89.28 and 92.85 per cent mortality, respectively and was moderately harmful to little bee Trigona iridipennis Smith against which the mortality was 93.10 per cent . On the other hand, field recommended dose of 400 g a.i. ha-1 could be considered harmless, (as suggested by Hassan,1989), to three bee species viz., A. cerana indica, A. florea and A. dorsata as it recorded mortality per cent lesser than 50.00 only. However, it was slightly harmful for little bee, T. iridipennis where the mortality rate was 63.33 per cent. The order of toxicity of different treatments to honey bees are quinalphos 25 EC 1200 g a.i. ha-1 > quinalphos 25 EC 600 g a.i. ha-1 > diafenthiuron 50 WP (NS) 1600 g a.i. ha -1 > diafenthiuron 50 WP (NS) 800 g a.i. ha -1 > diafenthiuron 50 WP (NS) 400 g a.i. ha-1 > diafenthiuron 50 WP (ES) 800 g a.i. ha-1. Similar studies conducted by Stanley, 2007 revealed that diafenthiuron at the highest dose of 800 g a.i. ha-1 was found toxic to bees by registering 71.43, 51.72, 55.56 and 85.19 per cent mortality at 24 h
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Trends in Biosciences 7 (13), 2014
Table 4. Toxicity of diafenthiuron 50 WP (NS) to to stingless bees - Trigona iridipennis (Smith)
12 HAT Treatments
Diafenthiuron 50WP Diafenthiuron 50WP Diafenthiuron 50WP Standard Diafenthiuron 50WP Quinalphos25EC Quinalphos25EC
Dose (ga.i. h-1)
24 HAT
48 HAT
Per cent mortality
Corrected mortality (%)
Per cent mortality
Corrected mortality (%)
Per cent mortality
Corrected mortality (%)
400
23.33 (28.88)b
23.33
40.00 (39.23)b
40.00
60.00 (50.77)c
58.62
800
50.00 (45.00)d
50.00
53.30 (46.89)cd
53.30
86.66 (68.58)d
86.20
1600
73.30 (58.89)e
73.30
80.00 (63.43)e
80.00
93.33 (75.03)e
93.10
800
26.66 (31.09)bc
26.66
43.33 (41.17)c
43.33
53.33 (46.91)b
51.72
600
80.00 (63.43)f
80.00
86.66 (68.58)f
86.66
93.33 (75.03)e
93.10
1200
90.00 (71.57)g
90.00
96.67 (79.79)g
96.67
100.00 (90.00)f
1.00
-
0.00 (0.00)a
Untreated check
0.00 (0.00)a
3.33 (10.51)a
Mean of three observations HAT – Hours after treatment In a column means followed by a common letter are not significantly different at P = 0.05 by LSD Figures in parentheses are arcsine P transformed values
after treatment (HAT) to A. dorsata., A. cerana indica., A. florea and T. irridipennis, respectively, of which rock bees and stingless bees were highly susceptible. Studies conducted by Perveen, et al., 2000 fall in line with our present study indicating that diafenthiuron 500 EC was found relatively safer to Italian bee Apis mellifera Capensis. However, it has been reported that repeated insecticidal sprayings in cardamom resulted in the reduction of honey bee visits (Murugan, et al., 2011). Cardamom blooms by early morning hours 4.30 to 6.30 AM (Belavadi, et al., 1997) and bees have been foraging on cardamom flowers in the morning hours between 8.00 AM and 12 Noon (Krishnamoorthy, et al., 1989). Apart from using diafenthiuron at correct and recommended dose, the toxicity on the bees can be avoided to a certain extent by having temporal asynchrony i.e., spraying diafenthiuron at time of minimum activity of bees to reduce the exposure/ contact to them especially in evening hours, studies should be done to know the selectivity of the chemical (Nasreen, et al., 2005).
ACKNOWLEDGEMENT The authors acknowledge Dr.S.Kuttalam Professor and Head, Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore with evine keen interest to acquaint with ideas and concepts. I thank Parijat
Industries pvt ltd, Newdelhi for funding the student research fellowship.
LITERATURE CITED Abbott, W.S. 1925. A method of computing the effectiveness of an insecticide. J. Econ. Entomol., 18: 265-267. Anonymous. 2005. Seventy Nineth Annual Report. UPASI Tea Research Foundation. pp. 28-85. Hassan, S.A. 1989. Testing methodology and the concepts of the IOBC/WPRS working group. In: Pesticides and non target invertebrates. (P.C. Japson, Ed.) Intercept, Nimborn, Dovset, pp.1-18. Ishaaya, I., Mendelson, Z. Horowitz. 1993. Toxicity and growthsuppression exerted by diafenthiuron in the sweetpotato whitefly, Bemisia tabaci. Phytoparasitica 21: 199-204 Javed, M. A., Matthews,G.A. 2002. Bioresidual and integrated pest management status of a biorational agent and a novel insecticide against whitefly and its key parasitoids. Intl. J. Pest Mgmt., 48: 13-17 Kranthi, K. R., Kranthi,S., Banerjee,S.K., Raj,S., Chaudhary,S., Narula,A.M., Barik,A., Khadi,B.M., Monga,D., Singh,A.P., Dhawan,A.K., Rao,N.H.P., Surulivelu,P., Sharma,A., Suryavanshi,D.S., Vadodaria, M. P., Shrivastava, V. K., and Mayee, C. D. 2004. IRM- Revolutionising cotton pest management in India. Resis. Pest Mgmt Newsl., 14: 33-36 Lingappa, S., Basavanagoud, K., Kulkarni, K. S., Roopa,P.,
ARAVIND and SAMIAYYAN, Safety Evaluation of Diafenthiuron 50WP (NS) to Non Target Organisms
1439
Kambrekar, K. 2004. Threat to vegetable production by diamondback moth and its management strategies: In: Disease Management in Fruits and Vegetables, Springer, Netherlands. pp.357- 396
Preetha, G., Stanley,J., Manoharan,T., Chandrasekaran,S., Kuttalam,S. 2009. Toxicity of imidacloprid and diafenthiuron to Chrysoperla carnea (Stephens) (Neuroptera: Chrysopidae) in the laboratory conditions. Journal of Plant Protection Research 49(3): 290-296.
Murugan, M., Shetty, P. K., Iremath, M. B., Ravi, R. Subbiah,A. 2011. Occurence and activity of cardamom pests and honeybees as affected by pest management and climatic change. International Multidisciplinary Research Journal 1(6): 3-12.
Rajathi, D. S., Krishnamoorthy, S. V., Regupathy,A. 2006. Selective toxicity and discriminating dose of lambda cyhalothrin 5 CS against Earias vitella Fab. Resistant Pest management Newsletter 15(2): 35-38.
Nasreen, A., Mustafa, G., Ashfaq,M. 2005. Mortality of Chrysoperla carnea (Stephens) (Neuroptera: Chrysopidae) after exposure to some insecticides; laboratory studies. South Pacific Studies 26(1): 1-6.
Scarpellani, J. R. 2000. Effect of thiamethoxam and diafenthiuron in the control of aphids, Myzus persicae and thrips, Frankliniella schultzei on tomato. In: Proc. of XXI Intl. Conf. of Entomology, Brazil Aug. 20- 26. 711p.
Patel, J. J., Patel, B. H., Bhatt, P. D., Manghodia, A. B. 2006. Bioefficacy of diafenthiuron 50 WP against sucking pests of brinjal (Solanum melongena L.). In: Biodiversity and Insect Pest Management. (S. Ignacimuthu Sj. and S. Jayaraj. eds.) Narosa Publishing House, New Delhi. pp. 57-58.
Stanley, J. 2007. Chemical and Behavioural Approaches for Pest Management in Cardamom. Ph.D. Thesis. Tamil Nadu Agric.Univ., Coimbatore, India, 210 p.
Perveen, N., Alhariri, A. B., Ahmad,M., Suhail,A. 2000. Insecticidal mortality, foraging behaviour and pollination role of honeybee (Apis mellifera L.) on sarson (Brassica campestris L.) crop. Int. J. Agri. Biol., 2(4): 322-333.
Torres J. B., Silva-Torres, C. S. A., Silva Murilo,R., Ferreira,J.F 2002. Compatibility of insecticides and acaricides to the predatory stinkbug Pod isus nigrispinus (Dallas) (Heteroptera: Pentatomidae) on cotton. Neotrop. Entomol., 31: 311-317. Received on 09-05-2014
Accepted on 21-05-2014
1440 in Biosciences 7(13): 1440-1443, 2014 Trends
Trends in Biosciences 7 (13), 2014
Base Line Toxicity of Lufenuron 5.4 EC against Diamondback Moth, Plutella xylostella L. for Resistance Monitoring K. SENGUTTUVAN1, S. KUTTALAM2 AND K. GUNASEKARAN3 1&2
Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu India. 3 Department of Nano Science and Technology,Tamil Nadu Agricultural University,Coimbatore, Tamil Nadu, India email :
[email protected], 2
[email protected] ,
[email protected]
ABSTRACT Studies were conducted to assess the baseline toxicity of lufenuron 5.4 EC on Diamondback moth, Plutella xylostella collected from three different locations in Tamil Nadu where cabbage and cauliflower crops are grown extensively. Acute toxicity of lufenuron indicated that the LC50 values from F1 to F8 generations declined from 15.41 to 3.21 ppm and LC95 declined from 458.11 to 33.62 ppm. The susceptibility index to lufenuron based on LC50 was 4.80, while it was 13.63 based on LC95. Rate of resistance decline (R) when selection pressure was stopped was -0.085. With regard to number of generations required for 10-fold decrease in LC50, it was calculated as 11.76. Considering the F8 P. xylostella population as the most susceptible, the tentative discriminating dose arrived at was 33.62 ppm for lufenuron. Resistance monitoring studies indicated that the per cent resistance of P. xylostella ranged from the lowest of 6.12 in Kotagiri to the highest of 24.49 in Ottanchathiram. Key words
Baseline toxicity, acute toxicity, susceptibility index, P. xylostella, resistance monitoring
The diamondback moth (DBM), Plutella xylostella L. is the most destructive pest of cruciferous vegetables viz., cabbage and cauliflower. High rate of multiplication, continuous cropping of susceptible hosts (cabbage and cauliflower) throughout the year and monocropping in larger areas are the reasons for DBM assuming the status of major pest. Among the various insect pests attacking cabbage, diamondback moth, P. xylostella is the most dreaded pest not only in India, but also throughout the world and the annual cost incurred for managing this pest is estimated to be one billion dollar as reported by Talekar, 1990. The DBM is an economically important pest of crucifers in Tamil Nadu, India causing severe damage to cauliflower (Uthamasamy, et al., 2011). Indiscriminate use of insecticides for controlling these insects is in practice by the farmers. These insecticides should be used very judiciously and safely, by taking into account the environmental and health concerns. At present, chemical
control is widely used for the management of cabbage and cauliflower pests. Continuous use of insecticides results in the destruction of natural enemies, development of resistance in target pest, residual problems and environmental pollution. Too many insecticides are used extensively now, monitoring of resistance development to these new molecules will be useful in the management strategy. Hence baseline susceptibility data were generated to monitor resistance in future.
MATERIALS AND METHODS Studies were conducted to assess the baseline toxicity of lufenuron 5.4 EC on Diamondback moth from August 2012 to December 2013 in the Insectary and toxicology unit at Tamil Nadu Agricultural University, Coimbatore.
Leaf disc bioassay method for acute toxicity: The procedure described by Tabashnik and Cushing, 1987 was adopted. Cauliflower leaves were collected from potted plants. The cauliflower leaf was cut into the size of 6×3 cm covering either side of the midrib. These leaf bits were dipped in the insecticides, for about a minute. Before this step, a drop of Triton x-100 was added (wetting agent) into the insecticidal solution. They were then removed, excess fluid drained and then it was held upside down on the plastic container about one hour for drying. After complete drying, the leaf bits were transferred to a small plastic container of 10 cm height and 5 cm diameter over a moistened filter paper. The leaf bits were placed slanting to rest on the side of the plastic container so that the larvae can move on each side. Ten larvae, starved for 12 hours, were then transferred to each leaf bit and the container was closed with perforated plastic lid and placed for further observation. The results were expressed in percentage mortality and correction for untreated mortality was also made using Abbott’s formula (Abbott, 1925). The median-lethal concentration LC50 and LC95 were computed using Finney’s method (Finney, 1971).
SENGUTTUVAN, et al., Base Line Toxicity of Lufenuron 5.4 EC against Diamondback Moth, Plutella xylostella L.
1441
Table 1. Toxicity of lufenuron on P. xylostella by leaf dip bioassay method Regression Equation (y =bx+a)
Chi square ( 2)
LC50
Fiducial limits
LC95
Fiducial limits
(ppm)
LL
UL
(ppm)
LL
UL
1.
y = 1.224x + 3.6082
1.801
15.41
5.18
45.84
458.11
23.84
8801.54
2.
y = 1.2567x + 3.6391
3.198
13.90
5.00
38.64
419.17
24.44
7188.01
3.
y = 1.3327x + 3.734
2.823
9.99
4.42
22.57
254.40
22.80
2837.90
4.
y = 1.4742x + 3.813
2.220
6.77
3.80
12.07
119.70
20.78
689.45
5.
y = 1.5396x + 3.9087
3.455
5.39
3.23
9.00
92.99
18.97
455.96
6.
y = 1.5531x + 4.156
1.503
3.25
2.38
5.01
45.51
14.03
147.61
7.
y = 1.7087x + 4.1644
1.117
3.22
2.21
4.69
37.36
11.95
116.79
8.
y = 1.743x + 4.164
0.9361
3.21
2.17
4.67
33.62
11.54
97.88
Generation
a- Initial deposition; b - Degradation reaction rate constant; LL – Lower limit; UL – Upper limit
Discriminating dose fixation:
Resistance Ratio or Resistance Factor:
) for the field Median lethal concentration ( collected F1 population of insect to different insecticides was obtained by conducting bioassays. Then the insects collected from the field were cultured continuously without any selection pressure (without any insecticide exposure) throughout Fn generations. Bioassays were conducted based on the doses obtained from the preliminary range finding test to construct log dose probit mortality line (LDPM) for the susceptible population known as baseline data. Discriminating dose was tentatively fixed based on the values obtained for ‘n’ generation of population maintained under insecticide free conditions.
The intensity of resistance of a population or a strain of insects to a particular insecticide, frequently quoted as the Resistance factor (RF) or Resistance ratio (RR) was calculated by the formula:
Monitoring insecticide resistance: The insecticidal dilutions for each insecticide were applied to the field collected larvae using leaf dip bioassay. The mortality count was taken at 12, 24, 48, 72h and up to 7 DAT. The bioassay method described by Tabashnik and Cushing (1987) was followed. The larvae collected from the fields at Coimbatore, Kotagiri, Ooty and Ottanchatram were used for the experiments.
Susceptibility index and rate of resistance decline:
RESULTS AND DISCUSSION
Susceptibility index was calculated based on LC50 and LC95 obtained for final generation maintained without insecticidal exposure (Regupathy and Dhamu, 2001).
The results of the studies carried out on the baseline toxicity of lufenuron 5.4 EC to P. xylostella and monitoring insecticide resistance to P. xylostella population collected from different locations are furnished hereunder.
Acute toxicity study of P. xylostella: The rate of resistance decline (R) used to quantify the rate of changing LC50 when the selection pressure is stopped was estimated by the formula;
Where, is the number of generations not exposed to insecticide, final is the after n generations without selection initial is the of the parental generation before n generations of selection The number of generations required for ten-fold decrease in the value was calculated using the formula; .
The LC50 and LC95 values for eight generations of P. xylostella are presented in Table 1. The lethal concentration (LC50) of lufenuron for P. xylostella was found to be 15.41 ppm for F1 generation and 3.21 ppm for F8 generation. The median lethal concentration (LC50) of lufenuron 5.4 EC for P. xylostella was found to be 15.41 ppm for F1 generation and 3.21 ppm for F8 generation. The LC95 values decreased from 458.11 (F1) to 33.62 ppm (F8). As the discriminating dose (DD) can kill 95 per cent of the subjected normal population, tentative discriminating dose of 33.62 ppm for lufenuron was arrived at based on the baseline toxicity studies with P. xylostella. Since the chemical under study is a newer one, the LC95 of the chemical could be considered as a discriminating dose for monitoring the field populations. For new insecticides, without well established baseline data,
1442
Trends in Biosciences 7 (13), 2014
LC95 can be taken as the discriminating dose (Senthilkumar and Regupathy, 2004). Sampathkumar, 2007 reported that tentative discriminating dose (DD) arrived at based on LD95 of Abamectin, emamectin benzoate, indoxacarb and spinosad for F6 generation of laboratory cultured population of E. vittella was 0.013, 0.02,0.80 and 0.012 mg larva-1. The LC50 and LC95 values of emamectin benzoate 5 SG for P. xylostella was found to be 0.053 and 0.989 ppm, respectively (Suganyakanna, 2003). Susceptible strain is obtained by continuous culturing of insects without exposure to insecticides over generations (Pradhan, 1983). The susceptibility index to lufenuron based on LC50 and LC95 was 4.80 and 13.63, respectively (Table 2). The rate of resistance decline (R) when selection pressure was stopped, was -0.085. With regard to number of generations required for 10-fold decrease in LD50, it was calculated as 11.76. Similar studies were also carried out by Salgado (1998). The low LC50 indicates the susceptibility of the larvae to lufenuron. The high susceptibility of these insects to lufenuron may be due to novel mode of action of the chemical.
Table 2. Susceptibility index of P. xylostella to lufenuron Assay method Leaf disc
Susceptibility index based on rate of resistance decline LC50/LC50
LC95/ LC95
R
G
4.80
13.63
-0.085
11.76
Monitoring of lufenuron resistance: Resistance monitoring techniques play an integral role in resistance management programme and therefore, it is important to detect resistance when it is at incipient levels and monitor its increase and geographical spread so that appropriate measures could be initiated to delay resistance development and thereby preserve the potentiality of new insecticide molecule. Monitoring of insecticide resistance is a pre-requisite for any insecticide resistance management (IRM) programme. Tabashnik and Cushing, 1987 opined that monitoring of insecticide use patterns and concomitant monitoring of resistance levels are essential in deciding the management strategy against a pest. Insecticide resistance in control failures of DBM population was first recorded in Punjab during 1968.
Subsequently, the DBM population developed resistance to several insecticides by Regupathy, 1996. Plutella xylostella population from Pakistan developing resistance to several insecticides (including lufenuron) up to 34 fold has been documented by Rafiq, 2005. Monitoring was done as one time survey in cabbage and cauliflower fields of Coimbatore, Kotagiri, Ooty and Ottanchathiram places in Tamil Nadu. The resistance in field population of P. xylostella to lufenuron 5.4 EC was monitored using discriminating doses (DD) (33.62 ppm). The level of resistance of diamondback moth varied from 6.12 to 24.49 per cent lufenuron. The larval population of Ottanchathiram registered a per cent resistance of 24.49 followed by Coimbatore, Ooty and Kotagiri (Table 3). Cheng et al. (1988) confirmed that DBM populations did not develop any resistance to teflubenzuron and chorfluazuron in Taiwan. While, diflubenzuron, triflubenzuron and ethofenprox were degraded by the MFO activity possessed by DBM larva. As resistance development is a common occurrence with respect to diamond back moth population, Cheng, 1986 suggested an insecticide alteration strategy with OP insecticides and cartap to avoid cross resistance. Resistance monitoring programmes generally involve comparisons of LD50’s, LD90’s and slopes between field collected populations and laboratory strains or both (Twine and Reynolds, 1980). The slopes of the dose mortality line of the LD95 might be better indicator of resistance (Roush and Miller, 1986). The chitin synthesis inhibitor, chlorfluazuron producing high levels of resistance in a short span of generation in DBM (Fahmy and Myata, 1992). Control failures of DBM in Brazil associated with development of resistance to several insecticides including lufenuron was reported by Santos et al. (2010). Based on LC 95 , discriminating doses for P. xylostella were fixed at 2 and 10 ppm for new molecules emamectin and Spinosad, respectively (Lavanya, et al., 2010). Revalidating the discriminating doses on field collected populations revealed that there was no resistance among the populations of P. xylostella recorded from the four locations. In-predicting the development of resistance of Helicoverpa armigera Hubner, it required 18.67 and 26.71 generations to obtain 5-fold and 10-fold increase of lufenuron in LC50, respectively under selection pressure at 90 per cent mortality for each generation of selection as observed by Liu, et al., 2003.
Table 3. Resistance monitoring of P. xylostella against lufenuron in four locations of Tamil Nadu. Location Coimbatore Kotagiri Ooty Ottanchathiram
No. of insect dosed
No. of dead insect
50 50 50 50
42 47 46 38
Corrected Per cent mortality 83.67 93.88 91.67 75.51
Resistance (%) SE 16.33 ± 3.91 6.12 ± 2.34 8.33 ± 2.84 24.49 ± 3.25
SENGUTTUVAN, et al., Base Line Toxicity of Lufenuron 5.4 EC against Diamondback Moth, Plutella xylostella L.
1443
From the present investigation, it could be seen that there are chances for the development of resistance in insect pests which is evident from the low level of resistance detected for P. xylostella in Ottanchathiram and Coimbatore field collected larval population.
Regupathy, A., Dhamu, K.P. 2001. Statistics Work Book for Insecticide Toxicology Second Edn. Softech Publishers, Coimbatore. pp. 206.
LITERATURE CITED
Salgado, V.L. 1998. Studies on the mode of action of spinosad : insect symptoms and physiological correlates. Pesticide Biochemistry and Physiology, 60: 91-92.
Abbott, W.S. 1925. A method of computing the effectiveness of an insecticide. Journal of Economic Entomology, 18: 265-267. Cheng, E,Y. 1986. The resistance, cross resistance, and chemical control of diamondback moth in Taiwan, In : (eds. Talekar NS and Griggs TD)Diamondback Moth Management : Proceedings of the First International Workshop. Asian Vegetable Research and Development Center, Shanhua, Taiwan pp. 329-345. Cheng, E.Y., Lin, D.F., Chiu, C.S., Kao, C.H. 1988. Purification and characterization of glutathione-S-transferases from diamondback moth, Plutella xylostella L. Journal of Agricultural Research of China, 37: 440-452. Fahmy, A.R., Miyata, T. 1992. Reversion of chlorfluazuron resistance after release from selection pressure in diamondback moth, Plutella xylostella. Journal of Pesticide Science, 17 : 83-85. Finney, D.J. 1971. Probit analysis, Cambridge University Press, London, pp. 333. Lavanya, D., Chandrasekaran, S., Regupathy, A. 2010. Baseline toxicity of emamectin and spinosad to Plutella xylostella (Lepidoptera: Noctuidae) for resistance monitoring. Resistant Pest Management Newsletter, 20: 33-36. Liu, T.X., Sparks, A.N., Chen, W. 2003. Toxicity, persistence and efficacy of indoxacarb and two other insecticides on Plutella xylostella (Lepidoptera; Plutellidae) immature in cabbage. International Journal of Pest Management, 49: 235-241. Pradhan, S. 1983. Agricultural Entomology and Pest Control. Indian Council of Agricultural research, New Delhi, India. 267p. Rafiq, M.N. 2005. Insecticide resistance in diamondback moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae) and strategies of its management. University of Arid Agriculture, Rawalpindi. Pakistan. 190p. Regupathy, A. 1996. Insecticide resistance in diamondback moth (DBM), Plutella xylostella (Linnaeus): status and prospects for its management in India. In: Third International Workshop on the Management of diamondback moth and other crucifer pests. Oct.29-Nov1.1996. Kualalumpur, Malaysia. pp. 233.
Roush, R.T., Miller, G.L. 1986. Considerations for design of insecticide resistance monitoring programmes. Journal of Economic Entomology, 79: 293-298.
Sampathkumar, M. 2007. Baseline toxicity and risk assessment of new molecules against spotted bollworm Earias vittella (Fab.) in cotton. M.Sc. Thesis, Tamil Nadu Agric. Univ., Coimbatore, India, 77p. Santos, V.C., De-Siqueira, H.A.A., Da-Silva, J.E., De-Farias, M.J.D.C. 2010. Insecticide Resistance in Populations of the Diamondback Moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae), from the State of Pernambuco, Brazil. Neotropical Entomology, 40 (2): 264-270. Senthilkumar, C.M., Regupathy, A. 2004. Resistance management from around the Globe. Resistant Pest Management Newsletter, 13: 5-6. Suganyakanna, S. 2003. Bioefficacy and selective toxicity of emamectin 5 SG against diamondback moth on cabbage and tomato fruit borer. M.Sc.(Ag.) Thesis, Tamil Nadu Agricultural University, Coimbatore, India. pp. 71. Tabashnik, B.E., Cushing, N.L. 1987. Leaf residue vs. topical bioassays for assessing insecticide resistance in the diamondback moth, Plutella xylostella L. FAO Plant Protection Bulletin, 35: 11–14. Talekar, N.S. 1990. Development of an integrated pest management program for the control diamondback moth on crucifers vegetables. In: Vegetable Research and Development in SADCC countries (R.T. Opena and M.L. Kyomo eds), AVRDC, Shanhua, Taiwan, pp. 147-157. Twine, P.H., Reynolds, H.T. 1980. Relative susceptibility and resistance of the tobacco budworm to methyl parathion and synthetic pyrethroids in southern California. Journal of Economic Entomology, 73: 239-241. Uthamasamy, S., Kannan, M., Senguttuvan, K., Jayaprakash, S.A. 2011. Status, damage potential and management of diamondback moth, Plutella xylostella (L.) in Tamil Nadu, India. In: Proceedings of the Sixth International Workshop on Management of the Diamondback Moth and Other Crucifer Insect Pests, AVRDCThe World Vegetable Centre, Taiwan, March 21 – 25, 2011. pp. 270 – 279. Received on 09-05-2014
Accepted on 21-05-2014
1444 in Biosciences 7(13): 1444-1448, 2014 Trends
Trends in Biosciences 7 (13), 2014
Combining Ability Analysis for Grain Yield and Its Related Characters in Rice (Oryza sativa L.) GIRISH CHANDRA TIWARI AND NARENDRA KUMAR JATAV Department of Plant Breeding and Genetics and Department of Plant Pathology , Parmanand Degree Collage Gajsinghpur , Sriganganagar Rajasthan affiliated to Swami Keshwanand Agricultural University, Bikaner Rajasthan India. email :
[email protected]
ABSTRACT Ten diverse genotypes of rice (Oryza sativa L.) were crossed in a diallel fashion to study gene action as well as their combining ability for 12 quantitative characters combining ability analysis suggested that additive gene action were involved in the inheritance of days to 50% flowering, days to maturity, plant height, number of leaves per tiller, number of spikelet per panicle. Non additive gene action was predominant for panicle length, panicle number per plant, 1000 grain weight, grain yield per plant, straw yield per plant, total biological yield and harvest index. Among the parents, I.R. 50, Ratna , Saket 4, Pusa 150 showed significant gca effects for more than one desirable trait indicating their utility in heterosis breeding programme. The hybrids Govind x Jaya, Basmati 370x Pusa 150, showed significant and positive sca effects. Key words
Diallel analysis, Combining ability, gca, sca
Information about the genetic architecture of parents and understanding the mode of gene action controlling seed yield and its related components is of major concern in devising systematic breeding procedures for quantitatively inherited traits. The combining ability analysis provides useful information on selection of parents and ilucidates the type of gene action involved in the expression of traits. It also helps to understand the nature of genetic variations present in population, which is essential to plan appropriate breeding strategy.
MATERIALS AND METHODS 10 diverse parental lines viz. (i) Basmati 370 (ii) Dular (iii) Govind (iv) H.U.R 52( v ) U.P.R. 79 ( vi ) I.R. 50( vii) Jaya (viii) Ratna (ix) Saket -4 (x) Pusa 150 were crossed in all possible combinations to produce 45 F1s (excluding reciprocals) during the rainy season of year 1996. In the next season, that is , in rainy season of 1997, F1 plants along with their parents were grown in a RBD with three replications at Research Farm of the Department of Agricultural Botany Kisan( Post Graduate) College, Simbhaoli, Ghaziabad, U.P. India. F1s and parents were
planted in a single and three rows respectively of 4 meter length. Obviously, there were 45 rows of F1s and 30 rows of parental lines so, there were 75 row in each replication. All together, there are overall 225 rows in the entire experiment. The rows spacing was maintained at 20 cm and plant to plant 15 cm .During transplanting single seedling were planted per hill for recording observation on 12 quantitative character viz., days to 50% flowering , days to maturity , plant height , number of leaves per tiller, number of spikelets per panicle, panicle length, panicle number per plant , 1000 grain weight, grain yield per plant, straw yield per plant , total biological yield per plant and harvest index. Combining ability analysis was carried out using method 2, modal-1of Griffing, 1956.
RESULT AND DISCUSSION The analysis of combining ability shows that, the highest general combining ability variance was found for number of spikelets per panicle, while it was lowest for number of leaves per tiller (Table 1) . The specific combining ability variance was also highest for number of spikelets per panicle and it was lowest for number of leaves per tiller (Table 1). Among the parent genotypes ,which show significant and positive gca effects (Table 2) are IR 50, Ratna, Saket 4, Pusa 150 for number of spikelets per panicle, Dular and HUR 52 for panicle length,Pusa 150 for grain yield per plant ,Jaya for straw yield per plant, Ratna and Pusa 150 for harvest index. The cross which showed significant and positive sca effects (Table 3) were Basmati 370 x Dular, Basmati 370x Govind, Basmati 370 x Saket 4, Dular x HUR 52, Dular x UPR 79, Dular x IR 50, Dular x Ratna, Dular x Pusa 150, Govind x IR 50, Govind x Ratna, Govind x Saket 4, Govind x Pusa 150, HUR 52 x Pusa 150,UPR 79 x Pusa 150, IR 50 x Jaya, IR 50 x Pusa 150, Jaya x Ratna, Jaya x Saket 4, Jaya x Pusa 150 for number of spiklets per panicle. Basmati 370 x Dular, Basmati 370 x UPR 79, Govind x HUR 52 for panicle length. Basmati 370 x Jaya for panicle number per plant. Govind x Jaya for 1000 grain
TIWARI and JATAV, Combining Ability Analysis for Grain Yield and Its Related Characters in Rice (Oryza sativa L.)
1445
Table 1. Depiction of general and specific combining ability variances for 12 metric characters in rice. CHARACTERS Estimate
2gca 2
sca
Days to Days to Plant 50% maturity height flowering (cm.)
Numbe Number r of of leaves spikelets per per tiller panicle
Panicle length (cm.)
Panicle number per plant
1000 grain weight (gm.)
Grain yield per plant (gm.)
Straw Total Harvest yield biological index per per plant plant (gm.) (gm.)
15.903
28.840
139.227
0.063
194.600
2.041
0.691
0.512
2.462
2.336
4.132
4.884
10.165
21.765
74.227
0.017
102.149
2.385
0.996
2.026
2.859
4.366
13.975
5.628
weight. Basmati 370 x Pusa 150 for grain yield per plant. Basmati 370x Govind, Basmati 370 x Saket 4, Dular x Jaya, Govind x UPR 79, Govind x Jaya , Jaya x Saket 4, Saket 4x Pusa 150 for straw yield per plant, Basmati 370 x Dular, Basmati 370 x Govind, Basmati 370 x HUR 52, Basmati 370 x UPR 79, Basmati 370x IR 50, Basmati 370x Ratna, Basmati 370 x Saket 4, Dular x Jaya, Dular x Pusa 150, Govind x UPR 79, Govind x Jaya, Govind x Pusa 150, HUR 52 x Pusa 150 for total biological yield per plant, Basmati 370 x UPR 79, Basmati 370 x Ratna, Basmati 370 x Pusa 150, Dular x Govind, Duluar x UPR 79, HUR 52 x UPR 79, HUR 52x Ratna, IR 50x Jaya for harvest index. The combining ability analysis indicated that days to 50% flowering, days to maturity, plant height, number of leaves per tiller, number of spikelets per panicle were
governed by additive gene action. However, non – additive gene action was predominant for panicle length, panicle number per plant, 1000 grain weight , grain yield per plant, straw yield per plant , total biological yield and harvest index as indicated by higher sca variances, which was also supported by Singh, et al., 1980, Shrivastava and Seshu, 1983 and Dhaliwal and Sharma, 1990. Pre dominance of additive gene action for plant height was evidenced by its high gea variance. Similar observation was made by Ram et al. (1989). Non additive gene action in harvest index reported by Sharma, et al., 1987 which conforms the present findings, for grain yield per plant which is govern by non additive gene action in the present findings which are in conformity
Table 2. General combining ability effects for yield and its component characters in rice. CHARACTERS Parents
Days to Days to 50% maturity flowering
Plant height (cm.)
Number Number of leaves of per tiller spikelets per panicle
Panicle Panicle length number (cm.) per plant
1000 grain weight (gm.)
Grain yield per plant (gm.)
Straw Total Harvest yield biological index per per plant plant (gm.) (gm.)
Basmati 370
3.36**
9.43**
23.29**
0.36
0.14
- 0.03
- 0.90
- 0.58
- 0.98
1.79
- 1.74
- 3.37**
Dular
- 6.97**
- 8.14**
11.51**
- 0.19
- 25.16**
2.54*
- 1.01
0.61
- 0.25
- 1.40
- 1.20
1.37
Govind
- 1.91
- 4.87**
5.65**
- 0.09
- 11.79**
- 1.11
- 0.70
1.55
- 1.35
- 1.81
- 2.66**
- 0.36
H.U.R. 52
6.56**
6.50**
- 5.23**
0.27
- 1.70
3.16**
- 0.09
- 0.70
- 1.20
0.50
- 0.18
- 2.18*
U.P.R. 79
1.49
- 0.64
- 9.59**
0.12
- 4.45**
- 0.30
0.43
0.10
0.56
- 0.65
0.49
1.41
I.R. 50
- 2.62**
- 4.00**
- 6.51**
0.03
4.88**
- 0.13
1.19
0.08
- 0.70
- 0.85
- 1.09
- 0.12
Jaya
6.54**
7.74**
15.22**
0.26
- 9.64**
- 1.07
- 0.55
- 0.96
- 0.72
3.33**
3.05**
- 3.69**
Ratna
- 1.40
- 1.21
- 9.73**
0.07
10.21**
- 1.68
1.76
1.22
1.10
- 0.54
1.02
2.07*
Saket 4
- 1.55
- 0.29
- 15.45**
- 0.47
4.35**
- 1.19
0.52
- 0.67
- 0.96
- 2.04* - 2.72**
Pusa 150
- 3.50**
- 4.51**
- 9.18**
- 0.36
33.16**
- 0.19
- 0.65
- 0.66
4.50**
1.67
5.04**
4.14**
VAR (GI)
0.009
0.027
0.002
0.001
1.449
0.0003
0.0001
0.001
0.002
0.001
0.227
0.017
VAR (GI-GJ)
0.020
0.060
0.004
0.003
3.322
0.0007
0.0002
0.002
0.004
0.003
0.506
0.038
*, ** Significant 5% and 1% levels of probability, respectively.
0.73
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Trends in Biosciences 7 (13), 2014
Table 3. Specific combining ability effects involving 10x10 diallel cross analysis (excluding reciprocals) for yield and its component characters in rice. CHARACTERS S. Crosses No.
Days to Days to 50% maturity flowering
Plant height (cm.)
Number of leaves per tiller
Number of spikelets per panicle
Panicle length (cm.)
Panicle number per plant
1000 grain weight (gm.)
Grain yield per plant (gm.)
Straw Total Harvest yield biologic index per al plant per (gm.) plant (gm.)
1
2
3
4
5
6
7
8
9
10
11
12
6.50**
8.30**
- 9.30**
- 0.01
20.63**
2.55*
- 0.78
0.74
0.56
1.42
4.56**
- 0.84
2 X Govind
0.11
6.70**
- 1.24
0.24
10.79**
- 0.41
- 0.62
1.88
-1.02
2.01*
3.55** - 2.97**
3 X HUR 52
- 0.36
- 3.67**
6.57**
0.03
- 1.42
1.51
0.76
0.37
- 0.92
0.71
2.31*
- 1.52
4 X UPR 79 - 2.79**
- 0.37
10.22**
0.04
- 4.17**
2.38*
1.21
1.57
1.72
0.79
4.98**
2.07*
5 X IR 50
1.49
8.88**
- 0.06
0.89
- 0.50
0.95
0.62
0.48
0.69
3.76**
0.37
0.04
- 3.09**
- 0.05
3.79**
- 2.18
0.30
- 0.47
2.43*
1.35
1 Basmati 370 x Dular
6 X Jaya
2.49*
- 3.68** - 4.08** - 12.99**
7 X Ratna
0.77
- 2.46*
9.91**
- 0.07
- 4.12**
0.56
- 1.09
1.17
1.99
0.37
4.95** 2.74**
8 X Saket 4
- 0.42
- 0.05
16.66**
0.10
8.26**
0.53
- 0.29
0.01
0.26
2.06*
5.05**
9 X Pusa 150
0.70
3.34**
11.56**
- 0.08
- 24.25**
- 1.94
- 0.83
0.46
4.78**
- 1.02
6.85** 12.08**
10 Dular x Govind
1.45
- 2.23*
6.16**
- 0.36
- 2.37*
0.39
- 0.52
- 2.98**
0.90
- 2.08*
11 X HUR 52 2.64**
11.73**
15.53**
- 0.02
4.64**
- 2.43*
0.07
- 0.59
0.80
0.69
1.02
0.08
12 X UPR 79
3.04**
3.70**
2.15*
0.29
10.95**
0.10
0.60
0.61
0.94
- 1.66
- 1.25
2.63**
0.99
0.56
14.13**
0.37
7.27**
- 0.85
0.59
1.39
0.55
0.88
1.01
- 0.55
1
2
3
4
5
6
7
8
9
10
11
12
14 X Jaya
2.99**
5.99**
- 2.80**
0.46
- 8.09**
1.37
- 0.73
1.47
0.17
15 X Ratna
1.93
3.60**
- 7.93**
0.11
14.36**
- 1.52
1.12
- 0.06
0.05
1.37
1.34
- 1.96
16 X Saket 4
1.08
3.69**
- 11.59
- 0.23
- 1.98*
1.93
0.49
1.20
0.01
- 0.66
- 1.89
0.16
17 X Pusa 150
2.70**
- 2.09*
- 15.16**
0.01
19.21**
0.62
- 0.30
1.77
1.35
2.52
5.04**
- 0.97
18 Govind X HUR 52
2.09*
- 6.37**
3.80**
- 0.23
0.45
2.87**
- 0.14
1.57
- 1.18
0.94
- 0.76 - 2.88**
19 X UPR 79
2.49*
0.77
2.34*
0.00
- 5.74**
1.06
0.34
- 1.53
2.05*
2.17*
3.65**
13 X IR 50
- 1.64
- 1.32
3.41**
2.84** 2.61** - 2.63**
0.92
20 X IR 50
- 0.90
- 0.87
- 5.17**
- 0.14
4.52**
0.58
0.38
0.95
0.02
1.46
1.02
- 0.42
21 X Jaya
- 1.90
- 4.60**
- 7.40**
0.12
- 10.15**
- 0.77
- 0.68
2.12*
- 0.01
2.58**
2.12*
- 2.34*
22 X Ratna
0.88
2.34*
6.15**
- 0.07
10.04**
- 0.56
1.17
- 1.65
0.90
- 0.02
0.75
1.35
23 X Seket 4
- 3.31**
- 0.74
- 2.06*
0.09
8.01**
- 0.89
- 0.05
1.54
1.26
- 0.41
- 0.44
0.69
24 X Pusa 150
- 2.02*
- 1.69
6.97**
- 0.32
18.19**
1.34
0.09
1.66
0.50
1.24
3.85**
- 0.87
25 HUR 52 X - 5.08** - 4.40** UPR 79
- 5.11**
0.07
- 0.34
0.38
- 1.53
0.74
0.90
- 0.23
0.84
2.54*
26 X IR 50
- 5.76**
- 0.11
- 0.73
1.20
0.33
0.44
0.62
- 0.01
0.14
0.77
- 1.37
- 0.24
27 X Jaya
- 3.54** - 6.98**
4.78**
- 0.04
- 1.26
0.25
- 0.62
- 0.23
0.15
0.26
- 0.05
1.05
28 X Ratna
- 2.93**
- 0.37
- 3.18**
0.29
- 2.62**
1.36
0.83
1.48
1.82
- 0.34
1.01
2.72**
29 X Saket 4
7.56**
- 2.95**
- 5.41**
0.08
- 1.74
1.17
0.29
- 0.60
0.31
1.09
1.10
- 0.86
TIWARI and JATAV, Combining Ability Analysis for Grain Yield and Its Related Characters in Rice (Oryza sativa L.)
1447
CHARACTERS S. Crosses No.
Days to Days to 50% maturity flowering
Plant height (cm.)
Number of leaves per tiller
Number of spikelets per panicle
Panicle length (cm.)
Panicle number per plant
1000 grain weight (gm.)
Grain yield per plant (gm.)
Straw Total Harvest yield biologic index per al plant per (gm.) plant (gm.)
1
2
3
4
5
6
7
8
9
10
11
12
30 X Pusa 150
- 0.50
3.94**
- 1.77
0.00
7.35**
0.18
0.21
- 0.53
1.33
0.43
2.86**
1.60
31 UPR 79 X IR 50
2.69**
- 4.10**
- 1.86
- 0.19
- 0.95
- 0.13
0.06
0.23
1.03
0.13
0.60
1.26
32 X Jaya
- 1.47
3.16**
7.83**
- 0.18
- 0.67
0.99
- 0.14
0.80
1.20
1.07
1.75
0.32
33 X Ratna
- 1.52
- 2.13*
- 4.12**
- 0.05
0.74
1.87
- 0.32
- 0.20
0.56
0.12
0.15
0.55
1
2
3
4
5
6
7
8
9
10
11
12
2.96**
- 2.81**
- 1.42
- 0.05
0.97
1.27
- 0.27
0.89
0.50
0.81
0.95
- 0.01
35 X Pusa 150
1.57
- 3.92**
- 2.75**
0.03
8.53**
0.06
- 0.05
0.58
- 1.80
- 0.72
- 1.47
- 1.52
36 IR 50 X Jaya
2.97**
5.52**
5.43**
0.07
7.21**
0.51
0.69
0.39
- 0.86 - 4.05** - 5.41** 2.85**
37 X Ratna
- 1.25
- 2.87**
- 2.72**
- 0.14
- 7.48**
1.13
- 0.59
0.63
1.21
38 X Saket 4
- 1.23
- 0.45
- 4.95**
0.21
- 4.80**
0.66
- 1.15
- 0.46
- 0.09
39 X Pusa 150
- 1.32
- 1.90
- 2.14*
- 0.02
2.77**
- 0.59
0.20
- 0.28
- 0.84
40 Jaya X Ratna
4.42**
0.23
9.59**
- 0.07
6.99**
- 0.14
0.51
1.19
1.31
1.35
41 X Saket 4
2.57**
1.81
11.80**
- 0.11
4.91**
- 0.46
1.86
- 0.24
- 0.40
2.90**
2.27* - 3.40**
42 X Pusa 150
2.02*
3.04**
7.96**
- 0.24
16.97**
0.60
- 0.33
- 0.14
0.81
- 0.03
1.90
1.29
43 Ratna X Saket 4
- 2.49*
- 2.58**
1.26
0.16
- 2.05*
- 1.02
- 0.49
0.71
0.58
- 0.97
- 0.63
1.74
44 X Pusa 150
- 0.87
- 1.02
- 3.31**
0.15
- 1.99
0.91
0.71
0.88
- 2.00*
- 0.23
- 1.06
- 2.30*
45 Saket 4 X Pusa 150
- 2.39*
0.06
0.10
0.19
0.93
0.39
0.21
- 0.55
0.04
4.07** 5.45** - 3.37**
VAR (SIJ)
0.106
0.307
0.024
0.016
16.392
0.003
0.001
0.014
0.025
0.016
2.579
0.194
VAR (SIJSIK)
0.230
0.665
0.052
0.036
35.420
0.007
0.002
0.031
0.054
0.035
5.572
0.419
VAR (SIJ-SKL)
0.209
0.604
0.047
0.33
32.200
0.007
0.002
0.029
0.049
0.0320
5.065
0.381
34 X Saket 4
0.08
0.88
1.51
1.69
1.36
- 2.08**
0.98
1.42
- 1.71
2.26*
0.48
*, ** Significant 5% and 1% levels of probability, respectively.
with earlier workers ( Li and Chang, 1970, Li, 1975, Singh and Nada, 1977), Shrivastava and Seshu, 1983 and Sharma et al., 1987). From this study, it was noted that both additive and non- additive gene action were important in controlling various characters.
ACKNOLDGEMENT Authors are grateful to Head, Department of Agricultural Botany Kisan (Post Graduate) Collage, Simbhaoli, Ghaziabad, U.P. India. for providing necessary facilities during the course of investigation
1448
Trends in Biosciences 7 (13), 2014
LITERATURE CITED Dhaliwal, T .S. Sharma, H. I. 1990. Combining ability and maternal effects for agronomic and grain characters in rice. Oryza. 27 :122124. Griffing, B. 1956. Concept of general and specific combining ability in relation to diallel crossing systems . Aust. J. Boil Sci,. 9 :46493.
nature of genetic variance for yield and its components in rice . Int. Rice Res. News. 14( 4) 6. Sharma. D. K. Shrivastava, M. N. Tiwari, D. K. 1987. Line X tester analysis for harvest index and related characters in rice (Oryza sativa L.) Indian J. Genet. 47: 211-218. Singh, D. P. Nada, J. S. 1977. Inheritance of yield and contributing characters in rice. Indian J. Agric. Sci. 37 (3 ):384-387.
Li, C. C. 1975. Diallel analysis of yield and its components trails in rice . Journal of Agricultural Association of China. 92: 41-59.
Singh, S. P. Singh, R. R. Singh, R. P. Singh, R. V. 1980. Combining ability in rice Oryza . 17 :104-108.
Li, C. C. Chang, T. T. 1970. Diallel analysis of agronomic traits in rice Oryza sativa L. Bot. Bull. Acad. Sinica . 11: 61-78.
Srivastav, M. N. Seshu, D. V. 1983. Combining ability for yield and associates characters in rice . Crop Sci . 23: 741-744.
Ram, T. Singh, J. Singh, R. M. 1989. Dominance relationship and Received on 09-05-2014
Accepted on 22-05-2014
Trends in Biosciences 7(13): 1449-1451, 2014
Study on Seed Physical Characteristics and Phytic Acid Content of Soybean Germplasm S. ABIRAMI1*, A. KALAMANI1 AND T. KALAIMAGAL2 1
Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore Department of Plant Breeding and Genetics, Anbil Dharmalingam Agricultural College and Research Institute, Trichy-9, Tamilnadu. *email:
[email protected] 2
ABSTRACT The present investigation was carried out with 250 soybean germplasm to study the seed physical characteristics and phytic acid content, which determine the quality of various soy foods. The seed phytic acid content ranges between 0.84 mg/g and 7.07 mg/g of dry seed weight. Results on seed physical characteristics showed 3.99 – 9.14 mm seed length, 3.28-6.70mm seed width and 2.15-5.72mm seed thickness. The seed weight fell in the range of 0.07-0.19g and the varieties recorded seed volume and seed density in the range of 0.07- 0.20ml and 0.63-1.45 g/ml respectively. The seed phytic acid content and seed physical parameters viz., seed length, seed width, seed thickness, seed weight and volume showed more heritability. All the seed physical characteristics except the seed density showed negative correlation with the phytic acid content and hence these traits can be used as physical markers after further conformational studies for selection of lines with low phytate content for improving the quality of various soy products. Key words
Soybean, Germplasm, Phytic acid, Seed physical characteristics
Soybean (Glycine max (L.) Merril), a “Miracle Crop” is one of the most important legume crops of the world for food, feed and oil. Soybean possesses ample of industrial uses in food and pharma industry. It contains 40 to 42% good quality protein and 18-22% of oil comprising 85% of unsaturated fatty acids free from cholesterol with much of nutritive element. Apart from this, soybean also contains secondary metabolites such as isoflavons (Sakai and Kogiso, 2008), saponins, phytic acid, oligosaccharides, goitrogens. The biochemical composition and physical appearance of soybean seeds affect the quality of various soy preparations such as soy milk, tofu, soy sprouts, miso and natto (fermented soy products). Large seeded soybeans with clear hilum are preferred in both soy-milk and tofu preparations. In tofu-making, phytic acid, a heat-stable anti nutritional factor in soybean seed, which chelates heavy metal ions Ca2+, Mg2+, Zn2+, Fe3+, also has an important implication. Soybean seeds with a higher phytic acid content necessitate higher requirement of coagulants,
namely CaSO4 (Schaefer and Love, 1992), thereby affecting the quality of tofu (Skurray, et al., 1980). Thus, soybean genotypes with a very low phytic acid content are good for tofu preparations. In other soy preparations such as soybean sprouts and natto, small seeded varieties with high protein content are preferred, while large seeded genotypes are preferred for miso and for use as vegetables (Edamame). The intent of this study was to study the variability for phytic acid content and physical characteristics of soybean seed.
MATERIALS AND METHODS A total of 250 soybean germplasm accessions being maintained at Department of Pulses, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University were used for the study. Phytic acid was estimated following the method described by Davies and Reid, 1979. One gram of seed material was ground and extracted with 0.5 M HNO3 by continuous shaking, filtered and made upto suitable volume with water. To 1.4 ml of the filterate, 1 ml of ferric ammonium sulphate (21.6 mg in 100 ml water) was added, mixed and placed in a boiling water bath for twenty minutes. The contents were cooled and 5 ml of iso amyl alcohol was added and mixed. To these, 0.1 ml of ammonia solution was added, shaken thoroughly and centrifuged at 3000 rpm for 10 minutes. The alcoholic layer was separated and the color intensity was read at 465 nm against amyl alcohol blank after 15 minutes. Sodium phytate standards were run along with the sample. The results were expressed as mg phytic acid/ g dry weight. Seed physical parameters of all the 250 genotypes were studied in detail. Seed length, width and thickness were measured using a digital vernier caliper and taking average values of 20 seeds. Seed weight was measured by taking average weight of 20 seeds, weighed individually in two replicates. Seed volume was determined by displacement of a known volume of water by 20 seeds in duplicate for each entry and calculated to volume per single seed. Seed density was calculated as ratio of mass to volume and expressed in g/ml. Analysis of variance was calculated using standard statistical procedures. Genetic parameters like variances, Genotypic Coefficient of Variability (GCV), Phenotypic Coefficient
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Trends in Biosciences 7 (13), 2014
Table 1. Descriptive statistics of phytic acid content and seed physical characteristics in 250 soybean genotypes. Characters
Mean
Median Mode
Maximum Minimum Range
Variance SD
SE
Skewness
Kurtosis
Phytic acid (mg/g)
3.90
3.79
4.67
7.07
0.84
6.23
1.26
1.12
0.07
0.085
-0.431
Seed length (mm)
6.72
6.79
6.90
9.14
3.99
5.14
0.50
0.71
0.05
-1.181
3.242
Seed width (mm)
5.75
5.85
5.99
6.70
3.28
3.42
0.37
0.60
0.04
-2.402
6.367
Seed thickness (mm) 4.57
4.67
4.62
5.72
2.15
3.58
0.39
0.63
0.04
-2.158
5.258
Seed weight (g)
0.12
0.12
0.12
0.19
0.07
0.12
0.0004
0.02
0.001 0.689
0.856
Seed volume (ml)
0.12
0.12
0.10
0.20
0.07
0.13
0.0005
0.02
0.002 1.074
1.194
Seed density (g/ml)
1.03
1.03
1.05
1.45
0.63
0.82
0.01
0.11
0.007 0.250
2.700
of Variability (PCV), broad sense heritability and Genetic advance as per cent of mean (GAM) were calculated by adopting suitable formulae. The mean values of the genotypes for phytic acid content and seed physical characteristics over two replications were subjected to descriptive statistics analysis using STATISTICA package.
RESULTS AND DISCUSSION The data recorded on soybean physiochemical parameters were subjected to descriptive statistics and properties of quantitative traits such as measures of central tendency (mean, median and mode), measures of dispersion or variability (range, variance, standard deviation and standard error) and measures of symmetry (skewness and kurtosis) were analysed and their respective values are furnished in the Table 1. The phytic acid content ranged from 0.84 mg / g (Williams) to 7.07 mg/ g (Co3), thus showing a large variability for this trait. A variability of 0.16 to 4.74 mg/g for phytic acid among 106 soybean germplasm lines was also recorded by Badigannavar and Manjaya, 2012. Out of the seven characters investigated, three traits viz., seed length, width and thickness showed negative skewness values, indicating the presence of additive gene action for these traits and they can be utilized as selection criteria while selecting the parents for hybridization
programmes. The remaining four characters such as phytic acid content, seed weight, seed volume and seed density were skewed positively, indicating the presence of non additive gene action for these characters. The analysis of variance showed highly significant differences among the genotypes for all the characters suggesting the presence of considerable range of variation expressed for the traits. To understand the extent to which the observed variation was due to genetic factors, the value of genotypic and phenotypic variance, phenotypic and genotypic coefficients of variability, broad sense heritability and genetic advance as per cent of mean for different characters under study were estimated and furnished in Table 2. Among the traits studied, phytic acid content had expressed the highest phenotypic and genotypic variances with the values of 1.26. The phenotypic coefficient of variation was maximum for phytic acid content (28.81) followed by seed volume (20.29). The genotypic coefficient of variation was highest for phytic acid content (28.80) followed by seed volume (17.01). Selection based on these characters may facilitate successful isolation of desirable genotypes. However phenotypic and genotypic coefficients of variability were found to be low for the characters seed length (10.53) and seed density (7.70.) respectively, indicating the limited utility of these traits in selection programme. High heritability is a good index of the
Table 2. Estimate of variance, coefficients of variation, heritability and genetic advance as per cent of mean for phytic acid and seed physical characteristics in 250 soybean genotypes. Characters
PV
GV
PCV
GCV
ECV
Heritability
GAM
Phytic acid (mg/g)
1.26
1.26
28.81
28.80
0.75
99.93
59.30
Seed length (mm)
0.50
0.49
10.53
10.46
1.26
98.57
21.39
Seed width (mm)
0.37
0.36
10.55
10.43
1.62
97.65
21.23
Seed thickness (mm)
0.40
0.39
13.76
13.64
1.77
98.34
27.87
Seed weight (g)
0.00
0.00
16.17
15.17
5.59
88.04
29.33
Seed volume (ml)
0.001
0.00
20.29
17.01
11.06
70.26
29.37
Seed density (g/ml)
0.018
0.006
13.14
7.70
10.64
34.37
9.30
ABIRAMI, et al., Study on Seed Physical Characteristics and Phytic Acid Content of Soybean Germplasm
Table 3. Correlation coefficients of phytic acid contents with seed physical parameters of 250 soybean germplasm lines. Seed physical parameters
Phytic acid %
Seed length (mm)
-0.180**
Seed width (mm)
-0.127*
Seed thickness (mm)
-0.124
Seed weight (g)
-0.281**
Seed volume (ml)
-0.247**
Seed density (g/ml)
0.014
* p