Mapping and Modeling Groundnut Growth and Productivity in Rainfed Areas of Tamil Nadu
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MAPPING AND MODELING GROUNDNUT GROWTH AND PRODUCTIVITY IN RAINFED AREAS OF TAMIL NADU By M. DEIVEEGAN, M. Sc. (Ag.), (I.D. No. 13-607-003) DEPARTMENT OF AGRONOMY DIRECTORATE OF CROP MANAGEMENT TAMIL NADU AGRICULTURAL UNIVERSITY COIMBATORE - 641 003 2017 MAPPING AND MODELING GROUNDNUT GROWTH AND PRODUCTIVITY IN RAINFED AREAS OF TAMIL NADU Thesis submitted in part fulfillment of the requirements for the award of the degree of DOCTOR OF PHILOSOPHY (AGRICULTURE) IN AGRONOMY to the Tamil Nadu Agricultural University, Coimbatore - 641 003 By M. DEIVEEGAN, M. Sc. (Ag.), (I.D. No. 13-607-003) DEPARTMENT OF AGRONOMY DIRECTORATE OF CROP MANAGEMENT TAMIL NADU AGRICULTURAL UNIVERSITY COIMBATORE - 641 003 2017 CERTIFICATE This is to certify that the thesis entitled “MAPPING AND MODELING GROUNDNUT GROWTH AND PRODUCTIVITY IN RAINFED AREAS OF TAMIL NADU” submitted in part fulfillment of the requirement for the award of the degree of DOCTOR OF PHILOSOPHY (AGRICULTURE) IN AGRONOMY to the Tamil Nadu Agricultural University, Coimbatore is a bona fide record of research work carried out by Mr. M. DEIVEEGAN under my supervision and guidance and that no part of this thesis has been submitted for the award of any other degree, diploma, fellowship or other similar titles. However, part of thesis work has been published in peer reviewed scientific journal of national/international repute (copy enclosed). Place: Coimbatore Dr. S. PAZHANIVELAN Date: Chairman Approved By Chairman: Dr. S. PAZHANIVELAN Co-Chairman Dr. MURALI KRISHNA GUMMA Members: Dr. S. PANNEERSELVAM Dr. R. SIVASAMY Dr. C. KARTHIKEYAN *Additional Member Dr. R. JAGADEESWARAN Date: EXTERNAL EXAMINER: Acknowledgement ACKNOWLEDGEMENT This thesis has been kept on track and been seen through to completion with the support and encouragement of numerous people including my well-wishers, my friends, colleagues and various institutions. At the end of my research, I would like to thank all those people who made this research possible and an unforgettable experience for me through my life time. It is a pleasant time to express my thanks to all those who contributed in many ways to the get it success of this study. First and foremost my heart lifts up to thank my parents, Jotting customary thanks is not enough for the care and support I enjoyed from my loving mother, Mrs. M. Malarkodi, and father, Mr. M. Murugesan. It is their dream and support that brought me up to this level. The Unconditional love and care I enjoyed from my sister, Mrs. Silambarasi Suresh Kumar, brother, Mr. M. Azhagiri, kids, Susin and Susith. Really I am gifted to have Dr. S. Pazhanivelan, Professor (Agronomy) and Head, Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore, as Chairman of my advisory committee, who guided me with full encouragement, involvement, follow ups, valuable suggestions, suitable techniques, correction of errors at correct time throughout the study period. Without his encouragement and strengthening words, the dissertation could not have come to this shape. There is no appropriate word to express my heartfelt thanks to him. I express my sincere thanks to the Co-Chairman Dr. Murali Krishna Gumma, Head - GIS & Remote Sensing Lab, Innovation Systems for the Drylands Program, ICRISAT, and members of advisory committee Dr. S. Panneerselvam, Professor and Head Department of Agronomy, Dr. R. Sivasamy, Professor (SS&AC), Department Remote Sensing and GIS, TNAU, Coimbatore, Dr. C. Karthikeyan, Professor (Agrl.Extn.), DEE, TNAU, Coimbatore, Dr. R. Jagadeeswaran, Asst. Professor (SS&AC), Department Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore, and, for their valuable suggestions, constant encouragement and kind help during the progress of this research work. I wish to place my deep sense of reverence, profound gratitude and immense heartfelt thanks to Dr. P. Devasenapathy, Professor and Head, Department of Agronomy, for their help and valuable suggestions during the course of this investigation. I esteem it a privilege to express my sincere regards to Dr. C. Chinnusamy, P.G. Co- ordinator Department of Agronomy. I am extremely thankful to Dr. K.P. Ragunath, Asst. Professor (SS&AC), Dr. R. Kumaraperumal, Assistant Professor (SS & AC), Dr. Balaji Kannan, Associate Professor (SWC), Department Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore, Manoj Yadav, GIZ, New Delhi for Spatial Analysis of remote sensing data and valuable suggestions. I place my profound sense of respect to Dr. Murali Krishnasamy, Professor (Agronomy). I sincerely thank to Mrs. Devi Pazhanivelan, and his family members (Parents, Neha and Hanya) for their constant encouragement and support in all my research efforts. I personally thank Sudar and Ranjani, for them timely help and encouragement during my study period and I take this opportunity to thank Shenbagaraj, Karizma, Savitha and Nasrin of RS&GIS for their valuable helps during my research. Also I wish to express my hearty thanks to my fellow scholars, Murali anna, Maruthu, Ram, Ashok, Anbarasi, Jeyajothi, John and my friends Science Sir, Hari, Suresh, Marimuthu, Muthukumar, Ezhil, Lokesh Sir, Satthiah, Oli annan, Mugilan, kalaimani, Rajesh, Rajadurai and all my friends, junior friends and senior friends for their ever-willing help and moral support during my research work. I express my thanks to the office staff members of Agronomy department and Department of Remote sensing and GIS for their support during my research programme. Besides this, several people have knowingly and unknowingly helped me in the successful completion of this research I owe thanks to all of them. I wish to place my sincere acknowledgement to RIICE project and sarmap, Switzerland for providing technical support in analysing satellite data and sparing MAPscape – Rice software. I also acknowledge the favours of numerous persons who, though not been individually mentioned here, have all directly or indirectly contributed during the course of the study. I am immensely thankful to the almighty for the blessings for lifting me uphill to this phase of life. (M. Deiveegan) Abstract ABSTRACT MAPPING AND MODELING GROUNDNUT GROWTH AND PRODUCTIVITY IN RAINFED AREAS OF TAMILNADU By M. DEIVEEGAN Degree : Doctor of Philosophy (Agriculture) in Agronomy Chairman : Dr. S. Pazhanivelan, Ph. D., Professor and Head Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore – 641 003, Tamil Nadu Year : 2017 A research study was conducted at Tamil Nadu Agricultural University, Coimbatore during kharif and rabi 2015 to estimate groundnut area, model growth and productivity and assess the vulnerability of groundnut to drought using remote sensing techniques. Multi temporal Sentinel 1A satellite data at VV and VH polarization with 20 m spatial resolution was acquired from May, 2015 to January, 2016 at 12 days interval and processed using MAPscape-RICE software. Continuous monitoring was done for ground truth on crop parameters in twenty monitoring sites and validation exercise was done for accuracy assessment. Input files on soil, weather and management practices were generated and crop coefficients pertaining to varieties were developed to assess growth and productivity of groundnut using DSSAT CROPGRO-Peanut model. Outputs from remote sensing and DSSAT model were assimilated to generate LAI thereby groundnut yield spatially and validated against observed yields. Being a rainfed crop, vulnerability of groundnut to drought was assessed integrating different meteorological and spectral indices viz., Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI) and Water Requirement Satisfaction Index (WRSI). Spectral dB curve of groundnut was generated using temporal multi date Sentinel 1A data. A detailed analysis of temporal signatures of groundnut showed a minimum at sowing and a peak at pod development stage and decreasing thereafter towards maturity. Groundnut crop expressed a significant temporal behaviour and large dynamic range (-11.74 to -5.31 in VV polarization and -20.04 to -13.05 in VH polarization) during its growth period. Groundnut area map was generated using maximum likelihood classifier integrating multi temporal features with a classification accuracy of 87.2 per cent and a kappa score of 0.74. The total classified groundnut area in the study districts was 88023 ha covering 17817 and 22582 ha in Salem and Namakkal districts during kharif 2015 while Villupuram and Tiruvannamalai districts accounted for 22722 and 24903 ha respectively during rabi 2015. Blockwise statistics on groundnut area during both seasons were also generated. To model growth and productivity of groundnut in DSSAT, weather and soil input files were generated using weatherman and ‘S’ build respectively besides deriving genetic coefficients for CO 6, TMV 7 and VRI 2 varieties of groundnut. Growth and development variables of groundnut were simulated using CROPGRO- Peanut model i.e., days to emergence (7-9 days) and anthesis (25-32 days), canopy height (63 to 70 cm), maximum LAI (1.12 to 3.07) and biomass (4176 to 9576 kg ha-1 across twenty monitoring locations spatially. The resultant pod yield was simulated to be 1796 to 3060 kg ha-1 with a harvest index of 0.28 to 0.43. On comparison of LAI between observed (2.01 to 4.05) and simulated values (1.12 to 3.07) the CROPGRO-Peanut model was found to under estimate the values with R2, RMSE and NRMSE of 0.82, 1.10 and 34 per cent. However, the model predicted the biomass of groundnut with an agreement of 89 per cent through the simulated values of 4176 to9576 kg ha-1 as against the observed biomass to 4620 to 9959 kg ha-1. The simulated pod yields of groundnut in the study area were 1796 to 3060 kg ha-1 as compared to the observed yields of 2115 to 2750 kg ha-1. The overall agreement between simulated and observed yields was 84 per cent with the average errors of 0.81, 342 kg ha-1 and 16 percent for R2, RMSE and NRMSE respectively. LAI values of groundnut, generated spatially through suitable regression models using dB from satellite images and LAI from DSSAT, ranged from 1.31 to 3.23 with R2, RMSE and NRMSE of 0.86, 0.78 and 24 per cent respectively on comparison with observed values.