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Subject: Agricultural Meteorology × End date: 2024 × Start date: 2020 × Type: Thesis × Thesis Type: Ph.D ×
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    ThesisItemOpen Access
    COMPARATIVE STUDY OF APSIM-WHEAT AND CERES-WHEAT MODEL FOR PRECISION MANAGEMENT OF WHEAT CROP AND MITIGATION OF THE ADVERSE IMPACT OF CLIMATE CHANGE UNDER TARAI REGION OF UTTARAKHAND
    (G. B. Pant University of Agriculture & Technology, Pantnagar-263145, 2022-09-01) Pareek, Neha; Nain, A. S.
    Climate change impacts on wheat production has widespread and national ramifications for food and nutritional security. A field study was conducted during rabi2017-18 and 2018-19 at agricultural farm of G.B. Pant University forcomparative study of APSIM-Wheat and CERES-Wheat model for precision management of wheat crop and mitigation of adverse impact of climate change Tarai region of Uttarakhand. APSIM-Wheat and CERES-Wheat, which simulates yieldhas been calibrated for winter wheat using experimental dataof 2017-18 crop season.The calibration was performed against anthesis (DAS), physiological maturity (DAS), grain and biomass yield for the three treatments of sowing dates (viz.,15th November, 25th November and 05th December) , three levels of irrigation (number of irrigation five, four and three) for two wheat varieties (HD-2967 and PBW-502).The calibrated APSIM-Wheat and CERES-Wheat model were then applied to validate anthesis (DAS), physiological maturity (DAS), yield and biomass for rabi season of 2018-19 for the three treatments of sowing dates (viz., 15th November, 25th November and 05th December) , three levels of irrigation (number of irrigation five, four and three) for two wheat varieties (HD-2967 and PBW-502). The simulated output by the CERES-Wheat model found closer to the experimental field data therefore this model was applied for other two objectives. In this study sowing time and irrigation (number of irrigations) were optimised to get economical yield. A sowing window starting from last week of October (25th October) to the first week of December (06th December) was selected for optimization of sowing date. Crop sown in the first week of November produced highest grain yield followed by sowing in second and third week in the rabi season of 2017-18 and 2018-19.The optimum number of irrigations for wheat was also evaluated by CERES-Wheat model through projected irrigation scenarios. Present study revealed that in this region three irrigations are sufficient for optimum yield. This model also applied to predict the wheat biomass and yield in the year 2030, 2050 and 2080 and found that biomass and yield will be decreased in the future years due to increase in temperature and decrease in rainfall. In-situ moisture conservation, rainwater harvesting and recycling, and efficient irrigation water use are all important adaptation strategies to combat the adverse effect of climate change. Breeders will need to develop some short-duration varieties in the coming years by changing the genetic makeup of existing varieties. Early flowering (photo- and thermo-insensitivity), early maturity, and higher productivity should be characteristics of the new varieties. Identification of crops and varieties/ hybrids with high water use efficiency, adapted to temperature extremes and high concentration of CO2 is needed in this climate change scenario.
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    ThesisItemOpen Access
    Paddy characterization and yield estimation using optical and SAR data
    (G. B. Pant University of Agriculture and Technology, Pantnagar, 2023-02) Sonam; Nain, A. S.
    Reliable and accurate monitoring of rice crop is vital for food security and the global economy. This study aims to characterize rice types and provide an early yield estimation using optical and Synthetic Aperture Radar (SAR) data. Dense time series Sentinel-1 SAR backscatter data were analyzed for summer and kharif rice to discriminate between different rice types and identify their optimum stages to represent crop growth profiles. Critical stages of discrimination were determined statistically for all rice types. Knowledge-based decision tree algorithm was employed to classify all major types of rice grown during both the seasons. Savitzky-Golay fitted temporal profile of remotely sensed indices from Sentinel-1 and Sentinel-2 satellite sensors were used to extract seasonality parameters using TIMESAT. Correlation analysis was performed at different phenophases to identify sensitive biophysical and remote sensing parameters. Finally, machine learning models were used to predict yield of rice at 45, 60 and 90 days after transplanting using remote sensing indices and biophysical parameters as inputs. The performances of ML models for early yield estimation were evaluated using standardized ranking performance index. Results revealed significant differences between backscatter profiles of different rice types especially in early vegetative stages with transplanting being the most critical stage. Transplanting/seeding, tillering, panicle initiation, peak vegetative/flowering and maturity were identified to be the most important stages to represent the backscatter profile of entire rice growth. Decision tree algorithm discriminated rice types with high overall accuracy and Kappa coefficient, viz., 94.74% and 0.94 for summer rice and 91.80% and 0.90 for kharif rice, respectively. The extracted seasonality parameters revealed that start time derived from SAR indices was found to accurately distinguish different types of rice. The correlation strength generally increased with the progress of crop growth in both seasons. All significant correlations were positive except that with moisture content. NRPB was strongly correlated with VV and VV/VH ratio at all stages in both the seasons. LAI and dry biomass were found to be the most sensitive biophysical parameters towards remote sensing data. Area under rice cultivation in the study area was found to be 37.1%, using random forest classifier that yielded an accuracy above 90% and kappa coefficient of 0.89. The machine learning models were able to achieve R2 and d-index values above 0.9 and RMSE below 0.5 t/ha. XGB was the best model for predicting rice yield using remote sensing and biophysical parameters. The findings of this study have the potential to contribute to the greater goal of sustainable agriculture and food security.