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2019 - 201912020 - 20231
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Subject: Agricultural Meteorology × Author: Sonam × End date: 2024 × Type: Thesis ×
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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.
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    ThesisItemOpen Access
    Application of crop simulation model and agrometeorological observations for optimization of inputs in chickpea under Tarai region of Uttarakhand
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-06) Sonam; Nain, A.S.
    The current focus of Indian agriculture is to maximizing the production by optimizing the limited resources so that production system could be sustained over a longer period of time. Considering this fact, the present study was conducted to optimize input resources in chickpea by calibrating the crop simulation model on experimental data set under Tarai region of Uttarakhand. The experiment was laid during rabi 2017-18 at Norman E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar with three dates of sowing and two irrigation levels. CROPGRO-Chickpea model was used as a tool to achieve the objective and was calibrated using rabi 2017-18 experimental field data. The important finding of the study is that sowing on 29th November resulted into highest number of root nodules per plant as well as maximum dry weight of nodules per plant. The relative increase in number of root nodules per plant was found to have negative correlation with temperature (R²=0.36) and positive correlation with RH (R²=0.34). Similarly, relative gain in dry weight of root nodules per plant possessed negative correlation with temperature (R²=0.32) and positive correlation with relative humidity (R²=0.21). The model could capture all phenological stages reasonably. The growth and yield parameters of chickpea could also be simulated very well with RMSE less than10%. Under non-limiting condition of other resources, the model identified first fortnight of November sowing date to produce maximum yield. Nitrogen and irrigation were optimized by considering four rabi seasons. Varying number of doses of nitrogen (18kg/dose) from one to three caused an increase in yield of chickpea but the relative increase per dose followed a decreasing trend. Another important finding was that if sowing date is delayed, nutrient use efficiency is also declined, therefore excess amount of nitrogen application results into wastages of resources. In case of failure of winter rains, model suggested two irrigation (one at pre-flowering stage and other at pod development stage) for crops sown up to first fortnight of December and three irrigation (at early vegetative stage, pre-flowering stage and pod development stage) for crops sown during 2nd fortnight of December. However, if limited water is available, one irrigation during the initiation of pod development stage was simulated to be optimum by the model. If winter rain occurs, only one irrigation during sensitive stage of chickpea facing stress should be applied.