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ThesisItem Open Access A study on impact of climate change and statistical models for pre-harvest forecast of wheat-yield in Haryana(CCSHAU, Hisar, 2023-05) Chetna; Monika DeviThis study aimed to improve the predictability of wheat yield in four districts of Haryana state using advanced statistical techniques. The best models for predicting weather variables were identified, and analyzed the impact of weather variables on crop yield during different growth stages. It was found that weather variables had varying effects on crop yield during different growth stages and across different districts. The observed positive effects of temperature on crop yield during the reproductive stages could be attributed to increased photosynthesis and growth rate of the crop, while the negative effects of temperature during the germination, milking, and harvesting stages could be due to increased plant stress and water loss. The study also found that the negative effects of rainfall on crop yield during certain growth stages could be attributed to waterlogging and soil compaction, while the positive effects of rainfall during certain growth stages could be due to increased soil moisture availability. The study developed models with high R2 values and low error values for predicting wheat yield in all four districts. Pre-harvest forecast models were developed to predict wheat yield before harvest in selected districts of Haryana, using discriminant function analysis and weekly meteorological variables. The models achieved high accuracy in correctly classifying the grouped cases in all districts, with varying effects of predictor variables and autocorrelation. The evaluation of various models for yield forecasting in different districts of Haryana State has yielded impressive results. Principal Component Analysis (PCA) was also utilized to investigate the impact of weather variables on the weather indices in various districts of Haryana State. The models showed a good fit with observed data and high accuracy in predicting yield, with different levels of complexity and performance depending on the district and the model used.ThesisItem Open Access Mathematical modeling for optimization of polyculture fish feed(CCSHAU, Hisar, 2021-09) Chetna; Tonk, Manju S.Linear programming is an important tool for optimization and shows considerable potential. It is a tool to find solution to a variety of very complex diet problems. In fish farming feed represents maximum of the production cost which increases cost of production. Nutritious diet plays an important role in fish farming for the optimal growth, health and life span of fish. The study on “Mathematical Modeling for optimization of polyculture fish feed” was planned to develop a linear programming model for feed formulation of polyculture fish farming. The data was collected from Dabra Shamsukh, Sundawas, Panihar chak,Rajli, Shahpur village and Bluebird lake of Hisar. The LP model based on farmer’s information was formulated and analyzed. The model suggested least cost composition with only one ingredient which was not practically acceptable because the farmers include all four ingredients. The model was modified using maximum/minimum constraints on ingredients quantity. Also the minimum nutrients’ constraints were relaxed. Three alternate feed plans were suggested for the polyculture fish (fry, fingerling and grower stage). The cost for farmer’s plan was found the highest in comparison to the other three stagewise feed mix plan. If the farmers use the recommendation of feed composition for fry which is maximum of all the three they can save at least ₹687.52 /100kg feed composition. Sensitivity analysis of the developed models showed minimum and maximum range of ingredients for feed mix, where the optimal LP solution will remain unchanged within these range of values of the ingredients
