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Chaudhary Charan Singh Haryana Agricultural University, Hisar

Chaudhary Charan Singh Haryana Agricultural University popularly known as HAU, is one of Asia's biggest agricultural universities, located at Hisar in the Indian state of Haryana. It is named after India's seventh Prime Minister, Chaudhary Charan Singh. It is a leader in agricultural research in India and contributed significantly to Green Revolution and White Revolution in India in the 1960s and 70s. It has a very large campus and has several research centres throughout the state. It won the Indian Council of Agricultural Research's Award for the Best Institute in 1997. HAU was initially a campus of Punjab Agricultural University, Ludhiana. After the formation of Haryana in 1966, it became an autonomous institution on February 2, 1970 through a Presidential Ordinance, later ratified as Haryana and Punjab Agricultural Universities Act, 1970, passed by the Lok Sabha on March 29, 1970. A. L. Fletcher, the first Vice-Chancellor of the university, was instrumental in its initial growth.

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2019 - 201912020 - 20232
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Author: Chetna × End date: 2023 ×
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    ThesisItemOpen 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 Devi
    This 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.
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    ThesisItemOpen 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
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    ThesisItemOpen Access
    Principal Component Analysis for weather based wheat yield modeling in Western Agro-climatic zone of Haryana
    (CCSHAU, 2019) Chetna; Verma, Urmil
    An efficient crop forecasting infrastructure is pre-requisite for information system about food supply especially export–import policies, procurement and price-fixation. Multiple linear regression and principal component analyses were used to obtain district-level wheat yield estimation in Hisar, Bhiwani, Sirsa and Fatehabad districts of Haryana. The zonal yield forecast models have been fitted using wheat yield/weather data for the period 1980-81 to 2011-12 of Hisar, Bhiwani and Sirsa districts and 1997-98 to 2011-12 of Fatehabad district. Models have been validated for the subsequent years i.e. 2012-13 to 2016-17, not included in the development of the models. The zonal models have been fitted by taking DOA yield as dependent variable and trend yield along with higher loading displaying weather variables obtained through PC analysis as regressors. The performance of zonal wheat yield forecast models has been compared on the basis of different statistics viz., Adj-R2, percent deviations of forecast yield(s) from the observed yield(s) and RMSEs. The overall results indicate the preference of using model-1based on higher loading displaying weather variables obtained through PC analysis and trend yield. Trend yield has been observed an important parameter appearing in all the models, which is an indication of technological advancement, improvement in fertilizer/insecticide/pesticide/weedicide used and increased use of high yielding varieties. District-level wheat yield(s) forecasting have improved significantly using zonal weather-yield models. The developed models provide reliable forecasts of wheat yield at least one month in advance of the crop harvest, while on the other hand, DOA yield estimates are obtained quite late after the actual harvest of the crop. The average absolute percent deviations of post-sample period forecasts falling between 3.5- 6 percent favour the use of zonal models for district-level wheat yield forecasting in western agro-climatic zone of Haryana.