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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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Subject: Statistics × Author: Aditi × End date: 2019 × Start date: 2010 ×
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    ThesisItemOpen Access
    Role of crop condition based dummy regressor alongwith weather parameters for pre-harvest yield prediction of cotton crop in Western Agro-climatic zone of Haryana
    (CCSHAU, 2019) Aditi; Verma, Urmil
    Crop yield models are abstract presentation of interaction of crop with its environment and can range from simple correlation of yield with a finite number of variables to the complex statistical models with predictive end. The pre-harvest forecasts are useful to farmers to decide in advance their future prospects and course of action. An efficient crop predicting infrastructure is pre-requisite for information system about food supply, especially export–import policies, procurement and price-fixation. Multiple Linear regression was used to develop zonal yield models for obtaining cotton yield prediction in Hisar, Bhiwani, Sirsa and Fatehabad districts of Haryana. Linear time-trend has been obtained using cotton yield data of the period 1980-81 to 2011-12. The fortnightly weather data along with trend yield have been utilized for the same period for building the zonal weather-yield models. Models have been validated for subsequent years i.e. 2012-13 to 2016-17, not included in the development of the models. The zonal models were fitted by taking DOA yield as dependent variable and fortnightly weather variables along with trend yield/CCT/dummy variables as regressors. The predictive performance(s) of the contending models were observed in terms of average absolute percent deviations of cotton yield forecasts in relation to the observed yield(s) and root mean square error(s). The adequacy of the fitted models was examined through histogram, normal-probability plot for the residuals and residual plot against fitted values for the selected models. The yield(s) estimated by zonal weather-yield models had sometimes higher percent deviations from the real-time yield(s) i.e. too high than considered to be tolerable for reliable yield prediction in the districts under consideration. Consequent upon, an attempt was made to improve the predictive accuracy of the developed models by adding trend yield based crop condition term to the zonal weather-yield model and that significantly improved the predictive accuracy of forecast models. The CCT is an indicator variable generated by splitting the DOA crop yield series into different non-overlapping classes. The level of accuracy achieved by zonal yield model(s) using CCT as categorical covariate along with weather variables was considered adequate for estimating the district-level cotton yield(s) at least 4-5 weeks in advance of the crop harvest. The average absolute percent deviations of postsample period forecasts falling between 4-9 percent favour the use of developed models for cotton yield prediction in western zone of Haryana. Zonal yield models incorporating CCT and weather variables consistently showed the satisfactory results pertaining to cotton yield prediction and performed well with lower error metrics as compared to the remaining models in all time regimes for the district under consideration.