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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: Naveen × Start date: 2019 × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Transfer Function Modeling for Cotton Yield Prediction in Haryana
    (CCSHAU, 2019) Naveen; Verma, Urmil
    Crop yield models are abstract presentation of interaction of the 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. The study has been categorized in two parts i.e. development of regression based weather-yield models and transfer function models for cotton yield prediction in Hisar, Sirsa, Bhiwani and Fatehabad districts of Haryana. Firstly, the multiple linear regression was used to develop zonal yield models for obtaining cotton yield estimates in the districts under consideration. Linear time-trend was obtained using cotton yield data of the period 1980-81 to 2009-10. The zonal models were fitted by taking DOA yield as dependent variable and fortnightly weather variables along with trend yield/crop condition term as regressors. The validity of fitted models have been checked for the post-sample years 2010-11 to 2014-15. Secondly, the ARIMA models with alternative combinations of weather variables were tried for fitting transfer function models. The fortnightly weather variables selected on the basis of stepwise regression method (viz., RH4, RF3, RF7, SSH4 and SSH7 over the crop growth period) were utilized as input series for fitting TF models. TF (2,1,0) model with RH4 and RF7 for Hisar and TF (0,1,1) model with SSH4 and SSH7 for Sirsa and Fatehabad districts respectively, were finalized to obtain the district-level cotton yield estimates for the post sample period. The performance(s) of the contending models were observed in terms of average absolute percent deviations and RMSEs. Transfer Function models consistently showed the superiority over regression based weather-yield models in capturing lower percent deviations in all time regimes. The results showed that the district-level cotton yield(s) prediction gives good agreement with DOA yield estimates. The average absolute percent deviations of post-sample period estimates falling between 5- 11 percent favour the use of TF models for cotton yield prediction in Haryana.
  • Thumbnail Image
    ThesisItemOpen Access
    Transfer Function Modeling for Cotton Yield Prediction in Haryana
    (CCSHAU, 2019) Naveen; Verma, Urmil
    Crop yield models are abstract presentation of interaction of the 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. The study has been categorized in two parts i.e. development of regression based weather-yield models and transfer function models for cotton yield prediction in Hisar, Sirsa, Bhiwani and Fatehabad districts of Haryana. Firstly, the multiple linear regression was used to develop zonal yield models for obtaining cotton yield estimates in the districts under consideration. Linear time-trend was obtained using cotton yield data of the period 1980-81 to 2009-10. The zonal models were fitted by taking DOA yield as dependent variable and fortnightly weather variables along with trend yield/crop condition term as regressors. The validity of fitted models have been checked for the post-sample years 2010-11 to 2014-15. Secondly, the ARIMA models with alternative combinations of weather variables were tried for fitting transfer function models. The fortnightly weather variables selected on the basis of stepwise regression method (viz., RH4, RF3, RF7, SSH4 and SSH7 over the crop growth period) were utilized as input series for fitting TF models. TF (2,1,0) model with RH4 and RF7 for Hisar and TF (0,1,1) model with SSH4 and SSH7 for Sirsa and Fatehabad districts respectively, were finalized to obtain the district-level cotton yield estimates for the post sample period. The performance(s) of the contending models were observed in terms of average absolute percent deviations and RMSEs. Transfer Function models consistently showed the superiority over regression based weather-yield models in capturing lower percent deviations in all time regimes. The results showed that the district-level cotton yield(s) prediction gives good agreement with DOA yield estimates. The average absolute percent deviations of post-sample period estimates falling between 5- 11 percent favour the use of TF models for cotton yield prediction in Haryana.