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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 - 20203
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Author: Naveen × Start date: 2019 × Type: Thesis × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Effect of brassinolide on morpho-physiological and biochemical parameters under drought stress in Indian mustard
    (CCSHAU, Hisar, 2020-02) Naveen; Kumari, Nisha
    The present experiment was conducted in Oilseed Research Area during Rabi 2018-19 with two Indian mustard varieties viz. drought-tolerant (RH-725) and drought-sensitive (RH-749) to find the efficiency of brassinolide in two concentrations of 10 ppm and 20 ppm sprayed at 42 and 52 days after sowing. The experiment was laid out in RBD design with three replications. Results revealed that: The activities of antioxidative enzyme (SOD, CAT and POX) increase in both the varieties but this increase was more pronounced in RH-725 with 20 ppm concentration of brassinolide spray. The concentration of non-enzymatic antioxidants (carotenoid, ascorbic acid and proline) also increase significantly in RH-725 as compared to RH-749 at 20 ppm spray of brassinolide. The oxidative stress indicators (hydrogen peroxide, malondialdehyde and electrolyte leakage) decrease to a significant level under brassinolide spray in tolerant variety at 50 % flowering. The exogenous application of brassinolide improved the physiological framework in the leaves of RH-725 at 50 % flowering. The yield and yield attributing characters such as plant height (cm), number of primary branches/plant, main shoot length (cm), number of siliquae on main shoot, number of seeds/siliqua, siliqua length (cm), seed yield/plant (g) were significantly increased among the treatments as well as varieties. Due to spraying of brassinolide the percentage of increase in seed yield ranged from 11.84 % in RH-725 and 5.77 % in RH-749 at 10 ppm concentration over control. In view of the present findings, the higher concentration of non-enzymatic antioxidants, higher activity of antioxidative enzymes and improved physiological parameters and maximum yield was found in RH-725 i.e. drought-tolerant at 20 ppm concentration of brassinolide spray.
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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.