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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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2016 - 20202
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Subject: Statistics × Author: Hooda, Ekta × Start date: 2003 ×
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    ThesisItemOpen Access
    State Space Modelling with Weather as Exogenous Input for Sugarcane Yield Prediction in Haryana
    (CCSHAU, Hisar, 2020-05) Hooda, Ekta; Verma, Urmil
    Parameter constancy is a fundamental issue for empirical models to be useful for forecasting, analyzing or testing any theory. This work addresses the concept of parameter constancy and the implications of predictive failure. Predictive failure is uniquely a post-sample problem. Unlike classical regression analysis, the state space models are time varying parameters models as they allow for known changes in the structure of the system over time and provide a flexible class of dynamic and structural time series models. The study has been performed in two parts i.e. the development of state space models in two forms (the state space and unobserved component approach), and the state space models with weather as an exogenous input for sugarcane yield prediction in Ambala, Karnal, Kurukshetra, Panipat and Yamunanagar districts of Haryana. The time series sugarcane yield data for the period 1966-67 to 2009-10 of Ambala and Karnal districts, 1972-73 to 2009-10 of Kurukshetra and 1970-71 to 2009-10 of Panipat and Yamunanagar districts were used for the development of different models. The validity of fitted models have been checked for the subsequent years i.e., 2010-11 to 2016-17, not included in the development of the yield forecast models. The selection of autoregressive orders, i.e., five, three, two, four and five looked reasonable for Ambala, Karnal, Kurukshetra, Panipat and Yamunanagar districts respectively helped in determining the amount of past information to be used in the canonical correlation analysis and further leading to the selection of state vector. Information from the canonical correlation and preliminary autoregression analyses were used to form preliminary estimate of the parameters of state space models and that provided the sugarcane yield estimates using Kalman filtering technique. The UCMs with level, trend and irregular components were fitted to study the trend of sugarcane yield. For all the five districts, the irregular component was found to be highly significant while both level and trend component variances were observed non-significant. Lastly, the state space models with weather as exogenous input using different types of growth trends viz., polynomial splines; PS(1), PS(2) and PS(3) were developed. The weather variables used for each district were selected on the basis of stepwise regression method and PS(2) with weather input was selected as the best suited model for all districts. The post-sample sugarcane yield estimates were obtained on the basis of fitted SS, UCM and SSM with exogenous input. The predictive performance(s) of the contending models were observed in terms of percent relative deviations and RMSEs of sugarcane yield forecasts in relation to observed yield(s). The SSMs with weather input consistently showed the superiority over SS and UCM models in capturing lower percent relative deviations. Thus, it is inferred that the state space models may be effectively used pertaining to Indian agriculture data, as it takes into account the time dependency of the underlying parameters which may further enhance the predictive accuracy of time-series models with parameter constancy.
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    ThesisItemOpen Access
    Study on principal dimensions of regional developmental disparities in Haryana
    (CCSHAU, 2016) Hooda, Ekta; Manocha, Veena
    Development is a multi-dimensional phenomenon and may be defined as a process, which improves the quality of life. Many programmes of development have been undertaken in our country after i ndependence through annual and five-year plans with main focus on agriculture, employment generation, population control, literacy, health etc. Such programmes have been implemented in almost all the states of India including Haryana. Accordingly, the overall quality of life has improved considerably after independence, however, the disparities in the level of development can still be observed at district and state levels. Therefore, the present study deals with the classification of districts of Haryana based on composite indices according to their level of development and identification of the principal dimensions of regional disparities for effective discrimination between backward and developed regions. The study indicated wide disparities in the level of development among various districts of Haryana over the periods of study. The overall development level revealed that the districts of Ambala, Faridabad and Gurgaon captured first position on the development scale in 1991-92, 2001-02 and 2011-12, respectively, whereas Mahendragarh had the last position in 1991-92 and 2001-02 and newly formed district Mewat in 2011-12. Separate principal component analysis of 19 indicators of development from agriculture sector and 9 indicators of socio-economic sectors indicated that first principal component comes out to be the principal dimension in each case with high component loading for most of the indicators. Principal indicators have also been identified for agriculture and socio -economic sectors by associating one indicator with each of the retained principal component. The rank correlations between first principal component based and composite indices based ranking of the districts for agriculture and socio-economic sectors too justified the degree of association between the two systems. Principal component based canonical analysis was performed to establish the relationship between agriculture and socio-economic sectors where first two canonical correlations were found significant in 1991-92 and one each in 2001-02 and 2011-12 periods.