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Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960. The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.

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  • ThesisItemOpen Access
    Modelling wheat yield based on weather parameters at its phenological stages
    (G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-08) Mariya, Merlin J.; Shukla, A.K.
    Crop yield prediction is an important aspect for a developing country like India in such a way that it helps decision makers, frame policies and strategies related to distribution, marketing and storage of agricultural products which ultimately lead to the sustainable growth and development of the country. The agricultural sector is severely affected by short term weather fluctuations and long term climate variations. Weather variability during important growth stages of a crop can result in uncontrolled crop yield variations. Wheat (Triticum aestivum) is one of the most widely grown cereal crops and an important staple food next to rice in India. The present study attempted to develop wheat yield prediction models for Udham Singh Nagar district of Uttarakhand state based on weather parameters during different growth stages of wheat. Maximum temperature, Minimum temperature, Relative Humidity A.M, Relative Humidity P.M, Total rainfall, Sunshine hours, Wind velocity and Evapotranspiration were the weather parameters considered for the study. Statistical and soft computing techniques namely Multiple Linear Regression, Artificial Neural Network and Ridge Regression were employed in the study using R software and SPSS software package. Correlations between rabi wheat yield and weather parameters during different growth stages of wheat were also analysed. The following conclusions were drawn from the study: •Correlation between rabi wheat yield and maximum temperature during the Dough stage was found to be positive but there was a negative correlation in the case of minimum temperature during the Dough stage. •Rabi wheat yield was found to be negatively correlated with minimum temperature during the milking stage whereas, rabi wheat yield was found to be positively correlated with morning relative humidity during the tillering stage and evening relative humidity during the Crown Root Initiation stage. •MLR-W (MLR model developed by using weather parameters at different growth stages of wheat used directly as predictors) model could perform better than the other two models developed using MLR method •ANN-WI (ANN model developed by using weather indices as predictors) model could perform better than the other two models developed using ANN. •RR-D (Ridge Regression model developed by using deviations of weather parameters from optimum value during important growth stages of wheat as predictors) model could perform better than the other two models developed using Ridge Regression •Evaluation based on statistical indices and error percentage during validation revealed that, ANN-WI (ANN model developed using unweighted and weighted weather indices as predictors, R2 = 0.96) out performed MLR-W model and RR-D model. •Crop yield prediction models based on weather parameters during important growth stages of the crop could provide reliable results.
  • ThesisItemOpen Access
    Assessment of trends, variability and climatic change for the district Lahaul And Spiti of Himachal Pradesh
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-06) Singh, Gaurav; Ahmad, Haseen
    The present investigation was carried out to study the weather parameters viz. precipitation, maximum temperature and minimum temperature for the district Lahaul and Spiti of Himachal Pradesh over the period of 102 years (1901-2002). The data were collected from Indian Water Portal website www.indianwaterportal.org. The study's objectives were to look at trends, variability, and distribution fitting for weather parameters on an annual, seasonal, and monthly basis, and to use Microsoft Excel and IBM SPSS software to calculate different measures of statistics such as mean, skewness, kurtosis, standard deviation etc. to test the normality of weather parameters. The values of shows that the weather parameters did not follow normal distribution. A comparative study was made among the weather parameters by fitting a trend line by method of least squares to obtained best fitted straight line trend. Then studied the best fit distributions by using Easy Fit 5.5 Software. The study showed significant and insignificant increasing trends for winter and monsoon season’s precipitation, and insignificant increasing and decreasing trends for pre-monsoon and post-monsoon seasons precipitation, whereas annual & monthly precipitation showed insignificant increasing trends. For maximum and minimum temperatures, premonsoon seasons showed significant increasing trends while winter, monsoon and postmonsoon seasons showed significant decreasing trends. Annual maximum and minimum temperatures showed significant increasing trends. Monthly maximum and minimum temperatures also showed significant increasing trends.
  • ThesisItemOpen Access
    Some statistical and soft computing models for crop yield and weather forecasting
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-08) Bhatt, Neha; Shukla, A.K.
    Crop yield forecasting and weather forecasting are two important aspects for developing economy so that adequate planning exercise is undertaken for sustainable growth and overall development of the country. However ,such forecast studies need to be done on continuing basis and for different agro-climatic zones due to visible effect of changing climatic conditions and weather shifts at different locations and area . Therefore, the present study was undertaken for forecasting the yield of two major crops viz. Rice and Wheat and to forecast the eight weather parameters namely Maximum temperature, Minimum temperature, Humidity A.M, Humidity P.M, Rainfall, Sunshine Hrs, Wind speed and Evapotranspiration the data for the last 20 years (w.e.f 2000- to 2020) of yield and eight weather parameters were obtained from G.B. Pant University of Agriculture and Technology ,Pantnagar, District Udham Singh Nagar, Uttarakhand, India. Statistical and soft computing techniques namely ETS ,TBATS, ARIMA, MLR, FIS ,ANN and ARIMAX were used to develop the forecasting models using R and MATLAB softwares. Correlations between yields of Rice and Wheat with the weather parameters were also calculated. On the basis of the present study following conclusions were drawn: 􀁸 Rice Yield was positively significantly correlated with Minimum Temperature, Humidity P.M., Rainfall, Number of Rainy Days and Sunshine Hours during the cropping season . 􀁸 Wheat yield was positively significantly correlated with Maximum Temperature , Minimum Temperature, Rainfall, Number of Rainy Days and Sunshine Hours during the cropping season . ARIMA model was found to be the best fit monthly weather prediction model for Maximum Temperature, Minimum Temperature, Humidity A.M., Humidity P.M., Sunshine Hours, Wind Velocity and Evapotranspiration as compared to ETS ,TBATS and ARIMA models. ANN model was found to be the best fit daily weather prediction model for Maximum Temperature, Minimum Temperature, Humidity A.M., Humidity P.M., Sunshine Hours and Evapotranspiration as compared to ETS and ARIMA models. 􀁸 For Rice yield prediction on the basis of seasonal average of weather data, various models were developed using ANN, FIS and ARIMAX techniques and it was found that ARIMAX-RY-II( ARIMAX-RICE YIELD – Based on 5 weather parameters) was the best fit model for Rice yield prediction. For Wheat yield prediction on the basis of seasonal average of weather data, various models were developed using ANN, FIS and ARIMAX techniques and it was found that FIS-WY (FIS- WHEAT YIELD) was the best fit model for Wheat yield prediction. 􀁸 For predicting the Rice yield on the basis of monthly weather data during the rice crop season , different MLR models were developed on the basis of different combinations of weather parameters and the best fit MLR-RY-A model with R 2 = 0.62 was selected It was found that November Maximum Temperature, September Minimum Temperature, July Humidity A.M., August Humidity A.M., August Sunshine Hours , November Sunshine Hours, July Maximum Temperature, October Humidity A.M. , November Humidity A.M. , September Rainfall and September Evapotranspiration could be used as best predictors for estimating the rice yield. For predicting the Wheat yield on the basis of monthly weather data during the Wheat crop season , different MLR models were developed on the basis of different combinations of weather parameters and the best fit MLRWY- A model with R 2 = 0.45 was selected . It was found that October Maximum Temperature March Maximum Temperature, March Humidity A.M., November Humidity P.M., April Sunshine Hours and April Wind Speed could be used as best predictors for estimating the wheat yield.
  • ThesisItemOpen Access
    Time series analysis of milk yield data and climatic effect on milk yield
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-06) Navneet Kaur; Vinod Kumar
    The Present study is concerned with the time series analysis of milk yield data and climatic, breed and fodder effect on milk yield. Major findings of the study are: 1. The milk yield from Crossbred cows, Sahiwal cows and Murrah buffaloes follow Burr, Kumaraswamy and General Preto distribution respectively. 2. The Milk Yield data of Crossbred and Sahiwal cows and Murrah buffaloes exhibit downward trend and are non-stationary. The stationarity is achieved by trend and seasonal differencing of the data. The ARIMA (0,1,1) (1,1,1)12 for Crossbred cows, ARIMA (1,1,0) (0,1,1)12 for Sahiwal cows and ARIMA (0,1,1) (2,1,0)12 for Murrah buffaloes are found to be the best fit model. The forecasting results showed good agreement between observed and predicted values. 3. The effect of climatic factors on Milk Yield performance is estimated using Multiple Linear Regression analysis using the Best model variables selection method. 4. Breed of the dairy animals is found to have a significant effect on average milk production, whereas seasons do not have a significant effect on average milk production in dairy animals. 5. The average milk production of the crossbredsand Sahiwal cows and Murrah buffaloes was higher during the first season (January-April) when dairy animals were fed a diet containing green fodder consisting of barley (Jau) and barseem. The results of the study are expected to provide useful information to dairy researchers and statisticians for future policy planning in the study area.