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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
    Some statistical models for crop yield forecasting
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2009-06) Garde, Yogesh Ashok; Shukla, A.K.
    Crop yield forecasting is an important aspect for a developing economy so that adequate planning exercise is undertaken for sustainable growth and overall development of the country. Weather fluctuations affect crop yield significantly during different stages of crop growing season, therefore several studies have been carried out to forecast crop yield using weather parameters. However, such forecast studies based on statistical models need to be done on continuing basis and for different agro-climatic zones, due to visible effects of changing environment conditions and weather shifts at different locations and areas. Therefore, present study was undertaken for forecasting yield of two major crops viz. rice and wheat based on time series data for 27 years (w.e.f. 1981-82 to 2007-08) of yield and weather parameters obtained from G. B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand, India. In this investigation an attempt has been made to develop statistical models for crop yield forecasting using various statistical techniques incorporating technical and statistical indicators. Two computer programs were developed in FORTRAN 77 language for generating the predictors used in proposed modified models. On the basis of developed statistical models following conclusions were drawn. I. Rice yield was found to have significant positive linear correlation with Average Weekly Maximum Temperature (4th week), Average Weekly Relative Humidity at 14.00 hrs (13th week) and Average Weekly Total Rainfall (13th week). However, rice yield was found to have significant non linear relationship (of the type Y =ab X ) with Average Weekly Max. Temp (4th week and 13th weeks), Average Weekly Total Rainfall (10th week and 13th weeks), Average Weekly Relative Humidity at 14.00 hrs (13th weeks) and Average Weekly Sunshine hrs (13th weeks). II. Wheat yield was found to have significant and positive linear correlation with Average Weekly Minimum Temperature (10th week), Average Weekly Sunshine hrs (20th week), and Average Weekly Pan Evaporation (20th week). However, wheat yield was found to have significant non linear relationship (of the type Y =ab X ) with Average Weekly Max. Temp (16th, 17th and 18th weeks), Average Weekly Min. Temp (10th week and 18th weeks), Average Weekly Relative Humidity at 7.00 hrs (20th weeks), Average Weekly Sunshine hrs (20th weeks) and Average Weekly Pan evaporation (20th weeks). III. For forecasting the yield of rice and wheat various statistical models viz. Model I and Model II (using Linear and Non Linear Regression Analysis), Model III (MLR) and Model IV developed by Hendricks and Scholl (1943) and Model V developed by Agrawal et al (2001) were applied. In addition to these models, two other modified models (Modified Model IV and Model VI) were suggested. IV. It was found that proposed Modified Model VI (A3) based on technical and statistical indicators for forecasting the rice yield was better than Model V (A5) suggested by Agrawal et al. (2001). V. It was found that proposed Modified Model VI (B5) based on technical and statistical indicators for forecasting the wheat yield was also better than Model V (B6) suggested by Agrawal et al. (2001).
  • ThesisItemOpen Access
    Studies of Roychoudhury method in unequal probability sampling
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2005-05) Gayatri, R.K.; Amdekar, S.J.
    The major objective of a sample survey is to make inferences about some characteristics of a population. Mainly, one is interested in estimating the population mean or population total of some characteristic called as study variable. Unequal probability sampling is one of the basic methods of sample selection. When the selection probability is based on the auxiliary variable it is commonly called as probability proportional to size (PPS) sampling. Estimator based on PPS sampling is expected to be better than simple random sampling, When there is proportionality between auxiliary and study variable. Roychoudhury (1957) gave a method in which the estimator has no sampling error even when the intercept of regression line is away from origin. Amdekar (2003) has generalized the Roychoudhury method. In the present study the performances of Roychoudhury and generalized Roychoudhury estimator are investigated empirically by considering two superpopulation models one involving normal distribution and other involving gamma distribution. From these distributions samples were drawn and by considering each sample as a population variances of various estimators are worked out. It is observed that in case of populations having normal distribution with increase in relative intercept the efficiency of Roychoudhury and generalized Roychoudhury generally increases and these estimators are better than PPSWR, ratio and regression estimators. Further, for the populations having moderate departure from symmetry generalized Roychoudhury estimator is better than other estimators and is less efficient when the distribution becomes more skewed. Some investigations were also carried out for estimators based on sample size two and it was observed that the weighted estimator has smaller variance than all the estimators included in the study.
  • ThesisItemOpen Access
    A statistical study of anthropometric data of children
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-08) Pathak, Shreya; Shukla, A.K.
    Present work deals withstatistical assessment of anthropometricand body parameters of children randomly selected fromthree different states of India namely Uttarakhand, Arunachal Pradesh and Gujarat. The secondary data on Age, Body Mass Index (BMI), Height (Ht), Weight (Wt) and Mid Upper Arm Circumference (MUAC) was obtained from College of Home Science, G.B. Pant University of Agriculture andTechnology, Pantnagar. From each state data of 500 children (250 boys and 250 girls) wererandomly selected.Following major conclusions were made on the basis of present study: 􀁸 The distributionpatterns of anthropometric parameters were carried out with the help of EasyFit5.6 Professional software and testing of their goodness of fit by Kolmogorov Smirnov and Chi-square tests. It was concluded that the distribution patterns of anthropometric parameters Height, Weight and MUAC were dissimilar and behave differently for different age groups in children.As none of the anthropometric parameters viz. Ht,Wt and MUAC follow normal distribution it was also concluded that the non -parametric test will provide more precise conclusions as compared to parametric test for statistical analysis of these parameters. 􀁸 Spearman’s Rank Correlation Coefficient was used to study inter relationship between anthropometric and body parameters of children from three different states and the significance of these coefficient was tested by t-test.It was concluded that all the parameters viz.Age, Ht, Wt, BMI and MUAC are significantly positively correlated to each other. 􀁸 Linear Regression (LR) and Multiple Linear Regression (MLR) analysis were used to develop prediction models for BMI and MUAC in children using IBM SPSS Statstics20 software. Out of the 14 LR and 7 MLR prediction models developed for BMI, it was concluded that the best fitted LR model to estimate BMI is LR4(r2=0.980) taking Age as the only predictor and the best fitted MLR model to estimate BMI is MLR1 (R2=0.988) taking MUAC and Age as the predictors.Similarly, out of the22 LR and14MLR prediction models developed for MUAC, it was concluded that the best fitted LR model to estimate MUAC is LR12 (r2=0.887) taking Htas the only predictor. Similarly, the best fitted MLR model to estimate MUAC is MLR9 (R2=0.89) taking Ht, Wt and Age as the predictors. 􀁸 Kruskal Wallis, One way ANOVA and Z test were used for age –wise, state- wise and gender - wise comparison of different parameters. It was concluded there is a significant difference with respect to all the parameters Ht, Wt, BMI and MUAC in different age groups of children within the state and between the states. Similarly, gender-wise significant difference was obtained with respect to all the parameters in each state. The findings of the study are expected to provide useful information for health experts and policy makers to initiate healthy intervention programmes at school level.
  • ThesisItemOpen Access
    Assessment of trends and variability of precipitation and temperature for Chandigarh
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-06) Singh, Tejendra; Ahmad, Haseen
    The present investigation was carried out to study the weather parameters viz. precipitation, maximum temperature, and minimum temperature for Chandigarh over the period of 102 years (1901-2002). The data were collected from Indian Water Portal website www.indianwaterportal.org. The objectives of the study were to analyze the trends, variability and distribution fitting for weather parameters on annual, seasonal and monthly basis. Different measures of statistics viz. mean, standard deviation, skewness, kurtosis, etc. were calculated to test the normality of weather parameters by using Microsoft Excel and IBM SPSS 16.0. The values of γ1≠0, β2≠3, 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 using the method of least squares to obtained best-fitted trend line. The study of best fit distribution was carried out by using EasyFit 5.5 software. The study showed significant increasing trends for winter and monsoon seasons precipitation whereas annual, monthly, premonsoon season and post-monsoon season precipitation showed insignificant increasing and decreasing trends. For maximum and minimum temperatures, winter and pre-monsoon seasons showed significant increasing trends while monsoon and post-monsoon seasons showed significant decreasing trends. Annual minimum temperature showed significant increasing trend whereas annual maximum temperature showed insignificant increasing trend. Monthly minimum and maximum temperatures showed insignificant increasing trends.
  • ThesisItemOpen Access
    Effects of tillage operations and weed control measures on soil variables and soybean yield
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-06) Wamanrao, Agashe Nehatai; Vinod Kumar
    The object of this study was to use an appropriate statistical method for analyzing the experimental data. The data were collected from the research farm of A.I.C.R.P. on Weed Management, Department of Agronomy, Dr. P.D.K.V., Akola (Maharashtra) during the year 2016-17. The effect of tillage practices on the soil characteristics and growth components were analyzed by two different methods viz., repeated measures in strip plot design and multi-observation data (measurement over time) in strip plot design. The power of the test for both the methods was nearly equal. Consequently any one of these two methods may be used for the analysis. The soybean yield data were analyzed by two methods viz., ANOVA and ANCOVA. Analysis of covariance with two covariates i.e. number of grains per plant and number of pods per plant was preferred for improving the precision of the experiment. The results obtained from correlation and path coefficient analysis strongly indicate that number of branches per plant, number of pods per plant and number of grains per plant should be considered as indices for selecting high yielding soybean variety.
  • ThesisItemOpen Access
    Statistical analysis of rainfall data in the hilly and plane zones: a study in Kumaon region of Uttaranchal state
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2004-06) Pandey, Rohit; Singh, J.B.
  • ThesisItemOpen Access
    Study of climatic parameters and climate change in capital of Himachal Pradesh and nearby districts
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-06) Chauhan, Sapna; Ahmad, Haseen
    Climatic variability, particularly, that of the precipitation, minimum temperature and maximum temperature has received a great deal of attention worldwide. The present study is carried out to determine the climatic conditions that are annually, monthly and seasonally variable to weather parameters in the Capital of Himachal Pradesh (Shimla) and nearby districts based on the data collected from Indian water portal website www.indianwaterportal.org. Three weather parameters were studied viz. precipitation, minimum temperature and maximum temperature. For the study of the three climatic parameters which showed linear trend annually, monthly and seasonally and different measures of statistics viz. mean, median, variance, skewness, standard error, range, minimum, maximum values and confidence interval for median etc. were calculated by Microsoft Excel and IBM SPSS software. A comparative study was made among the three climatic parameters for 102 years (1901-2002) by fitting a trend line by method of least squares to obtained best fitted straight line trend. The normality of climatic parameters were also studied by calculating the skewness and kurtosis which showed that the climatic parameters were skewed that is _1_ 0, _2_3 which showed a nonnormal distribution. Then studied the best fit distributions by using Easy Fit software. The graphs of precipitation showed a decreasing trends while minimum and maximum temperature showed an increasing and decreasing trend annually, monthly and seasonally in Capital of Himachal Pradesh (Shimla) and nearby districts.
  • ThesisItemOpen Access
    Statistical analysis of soil characteristics data
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-06) Mishra, Baishali; Vinod Kumar
    The object of this study was to analyze the soil characteristics (pH, EC, OC, Nitrogen, Phosphorus, Potassium, Sulphur, Maganese, Iron, Zinc, Copper and Boron) data through Distribution fitting, ANOVA, Multiple Correlation and Regression analysis and Path coefficient analysis. Secondary data on the above said soil characteristics of Mirzapurdistrict were analyzed with the help of different softwares. Different soil characteristics are found to follow different best fitted distributions. Average soil characteristic contents are found to be significantly different in all tehsils and also at different elevation ranges. pHhas shown significant positive correlation with Sulphur and Boron and significant negative correlation with Nitrogen, Maganese and Iron. EC has shown significant and positive correlation with Potassium, Sulphur and Boron and significant negative correlation with Iron, Maganese and Zinc. Moreover, OC is found to have positive and significant correlation with all the nutrients except Nitrogen. Path coefficient analysis reveals that EC, OC, Sulphur, Copper and Boron have positive direct effects on soil pH and remaining have negative direct effects on soil pH. Multiple Linear Regression Models are developed for estimating soil properties (pH, EC, OC) on the basis of given values of soil nutrients. These models are found to be quite efficient for the said purpose on the basis of large value of R2and small value of Root Mean Square Error.
  • ThesisItemOpen Access
    Forecasting of production and prices of selected agricultural commodities -an application of statistical models
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2008-06) Joshi, Seema; Shukla, A.K.
    Present work, Forecasting of production and prices of selected agricultural commodities-an application of statistical models treated the issue of analysis of growth trends and forecasting of production and prices of agricultural commodities and livestock products using various statistical models. The study has been presented in the form of thesis, comprising of five chapters. The chapter 1 is introductory, giving the motivation and the objectives of the present study. Chapter 2 accommodates majority of available past research work directly or indirectly related with the present work. Chapter 3 covers the sources of data available and methods currently being adapted and needed for the present study in examining the growth trends and building the forecasting models for time-series data. It also includes the detailed description of the model development and computation. Chapter 4 describes the application of different models which are used to analyze growth trend and to forecast the production and prices of the agricultural commodities and livestock products. For analysis of growth trend in area, production, yield and minimum support prices of rice, wheat, coarse cereals, pulses and for trend in prices of egg and broiler Semi-log (exponential) model was used. Compound growth rates were also calculated for each commodity. To forecast the production and minimum support prices of rice, wheat, coarse cereals and pulses two models namely Multiple Linear Regression model and Holt’s Linear Exponential Smoothing Model were used. However, for the forecasting of prices of egg and broiler two models namely Winter’s forecasting model and Holt’s Linear Exponential Smoothing Forecasting model were used. The results obtained and their interpretation is presented in this chapter. The work has been summarized in chapter 5 in view of the set objectives and findings of the study. The literature used in the research work has been referred under the section Literature Cited. A computer program developed for the application of Holt’s Linear Exponential Smoothing Forecasting model is given in the Appendix.