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ThesisItem Open 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).ThesisItem Open 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.ThesisItem Open 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.ThesisItem Open 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.