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Bihar Agricultural University, Sabour

Bihar Agricultural University, Sabour established on 5th August, 2010 is a basic and strategic institution supporting more than 500 researchers and educationist towards imparting education at graduate and post graduate level, conducting basic, strategic, applied and adaptive research activities, ensuring effective transfer of technologies and capacity building of farmers and extension personnel. The university has 6 colleges (5 Agriculture and 1 Horticulture) and 12 research stations spread in 3 agro-ecological zones of Bihar. The University also has 21 KVKS established in 20 of the 25 districts falling under the jurisdiction of the University. The degree programmes of the university and its colleges have been accredited by ICAR in 2015-16. The university is also an ISO 9000:2008 certified organisation with International standard operating protocols for maintaining highest standards in teaching, research, extension and training.VisionThe Bihar Agricultural University was established with the objective of improving quality of life of people of state especially famers constituting more than two third of the population. Having set ultimate goal of benefitting society at large, the university intends to achieve it by imparting word-class need based agricultural education, research, extension and public service.

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20172
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Subject: Agricultural Statistics and Informatics ×
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    ThesisItemOpen Access
    Assessment of non-response in the estimation of population mean in sample survey
    (Department of Statistics, Mathematics and Computer Application, BAU, Sabour, 2017-08) Kumar, Sanjay; Homa, Fozia
    Sample surveys are generally planned to obtain reliable estimates of parameters of the population. The mail questionnaire method is commonly used to collect data in surveys as the data collection costs following this method are considerably reduced. However, non-response can be a serious problem with this method of data collection. The presence of non-response may result in biased estimates, particularly, when the respondents differ from the non respondents. Although necessary steps are taken in situations where non-response is encountered, the problem persists. The presence of non-response not only introduces an element of bias in the survey results, but the estimators also become less precise. The presence of non-response may result in biased estimates, particularly, when the respondents differ from the non respondents. In this study, we have considered the problem of estimation of population mean in the presence of non-response. Accordingly, estimators were developed for the estimation of population mean. The proposed class of estimator was To deal with the problem of non-response, Hansen and Hurwitz (1946) developed new technique to estimate the population mean in case of missingness. This technique was based on the concept of sub-sampling of the non-respondents. A general class of modified ratio type exponential estimators was developed in the function of A and B which give eleven different estimators for different values of A and B. Properties of the developed estimators (biases, Mean Square Error’s (MSE’s)) have been studied and optimum mean square errors have been derived. The percent relative efficiencies of the proposed estimators have been studied empirically and found to be efficient as compared to sample mean estimator. There are many cases, when sub-sampling is not possible, so we imputed the missing value with some function of the responding class, developed by Rubin in 1987. The developed estimators have been checked in imputation technique and percent relative efficiency have been studied and found to be efficient as compared to the sample mean estimator. It is worthwhile to mention that simulations were performed involving population generated through gamma distribution.
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    ThesisItemOpen Access
    Pre-harvest forecast of rice yield for Bhagalpur District in Bihar
    (Department of Statistics, Mathematics and Computer Application, BAU, Sabour, 2017-07) Nath, Bhola; Singh, S. N.
    In Bihar, rice is the most leading food crop which is grown in 3.151 mha with production of 6.601 mt and productivity 20.95 q/ha during the season 2014-15 [(Directorate of Economics and Statistics, G.O.B. 2015-16)].Reliable and timely forecast of crop production are required for various policy decisions relating to storage, distribution, pricing, marketing, import-export etc. Pre-harvest forecast of rice yield has a great importance in Bihar as there is much more area and production of paddy in this state [Kumar et al. (2013)]. Therefore, present investigation was carried out on “Pre-harvest forecast of rice yield for Bhagalpur district in Bihar” with the objectives, (i) To establish the relationship between crop yield (Y) and biometrical characters as well as farmer’s appraisal (X's), (ii) To test the validity of forecasting model through suitable statistical tools and (iii) Pre-harvest forecasting of rice yield based on biometrical characters as well as farmers' appraisal for Bhagalpur district in Bihar for the year 2016-17. In this study 50 samples were collected from five blocks of Bhagalpur district in Bihar.Multistage sampling was adopted and at each stage samples were selected randomly.Data collected from these sampling procedures, 1024 regression models were developed. To establish the relationship among Yield (Y), Average Plant Population (X1), Average Plant Height (X2), Average number of effective Tillers (X3), Average Length of Panicle (X4), Nitrogen (X5), Phosphorous (X6), Potash (X7), Number of Irrigations (X8), Pest and Disease Infestation (X9) and Average Plant Condition (X10), a questionnaire based on biometrical character and farmer’s appraisal for rice crop was developed. The variable Y was used as dependent and all other X’s were as independent.Out of these models, ten regression models were highlighted with least RMSE (Root Mean Square Error) value. Further, five regression models were selected for minimum RMSE. Regression analysis was performed for each model. All five models were highly significant. Out of five selected regression models,Model-V i.e.Y ̂=16.74955+1.55753X_3-0.03582X_5+0.10947X_6+1.22290X_8had the minimum Standard Error of Mean Predicted (1.08670) value. Its residuals value was zero and RMSE value was 3.36722. From these analyses it was reflected that Model-V is the best whichwas used for pre-harvest forecast of rice yield.By using this model pre-harvest forecast of rice yieldin Bhagalpur is about 43.80967 (q/ha) for the year 2016-17 based on biometrical characters and farmer’s appraisal.