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Dr. Rajendra Prasad Central Agricultural University, Pusa

In the imperial Gazetteer of India 1878, Pusa was recorded as a government estate of about 1350 acres in Darbhanba. It was acquired by East India Company for running a stud farm to supply better breed of horses mainly for the army. Frequent incidence of glanders disease (swelling of glands), mostly affecting the valuable imported bloodstock made the civil veterinary department to shift the entire stock out of Pusa. A British tobacco concern Beg Sutherland & co. got the estate on lease but it also left in 1897 abandoning the government estate of Pusa. Lord Mayo, The Viceroy and Governor General, had been repeatedly trying to get through his proposal for setting up a directorate general of Agriculture that would take care of the soil and its productivity, formulate newer techniques of cultivation, improve the quality of seeds and livestock and also arrange for imparting agricultural education. The government of India had invited a British expert. Dr. J. A. Voelcker who had submitted as report on the development of Indian agriculture. As a follow-up action, three experts in different fields were appointed for the first time during 1885 to 1895 namely, agricultural chemist (Dr. J. W. Leafer), cryptogamic botanist (Dr. R. A. Butler) and entomologist (Dr. H. Maxwell Lefroy) with headquarters at Dehradun (U.P.) in the forest Research Institute complex. Surprisingly, until now Pusa, which was destined to become the centre of agricultural revolution in the country, was lying as before an abandoned government estate. In 1898. Lord Curzon took over as the viceroy. A widely traveled person and an administrator, he salvaged out the earlier proposal and got London’s approval for the appointment of the inspector General of Agriculture to which the first incumbent Mr. J. Mollison (Dy. Director of Agriculture, Bombay) joined in 1901 with headquarters at Nagpur The then government of Bengal had mooted in 1902 a proposal to the centre for setting up a model cattle farm for improving the dilapidated condition of the livestock at Pusa estate where plenty of land, water and feed would be available, and with Mr. Mollison’s support this was accepted in principle. Around Pusa, there were many British planters and also an indigo research centre Dalsing Sarai (near Pusa). Mr. Mollison’s visits to this mini British kingdom and his strong recommendations. In favour of Pusa as the most ideal place for the Bengal government project obviously caught the attention for the viceroy.

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20224
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Subject: Agricultural Statistics and Informatics × End date: 2022 × Start date: 2022 × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    STUDY ON CROP YIELD RESPONSE TO CLIMATE VARIABILITY IN MUZAFFARPUR DISTRICT OF BIHAR
    (Dr.RPCAU, Pusa, 2022) PRAVEENKUMAR; Nidhi
    In India, a warming trend has become pronounced over the past couple of decades and is predicted to intensify in the years to come. Climate change poses an increasing threat to agricultural production. In order to project the future impact of climate change on crop production, crop models and climate change scenarios have been widely used. While climate change has limited impacts on crop production, some evidence of its impacts has been found. A variety of view points are taken into account when studying the effect of climate change in different parts of the country. According to the study, agriculture is more affected than any other sector in the country. Over the past few years, numerous studies have been conducted to illustrate the fact that annual temperature changes, changes in relative humidity, changes in evaporation, and changes in rainfall patterns have all become more evident on a global scale. Different statistical methods are used to examine the effects of climatic factors, such as Temperature (Maximum and Minimum), Rainfall, and Evapotranspiration variation on wheat and rice yields in Muzaffarpur district of Bihar. Data on wheat and rice was obtained from ICRISAT Hyderabad for period 52 years (1966-2017) and data on weather variables were obtained from Centre for Advanced Studies on Climate Change, RPCAU, Pusa. Annual data on weather variable was obtained from ICRISAT Hyderabad for time period 1958-2015. In this study, investigation of significant trends in climate variables over period of time was done for Muzaffarpur district. Trend was detected using non-parametric (Mann-Kendall) trend test. Theil Sen's slope estimator was used to determine the magnitude of the trend, and percentage change in various variables was calculated. Detection of shift in weather variables was found out using change point analysis (Pettitt test). Positive trends were observed in maximum and minimum temperature, Evapo-transpiration. Negative trend in rainfall. Weather variables showed significant change point at 1992 (Maximum temperature), 1987 (minimum temperature), 1988 (Rainfall), and non-significant change was detected in Evapo-transpiration (1991). The effect of weather variable on yield of Wheat and Rice analysed by multiple linear regression. Model explained 24.3 % and 2.9 % of variability in wheat and rice respectively.
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    ThesisItemOpen Access
    CHARACTERIZATION OF VARIABILITY OF SOIL PROPERTIES USING GEOSTATISTICAL APPROACH: A CASE STUDY OF MUZAFFARPUR DISTRICT IN BIHAR
    (DRPCAU, PUSA, 2022) KUMAR, ABHINEET; Nidhi, Dr.
    In this study an attempt is made to assess spatial variability of soil properties Muzaffarpur district in Bihar. The geostatistical approach has been applied to study the spatial variability of soil properties like pH, electrical conductivity (EC), organic carbon (OC), phosphorous(P), potassium(K), sulphur(S), zinc (Zn), copper (Cu), iron (Fe), manganese (Mn) and boron(B). Data on soil properties were obtained from AICRP on micronutrients from department of soil science, RPCAU, Pusa. Variogram has been used to express the spatial variability where variogram is a plot of the variances of subsequent point in the space vs. distance. Variogram models are used to create the spatial dependence of all the soil parameters. The four variogram models are linear, spherical, exponential, gaussian. Ordinary kriging has been used to interpolate for unsampled locations. Kriging is a spatial interpolation technique that create spatial distribution map as well as map for prediction of variance of soil parameters. To check the accuracy of soil nutrient maps cross validation approach has been used where mean absolute error (MAE), Mean square error (MSE), Goodness of prediction(G) values are computed for accuracy check. It is found that pH has very lowest CV (6.37%) indicating homogeneous soil for pH across the study region while highest CV (78.44%) is found for zinc indicating high variation across the study region. EC, OC, P, K, S, Zn, Cu, Fe, Mn, and B with respect to their CV are under moderate variation across the study region. In different classes, almost 40% of soil sample have soil pH between 8.0 to 8.5 which mean moderate alkaline. Almost 85% of soil sample have OC less than 0.5%. 98% of soil sample have EC less than 1ds/m. 60% of soil sample have phosphorous between 25-50ppm. 63% of soil sample have potassium between 125 -300ppm. 82% of soil sample have Sulphur less than 22.4ppm. 54% of soil samples have Zn above 1.20ppm. 96% of soil samples have greater than1.20ppm. almost 80% of soil samples have greater than 12.0ppm. 88% of soil samples have Mn greater than 5.0ppm. 52% of soil samples have boron between 0.5-1.0ppm. Spherical and exponential model has been used to fit the experimental variogram of the soil parameters. Range of spatial dependence for each parameter has been established. values are computed to analyse the goodness of the variogram model. Based on nugget-sill ratio, the spatial dependence for each parameter has been classified as weak, moderate and strong. Mn, B, EC, OC and Zn values influenced their neighbouring values over greater distances than pH, P, K, S, Cu and Fe, all of which have range of below 10 km. The highest range of 72 km is observed for Manganese means the concentration is highly correlated spatial up to a large distance. The nugget sill ratio also proved its moderate spatial dependence in area under study. Range of Boron (B) concentration is 66 km while the largest nugget effect is observed for K and moderate dependence is observed for its concentration under study area due to large nugget effect. The goodness of fit statistic indicates a moderate fit for the variogram model. The soil pH is also observed to be spatially correlated to a distance of 10.6 km in the study area and the degree of spatial continuity is moderate. All the other parameters are observed to have low values of range varying below 10 km. It might be due to cumulative effect of climate, parent material and adopting of different land management, the range values of soil parameters are different. This observed spatial dependency can be used to support spatial sampling for detailed soil mapping in site specific soil management. Predictive spatial maps have been generated for each soil parameter by interpolating the values using ordinary kriging method using variogram model. The different distribution pattern is exhibited by the kriged surface maps developed for soil parameters. These maps could help for site specific nutrient management and also in designing future soil sampling strategies in the intensively cultivated alluvial soil of Muzaffarpur. The predictive maps produced by interpolating the values using ordinary kriging show distinct patchy distribution of pH, K, P, S, B, Zn, EC, OC and Mn across different parts of Muzaffarpur. These types of maps can help in identifying the pockets of soil available nutrients according to their concentration and may in turn help for region specific management.
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    ThesisItemOpen Access
    GROWTH ANALYSIS OF PIGEON PEA AND IT’S YIELD FORECASTING IN BIHAR AND KARNATAKA USING AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODEL
    (DRPCAU, PUSA, 2022) NAVEEN; Kumar, Mahesh
    The present study entitled examine “Growth Analysis of pigeon pea and it’s yield forecasting in Bihar and Karnataka using Auto-Regressive Integrated Moving Average (ARIMA) model” is based on the growth trends and ARIMA models for forecasting pigeonpea yield in Bihar and Karnataka. The secondary data for the years 1980 to 2021 was retrieved from reliable websites like the Department of Economics and Statistics and India Agri Stat. For the purpose of forecasting pigeonpea yield, data up to the year 2019 were used to build the prediction model, and data from the following two years were retained for the forecast model's validation. Trend analysis and validity tests were also calculated. With the help of above facts, it was found that the ARIMA (1,0,1) model is best fitted for pigeonpea yield in Karnataka among all the models namely ARIMA (0,1,1), ARIMA (0,1,2), ARIMA (0,0,1), ARIMA (1,0,0), ARIMA (1,1,1), ARIMA (2,0,0) and ARIMA (2,0,1). The ARIMA (1,1,1) model is best fitted for forecasting of pigeonpea yield in Bihar among all the other models namely ARIMA (0,0,1), ARIMA (1,0,1), ARIMA (0,1,1), ARIMA (1,1,1), ARIMA (0,1,2), ARIMA (2,0,0) and ARIMA (2,0,1). The parameters of all these models were computed and tested for their significance. Various statistics were also computed for selecting the adequate and parsimonious model i.e., t-test and chi-square test. This is supported by low values of MAPE, MAE, RMSE and BIC for forecasting of pigeonpea yield in Karnataka and Bihar. Forecasting of pigeonpea yield for the upcoming two years was done using ARIMA models. The results showed that there was a steady decrease in the yield of pigeonpea in Karnataka as well as Bihar. Selected ARIMA model for forecasting of yield of Pigeonpea in Karnataka and Bihar are as below: Zt – Zt-1 = 524.811 + 0.931 (zt-1 - zt-2) - 0.717 (at-1 - at-2) + at (for Karnataka) Zt – Zt-1 = 16.019 + 0.485 (zt-1 - zt-2) – 0.995 (at-1 - at-2) + at ( for Bihar) In this study, lower and upper limits of the forecasted yield were also calculated with 95% of confidence interval. The forecasts done five years period ahead for the time series data of yield of pigeonpea by using the best fitted ARIMA (1, 0, 1) and ARIMA (1,1,1) models, respectively for Karnataka and Bihar. Further study was done for the trend analysis and it is found that the trend of area, production and yield of pigeonpea in Karnataka is in increasing order whereas as in Bihar area and production shows a decreasing trend but the yield is increasing in Bihar. For accuracy coefficient of determination is calculated. Compound Annual Growth Rates were also calculated and it was found that all are highly significant. Annual income of the majority of the farmers of the study area are in between 1 lakh to 5 lakh and Age, Caste, Occupation, Education, Family size, Size of operational land holding, Farming experience shows a positive correlation with the farmer’s income. The size of operational land shows highly significant with the dependent variable which is farmers income.
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    ThesisItemOpen Access
    TREND ANALYSIS AND FORECASTING OF CHICKPEA YIELD IN MUNGER DISTRICT (BIHAR) AND BILASPUR DISTRICT (CHHATTISGARH) USING NON LINEAR MODELS
    (DRPCAU, PUSA, 2022) PAIKRA, KALYAN SINGH; Kumar, Mahesh
    This study is done with objective of trend analysis of area, production and productivity of chickpea for Munger district (Bihar) and Bilaspur district (Chhattisgarh) and forecast is done on chickpea yield for these districts using some nonlinear models. For conducting this study, secondary time series data is obtained from official sites of directorate of Economics and Statistics of respective states and ICRISAT Hyderabad from time period of 1990-91 to 2019-20. For achieving objective, data from 1990-91 to 2018-19 are analysed while for validation, data from year 2019-20 is taken. The graphical method is used for trend analysis for area, production and yield under chickpea. Validation of trend is checked using correlation test by Pearson and Spearman test. Forecasting of chickpea yield is done with non linear model i.e., Monomolecular, Gompertz and Logistic model and OSAF is used for validation. Although, over the time there was too much fluctuation in actual data of area, production and productivity of chickpea over all it is found decreasing for area as well as production while increasing trend for productivity for Munger district as well as for Bilaspur district. Even Pearson and Spearman coefficients are highly significant with negative values for area and production while positive values for productivity. All non-linear models are fitted to data by using Statistical software R. After fitting non-linear models, models are compared by ten different statistics R2, R27 , R28 , RSS, MAPE, MAE, MSE, RMSE, RSE and MSE. nn. So, Logistic model is found better for forecasting chickpea yield for Munger district with FE% of 22 % and Gompertz is found better for Bilaspur district with FE% of 22.3 % than other two models. Selected models for Munger district (Bihar) and Bilaspur district (Chhattisgarh) are given by Ŷ =1.16982/ (1 +(1.16982/0.60629-1) *exp(-0.10993*t)) (Munger district) Ŷ =1.12087*exp(log(0.44693/1.12087)*exp(-0.0532*t)) (Bilaspur district)