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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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20242
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    ThesisItemOpen Access
    GROWTH TREND AND YIELD FORECASTING OF MAIZE IN KARNATAKA AND BIHAR THROUGH AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODEL
    (RPCAU, Pusa, 2024) H, GIRIJASWATHI; Kumar, Mahesh
    The present study entitled examine “Growth Trend and Yield Forecasting of Maize in Karnataka and Bihar through Auto Regressive Integrated Moving average (ARIMA) Model” is based on the growth trends and ARIMA models for forecasting Maize yield in Bihar and Karnataka. The data spanning from 1990 to 2021 was gathered from reputable online sources such as the Department of Economics and Statistics and India Agri Stat. To create the maize yield prediction model, data up until 2020 were employed, reserving the subsequent two years' data for validating the forecast model. Additionally, trend analysis and tests for model validity were conducted. Based on the aforementioned information, it was determined that among a range of models including 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 (2,0,1) model offers the best fit for forecasting maize yield in Karnataka. Similarly, for Bihar, the ARIMA (2,0,0) model emerged as the most suitable choice from the selection of models, which also included 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 these models were calculated and subjected to significance tests. Various statistical measures were also computed to identify the appropriate and efficient model, involving t-tests and chi-square tests. This is reinforced by the presence of low values for MAPE, MAE, RMSE, and BIC in the prediction of maize yield for both Karnataka and Bihar. Forecasts for the next three years' maize yield were generated using ARIMA models. The outcomes revealed a consistent decline in maize yield, both in Karnataka and Bihar. Selected ARIMA model for forecasting of yield of maize in Karnataka and Bihar are as below: Zt – Zt-1 = 15.841+0.249(zt-1-zt-2)+0.196(zt-2 - zt-3)-0.19(at-1-at-2)+at (for Karnataka) Zt – Zt-1 = 22.810+0.29 (zt-1 - zt-2) + 0.20 (zt-2 - zt-3) + 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 three years period ahead for the time series data of yield of maize by using the best fitted ARIMA (2,0,1) and ARIMA (2,0,0) 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 maize in Karnataka is in increasing order whereas in Bihar area is in decreasing order and production and yield shows 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
    Sustainable Water Resource Planning for Burhi Gandak River Basin using Soft-Computing Techniques
    (RPCAU, Pusa, 2024) PRAJAPATI, RAJAT; Chandra, Ravish
    The present research work was conducted in the Burhi Gandak river Basin which is anintegral part of major basins in north Bihar. The assessment ofavailable groundwater resource was accomplished by the latest data provided by the CGWB and methods of surface water assessment was done using the discharge data of the gauge stations lying within the basin. The demand side of the basin that considered major water demanding sectors like crop water demand , domestic, livestock and industrial water demand. The CWR of major crops was estimated using FAO’s CROPWAT 8.0 model. The domestic and livestock water demand was estimated using the estimated and projected population from the latest census reports available. The industrial water demand was assessed by the data provided by CGWB, 2022 ArcGIS software and Google Earth Engine, a cloud-basedplatform, were primarily used toassess thedynamics of land use and cover change.Three sets oftime periods, 2013, 2017, and 2022, were examined using LANDSAT pictures to assess theclassificationoflanduseandlandcoverwithchangingdynamics.Totalsixclasses- forest, vegetation, waterbodies, built-up area, agricultural area and barren land wereusedinthe LULCclassificationscheme. Thesupervisedclassificationscheme,RandomForest algorithm was used by the Google Earth Engine to analyze this LULC categorizationThe overallaccuracy and Kappa statistic for LULC classification were found to be 66.67 % and 66.67 %,72.22 % and 0.53, 0.53 and 0.72 for year 2013, 2017 and 2022 respectively.The average surface water available in the BG basin was 4.51 BCM and the total ground water available was 2.85 BCM making the total water resource available in the basin to be 7.36 BCM. Total water demand of all the sectors including crop water requirements, domestic water demand , livestock water demand and industrial water demand for year was estimated to be 3.85 BCM and total water demand for the projected year 2027 was 4.21 BCM. Total water balance of the basin for year 2022 was estimated to be 3.50 BCM and 3.15 BCM for the projected year 2027. The LULC change dynamics were observed between the years of 2013 and 2022, and it wasfoundthat,asaresultofindustrializationandpopulationgrowth,theareaextentofvegetation, agriculturalland,andbarren landdecreasedby -8.18 percent,-25.40 percent and -56.28 percent, respectively, while forest and waterbodies increased by 11.26 and 65.95 percent. Finally, a sustainable water resource management plan was compiled that suggested conjunctive use of water available water, resources resource conservation techniques with utilization of MIS technology incorporating with promotion of IFS (integrated farming system).