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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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  • ThesisItemOpen Access
    TREND ANALYSIS OF WHEAT PRICE IN BIHAR AND FORECASTING OF WHEAT PRODUCTIVITY BASED ON BIOMETRICAL CHARACTERS IN SIWAN DISTRICT OF BIHAR
    (DRPCAU, PUSA, 2021) kumari, Pratibha; Kumar, Mahesh
    The present study deals with the forecasting of price of wheat (Triticum aestivum L) in Bihar through Autoregressive Integrated Moving Average ARIMA model for the five year ahead forecasting. Study also deals yield forecasting of wheat in Siwan district of Bihar based on biometrical characters along with farmers appraisal with their socioeconomic condition. For this research for forecasting of price trend analysis with ARIMA model were used. There were found to be ARIMA (0 1 1) is best and it forecast model for wheat price of Bihar is as below equation -1. It were found that its statistic values are R-square=0.944, RMSE= 87.966, MAPE= 6.751, MAE-70.295 and BIC=9.47. It were found that the forecasted price of wheat for the year 2018, 2019, 2020, 2021 and 2022 were Rs.1849.87/q, Rs.1891.98/q, Rs.1970.92/q Rs.2049.86/q and Rs.2128.80/q respectively for Bihar with error percentage 5.84 % for the year 2018. For forecasting of yield of wheat in Siwan district observations on plant biometrical characters were taken, such as average plant population per m2 (X1), average plant height in cm (X2), average number of tillers per m2 (X3), average length of Panicle in cm (X4), application of nitrogen (N) in kg/ha (X5), application of phosphorus (P2O5) in kg/ha (X6), application of potassium (K2O) in kg/ha (X7), irrigation level in numbers (X8), disease infestation in percentage (X9) and average plant condition (X10) according to eye estimates of farmers.There were recorded from data from 50 farmers of Siwan district of Bihar. Multistage (three stage) sampling was used for selecting samples.. The block were first stage unit, village as second stage unit and farmers were third stage unit of selection. All possible regression analyses were carried out to select the best combination of variables on the basis of some important statistics such as , RMSE= 0.6738, R2 =0.9598, Adj-R2 =0.9568 ,CV= 5.2132,Dependent mean = 5.2132. Graph of Fit diagnostics for yield, Superimposition of Graph of model predicted value and its residual as well as its actual value , clearly indicate the suitability of model developed. Further assessment regarding socioeconomic condition of people of Siwan various statistic were used like correlation, mean, regression for assessment of income and employment opportunity in Siwan district of Bihar. Forecast Model for wheat price in Bihar: - Zt – Zt-1 = -130.681+ 0.317 (at-1 - at-2) + at ……(1) Forecast Model for wheat yield in Siwam district of Bihar: = -3.1602+ 0.0339X5 + 0.0140X7 + 2.5849X10 ……(2)
  • ThesisItemOpen Access
    GENOTYPE × ENVIRONMENT INTERACTION BY GGE BIPLOT IN MANGO (Mangifera indica L.)
    (DRPCAU, PUSA, 2021) KRISHNA, K SAI; Choudhary, Ram Kumar
    Mango is one of the most important perennial fruit crop grown in India, with vast varieties. India has first position in mango production among the mango growing countries. Due to its taste and diverse uses it is known as “king of fruits”. The differential performance of a genotype in different environments is known as “Genotype × Environment Interaction”. Multi location trials are being carried out to study the behaviour of genotypes over different locations. Identification of stable genotypes of mango is important to increase the income of farmers. In present investigation an attempt has been made to identify stable genotypes of mango fruit crop from secondary data of multi-location trials collected from AICRP-STF and CISH, Lucknow. Data comprises of 16 genotypes grown in 4 locations over nine years have been analysed on Genotype × Environment interaction using GGE biplot. Two characters have been taken for the empirical analysis i.e. number of fruits per tree and fruit yield per tree. Results obtained by GGE biplot have been compared with results obtained by AMMI. Sixteen genotypes common across 4 locations viz., Rewa, Sabour, Sangareddy and Vengurla over a period of nine years was considered for the analysis. These genotypes, locations and years were coded accordingly. On the basis of AMMI analysis the superior genotypes were Totapari, Zardalu for Rewa; Kesar, Mankurad for Sabour; Suvarnarekha, Mankurad for Sangareddy; Alphanso, Totapari for Vengurla. AMMI analysis identified Zardalu as superior genotype for all four locations under study. Likewise, from GGE biplot analysis, genotypes Totapari, Neelum for Rewa; Kesar, Suvarnarekha for Sabour; Suvarnarekha, Mankurad for Sangareddy; Totapari, Bombai for Vengurla were found to be superior. GGE biplot analysis identified Totapuri as superior genotype for all four locations under study. Two mega environments have been identified based on GGE biplot analysis, the test locations Rewa and Sangareddy constitute first mega environment with Neelum as winner genotype; Vengurla and Sabour collectively forms second mega environment with Suvarnarekha as winner genotype. From the present study it is concluded that, GGE biplot analysis is a better approach for evaluating Genotype × environment interaction and identifying superior genotypes of mango fruit crop. Since, the interpretation of GGE biplots relatively to AMMI analysis is easier. “Which-won-where” view of GGE biplot facilitates to determine location specific genotypes which is challenging in AMMI analysis.
  • ThesisItemOpen Access
    TREND OF GROUNDNUT AREA, PRODUCTION, YIELD IN BIHAR AND TAMIL NADU ALONG WITH ITS YIELD FORECASTING THROUGH AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS
    (DRPCAU, PUSA, 2021) S, EZHILMATHI; Kumar, Mahesh
    The present study entitled “Trend of groundnut area, production, yield in Bihar and Tamil Nadu along with its yield forecasting through Auto-Regressive Integrated Moving Average (ARIMA) models” is based on the growth trends and ARIMA models for forecasting groundnut yield in Bihar and Tamil Nadu. The secondary data on groundnut area, production and yield were collected from the year 1980 to 2018 from the authenticated portals like Directorate of Groundnut Research, Directorate of Oilseeds Development and India Agri Stat. The data from 1980 to 2016 were used for analysis of forecasting groundnut yield and the data for 2017 to 2018 were kept for model evaluation. 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 Bihar 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 (1,0,0) model is best fitted for forecasting of groundnut yield in Tamil Nadu 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 groundnut yield in Bihar and Tamil Nadu. Forecasting of groundnut yield for the upcoming two years was done using ARIMA models. The results showed that there was a steady increase in the yield of groundnut in Bihar as well as Tamil Nadu. 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 groundnut by using the best fitted ARIMA (1, 0, 1) and ARIMA (1, 0, 0) models, respectively for Bihar and Tamil Nadu. Further study was done for trend analysis and it was found that the trend is likely to be in decreasing order for groundnut area and production of both the states. The trend of groundnut yield in Bihar is stable whereas it is in increasing order in case of Tamil Nadu. For the study of inter-state disparities, Compound Annual Growth Rates were also calculated and it was found that all are highly significant.
  • ThesisItemOpen Access
    FORECASTING OF PINEAPPLE YIELD IN TRIPURA AND BIHAR USING NON LINEAR MODELS AND IT'S TREND ANALYSIS
    (DRPCAU, PUSA, 2021) DE, ARUP; Kumar, Mahesh
    The present study is based on time series data collected for 32 years (1987-88 to 2018-19) to study the trend of area, production and productivity of pineapple in Tripura and Bihar and also to forecast the pineapple yield by using different non-linear models namely Logistic model, Gompertz model and Monomolecular model. Data from 1987-88 to 2017-18 are used for analysis purpose and 2018-19 data is used for model validation purpose. Socio economic condition of the farmers of Gomati district of Tripura, also taken as a objective of these experiment. Though the actual values of area, production and productivity were fluctuating over the period but overall trend is showing an linearly increasing pattern. Kendall, Spearman and Pearson test is done for testing the validity and result shows a highly significant positive correlated value for all the test. After comparing these three non-linear models with eleven statistics it is clear that Logistic model is most appropriate model for forecasting the pineapple yield in both the sate as it shows highest value of R2, adj. R2, R27 and R28 and lowest value of RSS, MAPE, MAE, MSE, RMSE, RSE and MSE. nn. Lowest Chi-square value of was found in Logistic model for both the state. In case of forecasting for productivity of pineapple in Tripura, the Logistic model shows closest value to actual productivity with a forecast error of 19.79% in OSAF method. In case of Tripura, percentage forecast error is some extent to high and that is due to highly variation in yield during that period of time. Whereas in case of forecasting the pineapple yield for Bihar, all the models shows almost similar result in OSAF method. Percentage forecast error for Bihar is 2.45 % for selected Logistic model. Forecasted Pineapple yield in Tripura for the year 2018 to 2022 are almost equal i.e.17.44 q/hac, and for Bihar for the year 2018-2022 are almost equal to 26.71 q/ha. Selected Logistic model for forecasting of yield of Pineapple for Tripura and Bihar are as below: Ŷ =17.4488/ (1 +3.4532 *exp(-0.2696*t) ) for Tripura Ŷ =26.72232/ (1 +10.50813*exp(-0.3022*t) ) for Bihar Annual income of the majority of the farmers of the study area are in between 1 lakh to 5 lakh and age, occupation, education, size of land holding, farming experience, farm mechanization and type of land shows a significant correlation with annual farmers income. The regression analysis shows that size of operational land shows highly significant with the farmer’s annual income.