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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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ThesisItem Open 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, MaheshThe 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)ThesisItem Open Access GENOTYPE × ENVIRONMENT INTERACTION BY GGE BIPLOT IN MANGO (Mangifera indica L.)(DRPCAU, PUSA, 2021) KRISHNA, K SAI; Choudhary, Ram KumarMango 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.ThesisItem Open 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, MaheshThe 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.ThesisItem Open Access FORECASTING OF PINEAPPLE YIELD IN TRIPURA AND BIHAR USING NON LINEAR MODELS AND IT'S TREND ANALYSIS(DRPCAU, PUSA, 2021) DE, ARUP; Kumar, MaheshThe 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.ThesisItem Open Access Designs for partial Diallel crosses(DRPCAU, Pusa, 1990) Singh, Karuna Shankar; Haque, M.N.ThesisItem Open Access A statistical analysis of growth performance of agriculture in Nepal(DRPCAU, Pusa, 1985) Jha, Naresh Chandra; Haque, M.N.ThesisItem Open Access Growth trend and yield forecasting of sugarcane for Tamil Nadu through non-linear growth models(DRPCAU, Pusa, 2020) R, Monisha; Kumar, MaheshSugarcane (Saccharumofficinarum),Family GRAMINEAE (POACEAE) is widely grown crop in India.It provides employment to over a million people directly and indirectly besides contributing significantly to the national exchequer. Sugarcane is the main source of sugar in India and holds a prominent position as a cash crop. India was the 2nd largest producer of sugar in the world after brazil in 2014-15.In India, sugarcane Area, Production area mainly concentrated in the states of Uttar Pradesh, Maharashtra, Tamil Nadu and Karnataka. Though Tamil Nadu accounts only for about 11% of the area under sugarcane of the country, this state has unique distinction of giving highest yield of 1,067.8 quintals/hectare. Looking to this and importance of sugarcane crop in Tamil Nadu, an attempt was made to apply some mechanistic growth models to sugarcane productivity data for complete Tamil Nadu. The present study was based on non-linear growth model such as Monomolecular, Gompertz and Logistic model. ThisThis This study is based upon the secondary data and these time series data were collected for the period from 1970-71 to 2018-19 from Directorate of Economics and Statistics Department Govt. of Tamil Nadu, (Chennai),Indian Sugar (the complete sugar journal), www.indiastat and also from Sugarcane Breeding Institute, Coimbatore(Tamil Nadu).The data from 1970-2019 were used for analysis of forecasting sugarcane yield and the data 2019-2020 were kept for model validation. Trend analysis and validity test were also calculated. The trend of sugarcane yield over a period of 1971-72 to 2018-19 seems likely to be linear in increasing order of yield (except some years). As an upward tendency seen in most of the trend data. However, the overall tendency seems to be upward. After doing trend analysis by different test like Kendall, Spearman, Pearson are found to be highly significant. The value has been obtained using the data collected and the method described Draper and Smith (1981). The three models has been compared considering R2, R27, R28, RSS, MAPE, MAE, MSE, RMSE. On the basis of these statistics, monomolecular model has been found to be most appropriate suitable for describing the sugarcane productivity data after comparison among all the models because this model have high value of R27 and R28 and low value of RSS,MAPE,MAE,MSE and RMSE and their fulfillment of assumption has been studied. After the analyzing all the model such as Monomolecular, Gompertz and logistic model, it is found that monomolecular model has good accuracy by interpreting low % FE, MAPE, RMSE. Pearson's Chi-squared test for Goodness of Fit for Monomolecular model for actual data on Area, Production & Yield of sugarcane in Tamil Nadu is highly significant. After comparison for area, production and productivity for trend and fitted for Monomolecular, Gompertz and Logistic, Monomolecular model is better superimposed to each other as compared to others model. It indicates that monomolecular model is good fitted among all three models.ThesisItem Open Access Forecasting of Wheat Yield (Triticum spp.L) yield using Non-linear Growth model including Weather parameters in Bihar(Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, 2018) Kumar, Awdhesh; Kumar, MaheshThe present study is based on non-linear growth models such as monomolecular, logistic, gompertz and compound growth model for forecasting the wheat yield in Bihar. The secondary data on wheat yield were collected for 1964-65 to 2015-16 from Directorate of Economics and evaluation department, Govt. of Bihar, Patna. The data from 1964-65 to 2014-15 were used for analysis of forecasting wheat yield and the data for 2015-16 were kept for model validation. In this study, on the basis of weather parameters, correlation matrix and regression models were also develop for relationship between weather and yield forecast. Trend analysis and validity tests were also calculated. With the help of above facts it was found that the monomolecular model is best fitted model among all models namely Logistic, Gompertz and Compound growth model.This is supported by the high value of R2, R_7^2, R_8^2and low value of RSS,MAPE, MAE,MSE, RMSE for forecasting of wheat yield in all three agro-climatic zones i.e. zone I,zone II, zone III and also for whole Bihar. Minimum % FE found in case of monomolecular model in zone I, zone II,zone III and whole Bihar are 6.69%, 15.32%, 2.56%, 0.85% respectively. Whereas maximum % FE are found in case of compound growth model in zone I,zone II,zone III and whole Bihar are 28.3%, 23.75%,31.62% and 25% respectively. It was also found that the minimum %FE in case of monomolecular model among all Zones including whole Bihar is 0. 89% which is incomplete Bihar condition. This study examine the effect of weather parameters i.e. Temperature(maximum and minimum) RH (7:00 hr& 14:00 hr) and rainfall (mm) on the wheat yield in Bihar by using correlation matrix and regression model were done. On the basis of above correlation matrix and regression model it was found that with increasing the temperature yield of wheat gradually decreases and also adverse effect of high rainfall during wheat growing period. With the increase in RH (7:00 hr& 14:00 hr) yield also increases and with decreasing in RH yield were also decreases. Further, study were done for trend analysis, it were found that the trend is likely to be increasing order of wheatyield (except few years). The overall trend seems to increasing and linear. A validity test were also done by using Kendall test, Spearman test and Pearson test, it was found that all are highly significant. The low value of MAPE, RMSE, OSAF, %FE also supported the accuracy test of forecast yield value of wheat in case of monomolecular model. Key words:Forecasting of wheat yield, non-linear growth models, forecasting based on weather parameters, trend analysis.ThesisItem Open Access Statistical Analysis of measuring impact of climate change on wheat yield in Samastipur district of Bihar(Dr. Rajendra Prasad Central Agricultural University, Pusa (Samastipur), 2016) Kumar, Subhash; Singh, S. P.The impact of climate change is studied in many aspects in different locations in the country and it is concluded that there is high impact on agriculture compared to any other sector in the country. Many studies have been conducted to illustrate the changes in annual temperature, relative humidity, evaporation and rainfall are becoming evident on a global scale. This study examines the effect of climatic factor e.g. Temperature (Maximum and Minimum), Relative humidity (Morning and Evening), Evaporation and Rainfall variation on the yield of wheat in Samastipur district of Bihar by using different statistical methods. The data of wheat yield of 29 Years (1984-2013) was taken from Department of Agricultural Economics, RAU, Pusa and Weather Variables (1984-2013) was taken from Agro-metrology Unit, RAU, Pusa. The time series information of yield and seasonal meteorological data (e.g., Temperature (Maximum and Minimum), Relative humidity (Morning and Evening), Evaporation and Rainfall will used trend to assess the using Mann Kendall Test, Theil Sen slope, Regression models and CART (Classification And Regression Tree) will used to estimate the impact of climate variables on the yield. The resultant Mann-Kendall test statistic (S) indicates the presence of trend in weather factors like Temperature (Maximum and Minimum), Relative humidity (Morning and Evening), Evaporation and Rainfall and whether it is increasing or decreasing. Theil Sen slope estimator has been used to assess the magnitude of trend and percentage change in different variables has been calculated. Statistically significant trends are observed in all the variables. It is observed that wheat yield increases 29 % over the period (1984 - 2013). Negative trend is observed in maximum temperature and rainfall. Positive trend is observed in minimum temperature, humidity and evaporation. The relationship between wheat yield and weather variables explained by the regression models with interaction terms included with 76.13 % variability explained by the model. With the help of regression and classification and regression trees (CART) we can also find out the importance of different climatic variables at different stages of wheat growth well identified. CART analysis allowed to: (i) unravel interactions and combined effects in a complex dataset; (ii) identify thresholds in the relationship between wheat yield and different weather variables. The approach provided insight into the structure of interrelationships within the dataset more easily as compared to multiple regression modeling. Key Words: Climate change, Wheat yield, CART.
