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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Open Access
Designs for partial Diallel crosses
(DRPCAU, Pusa, 1990) Singh, Karuna Shankar; Haque, M.N.
Open Access
A statistical analysis of growth performance of agriculture in Nepal
(DRPCAU, Pusa, 1985) Jha, Naresh Chandra; Haque, M.N.
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, Mahesh
The 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.
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.