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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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Author: Dinkar, Humbare Mrunalini × End date: 2022 × Start date: 2022 × Thesis Type: M.Tech. ×
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    ThesisItemOpen Access
    Study on different Machine Learning Techniques (MLT) in forecasting Crop Water Requirement
    (DRPCAU, PUSA, 2022) Dinkar, Humbare Mrunalini; Bhagat, I. B.
    Crop water requirements must be accurately determined for irrigation scheduling, water resources management, and environmental analysis. There are several locations where different climatic data may not be accessible for its determination. In these circumstances, crop water requirement modelling is a suitable method for predicting crop water requirements. To predict the crop water requirements of rice, wheat, maize, and sugarcane in the Samastipur area of Bihar, Machine Learning Techniques such as Random Forest (RF), Multivariate Adaptive Regression Splines (MARS), Support Vector Machine (SVM) and Multiple Linear Regression (MLR) were utilised in the current study. The Meteorological department of RPCAU, Pusa, Bihar provided the data including relative humidity (maximum and minimum), and temperature (maximum and minimum) while data about solar radiation and wind speed was obtained from Prediction of Worldwide Energy Resources (NASA/POWER) website. For this study, data spanning 20 years (2001–2020) was collected. The FAO-56 Penman-Monteith method and crop coefficient approach were used to determine the water requirement for the selected crops. The Gamma test was utilized for the best input determination. The entire dataset was divided into training (80%) and testing (20%) datasets. The assessment of the models was by the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE). It can be inferred that the MLR and MARS models predicted rice crop water requirements finer than they did for other crops. The SVM model accurately predicted the water requirement of the wheat crop compared to other crops. More precisely than other crops, the RF model forecasted the water requirement for sugarcane. For all crops, the overall models' performance-wise rankings were RF, MARS, SVM, and MLR. From the results attained, the RF model beat the other three models throughout training and testing for all crops and can be highly recommended for crop water requirement prediction.