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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: KUMAR, ABHINEET × Start date: 2012 ×
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    ThesisItemOpen Access
    CHARACTERIZATION OF VARIABILITY OF SOIL PROPERTIES USING GEOSTATISTICAL APPROACH: A CASE STUDY OF MUZAFFARPUR DISTRICT IN BIHAR
    (DRPCAU, PUSA, 2022) KUMAR, ABHINEET; Nidhi, Dr.
    In this study an attempt is made to assess spatial variability of soil properties Muzaffarpur district in Bihar. The geostatistical approach has been applied to study the spatial variability of soil properties like pH, electrical conductivity (EC), organic carbon (OC), phosphorous(P), potassium(K), sulphur(S), zinc (Zn), copper (Cu), iron (Fe), manganese (Mn) and boron(B). Data on soil properties were obtained from AICRP on micronutrients from department of soil science, RPCAU, Pusa. Variogram has been used to express the spatial variability where variogram is a plot of the variances of subsequent point in the space vs. distance. Variogram models are used to create the spatial dependence of all the soil parameters. The four variogram models are linear, spherical, exponential, gaussian. Ordinary kriging has been used to interpolate for unsampled locations. Kriging is a spatial interpolation technique that create spatial distribution map as well as map for prediction of variance of soil parameters. To check the accuracy of soil nutrient maps cross validation approach has been used where mean absolute error (MAE), Mean square error (MSE), Goodness of prediction(G) values are computed for accuracy check. It is found that pH has very lowest CV (6.37%) indicating homogeneous soil for pH across the study region while highest CV (78.44%) is found for zinc indicating high variation across the study region. EC, OC, P, K, S, Zn, Cu, Fe, Mn, and B with respect to their CV are under moderate variation across the study region. In different classes, almost 40% of soil sample have soil pH between 8.0 to 8.5 which mean moderate alkaline. Almost 85% of soil sample have OC less than 0.5%. 98% of soil sample have EC less than 1ds/m. 60% of soil sample have phosphorous between 25-50ppm. 63% of soil sample have potassium between 125 -300ppm. 82% of soil sample have Sulphur less than 22.4ppm. 54% of soil samples have Zn above 1.20ppm. 96% of soil samples have greater than1.20ppm. almost 80% of soil samples have greater than 12.0ppm. 88% of soil samples have Mn greater than 5.0ppm. 52% of soil samples have boron between 0.5-1.0ppm. Spherical and exponential model has been used to fit the experimental variogram of the soil parameters. Range of spatial dependence for each parameter has been established. values are computed to analyse the goodness of the variogram model. Based on nugget-sill ratio, the spatial dependence for each parameter has been classified as weak, moderate and strong. Mn, B, EC, OC and Zn values influenced their neighbouring values over greater distances than pH, P, K, S, Cu and Fe, all of which have range of below 10 km. The highest range of 72 km is observed for Manganese means the concentration is highly correlated spatial up to a large distance. The nugget sill ratio also proved its moderate spatial dependence in area under study. Range of Boron (B) concentration is 66 km while the largest nugget effect is observed for K and moderate dependence is observed for its concentration under study area due to large nugget effect. The goodness of fit statistic indicates a moderate fit for the variogram model. The soil pH is also observed to be spatially correlated to a distance of 10.6 km in the study area and the degree of spatial continuity is moderate. All the other parameters are observed to have low values of range varying below 10 km. It might be due to cumulative effect of climate, parent material and adopting of different land management, the range values of soil parameters are different. This observed spatial dependency can be used to support spatial sampling for detailed soil mapping in site specific soil management. Predictive spatial maps have been generated for each soil parameter by interpolating the values using ordinary kriging method using variogram model. The different distribution pattern is exhibited by the kriged surface maps developed for soil parameters. These maps could help for site specific nutrient management and also in designing future soil sampling strategies in the intensively cultivated alluvial soil of Muzaffarpur. The predictive maps produced by interpolating the values using ordinary kriging show distinct patchy distribution of pH, K, P, S, B, Zn, EC, OC and Mn across different parts of Muzaffarpur. These types of maps can help in identifying the pockets of soil available nutrients according to their concentration and may in turn help for region specific management.