Scientific information concerning spatial variability and distribution of soil properties is critical for farmers attempting to increase fertilization efficiency and crop productivity; fertilization based on maps with recommendations related to soil fertility may lead to reduced fertilizer inputs without reducing yield. In the present study, GPS based one hundred fifty surface soil samples (0-15 cm) were collected from dominant cropping system. After processing, the soil samples were analyzed for different soil characteristics in laboratory using standard procedures. The data obtain from laboratory analysis was statistically and geo-statistical interpreted. The results revealed that the 23.6, 28.30, 48.6, 13.9, 25.5 and 54.7% soil samples were found to be deficient in OC, N, P, K, S and Zn, respectively. None of the soil samples were tested low in Cu, Fe, Mn and B. Exponential model was found as the best fit for considered soil parameters whereas, spherical model was found as the best fit for Mn. The best model was used to generate the spatial distribution maps. Spatial maps showed that the soil pH, EC, organic carbon, available N, P, K, S, Zn, Cu, Fe, Mn and B spatially varied and N, P, K, S and Zn were deficient in major areas. Therefore these maps are more useful for guiding site-specific field management for agricultural production and environmental protection. In addition, reduce the losses of nutrients and could be save time and money for fertilizers.
Key words: Geo-statistical, soil types, land use, semi-variogram, kriging, nutrient status.
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