Geospatial variability mapping of soil nutrients for site specific input optimization in a part of central India. (4th April 2022) - Sharma, R. P.; Chattaraj, Sudipta; Jangir, Abhishek; Tiwari, Gopal; Dash, Benukantha; Daripa, Amrita; Naitam, Ravindra K.
Abstract: The ability to define site‐specific variability affects the acceptability of precision farming. A study was conducted to determine the soil nutrient spatial variability in the Nagpur Rural block of Nagpur district, Maharashtra, India. A total of 1707 surface soil samples (0‐to‐15‐cm soil depth) were collected from a 65, 386‐ha region. Soil samples were analyzed for pH, electrical conductivity (EC), soil organic carbon (SOC), and available macronutrients (N, P, K, and S) and micronutrients (Fe, Mn, Cu, and Zn). Geostatistical modeling (kriging) was run on soil geodatabase to obtain the best‐fit experimental semivariogram having the lowest RMSE. Exponential (for pH, SOC, and N) and spherical (for P, K, and S) variogram model of ordinary kriging type as well as exponential variogram of simple kriging (for EC) were found to be best fitted to the soil parameters studied. The spatial distribution of maps showed deficiency of available N, P, and Zn, whereas soil test K, Mn, and Cu was high in most of the study area. Spatial dependence was moderate for all soil fertility parameters (N/S ratio 0.25–0.75), whereas soil test P exhibited strong spatial dependency (N/S ratio 0.19). The soil fertility maps developed through geostatistical techniques can be successfully used for managing site‐specific nutrients, enhancing fertilizer use efficiency, minimizing cost of cultivation, improving socioeconomic status of farmers, and increasing farm income without adverse effects on soil environment. Core Ideas: High‐density sampling produces better soil fertility maps. GIS and remote sensing supports cadastral‐level planning for plant nutrient management. Precision soil fertility maps can reduce cultivation costs and improve socioeconomic returns.