In the present study, different soil properties such as pH, electrical conductivity (EC), organic carbon (OC) and the heavy metals chromium (Cr), lead (Pb) and zinc (Zn) were mapped spatially using geostatistical methods. Geostatistical tools including ordinary kriging interpolation technique were utilized to investigate the variability. Accurate and rapid assessment of soil properties is a key component of agriculture. A total of 116 surface soil samples (0–25 cm) were obtained at an interval of 250 m with GPS coordinates from the study area. The highest (93.76 %) variation was observed in the total Cr, whereas the lowest (4.75 %) was found in soil pH. The best variogram model fit in ordinary kriging was selected based on the accuracy of standardized mean error (MSE) nearest to zero, the smallest root-mean-squared prediction error (RMSE), the average standard error (ASE) nearest the root-mean squared prediction error and the standardized root-mean-squared prediction error (RMSSE) nearest one. The results indicated that soil pH, EC and Cd were best fitted to the spherical model, whereas soil OC, Pb, Cr and Zn best fitted to the Gaussian model. All the soil pollutants (Cr, Cd, Pb and Zn) were found above the permissible limits in soil, with Cr around 10 times higher than the recommended threshold. The spatial variability of important soil properties is valuable for planning remediation and management practices at regional scale.
Nisha et al. (Fri,) studied this question.