A better understanding of the spatial variability of soil parameters is important for refining agricultural management practices and for improving sustainable land use. It provides a valuable base against which subsequent and future measurements can be evaluated. Relating soil parameters to site characteristics may help in formulating and evaluating process models and in assessing the effects of land use and climate change on soil nutrient stores.

Statistical analysis represents that the coefficient of variation for all the variables observed was very different ranging within 3.35–76.54% at 0–20 cm depth of soil. The value of nugget for geostatistical analysis of soil variables: sill ratio ranges from 0% (Av Na, TN, SOC) to 100% (EC), which indicates that internal (e.g., the soil-forming processes) factors were dominant over external (e.g., human activities) factors. However, the soil properties Na, Total N, SOC had a strong spatial dependency with a nugget : sill ratio of <25% since it was induced by structural factors. Meanwhile, other soil variables (CEC, Av S, and pH) had moderately spatially dependency with nugget : sill ratio of 25–75% since these variables were mostly determined by both internal and external factors. The autocorrelation distances of all variables differed from 0.0216 degrees (SOC) to 1.0156 degrees (Total N), which implies the sampling design is reasonable. Kriging interpolation produced scattering maps that showed these studied areas were categorized by an opposite trend for Av S (higher in northwestern site) and EC (higher in east site). By contrast, Total N and soil SOC% differed all over the studied area, whereas pH and had a high percentage at the west region of the study area. The areas with low SOC% contents (0.724–0.729%) were mainly detected in the southwestern site of the study area. The kriging interpolation method offered the least interpolation error for the majority of soil properties, according to cross-validation results. It also demonstrates how well GIS techniques work for interpolating data that hasn't been sampled. These findings can be used to suggest the best agricultural management techniques in the area and to raise the standard of living for smallholder farmers.