The spatial variability of soil physicochemical properties significantly influences irrigation efficiency, nutrient availability, and the long-term sustainability of irrigated agriculture in semi-arid regions. This study aimed to quantify and model the spatial distribution of soil properties in a semi-arid irrigation district in central Mexico (Irrigation District 001 “Pabellón de Arteaga”, Aguascalientes), providing spatially explicit information for differential irrigation and fertilization management. Ninety-seven crop and four natural sampling sites were established under a stratified random design at two soil depths (0–30 and 30–60 cm). Geostatistical and machine learning models (Ordinary Kriging, OK; Generalized Additive Models, GAM; and Random Forest, RF) were applied to predict spatial patterns, and their performance was evaluated using statistical metrics. The findings reveal high spatial and vertical variability, with most properties (such as organic matter, total nitrogen, and texture) showing significant stratification with depth. In contrast, others (pH and electrical conductivity, EC) remained remarkably homogeneous vertically. Correlation patterns were identified, highlighting the negative influence of alkaline pH (≈8.0) on the availability of micronutrients (Fe2+ and Mn2+) and the positive association between EC and soluble cations (Ca2+, K+, and Na+). Moran’s Index confirmed significant spatial autocorrelation for most properties, reducing the effective sample size by 30–70%. The comparative evaluation of predictive models demonstrated the superiority of RF over OK and GAMs for predicting chemical properties, thanks to its ability to capture nonlinear relationships and complex interactions. However, the overall predictive performance was moderate, reflecting the multifactorial complexity of the edaphic system. This study lays the foundation for the development of an accessible, low-cost Decision Support System by providing a robust methodological framework for spatial soil characterization and contributing to more sustainable, resilient agriculture, where decision-making is based on quantitative data and predictive models.
Galván-Cano et al. (Sat,) studied this question.