Spatial Variability and Temporal Changes of Soil Properties Assessed by Machine Learning in Córdoba, Argentina

Understanding the temporal dynamics and spatial distribution of key soil properties is essential for sustainable land management and informed decision-making. This study assessed the spatial variability and decadal changes (2013–2023) of topsoil properties in Córdoba, central Argentina, using digital soil mapping (DSM) and machine learning (ML) algorithms. Three ML methods—Quantile Regression Forest (QRF), Cubist, and Support Vector Machine (SVM)—were compared to predict soil organic matter (SOM), extractable phosphorus (P), and pH at 0–20 cm depth, based on environmental covariates related to site climate, vegetation, and topography. QRF consistently outperformed the other models in prediction accuracy and uncertainty, confirming its suitability for DSM in heterogeneous landscapes. Prediction uncertainty was higher in marginal mountainous areas than in intensively managed plains. Over ten years, SOM, P, and pH exhibited changes across land-use classes (cropland, pasture, and forest). Extractable P declined by 15–35%, with the sharpest reduction in croplands (−35.4%). SOM decreased in croplands (−6.7%) and pastures (−3.1%) but remained stable in forests. pH trends varied, with slight decreases in croplands and forests and a small increase in pastures. By integrating high-resolution mapping and temporal assessment, this study advances DSM applications and supports regional soil monitoring and sustainable land-use planning.