GIS-Based Spatial Analysis and Explainable Gradient Boosting of Heavy Metal Enrichment in Agricultural Soils
Sadenova M. Beisekenov N.
January 2026Multidisciplinary Digital Publishing Institute (MDPI)
Applied Sciences (Switzerland)
2026#16Issue 1
Featured Application: This study presents a GIS-based and explainable machine learning framework for screening heavy metal enrichment in agricultural topsoils at the field scale using only routine soil indicators. Farm managers and environmental agencies can use the Heavy Metal Enrichment Index (HMI) and associated field maps to identify persistently enriched fields and emerging hot spots, prioritize follow-up sampling, and focus management review on locations where enrichment signals are most consistent. Because the framework relies on widely available laboratory measures of soil acidity (pH in H2O and pH in KCl) and humus content, it is suitable for operational monitoring programs and can be extended by incorporating satellite-derived covariates to support scalable screening in data-limited regions, including Central Asia. Heavy metal enrichment in agricultural soils can affect crop safety, ecosystem functioning, and long-term land productivity, yet farm-scale screening is often constrained by limited routine monitoring data. This study develops a GIS-based framework that combines field-scale spatial analysis with explainable machine learning to characterize and predict heavy metal enrichment on an intensively managed cereal farm in eastern Kazakhstan. Topsoil samples (0 to 20 cm) were collected from 34 fields across eight campaigns between 2020 and 2023, yielding 241 composite field–campaign observations for eight metals (Pb, Cu, Zn, Ni, Cr, Mo, Fe, and Mn) and routine soil properties (humus, pH in H2O, and pH in KCl). Concentrations were generally low but spatially heterogeneous, with wide observed ranges for several elements (for example, Pb 0.06 to 2.20 mg kg−1, Zn 0.38 to 7.00 mg kg−1, and Mn 0.20 to 38.0 mg kg−1). We synthesized multi-metal structure using an HMI defined as the unweighted mean of z-standardized metal concentrations, which supported field-level screening of persistent enrichment and emerging hot spots. We then trained Extreme Gradient Boosting models using only humus and pH predictors and evaluated performance with field-based spatial block cross-validation. Predictive skill was modest but nonzero for several targets, including HMI (mean R2 = 0.20), indicating partial spatial transferability under conservative validation. SHAP analysis identified humus content and soil acidity as dominant contributors to HMI prediction. Overall, the workflow provides a transparent approach for field-scale screening of heavy metal enrichment and establishes a foundation for future integration with satellite-derived covariates for broader monitoring applications.
agricultural soils , explainable machine learning , GIS-based spatial analysis , heavy metal contamination , spatial risk mapping
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D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, 070004, Kazakhstan
Department of Ecology and Conservation Biology, Texas A&M University, College Station, 77843, TX, United States
D. Serikbayev East Kazakhstan Technical University
Department of Ecology and Conservation Biology
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