Machine Learning-Based Analysis of Arsenic Migration from Soil to Highland Barley in High Geological Background Areas

To investigate the effect of high-arsenic (As) soil on the absorption of As by highland barley, 135 pairs of soil–crop samples were collected in the main producing areas of highland barley in the middle reaches of the Yarlung Zangbo River. Eight soil variables, including pH, redox potential (Eh), soil organic matter (SOM), total arsenic (T-As), total iron (T-Fe), total manganese (T-Mn), chemically extractable As (KH2PO4-As), and bioavailable As determined by diffusive gradients in thin films (DGT-As), were measured, along with As concentrations in barley grains (HB-As). Machine learning approaches were employed to construct predictive models for HB-As accumulation, and feature influence mechanisms were interpreted using SHapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP) analyses. The results showed that: (1) among models constructed using the full feature set, the random forest (RF) model exhibited the best predictive performance for HB-As, with R2 values of 0.756 and 0.651 for the training and testing datasets, respectively; (2) SHAP analysis indicated that DGT-As had the greatest contribution to the model (30.5%), followed by T-As and T-Fe/Mn; and (3) significant interaction effects among soil variables jointly influenced HB-As accumulation. This study provides scientific support for agricultural product safety, soil security, and sustainable land use in plateau agroecosystems.