
This dataset contains paired soil and crop measurements used to evaluate thorium-232 (²³²Th) soil-to-crop transfer in calcareous semi-arid agricultural systems of the Sokoto Basin, Nigeria. The dataset includes activity concentrations of ²³²Th in soil and edible crop components (beans, maize, and pepper), along with calculated soil-to-plant transfer factors (TF).
Additional variables include soil physicochemical properties (pH, CaCO₃ equivalent, organic carbon, clay content), crop type, and geographic location (Sokoto and Kebbi States). The dataset was used to develop and validate machine learning models (Random Forest, XGBoost, Artificial Neural Networks, Support Vector Regression, and ensemble methods) for predicting radionuclide transfer and assessing the influence of soil geochemical conditions on thorium bioavailability.