Simulation and prediction for the spatial heterogeneity of soil selenium bioavailability at different stratigraphic scales.

Stratigraphic lithology strongly influences the spatial heterogeneity of soil available selenium (ASe), however, it is often neglected in regional simulation. Therefore, taking the Jiangjin District, where the soil is richer in selenium (Se), as the research area, the changes of soil ASe at different spatial scales have been simulated by combining Geodetector and three popular models (Multiple linear regression (MLR), Random forest (RF) and BP neural network (BPN)). The results showed that modelling with 'Formation' as the spatial scale could reduce the influence of stratum lithology difference on the spatial heterogeneity of soil ASe and improve the model's prediction accuracy. Compared with the MLR (R = 0.52, root mean squares error (RMSE) = 13.217 μg kg) and BPN (R = 0.55, RMSE = 13.79 μg kg), the RF (R = 0.67, RMSE = 10.85 μg kg) exhibited higher R and smaller RMSE, and the simulation effect of soil ASe is the best in the Middle Jurassic Shaximiao Formation (Js). The outcomes of variable importance analysis revealed that soil total selenium (TSe) and soil organic matter (SOM) were the imperative factors for predicting ASe. The scenario simulation prediction showed that in the next 40 years, due to the combined influence of SOM and pH, the content of ASe in soil developed in the Js would decrease from 40.8 μg kg to 37.8 μg kg, a 7.8 percent drop. The main areas of soil ASe loss were in the western farming areas. The ASe content in dry land and paddy fields decreased by 12.0% and 4.9%, respectively. Therefore, long-term agricultural production activities would lead to soil ASe loss. The present results could provide a new scheme for the simulation and prediction of regional soil ASe, which is helpful for scientific planning, utilization of selenium-rich soil resources, and development of regional agricultural economy.