Holistic environmental soil-landscape modeling of soil organic carbon

In environmental soil-landscape modeling (ESLM), the selection of predictive variables is commonly contingent on the researchers' domain expertise on soil-environment processes. This variable selection strategy may suffer bias or even fail in regions where the process knowledge is insufficient. To overcome this problem, this study demonstrates a holistic ESLM framework which consists of five components: model conceptualization, data compilation, process identification, parsimonious model calibration, and model validation. Based on the STEP-AWBH conceptual model, a comprehensive pool of 210 potential environmental variables that exhaustively cover pedogenic and environmental factors was constructed. This was followed by strategic variable selection and development of parsimonious prediction models using machine learning techniques. The all-relevant variable selection successfully identified the major and minor factors relevant to the SOC variation, showing that the major factors important for explaining SOC variation in Florida were vegetation and soil water gradient. Topography and climate showed moderate effects on SOC variation. Parsimonious SOC models developed using four minimal-optimal variable selection techniques and simulated annealing yielded optimal predictive performance with minimal model complexity. The holistic ESLM framework not only provides a new view of selecting and utilizing variables for predicting soil properties but can also assist in identifying the underlying processes of soil-environment systems of interest. Due to the flexibility of the framework to incorporate various types of variable selection and modeling techniques, the holistic environmental modeling strategy can be generalized to other environmental modeling domains for both prediction and process identification.