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Machine Learning For Predicting Soil Classes In Three Semi-Arid Landscapes, Colby W. Brungard, Janis L. Boettinger, Michael C. Duniway, Skye A. Wills, Thomas C. Edwards Jr.

Plants, Soils, and Climate Faculty Publications

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set …