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Modeling soil properties at a regional scale using GIS and Multivariate Adaptive Regression Splines

In this paper, five topsoil properties (clay, silt, sand, organic matter and bulk density) have been modeled using accessible environmental information in order to improve pedological cartography in the Extremadura region (Spain). Independent variables related to topography, climate, lithology and vegetation cover were used to generate the models. The statistical approach was based on using Multivariate Adaptive Regression Splines (MARS) to produce the models. The performance of the models was tested using Root Mean Square Error (RMSE), Generalized Cross Validation (GCV) and the r coefficient (classical regression analysis). The models presented moderate power prediction; with r ranging from 0.23 for organic matter to 0.47 for silt. The most important independent variables supporting the models were location (X and Y UTM coordinates), lithology and altitude. Finally the models were compiled and implemented into a Geographical Information System (GIS) to obtain continuous maps of target variables.