Comparing Linear Regression and Regression Trees for Spatial Modelling of Soil Reaction in Dobrovăţ Basin (Eastern Romania)

Abstract

Our study compares the performances of two statistical methods, namely multiple linear regression and classification and regression trees, for deriving spatial models of soil reaction in the surface horizon. The applications were carried out within a 186 km2 hydrographic basin situated in eastern Romania. Statistical models were computed from a sample of 235 soil profiles, scattered in the eastern half of the basin. An independent sample of 237 expeditionary pH measurements was used to validate the results within the interpolation area, whereas an independent sample of 50 soil profiles was used to validate the results within the extrapolation area (the western half of the basin). The predictors included geomorphometrical parameters, derived from a 10x10 m digital elevation model, X and Y coordinates of soil profiles and the main soil types for the regression trees approach. The stepwise selection procedure indicated Y coordinate, digital elevation model, wetness index and surface ratio as the best predictors for soil reaction. The correlation between observed and predicted pH values for the training sample suggests a much higher quality of the regression trees spatial model. However, the validation using the two independent samples points out the instability of this model and recommends the regression model more reliable.