Evaluating pedotransfer functions for predicting soil bulk density using hierarchical mapping information in Campania, Italy.

In this study, the performance of 63 existing pedotransfer functions (PTFs) is evaluated to estimate oven-dry soil bulk density (BD) by using a dataset of 3,316 soil cores taken mainly in the farmlands of Campania (southern Italy). As expected, the lack of direct calibration yields prediction accuracy from unsatisfactory to rather weak. Therefore, we advance the working hypothesis that the use of hierarchical soil mapping information can make the application of existing PTFs more reliable. We show that grouping data according to land-systems classes or soil groups considerably improves the prediction ability quantified through the root mean squared error (RMSE) and the coefficient of determination (R2). An independent data set of 105 soil cores taken from two hillslopes in the Upper Alento River Catchment in southern Campania was used to verify our assumption. The validation step shows that the knowledge of a soil-landscape map is an efficient tool for improving the prediction of BD. This approach will be employed in a subsequent study to develop site-specific PTFs for the study region.