Mapping soil erodibility from composed dataset in Sele River Basin, Italy.

Evaluation of soil erodibility is an important task for Mediterranean lands, in which fertility and crop yield are significantly affected by soil erosion. The soil physicochemical parameters affecting soil erodibility are highly variable in space and, as for many other environmental variables, sample measurements are generally not enough for assessing its spatial variability with an acceptable level of uncertainty at the scales of practical interest. This study illustrates the procedure applied for estimating the pattern of soil erodibility across the Sele Basin (Southern Italy), where soil properties have been measured on a limited number of sparse samples. Sampled data were integrated with other sparse data estimated by local regression functions, which relate soil erodibility to auxiliary variables, such as terrain attributes and land system class memberships. Sampled and estimated data were merged in a composed data set to assess the spatial pattern of soil erodibility by ordinary kriging. The proposed approach offers effective spatial predictions, and it is exportable to regions where financial costs for soil sampling are not feasible.