Investigating spatial relationships of soil friability and driving factors through co-regionalization with state-space analysis in a subtropical watershed

•TP and Ele co-driven attributes manifested the highest crosscorrelation with F.•Soil slope and land-use could be used for estimating the spatial variation of F.•The path chosen affected the performance of all F state-space models.•Autoregressive models depicted the spatial behavior of F along the transect. Soil friability (F) is one of the key indicators of soil structural conditions and can be used to assess the environmental impacts of different tillage and no-tillage practices on crop growth and yield. F can be affected by several driving factors such as soil (physical and chemical parameters) and topographic attributes and different land uses as well. Few studies have been carried out to identify those driving factors as well as to quantify their relationships with F at watershed scale. The objectives of this study were (i) to characterize the spatial variability of F and further soil and topographic attributes across a spatial transect of 11.2 km in length using classical statistics; (ii) to quantify the spatial relationships of F and driving factors using the state-space approach; and (iii) to compare the performance of state-space models in estimating F with those of corresponding linear and multiple regression models. The transect was established in the Micaela river watershed (MRW), Southern Brazil, with equidistantly distributed soil sampling points. Soil textural fractions (sand, clay and silt contents), organic matter content, bulk density, macroporosity, and F were determined in the 0−0.10 m layer at each sampling point. The digital elevation model provided elevation and soil slope as topographic attributes while a land use map was obtained from satellite images. All state-space models achieved better results in describing the spatial relationships of F and the further soil and topographic attributes than the corresponding multiple linear regression models. State-space analysis showed that soil slope could be used as a proxy to predict local variations of F in the MRW. The spatial covariance information should be included in the development of pedotransfer functions for estimating the spatial variation of F and the type of land use (as a soil structural indicator) should be investigated in future studies at watershed scale.