Spatial analysis of soil aggregate stability in a small catchment of the Loess Plateau, China: II. Spatial prediction

摘要

As indicators of soil degradation vulnerability, soil aggregate stability indices play important roles in representing soil resistance to water erosion, and their spatial variability provides both agriculturally and environmentally important information. The spatial variability of aggregate stability indices is synergistically affected by the soil, topography, vegetation, and human factors. To understand the formation processes of aggregates by a spatial analysis, a prediction model combining soil properties with natural and human factors should be developed to improve the accuracy of the spatial interpolation of soil aggregate stability indices. In this study, the mean weight-diameter (MWD, mm), water-stable aggregates greater than 0.25 mm (WSA (> 0.25), %) and soil erodibility factor (K factor) were satisfactorily predicted by multiple stepwise regression (MSR) and regression kriging (RK) based on soil properties and natural and human factors (0.436 = R-2 = 0.578). In addition, spatial variability and prediction modeling of aggregate stability indices were highly dependent on the quantification of land use type and landscape structure (the spatial structure of landscape elements and the connections between the different ecosystem types or landscape elements). It has received little attention in previous studies. The exclusion of all soil variables did not affect the predictions of K factor, and for MWD and WSA (> 0.25.) even though the performance of the models may appear relatively low, but also significant (0.183 = R-2 = 0.312), indicating that the prediction of the spatial distributions of aggregate stability indices with easily available auxiliary data is practicable and effective. Residual maps showed that high residuals are distributed around built-up land (transportation land and residential land) or farmland, indicating that anthropogenic factors increase the uncertainty of the models. The spatial distribution maps of MWD, WSA (> 0.25) and K factor can be useful in landscape planning and decision making to minimize water erosion risks.