Predicting and mapping soil magnetic susceptibility in an agro-pastoral transitional zone: Influencing factors and implications

Soil magnetic susceptibility (MS) is a basic soil physical property that is influenced by environmental factors and processes. Mapping MS is fundamental to exploring its spatial pattern, influencing factors and practical applications. However, the traditional spatial interpolation method is difficult to implement in detailed large-scale mapping. Digital soil mapping (DSM) based on environmental factors provides a more effective method. It has been widely adopted in the prediction of soil physiochemical characteristics, but not yet in MS mapping. This study aimed to explore the spatial pattern of soil MS in an agro-pastoral transitional zone (APTZ) of north China, reveal influencing factors of MS, and extract the significance of DSM of MS and its possible applications. Soil samples were collected from two layers (0-5 and 35-40 cm) in an APTZ. For each sample, chi(lf) and chi(fd) were measured. The quantile regression forest (QRF) model was used to predict MS and assess uncertainties. The results indicated substantial spatial variation of MS in APTZ. QRF predicted MS with satisfying accuracy (adjusted R-2 = 0.37-0.64). Influencing factors of MS included parent material, topography, and vegetation. The predominant factors varied with MS attributes, depth, and environment. Soil redistribution further complicated the MS spatial pattern. Water erosion resulted in declining topsoil chi(fd) at steep slopes. Topsoil chi(fd) of grassland transect showed higher covariate of variation (8.23%) compared with forest (4.83%). Wind brought exogenous particles with low MS and led to a weaker correlation between topsoil chi(lf) and chi(fd) (p > 0.1) compared with subsoil (p < 0.05). DSM of MS based on environmental factors reveals the undetected influencing factors at a small scale and helps to identify dominant factors in different environments, which provides the possibility of adopting MS in practical works at a large scale.