The impact of grazing on soil carbon (C) and nitrogen (N) cycles is complex, and across a large area it can be difficult to uncover the magnitude of the effects. Here, we have linked two common approaches to statistical modelling regression trees and linear mixed models - in a novel way to explore various aspects of soil C and N dynamics for a large, semiarid bioregion where land use is dominated by grazing. The resulting models, which we term RT-LMM, have the pleasing visual appeal of regression trees, and they account for spatial autocorrelation as per a linear mixed model. Our RT-LMM were developed from explanatory variables that related information on climate, soil and past land management. Response variables of interest were: stocks of soil total organic carbon (TOC), soil total nitrogen (TN), and particulate organic C (POC); the ratio of TOC stock to TN stock; and the relative abundance of stable isotopes delta C-13 and delta N-15 in the soil. Each variable was sampled at the depth interval 0-0.3 m. The interactions of land use with, in particular, air temperature and soil phosphorus were strong, but three principal management-related effects emerged: (i) the use of fire to clear native vegetation reduced stocks of TOC and TN, and the TOC: TN ratio, by 25%, 19% and 9%, respectively, suggesting that TOC is more sensitive to fire than TN; (ii) conversion of native vegetation to pasture enriched soil with delta C-13 by 1.7 %; subsequent regrowth of the native vegetation among the pasture restored d13C to its original level but there was no corresponding change in TOC stock; and, (iii) the time elapsed since clearing reduced POC stocks and the TOC : TN ratio.