The implications of transposing regional digital soil models to continental and global scales are still poorly understood. This paper presents a 'controlled landscape experiment' where soil organic carbon (SOC) stocks were predicted using a standardized set of nationally available environmental predictor variables and sampling density of soil observations. Our specific objective was to compare the prediction performance of SOC in two contrasting regions in the United States. We used soil samples in the topsoil (0-20 cm) depth across Colorado and Florida from the U.S. National Soil Survey Database (Natural Resource and Conservation Service, NRCS). Environmental covariate sets were assembled representing a subset of STEP-AWBH factors (S: soils, T: topography, E: ecology, P: parent material, A: atmosphere, W: water, B: biota, and H: human). We used single Regression Trees (RT) and Support Vector Machines (SVMs) and various error metrics to assess the prediction performance of SOC stocks. Our results demonstrate that in ecologically contrasting states, both RT and SVM could produce moderate good predictions with R2 of 0.76 (SVMs) in Florida and R2 of 0.62 (RT) in Colorado. The differences in model results elucidate on the contrasting relationships between SOC and environmental predictors that were climate, soil and vegetation driven in Colorado and soil and vegetation driven in Florida. These findings have implications for upscaling of regional digital soil models to continental and global scales, specifically for SOC modeling across the U.S.