Digital Soil Mapping of Soil Organic Carbon in Smallholder Robusta Coffee Landscapes of South–Central Uganda

Abstract

Reliable spatial information on soil organic carbon (SOC) is important for soil-fertility management, climate-resilient coffee production, and land-restoration planning, yet the heterogeneity of smallholder agricultural landscapes is difficult to represent with field observations alone. This study characterised SOC variability, compared four machine-learning (ML) algorithms—Random Forest (RF), Cubist, Gradient Boosted Machines (GBM), and Support Vector Machine (SVM)—identified the main predictive environmental covariates, and generated SOC prediction maps for a smallholder Robusta coffee landscape in South–Central Uganda. A total of 126 georeferenced surface-soil observations (0–30 cm) were analysed for SOC using the Walkley–Black dichromate wet-oxidation method and related to climatic, terrain, spectral, soil, land-cover, and spatial-position covariates within the SCORPAN framework. Models were tuned within an SOC-stratified training subset and evaluated using a withheld 20% random holdout. Observed SOC ranged from 0.31% to 5.30%, with a mean of 2.06% and a coefficient of variation of 62.7%. Independent holdout performance was limited. SVM performed best (R2 = 0.36; RMSE = 1.05), followed by Cubist, RF, and GBM. Annual mean temperature and spatial position had the highest predictive importance, followed by soil type, Landsat 9 Band 5, and annual precipitation. All four models reproduced a broad pattern of relatively higher SOC in Kalungu and central Masaka and lower SOC in Southern Kyotera and parts of Lwengo, although the magnitude of local variation differed among algorithms. The resulting maps are, therefore, best suited for landscape-level screening, sampling prioritisation, and identifying areas requiring field verification, rather than farm-level SOC prescription.