Spatial Variability in and Geostatistical Mapping of Soil Organic Carbon Stocks Under Contrasting Land Uses in Foumbot, Western Cameroon

Accurate assessment of soil organic carbon (SOC) distribution is essential for carbon accounting and sustainable land management, yet spatial information remains limited in Central African landscapes. This study evaluated SOC stocks across cropland, grassland, and forest ecosystems in the Foumbot agricultural basin, western Cameroon, at depths of 0–10 and 10–20 cm. Spatial dependence was assessed using semivariograms, while a two-step, land use-based residual interpolation framework was applied. Land use-specific means were used to account for broad land use-related variation, and the resulting residuals were interpolated using Ordinary Kriging (OK) and Inverse Distance Weighting (IDW). Predictive performance was evaluated using leave-one-out cross-validation (LOOCV) and compared with direct interpolation and a mean per land use baseline. SOC stocks showed heterogeneity associated with land use, topography, and soil development. The fitted Gaussian semivariogram model described the spatial dependence of SOC stock residuals, with ranges of 167 and 144 m at 0–10 and 10–20 cm, respectively. At 0–10 cm, residual IDW achieved the lowest root mean square error (RMSE; 15.69 t ha−1), closely followed by residual OK (15.82 t ha−1), compared with direct IDW (19.60 t ha−1), direct OK (18.73 t ha−1), and the land use mean baseline (17.76 t ha−1). At 10–20 cm, residual OK performed best (RMSE = 14.28 t ha−1), closely followed by residual IDW (14.44 t ha−1), outperforming direct interpolation and the baseline. Overall, residual-based interpolation reduced prediction errors at both depths, although performance varied by depth and evaluation metric. These findings support its application in SOCS mapping and provide spatial information for targeted carbon management and sustainable soil management in tropical volcanic landscapes.