Impacts of Soil Environmental Factors on Variability of Soil Organic Carbon and Particle Size Fractions in Obudu Cattle Ranch, Nigeria - Afu, Sunday; Olim, Denis; Aberagi, Fidelis; Akpama, Afrekpe; Afangide, Akaninyene; Uquetan, Uquetan; Modey, Alice (Malaysian Journal of Science (MJS), 2025)
The knowledge of the influence of environmental factors on soil properties and spatial distribution of soil organic carbon (SOC) and soil particle size fractions is crucial to soil management and sustainable productivity. SOC provides an insight about soil capacity to perform ecosystem services while soil particle size fractions influence several key soil characteristics. This study assessed the impacts of environmental elements on spatial changes in SOC and sand, silt and clay using random forest (RF), regression kriging (RK), cubist regression (CR), multiple linear regression (MLR) and ordinary kriging (OK) models. Sixty (60) composite soil samples were obtained at 0-30 cm depth and distance of 200-500 m apart, and analyzed for physicochemical properties. The digital elevation model (DEM) of the area was acquired at the spatial resolution of 30 m from USGS and processed. The models were evaluated using bias, coefficient of determination (R2), correlation concordance coefficient (CCC), mean square error (MSE) and root mean square error (RMSE). The soil had sandy clay loam, sandy loam and loam texture with strongly acidic pH (pH <5.5) and high OC (2%). Available P and exchangeable cations were all low while cation exchange capacity and base saturation were high. Soil pH> SAVI (soil adjusted vegetation index)> NDVI (normalized difference vegetative index) > rainfall were found to be the top four environmental variables influencing OC prediction while temperature and slope had the least effect. Again, MLR model better predicted OC (R2 of 0.324, CCC of 0.537, MSE of 0.585, RMSE of 0.764), OK better predicted clay (MSE=2.680, RMSE=3.490), CK in sand (MSE = 7.434, RMSE =5.568). Also, MLR, CK and OK proved to have the best capacity in prediction SOC and sand, silt and clay in mountainous soils. The findings could therefore could be used by policy makers and planners as tools for decision making on sustainable soil and environmental management alternatives and precision agriculture.