Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty (n = 60) soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley–Black method. From these samples, 80% were used for model training and 21 auxiliary data were included as predictors. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), calcium to magnesium ratio (Ca_Mg), potassium to magnesium ratio (K_Mg), potassium to calcium ratio (K_Ca), elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST). Mean absolute error (MAE), root-mean-square error (RMSE) and R2 were used to determine the model performance. The result showed the mean SOC to be 1.62% with a coefficient of variation (CV) of 47%. The best performing model was RF (R2 = 0.68) followed by the cubist model (R2 = 0.51), SVM (R2 = 0.36), ANN (R2 = 0.36) and MLR (R2 = 0.17). The soil nutrient indicators, topographic wetness index and total catchment area were considered an indicator for spatial prediction of SOC in flat homogenous topography. Future studies should include other auxiliary predictors (e.g., soil physical and chemical properties, and lithological data) as well as cover a broader range of soil types to improve model performance.

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This paper reports the soil properties and agricultural potential of floodplain soil in Obufa Esuk Orok, Calabar and Awi, Akamkpa, Cross River State and characterized the soils to develop a baseline for soil improvement and increased agricultural productivity. Floodplain soil developed on coastal plain sand and basement complex soils were examined with regards to their physical and chemical properties. The results showed that both soils were coarse-textured with a high content of sand exceeding 70 %, giving dominant textural classes of loamy sand and sandy loam. The soils were strongly to slightly acid in reaction with mean surface pH values of 5.54 and 5.43 for soil developed on coastal plain sand and basement complex. The surface soil developed in coastal plain sand within Obufa Esuk Orok, Calabar contains moderate amount of exchangeable Mg (0.8-2.2 cmol/kg), available P (10.0 -11.83 mg/kg), total N (0.20-0.25 %) and organic carbon (2.51-3.05 %) and was high in base saturation (93.11-95.50 %) and low in exchangeable acidity. Conversely, the floodplain soil developed on basement complex in Awi, Akamkpa was rated low in key fertility indicators. Hence, floodplain soil under this study notably, those developed on coastal plain soil can be exploited for the cultivation of rice, oil palm, coconut, plantain, pineapple, pepper, fluted pumpkin and sugar cane with judicious application of lime, and N and K fertilizer.