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Cation exchange capacity (CEC) is an important soil property because it affects the assimilation of nutrients and buffers against soil acidification. Thus, knowledge of CEC is considered key to developing agricultural and environmental models for land management planning. However, in developing countries such as Sudan, there is a lack of soil CEC data due to the absence of research projects and funding to develop this information. Therefore, this research was conducted to predict CEC for large areas using specific soil physical characteristics, including soil texture and saturation percentage (SP), for which there is potentially available data. To achieve this goal, the properties of 430 soil samples (301 for training and 129 for validation) were obtained from the soil database of the Soil Survey Administration, Ministry of Agriculture, Sudan, which had different soil depth intervals (0–0.3 m, 0.3–0.6 m, 0.6–0.9 m, 0.9–1.5 m, and >1.5 m) from Entisols in the Northern State of Sudan. The data were stratified into homogeneous groups based on the textural classes of the main soil order. Then, regression models were performed and evaluated using the coefficient of determination ( R2), standard error of the estimate (SEE), and root mean square error (RMSE). The results indicated that in individual Entisols and textural classes, the combined soil covariates silt, clay, and SP were the best properties to predict CEC values ( R2 ranged from 0.86 to 0.99). The regression models did not provide statistically significant results for the silty clay loam textural class ( R2 ranged from 0.01 and 0.35). The findings of this modeling study could be applied to other Entisols worldwide with divergent textural classes, which could be used to verify the suggested CEC pedotransfer functions and/or improve them. This would help farmers correctly design soil management plans and prevent acidification issues if combined with other soil properties data. |
