Machine learning for cation exchange capacity prediction in different land uses

Abstract

Cation exchange capacity (CEC) is a major indicator of soil quality and nutrient retention capacity. Despite the considerable progress in CEC prediction using various models, studies to develop CEC pedotransfer functions (PTFs) using machine learning algorithms precisely, such as support vector regression (SVR) and random forest (RF), have not yet been performed in various land uses globally. This study aims to develop, evaluate, and compare the effectiveness of RF and SVR algorithms in determining CEC in different land uses that included agriculture, plantations, grasslands, forests, fallow land and deserts in five countries (Sudan, India, Italy, Iran, and Senegal). A total of 2418 soil samples were fully analyzed and clay, silt, sand, pH, and soil organic carbon (SOC) were the selected covariates for modelling. Both RF and SVR were calibrated with a training dataset (70%, 1693 samples) and validated by the remaining data (30%, 725 samples). The performance and accuracy of both models were evaluated using the Lin’s concordance correlation coefficient (LCCC), root mean square error (RMSE), and normalized root mean square error (NRMSE). The accuracy of the modeling predictions was further analyzed via the Taylor diagram. The findings revealed that clay content showed a positive significant correlation with CEC in all land uses, with highest correlation in desert land use (r = 0.94; p < 0.05). Conversely, CEC was significantly and negatively correlated with sand in all land uses, with highest negative correlation obtained in desert land use (r = −0.84; p < 0.05). The RF algorithm was able to predict the CEC better than SVR in nearly 67% of the validated land use datasets precisely in desert (RMSE = 2.68 cmolckg−1, NRMSE = 29.9%, and LCCC = 0.94), fallow land (RMSE = 5.12 cmolckg−1, NRMSE = 55.6%, and LCCC = 0.82), forest (RMSE = 4.78 cmolckg−1, NRMSE = 78.2%, and LCCC = 0.59), and grassland (RMSE = 8.39 cmolckg−1, NRMSE = 50.5%, and LCCC = 0.84). Conversely, SVR better predicted CEC in agriculture (RMSE = 5.82 cmolckg−1, NRMSE = 57.9%, and LCCC = 0.78) and plantation (RMSE = 4.64 cmolckg−1, NRMSE = 57.9%, and LCCC = 0.74). Therefore, RF represents a promising technique to estimate soil CEC and can be used to derive effective CEC-PTFs in case of limited data availability, due to the lack of time and financial resources when the few basic soil properties are available. The findings reported in this study can be used to verify the suggested CEC-PTFs and/or their improvement. We recommend that further similar studies based on RF and SVR algorithms should consider including land use type in the Whole dataset and clay minerals in the modelling, and then compare the performance of both algorithms considering the climatic regions of the different studied countries.