Rapid estimation of cation exchange capacity from soil water content

Estimates of cation exchange capacity (CEC) for soil or earth materials are vital for several applications in agriculture and geotechnical engineering. The time-consuming and laborious nature of standard methods of CEC determination prompted the development of regression-based techniques and more sophisticated methodologies, such as artificial neural networks (ANNs), to estimate CEC from other soil properties (e.g. clay, organic matter and pH). The present study proposes regression relations for estimating CEC from soil water content at different relative humidity (RH) values that range from 10 to 90% and considering sorption hysteresis. For model development, water adsorption and desorption isotherms were measured for 203 soil samples from different parts of the world with a vapour sorption analyser within an RH range from 3 to 93%. Regression models were developed for both adsorption and desorption for nine RH values (10, 20, 30, 40, 50, 60, 70, 80 and 90%). For 35 independent soil samples, the models predicted CEC accurately (root mean squared error (RMSE): adsorption= 2.47 (0.04) cmol(+) kg(-1); desorption=2.52 (0.09) cmol(+) kg(-1)) with no clear effect of RH value or sorption direction on the accuracy of prediction. The new models were superior to three models from the literature based on clay, organic matter and pH; the smallest RMSE from these models was 4.13 cmol(+) kg(-1) for the same 35 soil samples. Furthermore, the water sorption-based models performed better than literature models based on data mining tools (ANNs, support vector machines) and visible near infrared spectroscopy when standardized RMSE values were compared. Thus, a simple measure of soil water content at a known RH can serve as an accurate substitute for measuring soil CEC.