Considering Organic Carbon for Improved Predictions of Clay Content from Water Vapor Sorption.

Accurate determination of the soil clay fraction (CF) is of crucial importance for characterization of numerous environmental, agricultural, and engineering processes. Because traditional methods for measurement of the CF are laborious and susceptible to errors, regression models relating the CF to water vapor sorption isotherms that can be rapidly measured with a fully automated vapor sorption analyzer are a viable alternative. In this presentation we evaluate the performance of recently developed regression models based on comparison with standard CF measurements for soils with high organic carbon (OC) content and propose a modification to improve prediction accuracy. Evaluation of the CF prediction accuracy for 29 soils with clay contents ranging from 6 to 25% and with OC contents from 2.0 to 8.4% showed that the models worked reasonably well for all soils when the OC content was below 2.4%. For soils with OC>2.4% and CF<20%, the prediction error of the models consistently increased with increasing OC content. Based on 20 soils with CF between 3 and 15% and OC between 2.6 and 8.4%, we propose correction factors to account for the sorbed water content associated with OC. Evaluation of the OC-corrected model based on 15 independent soils with a wide range in OC showed significantly improved predictive capabilities when compared to the original model.