Using rule-based regression models to predict and interpret soil properties from X-ray powder diffraction data

In terms of predictive performance, XRPD surpassed NIRS for prediction of six of the eight soil properties investigated. Notably, diffuse scattering from X-ray amorphous organic matter facilitated relatively accurate predictions of total carbon and nitrogen from XRPD. Aqua regia extractable potassium was predicted with substantial accuracy and confirmed to reflect the phyllosilicate potassium. The particle size fractions were predicted with moderate-substantial agreement using combinations of quartz, phyllosilicate and feldspar variables. This approach introduces the value of XRPD datasets in enhancing the understanding of soil mineralogy-property relationships whilst contributing to soil mineralogy's advance into the digital soil typing paradigm.