Abstract
Soil cation exchange capacity (CEC) is an important measure of soil fertility, owing to the large number of plant essential nutrients which are taken up by plants as cations. However, current methods for evaluating soil CEC are arduous and require analysis in a laboratory. Portable X-ray fluorescence (PXRF) spectrometry is a proximal sensing technique which provides elemental data in-situ, in seconds. This study examined the potential of using PXRF for soil CEC prediction by evaluating 450 soil samples from California and Nebraska, USA representing a wide variety of soil textures found in active farm fields. Multiple linear regression was applied to a modeling dataset to establish the relationship between lab-determined CEC and PXRF elemental data. A second model also included auxiliary input data (soil clay, pH, organic matter) as potential modeling variables. Both models were shown to perform similarly, with the auxiliary input model providing slightly higher R2 (0.926 vs. 0.908) and slightly lower RMSEs (2.236 vs. 2.498) compared to pure elemental data models. Independent validation datasets were compelling for both pure elemental models (0.904) and auxiliary input models (0.953). Summarily, PXRF was able to predict soil CEC accurately, thereby minimizing the need for lab-based CEC data for many applications.
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