Characterization of soils via portable X-ray fluorescence spectrometer

Characterization of soil is critically important for any soil scientist initiating their research. Soil pH and soil cation exchange capacity (CEC) are two of the important soil properties that cannot be disregarded when considering the quality of soil. Current methods for evaluating these properties are arduous and require laboratory analysis. Laboratory approaches, while accurate, are destructive in nature and require sample modification. 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 pH and CEC prediction, using a diverse set of soil samples. The use of PXRF spectrometry for pH determination was investigated using elemental data as a proxy for soil pH. Two datasets representing a wide range of soil pH (4.17–8.70) were evaluated via PXRF followed by standard laboratory techniques. Datasets were divided into modeling and validation datasets. Simple and multiple linear regressions were used to develop models associating both pure elemental data from PXRF as well as PXRF elemental data with auxiliary input data (clay content, sand content, organic matter content). Al, Si, Mn, Fe, K, Ca, and Zn were used to predict pH when using only PXRF elemental data for dataset A. Multiple linear regression with auxiliary input data enhanced the model performance (R2: 0.825; RMSE: 0.541); model that included more PXRF elemental data and higher sample size performed much better (R2: 0.772; RMSE: 0.685); simple linear regression was ineffective at producing significant model predictions. Validation via correlation analysis supported the significance of the developed models. Similarly, soil CEC evaluation was made on 450 soil samples from active farm fields in California and Nebraska, USA representing a wide variety of soil textures. Multiple linear regression was applied to a modeling dataset to establish the relationship between lab-determined CEC and PXRF elemental data. When predicting CEC using PXRF data only, Ca, Ti, V, Cr, Fe, Cu, Sr, and Zr were used in the model. A second model also included auxiliary input data (soil clay, pH, organic matter) as a 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 shows considerable promise in rapid prediction of soil pH and CEC with reasonable accuracy, thereby minimizing the need for tedious laboratory operations in many applications.

This thesis won 1st Place in the Texas Tech University Outstanding Thesis and Dissertation Award, Biological Life Sciences, 2015.

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