Assessing models for prediction of some soil chemical properties from portable X-ray fluorescence (pXRF) spectrometry data in Brazilian Coastal Plains

Portable X-ray fluorescence (pXRF) spectrometry is becoming increasingly popular for predicting soil properties worldwide. However, there are still very few works on this subject under tropical conditions. Therefore, the objectives of this study were to use pXRF data to characterize the Brazilian Coastal Plains (BCP) soils and assess four machine learning algorithms [ordinary least squares regression (OLS), cubist regression (CR), XGBoost (XGB), and random forest (RF)] for prediction of total nitrogen (TN), cation exchange capacity (CEC), and soil organic matter (SOM) using pXRF data. A total of 285 soil samples were collected from the A and B horizons representing Ultisols, Oxisols, Spodosols, and Entisols. The pXRF reported elements helped in the characterization of the BCP soils. In general, the RF model achieved the best performances for TN (R² = 0.50), CEC (0.75), and SOM (0.56) when A and B horizons were combined, although better results have been reported in the literature for soils from other regions of the world. The results reported here for the BCP soils represent alternatives for reducing costs and time needed for assessing such data, supporting agronomic and environmental strategies.