Evaluating pXRF accuracy for predicting soil fertility: effects of moisture and soil properties

Portable X-ray fluorescence (pXRF) has emerged as a rapid, cost-effective tool for assessing soil fertility in precision agriculture. While the influence of soil moisture on pXRF elemental detection has been widely studied, its specific impact on nutrient prediction accuracy, particularly when combined with key soil properties, remains less well quantified. This study investigates how moisture conditions affect pXRF-based detection of soil nutrients and evaluates whether integrating soil properties enhances prediction accuracy. Using stepwise multiple linear regression (SMLR), available macronutrients and micronutrients were modeled from pXRF data under dry soil (DS), field moisture (FM), saturated paste (SP), and after the removal of excess water (AREW) from SP. At both DS and SP, pXRF yielded strong predictions for macronutrients, with R² values ranging from 0.54 to 0.74 for K, 0.49–0.59 for Ca, and 0.60–0.69 for Mg (all p < .0001), while predictions for Cu and Zn were low to moderate. Incorporating soil pH and organic matter (OM) improved model performance, increasing R² values by 1.1- to 6.1-fold compared to models based solely on pXRF data. These findings highlight the need for standardized sample preparation, especially regarding moisture, and demonstrate that combining pXRF with key soil attributes enhances its utility as a reliable tool for rapid nutrient assessment in diverse soil conditions.