Precision agriculture provides detailed information on the spatial variability of soil properties, including nutrient content, allowing for local-specific decision making. Recently, proximal sensors have been used to accurately predict soil properties, contributing to reduce costs of conventional wet-chemistry analyses for soil characterization. However, further investigations on this approach in tropical soils are needed. This work aimed to use portable X-ray fluorescence (pXRF) spectrometry data for prediction of exchangeable Ca²⁺ and available K⁺ and P contents in soils of a highly heterogeneous tropical area and evaluating its practical applications. 90 samples from soil A horizon were collected in a regular grid design, and analyzed through pXRF and for nutrient contents. Such data were split into modeling (63 samples) and validation (27 samples) datasets. Linear regression (LR), polynomial regression (PR), power regression (PwR) and stepwise multiple linear regression (SMLR) were tested for predictions. The models were used to spatially represent nutrient contents across the area and to compare the practical effects of varying regression models. PXRF elemental data provided reliable predictions of exchangeable Ca²⁺ and available P via SMLR and PwR, respectively, reaching root mean square errors (RMSE) of 5.66 cmol_c dm⁻³ for Ca²⁺ and 9.13 mg dm⁻³ for P. Available K⁺ predictions were not successful. Different models yielded contrasting maps showing the classes of soil fertility across the area, drawing attention to the importance of testing multiple prediction models and using the best one for precision agriculture. Fusion of data from different proximal sensors may enhance available K⁺ predictions.

精准农业提供有关土壤特性空间变异性的详细信息，包括养分含量，从而可以针对特定地方做出决策。最近，近端传感器已被用于准确预测土壤特性，有助于降低用于土壤表征的传统湿化学分析的成本。然而，需要在热带土壤中进一步研究这种方法。这项工作旨在使用便携式 X 射线荧光 (pXRF) 光谱数据来预测可交换 Ca ²⁺和可用 K ⁺和高度异质热带地区土壤中的 P 含量并评估其实际应用。在常规网格设计中收集了来自土壤 A 层的 90 个样品，并通过 pXRF 和养分含量进行分析。此类数据分为建模（63 个样本）和验证（27 个样本）数据集。测试了线性回归 (LR)、多项式回归 (PR)、幂回归 (PwR) 和逐步多元线性回归 (SMLR) 的预测。这些模型用于在空间上代表整个地区的营养成分，并比较不同回归模型的实际效果。PXRF 元素数据分别通过 SMLR 和 PwR提供了对可交换 Ca ²⁺和可用 P 的可靠预测，达到了 5.66 cmol _c dm 的均方根误差 (RMSE)^-3 Ca ²⁺和9.13 mg dm ^-3 P. 可用的K ⁺预测不成功。不同的模型产生了对比图，显示了整个地区的土壤肥力等级，提请注意测试多个预测模型的重要性，并使用最好的模型进行精准农业。来自不同近端传感器的数据融合可以增强可用的 K ⁺预测。