Soil enzymes act in biogeochemical cycles of elements and are indicators of soil quality since they rapidly reflect changes of the environmental conditions. Moreover, enzymes are related to soil physicochemical properties, but their spatial distribution has been rarely evaluated. The hypothesis of this work is that soil properties related to fertility and texture (F), total contents of chemical elements obtained by portable X-ray fluorescence (pX) spectrometry and terrain attributes (T) can be used as predictor variables to soil enzyme activity, along with phytophysiognomy and season information. The objective of this work was to predict soil enzymes activity and assess its spatial variability in the most common phytophysiognomies of the Quadrilátero Ferrífero mineral province, in Brazil. Soil samples were collected in four phytophysiognomies during both dry and humid seasons. Activity of β-glucosidase, acid phosphatase, alkaline phosphatase, urease, and hydrolysis of fluorescein diacetate (FDA) was determined. Phytophysiognomy, season, F, T, and pX, were used together or separately to predict the enzymes activity through conditional random forest algorithm and the accuracy was assessed via leave-one-out cross validation. The generated models were accurate, with coefficient of determination (R²) varying from 0.63 (FDA by pX) to 0.82 (β-glucosidase by F). F variables were more important for the predictions, while pX variables were more important for predicting acid phosphatase and urease. The accurate models using T variables allowed the generation of maps showing the enzymes variability along the phytophysiognomies. This approach can accelerate the determination of soil enzymes activity across the landscape.

土壤酶在元素的生物地球化学循环中起作用，是土壤质量的指标，因为它们会迅速反映环境条件的变化。此外，酶与土壤的理化性质有关，但很少评估其空间分布。这项工作的假设是，与肥力和质地（F），通过便携式X射线荧光（pX）光谱法获得的化学元素的总含量以及地形属性（T）有关的土壤特性可以用作土壤酶活性的预测变量。 ，以及植物生理和季节信息。这项工作的目的是预测QuadriláteroFerrífero最常见的植物生理学中的土壤酶活性并评估其空间变异性。巴西的矿产省。在干燥和潮湿的季节，在四个植物生理学中收集土壤样品。确定了β-葡萄糖苷酶，酸性磷酸酶，碱性磷酸酶，脲酶和荧光素二乙酸酯（FDA）的水解活性。根据条件随机森林算法，将植物生理学，季节，F，T和pX一起或分别用于预测酶的活性，并通过留一法交叉验证来评估准确性。生成的模型是准确的，具有确定系数（R ²）范围从0.63（FDA通过pX）到0.82（β-葡萄糖苷酶（F））。F变量对预测更重要，而pX变量对预测酸性磷酸酶和脲酶更重要。使用T变量的精确模型可以生成显示酶沿植物生理学变异性的图谱。这种方法可以加快整个景观中土壤酶活性的确定。