Exploring the driving forces and digital mapping of soil biological properties in semi-arid regions

Soil biological properties (SBPSs) are crucial for soil health and ecosystem functioning. Mapping these properties can provide insights into soil ecosystems and help develop management and conservation strategies. The objective of this study was to map five important indicators of SBPSs, namely urease (Ur), acid phosphatase (ACP), alkaline phosphatase enzyme (ALP) activity, metabolic quotient (qCO 2 ), and soil microbial biomass to soil organic carbon ratio (MBC: SOC) using machine learning algorithms (MLA) and to identify the most important driving factors of these properties. The study was conducted in Honam sub -basin, Lorestan, Iran, on an area of 14,000 ha. 90 surface soil samples (0 - 30 cm) were collected and analyzed in the laboratory. 60 soil and environmental covariates, including soil, topography, and remote sensing data (RS), were used to represent influencing factors. The most relevant covariates were selected based on correlation and recursive feature elimination (RFE) analysis using two state-of-the-art MLAs, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) trees, were used to predict SBPSs based on the selected covariates. Relative importance analysis and partial dependence plots were used to evaluate the effects of different factors on the prediction of SBPSs. The results showed that 28 soil and environmental covariates were selected as relevant factors. Both RF and XGBoost models performed well and achieved acceptable accuracy (as measured by R 2 value). XGBoost outperformed RF on qCO 2 (R 2 = 0.61), ALP (R 2 = 0.73) and Ur (R 2 = 0.75), while RF performed better on ACP (R 2 = 0.65) and the MBC: SOC (R 2 = 0.81). Relative importance analysis showed that ACP and Ur were most strongly associated with the soil variables at 79 % and 42 %, respectively. ALP and qCO 2 were strongly correlated with topographic attributes at 56 % and 52 %, respectively. MBS: SOC was about 40 % associated with the RS indices. The influence of each SBPS varied depending on the specific types of covariates. These results highlight the potential of targeted strategies for effective management and protection of soil ecosystem leading to improved soil health and ecosystem function. Further research should focus on developing methods that encompass a broader range of indices that influence soil microbial activity in drylands.