Assessing changes in soil quality between protected and degraded forests using digital soil mapping for semiarid oak forests, Iran

Soil quality, defined as the capacity of a soil to function, is one of the most important characteristics of soil. Methods for modelling and monitoring soil quality are needed for sustainable soil management and evaluating soil degradation. In Iran, resource demands have led to the deforestation of the mixed semiarid oak forests; however, the impacts of these activities on the spatial patterns of soil quality remains unclear. This study calculates a soil quality index (SQI) from an integrated suite of soil biological, physical, and chemical properties and compares the SQI between a paired degraded/deforested area and a protected forested area in Iran using a digital soil mapping (DSM) approach via geostatistical and machine learning techniques. Here, 50 soil samples were acquired for each of the degraded/deforested and protected forested areas, whereby 14 soil attributes were measured. Results showed that the soil organic carbon, total nitrogen, available potassium, cation exchange capacity, pH, clay, saturated water content, and basal respiration in the protected area were significantly higher than the degraded forest area. Furthermore, the soil quality in the protected area was substantially higher than the degraded area. To select the best modelling approach for mapping SQI, machine learning approaches using regression tree (RT), artificial neural networks (ANN), and Random Forest (RF) models were compared against geostatistical approaches using inverse distance weighted interpolation, global polynomial interpolation, radial basis function interpolation, local polynomial interpolation, and kriging (ordinary, simple, and universal). Of the machine learning techniques, the RF model (R² = 0.66) outperformed ANN and RT, while Universal Kriging outperformed all geostatistical approaches (R² = 0.71). By comparing the SQI maps between the degraded/deforested and protected forested areas, the soil quality was substantially higher for the protected areas. This study demonstrates a framework for assessing the impacts of deforestation on the spatial patterns of soils using DSM techniques, which will facilitate effective land use planning and sustainable forest resource management strategies.