The Influence of soil organic matter on soil erodibility (K-factor) in alpine soils: insights from Aosta Valley (NW, Italy)

Understanding soil erodibility (K-factor of USLE – Universal Soil Loss Equation) drivers in mountain environments is particularly challenging due to the complex interplay among the soil forming factors and human-driven processes (e.g. climate change, land use change). This complexity is particularly evident in the Aosta Valley (NW Italian Alps), where the degree of pedodiversity is very high. Building on existing soil data and knowledge, we compiled a soil database covering the whole region (n=308), and computed topsoil erodibility (K factor) using two empirically-derived formulas (Wischmeier and Smith, 1978 and Williams et al., 1983) both incorporating SOM (soil organic matter) content. For the Wischmeier and Smith formula, a stoniness correction factor was additionally applied to account for the influence of surface coarse fragments on soil erodibility. We focused on the statistical analysis of point-based field data, including soil-related variables (such as humus system soil type, soil horizon, and land use). These inputs were analyzed to assess their relationship with K values and better understand how these variables relate to erosion vulnerability. Our results showed that (i) the RUSLE K equation (Wischmeier and Smith, 1978 ) seems to more effectively capture the complexity of mountain environments; (ii) SOM—particularly its content and type—emerges as a key factor reducing soil erodibility in our study area, with additional effects related to stone cover and maximum SOM thresholds to be used in the formula (iii) K values vary significantly across different soil horizons, humus systems, land uses and soil types. These findings underscore the central role of SOM in enhancing soil stability in alpine environments and demonstrate how specific land covers and pedological features can be associated with the degree of vulnerability to erosion. This knowledge will provide a basis for spatial prediction of erosion-prone areas, offering valuable support for the development of targeted soil monitoring, conservation, and restoration strategies in mountain landscapes.