Biosystems Engineering Volume 258, September 2025, 104255 - A Recent Paper

Soil deformation is a key challenge in sustainable timber harvesting, particularly in environments with low bearing capacity. In mechanised forestry, this issue is especially pronounced in peatlands, where rutting arises from soil displacement and root shearing within the soft, organic substrate. While tree roots are known to reinforce soil, the specific role of stump-root systems in mitigating rut formation remains underexplored. This study examines the influence of stump presence on rut depth using Unmanned Aerial Vehicle (UAV) based digital terrain models (DTMs), manual field measurements, spatial modelling, and machine learning techniques. UAV-derived rut depth estimates were first compared with manual data, revealing slightly lower values in deeper ruts, particularly in curved trails, with mean discrepancies of 3 cm. Statistical analysis confirmed that cumulative stump influence significantly reduced rut depth, with a small to medium effect in straight trails (ɛ = 0.04–0.20) and a moderate to large effect in curved trails (ɛ = 0.02–0.32). Machine learning models achieved high predictive accuracy ( = 0.69–0.85), identifying stump-related variables and soil shear modulus as key predictors of rut formation. These findings emphasise the importance of incorporating stump-root reinforcement into forest planning to optimise machine path selection and minimise soil disturbance. Future research should refine species-specific reinforcement models and explore advanced root mapping techniques, such as ground-penetrating radar (GPR), to strengthen decision-support tools for sustainable forestry.