Soil compaction due to heavy forest traffic: measurements and simulations using an analytical soil compaction model

Abstract

Models for predictions of soil compaction following forest traffic represent important decision tools for forest managers in order to choose the best management practices for preserving soil physical quality. In agricultural soil compaction research, analytical models are widely used for this purpose. Our objective was to assess the ability of an analytical model to predict forest soil compaction under forwarder traffic. We used the results from two experimental sites set up in north-eastern France in 2007 and 2008 to compare simulations using the SoilFlex model with observed bulk density following forwarder traffic. The best model-based predictions were found when considering the mean initial soil conditions and an increased rebound parameter in the upper soil layers (0–10 cm) in comparison to the deeper layers (10–50 cm). The need to increase the rebound parameter in the soil surface layer to improve model accuracy was attributed to a large soil organic matter content in the uppermost layers of forest soils. For the site where initial soil mechanical parameters were measured as a function of soil bulk density and water content, the model performance was good, with a root mean square error (RMSE) of 0.06. The model performed poorer (RMSE of 0.11), especially for the surface soil layer, for the second site that was wetter at the time of traffic and where soil mechanical properties were not measured but estimated by means of pedo-transfer functions. SoilFlex was found to yield satisfactory predictions and could help forest managers estimate the risk of compaction and to select the most appropriate machinery for given soil conditions in order to preserve the soil from physical degradation during traffic in forest ecosystems. However, our results emphasise the need for research on soil mechanical properties of forest soils, in particular on the role of soil organic matter and roots on soil compressive properties.