Assessment of two modelling approaches for soil compaction risk based on wheeling experiments
- 1Department of Soil Science, Faculty of Regional and Environmental Sciences, Trier University, Trier, Germany (weimper@uni-trier.de)
- 2Chair of Soil Science and Geomorphology, Department of Geosciences, Eberhard Karls University, Tübingen, Germany
- 3Dienstleistungszentrum Ländlicher Raum Rheinhessen-Nahe-Hunsrück, Bad Kreuznach, Germany
- 4RLP Agroscience GmbH, Neustadt an der Weinstraße, Germany
- 5Department of Physical Geography, Faculty of Regional and Environmental Sciences, Trier University, Trier, Germany
Soil compaction by agricultural vehicles is regarded as a threat to soil functions. An important strategy to mitigate traffic-induced compaction might be avoidance of traffic on soils which are currently highly susceptible to compaction and adaption of machinery to site conditions. A spatial decision support system (sDSS) for farmers might help to reduce compaction risks by providing model-based information on site-specific, current compaction risk. As one part of the project "Smart Soil Information for Farmers", published models for compaction risk were assessed regarding their potential for implementation in an app-based-sDSS. As a first step, these models are evaluated based on wheeling experiments for selected sites and vehicles. Pre-selection of models resulted in two combinations that differ in terms of the required input data and the underlying modelling concept:
- Combination 1 (C1) derives the precompression stress as a measure of soil strength parameter using pedotransfer functions and calculates compaction risk based on semi-analytical solutions for stress transmission (according to Keller et al., 2007).
- Combination 2 (C2) derives the compaction risk according to Lorenz et al. (2016) as a combination of a susceptibility class (based on soil texture and moisture) and a load-input class from machinery parameters.
Evaluation of modelling results is based on wheeling experiments on two test sites (loamy sand vs. clayey loam) and different agricultural vehicles (total mass 10 to 38 t). Compaction by vehicles was assessed by measuring soil physical and mechanical parameters before and after wheeling. Soil physical measurements included dry bulk density, pore size distribution, water and air conductivity. Mechanical parameters included in situ soil stress during passage of vehicles, precompression stress and shear strength.
In all experiments, traffic had clear negative effects on physical properties in the topsoil (increase in bulk density, decrease in air capacity and water/air permeability). In the subsoil, only small effects were found for changes in physical and mechanical properties. This can presumably be explained by a “plough-pan” that increased load-bearing capacity.
Comparing both models, it was found that C1 generally tends to predict higher compaction risks than C2. For the topsoil, C1 was able to predict the observed effects better than C2. For the subsoil, relatively small observed effects were generally better represented by model C2, which predicted lower risks than C1 for the subsoil.
References
- Keller et al.: SoilFlex: A model for prediction of soil stresses and soil compaction due to agricultural field traffic. Soil and Tillage Research 93 (2007), 2/391–411
- Lorenz et al: Anpassung der Lasteinträge landwirtschaftlicher Maschinen an die Verdichtungsempfindlichkeit des Bodens. Landbauforschung (2016), 66/101–144
How to cite: Weimper, J. J., Schneider, R., Koschorke, J., Wald, L., Trapp, M., Casper, M., and Emmerling, C.: Assessment of two modelling approaches for soil compaction risk based on wheeling experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7914, https://doi.org/10.5194/egusphere-egu22-7914, 2022.
