A new model framework for infield spatialization of traffic induced soil compaction

Commonly used decision support tools used for estimating soil compaction are often based on single-point soil characteristics and thus not able to capture traffic and soil spatial variability in the fields. The aim of this study is to present a model framework that combines spatialization of wheel tracks, traffic intensity, soil deformation and its consequences in key soil physical properties. We compared three machines with different wheel loads, tyre dimensions and axle widths and three different route plans with working widths of 3, 6, and 12 m over a mechanical weeding operation occurring in a 5 ha moist field (pF 2). The field consisted of two different soil types, loamy sand and sandy loam with high and low bulk density spots. To model the effects of traffic we used the empirical model for estimating topsoil deformation from Schjønning (2023). We also estimate risk of subsoil compaction from PTFs. From the spatialization of wheel tracks we were able to identify the different traffic intensities and number of wheel passes for each machinery configuration. By combining topsoil deformation with traffic intensities we were able to identify areas where degree of compactness and air-filled porosity was detrimental for crop growth, and gas diffusion and water-filled pore space indicated high risk of greenhouse gas emissions. This simple model framework has shown promising potential for future decision support tools for assessing the impacts of traffic or estimating the risk of soil compaction during agricultural operations.