Avoiding soil compaction is one of the objectives to ensure sustainable agriculture. Subsoil compaction in particular can be irreversible. Frequent passages by (increasingly heavy) agricultural machinery are one trigger for compaction. The aim of this work is to map and analyze the extent of traffic intensity over four years. The analysis is made for complete seasons and individual operations. The traffic intensity is distinguished into areas with more than five wheel passes, more than 5 Mg and 3 Mg wheel load. From 2014 to 2018, 63 work processes on a field were recorded and the wheel load and wheel passes were modeled spatially with FiTraM. Between 82% (winter wheat) and 100% (sugar beet) of the total infield area is trafficked during a season. The sugar beet season has the highest intensities. High intensities of more than five wheel passes and more than 5 Mg wheel load occur mainly during harvests in the headland. At wheel load ≥3 Mg, soil tillage also stresses the headland. In summary, no work process stays below one of the upper thresholds set. Based on the results, the importance of a soil-conserving management becomes obvious in order to secure the soil for agriculture in a sustainable way.

Increasing traffic intensities in agricultural work processes increase the risk of subsoil compaction. The objective of this study is to investigate whether the geometry of the field and size of the machine influence the traffic intensity. This study focuses on analysing the influence of the machinery and field geometry on the traffic intensities in the field based on wheat harvest operations performed with one combined harvester. For this purpose, 177 routes on 59 different fields are simulated with a route planner, and subsequently, the tracks and traffic intensities are modelled. The structure of the field was specified using eight different shape indices. The analyses showed that the configuration of the machines mainly influences the total passed area and the wheel load distribution on the field. Independent of the machine, the geometry of the field has an influence on the area proportions in the headland with wheel passes above 5 and 10. The indices that highly correlate with traffic intensity are the mean interior edge ratio, the interior area ratio and the mean fractal dimension. It means that the larger the field area in proportion to the perimeter and the infield area, the larger the area fraction with more than 5 or 10 passes. Moreover, this area share increases with the complexity of the field geometry, expressed by the mean fractal dimension. Consequently, the size of the field mainly influences the traffic in the headland because of the removal of the crop.

Soil compaction due to intensive field traffic is one of the main threats to agricultural soils. Besides lower biomass productivity, compacted soils have a reduced regulation function which affects the air, water and nutrient cycles. To evaluate and mitigate soil degradation by field traffic, it is important to know where, when and to what extent soil compaction may occur during certain work processes.For this purpose, we modelled and analysed the soil compaction risk for each work process during a 4-year crop rotation (winter wheat, maize, winter wheat, sugar beets) for one field in Lower Saxony, Germany. Based on RTK-GPS tracks recorded by the farm machineries, FiTraM (field traffic model) was used to model the spatial representation and the wheel load for each tire of each work process. Subsequently, these data were used for the soil compaction risk assessment by the SaSCiA-model (Spatially explicit Soil Compaction risk Assessment). In total, 63 work processes were modelled with a spatial resolution of 20 cm.The model results revealed that the soil compaction risk at field scale is highly variable in space and time. The spatial variations in soil compaction risk are mainly determined by the spatial distribution of soil properties like soil texture, carbon content and soil moisture. In combination with changing wheel loads, e.g. during harvest processes, the soil compaction risk ranges from low to extremely high during one work process. The temporal variation is mainly caused by the weather conditions. A prolonged period with low precipitation resulted in no subsoil compaction risk during maize harvest, although maize harvest is often a major cause of soil compaction.This study shows that the soil compaction risk varies in a wide range within a certain field. Efforts to mitigate soil compaction should consider spatio-temporal dynamics at high resolution to achieve a sustainable soil management.

Land degradation (LD) threatens soil health and ecosystems globally, which is why the European Commission formulated the objective of transitioning towards healthy soils by reducing LD by 2030. Water erosion, one of the most severe forms of LD, is influenced by management direction. This is because it affects the roughness created by tillage, seeding ridges, and tramlines, which, in turn, can either exacerbate or mitigate erosion. Therefore, management direction is an important variable in the RUSLE-framework (Revised Universal Soil Loss Equation) used within European Union Soil Observatory (EUSO) to assess and monitor soil health (Panagos et al. 2024). However, spatial data on management direction, especially at policy-relevant scales, is scarce and often not adequately integrated into model-based assessments. This study develops a straightforward and fast approach to derive management direction and assesses its implications for soil erosion risk at the national level of Germany. Using field geometries from Tetteh et al. (2024) for 2021, we assumed that management direction follows the longest field side of the “minimum rotated rectangle” around each field. The derived management direction was validated using 1155 mapped arable fields randomly selected across Germany. In combination with a 10x10 m DEM, we then derived the contouring index (CI, 0-100%) as a measure to represent the relationship between management direction and the contour for each cell. The CI was then used to calculate the support management practice factor (P) based on the RUSLE to estimate the implications for soil erosion risk. Results showed that the management direction was correctly modelled in 79% of observations assuming a 10° tolerance. The CI averaged at 37±26%, indicating high variability both between and within fields. Management direction of 2021 was found to reduce soil erosion risk by 7.6%. The currently low CI for Germany shows high potential for further reducing soil erosion risk by implementing contour parallel management in cropland and enhance model predictions by providing P-factor maps with high spatial resolution. Despite these promising results, challenges persist in accurately predicting management direction in fields with highly irregular shapes or unique environmental conditions. Our approach provides a feasible method to predict and monitor management direction with sufficient accuracy for large scales. The approach can be incorporated into other models requiring management direction of cropland (e.g. tillage erosion) and applied for other data sources (e.g. LPIS geometries). This can improve modelling and monitoring LD on field level for large scales, supporting landowners and policy makers towards improving soil health. References: Panagos, P., Borrelli, P., Jones, A., & Robinson, D. A. (2024). A 1-billion-euro mission: A Soil Deal for Europe. European Journal of Soil Science, 75(1), e13466. Tetteh, G. O., Schwieder, M., Blickensdörfer, L., Gocht, A., & Erasmi, S. (2024). Agricultural land use (vector): National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021).https://doi.org/10.5281/zenodo.10619783 Acknowledgement: P.S., F.M., K.K. and P.B, were funded by the European Union Horizon Europe Project AI4SoilHealth (Grant No. 101086179).

Soil compaction by field traffic is one of the main threats to all agricultural soils. Besides lower biomass productivity, compacted soils have a reduced regulation function which affects the air, water and nutrient cycles. To evaluate and mitigate soil degradation by field traffic, it is important to know where, when and to what extent soil compaction may occur during certain traffic events.This study presents an approach to assess soil compaction risk at the field scale, considering the spatio-temporal changes of soil strengths and the machinery-induced changes in load and stress. Two newly developed models, the field traffic model “FiTraM” and the spatially explicit soil compaction risk assessment model “SaSCiA”, were used to evaluate the individual soil compaction risk for each field traffic activity during the maize cropping season. RTK-GPS data recorded by all farm vehicles served for the spatial calculation of traffic intensity and changing wheel loads at high spatial resolution (< 30 cm). These data were subsequently used for soil compaction risk assessment based on readily available soil and weather data.Our model results indicated that nearly 95% of a field was trafficked throughout the maize-season; harvest traffic at high wheel load contributed to more than the half of the total trafficked area. Furthermore, the analyses showed that soil compaction risk varies greatly within individual fields. Soil moisture and soil texture variation inside the field results in varying soil strength and, therefore, in varying effects of field traffic on soil functions. Thus, one part of a field can be negatively affected by field traffic through an increase in dry bulk density and a decrease in hydraulic conductivity, while the other part is not affected.In addition to the spatio-temporal assessment of field traffic intensity and soil compaction risk, the presented approach enables the calculation of maximum allowable wheel load until no harmful soil degradation occurs. Thus, the approach may support farmers in their decision-making for a more sustainable soil management.