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The (R)USLE and its many derivates are the most used models to estimate soil loss by water at regional scale. Within the (R)USLE the C-factor is used to assess the impact of crop and soil management on erosion. The exact, temporal and spatial explicit determination of the C-factor is crucial to create reliable maps on soil loss estimates, monitoring the impact of agricultural practices on soil erosion and evaluating the efficiency of policy instruments.The derivation of accurate high-resolution C-factor data for larger regions remains a challenge, as a variety of spatio-temporal data (crops/crop rotation, tillage management, interannual variation of rainfall erosivity) is needed. Based on recent improvements in calculation methods and the availability of earth observation data, the C-factor can be estimated using agricultural statistics and parcel-based information on crop cultivation. Consequently, we developed two types of spatially explicit and regional C-factor datasets for Germany, mapping the impact of agricultural practice on soil erosion in a more reliable and spatially detailed way: i) harmonized mean C-factors for municipalities for 1999, 2003, 2007, 2010, 2016 and 2020, and ii) parcel-based C-factors for the period 2017-2020 based on crop type maps from satellite data.In this contribution, we will present the implemented methods and will discuss the impact of the new detailed C-factor maps on soil loss estimates in a spatial resolution of 10 x 10 m for German cropland. The results show an increase in the C-factors for municipalities during the last two decades by 17 %. The parcel-based data reveals the C-factor ranges within the municipalities: While the mean C-factor for all parcels across Germany is 0.107 (conventional cropping; 2017-2020), the standard deviation within the municipalities ranges from 0 to 0.141 at a mean standard deviation of 0.045. This results in differences in erosion rates of up to 7 t / (ha • a), highlighting the importance of spatially explicit C-factor estimates.

Soil erosion by water is considered as one of the most serious processes degrading the soil functionality of arable soils around the globe. To assess the risk of water erosion at larger scales, the universal soil loss equation (USLE) and its derivates are commonly used for the implementation of policy instruments.An important factor needed within (R)USLE model exercises is the K-factor, which reflects the natural erodibility of the topsoil. The spatial explicit determination of the K-factor is crucial to create reliable soil loss estimate maps. However, the accurate estimation of the K-factor at the regional level is challenging due to different existing calculation methods and as a variety of spatial data (e.g. soil texture fractions, rock fragments, soil organic matter) is needed. The latter severely limits the choice of available data, which differ in spatial resolution and information content of the required parameters. This leads to a high potential of uncertainty in the regional estimation of K-factors, but also of the soil erosion estimates at regional level.Therefore, the aim of the study presented is to determine the spatial and quantitative accuracy of different soil data sets and calculation methods for estimating the K-factor for Germany. Furthermore, the influence of possible uncertainties on the estimation of soil erosion risk by combining all factors of the (R)USLE should be evaluated. Based on the aim of this study we modelled K-factors for three available harmonized German-wide datasets: I) LUCAS soil dataset of Europe), II) BÜK1000 and III) BÜK200 (soil overview maps for Germany) using the existing equations of A) Wischmeier and Smith (1978) and its extension of B) Auerswald et al. (2014) representing the original K-factor nomograph. For the validation of the three datasets, K-factors based on 2234 arable soil profiles across Germany from the Agricultural Soil Inventory (BZE_LW) with most detailed soil information were calculated.The results reveal significant differences between calculation methods and data sets for the German-wide assessment of K-Factors. The LUCAS soil dataset overestimates average K-Factors of the BZE-LW by ~ +21 % (RMSE = 0.015, R² = 0.30), while BÜK200 underestimates K-Factors by ~ -30 % (RMSE = 0.016; R² = 0.34). In contrast the calculation method has a low impact on the average K-Factor estimation (~ 5 %) while the RMSE is comparable high with 0.012 (R² = 0.57).The results imply that the choice of the calculation method and dataset at regional level is important and that detailed information on soil texture (e.g. very fine sand fraction) are crucial and strongly needed in order to improve the estimation of reliable K-Factors and soil erosion risk on regional-scales.

Agriculture has been identified as one of the main drivers of environmental degradation in the European Union (EU). It can have negative impacts on air, water, soil and biodiversity. The condition of agroecosystems is affected by soil degradation, especially by soil erosion, which reduces agroecosystems' capacity to provide essential ecosystem services. Therefore, it is necessary to explore synergies and trade-offs between pressures, ecosystem condition and services to create relevant information for policy and decision-makers to implement sustainable response actions. As part of the EU environmental policy, the Mapping and Assessment of Ecosystems and their Services (MAES) Working Group developed appropriate concepts to assess and link ecosystem condition and services. This study aims to test the indicators proposed by MAES to assess ecosystem condition and link it with the ecosystem services provision. For this, we applied a suggested operational framework developed in the context of the Biodiversity Strategies 2020 and 2030 for the integrated assessment of agroecosystems and regulating ecosystem service control of erosion rates supply at European scale. We quantified and mapped indicators for ecosystem condition, environmental and anthropogenic pressures and soil erosion control. We explored the relationships between the respective indicators and the capacity of agroecosystems to control soil erosion across the different Environmental Zones (EZ). Our results indicate that, in general, European agroecosystems have a high capacity to control soil erosion with some variations within the EZ. Supply capacity is positively, negatively and not correlated with the various pressure and condition indicators. Management and climate indicators play a significant role in the assessment of this service. These results highlight that conservational management practices are fundamental to reduce soil loss and improve agroecosystem condition. However, the design and implementation of such management practices must consider regional and local landscape characteristics, agricultural practices and the needs and opportunities of stakeholders. © 2021 The Author(s)

The ecosystem service (ES) approach usually addresses soil erosion as the regulating service control of erosion rates or soil retention. In addition to the assessment of this regulating ES, mitigated impacts on soil-related ES by preventing soil erosion can be assessed. This study presents a scenario-based approach for the assessment of the impact of soil erosion on soil-related ES. The assessment approach was tested in agricultural landscapes in Northern Germany, combining mapping and assessment of soil-related ES. In six scenarios, the degradation of soils due to soil erosion was simulated by the calculation of soil profile reductions. The scenarios represent two levels of impact with three time steps (+50, +100, +150 years). In the scenarios for the structural impact, the potential soil erosion rates were extrapolated into the future to generate spatially explicit information on degraded soils. In the scenarios for the mitigated impact, the actual soil erosion rates were extrapolated. Four soil-related ES were assessed for the initial state and the scenarios crop provision, water filtration, water flow regulation and fresh water provision. The comparison of the potential service supply of the four soil-related ES in the scenarios enabled the assessment of the long-term effect of the ES control of erosion rates. The mitigated reduction in the potential service supply for three of the considered ES (crop provision, water filtration, water flow regulation) is large and highlights the importance of sustainable soil management. Contrary to this, the ES fresh water provision benefits of erosion-induced soil profile reductions.

Ecosystems provide multiple services that are necessary to maintain human life and activities. Agroecosystems are very productive suppliers of biomass-related provisioning ecosystem services, e.g. food, fibre and energy. At the same time, they are highly dependent on respective ecosystem condition and regulating ecosystem services such as soil fertility, water supply or soil erosion regulation. Assessments of this interplay of ecosystem conditions and services are very important to understand the relationships in highly managed systems. Therefore, the aim of this study is twofold: First, to test the concept and indicators proposed by the European Union Working Group on Mapping and Assessment of Ecosystems and their Services (MAES) for the assessment of agroecosystem condition at a regional level. Second, to identify the relationships between ecosystem condition and the delivery of ecosystem services. For this purpose, we applied an operational framework for integrated mapping and assessment of ecosystems and their services. We used the proposed indicators to assess the condition of agroecosystems in Northern Germany and the provision of the regulating ecosystem service control of erosion rates. We used existing data that are available from official databases for the calculation of the different indicators. We show maps of environmental pressures, ecosystem condition and ecosystem service indicators for the Federal State of Lower Saxony. Furthermore, we identified areas within the state where pressures are high, conditions are limited, and more sustainable management practices are needed.Despite the limitations of the indicators and data availability, our results show positive, negative and no significant correlations between the different pressures and condition indicators, and the control of erosion rates. Although the idea behind the MAES framework is to show the general condition of an ecosystem, when looking at the relationships between condition and ecosystem services, we identified that not all the indicators - as they are proposed- are suitable to explain to what extent ecosystems are able to provide certain ecosystem services. Further research on other ecosystem services provided by agroecosystems would facilitate the identification of synergies and trade-offs. Moreover, the definition of a reference condition, although complicated for anthropogenically highly modified agroecosystems, would provide a benchmark to compare information on the condition of the ecosystems, leading to better land use policy and management decisions

Rainfall erosivity exhibits a high spatiotemporal variability. Rain gauges are not capable of detecting small-scale erosive rainfall events comprehensively. Nonetheless, many operational instruments for assessing soil erosion risk, such as the erosion atlas used in the state of Hesse in Germany, are still based on spatially interpolated rain gauge data and regression equations derived in the 1980s to estimate rainfall erosivity. Radar-based quantitative precipitation estimates with high spatiotemporal resolution are capable of mapping erosive rainfall comprehensively. In this study, radar climatology data with a spatiotemporal resolution of 1 km2 and 5 min are used alongside rain gauge data to compare erosivity estimation methods used in erosion control practice. The aim is to assess the impacts of methodology, climate change and input data resolution, quality and spatial extent on the R-factor of the Universal Soil Loss Equation (USLE). Our results clearly show that R-factors have increased significantly due to climate change and that current R-factor maps need to be updated by using more recent and spatially distributed rainfall data. Radar climatology data show a high potential to improve rainfall erosivity estimations, but uncertainties regarding data quality and a need for further research on data correction approaches are becoming evident.

The concept of ecosystem services (ES) creates understanding of the value of ecosystems for human well-being. With regard to soils, it provides a framework for assessments of soil contributions and soil management impacts. However, a lack of standardization impedes comparisons between assessment studies and the building of synthesis information. The Common International Classification of Ecosystem Services (CICES) is an important step forward, although its application to soils is not without difficulty. CICES version 5.1 defines 83 ES classes, of which only some are relevant for soils. We compiled two subsets of CICES classes: one set of soil-related ES comprising 29 services defined as directly and quantifiably controlled by soils and their properties, processes and functions, and another set of 40 ES defined as being affected by agricultural soil management. Additionally, we conducted a systematic literature review, searching for published lists of soil-related ES that claim completeness. We identified 11 relevant lists. Of all CICES classes, 12 were included in more than 75% of the lists, whereas another 36 classes were included in 25–75% of them. Regarding the suitability of the CICES classification for addressing ES in the context of soils and their agricultural management, we identified constraints, such as overlaps, gaps, and highly specific or very broad class definitions. Close cooperation between the soil research and ES communities could ensure better consideration of soils in future CICES updates. A shortlist of 25 service classes affected by agricultural soil management facilitates a standardized approach and may function as checklists in impact assessments. Highlights: Standardized definitions are needed to allow meta-analysis of ecosystem service studies and improve assessments. CICES defines 83 detailed classes of ecosystem services, suggested as a “default list”. We identified 29 classes as soil related and 40 classes as affected by agricultural soil management. Both subsets facilitate ecosystem service assessments in soil research and comparability of results.

Soil erosion caused by water has been monitored for 17 years on 465 ha cropland in Lower Saxony (Northern Germany). The 86 monitored fields are representative for three different typical agricultural landscapes with an intermediate to high water erosion risk in Northern Germany. The monitoring scheme included regularly conducted erosion damage mapping and cultivation surveys. The collected data encompasses 1275 field years and 1355 mapped erosion systems, giving evidence on the extent, frequency and rate of soil erosion by water. Mean mapped soil loss for all fields was relatively small at 0.85 t ha−1 a−1. The highest rate for a single field amounted to 7.02 t ha−1 a−1. Variability of soil loss in time and space was high: annual losses (mean of all fields) ranged between 0.04 and 2.81 t ha−1 a−1. The highest annual loss on a single field was 53.07 t ha−1. Every year, at least 24% of the monitored fields were not affected by erosion, only 1.3% of the monitored area eroded at least once a year. Spatial analysis of mapped erosion features shows that the highest soil loss is located in topographically-defined flow paths and in wheel tracks oriented in line of the steepest slope. Cultivated crops and tillage management have a high influence on loss rates ranging from 0.07 for catch crop to 2.78 t ha−1 a−1 for potato. Additionally, the results prove that farmers in the investigation areas were able to significantly reduce soil loss rates of the five most important crops from 0.6 to 0.37 t ha−1 a−1 by using conservation tillage measures. The annual variability in the collected data emphasises the importance of appropriate long-term monitoring programmes to create sound data on the extent, frequency and rate of soil erosion by water at a field to landscape scale.

Control of erosion rates (CER) is a key ecosystem service for soil protection. It is mandatory for sustaining the capacity, especially of agroecosystems, to provide ecosystem services. By applying an established framework to assess soil regulating services, this study compares two approaches to assess CER provision for 466 ha of cropland in Lower Saxony (Central Northern Germany). In a "sealed modelling approach", the structural and the mitigated structural impact were modelled by applying the Universal Soil Loss Equation (USLE). The second approach uses spatially explicit long-term monitoring data on soil loss rates obtained in the investigation area as an alternative to the USLE-based modelled mitigated structural impact. Assuming that the monitoring data have a higher reliability than the modelled data, the comparison of both approaches demonstrated the uncertainties of the USLE-based assessment of CER. The calculated indicators based on a sound monitoring database on soil loss rates showed that, due to limitations of the USLE model, the structural impact in thalwegs has been underestimated. Incorporating models with the ability to estimate soil loss by rilling und gullying can help to overcome this uncertainty. The produced set of complementary large-scale CER maps enables an integrated analyses of CER. In the entire investigation area, the provision of CER regulating ecosystem services was generally high, indicating good management practices. Differences at the field scale and between the different regions can be explained by variations of the structural impact and the management practices.