Assessment of the relationships between agroecosystem condition and the ecosystem service soil erosion regulation in Northern Germany

Ecosystems provide multiple services that are necessary to maintain human life. 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 good ecosystem condition and regulating ecosystem services such as soil fertility, water supply or soil erosion regulation. Assessments of this interplay of ecosystem condition and services are needed 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 assessing 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 regulating ecosystem service control of erosion rates. We used existing data from official databases to calculate the different indicators and created 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 unfavourable, 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. The idea behind the MAES framework is to indicate the general condition of an ecosystem. However, we observed that not all proposed indicators can explain to what extent ecosystems can provide specific ecosystem services. Further research on other ecosystem services provided by agroecosystems would help to identify 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.

Mean annual precipitation: Corresponds to the annual sum of monthly total precipitation given in mm for the period between 1988 and 2018.
Drought index: The data of annual drought index (from de Martonne [3]) dMI was calculated with: = ( + 10) ⁄ (1) Where T [in degree Celsius] was obtained from temperature grids and P [in mm] from precipitation grids for the period between 1995 and 2018.
Precipitation 10 mm, 20 mm and 30 mm: Corresponds to the number of days with precipitations equal or higher than 10, 20 and 30 mm respectively, averaged for the period between 1988 and 2018.
Beginning of vegetation period: Corresponds to the consecutive days of the year in which the first spring begins averaged for the period between 1992 and 2018.
Summer soil moisture was obtained from the DWD who used the AMBAV model that calculates the evapotranspiration and the soil-water balance for different crops [4]. This was done based on the soil moisture in 60 cm depth under grass derived from selected stations for the period between 1991 and 2010, for June, July and August.
Soil erosion corresponds to the mean actual soil loss and was calculated with the Universal Soil Loss Equation (USLE), applying the German standard DIN 19708 [5].
The loss rates were modelled as raster GIS layers for Lower Saxony in a resolution of 50 m based on the methodology applied by [6]. The USLE was calculated as: Where K is the soil erodibility factor [t h ha -1 N -1 ] for Germany based on the soil overview maps (Bodenkundliche Übersichtskarten [7]) and the approach of Auerswald  Loss of organic matter: This indicator was obtained from the average eroded soil organic carbon dataset calculated for the EU [10]. This was done by applying the CENTURY model, which simulates the carbon and nitrogen dynamics in cultivated or natural systems, coupled with soil erosion [11]. We calculated the average soil organic carbon loss per municipality based on a 1 km raster data set.

Ecosystem condition indicators
Crop diversity was calculated based on the number of field blocks in the year 2018. These data were obtained from the Land Development and Agricultural Support programme of Lower Saxony (LEA) Portal [12] of the Lower Saxonian Ministry of Food, Agriculture, and Consumer Protection. We calculated this indicator by estimating the number of crops in a regular 1km raster by applying a moving window analysis with a radius of 5 km. By counting the overlaying field, we added them up and obtained the diversity of crops in a diameter of 10 km around the midpoints of each raster cell. The raster data were combined with the shapes of the municipalities to calculate the average number of crops per municipality. The share of fallow land in Utilized Agricultural Area (UAA) was calculated by dividing the number of hectares of fallow land and set aside or decommissioning land over the number of hectares of UAA per municipality. The same approach was used for the calculation of the share of arable land and permanent crops in the UAA. The data for the estimation of these indicators were obtained from the Thünen-Atlas (Collection of agricultural data from Germany) [14,15] for the year 2010.

Density of semi-natural areas
Livestock density was calculated by dividing the number of livestock units over the number of hectares of UAA per municipality, both were obtained from the Thünen-Atlas [14,15] for the year 2010.
Soil Organic Carbon was calculated based on the datasets on humus content in the topsoil and the usage-differentiated soil survey map obtained from the Federal Institute for Geosciences and Natural Resources (BGR for its acronym in German) [16]. We used depths from 0 to 10 cm below the soil surface for grassland, pasture and forestry and depths from 0 to 30 cm under the soil surface for crop lands. To convert the humus content data into soil organic carbon data, we used the following equations: = × 1.72 (3) For mineral soil (van Bemmelen factor) [17] = × 2 (4) For peat Soil erodibility was calculated as the K factor explained before in the USLE equation [6].
Bulk density was obtained from the bulk density calculated for the EU [18]. This physical property was derived from soil texture data sets as described by Ballabio et al [19].

Ecosystem service indicators
The ecosystem service control of erosion rates is a regulating ecosystem service that mitigates the structural impact potential soil loss. In this study, we adapted the conceptual framework for assessing the provision of regulating ecosystem services developed by Guerra et al [20]. Fig S1 is a graphical representation of the adapted framework, where A shows the concept for the assessment of the ecosystem service control of erosion rates and B shows the implementation in this study.  [20].
Soil erosion risk is the mean potential soil loss defined as the amount of soil that is lost when there is no vegetation covering the ground [21]. Potential soil loss is determined by the USLE factors rain erosivity (R), soil erodibility (K) and topography (LS) as shown in the following equation [6] (also described before for the pressure indicator soil erosion): The results from the calculations were clipped to the arable land according to the land use type Non-irrigated arable land (2.1.1) of the CORINE Land Cover 2012.
The actual control of erosion rates denotes the prevented soil erosion calculated as the difference of potential soil loss (soil erosion risk) and actual soil loss (soil erosion -described before).
Another indicator used to calculate the ecosystem service control of erosion rates is the provision capacity. This is defined as the fraction of the potential soil loss that was mitigated by the actual service provision. It ranges from 0 to 1, where 0 represents no mitigation and 1 complete mitigation and was calculated based on the USLE model results for Lower Saxony (see [21] for more detailed explanation of the approach).
The data of these indicators were combined with the shapes of the municipalities to calculate the average value per municipality.