A. Relevance and sustainability context
A.1 Policy relevance
This indicator describes the share of structurally rich landscapes in the total agricultural area or agriculture landscape, measured in %. It considers areas in agricultural landscapes that are potentially beneficial for increasing biodiversity and provision of ecosystem services.
The suggested indicator is relevant for biodiversity/nature protection/ CAP evaluation policy. It characterises agricultural landscape composition and structural diversity. When data from several years become available, landscape composition changes over time can be observed.
A.2 Sustainability and / or comparative evaluation options
Landscapes with higher shares of semi-natural habitats and landscape features will likely be more beneficial for ecosystem services and biodiversity than intensively cultivated areas (e.g. crop fields, intensively managed grasslands). Increasing the share of habitats with a tested positive effect on biodiversity and ecosystem functioning in agroecosystems is aimed to ensure high agroecosystem quality. The higher the share, the more potential habitats are available for wild species. However, once optimal provision has been achieved, an increase in the share no longer contributes to an improvement in species diversity.
To interpret this indicator on a landscape level, the definition of thresholds is needed. The target of the EU Biodiversity Strategy for 2030 is to bring back at least 10% of agricultural area under high-diversity landscape features (Vallecillo et al., 2022). This could be used as a benchmark. Low and high thresholds can be defined based on scientific publications. For instance, Martin et al., (2019) assessed <5% as low and >20% as high semi-natural habitat shares in the landscape. Furthermore, when setting target values for this indicator, regional conservation goals and the requirements of the species/species groups to be promoted should be considered (MonViA, 2024).
B. Data availability
B.1 Stage of development
It is a semi-established indicator. The indicator in different variations is reported in the scientific literature and research project reports (e.g., percentage of semi-natural habitats in farmland by Herzog et al., 2012 and Martin et al., 2019; the proportion of natural and semi-natural areas and small structures in the agricultural landscape by Albert et al., 2015).
EU wide initiatives (e.g., Mapping and Assessment of Ecosystems and their Services) consider some part of ecosystems benefiting areas, for example the share of fallow land and high nature value farmland (Maes et al., 2020 and Vallecillo et al., 2022).
On the national level, the share of non-productive agricultural area and the share of landscape elements are reported as indicators in the nationwide monitoring of biological diversity in agricultural landscapes (MonViA): https://www.agrarmonitoring-monvia.de/en/monvia-the-nationwide-monitoring-of-biodiversity-in-agricultural-landscapes/biodiversity-monitoring. There, the share of non-productive areas is calculated as a percentage based on the total area of non-productive land (fallow land, hedgerows, etc., on and adjacent to arable land, grassland, and special-purpose crops) relative to the total agricultural land area of a region. And the area index of landscape elements provides information on the proportion of landscape element area relative to the total agricultural land area. The index of landscape elements considers areas of woodlands, hedges/rows of trees, orchards, heaths, moors, swamps, wasteland, and field margins. Linear features such as hedges or field margins are calculated as an area based on their length and an assumed width value in the indicator.
The results of these different approaches are difficult to compare because there is no joint definition of exact areas that can be considered as ecosystems benefiting areas. For example, fallow land, depending on the existing land management practices and the surrounding landscape, can be beneficial or have negative impacts on ecosystems and biodiversity (e.g. in case of bare or improperly vegetated fallow).
In the SYMOBIO 2PLUS project we define areas that benefit ecosystems based on the land cover classification applied in the Integrated Administration and Control System (IACS), using the Weser-Ems Region as a case study. We calculate and map the share of ecosystem benefiting areas on a landscape level using a hexagon raster. The size of each hexagon is equal to 10 km2.
Apologies if I missed this in all the copy paste, but where are the references? Could the be added to end of the individual profile under further information
B.2 Data sources
In the SYMOBIO 2PLUS Weser-Ems case study two data sets are used for indicator calculation:
- Hedgerow data from the Thünen Institute (Muro et al., 2025) and
- Integrated Administration and Control System (IACS) data covering flowering areas, other landscape elements (wet areas, ponds, etc.), land taken-out of production, set-aside areas and other areas under greening schemes of CAP.
Examples of used IACS land cover classes as ecosystems benefiting areas include catch crops/cover crops, strips at the edge of forest (without production), riparian vegetation, field edges and buffer strips, short rotation coppice areas, fallow land, flowering strips, hedges, trees, field copses, wetlands, ditches, afforestation areas, set-aside for nature conservation and landscape management etc.
B.3 Coverage over time and replicability
The SYMOBIO 2PLUS Weser-Ems case study indicator was calculated for the year 2022 because the hedgerow data was available only for this year.
For comparison, in MonViA the indicator ‘share of non-productive agriculture areas’ is calculated on federal state level on an annual basis and given for years 2010 – 2021. The indicator ‘area index of landscape elements’ is calculated on national level on annual basis and given for years starting from 2021. Both indicators have been reported for the first time in 2024 (pilot monitoring report).
For bioeconomy monitoring the same annual reporting frequency could be adopted.
B.4 Coverage across scales and sectors; compatibility
The case study developed in the SYMOBIO 2PLUS project calculated the indicator values on the agricultural landscape level for the Weser-Ems region.
Similar indicators in MonViA are reported either on a national scale covering all of Germany (area index of landscape elements) or on the federal state level – Hesse, Lower Saxony and North Rhine-Westfalen (share of non-productive agriculture areas). They can be aggregated on the level of administrative units (e.g. federal states, municipalities) or related per hexagons (100 ha = 1 km2) of agricultural landscape. Optionally, both indicators can be calculated per utilised agricultural area (UAA) or per any other land cover class. Therefore, reporting at regional (administrative unit) or detailed landscape level is possible.
C. Considerations for operationalization
C.1 Feasibility: Fit for monitoring
The indictor is considered sufficient. To make the indicator more fit for monitoring, three main challenges shall be solved: First, it is necessary to define the areas and landscape elements that can be considered as ecosystems benefiting areas. There is no joint definition of areas to be considered. Different approaches and definitions are currently being used. For example, non-productive agriculture areas (fallow land, flower strips) as well as semi-natural landscape elements (hedges, hedgerows, field margins, and riparian strips, water elements, stone, rock etc.) are being accounted for in different indicators in ecosystem monitoring.
Second, the link of the indicator to bioeconomy specific activities is not clearly established. For example, it is not clear to what extent bioeconomy specific activities are drivers of a positive or negative landscape composition and structural diversity change.
The third challenge is related to the availability of data. Integrated Administration and Control System (IACS) data are collected on the federal state level and includes different land cover classification. Moreover, even in the same federal state data are not consistent over the years. Also, not all IACS data are easily made available for research and monitoring purposes. Combining IACS data with remote sensing data may help. However, datasets obtained through remote sensing methods are still scarce, developed on a research project basis with a limited spatial and temporal coverage and lack of continuity.
C.2 Institutions
Currently under the nationwide monitoring of biological diversity in agricultural landscapes the calculation of the share of non-productive agriculture area is done by the Thünen-Institut, Institut für Betriebswirtschaft, Institut für Lebensverhältnisse in ländlichen Räumen and Julius Kühn-Institut, Institut für Pflanzenbau und Bodenkunde. The share of landscape elements is calculated by Julius Kühn-Institut (JKI), Bundesforschungsinstitut für Kulturpflanzen, Institut für Strategien und Folgenabschätzung.
The hedgerow dataset that was used in the SYMOBIO 2PLUS project indicator calculations was developed by Thünen-Institute as part of the project “Remote Sensing for Improved Climate reporting” (KlimaFern).
C.3 Future prospects
It is likely that continuity of the monitoring under the nationwide monitoring of biological diversity in agricultural landscapes will be ensured. The monitoring system is intended to be used to report Germany’s progress toward meeting the binding targets under the international agreements on the biodiversity protection.
C.4 Costs, considerations and reproducibility
Cost implications are currently not clear.
C.5 Potential for automation
If the initial automated workflow is established, the effort of generating a new update (annual actualisation) is not too high.
C.6 Presentation options and breakdown / sub-indicator needs
Currently the indicator calculated for the Weser-Ems region is available in a form of a map. Potentially, comparison to a benchmark could be added, if benchmarks or thresholds of low, high and optimal ecosystem benefitting areas are defined.
C.7 Potential for scenario integration