A. Relevance and sustainability context
A.1 Policy relevance
The agricultural land footprint quantifies the total global agricultural land area (cropland and grassland), both within Germany and abroad, associated with German final consumption of bio-based products, including food, feed, fibre and energy.
By accounting for agricultural land use along global supply chains, the indicator complements territorial land-use statistics and provides a consumption-based perspective on the total land requirements associated with the German bioeconomy, including their geographical distribution between Germany and other countries. It can thereby inform policy on the scale and geographical distribution of land requirements associated with German bioeconomy consumption and the extent to which this consumption relies on agricultural land resources outside Germany.
This provides a critical evidence base for ministries such as BMFTR and BMLEH to steer the bioeconomy transition within ecological boundaries. Specifically, it answers how much land is globally appropriated by German consumption and how policy shifts (e.g., dietary changes) impact this demand. It is strongly related to the SDG framework and especially SDG 12 on responsible consumption.
A.2 Sustainability and / or comparative evaluation options
The agricultural land footprint can be evaluated against different comparative benchmarks. The total footprint can be compared with Germany’s domestic agricultural land area to put consumption-based land requirements into perspective. In addition, the cropland component can be compared with global average cropland availability per capita and with proposed “safe and equitable” cropland benchmarks to assess relative and sustainable levels of land use).
These comparisons require compatible benchmark data in terms of scope, units and reference year; in particular, sustainability benchmarks need to be scientifically established and periodically reviewed. In addition to these comparative benchmarks, the spatially allocated footprint can be characterised by soil erosion risk to provide information on the susceptibility of the agricultural land associated with German consumption.
B. Data availability
B.1 Stage of development
The indicator is considered "new". The methodology is robust and well-documented through the SYMOBIO 2.0 research framework and the 2021 Pilot Report. While not yet part of routine official federal statistics, it is a primary component of systemic bioeconomy monitoring.
B.2 Data sources
GLORIA database: Global Multi-Regional Input-Output (MRIO) data used to trace biomass and bio-based products along global supply chains and identify their regions of origin.
LandSHIFT and underlying datasets: A spatially explicit global land-use model used to spatially allocate the agricultural land requirements derived from demands primary given by Gloria. The model utilises further land-cover data (CCI), climate (LPJml) and topography (e.g. elevation, slope, infrastructure) data, and socioeconomic demands of population and forest (Gloria).
Biophysical data: Soil erodibility maps and land-use statistics for qualitative overlays.#
B.3 Coverage over time and replicability
Temporal Scope: Continuous time series data are available from 2000 to 2021. Updates depend on the release of new MRIO (GLORIA) and land-use data. Annual updates are technically feasible, while a five-year update cycle could provide a practical interval for monitoring longer-term trends.
B.4 Coverage across scales and sectors; compatibility
Scales: Capable of reporting at national (Germany), EU, and global scales.
Sectors/products: Differentiates between cropland and grassland. It further allows to differentiate by relevant biomass commodities and product/consumption groups, depending on the level of detail provided by the underlying MRIO (multi-regional input-output) and land-use data.
Compatibility: Compatibility with relevant regional and international reporting frameworks depends on the consistency of definitions, system boundaries and underlying data and should be assessed for the respective monitoring framework.
C. Considerations for operationalization
C.1 Feasibility: Fit for monitoring
Ranking: Robust/Fit. The method is scientifically sound and fit for systemic monitoring and has been successfully applied within systemic bioeconomy monitoring. Though it carries inherent uncertainties related to input data resolution and model assumptions, as well as the complexity of the required data and modelling workflow.
C.2 Institutions
In the SYMOBIO project the Research Group Global and Regional Dynamics (GRID), at the University of Kassel, is responsible for modelling the spatially allocated agricultural land requirements with the LandSHIFT model, and the calculation of the spatially explicit agricultural land footprint. The Institute of Economic Structures Research (GWS) is responsible for the GLORIA multi-regional input-output (MRIO) framework. The models and methods are well-documented, and the underlying results are intended for free public availability.
C.3 Future prospects
Continuation is likely as part of integrated bioeconomy monitoring. The indicator is already a core component of long-term sustainability scenarios through 2050, assessing the impact of wedges like organic farming or dietary shifts.
C.4 Costs, considerations and reproducibility
Information avaiable per request.
C.5 Potential for automation
The current workflow, combining GLORIA-based MRIO analysis with spatial land-use modelling using LandSHIFT, involves several data-processing and modelling steps that could potentially be standardised and partly automated for regular monitoring. This could include recurring data processing, model execution and integration of outputs. However, data updates, model configuration, consistency checks and interpretation of results would continue to require specialised expertise and quality control. Further technical development and dedicated institutional capacities would be required to establish a robust automated monitoring workflow.
C.6 Presentation options and breakdown / sub-indicator needs
Primary presentation formats include time-series graphs and maps (regional and pixel-level spatial allocation) showing the geographical distribution of land requirements associated with German consumption. Relevant breakdowns include cropland and grassland, domestic and foreign land use, countries or regions of origin, and biomass commodity or product groups.
Additional spatial characterisations, such as soil erosion risk, can complement the core indicator by providing information on the environmental conditions of the land associated with German consumption. Related indicators, such as land use change (LUC)-related CO₂ emissions, can further link land requirements to associated environmental impacts, but should be treated as interlinked indicators rather than sub-indicators of the agricultural land footprint.
C.7 Potential for scenario integration
Highly suitable; the indicator is already utilized for 2050 foresight activities (see Section C.3), showing that shifting to recommended diets could reduce the land footprint by 14%. The indicator can be used to assess how alternative developments and policy interventions, such as dietary change or increased organic farming, affect future agricultural land requirements and their geographical distribution.
C.8 Interlinkages and alignment with other monitoring activities
This indicator is part of a systemic cluster (see Section A.1) and has direct connections and potential for alignment with several key national and international monitoring activities:
- Destatis (Environmental-Economic Accounting): Offers direct alignment with the official domestic "Flächenbelegung von Ernährungsgütern" footprint.
- EU Bioeconomy Monitoring System (JRC): Standardizes trade footprint metrics at the European level.
- FAOSTAT: Serves as a vital biophysical reference for model calibration.
- German Sustainable Development Strategy (DNS): Detects international leakage effects from domestic sustainability strategy shifts.
Further Comments:
The interactive Footprint Data Explorer provides access to detailed footprint results, including geographical and commodity-specific breakdowns.