Monitoring the Environmental Impact of the Bioeconomy: Indicators and Models for Ex-Post and Ex-Ante Evaluation in Agriculture

Abstract

The transformation towards a sustainable bioeconomy presents new challenges and opportunities for the agricultural sector. This paper investigates how the environmental impacts of this transformation can be effectively monitored using existing agricultural models and sustainability indicators. Drawing on comprehensive model and indicator reviews from the SYMOBIO project, we identify a set of suitable models and propose a harmonised set of 21 SMART-aligned indicators for immediate use for national and regional monitoring. The key findings highlight strong capabilities for modelling GHG and nutrient indicators, persistent gaps in biodiversity and water indicators, and the need for improved methods for using biomass as well as for representing scenario-based spatially disaggregated approaches. The findings highlight the importance of having harmonised indicators, quantitative targets, and integrated monitoring systems in place. The results mark an intermediate step in the ongoing development of national bioeconomy monitoring in Germany.

1. Introduction

The bioeconomy is considered an alternative to the fossil fuel economy because it is based on renewable biomass and oriented towards natural cycles, making greater use of residual and waste materials [1,2]. The EU Bioeconomy Strategy [3] frames the bioeconomy as a system that integrates biomass production and its conversion into food, materials, products, and bioenergy. It is central to achieving the Union’s climate and energy objectives for 2030 and climate neutrality by 2050 while addressing biodiversity loss and pollution through sustainable and circular practices. The regional and local level plays a crucial role in this transition by implementing local bioeconomy solutions [4]. This helps to create more sustainable and resilient societies while also fostering innovation in sectors such as agriculture and biotechnology.

Science has already developed key objectives of the bioeconomy in more detail, with criteria and a first set of indicators for each dimension of sustainability as well as their intersections [5] covering all sources of biomass production (agriculture, forestry, aquaculture, harvesting from natural systems, etc.).

Agriculture constitutes a key sector within the bioeconomy, and the global shift towards a bioeconomy introduces significant changes to agricultural systems [6]. Agriculture plays a dual role in this transition as a key provider of biomass with substantial environmental impacts and as an economic sector that depends on ecosystem services provided by natural, minimally managed ecosystems, e.g., for pollination, nutrient cycling, water regulation, and biodiversity [7].

The pressure to provide food, feed, and raw materials for bio-based industries has intensified, often challenging the ecological integrity of farming landscapes [8]. Germany, like many EU countries, has committed to achieving climate neutrality, halting biodiversity loss, and promoting circular economic principles. These objectives are also set out in the German Bioeconomy Strategy [1]. These goals demand sophisticated monitoring tools that contract changes in land use, emissions, and resource efficiency. In this context, it is also necessary to examine to what extent clear goals have already been formulated and whether progress can be quantitatively monitored using indicators. Furthermore, it must be determined whether the achievement of objectives can be measured reliably or not.

This analysis should focus not only on the environmental impacts that have already occurred (ex-post perspective) but should also be capable of recognising potential future impacts in order to inform political countermeasures (ex-ante perspective). Consequently, monitoring the environmental impact of the bioeconomy and agriculture requires quantitative tools that can project and assess trends under different policy and market scenarios [9]. This includes both agricultural production models capable of simulating management and land use changes and indicator systems that quantify ecological pressures and impacts. Effective monitoring frameworks must be spatially explicit, policy-relevant, and compatible with data from existing reporting systems. This latter aspect is important to keep extra effort to a minimum to ensure that, in times of tight government budgets, sufficient monitoring is guaranteed.

This paper was produced as part of the SYMOBIO 2.0 project. SYMOBIO is a research project of the Federal Ministry of Research, Technology, and Space. The project is developing the scientific basis for systemic monitoring and modelling of the German bioeconomy from a sustainability perspective to effectively manage the transition to a circular and more sustainable bioeconomy. The results are intended to pave the way for continuous monitoring. Building on the SYMOBIO findings, this work focuses on evaluating the capacities of current agricultural models and identifying indicators suitable for tracking bioeconomy-related environmental impacts in Germany, with a focus on their applicability in guiding sustainable development. The results mark an intermediate step in developing a national bioeconomy monitoring system in Germany. The indicators identified in this phase will be further refined a consolidated into a concise set of core indicators that comprehensively cover all origin types and all three dimensions of sustainability. The analysis of existing environmental reporting obligations across different environmental objectives, such as climate protection, water quality and quantity, air quality, soil fertility, biodiversity, land use, and sustainable agricultural production, is crucial for designing a monitoring system for the bioeconomy in the field of agriculture. Relevant frameworks include the United Nations Framework Convention on Climate Change and the National Emission Ceilings (NEC) Directive (2016/2284/EU) [10,11], the EU Nitrates Directive (91/676/EEC), the Water Framework Directive (2000/2284/EU) and the Sustainable development goals of the United Nations [12,13,14], and the Convention on Biological Diversity (CBD) [15].

Environmental indicators serve not only as analytical tools but also as essential instruments of communication between science, policy, and society [16]. By translating complex ecological processes into measurable and understandable metrics, indicators enable transparent monitoring of bioeconomy impacts on progress towards sustainability goals. Their communicative function is crucial for informing stakeholders, fostering accountability, and guiding evidence-based decision making. Effective monitoring systems ensure that indicators are timely, relevant, and aligned with policy objectives, thereby enabling adaptive governance and the strategic steering of bioeconomy policies in response to emerging challenges and opportunities [16].

Testing environmental indicators using concrete examples reveals that certain levels of differentiation, such as spatial resolution, land use specificity, or temporal dynamics, are often not available through existing monitoring data alone. For instance, assessing the long-term carbon sequestration potential of agroforestry systems requires detailed temporal data that spans decades, which is rarely captured in standard datasets [17]. Similarly, distinguishing between management practices at the plot level or capturing sub-annual variations in nutrient run-off is typically beyond the scope of available statistics. In such cases, modelling becomes essential to generate differentiated outputs, simulate scenarios, and fill data gaps, thereby enabling more precise and policy-relevant indicator assessment.

This study supports the implementation of Germany’s National Bioeconomy Strategy [1] and the EU Bioeconomy Strategy [3] by identifying relevant indicators for assessing the environmental impact of agricultural production on the bioeconomy in advance (ex-ante). As the implementation of the bioeconomy affects the regional and local levels, this study also examines the spatial level at which these indicators can be recorded.

The development of this framework is guided by key questions: How can the environmental impact of the bioeconomy in the agricultural sector be recorded systematically and based on indicators? Which environmental indicators are most relevant for capturing the impacts of bioeconomy-related agricultural production on ecosystems, natural resources, and climate? Which existing agricultural models are suitable for modelling these impacts under different political and economic scenarios? What gaps currently exist in the modelling and reporting of environmental indicators in the context of the bioeconomy?

2. Materials and Methods

2.1. Framework for Assessing Environmental Indicators in the Agricultural Bioeconomy

Bioeconomy monitoring in Germany has been established as part of a research project since 2016 and is now in the consolidation phase, which began in 2022. So far, a conceptual approach to bioeconomy monitoring in Germany has been developed, setting out the key objectives of the bioeconomy in the three dimensions of sustainability and providing a categorisation of environmental impacts (criteria) along with potential indicators [5]. In addition, we focus on quality criteria for each indicator by using the SMART principles [18]. Building on this framework, the work presented here is focused on the next steps, with an emphasis on ensuring effective monitoring through the application of existing agricultural models and sustainability indicators. This work involves integrating existing monitoring systems, assessing model capabilities, and validating the suitability of indicators. The following steps outline the methodology (see also Figure 1):

• Basis and further development of the monitoring concept: This step built on key issues identified in SYMOBIO-1 as a foundation for the monitoring concept while also addressing additional development needs.
• Evaluation of model capabilities to reflect environmental impacts: Step two assessed whether agricultural models are capable of modelling and predicting the environmental impacts of the bioeconomy using two steps:
  • Identifying suitable agricultural models: Step 2a involved identifying suitable agricultural production models currently in use, which can simulate the environmental impacts of farming activities, including the generation of relevant environmental indicators as model outputs.
  • Identification of environmental indicators represented in agricultural models: This step focused on determining which environmental indicators are already represented within these models, and it also assessed the model’s ability to simulate these indicators under varying bio-economic conditions.
• Identification and compilation of indicators from existing monitoring frameworks: Step 3 entailed compiling a comprehensive indicator set based on existing monitoring systems (e.g., EU-Bioeconomy, FAO-Bioeconomy, SDG indicators, G20 Bioeconomy principles, national sustainability metrics).
• Definition and characterisation of a bioeconomy indicator set: Step 4 included setting up an environmental indicator set relevant to the bioeconomy, with a focus on those that agricultural models can currently provide, those planned for future integration, and those identified as necessary for further development.

During the preparation of this manuscript, we used Microsoft 365 Copilot (Chat|M365 Copilot) for editorial assistance and drafting the text. Microsoft 365 Copilot was employed to revise and improve the clarity of sentence formulation. For indicator selection and analysis we used Microsoft Excel, version 2505 (Microsoft Corp., Redmond, WA, USA).

2.2. Basis and Further Development of the Monitoring Concept

This analysis builds on ongoing work within the SYMOBIO project (Systemic Monitoring and Modelling of the Bioeconomy) and draws on results from the first project phase, SYMOBIO 1. It continues and expands these processes to further develop systematic approaches for monitoring and assessing the environmental impacts of the bioeconomy. The monitoring concept is being developed further by reviewing data availability, identifying gaps, and refining indicators in line with new environmental targets. The process also involves assessing the availability of models that can map these indicators to ensure that observed and modelled data can be integrated for comprehensive monitoring and scenario analysis.

2.2.1. DPSIR Concept

In our analysis, we adopted the DPSIR (Drivers, Pressures, State, Impact, Response) framework as a basic concept, which was also used in the previous SYMOBIO project [5], to represent the environmental impacts of the bioeconomy as a coherent chain of cause and effect and to develop a systematic indicator-based monitoring approach. It was developed by the European Environmental Agency (EEA) [19] as an extension of the OECD’s Pressure–State–Response model [20]. It is widely applied in environmental assessments, policy evaluations, and sustainability reporting and is well-suited to the current analysis.

The DPSIR framework structures the complex socio-ecological interactions in agriculture by linking socio-economic drivers (e.g., market or policy shifts) to the pressure they generate (e.g., inputs, emissions, and land use change), the resulting environmental state (e.g., soil organic carbon, biodiversity, and water quality), the impacts on ecosystems and human well-being, and societal responses (e.g., regulations, incentives, and management practices). The framework enables the selection of policy-relevant indicators and can be used to model integrated scenarios and assess response options. It can also be used to link interventions to economic and environmental outcomes.

The reality is far more complex, but the DPSIR model highlights the need for information for bioeconomy monitoring. This is not limited to (environmental) observation but also offers policymakers possible solutions in the case of missed targets or conflicts due to alternative pathways, thanks to the scenario analysis.

2.2.2. Key Objectives of an Environmentally Sustainable Bioeconomy

This research evaluated various environmental assessment frameworks relevant to agriculture in the bioeconomy. A central reference point is the SDG framework [21], which represents an internationally recognised sustainability concept. The SDGs are widely used in bioeconomy monitoring initiatives.

Our work continues the foundation laid in SYMOBIO 1 [5], in which key objectives were developed. Five key objectives are used to describe environmental sustainability:

• Contribution to climate protection;
• Preservation of and improvement in air quality;
• Preservation of water balance and quality;
• Preservation and strengthening of biodiversity;
• Preservation of soil fertility and function.

In addition to these five key objectives, further key objectives resulted from the overlap between the environmental, economic, and social dimensions [5]. The issues of land degradation neutrality, sustainable consumption, production and infrastructure, and food security were also referred to.

As part of the further work on indicator selection, we also reviewed other monitoring systems to understand the key objectives that they address. This comparison helped to ensure that our approach aligns with established practices and captures relevant policy and sustainability goals.

2.3. Identification of Agricultural Models

Projections play a vital role in showing the potential future impacts of the bioeconomy on agriculture in Germany. These projections are typically generated using quantitative models, which means that the choice of indicators is closely tied to the capabilities of these models [9]. To evaluate this relationship, we conducted an analysis to determine how well current agricultural models can represent the selected indicators. Additionally, we identified ongoing model developments that could support the inclusion of further indicators in the future.

To identify relevant agricultural models applicable to bioeconomy monitoring in Germany, we conducted a literature review. This process involved the analysis of scientific publications and grey literature sources, including technical reports and policy documents. The search was carried out in the English and German languages using Google, Google Scholar, and expert assessment guided by a set of targeted keywords: agricultural production models, agricultural sector model, agroecosystem model, farm management model, integrated farm system model, and bio-economic farm model. Building on this broad inventory, we applied a selection criterion focused on practical applicability within the German context. Specifically, we prioritised models that are already used in Germany for official reporting requirements, such as those related to agricultural policy, environmental monitoring, or sustainability assessments. This approach ensured that the selected models are not only scientifically sound but also institutionally embedded, making them more suitable for integration into bioeconomy monitoring frameworks and aligned with existing data infrastructures and policy processes.

In terms of model selection, we prioritised models applicable at a national scale in Germany and focused on agricultural production rather than fine-scale biophysical processes, which require field-level detail and high temporal resolution.

2.4. Identification of Indicators

2.4.1. Evaluation of Existing Monitoring Systems

To identify indicators suitable for monitoring the environmental impacts of the bioeconomy in agriculture, a systematic review of existing indicator systems related to the bioeconomy in Germany was conducted. This included an in-depth examination of the EU bioeconomy monitoring system [22], the FAO Bioeconomy monitoring indicators [23], and the UN Sustainable Development Goal SDG indicators, as implemented in Germany [24]. The assessment focused on the most important environmental sustainability goals identified as part of the SYMOBIO1 project [5]. At the same time, it examined whether the monitoring systems contained any other key objectives relevant to measuring environmental sustainability.

2.4.2. Method and Criteria for Selection

In total, over 400 indicators were reviewed and catalogued. To ensure methodological clarity and avoid redundancy, indicators with overlapping definitions or measurement scopes were systematically consolidated. This step involved categorising indicators by thematic relevance (e.g., soil, water, emissions) and eliminating duplicates to produce a streamlined and coherent indicator set suitable for further validation. Each indicator was assessed for its thematic relevance and its potential applicability within agricultural models.

Reporting an excessive number of indicators does not constitute an effective monitoring system, as this can lead to complexity and redundancy, resulting in reduced interpretability. To enable meaningful assessment and decision making, the most relevant indicators must be prioritised and identified to capture the core aspects of environmental sustainability. For indicator selection, we prioritised those that meet the following criteria:

• Indicators within existing monitoring systems;
• Primary indicators;
• Indicators used in the models;
• Indicators that are already used for existing reporting requirements;
• New indicators for emerging environmental targets.

Complementing these criteria, we used the SMART concept (specific, measurable, achievable, relevant, and time-bound) to help us select indicators [18]. This approach was used to evaluate the chosen indicators based on five criteria: their scope (clearly defined), measurability (supported by available or collectable data), achievability (feasible within existing data and resource constraints), relevance (aligned with bioeconomy and sustainability goals), and time boundedness (ability to reflect changes over defined periods). We applied the SMART criteria to assess the quality of each indicator and verify whether sufficient information was available. If the “specific” criteria were not fully met, this did not automatically lead to exclusion. Indicators that were relevant but lacked specificity were adapted to ensure none were lost. Some indicators required adjustments because they represented similar concepts or used different units. The reasons for these adaptations are documented in the Supplementary Materials.

In addition to the indicator selection criteria described above, we applied the DPSIR framework [19] to structure the selection process. This approach provided a systematic way to categorise indicators according to their role in the cause–effect chain, ensuring comprehensive coverage of environmental dynamics and supporting the prioritisation of indicators relevant for monitoring and modelling purposes.

2.4.3. Development of an Operational Indicator List

A targeted set of environmental indicators relevant to the bioeconomy was defined, focusing on those that agricultural models can currently provide, those that are planned for future integration, and those that require further development. Each indicator was characterised in terms of its spatial and temporal resolution, data availability, and modelling requirements. With particular attention to existing data gaps, the assessment also considered the feasibility of generating time series data retrospectively up to a defined reference year, ensuring the indicators support both historical analysis and forward-looking policy evaluation. In addition, we identified whether a quantitative target was available for each indicator. The detailed indicator selection process and adaptation steps are provided in the Supplementary Materials.

2.5. Expert Workshops

To validate and refine the preliminary indicator list, structured expert workshops were held. The workshops brought together stakeholders from different institutions and different backgrounds, including modellers, biologists, and policy advisors. The workshops were used to assess the relevance, feasibility, and policy alignment of each indicator. They also served to identify any gaps in the list and to gather expert consensus on the methodological soundness of the proposed monitoring approach.

The first workshop was designed to discuss the conceptual framework and to discuss the final set of indicators, including the identification of gaps. It brought together experts from across the bioeconomy research and policy landscape, including members of the SYMOBIO, MoBi II, and MonBio project teams and additional researchers from several German scientific institutions (TI, DBFZ, ifeu, Ecologic, and the Federal Environment Agency). The second workshop was used to address implementation issues and assess whether the monitoring system was ready. Model experts from TI, ZALF, University of Gießen, ifeu, and the Federal Environment Agency attended and evaluated which indicators and methodologies are already integrated in existing models, which are under development, and which are currently lacking. The workshops were guided by a set of key questions designed to ensure the robustness and applicability of the selected indicators. The suitability of the indicators for reflecting environmental aspects was assessed, as was the completeness of the set. Discussions also addressed the most appropriate units in which to present each indicator, as well as the desired level of regional resolution. Furthermore, the workshops examined whether the indicators had already been integrated into existing models and explored the feasibility of extending these models to incorporate additional indicators where necessary.

3. Results

3.1. Identified Key Objectives and Indicators

By analysing major monitoring initiatives, such as the EU Bioeconomy monitoring system [22], the FAO’s global indicators set for monitoring and evaluating the sustainability of the bioeconomy [23], and Germany’s SDG-based indicator system [24], we identified over 400 sustainability indicators, although some are repeated across systems. These indicators form a broad target catalogue spanning social, economic, and environmental dimensions. In the further analysis, we focus on environmental sustainability. The analysis we carried out confirmed the key objectives for the indicators, which we divided into seven indicator groups. These include climate, water quality, water quantity, air, soil fertility, biodiversity, and land use. In addition, we added sustainable production as an eighth indicator group.

Table 1. Agriculture-related environmental indicators in the monitoring systems.

Indicator Groups for the Key Objective	Legal Framework or Strategy for Target Setting	Sum of Indicators		Recommended for Monitoring System
		With Overlap	Without Overlap
Climate	Paris Agreement	11	9	3
Air	Göteburg Protocol	12	10	2
Water Quality	Water Framework Directive Nitrate Directive	22	11	2 + 1 new
Water Quantity	SDG Targets	7	5	1
Biodiversity	Kunming–Montreal Global Biodiversity Framework	24	19	2 + 2 new
Soil Fertility	Future developments: Soil Monitoring Law, related to Paris Agreement	7	6	2
Land Use	No concrete targets, related to Paris Agreements due to emissions from land use change	5	4	2
Sustainable Production	SDG Targets	14	10	2 + 2 new

shows the indicator groups for the key objectives that we identified, along with the number of indicators associated with each group across the various monitoring systems. Most of the identified indicators fall under the pressure category of the DPSIR framework, while a smaller subset corresponds to the state category.

Most of the identified indicator groups are already integrated into existing reporting obligations at various governance levels, ensuring alignment with current monitoring frameworks. Indicators within the group of climate and soil fertility are aligned with reporting obligations under the UNFCCC [25], while air quality indicators are reported through the National Emission Ceilings (NEC) Directive (2016/2284/EU). Water quality indicators fall under the scope of the EU Water Framework Directive (2000/60/EC) and the EU Nitrate Directive (91/676/EEC). Biodiversity indicators refer to the Convention on Biological Diversity (CBD) [15], and indicators related to water quantity and sustainable agricultural production contribute to monitoring progress towards the Sustainable Development Goals. Table 1 shows the number of indicators identified in existing monitoring systems, as well as the recommended number after duplicates were removed and the selection criteria for inclusion in the monitoring framework were applied (see also the Supplementary Materials for the selection process).

Our study reveals significant duplication across monitoring systems, with many indicators representing the same concept under different names. For most indicator groups, more than half of the indicators could be removed. Applying our selection criteria would further reduce the set, ensuring more clarity and robustness.

The G20’s 10 High-Level Principles for the Bioeconomy, introduced in 2024 [26], also include the restoration and regeneration of degraded areas and ecosystems. In Germany, the rewetting of peatlands is a particularly relevant topic in this context. Nevertheless, developments in this area would be adequately captured by the criteria on climate protection, soil carbon, and biodiversity [27].

3.2. Suitable Agricultural Models

In our analysis, we draw on findings from literature reviews, expert workshops, and stakeholder interviews to identify the key models employed for agricultural reporting in Germany. We distinguish four main types of models: market, production, accounting and process-based biophysical. Each type of model contributes unique perspectives and data structures.

• Agricultural market models simulate supply, demand, and price formation across agricultural sectors, often at national or global scales. They are primarily used for policy impact assessments and scenario analysis, focusing on economic interactions and trade dynamics. (MAGNET, Aglink-Cosimo, GLOBIOM).
• Agricultural production models include representations of production, markets, and often economic decisions. These models translate optimised farm management into input and output flows, supplies, production, and emissions on the farm or regional level. Most of them incorporate environmental modules (CAPRI, RAUMIS, FARMIS).
• Emission and accounting models quantify GHG emissions and pollutant emissions based on activity data and emission factors, providing inventories and scenario analysis without simulating production decisions. These models can be linked to agricultural production models using outputs such as livestock numbers, crop areas, and management data as inputs for emission calculations (PY-GAS-EM).
• Process-based/biophysical models simulate soil, crop, water, and environmental processes. They are often linked to agricultural production models using management and land use data as inputs (AGRUM Model Network for water, RUSLE model for soil erosion, ROTH-C for soil carbon, SYNOPS for risk of synthetic plant protection).

CAPRI, FARMIS, and RAUMIS are core models used to analyse agricultural production and regional adjustment reactions within the EU and Germany. CAPRI enables analysis of agricultural supply at the regional (NUTS2) level in the EU. FARMIS takes a bottom-up approach at the farm or farm group level to extrapolate results to the sector level. RAUMIS focuses on the regional adaptation process in German agriculture. To assess environmental impacts, particularly emissions from agriculture, the Py-Gas-EM model is integrated with the outputs of CAPRI, FARMIS, and RAUMIS. It uses the projected production volumes and structural data from these models to estimate the development of selected pollutant emissions. The agricultural production models are coupled with market-oriented models, such as MAGNET, which simulates global and regional economic developments and policies, and AGMEMOD, which maps key agricultural markets across EU Member States, capturing interactions between the agricultural and food sectors.

To examine which indicators are currently integrated into the models, this analysis concentrates on those explicitly used in Germany and focuses on agricultural production models and emission accounting models. Although additional models exist at the European level that also incorporate data for Germany, they are not considered further in this context. In addition to agricultural production and emission accounting models, process-based biophysical models are used to simulate state indicators such as nitrate concentrations in groundwater. These models often operate within model networks using input from agricultural models to assess impacts. For example, the AGRUM network combines four models from three institutions based on outputs from RAUMIS [28]. Similarly, the RUSLE model uses agricultural data from CAPRI to estimate soil erosion at the NUTS2 level across Europe [29].

The following

Table 2. List of identified models, agricultural models, and accounting models.

Model Name	Model Type	Spatial Resolution	Institution	Reference to Model
CAPRI Common Agricultural Policy Regional Impact Analysis	Agricultural production model	NUTS2	Co-ownership (EU and third parties)	Model CAPRI—Common Agricultural Policy Regional Impact Analysis|Modelling Inventory and Knowledge Management System of the European Commission (MIDAS) [30]
FARMIS Farmgruppenmodell für die Agrarsektor-modellierung	Agricultural production model	Farm Level	Thünen- Institut	Thuenen: FARMIS [31]
RAUMIS Regionalisiertes Agrar- und Umweltinformationssystem	Agricultural production model	NUTS3	Thünen- Institut	Thuenen: RAUMIS [32]
Py-GAS-EM German Agricultural Emission Model	Emission accounting model	NUTS0, NUTS1, NUTS3	Thünen- Institut	Calculations of gaseous and particulate emissions from German agriculture 1990–2022: Input data and emission results [33]

presents selected agricultural and accounting models used in Germany to meet reporting requirements at high spatial resolution (NUTS2, NUTS 3) and the farm level.

Several scenario studies exist in which at least some models from the modelling family are applied. For Germany, the primary scenario is the Agri-Economic-Baseline, published every two years, which projects the future development of the agricultural sector over the next 10 years using the entire modelling family [34]. Another key scenario is the projection report by the German government [35], prepared every two years as part of an EU reporting obligation. This report has gained additional relevance as it now serves to demonstrate compliance with the Federal Climate Change Act. In this context, the agricultural sector is modelled up to 2050 based on results from the CAPRI and GAS-EM models.

Another important scenario is the impact assessment for the EU 2040 targets, which also relies on the CAPRI model [36]. Beyond this, there are additional relevant scenarios that apply one or more of the aforementioned models [37,38,39], but these are not modelled for the purpose of fulfilling any reporting requirements or on a regular basis.

Analysis of agricultural models, supported by workshop and interview discussions, confirmed that these models can generate the necessary indicators to assess the environmental impacts of agricultural production. Nevertheless, no scenario study currently publishes results for all relevant indicators. Most studies concentrate on GHG emissions, reflecting the primary focus in recent years. Moreover, indicator reporting varies over time. For example, the 2022 edition of the Agri-Economic-Baseline included a broader set of indicators (e.g., NH₃ emissions, GHG emissions, nitrogen balance), whereas the following edition reported only a smaller subset (e.g., nitrogen balance) [34,40].

3.3. Selected Indicators and Application of Practical Example

3.3.1. Climate

Agriculture is both a source and a sink of GHGs. Climate protection indicators are crucial for monitoring agricultural emissions as part of national greenhouse gas inventories under the UNFCCC and EU regulations and for evaluating progress towards national GHG reduction targets. Monitoring emissions helps assess the sector’s contribution to climate change and the effectiveness of mitigation strategies. In the monitoring systems reviewed, a total of 11 indicators (see Table 1) related to climate protection were identified. After prioritising primary indicators and eliminating duplicates, three key indicators remain as particularly relevant: (1) greenhouse gas GHG emissions from agricultural production, differentiated by gases such as CH₄, N₂O, and CO₂, (2) GHG emissions from land use, (e.g., deforestation or peatland drainage), and (3) carbon sinks from land use, such as carbon sequestration in soils and vegetation. Under the Federal Climate Change Act [41], annual emission budgets are defined. Agricultural emissions are targeted to fall to 56 million tonnes CO₂e by 2030. For the land use sector, the target specifies average net sequestration levels but does not distinguish between carbon sequestration and removals. This creates ambiguity when assessing progress. For peatlands, the National Peatland Strategy [42] includes a reduction target to reduce emissions from drained peatlands by 5 million tonnes of CO₂ by 2030.

For all three indicators, EU and national frameworks impose strict reporting obligations on greenhouse gas emissions from agriculture and land use. The EU Governance Regulation (2018/1999) [43] requires annual GHG inventories, biennial progress reports, and projections of future emissions. These are then consolidated in national energy and climate plans (NECPs). At the national level, the Federal Climate Change Act strengthens these requirements by setting legally binding targets for reducing emissions and requiring regular monitoring of performance. Historical data for these climate-related indicators is widely available, largely due to existing reporting obligations under the UNFCCC (see also the national inventory report [44]). Additionally, greenhouse gas emissions are regularly included in modelling efforts as their reporting is mandated through instruments such as the EU’s GHG projection reports [35], which all Member States are required to submit. Climate indicators are typically expressed in kilotons of CO₂ equivalent (kt CO₂e). Individual greenhouse gases are converted into CO₂ equivalents using their respective global warming potential (GWP) values. Spatial resolution varies from the farm level to regional scales (e.g., NUTS2 or NUTS3) and the national level. The reporting period for climate protection indicators is typically annual.

Most models include GHG emissions, as this is an important issue when considering the complex reporting requirements. Models consider different levels of detail by applying the methodology. Models run within the EU (e.g., CAPRI) context do not use country-specific emission factors in the main analysis, while models run explicitly in Germany and used to fulfil Germany’s reporting requirements apply detailed country-specific emission factors (PY-Gas-EM). However, these two models can be coupled to enable an integrated analysis.

3.3.2. Water Quality

Agriculture significantly influences water quality through nutrient run-off and leaching. Water quality indicators are essential for assessing the environmental impact of agricultural practices. There are currently 22 indicators related to water quality mentioned across various agricultural and environmental monitoring systems. After accounting for duplications, only eight distinct indicators remain. Most of these indicators are based on quantitative values, such as the concentration of phosphorus in rivers (e.g., mg PO₄/L), and rely on direct measurements or complex modelling approaches. State indicators, such as the concentration of nitrates in groundwater and the levels of phosphorus in surface waters, are subject to EU targets under the Nitrates Directive (91/676/EEC) [13] and the Water Framework Directive (2000/60/EEC) [12], respectively. However, when focusing on primary indicators and evaluating them against the SMART criteria, only two indicators meet the requirements. These include nitrogen field balance and phosphorus balances. These indicators reflect the surplus or deficit of nutrients in agricultural systems and are key to evaluating risks of water pollution and eutrophication.

Indicators for nitrogen field balance and phosphorus balance form part of the set of agri-environmental indicators defined under European statistics. This is based on the European Commission’s Communication COM (2006) 508 final regarding the development of agri-environmental indicators [45]. Although there are currently non-binding national targets for these indicators in Germany, the EU Farm-to-Fork Strategy [46] introduces an overarching goal of reducing nutrient losses, including those from fertilisers, by at least 50% by 2030. This significantly increases the importance of these indicators for monitoring progress towards sustainable agricultural practices and ensuring that national reporting aligns with EU policy objectives. Historical data on nitrogen field balances is widely available due to long-standing reporting obligations at the national and EU levels [47,48]. This ensures a consistent time series for monitoring and policy evaluation. In contrast, official national statistical reporting of phosphorus balances is lacking in Germany. Nevertheless, the Eurostat database includes phosphorus balance data as part of the agri-environmental indicator set [48]. Regionalised phosphorus area balances are calculated in research projects and by the Federal Environment Agency [49]. However, they are neither part of the official agricultural statistics nor are they reported on a consistent basis. Typically, these indicators are measured in kilogrammes per hectare with spatial resolution ranging from farm-level assessments to regional (NUTS2 or NUTS3) if sufficient data is available. Currently, regular data is only available for NUTS0 and NUTS1 [50]. Reporting is generally conducted on an annual basis. It is crucial to define the scope of nitrogen balances, as field balances reflect nitrogen losses to soil and water, whereas total nitrogen balances also account for emissions to the air and serve as indicators of atmospheric pollution.

Nitrogen and phosphorus balances are a typical output parameter of most agricultural production models coupled to an environmental module. These are also included in CAPRI [51] and RAUMIS [52]. In addition to agricultural models such as CAPRI and RAUMIS, the AGRUM-DE modelling network is used in Germany and covers a broader range of water-related indicators, such as nitrogen leaching and groundwater contamination [53]. However, it should be noted that these indicators reflect influences beyond agriculture, including urban development, wastewater management, and atmospheric deposition.

Notable gaps are evident in water-related indicators in agricultural monitoring systems, particularly regarding the impact of pesticides on water quality. Of the indicators reviewed, only one herbicide concentration, as a proxy for toxicity, addresses this issue. Although National Statistics on the sale of crop protection products are available, there is a lack of systematically reported regional data and no comprehensive information on the application of pesticides. In response to these shortcomings, the EU is developing more refined indicators to better capture pesticide toxicity [54]. These indicators align with the Farm-to-Fork Strategy objective to reduce the use and risk of chemical pesticides by 50% by 2030 [46]. In parallel, the CAPRI model development team is working to improve the representation of pesticide indicators within the model framework [55].

3.3.3. Water Quantity

Agriculture is a major user of freshwater resources, making water quantity indicators essential for evaluating the sector’s impact on water availability and use. A total of seven indicators related to water quantity were identified in the reviewed monitoring systems (see Table 1). The most applied indicator is the irrigated agricultural area, which provides a general measure of water demand in farming systems. Other modelled indicators, such as actual water withdrawal volumes, irrigation efficiency, or regional water stress levels, are often not systematically available or may currently be too complex to integrate into standard monitoring frameworks. These indicators require high-resolution data and sophisticated modelling approaches, which limit their widespread application. After prioritising the primary indicators and eliminating duplicates, only one key indicator emerged as particularly relevant. This indicator on irrigated area consists of two parts: irrigable area, which is the area equipped for irrigation, while the irrigated area measures the actual amount of land irrigated.

The indicator on irrigated area has been part of Eurostat reporting requirements, with the latest data from 2016. Annual national data is available on an annual basis, as the indicator is gaining relevance as part of the German Strategy for Adaptation to Climate Change under agricultural irrigation [56]. The indicator consists of two components: irrigated area, measured in hectares, and water volume, measured in cubic metres. The spatial resolution ranges from the farm level to the national scale, depending on the availability of data. Currently, data is only available at the NUTS1 level. The CAPRI water module incorporates water usage into agricultural policy analysis by simulating the consumption of water for irrigation and livestock, treating water as a production factor, and facilitating scenario analysis [57]. The modules support scenario analysis related to water scarcity and policy measures like water pricing. It uses indicators such as irrigation, water use, etc. In addition, there is a model network coupling RAUMIS with external hydrological models like GROWA and mGROWA, which provide detailed assessments of water availability, groundwater recharge and irrigation needs [53].

3.3.4. Air

Agriculture is a significant contributor to air pollution, particularly through the release of reactive nitrogen compounds. Air quality indicators are essential for assessing the sector’s impact on atmospheric emissions. A total of 12 indicators related to air were identified in the reviewed monitoring systems (see Table 1). After prioritising the primary indicators and removing duplicates, it emerged that two key indicators were particularly relevant, with ammonia (NH₃) emissions serving as the primary indicator. These emissions result mainly from livestock housing, manure management, and fertiliser application. In addition, the total nitrogen balance is a relevant supporting indicator as it reflects the potential for volatilisation and subsequent air pollution. Under the EU National Emission Ceilings (NEC) Directive (2016/2284/EU) [10], Germany is committed to reducing ammonia emissions, with targets set at a 5% reduction by 2020 and a 29% reduction by 2030 compared to 2005 levels, equivalent to a maximum of 444 kt NH₃ by 2030. Around 95% of the ammonia emissions in the country are accounted for by agriculture, meaning it plays a central role [58,59]. In parallel, the total nitrogen balance is recognised as a key sustainability indicator [60]. As part of Germany’s sustainable development strategy [61], the national target is to reduce the average nitrogen surplus to 70 kg/N/ha by 2030.

The reporting of ammonia emissions is mandated under the NEC directive (2016/2284), which requires EU Member States to monitor and report emissions of clear pollutants including NH₃ [58,59]. Historical data on ammonia emissions is widely available through national inventories and EU databases [62]. The total nitrogen balance is a core indicator within Germany’s national sustainability strategy, and historical data is available through official agricultural statistics and monitoring of SDG indicators [47,60]. Ammonia emissions are typically measured in kt NH₃/a, while nitrogen balances can also be transferred into kg N/ha/a, with a spatial resolution ranging from the farm level to regional (e.g., NUTS2 or NUTS3) if sufficient data is available. Currently, regular data is only available at the NUTS0 level.

The PY-GAS-EM model is used for official reporting requirements to estimate NH₃ emissions in Germany, as it incorporates the most up-to-date emission factors and agricultural production data [33]. NH₃ emissions are also an output parameter of the CAPRI model. However, for official reporting purposes, the PY-GAS-EM model uses agricultural production data from the CAPRI model as input since the PY-GAS-EM model contains the most current national emission factors [63]. Although integrating activity data from CAPRI to PY-GAS-EM does not generate additional indicators, PY-GAS-EM does apply the most up-to-date emission factors, which are aligned with national inventory data. While CAPRI relies on Tier 1 and Tier 2 emission factors, PY-GAS-EM uses Tier 3 factors where available.

Although the total nitrogen balance is an official indicator under the SDG framework, it is not currently a standard output from CAPRI and RAUMIS. However, it is possible to calculate a total nitrogen balance based on the available output data from these models.

3.3.5. Soil Fertility

Soil fertility is a key component of sustainable agriculture, with soil carbon indicators playing a central role in assessing long-term productivity and environmental health. A total of seven soil fertility-related indicators were identified in the examined monitoring systems (see Table 1). After prioritising the main indicators and removing duplicates, two particularly relevant key indicators emerged. The most widely used indicator is (1) soil organic carbon (SOC) content, which serves as a proxy for the soil’s ability to retain nutrients, support microbial activity, and sequester carbon. (2) Soil erosion is another important indicator, as it signals the loss of fertile topsoil and the degradation of land resources.

Historical data on SOC is available through national soil monitoring programmes and long-term field trials, although coverage and consistency may vary regionally. SOC is typically measured in grammes per kilogramme (g/kg) or tonnes per hectare (t/ha), while erosion is often quantified in tonnes per hectare per year (t/ha/a). Spatial resolution ranges from plot-level measurements to regional and national assessments, and reporting is generally conducted at multi-year intervals due to the slow-changing nature of soil properties. Soil carbon monitoring in Germany currently relies on measurements from the National Soil Condition Survey of Agricultural Land, which is conducted every 10 years [64]. Since 2024, emissions from soil carbon losses have also been included in the national greenhouse gas GHG inventory [44], with the ROTHC model being used to estimate changes in the carbon stock of mineral soils under cropland and grassland [65]. The ROTHC model is already applied in a range of case studies and scenario analyses to estimate soil organic carbon dynamics under different land use systems, management practices, and climate conditions, providing a basis for greenhouse gas inventories, policy assessments, and long-term sustainability evaluations [66,67]. Although agricultural production models do not directly output soil carbon content or apply alternative methods (e.g., accounting for catch crops), their output data can be used as input data for soil carbon models. Tracking SOC is becoming increasingly important due to new regulations: the proposed soil monitoring law [68] identifies SOC concentration as a key indicator, and the nature restoration regulation (2024/1991) [69] may require an increase in SOC reserves in arable mineral soils from croplands and the EU’s carbon. The removal certification framework [70] will establish standards for SOC-based carbon removals to support carbon farming and ensure robust monitoring, reporting, and verification (MRV).

Soil erosion is an EU agri-environmental indicator established under COM (2006) 508 [45], but there is no national reporting obligation. Instead, erosion rates are estimated for Europe using the RUSLE2015 model at the NUTS2 level [71]. These results are based solely on modelling, and validation is difficult due to limited measurement data. Agricultural production models only sometimes include soil erosion as part of their environmental model, but in general, their outputs (e.g., land use and crop dynamics) can feed into biophysical models like RUSLE for projections, including future soil loss scenarios [29]. The issue is gaining importance under new regulations: the proposed Soil Monitoring Law [72] lists soil erosion rate (t/ha/a) as a key indicator with a healthy soil target of less than or equal to two tonnes per hectare and year.

3.3.6. Biodiversity

Biodiversity is often severely reduced in intensively managed agricultural landscapes due to habitat simplification and high input use, and, at the same time, biodiversity plays a vital role in maintaining ecosystem services and agricultural resilience. This is the result of a broad meta-study (fact check) carried out by Wirth et al. [73]. Biodiversity indicators can describe the quality and diversity of habitats and the quality of biodiversity itself, namely, species diversity (and abundance) and genetic diversity [74]. The use of state indicators for biodiversity is associated with methodological problems since direct impacts on species (biocoenosis and abundances) are, thus far, difficult to capture comprehensively due to the complexity of the systems, as they become apparent with a time delay and are overshadowed by annual and weather influences. Surveys are conducted only at selected sites, and modelling of biogenic systems is still in its early stages (and was also part of the SYMBIO project). Describing biodiversity indicators, therefore, necessarily relies on proxies and composite indicators. At this point in time, habitat indicators can better help to assess both the ecological quality of landscapes and the pressure exerted by farming practices. Our analysis of monitoring systems identified a total of 24 indicators related to biodiversity with overlaps (see Table 1). After applying the selection criteria for indicators used in existing models and already used for existing reporting requirements, two key indicators emerged as particularly relevant.

Firstly, the share of high nature value (HNV) continuous species sampling on HNV monitoring plots (4a cycle) reflects areas with rich biodiversity (presence of indicator species) linked to low-intensity agriculture and structural diversity. The HNV indicator was developed for impact assessments of agri-environmental measures in the framework of the Common Agricultural Policy (GAP) and is now also used in other monitoring frameworks, e.g., the National Strategy on Biological Diversity [75]. Within this framework, the aim is to increase the proportion to 20% of the total utilised agricultural area. Since 2009, the HNV indicator has been collected by the federal states for special sample sites. Nationwide data is compiled by the National Monitoring Centre at the Federal Agency for Nature Conservation (BFN). Secondly, the share of agricultural land with structurally rich landscape elements, such as hedgerows, field margins, and small water bodies, is another indicator and can cover the share of wooded landscape elements and the density index of fringe structures. A target has not yet been formulated for this indicator, but it is particularly relevant for tracking progress under the EU Nature Restoration Regulation (2024/1991), which provides the indicator as one of three reporting indicators for agriculture in Article 11. The inventory of small-scale structures in the agricultural landscape can serve as a starting point for the reporting. This is a GIS-based database at the municipal level, which has been compiled annually since 2004 on behalf of the Federal Office of Consumer Protection and Food Safety [76]. The habitat suitability of landscape elements depends on the requirements of individual species [74]. Accordingly, the following should be considered when setting a target value (e.g., by reference to landscapes).

In addition, livestock density is proposed, as this indicator serves as a proxy for the intensity of grassland. This indicator can be further differentiated as the total number of animals on agricultural land or the number of ruminants on grassland, with the latter being more closely related to grassland, as it shows, for example, the influence on butterflies [77] and ground-nesting birds. Ruminant density can be derived from official statistical data available for both livestock census and land use data at the NUTS2 level, and it can be more detailed based on Germany’s Central Livestock Identification and Traceability System. There is no political target for livestock density, neither in general nor for grassland, but maximum limits can be found in the field of support schemes (e.g., for extensive grasslands or for stable buildings).

Lastly, the additional indicator pesticide load in water samples or pesticide sales has been identified to gain importance aligned with the goals of the European Green Deal [78]. The first variable represents an overlap with water quality (see above). The second can be differentiated by risk categories, offering a more nuanced understanding of potential ecological impacts. The use of insecticides is not currently recorded; application data will be available to the authorities from 2028 Regulation 2022/2379 [79]. Currently, only the sales figures and findings of pesticides (and metabolites) in ground and surface water are recorded for monitoring.

The HNV share in scenarios and projections is modelled with CAPRI SAT, a spatial sub-tool of the CAPRI model, which works on a 1 by 1 km grid for each pixel. So-called HNV scores are determined individually for arable land, grassland, and permanent crops and then aggregated to a total value. The scores range from 0 to 1, with a high value meaning a good biodiversity assessment [72]. At each grid point, the components included are crop diversity, stocking densities, fertiliser application rates, and set aside rates [80]. The indicator of structurally rich landscapes requires a more detailed database and modelling to capture linear landscape elements. Detailed ex-ante modelling based on farm decisions represents a future challenge. This would require a combination of CAPRI SAT and an especially explicit land use model or detailed remote sensing data. In contrast, modelling livestock density is common practice. Livestock numbers and land use are common output variables in farm modelling with CAPRI or FARMIS/RAUMIS. Another challenge is the modelling of the proposed proxy for exposure risks (pesticides in water or pesticide sales). In CAPRI, pesticide use serves as an economic input and is determined based on statistical data (sales, farm statistics), production methods, and crop rotation. Another option for modelling exposure risk is the SYNOPS-GIS model, which assesses aquatic and terrestrial risks aggregated at various spatial scales and is used in the frame of the National Action Plan on the Sustainable Use of Pesticides [81,82]. The model requires input data on location, weather, climate, and pesticide application.

3.3.7. Land Use

Land use indicators are becoming important due to the rising competition for land. While agriculture continues to demand large areas for food production, other sectors, such as renewable energy (e.g., wind and solar farms, biomass), infrastructure development, and urban expansion, are also placing growing pressure on available land resources. Land use indicators reveal how agricultural expansion, intensification, or abandonment affect ecosystems, carbon stocks, and landscape structures. Among the reviewed monitoring systems, five indicators related to land use and land use change were identified. After removing duplicates, three remained relevant, and applying the selection criteria further narrowed them down to two key indicators. The key indicators include the area of cropland and grassland, which reflect the dominant land use types in agricultural systems. Emerging indicators, such as the extent of agroforestry systems, rewetted organic soils (e.g., through paludiculture), and fallow cropland or grassland, provide insights into land use transitions and restoration efforts. These indicators are particularly relevant for monitoring progress toward climate and biodiversity goals, including those outlined in the EU Nature Restoration regulation (2024/1991). While historical data on cropland and grassland area is widely available through national and EU agricultural statistics or GHG inventories, data on newer land use types, such as paludiculture or structurally rich fallow land, are still limited and often not consistently reported. Units typically include hectares or the percentage of total agricultural land, with a spatial resolution ranging from the farm level to regional (NUTS2/NUTS3) and national scales. Reporting frequency varies, but most land use indicators are updated annually or in multi-year intervals, depending on the data source.

All agricultural production models include agricultural areas, such as cropland and grassland, as well as fallow land. Peatland rewetting is reflected as a land use category in RAUMIS and CAPRI but not in FARMIS. Other new land use categories, such as agroforestry, agri-PV, and paludiculture, are not standard land use categories in the models, but they can be integrated.

3.3.8. Sustainable Agricultural Production and Use

Sustainable agricultural production indicators are essential for assessing the environmental and resource efficiency of farming systems. Fourteen indicators related to sustainable agricultural production were identified among the reviewed monitoring systems. After removing duplicates, ten remained relevant. Applying the selection criteria narrowed these down further to four. Two of these are already included in existing statistics, while the other two are new recommendations or have been adapted from existing indicators. The key indicators include the following: (1) the share of organic farming, which reflects the adoption of environmentally friendly practices and reduced chemical input, (2) the development of plant and animal production, as well as the use of biomass for energy or material purposes, highlighting the multifunctionality of agriculture in contributing to both food and renewable resource supply. Another indicator is (3) the share of human edible production, which measures how efficiently agricultural outputs contribute to direct human nutrition. Additionally, on-farm resource-saving practices, such as (4) the reuse of residual materials for fodder or biogas production, are an important indicator to demonstrate circularity and reduce waste, supporting a more sustainable and resilient agricultural system.

The share of organic farming is a widely used SDG indicator reported in current statistics, with targets of 30% of UAA nationally [83] and 25% at the EU level by 2030 [46]. Data is available as a percentage of UAA and hectares. Organic farming has recently been integrated into the CAPRI model’s supply module [84], and FARMIS includes organic farms as a distinct group to capture differences in practices and performance [85].

Agricultural biomass production measures the total biomass on utilised agricultural area, expressed in cereal units; differentiating by use (plant-based, animal feed, energy, material) adds value when data is available. National Statistics provide annual data in cereal units, and model outputs in tonnes can be converted using official keys. While plant and animal feed shares can be derived from model demand data, estimating energy and material use requires additional sectoral information, which is not always available.

The share of human edible production measures the proportion of agricultural output directly consumable by humans, indicating food system efficiency [86]. No official statistics exist, but the concept is used in scenario studies based on model outputs and post-processing. While not a standard model indicator, most models provide the data needed to calculate it. An additional indicator of residues used for fodder, bioenergy, and biomaterials would measure the share of agricultural residues repurposed for feed or energy, supporting circular economy principles, resource efficiency, and waste reduction.

3.3.9. Operational Indicator List

Based on the above analysis, we compiled a core list of indicators. For the eight key objectives, we identified 21 indicators suitable for monitoring the bioeconomy’s impact on the agricultural production system and the environment.

Table 3. Suitable indicators for monitoring the impact of the bioeconomy on agricultural production and underlying policy frameworks.

Indicator Groups	Indicators	Unit	Framework
Climate	GHG emissions from agriculture; possible differentiation by gases (CH4, N2O, CO2) and production (animal, plant-based, biomass for energy, and material use).	kt CO2e	German Climate Law
	2. GHG emissions from land use and land use change	kt CO2e	German Climate Law
	3. Carbon sinks from land use.	kt CO2e	German Climate Law
Water quality	Nitrogen field balance.	kg N/ha	EU Nitrate Regulation
	2. Phosphorus balance.	kg P/ha	EU Nitrate Regulation
	3. New: Pesticide load to be specified in detail		Farm-to-Fork Strategy
Water quantity	Share of irrigable and irrigated areas in utilised agricultural area and irrigation water use.	% of UAA, m3 water	German Strategy for Adaptation to Climate Change
Air	NH3 (ammonia) emissions from agriculture.	kt NH3	NEC Directive
	2. Total nitrogen balance.	kg N/ha	German Sustainable Development Strategy
Soil fertility	Soil organic carbon content.	Corg/ha	German Climate Law
	2. Soil erosion	t/ha	Soil Monitoring Law
Biodiversity	Livestock density.	LSU/ha	-/-
	2. High nature value farmland (HNV).	% of UAA	German Sustainable Development Strategy
	3. New: Pesticide load to be specified in detail.		Farm-to-Fork Strategy
	4. New: Share of agricultural area with structurally rich landscapes.	% of UAA	Nature Restoration Law
Land use	Area of cropland, grassland, and fallow.	ha	-/-
	2. Area of woody plants, rewetted organic soils/paludiculture, Agri-PV.	ha	-/-
Sustainable agricultural production and use	Share of organic farming.	% of UAA	German Sustainable Development Strategy
	2. Agricultural biomass production, possible differentiation by plant, animal, energy, and material	kt cereal units	-/-
	3. New/own proposal: Share of human edible production	% of total production	-/-
	4. New/own proposal: Residues used for fodder, bioenergy, and biomaterial. To be specified in detail		-/-

summarises the indicator groups and their corresponding indicators, including established measures and newly proposed metrics for comprehensive agricultural monitoring.

4. Discussion

4.1. Conceptual Framework

The DPSIR framework [19] was useful for structuring indicator selection, particularly in distinguishing pressure from state variables, which supported prioritisation for modelling purposes. For monitoring frameworks that serve as early warning systems, pressure indicators are especially relevant because they can signal emerging risks before state indicators reflect actual environmental degradation. This is especially helpful in buffered systems. For example, as a pressure indicator, nitrogen balance can reveal trends earlier than groundwater nitrate concentrations, enabling timely interventions. Pressure indicators are also practical for modelling, as they are often easier to quantify, linked directly to agricultural practices, and require fewer resources than many state indicators. Nonetheless, state indicators remain necessary in some cases to validate model outputs and capture actual environmental conditions.

We applied the SMART concept [18] for indicator selection, which provided a systematic framework to address all relevant aspects of establishing a monitoring system. This approach helped ensure that indicators were specific, measurable, achievable, relevant, and time-bound. However, we observed a limitation when considering new indicators required by emerging environmental policies, such as the Farm-to-Fork Strategy [46] or the Nature Restoration Law (2024/1991). For newly developed indicators, some SMART criteria may not be fully met, depending on the stage of the process. For instance, achievability, defined as feasibility within existing data and tools, is often not fulfilled because developing indicators, such as those for soil erosion, typically requires new datasets and analytical tools. For newly emerging indicators, the SMART concept provides indications of where definitions and data need to be improved for the monitoring process.

4.2. Indicator Selection Gaps and Weaknesses

The results of our model and indicator assessment reveal a mixed picture of current monitoring capabilities for bioeconomy impacts. While core indicators for GHG emissions and nutrient balances are well-established, concretely defined, and covered by existing models, significant gaps persist for biodiversity, water management, and indicators linked to circularity and efficiency of biomass use.

When applying the SMART concept to indicators, common weaknesses often arise in regard to an indicator’s specificity. If indicators are not clearly defined, uncertainty emerges regarding both measurement and achievability, making it difficult to track progress effectively. Additionally, as the concept of the bioeconomy is broad and interdisciplinary, some of the indicators designed for this framework do not align well with agricultural production, which reduces their relevance. Indicators that do not meet the required relevance criteria are not selected.

Our analysis shows that for biodiversity and other key objectives, a greater number of descriptive indicators is required to describe more complex interactions. However, this increasing number can also reduce clarity and hinder effective communication. For instance, while greenhouse gas emissions and nutrient balances are well represented in existing models, domains such as biodiversity, water use, and circularity remain fragmented, are covered by proxies, and often lack historical data. This underscores the need for a more strategic and harmonised indicator selection guided by clear definitions, policy relevance, and model compatibility. To improve communication, several indicators could be merged into a composite indicator. However, this would require weighting factors from several indicators and incorporation into a composite index. This has not yet been accomplished. A corresponding process within the framework of BE monitoring would have to take place as part of an expert consultation and be presented transparently.

Many of the indicators selected for bioeconomy monitoring are already covered by existing frameworks, such as those related to climate protection and climate adaptation, as well as the Sustainable Development Goals (SDGs). However, for indicators that currently lack national targets, such as phosphorus levels and soil erosion, historical data availability remains limited. Some of these indicators form part of the agri-environmental set and must be reported to Eurostat. Efforts are underway to develop new indicators that can measure emerging targets stemming from initiatives such as the Farm-to-Fork strategy [46] and the Nature Restoration Law (2024/1991).

Within the context of sustainable production, new indicators have been proposed to reflect the proportion of human edible products and agricultural waste, both of which are becoming increasingly relevant in the bioeconomy. While data on the former is expected to be available through existing models, further methodological development is required to ensure reliable measurement of the latter.

Besides the use of these environmental indicator groups, it is essential to include indicators that link production with consumption to fully assess the environmental impact. Environmental footprints provide an integrated approach to monitoring the bioeconomy by linking production and consumption [87]. This is particularly important given the limitations of “greening production”. While more extensive farming methods are environmentally beneficial, they often result in lower yields. As a result, maintaining the same output in terms of quantity and quality may require additional land, which can lead to negative ecological consequences either domestically or through indirect land use changes abroad.

To enable an overarching assessment of the environmental impacts on the bioeconomy, a large number of individual indicators often hinders a quick and comprehensive overview. Therefore, further aggregation of indicators can be helpful, which raises the question of whether aggregation should aim for an equal or at least similar number of indicators for each environmental medium to facilitate balanced assessment. Interrelationships between indicators within the total set must be addressed methodologically, such as reactive nitrogen emissions that affect both climate change and biodiversity categories. The monitoring system could also consider assigning additional weight to aspects where planetary boundaries, as defined by Steven et al. [88], are already close to being reached or exceeded.

4.3. Target Setting and Lack of Targets

Monitoring results and model-based projections are important for providing advice by highlighting risks, trade-offs, and potential policy action pathways. However, their effectiveness hinges on the existence of clearly defined targets for each indicator. Many of the recommended indicators identified for bioeconomy monitoring lack clearly defined quantitative targets, which limits their effectiveness for policy evaluation and scenario interpretation.

With the introduction of the Farm-to-Fork Strategy [46] and the Nature Restoration Law (2024/1991), the EU has defined new targets for environmental indicators, such as phosphorus, nitrogen, and biodiversity, making robust monitoring systems increasingly important. Also, the Federal Environment Agency proposed an integrated national target for nitrogen. This target should also contribute to improving public awareness of nitrogen-related issues, which is often limited by the complexity of the topic [89]. For monitoring to be effective, targets must be clearly defined, including reference years and baselines, and supported by reliable historical data. Some of these targets have not yet been further regionalised. However, this would be very sensible given the heterogeneity in German agriculture, e.g., regarding the important field of regional nitrogen surpluses. While nitrogen balances can be presented regionally, the nitrogen targets of both the National Sustainability Strategy and the Farm-to-Fork Strategy [46] are currently only defined at the national level (NUTS0), which limits the ability to assess regional progress and tailor local interventions. The example of nitrogen illustrates a key dilemma in environmental monitoring: while national targets may appear to be met, significant exceedances often persist at regional levels. This raises the question of what the target state should be, either compliance at the national level or at a more granular level, such as administrative districts. Without clear guidance on the spatial scale of targets, monitoring results risk obscuring local hotspots, which limits the effectiveness of policy measures and undermines efforts to achieve meaningful environmental improvements.

Another aspect is the extent to which the evaluation of policy interventions should be included in the monitoring process. Including the evaluation of policy interventions is a crucial component of comprehensive environmental monitoring. While focusing on pressure and state indicators forms the foundation of an environmental status report, response indicators are equally important for effective governance. Response indicators capture the measures taken in response to environmental trends and policy targets, providing insights into whether corrective actions are implemented when thresholds are exceeded. A notable example is the former Federal Climate Change Act in Germany, which required the responsible ministry to initiate additional measures whenever sectoral emission budgets were exceeded. This ensures that monitoring not only reports conditions but also supports adaptive management and continuous improvement in environmental governance.

4.4. Suitability of Models

Agricultural production models, like CAPRI, RAUMIS, and FARMIS, simulate key indicators using simplified assumptions. However, they provide essential production data that serves as inputs for detailed emission balance models or process-based biophysical models, which calculate environmental indicators, such as soil carbon or emissions, using process-based approaches and detailed emission factors. To enhance the monitoring of environmental outcomes, agricultural models are increasingly being linked to specialised submodules or external models through common standards. This integration allows for more detailed and targeted assessments. For example, the CAPRI Water Module extends the core CAPRI model by incorporating irrigation and water use indicators, other modules include pesticide application and organic farming, and GAS-EM builds on outputs from CAPRI or RAUMIS to estimate greenhouse gas emissions from agricultural production at the regional level. These modular approaches enable the use of high-resolution data and production-specific methodologies, allowing for a more nuanced understanding of environmental impacts. From a technical perspective, combining production models with environmental submodules makes agricultural modelling a powerful tool for supporting national and EU-level bioeconomy monitoring and policy evaluation.

Using models that have already been applied for official reporting requirements has several advantages. From an organisational perspective, these models are usually well maintained and regularly updated based on the latest reliable data. Their repeated application and ongoing development ensure they remain aligned with evolving policy needs and scientific standards, making them robust tools for monitoring and scenario analysis. The modelling network of the Thünen-Institut provides a sufficient modelling framework. This framework is also capable of modelling the environmental impact of the bioeconomy.

Altogether, the models described analyse the situation and development within the EU and within Germany, respectively. Thus far, they do not capture transboundary effects of domestic production and consumption on other regions. This gap was addressed in the SYMOBIO project through footprint analysis, combining spatially explicit land use change modelling (LandSHIFT) with detailed multiregional input–output analysis (MRIO and EXIOBASE) to capture socio-economic and environmental drivers [90].

4.5. Relevance of Scenarios, Spatial Resolution, and Existing Gaps

Scenario studies are a vital tool in the decision-making process, enabling policymakers and stakeholders to explore the environmental consequences of different actions prior to implementation. By simulating alternative pathways, they help to identify trade-offs, synergies, and potential risks, enabling proactive and adaptive governance.

However, the insights they can provide depend on the availability of the right indicators and their consistent reporting. Without robust policy-relevant indicators integrated into scenario analysis, the results will be incomplete and difficult to translate into actionable strategies. Therefore, ensuring that core indicators, such as those related to climate, water, biodiversity, and land use, are systematically included in scenario studies is essential for evidence-based decision making in the bioeconomy.

Scenarios developed using models such as CAPRI or RAUMIS can provide spatially resolved outputs at least at the NUTS 2 level. This resolution enables meaningful analysis of regional patterns in indicators such as nitrogen surplus, livestock density, or fallow land shares. For instance, intensive livestock regions in north-western Germany consistently show high nitrogen loads, suggesting localised pressures on water quality and air emissions. Conversely, eastern German regions with lower stocking densities show more favourable balances but often face economic viability challenges. The ability to integrate scenario-based projections with maps and spatial dashboards adds significant value for policymakers. For example, visualising changes in structural landscape elements alongside erosion risk could inform spatial targeting of agri-environmental measures. However, technical challenges remain in harmonising datasets, standardising temporal baselines, and aggregating outputs from different models.

Given the lack of indicator outputs in current scenario studies, it is essential to establish a standardised reporting format specifying which indicators should be published in bioeconomy-related modelling projects. A similar approach was recently introduced for the Greenhouse Gas Projection Report for Germany, which requires each sector to report further projection data in addition to greenhouse gas emissions and framework data. These tables are published on the Federal Environment Agency’s data portal Data Cube [91]. However, for agriculture, these indicators are limited to activity data, such as livestock numbers and fertiliser use, excluding most indicators identified in our analysis. For the LULUCF sector, it is limited to existing land use categories like grassland and arable land on mineral and organic soils, which may include new categories in future (agroforestry, rewetted organic soils, paludiculture, agri-PV). Nevertheless, some of these indicators can be derived from the reported activity data. Overall, it seems advisable to introduce a new category of reporting for bioeconomy scenarios and align them with existing greenhouse gas projections. The two are strongly connected thematically, as both aim to reduce greenhouse gas emissions and the use of fossil resources. This is particularly important because bioeconomy monitoring covers all aspects of biomass use, including human nutrition, animal feed, material use, and energy production, allowing for a comprehensive assessment of land use efficiency.

4.6. Research and Development Needs

While existing agricultural models and environmental indicators provide a solid foundation for assessing the impact of the bioeconomy, there are ongoing development needs:

Further strengthening indicators and model development:

To ensure that the environmental impacts of the bioeconomy are monitored comprehensively, it is crucial to strengthen the set of indicators for underrepresented areas, such as biodiversity, water, and circularity metrics, including the efficiency of biomass use. These areas are vital for understanding the complexity of sustainability transitions, yet they are frequently underrepresented in existing monitoring frameworks. At the same time, there is a need for continuous model development to integrate newly emerging indicators and align modelling capabilities with evolving policy targets, such as those set out in the EU Green Deal [78] and the Nature Restoration Regulation (2024/1991).

Need for regular indicator updates:

As environmental regulations evolve, the demand for relevant indicators increases. Monitoring systems must therefore be dynamic to ensure that indicators remain aligned with new policy targets and sustainability objectives. This requires regular reviews to verify whether existing indicators still capture the most critical environmental aspects and to integrate new ones where gaps emerge. Without these updates, monitoring frameworks risk becoming outdated and ineffective as tools for policy guidance and communication.

Standardising indicator reporting in scenario studies:

In light of the current absence of indicator outputs in many scenario studies, it is crucial to establish a standardised reporting format that clearly defines which indicators must be included and published in bioeconomy-related modelling projects. This would ensure consistency, comparability, and policy relevance across studies, enabling decision makers to use the results of scenario analysis effectively.

Regional targets and aggregation reflecting local progress at the national level:

To make monitoring results both informative and communicative, there is a clear need to develop methods for aggregating and illustrating regional developments at the national level over time. While providing data for all districts (NUTS2/NUTS3) ensures transparency, it is not practical for communication purposes and risks overwhelming stakeholders. Instead, aggregation techniques that preserve regional dynamics while presenting clear national trends are essential.

Improving transparency through clear documentation:

The clarity of model and indicator descriptions can limit transparency in monitoring systems. This ambiguity makes it difficult to determine whether specific measures, such as the total nitrogen balance, are included in standardised outputs. Clear model descriptions, definitions, and a well-defined scope for each indicator are therefore essential to ensure consistent interpretation and usability.

4.7. Transferability of Results

Although this study focuses exclusively on Germany, the findings are generally transferable to other countries, particularly within Europe. This is because the applied indicators align with the EU Bioeconomy Strategy, and the CAPRI model is explicitly designed for EU-wide applications. Regional differences are addressed by evaluating indicators in relation to nationally or regionally defined targets for specific environmental impacts.

5. Conclusions

A transition to a sustainable bioeconomy requires robust monitoring systems to capture the environmental impacts of agricultural production and inform evidence-based policy decisions. While existing agricultural models and indicator frameworks provide a solid foundation, particularly regarding greenhouse gas emissions and nutrient balances, significant gaps remain in areas such as biodiversity, water management, and circular resource use.

Using the DPSIR framework and SMART criteria, we developed a harmonised set of indicators that align with EU and national sustainability targets. Our analysis emphasises the importance of pressure indicators for early warning systems and scenario-based assessments while recognising the complementary role of state indicators in validation and long-term trend analysis. Linking these indicators with well-established agricultural models, such as CAPRI, RAUMIS and FARMIS, allows for the creation of spatially explicit projections and facilitates their integration into existing reporting systems.

However, this study also reveals several challenges, including inconsistent definitions, limited historical data for emerging indicators, and insufficient coverage of new land use practices and biodiversity metrics. Furthermore, the absence of clearly defined quantitative targets (at national and regional scales) limits the policy relevance of available indicators. This also highlights the importance of setting concrete, quantitative targets in national policies. Without such targets, indicators often remain insufficiently integrated into models. Moving forward, closing these gaps will require quantitative target setting at the national (and also the regional) level, improved data infrastructures, and the development of models to incorporate new sustainable priorities. The need for detailed indicators that reflect the complexity of agricultural production conflicts with the demand for a concise, targeted set of indicators that provide quick policy insights. This issue remains unresolved and has been identified as a key area for future work.

Ultimately, a future-ready monitoring system for the agricultural bioeconomy must combine empirical data with model-based projections, ensure interoperability across tools, and provide regionally differentiated insights. To increase effectiveness, monitoring should also include response indicators that evaluate policy outcomes and enable adaptive governance, as well as clear responsibilities and integration with existing reporting obligations.

A system with these features would improve transparency and accountability, thereby improving policy steering of the bioeconomy towards circularity and sustainability. Currently, the greatest challenges remain in the field of biodiversity, whereas many other environmental indicators for agriculture are already well advanced and can be considered “monitoring-ready”.

By offering an ex-ante assessment of agricultural bioeconomy indicators, this study supports the implementation of Germany’s National Bioeconomy Strategy [1] and complements both the EU Bioeconomy Strategy [3] and the European Green Deal [78]. By visualising potential environmental risks and resource use conflicts linked to biomass extraction and utilisation through indicators, this study provides a foundation for guiding the sustainable development of the bioeconomy.