Climate-change impact assessments using agro-hydrological models often assume that farmers will stick to the same planting and harvest dates in the future, even as the climate warms. This "fixed-calendar" assumption conflicts with reality, as farmers naturally adapt to shifting seasons, leading models to miscalculate crop yields and water use. To address this, we developed a workflow in the SWAT+ model that replaces static calendar dates with dynamic rules that respond to the environment. We first analyzed historical management records to identify the specific weather conditions, such as accumulated heat or dry days, that actually prompt farmers to plant or harvest. We then translated these observations into "if-then" rules (decision tables) and tuned them to ensure they accurately reproduced historical farming timing. We applied this dynamic approach to winter wheat and corn silage in a German catchment under cool-dry, cool-wet, and warm-wet late-century climates. Rule-based management successfully adapts to future warming without requiring manual adjustments. For winter wheat, the median growing season is shortened from a historical similar to 312 days to roughly 293-306 days in the future due to faster maturation. Conversely, corn silage growing seasons extended from a historical 144 days to 162-172 days in wet scenarios, driven by significantly earlier planting. Dynamic management consistently improved corn silage yields (+28.0% to + 47.9%) compared to the fixed-calendar approach. Environmentally, the dynamic rules were far more effective at mitigating pollution: nitrate runoff decreased by 48.5% to 86.6% for winter wheat relative to the baseline. In contrast, sticking to fixed dates resulted in erratic outcomes, such as a 30.6% increase in nitrate losses under cool-dry conditions. This study demonstrates that using adaptive rules instead of fixed dates reduces bias and produces more realistic climate-change impact assessments.

CONTEXT Agri-environmental schemes (AES) are intended to promote sustainable agricultural management. However, in the context of the European Union's Common Agricultural Policy, AES in their current implementation have not met expectations regarding their potential to protect biodiversity and ecosystem services. Model-based analyses are particularly suitable for investigating how policy measures should be designed to achieve greater acceptance. OBJECTIVE The aim of this study is to use an agent-based model to critically evaluate the impact of changes in agricultural policies on AES adoption rates taking the Mulde River Basin in Germany as an example. METHODS Based on data from semi-structured interviews and an online survey, farmers' decision-making in the agent-based model is divided into two main steps: First, it is determined whether farmers are open to adopt an AES, regardless of economic AES contract characteristics. If so, in a second step, their actual adoption is calculated based on the compensation individual farmers require for specific contract designs. RESULTS AND CONCLUSIONS The model suggests that changes of AES contract design have more influence on adoption rates than changes in openness of the farmers. At present, limited openness is not the decisive reason for overall low adoption rates. Nevertheless, we find that an expansion of advisory support only fosters adoption if advice promotes farmer openness, which is currently not the case for all considered AES. In addition, social influence between farmers further increases adoption, especially if a farmer's network is large and influential. SIGNIFICANCE By systematically assessing the impact of changes in policy design, we show how the potential of the AES could be exploited to a greater extent. Furthermore, we discuss how such a model can be used in biophysical analyses to quantify the environmental impact of AES adoption.

CONTEXT: Farm typologies help to identify patterns across a wide range of farm systems and describe hetero-geneity in agriculture concisely. They can also support the design of agricultural policies by providing infor-mation and knowledge about policy target groups. For example, voluntary agri-environmental schemes could be tailored to specific agricultural landscapes and farm types. Farm typologies, however, are often developed from scratch, with limited connection to previous studies and policy making. OBJECTIVE: The objective of this study is to clarify the purposes of farm typologies in research and agricultural policy making and to develop a framework that allows to increase the usefulness and usability of farm typologies for agricultural policy making.METHODS: Based on a review of 13 systematically identified overview studies on farm typologies, we develop a framework that establishes connections between the purposes of farm typologies along the different stages of the policy process.RESULTS AND CONCLUSIONS: We find multiple purposes for farm typologies, the two most common of which are for understanding the characteristics, heterogeneity, and development of farm systems and for policy making. The newly developed framework suggests that connecting knowledge across these purposes could improve the validity, transferability, and relevance of farm typologies for agricultural policy making. Our framework also provides an entry point for encouraging cooperation between developers and users of typologies, and for the improvement of typologies through new data (including behavioural data) and methods such as machine learning. We conclude that future research can build on the existing work on farm typologies but must be aware of the specific challenges that are associated with the use of farm typologies in the policy process.SIGNIFICANCE: Knowledge of the prospects and challenges of using farm typologies allows to increase the usefulness and usabilityof these typologies and can contribute to the design of targeted and tailored agricultural policy instruments. By increasing the acceptance, perceived fairness, and legitimacy, this can improve their effectiveness and efficiency, which is urgently needed for a successful transformation to a more sustainable agricultural sector.

This deliverable provides a report on the agent-based models (ABMs) for each of the case studies, developed in the Work Package 4 (WP4) – Agent-Based Modelling and Analysis of BESTMAP. In particular, it includes a description of how the models can be accessed and which input data is needed. This document is accompanied by a description of each case study model in a structured form (see Appendix) which follows the ODD+D protocol (Müller et al., 2013). Deviations from the main processes as described in Milestone M6 (First versions of ABMs for CS) are discussed for each case study.

This Deliverable provides a roadmap to expansion of BESTMAP towards a operational pan-European modelling platform, as well as explore via pilot analyses several areas for improvement and future research. Considering new case studies, we analyse the locations where models parameterized in those regions can transfer to cover the most area. We conclude that in future case studies, they should be located in northern Spain, north-west Italy, central Italy, Montenegro/Albania, and Bulgaria. Testing if one can model water quality at the European scale, our modelling shows the NDR model (used in BESTMAP CS work) has generally good performance at EU scale, despite it being a rather simple process-based model. There is an overestimation of Nitrogen at low N, and underestimation of Phosphate at high P, which need to be considered in future work.

AbstractSoil is central to the complex interplay among biodiversity, climate, and society. This paper examines the interconnectedness of soil biodiversity, climate change, and societal impacts, emphasizing the urgent need for integrated solutions. Human‐induced biodiversity loss and climate change intensify environmental degradation, threatening human well‐being. Soils, rich in biodiversity and vital for ecosystem function regulation, are highly vulnerable to these pressures, affecting nutrient cycling, soil fertility, and resilience. Soil also crucially regulates climate, influencing energy, water cycles, and carbon storage. Yet, climate change poses significant challenges to soil health and carbon dynamics, amplifying global warming. Integrated approaches are essential, including sustainable land management, policy interventions, technological innovations, and societal engagement. Practices like agroforestry and organic farming improve soil health and mitigate climate impacts. Effective policies and governance are crucial for promoting sustainable practices and soil conservation. Recent technologies aid in monitoring soil biodiversity and implementing sustainable land management. Societal engagement, through education and collective action, is vital for environmental stewardship. By prioritizing interdisciplinary research and addressing key frontiers, scientists can advance understanding of the soil biodiversity–climate change–society nexus, informing strategies for environmental sustainability and social equity.

Despite the increasing use of standards for documenting and testing agent-based models (ABMs) and sharing of open access code, most ABMs are still developed from scratch. This is not only inefficient, but also leads to ad hoc and often inconsistent implementations of the same theories in computational code and delays progress in the exploration of the functioning of complex social-ecological systems (SES). We argue that reusable building blocks (RBBs) known from professional software development can mitigate these issues. An RBB is a submodel that represents a particular mechanism or process that is relevant across many ABMs in an application domain, such as plant competition in vegetation models, or reinforcement learning in a behavioural model. RBBs need to be distinguished from modules, which represent entire subsystems and include more than one mechanism and process. While linking modules faces the same challenges as integrating different models in general, RBBs are “atomic” enough to be more easily re-used in different contexts. We describe and provide examples from different domains for how and why building blocks are used in software development, and the benefits of doing so for the ABM community and to individual modellers. We propose a template to guide the development and publication of RBBs and provide example RBBs that use this template. Most importantly, we propose and initiate a strategy for community-based development, sharing and use of RBBs. Individual modellers can have a much greater impact in their field with an RBB than with a single paper, while the community will benefit from increased coherence, facilitating the development of theory for both the behaviour of agents and the systems they form. We invite peers to upload and share their RBBs via our website - preferably referenced by a DOI (digital object identifier obtained e.g. via Zenodo). After a critical mass of candidate RBBs has accumulated, feedback and discussion can take place and both the template and the scope of the envisioned platform can be improved.

Model-based analyses can effectively contribute to investigating leverage points for sustainability transformations in agriculture. They allow for a systematic evaluation of policies under changing environmental, economic, or institutional conditions, and can be used to assess the effectiveness and efficiency of different policy designs. For analyzing agricultural systems, agent-based modeling is particularly useful because it can represent individual farmers—the crucial actors in land use systems—their interactions and emerging patterns at the landscape level. In order to provide policy-makers with relevant and accurate information, an adequate representation of farmers’ decision-making is essential. However, formalizing empirically observed farmers’ behavior into model rules is challenging, in particular when the observations are qualitative. With this article, we aim to guide modelers through the process of formalizing farmers’ decision-making based on empirical findings. First, we discuss which primary data collection designs are appropriate for inferring particular aspects of farmers’ behavior, focusing in particular on when a theory-driven design is helpful and when inductive approaches are needed. Second, we compile aspects that need to be covered in empirical data to best inform agent-based models. Finally, we present approaches for translating empirical findings into formalized decision rules. We underpin our discussion with model examples from the literature and our own model developed to represent farmers’ decision-making on the adoption of agri-environmental schemes in Europe. With this methodological contribution, we aim to help make agent-based models less stylized, thereby providing greater potential to support policy-makers in identifying leverage points for a sustainable transformation of agriculture.

The Case Study Base Layer, described in this deliverable, is a harmonised geospatial database spanning across the five case study areas of BESTMAP: South Moravia (Czech Republic), Mulde (Germany), Humber (Great Britain), Backa (Serbia), and Catalonia (Spain). It is based on the Preliminary Case Study Base Layer (MS3) and includes geospatial information on climatic and soil conditions, biodiversity, land use/land cover (including crop types), farm structure and socio-economic data. It serves as a base for biophysical ecosystem service (ES) models as well as socio-economic statistical models. Thereby, the Case Study Base Layer will be crucial for mapping Farming System Archetypes (FSAs) and for building a common agent-based modeling (ABM) framework across all case studies. This report accompanies the data in Deliverable D3.1 and describes the compilation, harmonization, meta-data structure, secure storage and access of case study baye layer data using the BESTMAP instance of the UFZ GeoNetwork.

This deliverable report provides an integration guide on how information gained in BESTMAP’s agent-based model can be used in the standard economic model to improve the assessment of agricultural policies in the European Union. First, the models used in the BESTMAP are explained. The integration guide discusses in detail the preconditions and challenges when linking agent-based models with standard economic models such as partial and general equilibrium models. As a result of an expert workshop, six challenges are identified. The report also presents suggestions on how to make use of the finding and presents a way forward to integrate the two types of models.

Agri-environment schemes (AES) are government-funded voluntary programs that incentivise farmers and land managers for environmental friendly farming practices. Understanding farmers’ decision-making process and its impact on AES adoption can aid policy makers in designing AES schemes that meet adoption goals and environmental targets. Farmers’ decision-making is complex and involves a range of social, behavioural, economic and ecological factors. In this paper, we present a spatially explicit agent-based model (ABM)—BESTMAP-ABM-UK that simulates farmers’ decision-making process, inclusive of farmers’ social, behavioural and economic factors, on adopting buffer strips, cover crops, grassland management and arable land conversion to grassland schemes in the UK. The model produces farmers’ AES adoption under varied AES scheme designs in term of the contract length, the offered payment level, the bureaucracy level and the required minimal area. We apply the Morris screening method to analyse the importance of the model parameters in a status quo scenario, in which current UK AES designs are used. The results show that the average accepted payments of farmers for buffer strips and grassland management and farmers’ intrinsic openness to buffer strips have the most significant impact on the farm adoption rate in the model.

Models of socio-environmental or social-ecological systems (SES) commonly address problems requiring interdisciplinary scientific expertise and input from a heterogeneous group of stakeholders. In SES modelling multiple interactions occur on different scales among various phenomena. These scale phenomena include the technical, such as system variables, process detail, inputs and outputs, which most often require spatial, temporal, thematic and organisational choices. From a good practice and project efficiency perspective, the problem scoping and conceptual model formulation phase of modelling is the one to address well from the outset. During this phase, intense and substantive discussions should arise regarding appropriate scales at which to represent the different phenomena. Although the details of these discussions influence the path of model development, they are seldom documented and as a result often forgotten. We draw upon personal experience with existing protocols and communications in recent literature to propose preliminary guidelines for documenting these early discussions about the scale(s) of the studied phenomena. Our guidelines aim to aid modelling group members in building and capturing the richness of their rationale for scoping and scale decisions. The resulting transcripts are intended to promote transparency of modelling decisions and provide essential support for the justification of the final model for its intended use. They also facilitate adaptive modifications of the pathway of model development via retracing decisions and iterative reflection upon alternative scale options.

Abstract Wind erosion from agricultural land is an underrated and understudied environmental challenge in Europe. Its societal and policy relevance will likely increase in the near future due to climate change and associated increases in the frequency, severity and patterns of atmospheric events such as droughts. We review the research on this issue and find it to be fragmented, siloed and dominated by natural sciences, leaving important research gaps. The most important gaps that circumscribe a research agenda for the future include specific effects of future climate change on wind erosion, the relevance of wind erosion for public health and ecosystem functioning, farmers' behaviour affecting erosion risk and feedback between land management and environmental change, and appropriate policy approaches to address wind erosion risks. Social science contributions are thus required to make wind erosion research relevant for addressing the related societally most pressing questions. We provide a social–environmental systems perspective to highlight the potential of inter‐ and transdisciplinary research into wind erosion in times of climate change and the increasingly recognized need to transform agriculture towards more sustainability and climate resilience. Read the free Plain Language Summary for this article on the Journal blog.

Summary In 2018 the last active German hard coal mines have been abandoned and transitioned into the postmining phase. In the postmining phase, mine water pumping, necessary during active mining, becomes technically and economically unnecessary and unprofitable. As a consequence, the controlled process of mine water rebound to predefined levels increases the pore fluid pressure of subsurface rocks and changes the local stress field. As a result, faults may be (re-)activated inducing ground movements and microseismicity. This study presents an interdisciplinary approach consisting of geology, geomechanics, gas technology, geodesy, and geophysics in order to provide a process understanding how subsurface and surface are geomechanically coupled in the eastern Ruhr hard coal mining area, western Germany. Thus, mine workings and favourably oriented fractures represent the major pathways for mine water. This in accordance with CO2, 222Rn, and O2 anomalies detected along a fault. Rock matrices, however, are tight (mean porosity <1 %, mean permeability <1 mD). Furthermore, ground subsidence and microseismic events (–0.8 to 2.6 MLV) are spatially and temporally correlated with the mine water rebound and mine workings, but unrelated to tectonic faults. The applicability of these findings to other hard coal mining areas (e.g. Saarland, Ibbenbüren) will be tested.

Effective risk management is key to strengthening the resilience of the most vulnerable. Insurance products can help achieve this goal. Yet, in particular in low-income countries, not all can afford the regular premiums. Instead, informal risk-sharing within social networks plays a crucial role in protecting against unpredictable environmental shocks such as droughts and floods. However, this support may not reach households in need if income is heterogeneously distributed and poor households are not connected to those with sufficient resources to share. To study the determinants of vulnerability to extreme events when insurance is available but not affordable to all, we aggregate outcomes of an empirically informed agent-based model in a regression model. We show that not only a household's own financial situation is important for shock resilience, but also the household's position in the network and the financial situation of connected households. We demonstrate the transferability of our results by achieving high prediction accuracy for an empirical risk-sharing network of a village in the Philippines. The study demonstrates how model-based results can help detect vulnerable households. This can be used to develop a reliable identification tool to leverage external support and effectively target subsidies for different types of shocks.

Increasing farmers’ adoption of sustainable nitrogen management practices is crucial for improving water quality. Yet, research to date provides ambiguous results about the most important farmer-level drivers of adoption, leaving high levels of uncertainty as to how to design policy interventions that are effective in motivating adoption. Among others, farmers’ engagement in outreach or educational events is considered a promising leverage point for policy measures. This paper applies a Bayesian belief network (BBN) approach to explore the importance of drivers thought to influence adoption, run policy experiments to test the efficacy of different engagement-related interventions on increasing adoption rates, and evaluate heterogeneity of the effect of the interventions across different practices and different types of farms. The underlying data comes from a survey carried out in 2018 among farmers in the Central Valley in California. The analyses identify farm characteristics and income consistently as the most important drivers of adoption across management practices. The effect of policy measures strongly differs according to the nitrogen management practice. Innovative farmers respond better to engagement-related policy measures than more traditional farmers. Farmers with small farms show more potential for increasing engagement through policy measures than farmers with larger farms. Bayesian belief networks, in contrast to linear analysis methods, always account for the complex structure of the farm system with interdependencies among the drivers and allow for explicit predictions in new situations and various kinds of heterogeneity analyses. A methodological development is made by introducing a new validation measure for BBNs used for prediction.

This deliverable provides a General Framework for the BESTMAP Policy Impact Assessment Modelling (BESTMAP-PIAM) toolset. An update of the framework will be provided later in the project in Deliverable 2.4. The BESTMAP-PIAM is based on the notion of defining (a) a typology of agricultural systems, with one (or more) representative case study (CS) in each major system; (b) mapping all individual farms within the case study to a Farm System Archetype (FSA) typology; (c) model the adoption of agri-environmental schemes (AES) within the spatially-mapped FSA population using Agent Based Models (ABM), based on literature and a survey with sufficient representative sample in each FSA of each CS, to elucidate the non-monetary drivers underpinning AES adoption and the relative importance of financial and non-financial/social/identity drivers; (d) linking AES adoption to a set of biophysical, ecological and socio-economic impact models; (e) upscaling the CS level results to EU scale; (f) linking the outputs of these models to indicators developed for the post-2020 CAP output, result and impact reports; (g) visualizing outputs and providing a dashboard for policy makers to explore a range of policy scenarios, focusing on cost-effectiveness of different AES.

Sustainability challenges in socio-environmental systems (SES) are inherently multiscale, with global-level changes emerging from socio-environmental processes that operate across different spatial, temporal, and organisational scales. Models of SES therefore need to incorporate multiple scales, which requires sound methodologies for transferring information between scales. Due to the increasing global connectivity of SES, upscaling – increasing the extent or decreasing the resolution of a modelling study – is becoming progressively more important. However, upscaling in SES models has received less attention than in other fields (e.g., ecology or hydrology) and therefore remains a pressing challenge. To advance the understanding of upscaling in SES, we take three steps. First, we review existing upscaling approaches in SES as well as other disciplines. Second, we identify four main challenges that are particularly relevant to upscaling in SES: 1) heterogeneity, 2) interactions, 3) learning and adaptation, and 4) emergent phenomena. Third, we present an approach that facilitates the transfer of existing upscaling methods to SES, using two good practice examples from ecology. To describe and compare these methods, we propose a scheme of five general upscaling strategies. This scheme builds upon and unifies existing schemes and provides a standardised way to classify and represent existing as well as new upscaling methods. We demonstrate how the scheme can help to transparently present upscaling methods and uncover scaling assumptions, as well as to identify limits for the transfer of upscaling methods. We finish by pointing out research avenues on upscaling in SES to address the identified upscaling challenges.

Extreme weather conditions in the face of due to climate change often disproportionately affects the weakest members of society. Agricultural insurance programs that are specifically designed specifically for smallholders in developing countries are valuable tools that can help farmers to cope with the resulting risks. A broad range of methods including household surveys, experimental games, and agent-based models have been used to assess and improve the effectiveness of such climate insurance products. In addition Furthermore, process-based crop models have been used to derive suitable insurance indices. However, climate change raises specific socioeconomic andas well as environmental challenges that need to be considered when designing insurance schemes. We argue that, in light of these pressing challenges, some of the methodological approaches currently applied to study climate insurance reach their limits when applied independently. This has fundamental implications. On the one hand, not all undesired side effects of insurance can be detected and, on the other hand, insurance indices cannot be derived sufficiently well. We therefore advocate a sound combination of different methods, especially by linking empirical analyses and modelling, and underline the resulting potential with the help of stylized examples. Our study highlights how methodological synergies can make climate insurance products more effective in supporting the most vulnerable households, especially under changing climatic conditions.