Understanding the future and evolution of agri-food systems: A combination of qualitative scenarios with agent-based modelling
Introduction
The European agricultural sector is concerned not only with food security, productivity and economic efficiency of agricultural systems, but also with the multi-functionality of its activities considering the provision of environmental and social public goods (Gómez-Limón et al., 2009) including the supply of ecosystem services (ESS). ESS are the contribution of ecosystems characteristics and functions to human well-being (Burkhard and Maes, 2017, Costanza et al., 2017). They encompass provisioning services, such as the provisioning of biomass or water, regulation & maintenance services such as the control of erosion, climate regulation through carbon sequestration, or maintaining habitats for biodiversity, and cultural services, such as options for recreation in nature (Haines-Young and Potschin, 2018, Zhang et al., 2007). On a higher level, the EU's Farm to Fork Strategy (European Commision, 2020) sets a new agenda for rethinking and reshaping the agri-food system. This agenda emphasizes the interplay between agricultural activity and dietary habits, in order to support the transition to a fair, healthy and environmentally-friendly food system and the development of practical measures to increase local food supply (Voglhuber-Slavinsky et al., 2021).
To capture the complexity of agri-food systems within their socio-ecological settings, we necessitate readiness for better strategic planning and improved understanding of the uncertainties accompanying the effects of today’s actions in the future. On the one hand, the need of farmers and land managers to be better prepared for future trends, possible disturbances and shocks due to environmental, market or societal developments, is increasing due to climate and other global crises and their local impacts. On the other hand, this leads to increasing demands on the methodological background and robustness of Decision Support Systems (DSS). Especially in multi-stakeholder processes established for co-developing pathways for agri-food system transformations, the integration of socio-empirical methods and modelling, respectively the coupling of qualitative and quantitative data, has been discussed as a feasible approach to connect and capitalize different types of knowledge (Poggi et al., 2021).
Foresight, as a structured futures dialogue among key stakeholders of a certain arena of change (Warnke & Heimeriks, 2008), is particularly promising in that respect. It is a systematic, participatory and multi-disciplinary approach of exploring and understanding futures (Bingley, 2016) as well as drivers of change (de Lattre-Gasquet and Treyer, 2016, Bourgeois, 2015), which recognizes the inherent uncertainty of the future and therefore supports reflection on different possible future trajectories. It structures the multi-stakeholder dialogue using a set of distinct qualitative and quantitative methods (e.g., scenario building, Delphi surveys, visioning, roadmapping) which generate collective intelligence out of the diversity of stakeholders’ perspectives and at the same time underpin long-term and creative thinking beyond well-trodden pathways and mere extrapolation of today’s trends. Foresight dialogues yield both a product and process benefit, while generating information on possible threats and opportunities and options for action that reinforce a more future-oriented and robust decision making. The interactive dialogue is strengthening the capacity of the system to evolve in phase with a changing environment and to better unlock the potential of the present as it forms linkages and shared understanding among diverse actors and domains.
Scenario building is one of the most established and widely used foresight methods. Scenarios are consistent and plausible images of alternative futures (Schirrmeister & Warnke, 2013) combining human, environmental, economic and political systems (Swart et al., 2004). The scenario description can include qualitative and/or quantitative elements (e.g., facts and figures). QS are available as a stand-alone result on how a system may evolve but can also serve as an input to quantitative methods, such as simulation models, to describe numerically the evolution of agri-food systems and the associated impacts via a set of well-defined parameters.
Smolenaars et al. (2021) claim that most of the foresight approaches generate qualitative pictures or stories of the future as has been evidenced by the lack of quantitative scenarios for the plausible dimensions of future resource security requirements under socio-economic development. After a long period of largely independent use of quantitative and qualitative approaches, it is now widely accepted that there are large areas of overlap and that a combination of the various methodological approaches has its advantages (Moschner and Anschütz, 2010, Stummer et al., 2021). It is increasingly emphasized that qualitative and quantitative research approaches can complement each other in order to describe subjects and systems more comprehensively (e.g., Gantar & Golobič, 2015; Gómez-Limón et al., 2009; Jouan et al., 2020; Smolenaars et al., 2021). For that reason, coupling and combining qualitative and quantitative scenarios is used frequently (Bingley, 2016) and became increasingly popular as the story and simulation (SAS) approach in which narratives are first generated by stakeholders and experts and then translated into quantitative parameters, formulations and input values for simulation models (Mallampalli et al., 2016, Alcamo, 2008, Houet et al., 2016).
While qualitative scenarios provide useful insights into potential futures, quantitative scenarios are necessary to provide accurate predictions of probabilities and potential impacts. In agri-food systems, farmers and land managers require such quantitative scenarios to assist in the process of informed decision-making (Brown et al., 2018). However, the challenge in developing quantitative scenarios for agri-food systems is that they have a strong social component, making it difficult to accurately model the relationship between system elements using equations. The complexity of systems leads to applying qualitative rather than quantitative analysis (Weimer-Jehle, 2006). This might be the reason why most researchers have relied on qualitative scenarios in the past. Despite this challenge, the use of ABM and the combination with the qualitative scenarios enables researchers to incorporate social and individual behavior into quantitative scenarios. ABMs provide a framework for simulating the behavior of individual agents within a system, allowing researchers to model complex social dynamics and interactions (Parker et al., 2003, Mallampalli et al., 2016). Therefore, the use of ABMs to quantify scenarios for agri-food systems can provide a more accurate representation of the system's behavior and can be valuable for decision-making processes.
Following the SAS concept, in this paper, we present an example of coupling QS techniques and modelling. We focus on agent-based modelling (ABM), as a simulation method underpinned by its ability to explore the outcomes of lower-level land-use decisions (e.g., field and farm) by focusing not only on system factors but also on a range of actors (agents) with unequal powers and endowments. This type of modelling permits, thus, to capture the complexity of agri-food systems within their socio-ecological settings and to consider diverse actor strategies. These strategies of alliances and conflicts are influenced by the growth of powers of the agents (de Lattre-Gasquet & Treyer, 2016), which take actions in response to changes in their common environment (Janssen, 2005, Parker et al., 2003) dynamically affecting the behavior of other agents and other parts of the system (e.g., land use) (Mallampalli et al., 2016). The application of ABM as a translation tool combined with QS in the agri-food system has not yet been explored in the literature.
The paper focuses on identifying the benefits of combining QS with quantitative ABM in the agri-food system, while exploring how to translate QS to quantitative ABM parameters. We present a new approach in which we take different pictures of the future in the agri-food system in Germany and implement an ABM as a translation tool for QS to simulate the evolution of the socio-ecological agroecosystem. To illustrate the approach, we focus on digitalization and the provision of ESS, in the frame of the “Digital Agricultural Knowledge and Information System (DAKIS)” project (https://adz-dakis.com/en/). The DAKIS project aims to support farmers in agricultural management choices with real-time digital data and spatial information to enable the resource efficient sustainable production of commodities, ESS and biodiversity and to stimulate cooperation among farms (Mouratiadou et al., 2021). For this exercise, we consider different management options regarding their impact on the provision of ESS as well as their impact on the capitals of the involved actors. Further, we assess the degree of contribution of digitalization in relation to these management options. Ultimately, we make a contribution in showing how QS and ABM can be combined to quantitatively project changes in the agri-food system and thus enable comparable quantifications distinguishing implications of different actors’ behaviour.
Section snippets
Materials and methods
Our approach is based on a five-step process as shown in Fig. 1. In the first step, we used the four QS developed in the DAKIS project. Second, we used the scenarios together with the literature to parameterize the ABM for the system under consideration. Third, in an internal focus group we estimated the impacts of the scenario-specific assumption on the parameters used in the ABM. Fourth, we validated our results via an expert workshop. Finally, we run a test of the ABM simulation to 2035 and
Scenario quantification outputs
Combining the QS and ABM resulted in a scenario specific assessment of changes in the different capital types, systems and the policy domain (Fig. 6). The detailed results for both approaches can be found in the supplementary materials (SM_2 and 3). In the following, we explain our justifications for these estimated values based on the internal exercise as well as on the discussions during the workshop.
Scenario 1: Reduced Consumption and De-growth by Necessity.
It is estimated that natural
Discussion
Combining a narrative qualitative element and a modelled quantitative section has the advantage of involving subjective judgment (of experts) and rational analysis (facts-based) (Al-Saleh et al., 2012). On the one hand, scenarios have been employed for the parameterization of the model. On the other hand, mathematical rules-based models are capable of translating qualitative narratives, giving quantitative insights to possible societal and ecological impacts and conditions in the future (
Conclusions
In this paper we present a new approach of combining QS with quantitative ABM in order to anticipate the probabilities of these future QS from individual actions and to identify the conditions that can support the evolution of a selected scenario. Combining QS with quantitative modelling to provide insights on possible societal and ecological impacts and conditions in the future of the agri-food system offers several advantages: i) it converts abstract agri-food future images into concrete
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was made possible through funding from the Digital Agriculture Knowledge and Information System (DAKIS) Project (ID: FKZ 031B0729A), financed by the German Federal Ministry of Education and Research (BMBF). Special thanks to the group of experts who took part in the workshop for the validation of the translation process.
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