Analysing stakeholder perceptions in organic farming using fuzzy cognitive maps

<ns3:p>Background In this study, we introduce the application of ensemble averages, a concept rooted in statistical mechanics, to the analysis of Fuzzy Cognitive Maps (FCMs). This novel approach is employed to investigate stakeholder perceptions of the organic and sustainable food chain across Europe and identify leverage points for its improvement. The FCMs were constructed through workshops held in seven countries participating in the European project Foodlevers, using diverse methodologies to ensure comprehensive and regionally relevant data collection. Methods By leveraging ensemble averages, we aggregate the outcomes of FCMs initialised with random conditions, ensuring robust and stable representations of the systems under study. This approach allows us to mitigate the variability inherent in individual initialisations and highlight the underlying structure and dynamics of the maps. Through this analysis, we identify the key variables and their roles within each country’s food system, providing insights into the factors perceived as most critical by stakeholders for promoting sustainability and organic practices. Results Our findings reveal both commonalities and country-specific differences in the perceived leverage points, reflecting the diverse socio-economic and cultural contexts within Europe. The ensemble averaging method proved particularly effective in achieving consistent rankings of variables, enabling a clearer comparison across the countries. This methodological advancement enhances the utility of FCMs in multi-country studies, offering a reliable tool for analysing complex systems and informing policy interventions. The results contribute to a deeper understanding of the systemic dynamics of the European organic food chain and provide actionable insights for policymakers and stakeholders aiming to strengthen sustainable practices. Conclusion Our study highlights the potential of integrating statistical mechanics techniques with cognitive mapping methodologies to address pressing challenges in food systems research and beyond.</ns3:p>