Analysing stakeholder perceptions in organic farming using fuzzy cognitive maps Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.12688/openreseurope.19124.1
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.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.12688/openreseurope.19124.1
- OA Status
- gold
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408995105
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408995105Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.12688/openreseurope.19124.1Digital Object Identifier
- Title
-
Analysing stakeholder perceptions in organic farming using fuzzy cognitive mapsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-31Full publication date if available
- Authors
-
Marco Ciolfi, Anthony Adame, Lisa Arguile, Robert Borek, Claudia Consalvo, Michael den Herder, Rowan Dumper-Pollard, Siavash Farahbakhsh, Adrian Gliga, Valerie Holzner, Deborah Lorenzoni, Rosario Michel‐Villarreal, Sandor Mignon, Nicola Noble, Timokleia Orfanidou, Elisa Petrangeli, Paweł Radzikowski, Nicklas Riekötter, Giuseppe Russo, Laurence Smith, Alina Syp, Sally Westaway, Hilde Wustenberghs, Tomasz Żyłowski, Marco LauteriList of authors in order
- Landing page
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https://doi.org/10.12688/openreseurope.19124.1Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.12688/openreseurope.19124.1Direct OA link when available
- Concepts
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Perception, Stakeholder, Organic farming, Cognition, Fuzzy cognitive map, Agriculture, Cognitive map, Fuzzy logic, Geography, Business, Psychology, Environmental resource management, Computer science, Environmental science, Artificial intelligence, Fuzzy set, Political science, Archaeology, Fuzzy classification, Public relations, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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