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Flow approaches in agri-food systems research: revealing blind spots to support social-ecological transformation

Author

Listed:
  • Sandrine Allain

    (UR LESSEM - Laboratoire des EcoSystèmes et des Sociétés en Montagne - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Fédération OSUG - Observatoire des Sciences de l'Univers de Grenoble - UGA - Université Grenoble Alpes)

  • Simon de Muynck

    (ULB - Université libre de Bruxelles = Free University of Brussels)

  • Pierre Guillemin

    (ASTER Mirecourt - Agro-Systèmes Territoires Ressources Mirecourt - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, ESO - Espaces et Sociétés - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UM - Le Mans Université - UA - Université d'Angers - UR2 - Université de Rennes 2 - CNRS - Centre National de la Recherche Scientifique - Nantes Univ - IGARUN - Institut de Géographie et d'Aménagement Régional de l'Université de Nantes - Nantes Université - pôle Humanités - Nantes Univ - Nantes Université - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement)

  • Kevin Morel

    (SADAPT - Sciences pour l'Action et le Développement : Activités, Produits, Territoires - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Tiago Teixeira da Silva Siqueira

    (UMR SELMET - Systèmes d'élevage méditerranéens et tropicaux - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement)

  • Lynda Aissani

    (UR OPAALE - Optimisation des procédés en Agriculture, Agroalimentaire et Environnement - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

Agrifood systems are called upon to undergo profound transformation. The development of "flow approaches" (including lifecycle assessment, carbon footprint, ecological footprint, and metabolism methodologies) has been crucial to point to the material side of human activities. More specifically, these approaches highlight the material and energetic costs of long agrifood value-chains, intensive farming practices, high levels of geographic specialization, as well as the production of non-food commodities. In the logical progression from diagnosis to action, flow approaches are currently being used as decision-support tools. But what are the biases induced by flow approaches when it comes to supporting real-world transformations? Based on our experience and interdisciplinary background, we argue that flow approaches provide a decontextualized and narrow framing of issues related to agrifood systems, such as accumulations and transfers across space and time, inequalities and asymmetries along the chain of activities, or long-lasting environmental impacts. Some aspects are measured and emphasized, while others are difficult to observe or neglected. Flow approaches, alone, are not well suited to inform issues of environmental justice, radical transformation, and local governance. As in most cases methodological advances will not suffice to overcome the biases induced; we call for hybridizing methods and for broadening analytical perspectives.

Suggested Citation

  • Sandrine Allain & Simon de Muynck & Pierre Guillemin & Kevin Morel & Tiago Teixeira da Silva Siqueira & Lynda Aissani, 2025. "Flow approaches in agri-food systems research: revealing blind spots to support social-ecological transformation," Post-Print hal-04773727, HAL.
  • Handle: RePEc:hal:journl:hal-04773727
    DOI: 10.1080/21683565.2024.2421977
    Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-04773727v1
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