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Diversity and potentiality of multi-criteria decision analysis methods for agri-food research

There is a growing demand for moving towards sustainable agri-food systems which per nature covers a complex network of activities and domains; such systems will benefit from multi-criteria decision analysis (MCDA) methods. Although some reviews on MCDA in agri-food research have been published, none of them covered the whole value chain. In this article, a corpus of 954 articles published by INRA scientists from 2007 to 2017 was used to study the diversity and potentiality of MCDA techniques. For the first time, experts from more than 10 agri-food domains worked altogether to annotate the articles, carry out a multivariate analysis, and finally interpret the statistical results to identify the specificities of certain domains and the complementarities between domains and to suggest avenues for future agri-food research. One-third of the studies were based only on a list of indicators, even when their purpose was to choose, sort, or rank options. Regardless of the scientific discipline in the agri-food sector, MCDA studies rarely considered temporal dynamics, spatial scale changes, or stakeholder contributions. As the agri-food system becomes increasingly sustainable in the near future, the use of MCDA methods will accelerate. To become more effective, they will have to include ecosystem services, even outside the scope of ecological studies. Similarly, MCDA studies will need to include participatory science to involve stakeholders (i.e., public authorities, governmental agencies) and end-users (i.e., farmers, producers, industrials, consumers) in the construction of the multi-criteria evaluation but also in the resulting decisions.
- Département Sciences sociales, agriculture et alimentation, espace et environnement France
- University of Lorraine France
- National Research Institute for Agriculture, Food and Environment France
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement France
- Laboratoire Agronomie Environnement France
Design, 330, [SDV]Life Sciences [q-bio], Environment, Choice, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Indicators, [INFO]Computer Science [cs], INRAE, 600, Agriculture, [SDV.IDA] Life Sciences [q-bio]/Food engineering, 001, [SDV] Life Sciences [q-bio], [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, Sustainability, Agro-industry, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Design, 330, [SDV]Life Sciences [q-bio], Environment, Choice, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Indicators, [INFO]Computer Science [cs], INRAE, 600, Agriculture, [SDV.IDA] Life Sciences [q-bio]/Food engineering, 001, [SDV] Life Sciences [q-bio], [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, Sustainability, Agro-industry, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).17 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
