Modelling world agriculture as a learning machine? From mainstream models to Agribiom 1.0

Dorin Bruno, Joly Pierre-Benoît. 2019. Modelling world agriculture as a learning machine? From mainstream models to Agribiom 1.0. Land Use Policy, 10 p.

Journal article ; Article de recherche ; Article de revue à facteur d'impact
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DORIN et JOLY 2019 - Modelling world agriculture as a learning machine. From mainstream models to Agribiom 1.0 (LUP - Online first).pdf

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Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Géographie-Aménagement-Urbanisme-Architecture

Abstract : Models of world agriculture and food systems are used widely to predict future scenarios of land and resource uses. Starting with a brief history of world agriculture models since the 1960s, which shows their hybrid character as well as their limitations in representing real world diversity and options, this article then presents an alternative modelling experience. We argue that models are tools of evidence, hence “truth machines”, but also tools of government, with a multi-faceted political dimension. For instance, the virtual realities that conventional models build incorporate value judgements about the future that remain invisible and difficult to challenge. For ease of computation and comparison, they standardise functional forms and parameters, eliding observable diversity and blacklisting sociotechnical policy options such as those based on agroecology and biological synergies. They are designed for prediction and prescription rather than for supporting public debate, which is also a (comfortable) political stance. In contrast, the Agrimonde experience – a foresight initiative based on the Agribiom model – shows that a model of world agriculture can be constructed as a “learning machine” that leaves room for a variety of scientific and stakeholder knowledge as well as public debate. This model and its partners unveiled some virtual realities, processes and actors that were invisible in mainstream models, and asserted a vision of sustainable agri-food systems by 2050. Agribiom and Agrimonde improved knowledge, policy-making and democracy. Overall, they highlighted the need for epistemic plurality and for engaging seriously in the production of models as learning machines.

Mots-clés Agrovoc : Système de production, Structure agricole, Utilisation des terres, gestion des ressources naturelles, Modélisation des cultures, Agriculture durable

Mots-clés géographiques Agrovoc : Monde

Mots-clés libres : Global modelling, Agriculture, Science and technology studies, Learning machine, Agrimonde

Classification Agris : E10 - Agricultural economics and policies
E90 - Agrarian structure
E21 - Agro-industry
P01 - Nature conservation and land resources
U10 - Computer science, mathematics and statistics

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

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