Modelling world agriculture as a learning machine? From mainstream models to Agribiom 1.0
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DOI: 10.1016/j.landusepol.2018.09.028
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- Bruno Dorin & Pierre-Benoît Joly, 2020. "Modelling world agriculture as a learning machine? From mainstream models to Agribiom 1.0," Post-Print hal-02106267, HAL.
References listed on IDEAS
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- Tanja Dergan & Aneta Ivanovska & Tina Kocjančič & Pietro P. M. Iannetta & Marko Debeljak, 2022. "‘Multi-SWOT’ Multi-Stakeholder-Based Sustainability Assessment Methodology: Applied to Improve Slovenian Legume-Based Agri-Food Chains," Sustainability, MDPI, vol. 14(22), pages 1-26, November.
- Pierre-Marie Aubert & Baptiste Gardin & Élise Huber & Michele Schiavo & Christophe Alliot, 2021. "Designing Just Transition Pathways: A Methodological Framework to Estimate the Impact of Future Scenarios on Employment in the French Dairy Sector," Post-Print hal-03653089, HAL.
- Pierre-Marie Aubert & Baptiste Gardin & Élise Huber & Michele Schiavo & Christophe Alliot, 2021. "Designing Just Transition Pathways: A Methodological Framework to Estimate the Impact of Future Scenarios on Employment in the French Dairy Sector," Agriculture, MDPI, vol. 11(11), pages 1-19, November.
- Jahel, Camille & Bourgeois, Robin & Bourgoin, Jérémy & Daré, William's & De Lattre-Gasquet, Marie & Delay, Etienne & Dumas, Patrice & Le Page, Christophe & Piraux, Marc & Prudhomme, Rémi, 2023. "The future of social-ecological systems at the crossroads of quantitative and qualitative methods," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
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Keywords
Global modelling; Agriculture; Science and technology studies; Learning machine; Agrimonde;All these keywords.
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