Risk, labour and climatic uncertainty in crop rotation optimization

Charlotte Daydé, Aude Ridier, Caroline Roussy and Karim Chaïb
Additional contact information
Charlotte Daydé: UBIA - Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) - INRA - Institut National de la Recherche Agronomique
Caroline Roussy: SMART-LERECO - Structures et Marché Agricoles, Ressources et Territoires - INRA - Institut National de la Recherche Agronomique - AGROCAMPUS OUEST
Karim Chaïb: INP - PURPAN - Ecole d'Ingénieurs de Purpan - Toulouse INP - Institut National Polytechnique (Toulouse) - UT - Université de Toulouse

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Abstract: The goal of this article is to give some guidelines when modeling farmers' rotation choices through optimization models. To improve the accuracy of such models, researchers can i) sophisticate the utility function or ii) specify the production function and the constraints of the model. Based on an interactive approach involving farmers, a preliminary discrete determinist model is built and tested under changing crops prices. Then, two discrete stochastic modeling approaches are compared; in the first one, yield risk is accounted as main source of income variability and, in the second one, risk is incorporated as a stochastic constraint of monthly inaccessible field days. Results show that risk aversion little affects rotation choice. A stochastic labour constraint accounting for field inaccessibility has considerable more impact on crops choice, especially in presence of imperfect labour market.

Keywords: crop rotation; labour constraint; interactive approach; discrete stochastic programming (search for similar items in EconPapers)
Date: 2014-08-26
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Published in 14. EAAE Congress "Agri-Food and Rural Innovations for Healthier Societies", Aug 2014, Ljubljana, Slovenia. , 6 p., 2014

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Working Paper: Risk, labour and climatic uncertainty in crop rotation optimization (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01123262

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