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A Dynamic Stochastic Programming model of crop rotation choice to test the adoption of long rotation under price and production risks

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  • Ridier, Aude
  • Chaib, Karim
  • Roussy, Caroline

Abstract

This article investigates the role played by both production and market risks on cash crop farmers’ decision to adopt long rotations considered as innovative cropping systems. We build a multi-period recursive farm model with Discrete Stochastic Programming. The model arbitrates each year between conventional and innovative, longer rotations. Yearly farming operations are declined according to a decision tree, so that production risk is an intra-year risk. Market risk is considered as an inter-year risk influencing crop successions. Simulations are performed on a specialized French cash crop farm. They show that when the long rotation is subsidized by an area premium, farmers are encouraged to remain in longer rotations. They also show that a high level of risk aversion tends to slow down the conversion towards longer rotations.

Suggested Citation

  • Ridier, Aude & Chaib, Karim & Roussy, Caroline, 2016. "A Dynamic Stochastic Programming model of crop rotation choice to test the adoption of long rotation under price and production risks," European Journal of Operational Research, Elsevier, vol. 252(1), pages 270-279.
  • Handle: RePEc:eee:ejores:v:252:y:2016:i:1:p:270-279
    DOI: 10.1016/j.ejor.2015.12.025
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    Cited by:

    1. Cervantes-Gaxiola, Maritza E. & Sosa-Niebla, Erik F. & Hernández-Calderón, Oscar M. & Ponce-Ortega, José M. & Ortiz-del-Castillo, Jesús R. & Rubio-Castro, Eusiel, 2020. "Optimal crop allocation including market trends and water availability," European Journal of Operational Research, Elsevier, vol. 285(2), pages 728-739.
    2. Lefebvre, Marianne & Midler, Estelle & Bontems, Philippe, 2020. "Adoption of environmentally-friendly agricultural practices with background risk: experimental evidence," TSE Working Papers 20-1079, Toulouse School of Economics (TSE).
    3. Robert, Marion & Bergez, Jacques-Eric & Thomas, Alban, 2018. "A stochastic dynamic programming approach to analyze adaptation to climate change – Application to groundwater irrigation in India," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1033-1045.
    4. Julia Jouan & Aude Ridier & Matthieu Carof, 2019. "Economic Drivers of Legume Production: Approached via Opportunity Costs and Transaction Costs," Sustainability, MDPI, Open Access Journal, vol. 11(3), pages 1-14, January.
    5. Marianne Lefebvre & Estelle Midler & Philippe Bontems, 2020. "Adoption of Environment-Friendly Agricultural Practices with Background Risk: Experimental Evidence," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(2), pages 405-428, July.
    6. Cholez, Celia & Magrini, Marie-Benoit & Galliano, Danielle, 2016. "Technical knowledge and production contracts between a company and its suppliers: lessons from a French case-study," 149th Seminar, October 27-28, 2016, Rennes, France 244775, European Association of Agricultural Economists.
    7. Mitra, Sovan & Lim, Sungmook & Karathanasopoulos, Andreas, 2019. "Regression based scenario generation: Applications for performance management," Operations Research Perspectives, Elsevier, vol. 6(C).

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