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The adoption of innovative cropping systems under price and production risks: a dynamic model of crop rotation choice

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

Abstract

In the paper we investigate the role played by both production and market risks on farmer’s decision to adopt long rotations (over 2 years), considered as innovative cropping systems. We build a multiperiod dynamic farm model (run under GAMS) that arbitrates each year between traditional and innovative rotations. With discrete stochastic programming, the production risk is accounted as an intra-year risk; yearly farming operations are declined according to a decision tree where probabilities are assigned. Subjective yield and cost distributions linked to this decision tree are elicited among a sample of 13 farmers that are experiencing this innovation in South-western France. The price risk is randomly distributed with a given market trend. The crop acreage can be revised according to the market situation. The simulations show that substantive sunk costs are incentive to remain in the long rotation when the farmer is already engaged and when he is supported for this engagement. They also show that both a high risk aversion and a highly positive market trend tend to slow down the conversion towards innovative systems.

Suggested Citation

  • Ridier, Aude & Chaib, Karim & Roussy, Caroline, 2012. "The adoption of innovative cropping systems under price and production risks: a dynamic model of crop rotation choice," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122440, European Association of Agricultural Economists.
  • Handle: RePEc:ags:eaa123:122440
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    References listed on IDEAS

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    1. Abadi Ghadim, Amir K. & Pannell, David J., 1999. "A conceptual framework of adoption of an agricultural innovation," Agricultural Economics, Blackwell, vol. 21(2), pages 145-154, October.
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    4. Trebeck, David B. & Hardaker, J. Brian, 1972. "The Integrated Use Of Simulation And Stochastic Programming For Whole Farm Planning Under Risk," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 16(02), August.
    5. Carpentier, Alain & Gohin, Alexandre & Letort, Elodie, 2011. "Accounting for agronomic rotations in crop production: A theoretical investigation and an empirical modeling framework," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103431, Agricultural and Applied Economics Association.
    6. Bocqueho, Geraldine & Jacquet, Florence & Reynaud, Arnaud, 2011. "Expected Utility or Prospect Theory Maximizers? Results from a Structural Model based on Field-experiment Data," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114257, European Association of Agricultural Economists.
    7. David A. Hennessy, 2006. "On Monoculture and the Structure of Crop Rotations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(4), pages 900-914.
    8. Szvetlana Acs & Paul Berentsen & Ruud Huirne & Marcel van Asseldonk, 2009. "Effect of yield and price risk on conversion from conventional to organic farming ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 53(3), pages 393-411, July.
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    11. Blazy, Jean-Marc & Tixier, Philippe & Thomas, Alban & Ozier-Lafontaine, Harry & Salmon, Frédéric & Wery, Jacques, 2010. "BANAD: A farm model for ex ante assessment of agro-ecological innovations and its application to banana farms in Guadeloupe," Agricultural Systems, Elsevier, vol. 103(4), pages 221-232, May.
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    14. Kenneth A. Baerenklau & Keith C. Knapp, 2007. "Dynamics of Agricultural Technology Adoption: Age Structure, Reversibility, and Uncertainty," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(1), pages 190-201.
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    Cited by:

    1. Canales, Elizabeth & Bergtold, Jason S. & Williams, Jeffery & Peterson, Jeffrey, 2015. "Estimating farmers’ risk attitudes and risk premiums for the adoption of conservation practices under different contractual arrangements: A stated choice experiment," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205640, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    2. 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.

    More about this item

    Keywords

    innovative cropping systems; dynamic model; crop rotation decision; risk; subjective probabilities; Risk and Uncertainty; C61; D0; Q12; Q55;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D0 - Microeconomics - - General
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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