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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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  • Aude Ridier
  • Karim Chaib
  • Caroline Roussy

Abstract

We investigate the role played by both production and market risks on farmers’ decision to adopt long rotations considered as innovative cropping systems. We build a multi-period dynamic farm model which arbitrates each year between conventional and innovative rotations. With discrete stochastic programming, the production risk is accounted for as an intra-year risk, yearly farming operations being declined according to a decision tree where probabilities are assigned. The simulations for a sample of 13 farmers who are currently experimenting this innovation in south-western France, show that substantive sunk costs act as incentives to remain in the long rotation when the farmer is supported for his 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

  • Aude Ridier & Karim Chaib & Caroline Roussy, 2012. "The adoption of innovative cropping systems under price and production risks: a dynamic model of crop rotation choice," Working Papers SMART - LERECO 12-07, INRA UMR SMART-LERECO.
  • Handle: RePEc:rae:wpaper:201207
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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.
    2. Charles A. Holt & Susan K. Laury, 2002. "Risk Aversion and Incentive Effects," American Economic Review, American Economic Association, vol. 92(5), pages 1644-1655, December.
    3. Tomomi Tanaka & Colin F. Camerer & Quang Nguyen, 2010. "Risk and Time Preferences: Linking Experimental and Household Survey Data from Vietnam," American Economic Review, American Economic Association, vol. 100(1), pages 557-571, March.
    4. 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.
    5. 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.
    6. David B. Trebeck & J. Brian Hardaker, 1972. "The Integrated Use Of Simulation And Stochastic Programming For Whole Farm Planning Under Risk," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 16(2), pages 115-126, August.
    7. 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.
    8. Isik, Murat & Khanna, Madhu & Winter-Nelson, Alex, 2001. "Sequential Investment In Site-Specific Crop Management Under Output Price Uncertainty," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 26(01), July.
    9. 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.
    10. Graeme J. Doole & David J. Pannell, 2008. "Optimisation of a Large, Constrained Simulation Model using Compressed Annealing," Journal of Agricultural Economics, Wiley Blackwell, vol. 59(1), pages 188-206, February.
    11. 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.
    12. 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.
    13. 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.
    14. Apland, Jeffrey & Hauer, Grant, 1993. "Discrete Stochastic Programming: Concepts, Examples And A Review Of Empirical Applications," Staff Papers 13793, University of Minnesota, Department of Applied Economics.
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    Cited by:

    1. 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.
    2. 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.

    More about this item

    Keywords

    innovative cropping systems; dynamic model; crop rotation decision; risk; subjective probabilities;

    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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