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Costs And Risks Of Testing And Segregating Gm Wheat


  • Wilson, William W.
  • Dahl, Bruce L.


Development of genetically modified crops is challenging the functions of the grain marketing system with many participants arguing for Identity Preservation systems prior to release of GM varieties. In this study, a stochastic optimization model was developed to determine optimal testing strategies. The model chooses the optimal testing strategy that maximizes utility (minimizes disutility) of additional system costs due to testing and rejection and allows estimation of the risk premium required for sellers to undertake a dual marketing system with GM/Non-GM segregations over the current Non-GM system. Elements of costs (testing costs, rejection costs, and risk premium) were estimated for a base model representing a grain export chain. The model includes elements of costs and risks for uncertainties within the marketing chain including risk of adventitious commingling at all stages of the marketing chain, grower truth-telling, variety declaration, and accuracy of testing technologies. Sensitivities were evaluated for effects of GM adoption, risk parameters, variety declaration, tolerance levels, and for a domestic market case.

Suggested Citation

  • Wilson, William W. & Dahl, Bruce L., 2002. "Costs And Risks Of Testing And Segregating Gm Wheat," Agribusiness & Applied Economics Report 23480, North Dakota State University, Department of Agribusiness and Applied Economics.
  • Handle: RePEc:ags:nddaae:23480
    DOI: 10.22004/ag.econ.23480

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    Cited by:

    1. Johnson, D. Demcey & Lin, William W., 2005. "The Economics of Testing for Biotech Grain: Application to StarLink Corn," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 30(2), pages 1-17, August.
    2. Matthew Rousu & Wallace E. Huffman & Jason F. Shogren & Abebayehu Tegene, 2007. "Effects And Value Of Verifiable Information In A Controversial Market: Evidence From Lab Auctions Of Genetically Modified Food," Economic Inquiry, Western Economic Association International, vol. 45(3), pages 409-432, July.
    3. Ge, Houtian & Gray, Richard & Nolan, James, 2015. "Agricultural supply chain optimization and complexity: A comparison of analytic vs simulated solutions and policies," International Journal of Production Economics, Elsevier, vol. 159(C), pages 208-220.
    4. Gawron, Jana-Christina & Theuvsen, Ludwig, 2007. "Costs of Processing Genetically Modified Organisms: Analysis of the Rapeseed and Corn Industries," 47th Annual Conference, Weihenstephan, Germany, September 26-28, 2007 7601, German Association of Agricultural Economists (GEWISOLA).
    5. Zhu, Manhong & Schmitz, Andrew & Schmtiz, Troy G., 2016. "Why Has not Genetically Modified Wheat Been Commercialized: A Game Theoretical Perspective," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230796, Southern Agricultural Economics Association.
    6. William W. Wilson & Xavier Henry & Bruce L. Dahl, 2008. "Costs and risks of conforming to EU traceability requirements: the case of hard red spring wheat," Agribusiness, John Wiley & Sons, Ltd., vol. 24(1), pages 85-101.
    7. Huffman, Wallace E. & Shogren, Jason F. & Rousu, Matthew C. & Tegene, Abebayehu, 2003. "Consumer Willingness to Pay for Genetically Modified Food Labels in a Market with Diverse Information: Evidence from Experimental Auctions," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(3), pages 1-22, December.
    8. Anderson, Kym & Jackson, Lee Ann, 2005. "GM crop technology and trade restraints: economic implications for Australia and New Zealand," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 49(3), pages 1-19.

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