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Costs And Risks Of Testing And Segregating Gm Wheat - Summary

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

Abstract

Development of genetically modified crops is challenging the functions of the grain marketing system with many participants arguing for Identity Preservation (IP) 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 - Summary," Agribusiness & Applied Economics Report 23502, North Dakota State University, Department of Agribusiness and Applied Economics.
    • Handle: RePEc:ags:nddaae:23502
    DOI: 10.22004/ag.econ.23502
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    File URL: https://ageconsearch.umn.edu/record/23502/files/aer501s.pdf
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    Citations

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    Cited by:

    1. Wisner, Robert N., 2004. "Round-Up® Ready Spring Wheat: Its Potential Short-Term Impacts on U.S. Wheat Exports Markets and Prices," Staff General Research Papers Archive 12205, Iowa State University, Department of Economics.
    2. 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.
    3. David J. Hertsgaard & William W. Wilson & Bruce Dahl, 2019. "Costs and risks of testing and blending for essential amino acids in soybeans," Agribusiness, John Wiley & Sons, Ltd., vol. 35(2), pages 265-280, April.
    4. 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.
    5. Matthew Rousu & Wallace E. Huffman & Jason F. Shogren & Abebayehu Tegene, 2004. "Are United States Consumers Tolerant of Genetically Modified Foods?," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 26(1), pages 19-31.
    6. William, Wilson & Dahl, Bruce & Hertsgaard, David, 2020. "Soybean quality differentials, blending, testing and spatial arbitrage," Journal of Commodity Markets, Elsevier, vol. 18(C).
    7. Ge, Houtian & Nolan, James & Gray, Richard & Goetz, Stephan & Han, Yicheol, 2016. "Supply chain complexity and risk mitigation – A hybrid optimization–simulation model," International Journal of Production Economics, Elsevier, vol. 179(C), pages 228-238.
    8. 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.

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    Keywords

    Crop Production/Industries;

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