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Spatio-temporal Risk and Severity Analysis of Soybean Rust in the United States

Author

Listed:
  • Bekkerman, Anton
  • Goodwin, Barry K.
  • Piggott, Nicholas E.

Abstract

Soybean rust is a highly mobile infectious disease and can be transmitted across short and long distances. Soybean rust is estimated to cause yield losses that can range between 1%-25%. An analysis of spatio-temporal infection risks within the United States is performed through the use of a unique data set. Observations from over 35,000 field-level inspections between 2005 and 2007 are used to conduct a county-level analysis. Statistical inferences are derived by employing zero-inflated Poisson and negative binomial models. In addition, the model is adjusted to account for potential endogeneity between inspections and soybean rust finds. Past soybean rust finds and inspections in the county and in the surrounding counties, weather and overwintering conditions, and plant maturity groups and planting dates are all found to be significant factors determining soybean rust. These results are then used to accordingly price annual insurance contracts or indemnification programs that cover soybean rust damages.

Suggested Citation

  • Bekkerman, Anton & Goodwin, Barry K. & Piggott, Nicholas E., 2008. "Spatio-temporal Risk and Severity Analysis of Soybean Rust in the United States," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 33(3), pages 1-21.
    • Handle: RePEc:ags:jlaare:46564
    DOI: 10.22004/ag.econ.46564
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    File URL: https://ageconsearch.umn.edu/record/46564/files/Bekkerman.pdf
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    References listed on IDEAS

    as
    1. Roberts, Michael J. & Schimmelpfennig, David E. & Ashley, Elizabeth & Livingston, Michael J. & Ash, Mark S. & Vasavada, Utpal, 2006. "The Value of Plant Disease Early-Warning Systems: A Case Study of USDA's Soybean Rust Coordinated Framework," Economic Research Report 7208, United States Department of Agriculture, Economic Research Service.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Bekkerman, Anton & Piggott, Nicholas E. & Goodwin, Barry K. & Jefferson-Moore, Kenrett, 2012. "A Market-based Mitigation Program for Wind-borne Diseases," Agricultural and Resource Economics Review, Cambridge University Press, vol. 41(2), pages 175-188, August.
    2. Thakur, Tiesta & Homans, Frances R. & Haight, Robert G. & Hurley, Terrance M., 2018. "Valuing Monitoring Networks for New Pathogens: The Case of Soybean Rust," 2018 Annual Meeting, August 5-7, Washington, D.C. 273870, Agricultural and Applied Economics Association.
    3. Gomez, Miguel I. & Nunez, Hector M. & Onal, Hayri, 2009. "Economic Impacts of Soybean Rust on the US Soybean Sector," Working Papers 51177, Cornell University, Department of Applied Economics and Management.
    4. repec:rre:publsh:v:40:y:2010:i:1:p:53-69 is not listed on IDEAS
    5. Bekkerman, Anton & Brester, Gary W. & Taylor, Mykel, 2016. "Forecasting a Moving Target: The Roles of Quality and Timing for Determining Northern U.S. Wheat Basis," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 41(01), pages 1-17, January.
    6. Thakur, Tiesta & Hurley, Terrance M. & Homans, Frances R. & Haight, Robert G., . "Valuing Monitoring Networks for New Pathogens: The Case of Soybean Rust in the United States," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 49(3).
    7. Onel, Gulcan & Karali, Berna, 2014. "Relative Performance of Semi-Parametric Nonlinear Models in Forecasting Basis," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169795, Agricultural and Applied Economics Association.

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