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Long-term prediction of soybean rust entry into the continental United States

  • Zaitao Pan
  • , X. B. Yang
  • , S. Pivonia
  • , L. Xue
  • , R. Pasken
  • , J. Roads
    • Saint Louis University
    • Iowa State University
    • Central-and Northern-Arava Research and Development
    • University of California at San Diego

    Research output: Contribution to journalArticlepeer-review

    64 Scopus citations

    Abstract

    This special report demonstrates the feasibility of long-term prediction of intercontinental dispersal of Phakopsora pachyrhizi spores, the causal agent of the devastating Asian soybean rust (SBR) that invaded the continental United States in 2004. The climate-dispersion integrated model system used for the prediction is the combination of the particle transport and dispersion model (HYSPLIT_4) with the regional climate prediction model (MM5). The integrated model system predicts the trajectory and concentration of P. pachyrhizi spores based on three-dimensional wind advection and turbulent transport while incorporating simple viability criteria for aerial spores. The weather input of the model system is from a seasonal global climate prediction. The spore source strength and distribution were estimated from detected SBR disease severity and spread. The model system was applied to the known P. pachyrhizi spore dispersal between and within continents while focusing on the disease entry into the United States. Prediction validation using confirmed disease activity demonstrated that the model predicted the 2004 U.S. entry months in advance and reasonably forecast disease spread from the south coast states in the 2005 growing season. The model also simulated the dispersal from Africa to South America and from southern South America to Columbia across the equator. These validations indicate that the integrated model system, when furnished with detailed source distribution, can be a useful tool for P. pachyrhizi and possibly other airborne pathogen prediction.

    Original languageEnglish
    Pages (from-to)840-846
    Number of pages7
    JournalPlant Disease
    Volume90
    Issue number7
    DOIs
    StatePublished - Jul 2006

    Keywords

    • Climate model
    • Hurricane

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