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Modeling spatiotemporal dynamics and time to regional outbreaks of soybean rust in southern Brazil

Modeling spatiotemporal dynamics and time to regional outbreaks of soybean rust in southern Brazil

54197e156adad1b100edde325b492b3d?s=128

Emerson M. Del Ponte

August 20, 2020
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  1. Modeling spatiotemporal dynamics and time to regional outbreaks of soybean

    rust in southern Brazil Kaique Alves | Adam Sparks | Emerson Del Ponte
  2. Why Soybean rust? More models?

  3. Fungicides are key, but.. AZOX + CYPR TEBU

  4. Background … from my talk last year

  5. Follow up: now using 15 years - SBR prevalence All

    years (2005 - 2019) 2 States (south) PR and RS Commercial soybean fields First (date) report in a county 2,027 records ~15,000 records PR RS
  6. Epidemic time MaxPrev AUDPC r Time_10 10% Prev90 Prev120 Temporal

    progress description / analysis Time to outbreak (Survival analysis)
  7. NND Nearest neighbour distances Epidemic area Monthly maps Spatial description

    and analysis Initial Final area
  8. Results: reports over time Peak in January Peak in February

    PR State RS State
  9. Early onset Late onset Results: reports space and time

  10. Results: NND over time

  11. Results: Epidemic area over time Weak correlation between Initial and

    final epidemic area
  12. Results: Correlations initial and final area Week after Sep 15

    Jan 15
  13. Results: Correlations initial and final area Jan 1st

  14. Results: Time to outbreak ~ 70 days

  15. Results: Principal Components

  16. Results: Principal Components

  17. From my talk last year, using 4 seasons

  18. Euclidean distances Clustering method "ward.D2" Is the evidence consistent with

    other years?
  19. Survival analysis Cox modeling Is time to outbreak affected by

    ENSO?
  20. From my talk last year, using 4 seasons

  21. What about the next season?

  22. Conclusions • Large scale spatiotemporal spread varies among seasons and

    states • Multiple inoculum sources affect initial epidemic area • Early- and mid- season weather plays a major role • ENSO conditions useful as early warning Tactical Strategical Pre-season Growing season Risk prediction Outlook Forecasting Warning What's next? Develop models for predicting and mapping disease risk at the regional level Thank you! @edelponte