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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

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

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  2. Why Soybean rust? More models?

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

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  4. Background … from my talk last year

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  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

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  6. Epidemic time
    MaxPrev
    AUDPC
    r
    Time_10
    10%
    Prev90
    Prev120
    Temporal progress description / analysis
    Time to outbreak
    (Survival analysis)

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  7. NND
    Nearest neighbour distances
    Epidemic area
    Monthly maps
    Spatial description and analysis
    Initial
    Final area

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  8. Results: reports over time
    Peak in
    January
    Peak in
    February
    PR State
    RS State

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  9. Early onset
    Late onset
    Results: reports space and time

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  10. Results: NND over time

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

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

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

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  14. Results: Time to outbreak
    ~ 70 days

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  15. Results: Principal Components

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  16. Results: Principal Components

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  17. From my talk last year, using 4 seasons

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

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

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  20. From my talk last year, using 4 seasons

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  21. What about the next season?

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  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

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