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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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Why Soybean rust? More models?

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Fungicides are key, but.. AZOX + CYPR TEBU

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Background … from my talk last year

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

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

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

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Early onset Late onset Results: reports space and time

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Results: NND over time

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

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

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Results: Correlations initial and final area Jan 1st

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Results: Time to outbreak ~ 70 days

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Results: Principal Components

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Results: Principal Components

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From my talk last year, using 4 seasons

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

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

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From my talk last year, using 4 seasons

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What about the next season?

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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