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