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Can rainfall be a useful predictor of epidemic risk across temporal and spatial scales?

Can rainfall be a useful predictor of epidemic risk across temporal and spatial scales?

Plant disease epidemics are largely driven by within-season weather variables when inoculum is not limiting. Commonly, predictors in risk assessment models are based on the interaction of temperature and wetness-related variables, relationships which are determined experimentally. There is an increasing interest in providing within-season or inter-seasonal risk information at the region or continent scale, which commonly use models developed for a smaller scale. Hence, the scale matching dilemma that challenges epidemiologists and meteorologists: upscale models or downscale weather data? Successful applications may be found in both cases, which should be supported by validation datasets whenever possible, to prove the usefulness of the approach. For some diseases, rainfall is key for inoculum dispersal and, in warmer regions (e.g., tropics) where temperature is less limiting for epidemics, rainfall extends wetness periods. The drawbacks of using rainfall at small scales relate to its discrete nature and high spatial variability. However, for pre- or early-season predictions at large spatial scales sources of reasonably accurate rainfall summaries are available and may prove useful. The availability of disease datasets at various scales allows the development and evaluation of new models to be applied at the correct scale. We will showcase examples and discuss the usefulness of rainfall as key variable to predict soybean rust and wheat scab from field to region.

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Emerson M. Del Ponte

July 31, 2018
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Transcript

  1. Can rainfall be a useful predictor of epidemic risk across

    temporal and spatial scales? Emerson Del Ponte Discussions with: Adam Sparks, Nik Cunniffe, Larry Madden Collaboration: Kaique Alves, Gustavo Beruski
  2. Epidemic risk context for this talk Fungal disease (inoculum not

    limited) Field crops ("large" scale) Weather is key predictor Risk → Fungicide use or planning
  3. Agrios (2005) "Classical" disease (infection) risk model Relative humidity and

    T Dew Rainfall Leaf / Hour Field / Day LWD measured or estimated LWD
  4. Which wetness-variable? What is the model-building approach? Data-driven or Concept-driven?

    Biological or technical reasons Are Data available/accurate for the Scale?
  5. Several rainfall-based variables have been selected/used in disease risk models

    Together with other variables Rainfall duration in hours (continuous) Presence/absence (binary) Number of rain days (count) Daily total (continuous) Weekly total (continuous) Weekly rain events (count) Rain gauges Satellite Gridded data Model estimations
  6. Dispersal Infection Cloud cover wet deposition Survival (build-up) moisture splash

    moisture splash Effects of rainfall on epidemics?
  7. Soybean rust (SBR) in Brazil Data-driven model - natural field

    epidemics Only rainfall (30-day after detection) explained variation in final disease severity Del et (2006)
  8. SBR epidemics and rainfall Frequency and amount of rainfall during

    the epidemics
  9. Three Uses of the SBR Rainfall Model 1) Risk analysis

    2) Warning system 3) Early-season risk SEV = -2.14 + 0.18CR30 + 1.28RD30 Historical rain (day) Maximum Risk Region Real-time rain (day) More likely Risk Field Real time/forecast rain Likely Risk Large region Cumulative rain n. of Rain Days
  10. 1) Risk analysis Del Ponte et al (2011) How frequent

    severe epidemics? When during the season? Are they influenced by ENSO?
  11. 1) Risk analysis Risk of SEV > 70% 1 in

    3-4 years 30 years daily rainfall (1979 to 2009) 30 disease onset dates > Jan 1st 24 locations
  12. ENSO effects Warm Warm = El Niño Cold = La

    Niña
  13. Time and space ENSO effects

  14. Was the model/result too wrong? Warm Warm Cold Cold Warm

    Cold
  15. Warning system Site-specific risk advisory system Daily severity values and

    action thresholds SEV15 = -2.14 + 0.18CR15 + 1.28RD15 15A 15B DSV SEV15A x 0.3 + SEV15B x 0.7 3-day mean daily severity value
  16. Rainfall warning system Site-specific risk advisory system DSV50 action threshold

    - more conservative DSV80 action threshold - less conservative
  17. Number of sprays (9 trials - 3 locations, Gustavo Beruski

    PhD Dissertation at Esalq) 4.6 4.0 2.6 4.8 5.3
  18. Yield gains from using the systems (9 trials - 3

    locations, Gustavo Beruski PhD Dissertation at Esalq)
  19. Is it worth not follow calendar? Fungicide efficacy (%)

  20. 3) Early Season Risk (regional) PR RS Database: first report

    of soybean rust in commercial fields of a municipality Source: Consórcio Antiferrugem
  21. 2014/15 2015/16

  22. 2014/15 2015/16

  23. Nov 2015 Rainfall These Rainfall data were obtained from the

    NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program Nov 2014 Rainfall
  24. These Rainfall data were obtained from the NASA Langley Research

    Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program Nov 2015 Rainfall Nov 2014 Rainfall
  25. These Rainfall data were obtained from the NASA Langley Research

    Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program Nov 2016 Rainfall Nov 2017 Rainfall
  26. http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php What happened in 2015? El Niño! ENSO predictions for

    OND 2018
  27. Past 30-day observed rainfall 7-day forecast rainfall Favorability risk maps

  28. So, can rain be useful? Yes! for some diseases and

    crop production situations, is a very important weather variable driving epidemics from field to regional scales Tha y ! @edelponte