BIG Data and META-approaches for analysing research data and improving DECISIONS in plant disease MANAGEMENT

Talk given at an online conference on Big Data in Agriculture organized in Ecuador on 10 February 2021

Transcript

1. BIG Data and META-approaches for
   analysing research data and improving
   DECISIONS in plant disease MANAGEMENT
   Emerson M. Del Ponte
   Jhonatan P. Barro, Kaique S. Alves and Felipe Dalla Lana
   

   View Slide

2. Epidemiology
   Research
   Control methods Chemicals Biological
   Genetic Cultural
   Biotechnology
   Remote sensing
   Modeling
   Machine learning
   Phytopathometry
   

   View Slide

3. Big data
   Decision
   Soil, crops, pests,
   diseases
   RISK
   Strategic or tactical
   PDM research
   Epidemiology
   Field trials
   (Regionwide)
   Information
   Sensors
   user-input
   Storage
   Processing
   Impact
   Knowledge
   

   View Slide

4. Digital farming
   Remote sensing
   → Field scale
   n > 5k?
   PDM research requires
   variation in disease and
   production situations → several fields
   (locations x years)
   n > 50?
   Context for BIG!
   Source: grupocultivar.com.br
   

   View Slide

5. Coordinated efforts (industry and public)
   One or more target (disease)
   Common treatments (fungicide, biocontrol, etc.)
   Chemicals from several industries (control bias?)
   New treatments added over years, some are kept
   Disease and yield data are obtained
   The uniform trials (network)
   

   View Slide

6. 2004
   Soybean
   Rust
   Soybean
   White mold
   2009 2011
   Soybean
   Target spot
   Wheat
   Blast & FHB
   Fungicides
   Biocontrol
   2018 2019
   Wheat
   Leaf blotches
   Wheat powdery
   mildew
   Cooperative trials
   

   View Slide

7. Rapid response
   Yearly summaries
   Within trial data/analysis
   "Combined" analysis
   Objectives & outcomes
   

   View Slide

8. Few (< 5) experiments
   Focus on statistical significance (P-value)
   Vote-counting approach: how many P < 0.05
   When combined, same weight is assigned to trials
   "Not good" trials are eliminated from analysis
   By tradition in academic research
   

   View Slide

9. Three examples of our research
   Data: soybean rust in fungicide trial network
   Meta-analysis
   Yield Loss
   Meta-analysis
   Fungicide performance
   Fungicide profitability
   Monte Carlo Simulation
   Cooperative
   trial datasets
   1
   3
   2
   

   View Slide

10. 210 trials
    57 locations
    40 fungicides
    9 seasons
    2004/05 a 2012/13
    

    View Slide

11. Variability in intercepts and slopes
    Intercepts slopes
    

    View Slide

12. Effect of disease pressure and onset time
    

    View Slide

13. Conclusion 1
    Yield loss can be predicted from
    severity data and is influenced by
    onset time and severity level
    

    View Slide

14. 250 field trials
    2004 - 2017 (14 years)
    > 30 Institutions/researchers
    Example 2: fungicide performance
    

    View Slide

15. Exploratory results
    

    View Slide

16. v
    v
    

    View Slide

17. Large within-year (spatial) variation
    

    View Slide

18. MA Results
    

    View Slide

19. Dual premixes should be
    encouraged and single a.i. not
    recommended due to low efficacy
    Conclusion 2
    

    View Slide

20. Example 3: premix performance
    

    View Slide

21. Fungicide a.i. Study code Commercial
    name
    CHECK
    AZOX + BENZ
    BIXF + TFLX + PROT
    PICO + BENZ
    PICO + CYPR
    PICO + TEBU
    PYRA + EPOX + FLUX
    TFLX + CYPR
    TFLX + PROT
    Fungicide treatments
    

    View Slide

22. Exploratory results
    

    View Slide

23. Exploratory results
    

    View Slide

24. 77.6 - 85.2
    81.4 - 85.6
    SBR control (%)
    Fungicidea Seasons Trials C CI
    L
    CI
    U
    BIXF + TFLX + PROT 4 115 76.80 74.39 78.98
    PICO + BENZ 4 116 74.02 71.24 76.54
    AZOX + BENZ 5 144 72.79 69.74 75.53
    PYRA + EPOX + FLUX 4 115 72.23 69.56 74.66
    TFLX + PROT 6 166 71.96 69.31 74.39
    PICO + TEBU 5 149 66.01 63.11 68.69
    TFLX + CYPR 5 143 57.89 54.68 60.88
    PICO + CYPR 6 169 56.25 53.25 59.06
    MA results
    Dalla Lana et al (2018)
    

    View Slide

25. View Slide

26. View Slide

27. View Slide

28. View Slide

29. View Slide

30. Dual premixes declining after 4 years, but
    triple premix still good
    Conclusion 3
    

    View Slide

31. Example 4: economic analysis
    

    View Slide

32. Start
    

    View Slide

33. Probability distributions for Monte carlo simulations
    Severity on untreated plots
    Soybean Price (2 years)
    Interceps
    Slopes
    Yield-severity relationship
    

    View Slide

34. Probability distributions for Monte carlo simulations
    Damage coefficients
    Empirical
    Simulated
    

    View Slide

35. Yield response
    

    View Slide

36. View Slide

37. View Slide

38. Profitability over time
    

    View Slide

39. Profitability over time
    

    View Slide

40. Rusty profits shiny app
    Website
    

    View Slide

41. Conclusion 4
    A decision tool for making profit with
    fungicides taking epidemiological,
    control and economic factors into
    account
    

    View Slide

42. Chemical breeding
    epidemiology
    Kaique Alves Felipe Dalla Lana
    Jhonatan Barro
    emersondelponte.netlify.app
    

    View Slide