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

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

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Emerson M. Del Ponte

February 16, 2021
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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
  2. Epidemiology Research Control methods Chemicals Biological Genetic Cultural Biotechnology Remote

    sensing Modeling Machine learning Phytopathometry
  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
  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
  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)
  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
  7. Rapid response Yearly summaries Within trial data/analysis "Combined" analysis Objectives

    & outcomes
  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
  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
  10. 210 trials 57 locations 40 fungicides 9 seasons 2004/05 a

    2012/13
  11. Variability in intercepts and slopes Intercepts slopes

  12. Effect of disease pressure and onset time

  13. Conclusion 1 Yield loss can be predicted from severity data

    and is influenced by onset time and severity level
  14. 250 field trials 2004 - 2017 (14 years) > 30

    Institutions/researchers Example 2: fungicide performance
  15. Exploratory results

  16. v v

  17. Large within-year (spatial) variation

  18. MA Results

  19. Dual premixes should be encouraged and single a.i. not recommended

    due to low efficacy Conclusion 2
  20. Example 3: premix performance

  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
  22. Exploratory results

  23. Exploratory results

  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)
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  30. Dual premixes declining after 4 years, but triple premix still

    good Conclusion 3
  31. Example 4: economic analysis

  32. Start

  33. Probability distributions for Monte carlo simulations Severity on untreated plots

    Soybean Price (2 years) Interceps Slopes Yield-severity relationship
  34. Probability distributions for Monte carlo simulations Damage coefficients Empirical Simulated

  35. Yield response

  36. None
  37. None
  38. Profitability over time

  39. Profitability over time

  40. Rusty profits shiny app Website

  41. Conclusion 4 A decision tool for making profit with fungicides

    taking epidemiological, control and economic factors into account
  42. Chemical breeding epidemiology Kaique Alves Felipe Dalla Lana Jhonatan Barro

    emersondelponte.netlify.app