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BIG Data and META-approaches for analysing rese...

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

Emerson M. Del Ponte

February 16, 2021
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  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. 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
  3. 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
  4. 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)
  5. 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
  6. 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
  7. 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
  8. Conclusion 1 Yield loss can be predicted from severity data

    and is influenced by onset time and severity level
  9. 250 field trials 2004 - 2017 (14 years) > 30

    Institutions/researchers Example 2: fungicide performance
  10. v v

  11. 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
  12. 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)
  13. Probability distributions for Monte carlo simulations Severity on untreated plots

    Soybean Price (2 years) Interceps Slopes Yield-severity relationship
  14. Conclusion 4 A decision tool for making profit with fungicides

    taking epidemiological, control and economic factors into account