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
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
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
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)
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
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