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Update on Fusarium Head BlightForecasting
Erick De Wolf, Denis Shah, Peirce Paul, and Larry
Madden
Brief History of Modeling EffortYears Location years Deployment
1999-2001 50 Individual states
2002-2003 120 Individual states and groups of states
2004-2014 527 Regional (30 states)
• Primarily logistic regression models• Now exploring Boosted Regression Tree (BRTs)
Boosted Regression Trees
• Origins in machine learning community • Fits individual trees in forward, additive
manner• New trees focus on cases misclassified by
previous trees• Combines many simple predictive trees into
single predictive model (1,000 models)
FHB Data Sets
• 527 cases; 70% training, 30% testing• Representing 15 states and 26 years• 350 weather-based predictors– 5, 7, 10, 14 days prior to or post-anthesis– Temp, atmospheric moisture, rain
• Binary predictors – Corn residue – Wheat type (winter or spring)– Genetic resistance of variety
Response Variable
• Binary representation of FHB epidemics– 1 if FHB severity is >10%– 0 if severity is <10%
Model Performance
Relative Influence Binary Predictors
• Corn residue and wheat type low relative influence dropped
• Genetic resistance retained
Relative Influence Weather Based Predictors
• Pre-anthesis– Mean RH% – Temperature and RH combination• Hours that temp. 9-30 and RH>90%
• Post-anthesis– Mean temperature– Rain– Temperature RH combination
Partial Dependence Plots
Variables summarize weather 7-days prior to anthesis
Partial Dependence Plots
Mean RH (%) Mean Temperature C
Variables summarize weather 7-days prior to anthesis
Visualize Interactions
Mean RH(%)
VS
S
MS & MR
Potential Value of BRTs?
• Helpful tools for variable selection– Removal of corn residue and wheat type– Addition of rain post-anthesis
• Insights on relationship between variables and FHB epidemics– RH and temp thresholds
• Visualization of interactions – RH and Level of genetic resistance
Reality Check
• Prediction accuracy improved over logistic models
• Application of models considerably more complex (1,000 predictive models)
• Looking to apply what we have learned in other model frameworks better suited for application
Questions
• For more information:– Shah et al 2014, Phytopathology 104:702-714