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