Table 9.

Pros and cons of the regression model, Bayesian network model, and mechanistic model. DFA: discriminant function analysis.

	Regression Model	Bayesian Network Model	Mechanistic Model
Prediction accuracy low DON	93.8%	90.2%	84.1%
Prediction accuracy medium DON	0%	0%	0%
Prediction accuracy high DON	0%	0%	50%
Possibility to apply in other conditions (e.g., countries)?	High data dependency. Only in those countries/regions with similar agricultural and weather conditions. Validation needed before its use in new agricultural contexts	High data dependency. Only in those countries/regions with very similar agricultural and weather conditions. Validation needed before its use in new agricultural contexts	Low data dependency. The model can be implemented in other countries/regions given that the fungal species are similar. The combination of model output with influencing agronomic practices in a new country/region needs calibration through a specific DFA.
Prediction time	One week before flowering, using 10 days’ weather forecast data	From beginning of the growing season	From heading date
Capability to predict unknown situations	No	No	Yes
Requirement for specific data	Low	Low. Possible to combine expert knowledge with statistical relationships.	High, e.g., heading date, and leaf wetness duration.