Using Weather Variables Pre- and Post-heading to Predict Deoxynivalenol Content in Winter Wheat

Area of Science:

• Agricultural Science
• Plant Pathology
• Mycotoxicology

Background:

• Deoxynivalenol (DON) contamination in wheat causes significant economic losses.
• Accurate prediction of DON concentrations is essential for implementing timely control strategies.

Purpose of the Study:

• To identify key weather variables and their critical timing for predicting DON in mature wheat grain.
• To develop a predictive model for DON concentrations at wheat heading.

Main Methods:

• DON concentrations were measured in 399 farm fields in southern Ontario from 1996-2000.
• Weather data (rainfall, temperature, humidity) were collected and normalized to wheat heading date.
• Stepwise multiple regression analysis was used to identify significant weather predictors.

Main Results:

• DON levels were significantly influenced by weather conditions in three critical periods around heading.
• Increased rainfall and cooler temperatures before heading, rainfall after heading, and high temperatures after heading were associated with higher DON.
• The model explained 73% of the variation in DON concentrations, with high accuracy for levels below 2.0 μg g⁻¹.

Conclusions:

• Weather variables around wheat heading are critical for predicting deoxynivalenol contamination.
• Predictive models based on weather data can aid in managing DON risks in wheat production.
• Early prediction allows for informed decisions on control strategies to mitigate economic impacts.