Stability of Hybrid Maize Reaction to Gibberella Ear Rot and Deoxynivalenol Contamination of Grain

Impact of Gibberella Ear Rot on Grain Quality and Yield Components in Maize as Influenced by Hybrid Reaction

The impact of Gibberella ear rot (GER; caused by Fusarium graminearum) on deoxynivalenol (DON) contamination of grain and yield components in maize were investigated using data from 30 environments in Ohio (3 years by 10 locations). Fifteen hybrids, later classified as susceptible (SU), moderately susceptible (MS), or moderately resistant (MR), based on the magnitude of differences in mean arcsine square-root-transformed GER severity (arcSEV) and log-transformed DON (logDON) relative to a reference SU check, were planted in each environment, and 10 ears per hybrid were inoculated with a spore suspension of F. graminearum. Relationships between GER severity and DON were well described by a Kono-Sugino-type nonlinear equation. Estimated parameters representing height (A) and steepness (β) of the curves were significantly higher for SU than MS and MR hybrids but A was not significantly different between MS and MR. Results from a surrogacy analysis showed that GER was a moderate trial- and individual-level surrogate for DON. Both grain weight per ear and ear diameter decreased with increasing arcSEV but the regression slopes varied among resistance classes. The rates of reduction in both yield components per unit increase in arcSEV were significantly greater for SU than for MS and MR. An estimated 50% reduction in grain weight occurred at 62% GER severity for SU, compared with 77% severity for MS and 83% for MR. These results show that GER severity can be used as a surrogate for early estimation of DON contamination and yield loss to help guide grain handling and marketing decisions.

Impact of agronomic practices on Fusarium mycotoxin accumulation in maize grain

In the Great Lakes region of North America, Gibberella ear rot (GER), caused by Fusarium graminearum, affects grain quality due to the accumulation of mycotoxins. GER severity is strongly influenced by environmental conditions; however, agronomic practices can also influence disease severity and mycotoxin accumulation. In this study, three separate small-plot experiments were conducted at Ridgetown, ON, Canada during 2019 and 2020 under an inoculated-misted system to determine Fusarium mycotoxin accumulation as affected by: (1) plant population density; (2) in-row-plant developmental variability; and (3) the effect of integrated Bt refuge genetics. In this study, DON concentrations were at least 49% higher in maize at 113,600 plants/ha compared to 79,000 plants/ha. Moreover, mycotoxin accumulation was higher in plants that were delayed developmentally in the crop row; total DON concentrations were at least 310% higher in late silked plants adjacent to early silked plants. Results of the plant population density and in-row-plant developmental variability suggest that the main driver for mycotoxin accumulation was stress induced by plant competition rather than environmental conditions; this highlights the importance of avoiding plant competitive stress as a strategy to reduce the risks of mycotoxin accumulation. In this study, there was no statistical difference in DON accumulation between the Bt component and the non-Bt component in each of the four hybrids tested; however, there was evidence that hybrids varied in susceptibility, including the Bt and non-Bt components that were paired commercially in a bag of seed maize. Reducing mycotoxins in maize requires integrated management, which includes agronomic considerations. These results indicate that mycotoxins are favoured with high plant populations and plant-to-plant variability in the row, especially in susceptible hybrids.

Logistic Models Derived via LASSO Methods for Quantifying the Risk of Natural Contamination of Maize Grain with Deoxynivalenol

Models were developed to quantify the risk of deoxynivalenol (DON) contamination of maize grain based on weather, cultural practices, hybrid resistance, and Gibberella ear rot (GER) intensity. Data on natural DON contamination of 15 to 16 hybrids and weather were collected from 10 Ohio locations over 4 years. Logistic regression with 10-fold cross-validation was used to develop models to predict the risk of DON ≥1 ppm. The presence and severity of GER predicted DON risk with an accuracy of 0.81 and 0.87, respectively. Temperature, relative humidity, surface wetness, and rainfall were used to generate 37 weather-based predictor variables summarized over each of six 15-day windows relative to maize silking (R1). With these variables, least absolute shrinkage and selection operator (LASSO) followed by all-subsets variable selection and logistic regression with 10-fold cross-validation were used to build single-window weather-based models, from which 11 with one or two predictors were selected based on performance metrics and simplicity. LASSO logistic regression was also used to build more complex multiwindow models with up to 22 predictors. The performance of the best single-window models was comparable to that of the best multiwindow models, with accuracy ranging from 0.81 to 0.83 for the former and 0.83 to 0.87 for the latter group of models. These results indicated that the risk of DON ≥1 ppm can be accurately predicted with simple models built using temperature- and moisture-based predictors from a single window. These models will be the foundation for developing tools to predict the risk of DON contamination of maize grain.