Article taken from https://agcrops.osu.edu/newsletter/corn-newsletter/2024-22/risk-corn-grain-contamination-vomitoxin-ohio-2024-july-8 by Jason Hartschuh, CCA, Pierce Paul, Stephanie Karhoff, CCA
Mycotoxins, specifically deoxynivalenol (DON), commonly referred to as vomitoxin, has become a significant problem for Ohio corn growers. What was once considered an every-ten-year problem, has now become a yearly challenge in some sections of the state. DON contamination of grain is often associated with Gibberella ear rot (GER), a disease caused by the fungus Fusarium graminearum. The Ohio State University Cereal Pathology Lab led by Dr. Pierce Paul has been researching and developing weather-based models to predict when weather conditions are favorable for DON contamination of corn grain. The current models have an 80% accuracy at predicting when conditions are favorable for grain to be contaminated with at least 1 ppm DON, meaning that based on data collected so far, the models are correct about 8 out of 10 times at predicting whether DON contamination will reach or exceed 1 ppm.
The fungus that produces DON infects corn ears during pollination while silks are wet (R1 growth stage). Each week, we will be updating the models and share estimates of the chance of grain in various parts of the state being contaminated with at least 1 ppm DON. This information will be made available through the C.O.R.N newsletter. Predictions generated by these models should only be applied to corn pollinating during the model’s prediction model’s prediction window, which is specific for each area of the state and field within that area. As a result, each week predictions will likely change for the corn that is pollinating based on changes in weather conditions. Similarly, during any given week, predictions will likely change from one field to another based on hybrid maturity, planting date, and weather condition, all of which affect the silking/pollination window.
It is important to remember that weather conditions are only one part of the disease triangle, and that all three sides of the triangle are needed for disease development, and in this case, for DON contamination to occur. The other two sides of the disease triangle are a susceptible corn hybrid and fungal spores being present at the time and growth stage when weather conditions favor infection, disease development, and toxin production. Consequently, the actual level of DON contamination will vary from field to field, depending on the susceptibility of the hybrid planted, tillage, and crop rotation, as well as weather conditions. Under favorable weather conditions, a highly susceptible hybrid planted no-till into corn stubble will likely be contaminated with DON well above 1 ppm compared to a tilled field of a moderately resistant hybrid planted after beans.
DON model predictions are based on data from publicly available weather stations located at OSU-CFAES research farms and airports around the state. The two weather factors used to make predictions are temperature and relative humidity as number of hours within certain ranges or above certain thresholds. These factors can vary significantly over a few miles or with changes in elevation. Based on weather data collected from the Piketon (Pike Co), Jackson (Jackson Co), Eastern (in Noble Co), Western (Clark Co), and Columbus CFAES weather stations, corn fields in those areas of the state that reached the R1 growth stage over the weekend or will reach that growth stage during this week have more that an 80% change of yielding grain contaminated with ≥ 1 ppm DON. However, this prediction does not guarantee lower or higher DON than levels set by grain buyers. The current models were not developed to predict whether DON will be 2, 5, 8, or 10 ppm, they only predict whether DON will be ≥ 1 ppm, but not how much greater.
Use predictions as one piece of information to help guide your DON management decisions. You should also use information about the susceptibility of your hybrid (see our previous CORN newsletter article for more on hybrids reaction to DON: https://agcrops.osu.edu/newsletter/corn-newsletter/2024-06/osu-deoxynivalenol-don-resistance-screening-program-2024) and production practices when making management decisions. Again, when weather conditions are favorable as suggested/predicted by the models, fields of highly susceptible hybrid planted no-till after corn will likely yield grain with DON above 1 ppm compared to a tilled field of a moderately resistant hybrid planted after beans.
Effective management of DON requires the combination of multiple strategies. The model prediction given above can help you determine if a fungicide for DON management would be beneficial. Of the many fungicides available for corn disease management, two are considered to be the most consistently effective at suppressing GER and DON. These two products are Proline and Miravis Neo. They have shown the best results when applied when silks are still wet (early- to mid-R1). The fungicide must penetrate the canopy and reach the corn silks when they are still wet to be effective. Applications made after silks are dry and brown are considerably less effective at reducing DON.
Hybrid resistance is critical even with a fungicide application in achieving low DON levels at harvest. A hybrid with partial resistance will have lower DON at harvest than a susceptible hybrid when both have received a fungicide application for DON management. Extended harvest periods allow more time for DON production, particularly if harvest is delayed by wet conditions. An additional tool is to scout fields that pollinate during high-risk times for GER and plan to harvest those fields early.
These models are the first step towards the development of a prediction tool that would be available to growers to run daily to assess DON risk at their field location during pollination. The current model development has been generously supported by the Corn Marketing Board through your corn check-off.
Estimates of the risk of DON contamination of corn grain are provided at no cost within the state of Ohio. The model developers, The Ohio State University, and funding agencies cannot guarantee prediction accuracy. Users should always consult extension educators and state and field specialist when making disease and mycotoxin management decisions.