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TECHNICAL SESSION: Predictive Disease Modeling

Pre and Post Silk Emergence Weather Variables Associated with Gibberella ear rot and Deoxynivalenol in Corn
Felipe Dalla Lana - The Ohio State University. Laurence Madden- The Ohio State University, Pierce Paul- The Ohio State University, Peter Thomison- Department of Horticulture and Crop Science, Ohio State University, Richard Minyo- OARDC/OSU

The objective of this study was to quantify associations between variables summarizing weather conditions before, during, and after silk emergence (the R1 growth stage) and the severity (SEV) of Gibberella ear rot (GER; Fusarium graminearum), one of the most economically important ear diseases of corn in the U.S. Midwest, and its associated mycotoxin, deoxynivalenol (DON). Fifteen hybrids were planted in each of 10 Ohio counties from 2015 to 2018. At R1, ten arbitrarily-selected primary ears per hybrid were tagged and inoculated via the silk channel with a spore suspension of F. graminearum. All inoculated ears plus 5-10 non-inoculated ears were hand-harvested at R6 for SEV and DON quantification. Temperature, relative humidity (RH), surface wetness, and rainfall data were recorded at each location and used to generate weather summaries (e.g. daily, daytime, and nighttime averages, totals, and number of hours within certain ranges) for 6 time window lengths (5 to 30 days) relative to R1. Associations between each weather summary by window length combination and SEV and DON were quantified with Spearman rank correlation coefficients. All tested weather summaries were significantly correlated SEV and DON (P < 0.001) for at least one time window length, regardless of inoculation. However, the magnitude and direction of these associations varied with window length and starting point relative to R1. These results will be used to identify potential predictors in order to develop risk assessment models for GER and DON contamination.