Documenting the Establishment, Spread, and Severity of Phyllachora maydis on Corn, in the United States

Tar spot on corn, caused by the fungus (Phyllachora maydis Maubl. [Phyllachorales: Phyllachoraceae]), is an emerging disease in the United States. In 2018 and 2019, significant but localized epidemics of tar spot occurred across the major corn producing region of the Midwest. After being first detected in 2015, tar spot was detected in 135 and 139 counties where the disease was not previously detected in 2018 and 2019, respectively, and is now established across 310 counties across the United Sates. Foliage with signs (stromata) of P. maydis and symptoms of tar spot were collected from 128 fields in 2018 and 191 fields in 2019, across seven states. Samples were assessed for severity of fungal stromata (percent leaf area covered with stromata) on foliage and the incidence of fisheye lesions (proportion of lesions with fisheye symptoms) associated with fungal stromata. Stromatal severity on samples in 2018 ranged from 0.5 to 67% and incidence of fisheye lesions ranged from 0 to 12%, whereas in 2019, stromatal severity ranged from 0.1 to 35% and incidence of fisheye lesions ranged from 0 to 80%, with 95% of samples presenting less than 6% incidence of fisheye lesions. Tar spot has spread substantially from where it was first reported in the United States. Collaborative efforts to monitor the spread and educate clientele on management are essential as this disease spreads into new areas

Uncovering the environmental conditions required for Phyllachora maydis infection and tar spot development on corn in the United States for use as predictive models for future epidemics

Phyllachora maydis is a fungal pathogen causing tar spot of corn (Zea mays L.), a new and emerging, yield-limiting disease in the United States. Since being first reported in Illinois and Indiana in 2015, P. maydis can now be found across much of the corn growing regions of the United States. Knowledge of the epidemiology of P. maydis is limited but could be useful in developing tar spot prediction tools. The research presented here aims to elucidate the environmental conditions necessary for the development of tar spot in the field and the creation of predictive models to anticipate future tar spot epidemics. Extended periods (30-day windowpanes) of moderate mean ambient temperature (18–23 °C) were most significant for explaining the development of tar spot. Shorter periods (14- to 21-day windowpanes) of moisture (relative humidity, dew point, number of hours with predicted leaf wetness) were negatively correlated with tar spot development. These weather variables were used to develop multiple logistic regression models, an ensembled model, and two machine learning models for the prediction of tar spot development. This work has improved the understanding of P. maydis epidemiology and provided the foundation for the development of a predictive tool for anticipating future tar spot epidemics