Investigations into Economic Returns Resulting from Foliar Fungicides and Application Timing on Management of Tar Spot in Indiana Hybrid Corn.

Tar spot, caused by Phyllachora maydis, is the most significant yield-limiting disease of corn (Zea mays L.) in Indiana. Currently, fungicides are an effective management tool for this disease, and partial returns from their use under different disease severity conditions has not previously been studied. Between 2019 and 2021, two separate field experiments were conducted in each year in Indiana to assess the efficacy of nine foliar fungicide products and nine fungicide application timings based on corn growth stages on tar spot symptoms and stromata, canopy greenness, yield, and influence on partial returns. All fungicides evaluated significantly suppressed tar spot development in the canopy and increased canopy greenness over the nontreated control. Additionally, applications of mefentrifluconazole + pyraclostrobin, metconazole + pyraclostrobin, cyproconazole + picoxystrobin at tassel, and propiconazole + benzovindiflupyr + azoxystrobin between the tassel and dough growth stages were the most effective at significantly reducing disease severity, increasing canopy greenness, protecting yield, and offered the greatest partial return. Fungicide products varied in their ability to protect yield under low and high disease severity conditions relative to the nontreated control. Consistently, positive yield increases were observed when disease severity was high, which translated to greater profitability relative to low severity conditions. On average, the yield increases across foliar fungicide products and timed application treatments were 544.6 and 1,020.7 kg/ha greater, and partial returns using a grain value of $0.17/kg were $92.6/ha and $173.5/ha greater, respectively, when high severity conditions occurred. This research demonstrates that foliar fungicides and appropriately timed fungicide applications can profitably be used to manage tar spot in Indiana under high disease severity conditions.

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.

Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning.

Introduction: Tar spot is a high-profile disease, causing various degrees of yield losses on corn (Zea mays L.) in several countries throughout the Americas. Disease symptoms usually appear at the lower canopy in corn fields with a history of tar spot infection, making it difficult to monitor the disease with unmanned aircraft systems (UAS) because of occlusion. Methods: UAS-based multispectral imaging and machine learning were used to monitor tar spot at different canopy and temporal levels and extract epidemiological parameters from multiple treatments. Disease severity was assessed visually at three canopy levels within micro-plots, while aerial images were gathered by UASs equipped with multispectral cameras. Both disease severity and multispectral images were collected from five to eleven time points each year for two years. Image-based features, such as single-band reflectance, vegetation indices (VIs), and their statistics, were extracted from ortho-mosaic images and used as inputs for machine learning to develop disease quantification models. Results and discussion: The developed models showed encouraging performance in estimating disease severity at different canopy levels in both years (coefficient of determination up to 0.93 and Lin's concordance correlation coefficient up to 0.97). Epidemiological parameters, including initial disease severity or y0 and area under the disease progress curve, were modeled using data derived from multispectral imaging. In addition, results illustrated that digital phenotyping technologies could be used to monitor the onset of tar spot when disease severity is relatively low (< 1%) and evaluate the efficacy of disease management tactics under micro-plot conditions. Further studies are required to apply and validate our methods to large corn fields.

Draft Genome Sequence Resource for Phyllachora maydis-An Obligate Pathogen That Causes Tar Spot of Corn with Recent Economic Impacts in the United States.

Phyllachora maydis is an important fungal pathogen that causes tar spot of corn and has led to significant yield loss in the United States and other countries. P. maydis is an obligate biotroph belonging to the Sordariomycetes class of Ascomycota. Due to the challenges posed by their obligate nature, there is no genome sequence available in the Phyllachora genus. P. maydis isolate PM01 was collected from a corn field in Indiana and the genome was determined by next-generation sequencing. The assembly size is 45.7 Mb, with 56.46% repetitive sequences. There are 5,992 protein-coding genes and 59 are predicted as effector proteins. This genome resource will increase our understanding of genomic features of P. maydis and will assist in studying the corn-P. maydis interaction and identifying potential resistant candidates for corn breeding programs.