ABSTRACT
Tar spot caused by the fungus Phyllachora maydis Maubl. is a significant foliar disease of corn (Zea mays L.). Threatening corn production across the Americas, this disease can reduce the quality of silage and grain yield (Rocco da Silva et al. 2021; Valle-Torres et al. 2020). Lesions caused by P. maydis are usually black, glossy, and raised stromata on the leaf surface and occasionally on the husk. (Liu 1973; Rocco da Silva et al. 2021). Samples consistent with tar spot of corn were collected between September and October of 2022 from 6 fields in Kansas, 23 in Nebraska, and 6 in South Dakota. One sample was selected from each of the three states for further microscopic evaluation and molecular analysis. Signs of the fungus were visually and microscopically confirmed in eight Nebraska counties in October 2021; however, in the 2021, season tar spot sings were not found in Kansas and South Dakota. In the 2022 season disease severity varied by location; some fields in Kansas had <1% incidence, whereas in South Dakota disease incidence approached 1-2%, and in Nebraska between <1-5%. Stromata were present on both green and senescing tissues. Morphological characteristics of the pathogen were similar and consistent with the description of P. maydis (Parbery 1967) from all examined leaves across all locations. Asexual spores (conidia) were produced in pycnidial fruiting bodies ranging in size 1.29 to 2.82 x 8.84 to 16.95 µm (n = 40, average 1.98 × 13.30 µm). The pycnidial fruiting bodies were often found adjacent to perithecia within the stromata. For molecular confirmation, stromata were aseptically removed from leaves collected at each location and DNA extracted using a phenol chloroform method. The internal transcribed spacer (ITS) regions of the ribosomal RNA gene were sequenced using ITS1/ITS4 universal primers (Larena et al. 1999). Amplicons were Sanger sequenced (Genewiz, Inc., South Plainfield, NJ), and a consensus sequence for each sample was deposited in GenBank: Kansas (OQ200487), Nebraska (OQ200488), and South Dakota (OQ200489). Using the BLASTn, sequences from Kansas, Nebraska and South Dakota showed 100% homology with 100% query cover to other P. maydis GenBank accessions (MG881848.1; OL342916.1; OL342915.1). Koch's postulates were not performed given the obligate nature of the pathogen (Muller and Samuels 1984). This report documents the first confirmation of tar spot on corn in Kansas, Nebraska, and South Dakota (Great Plains).
ABSTRACT
During the past two decades, the wheat-producing areas of the Great Plains region in North America experienced frequent, severe yield losses to stripe rust (Puccinia striiformis f. sp. tritici). In general, outbreaks of rust diseases in the Southern Great Plains region often precede disease problems in the Central and Northern Great Plains. However, these generalizations provide little information, and our objective for this study was to identify weather variables, geographical areas, and time periods that influence the early stages of stripe rust epidemics in the Great Plains. Data used in this analysis consisted of monthly summaries of temperature, precipitation, and soil moisture from 10 climate districts in Texas of the United States. These environmental variables were paired with estimates of wheat yield losses to stripe rust in Kansas from 2000 to 2019, with yield loss coded as a binary variable (1 = >4% statewide yield loss). An ensemble of simple models representing weather variables, time periods, and geographical locations were hypothesized to be influential in the development of stripe rust epidemics. Model performance was verified with observations not used in model development. Results of this study indicated that soil moisture within two to three climate districts in Texas were particularly influential in regional disease development. These areas of Texas were 700 to 1,000 km away from locations in Kansas where the disease-related yield losses were observed, and they often preceded disease losses by 3 to 6 months. In the future, these models could help establish priority locations and time periods for disease scouting and inform regional estimates of disease risk.
