Tar Spot Research Articles

Tar Spot of Maize in Florida

Tar spot, a fungal foliar disease, affects most maize types commonly grown in Florida, such as sweet corn, field corn, and criollo corn. This disease is characterized by brown-to-black, raised structures called stromata, which are embedded in leaf tissue. As a polycyclic disease, tar spot lesions can increase in number as the disease progresses throughout the growing season, leading to reduction of photosynthetic area on the leaves. Tar spot has recently emerged as a significant threat in Florida and in the United States, with reports of up to 100% incidence in maize fields in south Florida. Tar spot occurrence and development in Florida is influenced by several factors, such as environmental conditions, maize types, crop maintenance, and corn growth stages. The purpose of this publication is to promote awareness among Florida growers regarding this newly identified disease and its management practices.

Optimizing Corn Tar Spot Measurement: A Deep Learning Approach Using Red-Green-Blue Imaging and Stromata Contour Detection Algorithm for Leaf-Level Disease Severity Analysis.

Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is a primary method for quantifying tar spot early in the season, as these structures are definitive signs of the disease and essential for effective disease monitoring and management. Here, we present Stromata Contour Detection Algorithm version 2 (SCDA v2), which addresses the limitations of the previously developed SCDA version 1 (SCDA v1) without the need for empirical search of the optimal Decision Making Input Parameters (DMIPs), while achieving higher and consistent accuracy in tar spot stromata detection. SCDA v2 operates in two components: (i) SCDA v1 producing tar-spot-like region proposals for a given input corn leaf Red-Green-Blue (RGB) image, and (ii) a pre-trained Convolutional Neural Network (CNN) classifier identifying true tar spot stromata from the region proposals. To demonstrate the enhanced performance of the SCDA v2, we utilized datasets of RGB images of corn leaves from field (low, middle, and upper canopies) and glasshouse conditions under variable environments, exhibiting different tar spot severities at various corn developmental stages. Various accuracy analyses (F1-score, linear regression, and Lin's concordance correlation), showed that SCDA v2 had a greater agreement with the reference data (human visual annotation) than SCDA v1. SCDA v2 achievd 73.7% mean Dice values (overall accuracy), compared to 30.8% for SCDA v1. The enhanced F1-score primarily resulted from eliminating overestimation cases using the CNN classifier. Our findings indicate the promising potential of SCDA v2 for glasshouse and field-scale applications, including tar spot phenotyping and surveillance projects.

Six Species of Phyllachora with Three New Taxa on Grass from Sichuan Province, China.

Phyllachora (Phyllachoraceae, Phyllachorales) species are parasitic fungi with a wide global distribution, causing tar spots on plants. In this study, we describe three newly discovered species: Phyllachora chongzhouensis, Phyllachora neidongensis, and Phyllachora huiliensis from Poaceae in China. These species were characterized using morphological traits and multi-locus phylogeny based on the internal transcribed spacer region (ITS) with the intervening 5.8S rRNA gene, the large subunit of the rRNA gene (LSU), and the 18S ribosomal RNA gene (SSU). Three known species of P. chloridis, P. graminis, and P. miscanthi have also been redescribed, because, in reviewing the original references of P. chloridis, P. graminis, and P. miscanthi, these were found to be relatively old and in Chinese or abbreviated. In addition, the illustrations were simple. In molecular identification, the ITS sequence is short, while the ITS, LSU, and SSU are incomplete. Therefore, this study provides new important references for the redescription of three known species and provides further evidence for the identification of new taxa.

Foliar Inoculation of Phyllachora maydis into Corn Induces Infection and Local Spread in Field Environments.

Tar spot of corn (Zea mays L.) is a significant disease in the United States and Canada caused by Phyllachora maydis, an obligate biotroph fungus. However, field research critical for understanding and managing the disease has been hindered by a need for methods to inoculate corn with P. maydis in field environments. In this study, we developed and demonstrated the efficacy of a method to initiate tar spot in field settings using inoculations of corn leaves with P. maydis inoculum that had been stored at -20°C for 10 months. Stromata of P. maydis were observed 19 days after inoculations in two field experiments, and stromata resulting from secondary spread were initially observed 39 to 41 days after the initial inoculations. Tar spot was not present in the fields beyond the inoculated areas or localized spread area, signifying that the establishment of initial disease resulted solely from inoculations. This study enhances our understanding of inoculation and infection of corn with P. maydis and tar spot development in field environments. The results will aid new research into understanding the corn tar spot pathosystem and improving management strategies.

Genome-Informed Trophic Classification and Functional Characterization of Virulence Proteins from the Maize Tar Spot Pathogen Phyllachora maydis.

Phyllachora maydis is an ascomycete foliar fungal pathogen and the causal agent of tar spot in maize. Although P. maydis is considered an economically important foliar pathogen of maize, our general knowledge of the trophic lifestyle and functional role of effector proteins from this fungal pathogen remains limited. Here, we utilized a genome-informed approach to predict the trophic lifestyle of P. maydis and functionally characterized a subset of candidate effectors from this fungal pathogen. Leveraging the most recent P. maydis genome annotation and the CATAStrophy pipeline, we show that this fungal pathogen encodes a predicted carbohydrate-active enzymes (CAZymes) repertoire consistent with that of biotrophs. To investigate fungal pathogenicity, we selected 18 candidate effector proteins that were previously shown to be expressed during primary disease development. We assessed whether these putative effectors share predicted structural similarity with other characterized fungal effectors and determined whether any suppress plant immune responses. Using AlphaFold2 and Foldseek, we showed that one candidate effector, PM02_g1115, adopts a predicted protein structure similar to that of an effector from Verticillium dahlia. Furthermore, transient expression of candidate effector-fluorescent protein fusions in Nicotiana benthamiana revealed two putative effectors, PM02_g378 and PM02_g2610, accumulated predominantly in the cytosol, and three candidate effectors, PM02_g1115, PM02_g7882, and PM02_g8240, consistently attenuated chitin-mediated reactive oxygen species production. Collectively, the results presented herein provide insights into the predicted trophic lifestyle and putative functions of effectors from P. maydis and will likely stimulate continued research to elucidate the molecular mechanisms used by P. maydis to induce tar spot.

INTEGRUOTI GAMTAMOKSLINIAI TIRIAMIEJI DARBAI

While conducting the research, students applied their wide-ranging knowledge, including biology, chemistry, mathematics, technology, and informatics. Our investigation focused on air pollution across five locations in Kuršėnai, utilizing lichen bioindicator species, the maple leaf tar spot (fungus Rhytisma acerinum) test, and metal corrosion indicators. Comparative analysis of the data revealed almost identical results across different methodologies. The cleanest air was near the gymnasium, likely due to the distance of the maple trees from the road and vehicular traffic. Conversely, the most polluted air was at Vilniaus St. 48, adjacent to a bustling street. Deviations noted at Ventos St. 6 were due to research conducted away from the immediate vicinity of the road. Notably, air pollution significantly decreased 100 meters away from the street. Metal corrosion indicators emerged as the most sensitive method employed in our study. Keywords: air pollution research, bioindicators, comparative analysis, metal corrosion

Uncovering the Infection Strategy of Phyllachora maydis During Maize Colonization: A Comprehensive Analysis.

Tar spot, a disease caused by the ascomycete fungal pathogen Phyllachora maydis, is considered one of the most significant yield-limiting diseases of maize (Zea mays) within the United States. P. maydis may also be found in association with other fungi, forming a disease complex that is thought to result in the characteristic fisheye lesions. Understanding how P. maydis colonizes maize leaf cells is essential for developing effective disease control strategies. Here, we used histological approaches to elucidate how P. maydis infects and multiplies within susceptible maize leaves. We collected tar spot-infected maize leaf samples from four different fields in northern Indiana at three different time points during the growing season. Samples were chemically fixed and paraffin-embedded for high-resolution light and scanning electron microscopy. We observed a consistent pattern of disease progression in independent leaf samples collected across different geographical regions. Each stroma contained a central pycnidium that produced asexual spores. Perithecia with sexual spores developed in the stomatal chambers adjacent to the pycnidium, and a cap of spores formed over the stroma. P. maydis reproductive structures formed around but not within the vasculature. We observed P. maydis associated with two additional fungi, one of which is likely a member of the Paraphaeosphaeria genus; the other is an unknown fungi. Our data provide fundamental insights into how this pathogen colonizes and spreads within maize leaves. This knowledge can inform new approaches to managing tar spot, which could help mitigate the significant economic losses caused by this disease.

DYNAMICS OF HEAVY METAL ACCUMULATION IN TROPHIC LEVELS OF FOREST ECOSYSTEMS

The aim of the study is to examine the accumulation of heavy metals (HMs) in trophic levels (soil, wood, tree leaves and fungi of various evolutionary origin) of forest ecosystems.
 Materials and Methods. The authors examined soil, wood, leaves and fungi in forests with different anthropogenic load intensity. The collecting grounds were selected according to forest management materials. In total, 26 grounds were examined. Sampling of soil and fungi of various trophic levels was carried out on different substrates, in different forest conditions and under different levels of technogenic impact. Leaf and wood samples were taken from phytopathogen-affected and healthy trees. Chemical sample analysis was carried out using a Kvant atomic absorption spectrophotometer.
 Results. The total heavy metal content in fungi is higher than in wood, but lower than in soil. The total heavy metal content in leaves of Norway maple affected by Rhytisma acerinum is higher compared to the leaves from heathy trees. Iron predominates among the heavy metals found in the fungi under consideration; cadmium content is minimal.
 Conclusion. The largest amount of accumulated heavy metals was found in the fruiting bodies of the facultative saprotroph, and the smallest amount was in the bodies of the facultative parasite. The forest type and growing conditions affect the heavy metal content in ecosystems.

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.

First report of Phyllachora maydis causing tar spot on corn in Delaware.

In October 2023, lesions consistent with descriptions of tar spot (Phyllachora maydis) were observed on corn (Zea mays) in Kent and Sussex County, Delaware (DE). Black, raised stromata were observed on leaves of commercially grown corn hybrids. Plants were at physiological maturity and disease severity was low with symptoms present on 1 to 10% of plants. In collected tissue, individual leaf severities ranged from 1 to 3% of leaf area with lesions. Hyaline conidia measuring approximately 15.5 µm in length and 0.5 µm in width were observed microscopically (n=5). Stromata were excised and sterilized in a 0.825% sodium hypochlorite solution for 30 s, rinsed in sterile deionized water for 30 s, and dried on a sterile paper towel for 30 s. Tissues were ground in a 1.5 mL microcentrifuge tube with a sterile plastic pestle. DNA was extracted using a DNeasy Plant Mini Kit (Qiagen). DNA was amplified at the internal transcribed spacer (ITS) region with ITS4 and ITS5 primers using polymerase chain reaction (PCR). NCBI BLAST search results yielded 100% sequence homology and 100% query cover (350/515 bp) to P. maydis accession MG881848.1 (Moura et al. 2023). Koch's postulates could not be completed due to the obligate nature of P. maydis. Tarspot was initially discovered in the United States in 2016 in Indiana and Illinois (Ruhl et al. 2016).This is the first report of tar spot on corn in DE. Yield losses from P. maydis can range depending on time of infection, environmental factors, and hybrid susceptibility and have been recorded up to 100% (Rocco da Silva et al. 2021). Because the disease did not enter the area until the end of the season, no yield impact was observed for 2023. Monitoring for the progression of disease will be crucial for future seasons (Telenko et al. 2020). High humidity and moisture levels favor disease development. Approximately half of DE corn acreage is irrigated due to sandy soils, current irrigation timing strategies may need to be reevaluated. Fungicide efficacy trials for management of tar spot have been conducted in other regions, but continued research will be needed to assess management options and optimize application timing for farmers in DE and the Mid-Atlantic region.

Comparing tar spot epidemics in high-risk areas in the United States and Honduras

Tar spot of corn is a disease that causes significant production losses in the Americas. However, the dynamics of tar spot epidemics in different countries is currently unknown. We assessed the temporal dynamics of tar spot epidemics from six efficacy trial experiments conducted in the United States (US) (three) and Honduras (three) from 2019 to 2021. Data collected corresponded to different canopy positions (lower, middle and upper). In all experiments and canopies, treatments contributed to reducing disease as compared to non-treated controls. In the US the time from disease onset (y ons ) to maximum disease level (y max ) was longer than in Honduras. In all experiments, the disease developed from the lower to the upper canopy. The logistic model described well the disease progression data in its linear and non-linear form. Overall, the linear rates (r L* ) were lower in the US than in Honduras, but in the US the highest rates occurred in the upper canopy, while in Honduras they occurred in the lower canopy. Logistic non-linear rates were in general higher in the US than in Honduras. Multi-treatment meta-analysis and the effect size showed that y ons , r L* , standardized AUDPC and y max were higher in Honduras than in the US in the lower canopy and higher in the US than Honduras in the upper canopy. The best correlations occurred in the lower canopy in Honduras or the upper canopy in the US. The information generated in this study helped identify differences in epidemiological dynamics in high-risk areas in two countries.

Application of sandpaper spray ionization mass spectrometry to comprehensively examine maple leaves infected with distinct fungi.

This study describes a novel application for sandpaper spray ionization mass spectrometry (SPS-MS), to examine the surface of maple tree (Acer sp.) leaves. By comparing mass spectrometry fingerprints, healthy leaves from those infected with powdery mildew and Rhytisma acerinum were distinguished. Leaves were grated with sandpaper, cut into triangles, and placed before the mass spectrometer, with the addition of a methanol-formic acid solution. Multivariate statistical analysis categorized the samples into three groups. Overall, SPS-MS effectively analyzed leaves with infectious microorganisms, potentially aiding in the creation of fungal identification databanks.

Development of a qPCR Assay for Species-Specific Detection of the Tar Spot Pathogen Phyllachora maydis

Phyllachora maydis is a fungal plant pathogen that causes tar spot of corn ( Zea mays) in North and South America, causing devastating yield losses under favorable conditions. Although the causal agent is relatively easy to diagnose via macroscopic and microscopic observations, other diseases and conditions, such as insect frass, have been mistaken for tar spot of corn. Furthermore, conidia and ascospores in isolation can be difficult to visually distinguish from other fungi, and the development of signs and symptoms of the disease may not be observed until 12 to 20 days after infection. Therefore, we developed a TaqMan quantitative polymerase chain reaction (qPCR) assay for the detection and quantification of this pathogen to be used for diagnostics and airborne spore quantification. The assay was designed for the internal transcribed spacer region of P. maydis. The specificity of the assay was confirmed and tested against various nontarget Phyllachora species, corn pathogens, endophytes, and P. maydis samples from several states in the Midwest and from Mexico. The detection limit of this assay was determined to be 100 fg of genomic P. maydis DNA. To demonstrate the transferability of this technology, the assay was tested in different labs using various qPCR thermal cyclers. This assay can be used in downstream research involving latency period, disease prediction, and diagnostics. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Tar Spot Disease Identification and Severity Estimation Using Deep Learning

HighlightsImage classification was used to identify tar spot, NLB, GLS, and NLS corn disease with 99.51% testing accuracy.YOLOv7 object detection was used to locate and identify tar spot lesions on infected leaves with mAP@0.5 of 40.46%.A novel tar spot severity estimation approach was developed using deep learning and color histogram thresholding.A web-based disease diagnosis tool was developed to help with an accurate, in-field diagnosis of tar spot in corn.Abstract. Management of tar spot disease in corn has traditionally relied on manual field scouting and visual analysis since it was first observed in the US in 2015. The disease has been identified using deep learning (DL) models as the application of computer vision and DL techniques for disease management is increasing. The severity of the disease has been estimated using close-range images of infected corn leaves under lab conditions with uniform backgrounds. However, DL models trained using images acquired under uniform lab conditions are limited in their ability to generalize to field conditions. Although recent studies have shown success in quantifying the disease, its analysis under field conditions with complex backgrounds to provide a field-ready solution in the form of an application has not yet been developed. Therefore, this study acquired a custom handheld imagery dataset of 455 images for the tar spot disease in a greenhouse with noisy backgrounds to simulate field conditions for training DL-based disease identification models. The dataset was combined with a publicly available Corn Disease and Severity (CD&S) dataset consisting of field-acquired diseased images corresponding to Northern Leaf Blight (NLB), Gray Leaf Spot (GLS), and Northern Leaf Spot (NLS). Image classification models were first trained to accurately identify tar spot, NLB, GLS, and NLS diseases. To accurately locate and identify tar spot disease lesions, YOLOv7 object detection models were then trained. In addition, semantic segmentation models were trained using the UNet architecture for leaf and lesion segmentation. After training the image classification models, the highest testing accuracy of 99.51% was achieved with the InceptionV3 model. For YOLOv7 object detection, the highest mAP of 40.46% was achieved for locating and identifying tar spot disease lesions on infected leaves. Finally, for UNet semantic segmentation, the mIoU for leaf and lesion segmentation were 0.80 and 0.28, respectively. Therefore, traditional color histogram thresholding was used for segmenting the tar spot lesions, and DL techniques were used to develop a novel severity estimation framework. After evaluating the models, the image classification model was deployed on a progressive web application accessible through a smartphone to enable real-time analysis. In this study, different DL models were trained and evaluated for tar spot disease identification and its severity estimation. In addition, a smartphone-based disease diagnosis tool was developed that has the potential for an accurate, in-field diagnosis of tar spot in corn. Keywords: Computer vision, Deep learning, Disease identification, Image classification, Precision agriculture, Severity estimation, Tar spot.

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.

Is Microdochium maydis Associated with Necrotic Lesions in the Tar Spot Disease Complex? A Culture-Based Survey of Maize in Mexico and the Midwestern United States.

Tar spot, caused byPhyllachora maydis, is an emerging disease of corn in the United States. Stromata ofP. maydisare sometimes surrounded by necrotic lesions known as fisheyes and were previously reported to be caused by the fungus Microdochiummaydis. The association ofM. maydiswith fisheye lesions has not been well documented outside of initial descriptions from the early 1980s. The objective of this work was to assess and identify Microdochium-like fungi associated with necrotic lesions surrounding P. maydis stromata using a culture-based method. In 2018, corn leaf samples with fisheye lesions associated with tar spot stromata were collected from 31 production fields across Mexico, Illinois, and Wisconsin. Cultures of pure isolates collected from Mexico believed to be M. maydis were included in the study. A total of 101 Microdochium/Fusarium-like isolates were obtained from the necrotic lesions, and 91% were identified asFusariumspp., based on initial ITS sequence data.Multi-gene (ITS, TEF1-α, RPB1, and RPB2)phylogenies were constructed for a subset of 55 isolates;Microdochium,Cryptostroma, andFusariumreference sequences were obtained from GenBank. All the necrotic lesion isolates clustered withinFusariumlineages and were phylogenetically distinct from the Microdochiumclade. All Fusarium isolates from Mexico belonged to theF. incarnatum-equisetispecies complex, whereas >85% of the U.S. isolates grouped within theF. sambucinumspecies complex. Our study suggests that initial reports of M. maydis were misidentifications of resident Fusarium spp. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

First report of tar spot on sorghum in West Lombok, West Nusa Tenggara province, Indonesia

First report of tar spot on sorghum in West Lombok, West Nusa Tenggara province, Indonesia

A new and effective method to induce infection of Phyllachora maydis into corn for tar spot studies in controlled environments

BackgroundTar spot of corn is a significant and spreading disease in the continental U.S. and Canada caused by the obligate biotrophic fungus Phyllachora maydis. As of 2023, tar spot had been reported in 18 U.S. states and one Canadian Province. The symptoms of tar spot include chlorotic flecking followed by the formation of black stromata where conidia and ascospores are produced. Advancements in research and management for tar spot have been limited by a need for a reliable method to inoculate plants to enable the study of the disease. The goal of this study was to develop a reliable method to induce tar spot in controlled conditions.ResultsWe induced infection of corn by P. maydis in 100% of inoculated plants with a new inoculation method. This method includes the use of vacuum-collection tools to extract ascospores from field-infected corn leaves, application of spores to leaves, and induction of the disease in the dark at high humidity and moderate temperatures. Infection and disease development were consistently achieved in four independent experiments on different corn hybrids and under different environmental conditions in a greenhouse and growth chamber. Disease induction was impacted by the source and storage conditions of spores, as tar spot was not induced with ascospores from leaves stored dry at 25 ºC for 5 months but was induced using ascospores from infected leaves stored at -20 ºC for 5 months. The time from inoculation to stromata formation was 10 to 12 days and ascospores were present 19 days after inoculation throughout our experiments. In addition to providing techniques that enable in-vitro experimentation, our research also provides fundamental insights into the conditions that favor tar spot epidemics.ConclusionsWe developed a method to reliably inoculate corn with P. maydis. The method was validated by multiple independent experiments in which infection was induced in 100% of the plants, demonstrating its consistency in controlled conditions. This new method facilitates research on tar spot and provides opportunities to study the biology of P. maydis, the epidemiology of tar spot, and for identifying host resistance.

First report of tar spot on corn caused by Phyllachora maydis in Kentucky.

In September 2021, signs of black circular to oval shaped fungal structures (stromata) were observed on corn (Zea mays L.) leaves on a non-commercial inbred line in Todd County, Kentucky. Signs were only observed in a small pocket within the larger field, with disease levels ranging from 1- 5% incidence and 1-25% severity on individual leaves affected in the field. Corn leaves had senesced and only fungal structures were available to aid in diagnosis. Microscopic examination of stromata uncovered ascomata within the clypei/stromata. Further examination of ascomata revealed multiple asci containing eight hyaline, uniseriate, aseptate, oval to ovoid ascospores ranging in size from 8 to 12 µm x 5 to 7 µm. Observed signs were consistent with published reports of tar spot caused by Phyllachora maydis (Parbery 1967; Valle-Torres et al. 2020). For molecular confirmation of the causal agent, corn leaves were surface sterilized in diluted bleach (10%) for 30 seconds and stromata were excised from the leaves using a sterile scalpel. Five to seven stromata were placed into each microcentrifuge tube. Liquid nitrogen was added to the microcentrifuge tubes and the frozen stromata were ground using a sterilized pestle. The ground stromata tissue was used for DNA extraction using a Synergy 2.0 plant DNA extraction kit (OPS Diagnostics, Lebanon, NJ). A portion of the internal transcribed spacer (ITS) region was amplified by PCR utilizing ITS-4 and ITS-5 primers. Amplicons were subjected to Sanger sequencing to obtain a consensus sequence. Using the BLASTn algorithm the consensus sequence shared 100% similarity to three P. maydis Genbank accessions: MG881848.1, MG8814847.1, MG881846.1. A representative sequence was deposited in GenBank (accession no. OQ034699.1). Due to P. maydis being an obligate parasite, Koch's postulates were not attempted.

First Report of Tar Spot on Corn Caused by Phyllachora maydis in the Great Plains.

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).