Fungicide efficacy on tar spot and yield of corn in the Midwestern United States

Tar spot of corn caused by Phyllachora maydis has recently led to significant yield losses in the eastern corn belt of the Midwestern United States. Foliar fungicides containing quinone outside inhibitors(QoI), demethylation inhibitors(DMI), and succinate dehydrogenase inhibitors(SDHI) are commonly used to manage foliar diseases in corn. To mitigate the losses from tar spot thirteen foliar fungicides containing single or multiple modes of action (MOA/FRAC groups) were applied at their recommended rates in a single application at the standard tassel/silk growth stage timing to evaluate their efficacy against tar spot in a total of eight field trials in Illinois, Indiana, Michigan, and Wisconsin during 2019 and 2020. The single MOA fungicides included either a QoI or DMI. The dual MOA fungicides included a DMI with either a QoI or SDHI, and fungicides containing three MOAs included a QoI, DMI, and SDHI. Tar spot severity estimated as the percentage of leaf area covered by P. maydis stroma of the non-treated control at dent growth stage ranged from 1.6 to 23.3% on the ear leaf. Averaged across eight field trials all foliar fungicide treatments reduced tar spot severity, but only prothioconazole+trifloxystrobin, mefentrifluconazole+pyraclostrobin+fluxapyroxad, and mefentrifluconazole+pyraclostrobin significantly increased yield over the non-treated control. When comparing fungicide treatments by the number of MOAs foliar fungicide products that had two or three MOAs decreased tar spot severity over not treating and products with one MOA. The fungicide group that contained all three MOAs significantly increased yield over not treating with a fungicide or using a single MOA.

Phyllachora species infecting maize and other grass species throughout the Americas represents a complex of closely related species which vary in their host and geographic range

The genus Phyllachora contains numerous obligate fungal parasites that produce raised, melanized structures called stromata on their plant hosts referred to as tar spot. Members of this genus are known to infect many grass species but generally do not cause significant damage or defoliation, with the exception of P. maydis which has emerged as an important pathogen of maize throughout the Americas, but the origin of this pathogen remains unknown. To date, species designations for Phyllachora have been based on host associations and morphology, and most species are assumed to be host specific. We assessed the sequence diversity of 186 single stroma isolates collected from 16 hosts representing 15 countries. Samples included both herbarium and contemporary strains that covered a temporal range from 1905-2019. These 186 isolates were grouped into 5 distinct species with strong bootstrap support. We found three closely related, but genetically distinct groups of Phyllachora are capable of infecting maize in the United States, we refer to these as the P. maydis species complex. Based on herbarium species, we hypothesize that these three groups in the P. maydis species complex originated from Central America, Mexico and the Caribbean. Although two of these groups were only found on maize, the third and largest group contained contemporary strains found on maize and other grass hosts, as well as herbarium specimens from maize and other grasses that include 10 species of Phyllachora. The herbarium specimens were identified based on morphology and host association, but our sequence data indicates some Phyllachora species are capable of infecting a broad range of host species and there may be significant synonymy in the Phyllachora genus and additional work on species delineation and host specificity should be considered.

Identification and fine-mapping of a major QTL (qRtsc8-1) conferring resistance to maize tar spot complex and production markers validation in breeding lines

Tar spot complex (TSC) is a major foliar disease of maize in many Central and Latin American countries and leads to severe yield loss. To dissect the genetic architecture of TSC resistance, a genome-wide association study (GWAS) panel and a bi-parental doubled haploid population were used for GWAS and selective genotyping analysis, respectively. A total of 115 SNPs in bin 8.03 were detected by GWAS and three QTL in bins 6.05, 6.07, and 8.03 were detected by selective genotyping. The major QTL qRtsc8-1 located in bin 8.03 was detected by both analyses, it explained 14.97% of the phenotypic variance. To fine-map qRtsc8-1, the recombinant-derived progeny test was implemented. Recombinations in each generation were backcrossed, and the backcross progenies were genotyped with Kompetitive Allele Specific PCR (KASP) markers and phenotyped for TSC resistance individually. The significant tests for comparing the TSC resistance between the two classes of progenies with and without resistant alleles were used for fine-mapping. In BC5 generation, qRtsc8-1 was fine mapped in an interval of ~721 kb flanked by markers of KASP81160138 and KASP81881276. In this interval, the candidate genes GRMZM2G063511 and GRMZM2G073884 were identified, which encode an integral membrane protein-like and a leucine-rich repeat receptor-like protein kinase, respectively. Both genes are involved in maize disease resistance responses. Two production markers KASP81160138 and KASP81160155 were verified in 471 breeding lines. This study provides valuable information for cloning the resistance gene, it will also facilitate the routine implementation of marker-assisted selection in the breeding pipeline for improving TSC resistance.

Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery

Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by Phyllachora maydis (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to human subjectivity. An objective and accurate method, which is also time and labor-efficient, is of an urgent need for tar spot surveillance and high-throughput disease phenotyping. Here, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves. Image blocks (n = 1,130) generated by uniform partitioning the RGB images of leaves, were analyzed for their number of stromata by two independent, experienced human raters using ImageJ (visual estimates) and the experimental stromata contour detection algorithm (SCDA; digital measurements). Stromata count for each image block was then categorized into five classes and tested for the agreement of human raters and SCDA using Cohen's weighted kappa coefficient (κ). Adequate agreements of stromata counts were observed for each of the human raters to SCDA (κ = 0.83) and between the two human raters (κ = 0.95). Moreover, the SCDA was able to recognize “true stromata,” but to a lesser extent than human raters (average median recall = 90.5%, precision = 89.7%, and Dice = 88.3%). Furthermore, we tracked tar spot development throughout six time points using SCDA and we obtained high agreement between area under the disease progress curve (AUDPC) shared by visual disease severity and SCDA. Our results indicate the potential utility of SCDA in quantifying stromata using RGB images, complementing the traditional human, visual-based disease severity estimations, and serve as a foundation in building an accurate, high-throughput pipeline for the scoring of tar spot symptoms.

Recovery Plan for Tar Spot of Corn, Caused by Phyllachora maydis

Tar spot is a foliar disease of corn threatening production across the Americas. The disease was first documented in Mexico in 1904 and is now present in 15 additional countries throughout Central America, South America, and the Caribbean. Researchers and growers in Central America, South America, and the Caribbean consider tar spot to be a disease complex caused by multiple fungal pathogens. When environmental conditions are conducive for infection, these regions have experienced yield losses can reach up to 100%. In 2015, tar spot was detected in the U.S. for the first time in Illinois and Indiana. Since that time tar spot has spread across the U.S. corn-growing region, and the disease has been found in Florida, Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, Pennsylvania, and Wisconsin. In 2020, tar spot was also found in southwest Ontario, Canada. Losses in the U.S. due to tar spot totaled an estimated 241 million bushels from 2018 to 2020. With the potential to continue to spread across the U.S. corn-growing states, much greater losses could result when environmental conditions are conducive.

Genomic Prediction of Resistance to Tar Spot Complex of Maize in Multiple Populations Using Genotyping-by-Sequencing SNPs

Tar spot complex (TSC) is one of the most important foliar diseases in tropical maize. TSC resistance could be furtherly improved by implementing marker-assisted selection (MAS) and genomic selection (GS) individually, or by implementing them stepwise. Implementation of GS requires a profound understanding of factors affecting genomic prediction accuracy. In the present study, an association-mapping panel and three doubled haploid populations, genotyped with genotyping-by-sequencing, were used to estimate the effectiveness of GS for improving TSC resistance. When the training and prediction sets were independent, moderate-to-high prediction accuracies were achieved across populations by using the training sets with broader genetic diversity, or in pairwise populations having closer genetic relationships. A collection of inbred lines with broader genetic diversity could be used as a permanent training set for TSC improvement, which can be updated by adding more phenotyped lines having closer genetic relationships with the prediction set. The prediction accuracies estimated with a few significantly associated SNPs were moderate-to-high, and continuously increased as more significantly associated SNPs were included. It confirmed that TSC resistance could be furtherly improved by implementing GS for selecting multiple stable genomic regions simultaneously, or by implementing MAS and GS stepwise. The factors of marker density, marker quality, and heterozygosity rate of samples had minor effects on the estimation of the genomic prediction accuracy. The training set size, the genetic relationship between training and prediction sets, phenotypic and genotypic diversity of the training sets, and incorporating known trait-marker associations played more important roles in improving prediction accuracy. The result of the present study provides insight into less complex trait improvement via GS in maize.

Phyllachora maydis (black spot of maize).

P. maydis, a perithecial ascomycete, causes a tar spot disease of maize that is usually a minor problem. More significant damage to leaves and yield is caused by the fungus Monographella maydis whose infection follows that of the tar-spot fungus, at least where studied in Mexico (Hock et al., 1992; 1995). The source of initial inoculum for both fungi is not determined. The disease they cause occurs in the cooler and higher elevations of Mexico, and Central and South America, and the West Indies, so their ability to spread over land through other climatic zones may be limited. Not known to be seedborne or to infect other species, P. maydis could be transported on fresh or dry maize leaves or husks, or products made from them, from which ascospores would have to be produced and carried by wind or rain splash to maize [Zea mays].