Management of Soybean Cyst Nematode and Sudden Death Syndrome with Nematode-Protectant Seed Treatments Across Multiple Environments in Soybean.

As soybean (Glycine max) production continues to expand in the United States and Canada, so do pathogens and pests that directly threaten soybean yield potential and economic returns for farmers. One such pathogen is the soybean cyst nematode (SCN; Heterodera glycines). SCN has traditionally been managed using SCN-resistant cultivars and rotation with nonhost crops, but the interaction of SCN with sudden death syndrome (SDS; caused by Fusarium virguliforme) in the field makes management more difficult. Nematode-protectant seed treatments have become options for SCN and SDS management. The objectives of this study were to evaluate nematode-protectant seed treatments for their effects on (i) early and full season SCN reproduction, (ii) foliar symptoms and root-rot caused by SDS, and (iii) soybean yield across environments accounting for the above factors. Using a standard protocol, field trials were implemented in 13 states and one Canadian province from 2019 to 2021 constituting 51 site-years. Six nematode-protectant seed treatment products were compared with a fungicide + insecticide base treatment and a nontreated check. Initial (at soybean planting) and final (at soybean harvest) SCN egg populations were enumerated, and SCN females were extracted from roots and counted at 30 to 35 days postplanting. Foliar disease index (FDX) and root rot caused by the SDS pathogen were evaluated, and yield data were collected for each plot. No seed treatment offered significant nematode control versus the nontreated check for in-season and full-season nematode response, no matter the initial SCN population or FDX level. Of all treatments, ILEVO (fluopyram) and Saltro (pydiflumetofen) provided more consistent increases in yield over the nontreated check in a broader range of SCN environments, even when FDX level was high.

First report of Calonectria ilicicola causing red crown rot of soybean in Indiana.

Soybean (Glycine max [L.] Merr.) samples from commercial fields in Decatur and Spencer counties, Indiana were submitted to the Purdue Plant and Pest Diagnostic Lab in August to October 2022. Plants exhibited whole-leaf to interveinal chlorosis of the foliage, red to dark brown external lesions on the crown spreading from the soil-line upward, and severe root rot. In the fields, patches of diseased plants were observed, with greater than 50% of the plants affected and yield loss up to 50%. Orange to red perithecia were present on the exterior of symptomatic stem tissue and ranged in size from 329 to 433 × 232 to 306 µm (n = 10). Stems were surface sterilized in 10% Clorox (0.825% NaOCl) for 1 min, then rinsed with sterile distilled water and dried. In a laminar flow hood, sections of symptomatic stem tissue were plated on using quarter-strength potato dextrose agar (QPDA) and incubated under fluorescent lights on a 12-hr light/dark cycle at 20°C. After 6 days, fungal colonies with fluffy aerial hyphae, which were white near the colony margins and orange to burnt-red near their center, grew uniformly from the stem tissue plated. Elongate, cylindrical hyaline conidia with zero to three septations measuring 45.5 to 73.8 × 4.4 to 6.7 µm (n = 22) grew in clusters from symptomatic stem tissue within the plate. Perithecia developed after 14 days. Falcate, hyaline ascospores with one to two septa measuring 29.4 to 54.7 × 4.6 to 6.8 (n = 23) µm developed within the perithecia. Calonectria ilicicola Boedijn & Reitsma was confirmed based on morphological characteristics (Padgett et al. 2015). Isolate PPDL 22-01457B was used for DNA extraction using the ZR Fungal/Bacterial DNA Miniprep kit (Zymo Research, Irvine, CA). The internal transcriber region (ITS), actin (ACT) and ß-tubulin (TUB2) genes were amplified (Carbone and Kohn 1999; Glass and Donaldson 1995; O'Donnell and Cigelnik 1997; White et al. 1990). Amplicons were sent for Sanger sequencing (Genewiz, Inc., South Plainfield, NJ), submitted to Genbank, and assigned accession numbers ITS: OQ932995, Actin: OR484986, and ß-tubulin: OR546281. Sequences were analyzed using the NCBI BLASTn tool with results showing 99.5 to 100% identical to C. ilicicola (GenBank accessions LC500063, OQ303403, CP085825, respectively). To perform Koch's Postulates, 90 soybean seeds (CP3620E) were planted in potting media (Berger, Saint-Modeste, Quebec, Canada) in a seed flat with 45 of the plants used as controls and grown under grow lights for 16hr light/8hr dark at 20℃. Individual seedling crowns were inoculated 3 days post-emergence with a 5 to 10 ml spore and hyphal suspension that was scraped from the surface of a 14-day old QPDA culture after adding 300 mL deionized (DI) to each plate grown at 20 to 22°C. The control plants received sterile-DI water. Plants were covered in a plastic bag for 72 h. Plant stems were sprayed with sterile-DI water once a day for seven days. Symptoms were observed after four days, but significant crown rot and lesions developed after two weeks before wilting and dying. Calonectria iliciola was isolated uniformly from symptomatic plants and identified morphologically. Control plants showed no symptoms. Inoculations were repeated 3 times with similar results. As of fall 2023, red crown rot has been confirmed in Adams and Rush counties in Indiana. Red crown rot has been confirmed in several Midwest states (Kleczewski et al. 2019, Neves et al. 2023), but the extent of its distribution and disease management strategies are still limited.

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 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 L.) within the United States. P. maydis may also be found in association with other fungi, forming a disease complex which is thought to result in the characteristic fish eye 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.

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.

Characterization of Virulence Phenotypes of Heterodera glycines during 2020 in Indiana.

The soybean cyst nematode (SCN, Heterodera glycines) is the most yield-limiting pathogen of soybean in the US. This study was carried out in order to provide updated information on SCN virulence phenotypes in Indiana. A total of 124 soil samples were collected from soybean fields in 2020 and all of them tested positive for SCN. The virulence phenotypes of 42 representative SCN populations were determined with seven soybean indicator lines using the standard HG type test. The most predominant HG types were 2.5.7 and 1.2.5.7, which accounted for 64% and 14% of the SCN populations tested, respectively. None of the SCN populations tested were rated as HG type 0, compared with 28% of the populations in a previous survey in Indiana during 2006-2008. Nearly 88% of the SCN populations evaluated in this study overcame the resistance provided by PI 88788, which is the most common source of resistance in soybean, up from 56% in the 2006-2008 survey. Approximately 14% of SCN populations tested were virulent to PI 548402 (Peking), in contrast to 0% in the 2006-2008 survey. This study reveals a trend of increasing virulence of SCN populations to resistant sources of soybean in Indiana. The results highlighted the importance of rotating soybean varieties with different types of resistance and identifying new sources of resistance for sustainable management of SCN.

A global-temporal analysis on Phytophthora sojae resistance-gene efficacy.

Plant disease resistance genes are widely used in agriculture to reduce disease outbreaks and epidemics and ensure global food security. In soybean, Rps (Resistance to Phytophthora sojae) genes are used to manage Phytophthora sojae, a major oomycete pathogen that causes Phytophthora stem and root rot (PRR) worldwide. This study aims to identify temporal changes in P. sojae pathotype complexity, diversity, and Rps gene efficacy. Pathotype data was collected from 5121 isolates of P. sojae, derived from 29 surveys conducted between 1990 and 2019 across the United States, Argentina, Canada, and China. This systematic review shows a loss of efficacy of specific Rps genes utilized for disease management and a significant increase in the pathotype diversity of isolates over time. This study finds that the most widely deployed Rps genes used to manage PRR globally, Rps1a, Rps1c and Rps1k, are no longer effective for PRR management in the United States, Argentina, and Canada. This systematic review emphasizes the need to widely introduce new sources of resistance to P. sojae, such as Rps3a, Rps6, or Rps11, into commercial cultivars to effectively manage PRR going forward.

Influence of Integrated Management Strategies on Soybean Sudden Death Syndrome (SDS) Root Infection, Foliar Symptoms, Yield and Net Returns.

Three soybean field trials were conducted in Indiana to evaluate the integration of seed treatment, cultivar selection, and seeding rate on sudden death syndrome (SDS) root rot, pathogen load in the root, foliar symptoms, yield, and net return. Two soybean cultivars, one moderately resistant and one susceptible to SDS, were planted at three seeding rates (272,277 seeds/ha, 346,535 seeds/ha, and 420,792 seeds/ha). Fluopyram and pydiflumetofen seed treatments were applied to both cultivars, and the cultivars were then compared with a control. Low foliar SDS disease pressure was observed in our study. Seed treatment with either fluopyram or pydiflumetofen and the use of a moderately resistant cultivar decreased Fusarium virguliforme DNA concentration in the root relative to the control and the use of a susceptible cultivar. Fluopyram significantly reduced visual root rot severity by 8.8% and increased yield by 105 kg/ha relative to the control but was not different from pydiflumetofen. However, pydiflumetofen performed the same as the control with respect to root rot severity and yield. Findings from this study support the use of a seed treatment to protect roots from infection and the use of a moderately resistant cultivar planted at a seeding rate of 346,535 seeds/ha to protect yield and maximize net returns when a field has low foliar SDS pressure.

Assessment of symptom induction via artificial inoculation of the obligate biotrophic fungus Phyllachora maydis (Maubl.) on corn leaves.

OBJECTIVE: Tar spot is a foliar disease of corn caused by Phyllachora maydis, which produces signs in the form of stromata that bear conidia and ascospores. Phyllachora maydis cannot be cultured in media; therefore, the inoculum source for studying tar spot comprises leaves with stromata collected from naturally infected plants. Currently, there is no effective protocol to induce infection under controlled conditions. In this study, an inoculation method was assessed under greenhouse and growth chamber conditions to test whether stromata of P. maydis could be induced on corn leaves. RESULTS: Experiments resulted in incubation periods ranging between 18 and 20 days and stromata development at the beginning of corn growth stage VT-R1 (silk). The induced stromata of P. maydis were confirmed by microscopy, PCR, or both. From thirteen experiments conducted, four (31%) resulted in the successful production of stromata. Statistical analyses indicate that if an experiment is conducted, there are equal chances of obtaining successful or unsuccessful infections. The information from this study will be valuable for developing more reliable P. maydis inoculation methods in the future.

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.

Parental Inbred Lines of the Nested Association Mapping (NAM) Population of Corn Show Sources of Resistance to Tar Spot in Northern Indiana.

Tar spot is a major foliar disease of corn caused by the obligate fungal pathogen Phyllachora maydis, first identified in Indiana in 2015. Under conducive weather conditions, P. maydis causes significant yield losses in the United States and other countries, constituting a major threat to corn production. Relatively little is known about resistance to tar spot other than a major quantitative gene that was identified in tropical maize lines. To test for additional sources of resistance against populations of P. maydis in North America, 26 parental inbred lines of the nested associated mapping (NAM) population were evaluated for tar spot resistance in Indiana in replicated field trials under natural infection for 3 years. Tar spot disease severity was scored visually using a 0-to-100% scale. Maximum disease severity (MDS) for tar spot scoring at reproductive growth stage ranged from 0 to 48.3%, with 0% being most resistant and 48.3% being most susceptible. Nine inbred lines were resistant to P. maydis with MDS ranging from 0 to 5.0%, six were moderately resistant (5.2 to 10.6% MDS), two were moderately susceptible (11.7 to 26.0% MDS), and the remaining eight inbred lines were rated as susceptible (30.0 to 48.3% MDS). There was some variability between years, due to higher disease pressure after 2019. Inbred B73, the common parent of the NAM populations, was rated as susceptible, with MDS of 30.0%. The nine highly resistant lines provide a potential source of new genes for genetic analysis and mapping of tar spot resistance in corn.

Field and Greenhouse Assessment of Seed Treatment Fungicides for Management of Sudden Death Syndrome and Yield Response of Soybean.

Seed treatments for the management of sudden death syndrome (SDS) caused by Fusarium virguliforme are available in the United States and Canada; however, side-by-side comparisons of these seed treatments are lacking. Sixteen field experiments were established in Illinois, Indiana, Iowa, Michigan, and Wisconsin, United States, and Ontario, Canada, in 2019 and 2020 to evaluate seed treatment combinations. Treatments included a nontreated check (NTC), fungicide and insecticide base seed treatments (base), fluopyram, base + fluopyram, base + saponin extracts from Chenopodium quinoa, base + fluopyram + heat-killed Burkholderia rinojenses, base + pydiflumetofen, base + thiabendazole + heat-killed B. rinojenses, and base + thiabendazole + C. quinoa extracts + heat-killed B. rinojenses. Treatments were tested on SDS moderately resistant and susceptible soybean cultivars at each location. Overall, NTC and base had the most root rot, most foliar disease index (FDX), and lowest yield. Base + fluopyram and base + pydiflumetofen were most effective for managing SDS. Moderately resistant cultivars reduced FDX in both years but visual root rot was greater on the moderately resistant than the susceptible cultivars in 2020. Yield response to cultivar was also inconsistent between the 2 years. In 2020, the susceptible cultivar provided significantly more yield than the moderately resistant cultivar. Treatment effect for root rot and FDX was similar in field and greenhouse evaluations. These results reinforce the need to include root rot evaluations in addition to foliar disease evaluations in the breeding process for resistance to F. virguliforme and highlights the importance of an integrated SDS management plan because not a single management tactic alone provides adequate control of the disease.

Determining the Distribution of QoI Fungicide-Resistant Cercospora sojina on Soybean from Indiana.

Frogeye leaf spot (FLS) is a foliar disease of soybean (Glycine max) caused by Cercospora sojina. Application of fungicide products that contain quinone outside inhibitor (QoI) active ingredients has been one of the major tools used in the management of this disease, but, since 2010, QoI-resistant C. sojina isolates have been confirmed in over 20 states in the United States, including Indiana. In summer 2019 and 2020, 406 isolates of C. sojina were collected from 32 counties across Indiana and screened for QoI resistance using a PCR-restriction fragment length polymorphism (RFLP) method. An in vitro fungicide sensitivity test was also performed on a subset of isolates to evaluate their sensitivity to three QoI fungicides: azoxystrobin, pyraclostrobin, and picoxystrobin. A discriminatory dose of picoxystrobin was established as 1 µg/ml by testing five concentrations (0.001, 0.01, 0.1, 1, and 10 µg/ml). QoI-resistant isolates were found in 29 counties, and 251 of the 406 isolates (61.8%) were confirmed to be resistant to QoI fungicides based on PCR-RFLP results. Partial nucleotide sequences of the cytochrome b gene from four resistant and four sensitive isolates corroborated the presence and absence, respectively, of the G143A mutation. Results from the sensitivity assays with discriminatory doses of azoxystrobin (1 µg/ml) and pyraclostrobin (0.1 µg/ml) also supported the findings from the PCR-RFLP assay, because all QoI-resistant isolates were inhibited less than 50% relative to a no-fungicide control when exposed to these doses. Resistant isolates harboring the G143A mutation also exhibited resistance to picoxystrobin. The effective concentrations to inhibit mycelial growth by 50% relative to the nonamended control (EC50) in QoI-sensitive isolates ranged from 0.087 to 0.243 µg/ml, with an overall mean of 0.152 µg/ml, while EC50 values in QoI-resistant isolates were established as >10 µg/ml for picoxystrobin. Results from this study indicated that QoI-resistant C. sojina isolates are spread throughout Indiana and exhibit cross-resistance to QoI fungicides.

Using Organic Contaminants to Constrain the Terrestrial Journey of the Martian Meteorite Lafayette.

A key part of the search for extraterrestrial life is the detection of organic molecules since these molecules form the basis of all living things on Earth. Instrument suites such as SHERLOC (Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals) onboard the NASA Perseverance rover and the Mars Organic Molecule Analyzer onboard the future ExoMars Rosalind Franklin rover are designed to detect organic molecules at the martian surface. However, size, mass, and power limitations mean that these instrument suites cannot yet match the instrumental capabilities available in Earth-based laboratories. Until Mars Sample Return, the only martian samples available for study on Earth are martian meteorites. This is a collection of largely basaltic igneous rocks that have been exposed to varying degrees of terrestrial contamination. The low organic molecule abundance within igneous rocks and the expectation of terrestrial contamination make the identification of martian organics within these meteorites highly challenging. The Lafayette martian meteorite exhibits little evidence of terrestrial weathering, potentially making it a good candidate for the detection of martian organics despite uncertainties surrounding its fall history. In this study, we used ultrapure solvents to extract organic matter from triplicate samples of Lafayette and analyzed these extracts via hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS). Two hundred twenty-four metabolites (organic molecules) were detected in Lafayette at concentrations more than twice those present in the procedural blanks. In addition, a large number of plant-derived metabolites were putatively identified, the presence of which supports the unconfirmed report that Lafayette fell in a semirural location in Indiana. Remarkably, the putative identification of the mycotoxin deoxynivalenol (or vomitoxin), alongside the report that the collector was possibly a student at Purdue University, can be used to identify the most likely fall year as 1919.

Phyllachora species infecting maize and other grass species in the Americas represents a complex of closely related species.

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 to 2019. These 186 isolates were grouped into five 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 specimens, 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 previously identified based on morphology and host association. This work represents the first attempt at molecular characterization of Phyllachora species infecting grass hosts and indicates some Phyllachora species can infect a broad range of host species and there may be significant synonymy in the Phyllachora genus.

Pathotype Complexity and Genetic Characterization of Phytophthora sojae Populations in Illinois, Indiana, Kentucky, and Ohio.

Phytophthora sojae, the causal agent of Phytophthora root and stem rot of soybean, has been managed with single Rps genes since the 1960s but has subsequently adapted to many of these resistance genes, rendering them ineffective. The objective of this study was to examine the pathotype and genetic diversity of P. sojae from soil samples across Illinois, Indiana, Kentucky, and Ohio by assessing which Rps genes were still effective and identifying possible population clusters. There were 218 pathotypes identified from 473 P. sojae isolates with an average of 6.7 out of 15 differential soybean lines exhibiting a susceptible response for each isolate. Genetic characterization of 103 P. sojae isolates from across Illinois, Indiana, Kentucky, and Ohio with 19 simple sequence repeat markers identified 92 multilocus genotypes. There was a moderate level of population differentiation between these four states, with pairwise FST values ranging from 0.026 to 0.246. There were also moderate to high levels of differentiation between fields, with pairwise FST values ranging from 0.071 to 0.537. Additionally, cluster analysis detected the presence of P. sojae population structure across neighboring states. The level of pathotype and genetic diversity, in addition to the identification of population clusters, supports the hypothesis of occasional outcrossing events that allow an increase in diversity and the potential to select for a loss in avirulence to specific resistance genes within regions. The trend of suspected gene flow among neighboring fields is expected to be an ongoing issue with current agricultural practices.

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.

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.

Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks.

Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.

Meta-Analysis of Soybean Yield Response to Foliar Fungicides Evaluated from 2005 to 2018 in the United States and Canada.

Random-effect meta-analyses were performed on data from 240 field trials conducted between 2005 and 2018 across nine U.S. states and Ontario, Canada, to quantify the yield response of soybean after application of foliar fungicides at beginning pod (R3) stage. Meta-analysis showed that the overall mean yield response when fungicide was used compared with not applying a fungicide was 2.7% (110 kg/ha). Moderator variables were also investigated and included fungicide group, growing season, planting date, and base yield, which all significantly influenced the yield response. There was also evidence that precipitation from the time of planting to the R3 growth stage influenced yield when fungicide was used (P = 0.059). Fungicides containing a premix of active ingredients from multiple groups (either two or three ingredients) increased the yield by 3.0% over not applying a fungicide. The highest and lowest yield responses were observed in 2005 and 2007, respectively. Better yield response to fungicides (a 3.0% increase) occurred when soybean crops were planted not later than 21 May and when total precipitation between planting and the R3 application date was above historic averages. Temperatures during the season did not influence the yield response. Yield response to fungicide was higher (a 4.7% increase) in average yield category (no spray control yield 2,878 to 3,758 kg/ha) and then gradually decreased with increasing base yield. Partial economic analyses indicated that use of foliar fungicides is less likely to be profitable when foliar diseases are absent or at low levels.