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Tar spot of corn has been a major foliar disease in several Latin American countries since 1904. In 2015, tar spot was first documented in the United States and has led to significant yield losses of approximately 4.5 million t. Tar spot is caused by an obligate pathogen, Phyllachora maydis, and thus requires a living host to grow and reproduce. Du...
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Context 1
... spot is characterized by the formation of black stromata, the fruiting bodies of P. maydis, on the foliage. The stromata resemble spots of tar ( Fig. 1). Like other species in the genus, P. maydis is an obligate biotroph, requiring a living host to grow and reproduce (Cannon 1991). In fields with infested corn residue, initial signs and symptoms of tar spot may appear in the lower canopy of the corn plant ( Bajet et al. 1994). In the U.S., "top down" patterns of disease frequently ...
Context 2
... maydis is endemic to parts of Mexico and Central and South America (Fig. 8, Table 1), where it was apparently restricted for over 100 years (Cline 2019;Hock et al. 1995). However, in 2015, it was detected for the first time in the continental U.S. and has spread significantly since then (Bissonnette 2015; Ruhl et al. 2016). P. maydis is now established in the U.S. in Illinois, Indiana, Iowa, Michigan, Minnesota, ...
Citations
... Such diseases cause an estimated 10 to 20% yield loss worldwide annually, with an additional 10 to 20% loss after harvest (Stukenbrock and Gurr 2023). Phyllachora maydis is an obligate fungal pathogen that causes tar spot, a foliar disease of maize (Zea mays L.) common in the United States, Mexico, and Central and South America (da Silva et al. 2021;Bajet et al. 1994;Mottaleb et al. 2019;Solórzano et al. 2023;Valle-Torres et al. 2020). Tar spot was first detected in the United States in 2015 da Silva et al. 2021;Malvick et al. 2020;McCoy et al. 2018;Mottaleb et al. 2019;Moura et al. 2023;Pandey et al. 2022;Ruhl et al. 2016;Valle-Torres et al. 2020;Wise et al. 2023). ...
... Phyllachora maydis is an obligate fungal pathogen that causes tar spot, a foliar disease of maize (Zea mays L.) common in the United States, Mexico, and Central and South America (da Silva et al. 2021;Bajet et al. 1994;Mottaleb et al. 2019;Solórzano et al. 2023;Valle-Torres et al. 2020). Tar spot was first detected in the United States in 2015 da Silva et al. 2021;Malvick et al. 2020;McCoy et al. 2018;Mottaleb et al. 2019;Moura et al. 2023;Pandey et al. 2022;Ruhl et al. 2016;Valle-Torres et al. 2020;Wise et al. 2023). Since then, P. maydis has caused an estimated $2.9 billion in maize yield loss e-Xtra: Supplementary material is available online. ...
... © 2024 The American Phytopathological Society (Mueller et al. , 2022. P. maydis produces dark-pigmented structures known as stromata, the characteristic "tar spots" visible on the leaf surface (da Silva et al. 2021;Solórzano et al. 2023;Valle-Torres et al. 2020). Later in the disease cycle, a necrotic halo may develop surrounding stromata, giving the appearance of a "fish eye" (da Silva et al. 2021;Hock et al. 1995;McCoy et al. 2019;Mottaleb et al. 2019;Valle-Torres et al. 2020). ...
Tar spot, a disease caused by the ascomycete fungal pathogenPhyllachora 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 differentfields in northern Indiana at three different time points during the growingseason. 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 are formed around, not within, the vasculature. We observed P. maydis associated with two additional fungi, one of which is likely a member of the Paraphaeosphaeriagenus; the other is an unknown fungus. Our data provide fundamental insights into how this pathogen colonizes and spreads within maize leaves. This knowledge can inform new approaches to managing tar spots, which could help mitigate the significant economic losses caused by this disease.
... Tar spot of corn is a relatively new disease in the United States (US) but, since the early-to-mid 1900s, has been endemic and prevalent in various other countries throughout the Americas, including Honduras (Maublanc 1904;Abbott 1931;Bell and Alandia 1957;McGuire and Crandall 1967;Castaño 1969;Malaguti and Subero 1972;Liu 1973, Bajet et al. 1994Valle-Torres et al. 2020). In the US, Phyllachora maydis (Maubl.) ...
... The disease has generally been understudied worldwide (Valle-Torres et al. 2020). Thus, there is a dearth of information on the understanding of tar spot epidemiology (Maublanc 1904;Hock et al. 1989;Dittrich et al. 1991;Bajet et al. 1994;Hock et al. 1995, Pereyda-Hernandez et al. 2009Kleczewski et al. 2019;Loladze et al. 2019;Groves et al. 2020;Valle-Torres et al. 2020;Oh et al. 2021). ...
... The disease has generally been understudied worldwide (Valle-Torres et al. 2020). Thus, there is a dearth of information on the understanding of tar spot epidemiology (Maublanc 1904;Hock et al. 1989;Dittrich et al. 1991;Bajet et al. 1994;Hock et al. 1995, Pereyda-Hernandez et al. 2009Kleczewski et al. 2019;Loladze et al. 2019;Groves et al. 2020;Valle-Torres et al. 2020;Oh et al. 2021). Although tar spot epidemics occur in different countries and areas at risk throughout the Americas, the epidemiological nature of these events is currently unknown. ...
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.
... Downy mildew is a fungal disease and is considered one of the most destructive pathogens of corn in the Philippines [20]. Tar spot is also a major foliar disease of corn that can greatly reduce grain yield and quality [21]. The new disease categories have two hundred fifty sample images each. ...
Despite the advances achieved in crop protection and management, crop diseases remain a problem for corn farmers. Numerous studies have presented the efficacy of corn disease detection and classification using machine learning-based vision detectors. However, many of these models rely on datasets with lab-based images that do not depict the real-world accurately. In this study, a vision-based corn disease detector and classifier is developed with EfficientNetV2 as base architecture. In addition, an algorithm for evaluating the severity of the disease has also been created. Two datasets were built to train the models, the common corn diseases (CCD) and corn disease severity (CDS). The EfficientNetV2-B0, B1, B2, B3, and S models, pre-trained on ImageN et, were explored for feature extraction. A custom classifier head is incorporated into the EfficientNetV2-based model to complete the architecture. It consists of a single convolutional neural network (CNN) and two fully connected layers. Transfer learning and fine-tuning were employed to improve the performance. The models were evaluated based on accuracy, cross-entropy loss, precision, recall, and F1-score. The EfficientNetV2B2 model performed best on the disease classification task, with an accuracy of 95.74%. The EfficientNetV2B3 is the top performer in the disease severity assessment task, with an accuracy of 98.73%. The EfficientNetV2S also surpassed other proposed models in PlantVillage (PV) dataset with an accuracy of 99.52%.
... The proposed tar spot disease cycle begins with the dispersal of ascospores onto corn leaves under favorable environmental conditions [8]. Early chlorotic symptoms then can develop followed by the formation of black stromata that are raised, embedded in the tissue, and may be surrounded by necrosis [9]. ...
... Early chlorotic symptoms then can develop followed by the formation of black stromata that are raised, embedded in the tissue, and may be surrounded by necrosis [9]. The stromata often extrude masses of ascospores [9] that allow multiple cycles of infection during the growing season [8]. Ascospores overwinter within the stromata on infected foliage and can be dispersed in the following growing season under favorable environmental conditions [10]. ...
... This study reveals how little is known about the biology of P. maydis. The stromata of P. maydis extrude masses of spores onto leaf surfaces under conditions that are unknown; however, spore release appears to occur under environmental conditions also favorable for germination and induction of tar spot [8]. When evaluating the induction of tar spot in the current study, variable results were achieved with a proximity inoculation test. ...
Background:
Tar 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.
Results:
We 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.
Conclusions:
We 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.
... One devastating disease is caused by Phyllachora maydis and is commonly referred to as tar spot because of its characteristic black, shiny, raised leaf spots which generally range from 2-4 mm in diameter. Infection by this pathogenic fungus can result in significant yield losses, and even death of plants if infection occurs early in a susceptible variety [2]. Tar spot disease has been endemic in much of Central and South America for several decades [2]. ...
... Infection by this pathogenic fungus can result in significant yield losses, and even death of plants if infection occurs early in a susceptible variety [2]. Tar spot disease has been endemic in much of Central and South America for several decades [2]. It was first reported in two U.S. "corn belt" states, Illinois and Indiana, in 2015 [3], and since then there have been major outbreaks in several regions in both 2018 and 2021 [4]. ...
... Management strategies for tar spot disease include use of resistant varieties and application of fungicides [2]. However, under high inoculum pressure, significant yield losses can still occur, even with the use of resistant varieties [5]. ...
Tar spot disease in corn, caused by Phyllachora maydis, can reduce grain yield by limiting the total photosynthetic area in leaves. Stromata of P. maydis are long-term survival structures that can germinate and release spores in a gelatinous matrix in the spring, which are thought to serve as inoculum in newly planted fields. In this study, overwintered stromata in corn leaves were collected in Central Illinois, surface sterilized, and caged on water agar medium. Fungi and bacteria were collected from the surface of stromata that did not germinate and showed microbial growth. Twenty-two Alternaria isolates and three Cladosporium isolates were collected. Eighteen bacteria, most frequently Pseudomonas and Pantoea species, were also isolated. Spores of Alternaria, Cladosporium, and Gliocladium catenulatum (formulated as a commercial biofungicide) reduced the number of stromata that germinated compared to control untreated stromata. These data suggest that fungi collected from overwintered tar spot stromata can serve as biological control organisms against tar spot disease.
... However, morphological and genetic data clearly separate Microdochium and Fusarium into different genera (Nicholson et al. 1996). Mischaracterization and misidentification of these genera is possible when relying solely on morphological characteristics, as many isolates will fail to sporulate in culture, while members of the genera that sporulate successfully share similarities in spore types and structures (Gerlach et al. 1982;Leslie and Summerell 2008;Summerell et al. 2003;Valle-Torres et al. 2020). Therefore, it is possible that previous identifications of Microdochium recovered from fisheye lesions from Mexico (Mueller and Samuels 1984) may belong to Fusarium. ...
Tar spot, caused by Phyllachora maydis, is an emerging disease of corn in the United States. Stromata of P. maydis are sometimes surrounded by necrotic lesions known as fisheyes and were previously reported to be caused by the fungus Microdochium maydis. The association of M. maydis with 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 as Fusarium spp., 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, and Fusarium reference sequences were obtained from GenBank. All the necrotic lesion isolates clustered within Fusarium lineages and were phylogenetically distinct from the Microdochium clade. All Fusarium isolates from Mexico belonged to the F. incarnatum-equiseti species complex, whereas >85% of the U.S. isolates grouped within the F. sambucinum species 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 .
... Tar spot of corn is an emerging disease caused by Phyllachora maydis Maubl. The disease is now distributed across the Americas [1]. In Mexico, the Caribbean, Central and South America, tar spot is considered to be caused by the interaction of three organisms: P. maydis, Monographella maydis, and Coniothyrium phyllachorae [2,3]. ...
... Currently, no reliable and reproducible infection assay is available to study tar spot of corn. The isolation of P. maydis in synthetic media remains elusive, limiting the production of pure inoculum for pathological studies (1). However, infection assays using spores produced on plants exist for Puccinia and other fungi considered obligate biotrophs [14][15][16]. ...
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.
... that could be reducing the grain yield when N is sufficient for crop growth, a functionality not currently in the APSIM framework. We coded and tested new algorithms within APSIM to simulate the potential for disease risk associated with greater amounts of corn residue (Valle-Torres et al., 2020;Jalli et al., 2021) which could decrease canopy photosynthesis (the radiation use efficiency parameter) and/or reduce roots' ability to extract water and N from the soil profile (the XF and KL parameters; Figs. S1 and S2). ...
CONTEXT
Process-based models are increasingly used to explain and predict crop yields and long-term changes in soil organic matter (SOM) and hence should be regularly evaluated for their accuracy. Currently, there is a knowledge gap of how well process-based models can estimate the economic optimum nitrogen rate (EONR) across environments and years.
OBJECTIVES
We evaluated the Agricultural Production Systems sIMulator (APSIM) software ability to simulate corn yield response to nitrogen (N) input and crop rotation in long term experiments. Furthermore, we explored causes for over/under prediction of the EONR.
METHODS
Measurements included crop yields from 14 long-term (N) fertilizer rate experiments representing major production regions in the U.S. Corn Belt and SOM distribution from seven long-term experiments. Corn yield response to N rate was analyzed with statistical models to estimate the EONR (386 N rate trials).
RESULTS And CONCLUSIONS
The model successfully captured spatiotemporal patterns in observed crop yields across N rates in the soybean-corn (SC) system, but overpredicted crop yield by 5–25% at high N rates in the corn-corn (CC) system. This overprediction was partially resolved by adding algorithms in APSIM to account for the well-known continuous corn yield penalty. The improved model simulated yield response to N and the EONR with a model agreement of 0.93 and 0.66, respectively, across rotations. The lower accuracy in predicting EONR compared to crop yields was attributed to 1) inherent model error in simulating yields: for example, a 10% error in yield simulation of a single point can result in a 34% error in EONR; and 2) the inability of the APSIM model to fully generate the quadratic nature of corn yield response to N rate, which resulted in the linear-plateau model being selected in most cases. Forcing use of only a quadratic plateau or quadratic regression model to fit the simulated yields, which is the expected biophysical yield response to N, improved EONR prediction by 23% while the accuracy of the statistical model fit was minimally decreased (<1%). The APSIM model simulated SOM distributions after 20 years of cropping with an agreement index of 0.93. We believe that APSIM is well suited to supplement N research in the U.S. Corn Belt, recognizing identified limitations between yield estimation and N response determination.
SIGNIFICANCE
This research provided a solution for process-based models to cope with the continuous corn yield penalty, thoroughly evaluated APSIM, and identified research priorities towards increasing EONR prediction.
... Removal of corn tassels from acquired images is a potential way to improve the performance and transferability of the proposed models. The spectral reflectance of corn tassels is different from that of corn leaves (Viña et al., 2004;Shao et al., 2022). Shao et al. reported that tassels lowered the canopy reflectance of the green region, and the impact of tassel reflectance on different vegetation indices varied between growth stages and corn varieties (Shao et al., 2022). ...
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.
... Existen distintos métodos de evaluación visual y escalas representativas que son usadas para fenotipado o evaluaciones epidemiológicas. Estos métodos están basados en secciones de hojas donde los síntomas muchas veces no son uniformes (Valle-Torres et al., 2020). Otros métodos, como el uso de drones que miden ref lectancia (Mahlein, Kuska, Behmann, Polder y Walter, 2018) o índices vegetativos pero requieren de equipos costosos y personal capacitado, aunque podrían ser más ef icaces en grandes hectáreas. ...
La enfermedad conocida como mancha de asfalto (Phyllachora maydis) afecta al cultivo de maíz (Zea mays). Esta enfermedad causa lesiones en las hojas que al progresar pueden ocasionar la muerte de la planta. Existen diferentes métodos para evaluar el progreso de una enfermedad en las plantas. Generalmente, se utilizan escalas de evaluación visual; sin embargo, su uso es bastante subjetivo. El procesamiento de imágenes ha sido utilizado como una alternativa para la evaluación de enfermedades. Este método evita sesgos y errores durante las evaluaciones. El objetivo de este ensayo fue utilizar el aplicativo telefónico Leaf Doctor como una alternativa a la evaluación de la enfermedad producida por P. maydis. Para el experimento se utilizó un diseño de bloques completamente al azar. Se sembraron tres variedades de maíz y se evaluó el nivel de tolerancia a la mancha de asfalto al ser tratadas con diferentes dosis de silicio. Las diferentes dosis de silicio no reducen la enfermedad, sin embargo, fue posible determinar aumento de la producción de maíz en dosis de silicio de 252 kg ha-1. Los resultados indican que el programa puede considerarse como una alternativa ef iciente para evaluar la mancha de asfalto debido a la alta correlación con la escala de evaluación visual (R2: 0.77-0.94). La variedad más resistente a la enfermedad fue INIAP-551.
