Season-Long Dynamics of Spinach Downy Mildew Determined by Spore Trapping and Disease Incidence

Free-Riding in Plant Health: A Social-Ecological Systems Approach to Collective Action

Plant disease epidemics often transcend land management boundaries, creating a collective-action problem where a group must cooperate in a common effort to maximize individual and group benefits. Drawing upon the social-ecological systems framework and associated design principles, we review variables of resource systems, resource units, actors, and governance systems relevant to collective action in plant health. We identify a need to better characterize how attributes of epidemics determine the usefulness of collective management, what influences actors' decisions to participate, what governance systems fit different plant health threats, and how these subsystems interact to lead to plant health outcomes. We emphasize that there is not a single governance structure that ensures collective action but rather a continuum of structures that depend on the key system variables identified. An integrated social-ecological systems approach to collective action in plant health should enable institutional designs to better fit specific plant health challenges.

It's a Trap! Part I: Exploring the Applications of Rotating-Arm Impaction Samplers in Plant Pathology

Although improved knowledge on the movement of airborne plant pathogens is likely to benefit plant health management, generating this knowledge is often far more complicated than anticipated. This complexity is driven by the dynamic nature of environmental variables, diversity among pathosystems that are targeted, and the unique needs of each research group. When using a rotating-arm impaction sampler, particle collection is dependent on the pathogen, environment, research objectives, and limitations (monetary, environmental, or labor). Consequently, no design will result in 100% collection efficiency. Fortunately, it is likely that multiple approaches can succeed despite these constraints. Choices made during design and implementation of samplers can influence the results, and recognizing this influence is crucial for researchers. This article is for beginners in the art and science of using rotating-arm impaction samplers; it provides a foundation for designing a project, from planning the experiment to processing samples. We present a relatively nontechnical discussion of the factors influencing pathogen dispersal and how placement of the rotating-arm air samplers alters propagule capture. We include a discussion of applications of rotating-arm air samplers to demonstrate their versatility and potential in plant pathology research as well as their limitations.

Transmission of Spinach Downy Mildew via Seed and Infested Leaf Debris

Spinach downy mildew, caused by the obligate oomycete pathogen Peronospora effusa, is a worldwide constraint on spinach production. The role of airborne sporangia in the disease cycle of P. effusa is well established, but the role of the sexual oospores in the epidemiology of P. effusa is less clear and has been a major challenge to examine experimentally. To evaluate seed transmission of spinach downy mildew via oospores in this study, isolated glass chambers were employed in two independent experiments to grow out oospore-infested spinach seed and noninfested seeds mixed with oospore-infested crop debris. Downy mildew diseased spinach plants were observed 37 and 34 days after planting in the two isolator experiments, respectively, in the chambers that contained one of two oospore-infested seed lots or seeds coated with oospore-infested leaves. Spinach plants in isolated glass chambers initiated from seeds without oospores did not show downy mildew symptoms. Similar findings were obtained using the same seed lot samples in a third experiment conducted in a growth chamber. In direct grow out tests to examine oospore infection on seedlings performed in a containment greenhouse with oospore-infested seed of two different cultivars, characteristic Peronospora sporangiophores were observed growing from a seedling of each cultivar. The frequency of seedlings developing symptoms from 82 of these oospore-infested seed indicated that approximately 2.4% of seedlings from infested seed developed symptoms, and 0.55% of seedlings from total seeds assayed developed symptoms. The results provide evidence that oospores can serve as a source of inoculum for downy mildew and provide further evidence of direct seed transmission of the downy mildew pathogen to seedlings in spinach via seedborne oospores.

Dynamics of Puccinia striiformis f. sp. tritici Urediniospores in Longnan, a Critical Oversummering Region of China

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici Erikss. (Pst), is a devastating disease resulting in yield reduction. Because the temperature limits the overwintering and oversummering of Pst, it cannot complete the whole year cycle in most areas of China. Longnan, located in the southeast of Gansu Province, is one of the annual cycle areas of Pst, which can supply urediniospores in autumn to eastern wheat-growing areas in China. In this study, a TaqMan real-time quantitative PCR (TaqMan-qPCR) detection system for Pst urediniospores was established, and the detection limit was a single urediniospore. The dynamics of Pst urediniospores in Longnan were monitored by spore trapping and TaqMan-qPCR for 3 years. Meanwhile, the meteorological conditions including air temperature, relative humidity, and precipitation were recorded. Results showed that Pst urediniospores can be captured from March to December, and two peaks of urediniospore density appeared in May and June, respectively. The density of urediniospore is closely related to temperature and precipitation from March to June. In addition, we found that the density of Pst urediniospore had the peak value when the average air temperature was 10 to 21°C, and the relative humidity was 60 to 85% from May to June. The exponential model could describe the variation of Pst urediniospore density based on average temperature and precipitation from March to June. It is worth mentioning that the exponential model based on average temperature 7 days before spore capture has a great advantage in predicting the urediniospore density in the air. This study laid a foundation for establishment of a prediction model for wheat stripe rust based on the density of urediniospores and meteorological factors.

Spore-Trapping Device: An Efficient Tool to Manage Fungal Diseases in Winter Wheat Crops

Leaf airborne diseases cause major shortfalls in agricultural crops. The introduction of technical means can significantly improve early-warning systems for plant diseases as well as provide timely and accurate forecasts. In this paper, we aim to evaluate the possibilities of detecting a phytopathogenic infection using a spore-catching device developed at the Federal Research Center of Biological Plant Protection (FRCBPP) on winter wheat varieties of different levels of susceptibility to major economically important leaf diseases, taking into account climatic conditions. The device captures spores in the surface layer of air among crop plants. We conducted research in the experimental fields of FRCBPP in 2019-2021. The objects of the study were four cultivars of winter wheat. They were selected according to the degree of resistance to various leaf diseases. We studied the progress of wheat diseases according to generally accepted international scales the onset of the first manifestations to their maximum development. We studied the aerogenic infection in wheat crops using the FRCBPP developed portable device for determining the infestation of plants. Sampling was carried out in the same period as the visual assessment. The samples were taken in the crops of each variety at five points. The sampling time was one minute. As a result of research on experimental crops of four varieties of winter wheat, we observed the development of such diseases as powdery mildew (Erysiphe graminis), yellow spot (Pyrenophora tritici-repentis), septoria leaf spot (Septoria tritici), yellow (Puccinia striiformis) and brown rust (Puccinia triticina). In a laboratory study of samples under a light microscope, all of the listed pathogens were found except for septoria leaf spot. Two-way analysis of variance confirmed the statistically significant separate and cumulative influence of the cultivar and year factor on winter wheat diseases. A generalized correlation analysis for three growing seasons (2019-2021) showed that an average statistically significant correlation coefficient (0.5-0.6) remains for the total groups for powdery mildew, yellow and brown rust. This indicator for the causative agent of yellow spot was equal to 0.4 with a high level of statistical significance. Thus, we conclude that by using a spore-catching device, it is possible to identify spores of economically significant pathogens in winter wheat crops and predict the further development of pathogens, taking into account the cultivar and annual climate factors.

Catching Spores: Linking Epidemiology, Pathogen Biology, and Physics to Ground-Based Airborne Inoculum Monitoring

Monitoring airborne inoculum is gaining interest as a potential means of giving growers an earlier warning of disease risk in a management unit or region. This information is sought by growers to aid in adapting to changes in the management tools at their disposal and the market-driven need to reduce the use of fungicides and cost of production. To effectively use inoculum monitoring as a decision aid, there is an increasing need to understand the physics of particle transport in managed and natural plant canopies to effectively deploy and use near-ground aerial inoculum data. This understanding, combined with the nuances of pathogen-specific biology and disease epidemiology, can serve as a guide to designing improved monitoring approaches. The complexity of any pathosystem and local environment are such that there is not a generalized approach to near-ground air sampler placement, but there is a conceptual framework to arrive at a "semi-optimal" solution based on available resources. This review is intended as a brief synopsis of the linkages among pathogen biology, disease epidemiology, and the physics of the aerial dispersion of pathogen inoculum and what to consider when deciding where to locate ground-based air samplers. We leverage prior work in developing airborne monitoring tools for hops, grapes, spinach, and turf, and research into the fluid mechanics governing particle transport in sparse canopies and urban and forest environments. We present simulation studies to demonstrate how particles move in the complex environments of agricultural fields and to illustrate the limited sampling area of common air samplers.

Comparison of microscopic and metagenomic approaches to identify cereal pathogens and track fungal spore release in the field

Wheat is one of the main staple food crops, and 775 million tonnes of wheat were produced worldwide in 2022. Fungal diseases such as Fusarium head blight, Septoria tritici blotch, spot blotch, tan spot, stripe rust, leaf rust, and powdery mildew cause serious yield losses in wheat and can impact quality. We aimed to investigate the incidence of spores from major fungal pathogens of cereals in the field by comparing microscopic and metagenomic based approaches for spore identification. Spore traps were set up in four geographically distinct UK wheat fields (Carnoustie, Angus; Bishop Burton, Yorkshire; Swindon, Wiltshire; and Lenham, Kent). Six major cereal fungal pathogen genera (Alternaria spp., Blumeria graminis, Cladosporium spp., Fusarium spp., Puccinia spp., and Zymoseptoria spp.) were found using these techniques at all sites. Using metagenomic and BLAST analysis, 150 cereal pathogen species (33 different genera) were recorded on the spore trap tapes. The metagenomic BLAST analysis showed a higher accuracy in terms of species-specific identification than the taxonomic tool software Kraken2 or microscopic analysis. Microscopic data from the spore traps was subsequently correlated with weather data to examine the conditions which promote ascospore release of Fusarium spp. and Zymoseptoria spp. This revealed that Zymoseptoria spp. and Fusarium spp. ascospore release show a positive correlation with relative humidity (%RH). Whereas air temperature (°C) negatively affects Zymoseptoria spp. ascospore release.

Clade-Specific Monitoring of Airborne Pseudoperonospora spp. Sporangia Using Mitochondrial DNA Markers for Disease Management of Cucurbit Downy Mildew

Management of cucurbit downy mildew (CDM) caused by Pseudoperonospora cubensis, relies on an intensive fungicide program. In Michigan, CDM occurs annually due to an influx of airborne sporangia and timely alerts of airborne inoculum can assist growers in assessing the need to initiate fungicide sprays. This research aimed to improve the specific detection of airborne P. cubensis sporangia by adapting quantitative real-time polymerase chain reaction (qPCR) assays to distinguish among P. cubensis clades I and II and P. humuli in spore trap samples from commercial production sites and research plots. We also evaluated the suitability of impaction spore traps compared with Burkard traps for detection of airborne sporangia. A multiplex qPCR assay improved the specificity of P. cubensis clade II detection accelerating the assessment of field spore trap samples. After 2 years of monitoring, P. cubensis clade II DNA was detected in spore trap samples before CDM symptoms were first observed in cucumber fields (July and August), while P. cubensis clade I DNA was not detected in air samples before or after the disease onset. In some commercial cucumber fields, P. humuli DNA was detected throughout the growing season. The Burkard spore trap appeared to be better suited for recovery of sporangia at low concentrations than the impaction spore trap. This improved methodology for the monitoring of airborne Pseudoperonospora spp. sporangia could be used as part of a CDM risk advisory system to time fungicide applications that protect cucurbit crops in Michigan.

Development, Validation, and Utility of Species-Specific Diagnostic Markers for Detection of Peronospora belbahrii

Peronospora belbahrii is an oomycete and the cause of basil downy mildew, one of the most destructive diseases affecting basil production worldwide. Disease management is challenging due to wind-dispersed sporangia and contaminated seed; therefore, identifying P. belbahrii in seed lots before sale or planting or in the field before symptoms develop could allow for timely deployment of disease management strategies. In this study, a draft genome assembly and next-generation sequencing reads for P. belbahrii, as well as publicly available DNA-seq and RNA-seq reads of several other downy mildew pathogens, were incorporated into a bioinformatics pipeline to predict P. belbahrii-specific diagnostic markers. The specificity of each candidate marker was validated against a diverse DNA collection of P. belbahrii, host tissue, and related oomycetes using PCR. Two species-specific markers were identified and used as templates to develop a highly sensitive probe-based real-time quantitative PCR (qPCR) assay that could detect P. belbahrii in leaf tissue and seed samples. Both markers were capable of reliably detecting as low as 500 fg/µl of P. belbahrii genomic DNA and as few as 10 sporangia. The qPCR assay was then validated with seed samples collected from a basil cultivar experiment. In total, 48 seed samples were collected and tested; P. belbahrii was detected in samples of all cultivars at estimated concentrations of 600 fg/µl up to 250 pg/µl and at as few as 10 sporangia up to >1,000 sporangia. The markers and assays are valuable for diagnostics and identifying P. belbahrii-contaminated seed lots to mitigate the effects of future basil downy mildew epidemics.

Early Detection of the Spinach Downy Mildew Pathogen in Leaves by Recombinase Polymerase Amplification

Downy mildew of spinach, caused by Peronospora effusa, is a major economic threat to both organic and conventional spinach production. Symptomatic spinach leaves are unmarketable and spinach with latent infections are problematic because symptoms can develop postharvest. Therefore, early detection methods for P. effusa could help producers identify infection before visible symptoms appear. Recombinase polymerase amplification (RPA) provides sensitive and specific detection of pathogen DNA and is a rapid, field-applicable method that does not require advanced technical knowledge or equipment-heavy DNA extraction. Here, we used comparative genomics to identify a unique region of the P. effusa mitochondrial genome to develop an RPA assay for the early detection of P. effusa in spinach leaves. In tandem, we established a TaqMan quantitative PCR (qPCR) assay and used this assay to validate the P. effusa specificity of the locus across Peronospora spp. and to compare assay performance. Neither the TaqMan qPCR nor the RPA showed cross reactivity with the closely related beet downy mildew pathogen, P. schachtii. TaqMan qPCR and RPA have detection thresholds of 100 and 900 fg of DNA, respectively. Both assays could detect P. effusa in presymptomatic leaves, with RPA-based detection occurring as early as 5 days before the appearance of symptoms and TaqMan qPCR-based detection occurring after 24 h of plant exposure to airborne spores. Implementation of the RPA detection method could provide real-time information for point-of-care management strategies at field sites.

Fantastic Downy Mildew Pathogens and How to Find Them: Advances in Detection and Diagnostics

Downy mildews affect important crops and cause severe losses in production worldwide. Accurate identification and monitoring of these plant pathogens, especially at early stages of the disease, is fundamental in achieving effective disease control. The rapid development of molecular methods for diagnosis has provided more specific, fast, reliable, sensitive, and portable alternatives for plant pathogen detection and quantification than traditional approaches. In this review, we provide information on the use of molecular markers, serological techniques, and nucleic acid amplification technologies for downy mildew diagnosis, highlighting the benefits and disadvantages of the technologies and target selection. We emphasize the importance of incorporating information on pathogen variability in virulence and fungicide resistance for disease management and how the development and application of diagnostic assays based on standard and promising technologies, including high-throughput sequencing and genomics, are revolutionizing the development of species-specific assays suitable for in-field diagnosis. Our review provides an overview of molecular detection technologies and a practical guide for selecting the best approaches for diagnosis.

Characterization of Pathogen Airborne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data

In a previous study, air sampling using vortex air samplers combined with species-specific amplification of pathogen DNA was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic uncertainty. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of pathogen abundance data. In particular, the analyses suggested that the length of time series data that it is practical or cost-effective to collect may limit the ability to definitively classify the uncertainty in the data. Over the two years of the study, five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating whether the pathogen abundance data were increasing revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves and (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence was positive.

Classification Methods for Airborne Disease Spores from Greenhouse Crops Based on Multifeature Fusion

Airborne fungal spores have always played an important role in the spread of fungal crop diseases, causing great concern. The traditional microscopic spore classification method mainly relies on naked eye observations and classification by professional and technical personnel in a laboratory. Due to the large number of spores captured, this method is labor-intensive, time-consuming, and inefficient, and sometimes leads to huge errors. Thus, an alternative method is required. In this study, a method was proposed to identify airborne disease spores from greenhouse crops using digital image processing. First, in an indoor simulation, images of airborne disease spores from three greenhouse crops were collected using portable volumetric spore traps. Then, a series of image preprocessing methods were used to identify the spores, including mean filtering, Gaussian filtering, OTSU (maximum between-class variance) method binarization, morphological operations, and mask operations. After image preprocessing, 90 features of the spores were extracted, including color, shape, and texture features. Based on these features, logistics regression (LR), K nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classification models were built. The test results showed that the average accuracy rates for the 3 classes of disease spores using the SVM model, LR model, KNN model, and RF model were 94.36%, 90.13%, 89.37%, and 89.23%, respectively. The harmonic average of the accuracy and the recall rate value (F value) were higher for the SVM model and its overall average value reached 91.68%, which was 2.03, 3.59, and 3.96 percentage points higher than the LR model, KNN model, and RF model, respectively. Therefore, this method can effectively identify 3 classes of diseases spores and this study can provide a reference for the identification of greenhouse disease spores.

Sporangiospore Viability and Oospore Production in the Spinach Downy Mildew Pathogen, Peronospora effusa

Downy mildew of spinach, caused by the obligate pathogen Peronospora effusa, remains the most important constraint in the major spinach production areas in the United States. This disease can potentially be initiated by asexual sporangiospores via "green bridges", sexually derived oospores from seed or soil, or dormant mycelium. However, the relative importance of the various types of primary inoculum is not well known. The ability of P. effusa sporangiospores to withstand abiotic stress, such as desiccation, and remain viable during short- and long-distance dispersal and the ability of oospores to germinate and infect seedlings remain unclear. Thus, the primary objectives of this research were to evaluate the impact of desiccation on sporangiospore survival and infection efficiency and examine occurrence, production, and germination of oospores. Results indicate that desiccation significantly reduces sporangiospore viability as well as infection potential. Leaf wetness duration of 4 h was needed for disease establishment by spinach downy mildew sporangiospores. Oospores were observed in leaves of numerous commercial spinach cultivars grown in California in 2018 and Arizona in 2019. Frequency of occurrence varied between the two states-years. The presence of opposite mating types in spinach production areas in the United States was demonstrated by pairing isolates in controlled crosses and producing oospores on detached leaves as well as intact plants. Information from the study of variables that affect sporangiospore viability and oospore production will help in improving our understanding of the epidemiology of this important pathogen, which has implications for management of spinach downy mildew.

Transcriptional analyses of differential cultivars during resistant and susceptible interactions with Peronospora effusa, the causal agent of spinach downy mildew

Downy mildew of spinach is caused by the obligate oomycete pathogen, Peronospora effusa. The disease causes significant economic losses, especially in the organic sector of the industry where the use of synthetic fungicides is not permitted for disease control. New pathotypes of this pathogen are increasingly reported which are capable of breaking resistance. In this study, we took advantage of new spinach genome resources to conduct RNA-seq analyses of transcriptomic changes in leaf tissue of resistant and susceptible spinach cultivars Solomon and Viroflay, respectively, at an early stage of pathogen establishment (48 hours post inoculation, hpi) to a late stage of symptom expression and pathogen sporulation (168 hpi). Fold change differences in gene expression were recorded between the two cultivars to identify candidate genes for resistance. In Solomon, the hypersensitive inducible genes such as pathogenesis-related gene PR-1, glutathione-S-transferase, phospholipid hydroperoxide glutathione peroxidase and peroxidase were significantly up-regulated uniquely at 48 hpi and genes involved in zinc finger CCCH protein, glycosyltransferase, 1-aminocyclopropane-1-carboxylate oxidase homologs, receptor-like protein kinases were expressed at 48 hpi through 168 hpi. The types of genes significantly up-regulated in Solomon in response to the pathogen suggests that salicylic acid and ethylene signaling pathways mediate resistance. Furthermore, many genes involved in the flavonoid and phenylpropanoid pathways were highly expressed in Viroflay compared to Solomon at 168 hpi. As anticipated, an abundance of significantly down-regulated genes was apparent at 168 hpi, reflecting symptom development and sporulation in cultivar Viroflay, but not at 48 hpi. In the pathogen, genes encoding RxLR-type effectors were expressed during early colonization of cultivar Viroflay while crinkler-type effector genes were expressed at the late stage of the colonization. Our results provide insights on gene expression in resistant and susceptible spinach-P. effusa interactions, which can guide future studies to assess candidate genes necessary for downy mildew resistance in spinach.

Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree-confusion matrix method

BACKGROUND

Rice smut and rice blast are listed as two of the three major diseases of rice. Owing to the small size and similar structure of rice blast and rice smut spores, traditional microscopic methods are troublesome to detect them. Therefore, this paper uses microscopy image identification based on the synergistic judgment of texture and shape features and the decision tree-confusion matrix method.

RESULTS

The distance transformation-Gaussian filtering-watershed algorithm method was proposed to separate the adherent rice blast spores, and the accuracy was increased by about 10%. Four shape features (area, perimeter, ellipticity, complexity) and three texture features (entropy, homogeneity, contrast) were selected for decision-tree model classification. The confusion-matrix algorithm was used to calculate the classification accuracy, in which global accuracy is 82% and the Kappa coefficient is 0.81. At the same time, the detection accuracy is as high as 94%.

CONCLUSIONS

The synergistic judgment of texture and shape features and the decision tree-confusion matrix method can be used to detect rice disease quickly and precisely. The proposed method can be combined with a spore trap, which is vital to devise strategies early and to control rice disease effectively. © 2019 Society of Chemical Industry.

Spinach Downy Mildew: Advances in Our Understanding of the Disease Cycle and Prospects for Disease Management

Downy mildew on spinach is caused by Peronospora effusa, an oomycete pathogen that poses a challenge to spinach production worldwide, especially in organic production. Following infection, P. effusa produces abundant amounts of asexual sporangia. Sporangia become windborne and initiate new infections locally or distantly, leading to widespread epidemics. Oospores produced from the union of opposite mating types have been observed within infected leaves and seeds and may remain viable for many years. Sexual reproduction increases the genetic diversity of P. effusa through sexual recombination, and thus, the movement of oospores on seed has likely fueled the rapid explosion of new pathotypes in different regions of the world over the past 20 years. This review summarizes recent advances in spinach downy mildew research, especially in light of the findings of oospores in contemporary commercial spinach seed lots as well as their germination. Knowledge of the role of the oospores and other aspects of the disease cycle can directly translate into new and effective disease management strategies.

Summary Measures of Predictive Power Associated with Logistic Regression Models of Disease Risk

For an ordinary least-squares regression model, the coefficient of determination (R²) describes the proportion (or percentage) of variance of the response variable explained by the model, and is a widely accepted summary measure of predictive power. A number of R²-analogues are available as summary measures of predictive power associated with logistic regression models, including models of disease risk. Tjur's R² and McFadden's R² are of particular interest in this context. Both of these metrics have transparent derivations, which reveal that they apply to different aspects of model evaluation. Tjur's R² is a measure of separation between (known) actual states (e.g., gold standard determinations of "healthy" or "diseased" status) whereas McFadden's R² is a measure of separation between predicted states (e.g., forecasts of disease status based on models of disease risk). This clarifies their interpretation in the context of evaluation of logistic regression models of disease risk. In addition, versions of both Tjur's R² and McFadden's R² may be obtained from analyses of disease risk that are not preceded by logistic regression analysis. Tjur's R² and McFadden's R² are shown to be useful, distinct summary measures of predictive power for epidemiological models of disease risk.

Automatic detection and counting of urediniospores of Puccinia striiformis f. sp. tritici using spore traps and image processing

The quantitative monitoring of airborne urediniospores of Puccinia striiformis f. sp. tritici (Pst) using spore trap devices in wheat fields is an important process for devising strategies early and effectively controlling wheat stripe rust. The traditional microscopic spore counting method mainly relies on naked-eye observation. Because of the great number of trapped spores, this method is labour intensive and time-consuming and has low counting efficiency, sometimes leading to huge errors; thus, an alternative method is required. In this paper, a new algorithm was proposed for the automatic detection and counting of urediniospores of Pst, based on digital image processing. First, images of urediniospores were collected using portable volumetric spore traps in an indoor simulation. Then, the urediniospores were automatically detected and counted using a series of image processing approaches, including image segmentation using the K-means clustering algorithm, image pre-processing, the identification of touching urediniospores based on their shape factor and area, and touching urediniospore contour segmentation based on concavity and contour segment merging. This automatic counting algorithm was compared with the watershed transformation algorithm. The results show that the proposed algorithm is efficient and accurate for the automatic detection and counting of trapped urediniospores. It can provide technical support for the development of online airborne urediniospore monitoring equipment.

Detection of Latent Peronospora effusa Infections in Spinach

Downy mildew disease of spinach, caused by Peronospora effusa, is managed in conventional fields by a combination of host resistance and scheduled fungicide applications. Fungicides are currently applied to prevent downy mildew epidemics regardless of the infection status of spinach crops. A more streamlined approach would be to develop methods to target either latent infections for fungicide application in conventional production systems or to hasten harvest in organic production. In this study, conventional polymerase chain reaction (PCR) was applied to detect P. effusa DNA in symptomless spinach leaves in three spatially and temporally separated field plots, each containing four 2-m beds, 35 m in length. Spinach leaves were sampled weekly at 3-m intervals at 48 locations throughout each plot. Initial samples were asymptomatic and yet PCR enabled detection of P. effusa DNA extracted from sampled spinach leaves. Detection of latent downy mildew infection in spinach leaves was confirmed by PCR as early as 7 days prior to symptom development. The limit of pathogen DNA detection in spinach leaves was calculated at 10 pg using the conventional PCR approach. Quantitative PCR with TaqMan methodology revealed the presence of inhibitors from spinach leaf DNA extracts and affected amplification efficiencies, but not when diluted, enabling detection of P. effusa DNA at a concentration of <0.1 pg. In conclusion, detection of latent infections may enable management decisions for earlier-than-normal harvest of infected, symptomless organic crops, and for timing fungicide applications on symptomless plants in conventional production.

Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model

Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.

A Framework for Optimizing Phytosanitary Thresholds in Seed Systems

Seedborne pathogens and pests limit production in many agricultural systems. Quarantine programs help prevent the introduction of exotic pathogens into a country, but few regulations directly apply to reducing the reintroduction and spread of endemic pathogens. Use of phytosanitary thresholds helps limit the movement of pathogen inoculum through seed, but the costs associated with rejected seed lots can be prohibitive for voluntary implementation of phytosanitary thresholds. In this paper, we outline a framework to optimize thresholds for seedborne pathogens, balancing the cost of rejected seed lots and benefit of reduced inoculum levels. The method requires relatively small amounts of data, and the accuracy and robustness of the analysis improves over time as data accumulate from seed testing. We demonstrate the method first and illustrate it with a case study of seedborne oospores of Peronospora effusa, the causal agent of spinach downy mildew. A seed lot threshold of 0.23 oospores per seed could reduce the overall number of oospores entering the production system by 90% while removing 8% of seed lots destined for distribution. Alternative mitigation strategies may result in lower economic losses to seed producers, but have uncertain efficacy. We discuss future challenges and prospects for implementing this approach.