Meta-analysis and other approaches for synthesizing structured and unstructured data in plant pathology

Seasonal Periodicity of the Airborne Spores of Fungi Causing Grapevine Trunk Diseases: An Analysis of 247 Studies Published Worldwide

Grapevine trunk diseases (GTDs) are among the most devastating grapevine diseases globally. GTDs are caused by numerous fungi belonging to different taxa, which release spores into the vineyard and infect wood tissue, mainly through wounds caused by viticultural operations. The timing of operations to avoid infection is critical concerning the periodicity of GTD spores in vineyards, and many studies have been conducted in different grape-growing areas worldwide. However, these studies provide conflicting and fragmented information. To synthesize current knowledge, we conducted a systematic literature review, extracted quantitative data from published papers, and used these data to identify trends and knowledge gaps that need to be addressed in future studies. Our database included 26 papers covering 247 studies and 3,529 spore sampling records concerning a total of 29 fungal taxa responsible for Botryosphaeria dieback (BD), Esca complex (EC), and Eutypa dieback (ED). We found a clear seasonality in the presence and abundance of BD spores, with a peak from fall to spring, more in the northern hemisphere than in the southern hemisphere, but not for EC and ED. Spores of these fungi were present throughout the growing season in both hemispheres, possibly because of higher variability in spore types, sporulation conditions, and spore release mechanisms in EC and ED fungi than in BD. Our analysis has limitations because of knowledge gaps and data availability for some fungi (e.g., basidiomycetes, which cause EC). These limitations are discussed to facilitate further research.

Reconstructing historic and modern potato late blight outbreaks using text analytics

In 1843, a hitherto unknown plant pathogen entered the US and spread to potato fields in the northeast. By 1845, the pathogen had reached Ireland leading to devastating famine. Questions arose immediately about the source of the outbreaks and how the disease should be managed. The pathogen, now known as Phytophthora infestans, still continues to threaten food security globally. A wealth of untapped knowledge exists in both archival and modern documents, but is not readily available because the details are hidden in descriptive text. In this work, we (1) used text analytics of unstructured historical reports (1843-1845) to map US late blight outbreaks; (2) characterized theories on the source of the pathogen and remedies for control; and (3) created modern late blight intensity maps using Twitter feeds. The disease spread from 5 to 17 states and provinces in the US and Canada between 1843 and 1845. Crop losses, Andean sources of the pathogen, possible causes and potential treatments were discussed. Modern disease discussion on Twitter included near-global coverage and local disease observations. Topic modeling revealed general disease information, published research, and outbreak locations. The tools described will help researchers explore and map unstructured text to track and visualize pandemics.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.

A Systematic Review and Quantitative Synthesis of the Efficacy of Quaternary Ammonium Compounds in Disinfesting Nonfungal Plant Pathogens

This quantitative review and systematic analysis of the effectiveness of quaternary ammonium compounds (QACs) in disinfesting nonfungal plant pathogens in agricultural and horticultural cropping systems is a complementary follow-up to a previous study that evaluated the efficacy of QACs against fungal plant pathogens. In the present study, a meta-analysis involving 67 studies was conducted to assess the overall efficacy of QACs against plant pathogenic bacteria, oomycetes, and viruses and to identify factors associated with observed differences in product efficacy. Across all studies, QACs resulted in a significant (P < 0.0001) reduction in either disease intensity or propagule viability with a mean Hedges' g ([Formula: see text]) of 1.75, indicating that overall QAC treatments were moderately effective against nonfungal pathogens. Significant differences in product efficacy were observed between organism types (P = 0.0001), with QAC interventions resulting in higher efficacy (P = 0.0002) against oomycetes ([Formula: see text] = 4.20) than against viruses ([Formula: see text] = 1.42) and bacteria ([Formula: see text] = 1.07), which were not different (P = 0.2689) from each other. As a result, bacterium and virus types were combined into a composite set (BacVir). QAC intervention against BacVir resulted in significant differences in efficacy within categorical moderator subgroups for genus (P = 0.0133), target material (P = 0.0001), and QAC product generation (P = 0.0281). QAC intervention against oomycetes resulted in significant differences in efficacy only for genus (P < 0.0001). For the BacVir composite, five random effect (RE) meta-regression models were significant (P = 0.05), where models with dose and time, dose and genus, time and genus, dose and target, and time and target accounted for 62, 61, 52, 83, and 88%, respectively, of the variance in true effect sizes (R2) associated with [Formula: see text]. For oomycetes, three RE meta-regression models were significant (P = 0.05), where models with dose and time, dose and genus, and time and genus accounted for 64, 86, and 90%, respectively, of R2 associated with [Formula: see text]. These results show that while QACs are moderately effective against nonfungal plant pathogens, the observed variability in their efficacy due to dose of active ingredient and contact time of these products can be influenced by organism type, genus within organism type, the target being treated, and the generation of QAC products.

Temperature-Dependent Growth and Spore Germination of Fungi Causing Grapevine Trunk Diseases: Quantitative Analysis of Literature Data

Grapevine trunk diseases (GTDs) are serious threats in all viticultural areas of the world, and their management is always complex and usually inadequate. Fragmented and inconsistent information on the epidemiology and environmental requirements of the causal fungi is among the reasons for poor disease control. Therefore, we conducted a quantitative analysis of literature data to determine the effects of temperature on mycelial growth and the effects of temperature and moisture duration on spore germination. Using the collected information, we then developed mathematical equations describing the response of mycelial growth to temperature, and the response of spore germination to temperature and moisture for the different species and disease syndromes. We considered 27 articles (selected from a total of 207 articles found through a systematic literature search) and 116 cases; these involved 43 fungal species belonging to three disease syndromes. The mycelial growth of the fungi causing Botryosphaeria dieback (BD) and the esca complex (EC) responded similarly to temperature, and preferred higher temperatures than those causing Eutypa dieback (ED) (with optimal temperature of 25.3, 26.5, and 23.3°C, respectively). At any temperature, the minimal duration of the moist period required for 50% spore germination was shorter for BD (3.0 h) than for EC (17.2 h) or ED (15.5 h). Mathematical equations were developed accounting for temperature-moisture relationships of GTD fungi, which showed concordance correlation coefficients ≥0.888; such equations should be useful for reducing the risk of infection.

Development of a model for Colletotrichum diseases with calibration for phylogenetic clades on different host plants

Fungi in the genus Colletotrichum cause serious pre- and post-harvest losses to several agricultural crops worldwide. Through a systematic literature review, we retrieved the published information on Colletotrichum anthracnose diseases on different host plants and developed a mechanistic model incorporating the main stages of the pathogen's life cycle and the effect of weather. The model predicts anthracnose progress during the growing season on the aerial organs of different crops, and was parameterized for seven Colletotrichum clades (acutatum, dematium, destructivum, gloeosporioides, graminicola, and orbiculare) and the singleton species, C. coccodes. The model was evaluated for the anthracnose diseases caused by fungi belonging to five clades on six hosts by using data from 17 epidemics that occurred in Italy, the USA, Canada, and Japan. A comparison of observed versus predicted data showed a concordance correlation coefficient of 0.928 and an average distance between real data and the fitted line of 0.044. After further validation, the model could be used to support decision-making for crop protection.

Susceptibility of pruning wounds to grapevine trunk diseases: A quantitative analysis of literature data

INTRODUCTION

Pruning wounds are the main entry points for fungi causing grapevine trunk diseases (GTDs). Several studies identified factors influencing the temporal dynamics of wound susceptibility, which include the fungal species and inoculum dose, weather conditions, grape variety, pruning date, and so forth. Here, we conducted a quantitative analysis of literature data to synthesise outcomes across studies and to identify the factors that most affect the length of pruning wound susceptibility.

METHODS

We extracted data on the frequency at which the inoculated wounds showed GTD symptoms or an inoculated pathogen was reisolated following artificial inoculation at the time of pruning or in the following days. A negative exponential model was fit to these data to describe changes in wound susceptibility as a function of time since pruning, in which the rate parameter changed depending on specific factors.

RESULTS AND DISCUSSION

The results show that wound susceptibility is high at the time of pruning, and they remain susceptible to invasion by GTD fungi for months after pruning. Infection incidence on wounds was higher for fungi associated with Botryosphaeria dieback than those associated with Eutypa dieback or Esca complex, and wound susceptibility decreased faster for Eutypa dieback than for other GTD agents. Grapevine variety and pruning season also affected the wound susceptibility period. Sauvignon Blanc remains susceptible to GTDs longer than other varieties. We also found that the time of pruning can affect infection dynamics, especially for more susceptible varieties. The results increase our understanding of GTD epidemiology and should help growers control infections.

A Systematic Review and Quantitative Synthesis of the Efficacy of Quaternary Ammonium Disinfestants Against Fungal Plant Pathogens

Quaternary ammonium compounds (QACs) have been used as disinfestants in plant production systems since the late 20th century. In studies on the control of fungal pathogens in agricultural and horticultural crop production systems, the efficacy of QAC disinfestants is variable, ranging from very high to ineffective. A systematic review and meta-analysis were performed to establish and understand how pathogen- and application-related factors influenced product efficacy. The meta-analysis was based on 124 studies involving 14 fungal plant pathogen genera, eight target materials, and four generations of QAC products that contained different mixtures of active ingredients. A significant (P < 0.0001) reduction in either disease intensity or propagule viability resulted following disinfestation using QAC products. Hedges' g standardized mean difference (g̅₊) across the studies was 2.16, indicating that QACs, on average, were highly effective against fungal pathogens. Heterogeneity was significant (P < 0.0001), indicating that effect sizes (g) were not representative of a common mean effect size and supported selection of a random effects model. In all, 78.5% of the observed variance consisted of variance in true effects with a high estimate of between-study variability (τ² = 2.15). For fungal genus, subgroup g̅₊ for genera Pseudonectria and Calonectria was significantly (P < 0.0038) higher than for all other genus subgroups, except Fusarium. For target materials, subgroup g̅₊ for solution, cloth, plant, and metal were significantly (P > 0.0071) higher than for inorganic material or wood. For product generation, subgroup g̅₊ for fifth-generation products was significantly (P > 0.0071) higher than for fourth-, third-, and second-generation products. Dose and time accounted for only 8 and 4%, respectively, of the true variance in effect sizes in the regression model dose, time, and dose-time (P = 0.0004). Genus accounted for 40 and 51% of the true variance in effect sizes in the regression models dose and genus (P = 0.0008) and time and genus (P = 0.0007), respectively. Target material accounted for 18 and 19% of the true variance in effect sizes in the regression models dose and target (P = 0.0001) and time and target (P = 0.0001), respectively. QAC product generation accounted for 24 and 21% of the true variance in effect sizes in the regression models dose and QAC generation (P = 0.0034) and time and QAC generation (P = 0.0189), respectively. These results show that the current recommended rates for dose and contact time are generally expected to result in effective disinfestation for commercial QAC products. However, the efficacy against fungal plant pathogens is likely to be influenced by the fungal genus and target being treated and the generation of the QAC product that is used for disinfestation.

Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation

Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.

Efficacy of Fungicides Used to Manage Downy Mildew in Cucumber Assessed with Multiple Meta-Analysis Techniques

A nationwide, quantitative synthesis of fungicide efficacy data on management of cucurbit downy mildew (CDM) caused by Pseudoperonospora cubensis is needed to broadly evaluate fungicide performance. Three-level meta-analysis, three-level meta-regression, and network meta-analyses were conducted on data from 46 cucumber (Cucumis sativus) CDM fungicide efficacy studies conducted in the eastern United States retrieved from Plant Disease Management Reports published between 2009 and 2018. Three response variables were examined in each analysis: disease severity, marketable yield, and total yield, from which percent disease control and percent yield return compared with nontreated controls was calculated. Moderator variables used in the three-level meta-analysis or three-level meta-regression included year, disease pressure, number of fungicide applications, and slicing or pickling cucumbers. In the network meta-analysis, fungicides were grouped by common combinations of Fungicide Resistance Action Committee Codes and modes of action. Overall, fungicides significantly (P < 0.001) reduced disease severity and increased marketable and total yields, resulting in a mean 54.0% disease control and 61.9% marketable and 73.3% total yield return. Subgroup differences were observed for several fungicide applications, control plot disease severity, and cucumber type for marketable yield. Based on the meta-regression analysis for disease severity by year, fungicide efficacy has been decreasing from 2009 to 2018, potentially indicating broad development of fungicide resistance over time. Treatments containing quinone inside inhibitors, pyridinylmethyl-benzamides, and protectants and treatments containing oxysterol binding protein inhibitors and protectants most effectively reduced disease severity. The most effective fungicide combinations for disease control did not always result in the highest yield return.

Temperature requirements of Colletotrichum spp. belonging to different clades

The fungal genus Colletotrichum includes plant pathogens that cause substantial economic damage to horticultural, ornamental, and fruit tree crops worldwide. Here, we conducted a systematic literature review to retrieve and analyze the metadata on the influence of temperature on four biological processes: (i) mycelial growth, (ii) conidial germination, (iii) infection by conidia, and (iv) sporulation. The literature review considered 118 papers (selected from a total of 1,641 papers found with the literature search), 19 Colletotrichum species belonging to eight clades (acutatum, graminicola, destructivum, coccodes, dematium, gloeosporioides, and orbiculare), and 27 host plants (alfalfa, almond, apple, azalea, banana, barley, bathurst burr, blueberry, celery, chilli, coffee, corn, cotton, cowpea, grape, guava, jointvetch, lentil, lupin, olive, onion, snap bean, spinach, strawberry, tomato, watermelon, and white bean). We used the metadata to develop temperature-dependent equations representing the effect of temperature on the biological processes for the different clades and species. Inter- and intra-clades similarities and differences are analyzed and discussed. A multi-factor cluster analysis identified four groups of clades with similar temperature dependencies. The results should facilitate further research on the biology and epidemiology of Colletotrichum species and should also contribute to the development of models for the management of anthracnose diseases.

Efficacy of Hypochlorite in Disinfesting Nonfungal Plant Pathogens in Agricultural and Horticultural Plant Production: A Meta-Analysis

Bleach products containing hypochlorite are commonly used as disinfectants to eliminate nonfungal plant pathogens from production surfaces, tools, plant surfaces, irrigation water, and produce dump tanks. Although bleach products are useful, their effectiveness has been reported to vary under specific settings. A meta-analysis was conducted of 86 studies to assess the overall efficacy of hypochlorite against plant pathogenic bacteria, oomycetes, and viruses and to identify factors that explain differences in product efficacy. Hypochlorite resulted in a significant (P < 0.0001) reduction in disease intensity or propagule viability, with a mean Hedges' g standardized difference ([Formula: see text]) of 3.01, indicating that overall, hypochlorite treatments are highly effective. However, heterogeneity in g was significant (P < 0.0001) between studies, wherein 69.8% of the variance observed in g was attributed to true effects. Furthermore, an estimate of between-study variability was moderate (τ² = 1.46). Random effects (REs) metaregression showed limited effects of moderator variables dosage, contact time, targeted material of treatment, and organism type on product efficacy when all organism types were considered together. Because subgroup [Formula: see text] was significantly higher (P = 0.0070) for oomycetes ([Formula: see text] = 3.30) than for bacteria ([Formula: see text] = 2.19), subsequent metaregressions were performed by organism type. For oomycetes, five RE metaregression models, each containing two moderators and their interaction, resulted in significant (P = 0.05) effects, where models with dosage and time, dosage and genus, time and genus, dosage and target, and time and target accounted for ≤50, 71, 57, 48, and 47%, respectively, of the variance in true effect sizes (R²) associated with [Formula: see text]. For viruses, only the RE metaregression model containing time and target and their interaction resulted in significant (P = 0.0435) effects accounting for 38% of the variance in true effect sizes associated with [Formula: see text]. None of the RE metaregression models for bacteria were significant, although they still accounted for ≤28% of the variance in true effect sizes associated with [Formula: see text]. These results show that although the current recommended rates for dosage and contact time for commercial bleach products are generally expected to result in effective disinfestation, the efficacy against nonfungal plant pathogens is expected to be influenced by the organism type and target being treated with hypochlorite.

Efficacy of Hypochlorite as a Disinfestant Against Fungal Pathogens in Agricultural and Horticultural Plant Production: A Systematic Review and Meta-Analysis

Hypochlorite is often used as a disinfestant of fungal pathogens in a range of agricultural and horticultural settings. However, reports of its effectiveness are variable across studies and it is unclear what factors could potentially influence the reported estimates of its efficacy. A systematic review and meta-analysis was conducted to assess the efficacy of hypochlorite against fungal pathogens and explore factors that may explain the observed heterogeneity in estimates of efficacy. Standardized mean effect size, Hedges' g, was calculated for each of the 109 selected studies, published from 1972 to 2019, that met the criteria defined for the systematic review. A random-effects model was used to estimate the overall mean effect size ([Formula: see text]) and determine the heterogeneity in g among studies. Hypochlorite resulted in a significant (P < 0.001) reduction in either disease intensity or propagule viability with [Formula: see text]= 2.25, suggesting a large overall effect. However, 95% prediction intervals ranged from -0.18 to 4.68, indicating that hypochlorite could be ineffective against some fungi or when targeting some substrate materials. An estimate of the within-study variability, τ², was 1.48 and the proportion of heterogeneity in g among studies due to true effects was 71.5%. Inclusion of categorical moderator variables in the random effects model showed that hypochlorite treatments were significantly (P < 0.0062) more effective when used to disinfest spores in an aqueous solution ([Formula: see text]= 4.58) than when used on plastic ([Formula: see text]= 2.13), plant ([Formula: see text]= 2.13), and wood ([Formula: see text]= 0.79). Similarly, hypochlorite treatments were significantly (P < 0.0083) more effective in disinfesting fungal propagules of Thielaviopsis spp. ([Formula: see text]= 2.51) than those of Verticillium spp. ([Formula: see text]= 1.21). A meta-regression indicated that the effect of dose (β = -3.54; P = 0.0398) and contact time (β = -0.05; P = 0.0001) on [Formula: see text] were highly significant. Further, [Formula: see text]was significantly affected by the dose × time interaction (β = -0.017; P = 0.0269). In the meta-regression models, dose and time explained 0 and 16% of the variance in true effects, respectively. In meta-regression models with a continuous variable of dose or time, a categorical variable of target or genus and their interaction term, genus and target explained an additional 7 to 19% of the variance in true effects. These results show that although the current recommended dose and contact time for commercial bleach products are expected to result in effective disinfestation, the target material and genera of the fungal pathogen of interest will likely influence their efficacy.

The persistent threat of emerging plant disease pandemics to global food security

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover, and evolution of new pathogen lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could help mitigate future plant disease pandemics.

A network meta-analysis provides new insight into fungicide scheduling for the control of Botrytis cinerea in vineyards

BACKGROUND

Control of Botrytis bunch rot (BBR) is currently based on the application of fungicides at four timings corresponding to specific growth stages of vines: end of flowering (A), pre-bunch closure (B), veraison (C) and before harvest (D). The current research provides a network meta-analysis of 116 studies conducted between 1963 and 2016 in nine countries, in which 14 strategies (based on combinations of 1, 2, 3, or 4 sprays applied in A, B, C, and/or D) were compared.

RESULTS

When a one-spray strategy was applied, BBR control was more effective with sprays applied in A, C, or D than B. With a two-spray strategy, strategy AC provided similar control as strategy BC; strategy CD also provided good control. For a 3-spray strategy, the best disease control was consistently obtained with strategy ACD. Four-spray strategy ABCD provided the best control but often involved needless sprays so that the routine application of four sprays is not justified.

CONCLUSIONS

Spraying at timing A seems to be very important for achieving efficient and flexible disease control. Flexibility is reduced by spraying at timing B rather than A. © 2018 Society of Chemical Industry.

Automated Analysis of Aggregated Datasets to Identify Climatic Predictors of Botrytis Bunch Rot in Wine Grapes

Botrytis bunch rot (BBR), caused by Botrytis cinerea, results in serious losses to wine-grape production in some seasons during the preharvest period. In order to predict seasons that are at risk from BBR, datasets consisting of 25 disease, weather and vine phenology variables were aggregated from 101 SiteYears across seven regions and nine growing seasons. Automated analyses were used to compare a range of statistical methods for their ability to predict BBR epidemics, including the Kruskal-Wallis test, logistic regression, receiver operating characteristic analysis, and skill-scores. Variables based on relative humidity and surface-wetness duration were significant and consistent predictors of BBR epidemics across the range of analyses applied. Variables integrating temperature and wetness duration, including the Bacchus and Broome models, also demonstrated high predictive ability; however, they did not outperform their constituent components in all analyses. Automation of data analyses was an effective way to compare a wide range of statistical methods and a large number of variables with minimal user input, following initial code development. Significant time was needed to check input data and software code, but a greater return on investment would occur should the analytical process be applied to new datasets, including those from other pathosystems.

Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization

Syndromic Surveillance aims at analyzing medical data to detect clusters of illness or forecast disease outbreaks. Although the research in this field is flourishing in terms of publications, an insight of the global research output has been overlooked. This paper aims at analyzing the global scientific output of the research from 1993 to 2017. To this end, the paper uses bibliometric analysis and visualization to achieve its goal. Particularly, a data processing framework was proposed based on citation datasets collected from Scopus and Clarivate Analytics’ Web of Science Core Collection (WoSCC). The bibliometric method and Citespace were used to analyze the institutions, countries, and research areas as well as the current hotspots and trends. The preprocessed dataset includes 14,680 citation records. The analysis uncovered USA, England, Canada, France and Australia as the top five most productive countries publishing about Syndromic Surveillance. On the other hand, at the Pinnacle of academic institutions are the US Centers for Disease Control and Prevention (CDC). The reference co-citation analysis uncovered the common research venues and further analysis of the keyword cooccurrence revealed the most trending topics. The findings of this research will help in enriching the field with a comprehensive view of the status and future trends of the research on Syndromic Surveillance.

Development and validation of a weather-based warning system to advise fungicide applications to control dollar spot on turfgrass

Dollar spot is one of the most common diseases of golf course turfgrass and numerous fungicide applications are often required to provide adequate control. Weather-based disease warning systems have been developed to more accurately time fungicide applications; however, they tend to be ineffective and are not currently in widespread use. The primary objective of this research was to develop a new weather-based disease warning system to more accurately advise fungicide applications to control dollar spot activity across a broad geographic and climactic range. The new dollar spot warning system was developed from data collected at field sites in Madison, WI and Stillwater, OK in 2008 and warning system validation sites were established in Madison, WI, Stillwater, OK, Knoxville, TN, State College, PA, Starkville, MS, and Storrs, CT between 2011 and 2016. A meta-analysis of all site-years was conducted and the most effective warning system for dollar spot development consisted of a five-day moving average of relative humidity and average daily temperature. Using this model the highest effective probability that provided dollar spot control similar to that of a calendar-based program across the numerous sites and years was 20%. Additional analysis found that the 20% spray threshold provided comparable control to the calendar-based program while reducing fungicide usage by up to 30%, though further refinement may be needed as practitioners implement this warning system in a range of environments not tested here. The weather-based dollar spot warning system presented here will likely become an important tool for implementing precision disease management strategies for future turfgrass managers, especially as financial and regulatory pressures increase the need to reduce pesticide usage on golf course turfgrass.

Meta-Analysis of a Web-Based Disease Forecast System for Control of Anthracnose and Botrytis Fruit Rots of Strawberry in Southeastern United States

Strawberry production in Florida and South Carolina is affected by two major diseases, anthracnose fruit rot (AFR) and Botrytis fruit rot (BFR), caused by Colletotrichum acutatum and Botrytis cinerea, respectively. The effective management of both diseases traditionally relied on weekly fungicide applications. However, to improve timing and reduce the number of fungicide sprays, many growers follow the Strawberry Advisory System (StAS), a decision support system for forecasting fungicide applications based on environmental conditions and previously developed models. The objective of this study was to perform a meta-analysis to determine the effectiveness of the StAS for AFR and BFR management compared with a calendar-based spray program. Thirty-nine trials were conducted from 2009 to 2014 in Florida and South Carolina commercial strawberry fields. Meta-analysis was conducted to quantify the treatment effects on four effect sizes, all based on the difference in response variables for StAS and the calendar-based treatments in each trial. The mean difference in BFR incidence, AFR incidence, yield, and number of marketable fruit between the two treatments was not significantly different from 0 (P < 0.05). However, the number of fungicide applications per season was reduced by a median of seven when using the StAS, a 50% reduction in sprays compared with the calendar-based approach. Effect sizes were not influenced by location or the favorability of the environment for disease development. These findings indicate that use of StAS in commercial fields is effective in controlling fruit rot diseases with no reduction in yield while substantially reducing fungicide applications.

Statistical Models and Methods for Network Meta-Analysis

Meta-analysis, the methodology for analyzing the results from multiple independent studies, has grown tremendously in popularity over the last four decades. Although most meta-analyses involve a single effect size (summary result, such as a treatment difference) from each study, there are often multiple treatments of interest across the network of studies in the analysis. Multi-treatment (or network) meta-analysis can be used for simultaneously analyzing the results from all the treatments. However, the methodology is considerably more complicated than for the analysis of a single effect size, and there have not been adequate explanations of the approach for agricultural investigations. We review the methods and models for conducting a network meta-analysis based on frequentist statistical principles, and demonstrate the procedures using a published multi-treatment plant pathology data set. A major advantage of network meta-analysis is that correlations of estimated treatment effects are automatically taken into account when an appropriate model is used. Moreover, treatment comparisons may be possible in a network meta-analysis that are not possible in a single study because all treatments of interest may not be included in any given study. We review several models that consider the study effect as either fixed or random, and show how to interpret model-fitting output. We further show how to model the effect of moderator variables (study-level characteristics) on treatment effects, and present one approach to test for the consistency of treatment effects across the network. Online supplemental files give explanations on fitting the network meta-analytical models using SAS.

Drivers for emerging issues in animal and plant health

The history of agriculture includes many animal and plant disease events that have had major consequences for the sector, as well as for humans. At the same time, human activities beyond agriculture have often driven the emergence of diseases. The more that humans expand the footprint of the global population, encroach into natural habitats, alter these habitats to extract resources and intensify food production, as well as move animals, people and commodities along with the pathogens they carry, the greater the potential for pathogens and pests to spread and for infection to emerge or re-emerge. While essential to human well-being, producing food also plays a major role in disease dynamics. The risk of emergence of pests and pathogens has increased as a consequence of global changes in the way food is produced, moved and consumed. Climate change is likely to increase pressure on the availability of food and provide newly suitable conditions for invasive pests and pathogens. Human population displacements due to economic, political and humanitarian crises represent another set of potential drivers for emerging issues. The overlapping drivers of plant, animal and human disease emergence and environmental changes point towards the concept of 'One Health'. This paradigm underlines the urgent need to understand the influence of human behaviour and incorporate this understanding into our approach to emerging risks. For this, we face two major challenges. One is cultural; the second is methodological. We have to look at systems not under the narrow view of specific hazards but with a wider approach to system dynamics, and consider a broad spectrum of potential outcomes in terms of risk. In addition, we have to make sense of the vast amounts of data that are available in the modern age. This paper aims to help in preparing for the cultural and methodological shifts needed in our approach to emerging risks.

The Ebb and Flow of Airborne Pathogens: Monitoring and Use in Disease Management Decisions

Perhaps the earliest form of monitoring the regional spread of plant disease was a group of growers gathering together at the market and discussing what they see in their crops. This type of reporting continues to this day through regional extension blogs, by crop consultants and more formal scouting of sentential plots in the IPM PIPE network (http://www.ipmpipe.org/). As our knowledge of plant disease epidemiology has increased, we have also increased our ability to detect and monitor the presence of pathogens and use this information to make management decisions in commercial production systems. The advent of phylogenetics, next-generation sequencing, and nucleic acid amplification technologies has allowed for development of sensitive and accurate assays for pathogen inoculum detection and quantification. The application of these tools is beginning to change how we manage diseases with airborne inoculum by allowing for the detection of pathogen movement instead of assuming it and by targeting management strategies to the early phases of the epidemic development when there is the greatest opportunity to reduce the rate of disease development. While there are numerous advantages to using data on inoculum presence to aid management decisions, there are limitations in what the data represent that are often unrecognized. In addition, our understanding of where and how to effectively monitor airborne inoculum is limited. There is a strong need to improve our knowledge of the mechanisms that influence inoculum dispersion across scales as particles move from leaf to leaf, and everything in between.

Five Reasons to Consider Phytophthora infestans a Reemerging Pathogen

Phytophthora infestans has been a named pathogen for well over 150 years and yet it continues to "emerge", with thousands of articles published each year on it and the late blight disease that it causes. This review explores five attributes of this oomycete pathogen that maintain this constant attention. First, the historical tragedy associated with this disease (Irish potato famine) causes many people to be fascinated with the pathogen. Current technology now enables investigators to answer some questions of historical significance. Second, the devastation caused by the pathogen continues to appear in surprising new locations or with surprising new intensity. Third, populations of P. infestans worldwide are in flux, with changes that have major implications to disease management. Fourth, the genomics revolution has enabled investigators to make tremendous progress in terms of understanding the molecular biology (especially the pathogenicity) of P. infestans. Fifth, there remain many compelling unanswered questions.

Data mining with iPlant: a meeting report from the 2013 GARNet workshop, Data mining with iPlant

High-throughput sequencing technologies have rapidly moved from large international sequencing centres to individual laboratory benchtops. These changes have driven the 'data deluge' of modern biology. Submissions of nucleotide sequences to GenBank, for example, have doubled in size every year since 1982, and individual data sets now frequently reach terabytes in size. While 'big data' present exciting opportunities for scientific discovery, data analysis skills are not part of the typical wet bench biologist's experience. Knowing what to do with data, how to visualize and analyse them, make predictions, and test hypotheses are important barriers to success. Many researchers also lack adequate capacity to store and share these data, creating further bottlenecks to effective collaboration between groups and institutes. The US National Science Foundation-funded iPlant Collaborative was established in 2008 to form part of the data collection and analysis pipeline and help alleviate the bottlenecks associated with the big data challenge in plant science. Leveraging the power of high-performance computing facilities, iPlant provides free-to-use cyberinfrastructure to enable terabytes of data storage, improve analysis, and facilitate collaborations. To help train UK plant science researchers to use the iPlant platform and understand how it can be exploited to further research, GARNet organized a four-day Data mining with iPlant workshop at Warwick University in September 2013. This report provides an overview of the workshop, and highlights the power of the iPlant environment for lowering barriers to using complex bioinformatics resources, furthering discoveries in plant science research and providing a platform for education and outreach programmes.