Natural Occurrence of Maize Gibberella Ear Rot and Contamination of Grain with Mycotoxins in Association with Weather Variables

Gibberella ear rot (GER) severity (percent area of the ear diseased) and associated grain contamination with mycotoxins were quantified in plots of 15 to 16 maize hybrids planted at 10 Ohio locations from 2015 to 2018. Deoxynivalenol (DON) was quantified in grain samples in all 4 years, whereas nivalenol, 3-acetyldeoxynivalenol, and 15-acetyldeoxynivalenol (15ADON) were quantified only in the last 2 years. Only DON and 15ADON were detected. The highest levels of GER and DON contamination were observed for 2018, followed by 2016 and 2017. No GER symptoms or DON were detected in 2015. Approximately 41% of the samples from asymptomatic ears had detectable levels of DON, and 7% of these samples from 2016 had DON > 5 ppm. Associations between DON contamination and 43 variables representing summaries of temperature (T), relative humidity (RH), rainfall (R), surface wetness, and T-RH combinations for different window lengths and positions relative to R1 growth stage were quantified with Spearman correlation coefficients (r). Fifteen-day window lengths tended to show the highest correlations. Most of the variables based on T, R, RH, and T-RH were significantly correlated with DON for the 15-day window, as well as other windows. For moisture-related variables, there generally was a negative correlation before R1, changing to a positive correlation after R1. Results showed that GER and DON can be frequently found in Ohio maize fields, with the risk of DON being associated with multiple weather variables, particularly those representing combinations of T between 15 and 30°C and RH > 80 summarized during the 3 weeks after R1.

Stability of Hybrid Maize Reaction to Gibberella Ear Rot and Deoxynivalenol Contamination of Grain

Trials were conducted to quantify the stability (or lack of G × E interaction) of 15 maize hybrids to Gibberella ear rot (GER; caused by Fusarium graminearum) and deoxynivalenol (DON) contamination of grain across 30 Ohio environments (3 years × 10 locations). In each environment, one plot of each hybrid was planted and 10 ears per plot were inoculated via the silk channel. GER severity (proportion of ear area diseased) and DON contamination of grain (ppm) were quantified. Multiple rank-based methods, including Kendall's concordance coefficient (W) and Piepho's U, were used to quantify hybrid stability. The results found insufficient evidence to suggest crossover G × E interaction of ranks, with W greater than zero for GER (W = 0.28) and DON (W = 0.26), and U not statistically significant for either variable (P > 0.20). Linear mixed models (LMMs) were also used to quantify hybrid stability, accounting for crossover or noncrossover G × E interaction of transformed observed data. Based on information criteria and likelihood ratio tests for GER and DON response variables, the models with more complex variance-covariance structures-heterogeneous compound symmetry and factor-analytic-provided a better fit than the model with the simpler compound symmetry structure, indicating that one or more hybrids differed in stability. Overall, hybrids were stable based on rank-based methods, which indicated a lack of crossover G × E interaction, but the LMMs identified a few hybrids that were sensitive to environment. Resistant hybrids were generally more stable than susceptible hybrids.

Predicting plant disease epidemics from functionally represented weather series

Epidemics are often triggered by specific weather patterns favouring the pathogen on susceptible hosts. For plant diseases, models predicting epidemics have therefore often emphasized the identification of early season weather patterns that are correlated with a disease outcome at some later point. Toward that end, window-pane analysis is an exhaustive search algorithm traditionally used in plant pathology for mining correlations in a weather series with respect to a disease endpoint. Here we show, with reference to Fusarium head blight (FHB) of wheat, that a functional approach is a more principled analytical method for understanding the relationship between disease epidemics and environmental conditions over an extended time series. We used scalar-on-function regression to model a binary outcome (FHB epidemic or non-epidemic) relative to weather time series spanning 140 days relative to flowering (when FHB infection primarily occurs). The functional models overall fit the data better than previously described standard logistic regression (lr) models. Periods much earlier than heretofore realized were associated with FHB epidemics. The findings were used to create novel weather summary variables which, when incorporated into lr models, yielded a new set of models that performed as well as existing lr models for real-time predictions of disease risk. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Integrated Effects of Genetic Resistance and Prothioconazole + Tebuconazole Application Timing on Fusarium Head Blight in Wheat

Integrated Fusarium head blight (FHB) management programs consisting of different combinations of cultivar resistance class and an application of the fungicide prothioconazole + tebuconazole at or after 50% early anthesis were evaluated for efficacy against FHB incidence (INC; percentage of diseased spikes), index (IND; percentage of diseased spikelets per spike), Fusarium damaged kernel (FDK), deoxynivalenol (DON) toxin contamination, grain yield, and test weight (TW) in inoculated field trials conducted in 11 U.S. states in 2014 and 2015. Mean log response ratios and corresponding percent control values for INC, IND, FDK, and DON, and mean differences in yield and TW relative to a nontreated, inoculated susceptible check (S_CK), were estimated through network meta-analyses as measures of efficacy. Results from the analyses were then used to estimate the economic benefit of each management program for a range of grain prices and fungicide applications costs. Management programs consisting of a moderately resistant (MR) cultivar treated with the fungicide were the most efficacious, reducing INC by 60 to 69%, IND by 71 to 76%, FDK by 66 to 72%, and DON by 60 to 64% relative to S_CK, compared with 56 to 62% for INC, 68 to 72% for IND, 66 to 68% for FDK, and 58 to 61% for DON for programs with a moderately susceptible (MS) cultivar. The least efficacious programs were those with a fungicide application to a susceptible (S) cultivar, with less than a 45% reduction of INC, IND, FDK, or DON. All programs were more efficacious under conditions favorable for FHB compared with less favorable conditions, with applications made at 50% early anthesis being of comparable efficacy to those made 2 to 7 days later. Programs with an MS cultivar resulted in the highest mean yield increases relative to S_CK (541 to 753 kg/ha), followed by programs with an S cultivar (386 to 498 kg/ha) and programs with an MR cultivar (250 to 337 kg/ha). Integrated management programs with an MS or MR cultivar treated with the fungicide at or after 50% early anthesis were the most likely to result in a 50 or 75% control of IND, FDK, or DON in a future trial. At a fixed fungicide application cost, these programs were $4 to $319/MT more economically beneficial than corresponding fungicide-only programs, depending on the cultivar and grain price. These findings demonstrate the benefits of combining genetic resistance with a prothioconazole + tebuconazole treatment to manage FHB, even if that treatment is applied a few days after 50% early anthesis.

Functional Data Analysis of Weather Variables Linked to Fusarium Head Blight Epidemics in the United States

In past efforts, input weather variables for Fusarium head blight (FHB) prediction models in the United States were identified after following some version of the window-pane algorithm, which discretizes a continuous weather time series into fixed-length windows before searching for summary variables associated with FHB risk. Functional data analysis, on the other hand, reconstructs the assumed continuous process (represented by a series of recorded weather data) by using smoothing functions, and is an alternative way of working with time series data with respect to FHB risk. Our objective was to functionally model weather-based time series data linked to 865 observations of FHB (covering 16 states and 31 years in total), classified as epidemics (FHB disease index ≥ 10%) and nonepidemics (FHB disease index < 10%). Altogether, 94 different time series variables were modeled by penalized cubic B-splines for the smoothing function, from 120 days pre-anthesis to 20 days post-anthesis. Functional mean curves, standard deviations, and first derivatives were plotted for FHB epidemics relative to nonepidemics. Function-on-scalar regressions assessed the temporal trends of the magnitude and significance of the mean difference between functionally represented weather time series associated with FHB epidemics and nonepidemics. The mean functional weather-variable curve for epidemics started to deviate, in general, from that for nonepidemics as early as 40 days pre-anthesis for several weather variables. The greatest deviations were often near anthesis, the period of maximum susceptibility of wheat to FHB-causing fungi. The most consistent separations between the mean functional curves were seen with the daily averages of moisture-related variables (such as average relative humidity) and with variables summarizing the daily variation in temperature (as opposed to the daily mean). Functional data analysis was useful for extending our knowledge of relationships between weather variables and FHB epidemics.

Meta-Analysis of the Effects of QoI and DMI Fungicide Combinations on Fusarium Head Blight and Deoxynivalenol in Wheat

Field trials were conducted in 17 U.S. states to evaluate the effects of quinone outside inhibitor (QoI) and demethylation inhibitor (DMI) fungicide programs on Fusarium head blight index (IND) and deoxynivalenol (DON) toxin in wheat. Four DMI-only treatments applied at Feekes 10.5.1, five QoI-only treatments applied between Feekes 9 or Feekes 10.5, three QoI+DMI mixtures applied at Feekes 10.5, and three treatments consisting of a QoI at Feekes 9 followed by a DMI at Feekes 10.5.1 were evaluated. Network meta-analytical models were fitted to log-transformed mean IND and DON data and estimated contrasts of log means were used to obtain estimates of mean percent controls relative to the nontreated check as measures of efficacy. Results from the meta-analyses were also used to assess the risk of DON increase in future trials. DMI at Feekes 10.5.1 were the most effective programs against IND and DON and the least likely to increase DON in future trials. QoI-only programs increased mean DON over the nontreated checks and were the most likely to do so in future trials, particularly when applied at Feekes 10.5. The effects of QoI+DMI combinations depended on the active ingredients and whether the two were applied as a mixture at heading or sequentially. Following a Feekes 9 QoI application with a Feekes 10.5.1 application of a DMI reduced the negative effect of the QoI on DON but was not sufficient to achieve the efficacy of the Feekes 10.5.1 DMI-only treatments. Our results suggest that one must be prudent when using QoI treatments under moderate to high risk of FHB, particularly where the QoI is used without an effective DMI applied in combination or in sequence.

Twenty-Five Years of the Binary Power Law for Characterizing Heterogeneity of Disease Incidence

Spatial pattern, an important epidemiological property of plant diseases, can be quantified at different scales using a range of methods. The spatial heterogeneity (or overdispersion) of disease incidence among sampling units is an especially important measure of small-scale pattern. As an alternative to Taylor's power law for the heterogeneity of counts with no upper bound, the binary power law (BPL) was proposed in 1992 as a model to represent the heterogeneity of disease incidence (number of plant units diseased out of n observed in each sampling unit, or the proportion diseased in each sampling unit). With the BPL, the log of the observed variance is a linear function of the log of the variance for a binomial (i.e., random) distribution. Over the last quarter century, the BPL has contributed to both theory and multiple applications in the study of heterogeneity of disease incidence. In this article, we discuss properties of the BPL and use it to develop a general conceptualization of the dynamics of spatial heterogeneity in epidemics; review the use of the BPL in empirical and theoretical studies; present a synthesis of parameter estimates from over 200 published BPL analyses from a wide range of diseases and crops; discuss model fitting methods, and applications in sampling, data analysis, and prediction; and make recommendations on reporting results to improve interpretation. In a review of the literature, the BPL provided a very good fit to heterogeneity data in most publications. Eighty percent of estimated slope (b) values from field studies were between 1.06 and 1.51, with b positively correlated with the BPL intercept parameter. Stochastic simulations show that the BPL is generally consistent with spatiotemporal epidemiological processes and holds whenever there is a positive correlation of disease status of individuals composing sampling units.

Plant Virus Epidemiology: Applications and Prospects for Mathematical Modeling and Analysis to Improve Understanding and Disease Control

In recent years, mathematical modeling has increasingly been used to complement experimental and observational studies of biological phenomena across different levels of organization. In this article, we consider the contribution of mathematical models developed using a wide range of techniques and uses to the study of plant virus disease epidemics. Our emphasis is on the extent to which models have contributed to answering biological questions and indeed raised questions related to the epidemiology and ecology of plant viruses and the diseases caused. In some cases, models have led to direct applications in disease control, but arguably their impact is better judged through their influence in guiding research direction and improving understanding across the characteristic spatiotemporal scales of plant virus epidemics. We restrict this article to plant virus diseases for reasons of length and to maintain focus even though we recognize that modeling has played a major and perhaps greater part in the epidemiology of other plant pathogen taxa, including vector-borne bacteria and phytoplasmas.

Epidemiology: Past, Present, and Future Impacts on Understanding Disease Dynamics and Improving Plant Disease Management-A Summary of Focus Issue Articles

Epidemiology has made significant contributions to plant pathology by elucidating the general principles underlying the development of disease epidemics. This has resulted in a greatly improved theoretical and empirical understanding of the dynamics of disease epidemics in time and space, predictions of disease outbreaks or the need for disease control in real-time basis, and tactical and strategic solutions to disease problems. Availability of high-resolution experimental data at multiple temporal and spatial scales has now provided a platform to test and validate theories on the spread of diseases at a wide range of spatial scales ranging from the local to the landscape level. Relatively new approaches in plant disease epidemiology, ranging from network to information theory, coupled with the availability of large-scale datasets and the rapid development of computer technology, are leading to revolutionary thinking about epidemics that can result in considerable improvement of strategic and tactical decision making in the control and management of plant diseases. Methods that were previously restricted to topics such as population biology or evolution are now being employed in epidemiology to enable a better understanding of the forces that drive the development of plant disease epidemics in space and time. This Focus Issue of Phytopathology features research articles that address broad themes in epidemiology including social and political consequences of disease epidemics, decision theory and support, pathogen dispersal and disease spread, disease assessment and pathogen biology and disease resistance. It is important to emphasize that these articles are just a sample of the types of research projects that are relevant to epidemiology. Below, we provide a succinct summary of the articles that are published in this Focus Issue .

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.

Does the P Value Have a Future in Plant Pathology?

The P value (significance level) is possibly the mostly widely used, and also misused, quantity in data analysis. P has been heavily criticized on philosophical and theoretical grounds, especially from a Bayesian perspective. In contrast, a properly interpreted P has been strongly defended as a measure of evidence against the null hypothesis, H0. We discuss the meaning of P and null-hypothesis statistical testing, and present some key arguments concerning their use. P is the probability of observing data as extreme as, or more extreme than, the data actually observed, conditional on H0 being true. However, P is often mistakenly equated with the posterior probability that H0 is true conditional on the data, which can lead to exaggerated claims about the effect of a treatment, experimental factor or interaction. Fortunately, a lower bound for the posterior probability of H0 can be approximated using P and the prior probability that H0 is true. When one is completely uncertain about the truth of H0 before an experiment (i.e., when the prior probability of H0 is 0.5), the posterior probability of H0 is much higher than P, which means that one needs P values lower than typically accepted for statistical significance (e.g., P = 0.05) for strong evidence against H0. When properly interpreted, we support the continued use of P as one component of a data analysis that emphasizes data visualization and estimation of effect sizes (treatment effects).

Revisiting Fungicide-Based Management Guidelines for Leaf Blotch Diseases in Soft Red Winter Wheat

Standard foliar fungicide applications in wheat are usually made between flag leaf emergence (Feekes [FK] 8) and heading (FK10.5) to minimize damage to the flag leaf. However, over the last few years, new fungicide programs such as applications prior to FK8 and split half-rate applications have been implemented, although there are few data pertaining to the efficacy of these programs. Eight experiments were conducted in Illinois, Indiana, Ohio, and Wisconsin from 2010 to 2012 to compare new programs to standard FK8 and FK10 programs in terms of disease control and yield response. The programs evaluated consisted of single full-rate applications of 19% tebuconazole + 19% prothioconazole (Prosaro) or 23.6% pyraclostrobin (Headline) at FK5 (pseudostem strongly erected), FK8, or FK10, or split half rates at FK5 and 8 (FK5+8), plus an untreated check (CK). Leaf blotch (LB) severity and yield data were collected and random effects meta-analytical models fitted to estimate the overall log odds ratio of disease reaching the flag leaf ( L¯OR ) and mean yield increase ( D¯ ) for each fungicide program relative to CK. For all programs, L¯OR was significantly different from zero (P < 0.05). Based on estimated odds ratios (OR = exp[ L¯OR ]), the two FK8 programs reduced the risk of LB reaching the flag leaf by 55 and 75%, compared with 62 and 69% and 67 and 70% for the two FK10 and FK5+8 programs, respectively, and only 32 and 37% for the two FK5 programs. D¯ was significantly different from zero (P ≤ 0.003) for all FK8, FK10, and FK5+8 programs, with values of 233 and 245, 175 and 220, and 175 and 187 kg ha^-1 for the FK10, FK5+8, and FK8 programs, respectively. Differences in mean yield response between Headline and Prosaro were not statistically significant (P > 0.05). The probability of profitability was estimated for each program for a range of grain prices and fungicide application costs. All FK8, FK10, and FK5+8 programs had more than an 80% chance of resulting in a positive yield response, compared with 63 and 67% for the two FK5 programs. The chance of obtaining a yield increase of 200 kg ha^-1, required to offset an application cost of $36 ha^-1 at a grain price of $0.18 kg^-1, ranged from 44 to 60% for FK8, FK10 and FK5+8 programs compared with 22 and 25% for the two FK5 programs. These findings could be used to help inform fungicide application decisions for LB diseases in soft red winter wheat.

Efficacy of Fungicide Applications During and After Anthesis Against Fusarium Head Blight and Deoxynivalenol in Soft Red Winter Wheat

Seven field experiments were conducted in Ohio and Illinois between 2011 and 2013 to evaluate postanthesis applications of prothioconazole + tebuconazole and metconazole for Fusarium head blight and deoxynivalenol (DON) control in soft red winter wheat. Treatments consisted of an untreated check and fungicide applications made at early anthesis (A), 2 (A+2), 4 (A+4), 5 (A+5), or 6 (A+6) days after anthesis. Six of the seven experiments were augmented with artificial Fusarium graminearum inoculum, and the other was naturally infected. FHB index (IND), Fusarium damaged kernels (FDK), and DON concentration of grain were quantified. All application timings led to significantly lower mean arcsine-square-root-transformed IND and FDK (arcIND and arcFDK) and log-transformed (logDON) than in the untreated check; however, arcIND, arcFDK, and logDON for the postanthesis applications were generally not significantly different from those for the anthesis applications. Relative to the check, A+2 resulted in the highest percent control for both IND and DON, 69 and 54%, respectively, followed by A+4 (62 and 52%), A+6 (62 and 48%), and A (56 and 50%). A+2 and A+6 significantly reduced IND by 30 and 14%, respectively, relative to the anthesis application. Postanthesis applications did not, however, reduce DON relative to the anthesis application. These results suggest that applications made up to 6 days following anthesis may be just as effective as, and sometimes more effective than, anthesis applications at reducing FHB and DON.

Rainfastness of Prothioconazole + Tebuconazole for Fusarium Head Blight and Deoxynivalenol Management in Soft Red Winter Wheat

Fungicides are most warranted for control of Fusarium head blight (FHB), a disease of wheat caused by the fungal pathogen Fusarium graminearum, when wet, rainy conditions occur during anthesis. However, it is unclear whether rainfall directly following application affects fungicide efficacy against FHB and its associated toxin, deoxynivalenol (DON). The objective of this study was to determine the rainfastness of the fungicide tebuconazole + prothioconazole and the residual life of tebuconazole when applied to wheat spikes at anthesis in combination with the nonionic surfactant Induce. Three field experiments were conducted during 2012 and 2013 in Wooster, OH. Simulated rainfall of a fixed intensity and duration was applied to separate plots at five different times after the fungicide treatment (0, 60, 105, 150, or 195 min). Spike samples were collected at 4-day intervals after fungicide application and assayed for tebuconazole residue. A similar set of greenhouse experiments was conducted using six post-fungicide-application rainfall timing treatments (0, 15, 30, 60, 120, or 180 min). All experiments were inoculated at anthesis with spores of F. graminearum, and FHB index (IND) and DON were quantified. In four of the five experiments, all fungicide-treated experimental units (EUs) had significantly lower mean IND and DON than the untreated check, regardless of rainfall treatment. Among rainfall treatments, EUs that received the earliest rains after fungicide application tended to have the highest numerical mean IND and DON, but were generally not significantly different from EUs that received later rain or fungicide without rain. In both years, fungicide residue on wheat spikes decreased rapidly with time after application, but the rate of reduction varied somewhat between years, with a half-life of 6 to 9 days. Rainfall treatment did not have a significant effect on the rate of residue reduction or the level of residue at a fixed sampling time after fungicide application. In this study, tebuconazole + prothioconazole mixed with a nonionic surfactant was fairly rainfast for a fixed set of rainfall characteristics, and tebuconazole residue did not persist very long after application on wheat spikes.

Predicting Fusarium head blight epidemics with boosted regression trees

Predicting major Fusarium head blight (FHB) epidemics allows for the judicious use of fungicides in suppressing disease development. Our objectives were to investigate the utility of boosted regression trees (BRTs) for predictive modeling of FHB epidemics in the United States, and to compare the predictive performances of the BRT models with those of logistic regression models we had developed previously. The data included 527 FHB observations from 15 states over 26 years. BRTs were fit to a training data set of 369 FHB observations, in which FHB epidemics were classified as either major (severity ≥ 10%) or non-major (severity < 10%), linked to a predictor matrix consisting of 350 weather-based variables and categorical variables for wheat type (spring or winter), presence or absence of corn residue, and cultivar resistance. Predictive performance was estimated on a test (holdout) data set consisting of the remaining 158 observations. BRTs had a misclassification rate of 0.23 on the test data, which was 31% lower than the average misclassification rate over 15 logistic regression models we had presented earlier. The strongest predictors were generally one of mean daily relative humidity, mean daily temperature, and the number of hours in which the temperature was between 9 and 30°C and relative humidity ≥ 90% simultaneously. Moreover, the predicted risk of major epidemics increased substantially when mean daily relative humidity rose above 70%, which is a lower threshold than previously modeled for most plant pathosystems. BRTs led to novel insights into the weather-epidemic relationship.

Predicting fusarium head blight epidemics with weather-driven pre- and post-anthesis logistic regression models

Our objective was to identify weather-based variables in pre- and post-anthesis time windows for predicting major Fusarium head blight (FHB) epidemics (defined as FHB severity ≥ 10%) in the United States. A binary indicator of major epidemics for 527 unique observations (31% of which were major epidemics) was linked to 380 predictor variables summarizing temperature, relative humidity, and rainfall in 5-, 7-, 10-, 14-, or 15-day-long windows either pre- or post-anthesis. Logistic regression models were built with a training data set (70% of the 527 observations) using the leaps-and-bounds algorithm, coupled with bootstrap variable and model selection methods. Misclassification rates were estimated on the training and remaining (test) data. The predictive performance of models with indicator variables for cultivar resistance, wheat type (spring or winter), and corn residue presence was improved by adding up to four weather-based predictors. Because weather variables were intercorrelated, no single model or subset of predictor variables was best based on accuracy, model fit, and complexity. Weather-based predictors in the 15 final empirical models selected were all derivatives of relative humidity or temperature, except for one rainfall-based predictor, suggesting that relative humidity was better at characterizing moisture effects on FHB than other variables. The average test misclassification rate of the final models was 19% lower than that of models currently used in a national FHB prediction system.

Effects of Temperature and Wetness Duration on the Sporulation Rate of Phomopsis viticola on Infected Grape Canes

Controlled-environment studies were conducted to examine effects of temperature (T) and wetness duration (W) on the sporulation rate of Phomopsis viticola on infected grape canes and to determine effects of interrupted wetness duration (IWD) on sporulation. A split-plot design was used to determine T and W effects, with T (5, 12, 15, 18, 20, 22, 25, 28, and 35°C) as the whole-plot and W (11, 23, 35, 47, and 71 h) as the subplot. Linear and nonlinear mixed models were fitted to the data. Lower and upper limits of sporulation were estimated to be 4 and 36°C, respectively, based on the modeling results, optimum sporulation was near 21°C, and sporulation increased monotonically with increasing wetness duration. Of the examined models, a generalization of the Analytis Beta model fit the data best, based on a collection of goodness-of-fit statistical criteria. To determine effects of IWD, a split-plot was used, with T (12, 15, and 20°C) as the whole-plot and IWD (0, 2, 4, 8, 12, and 24 h) as the subplot. Generally, sporulation declined with increasing IWD. An IWD of 8 h or more resulted in significantly and substantially less sporulation compared to the control (0 h IWD) (P < 0.01). Temporal patterns of spore density in the field were determined using a repeated-measures design, in which spore density and environmental data were measured in the vineyard during and following individual rain events over 3 years. The developed model from the controlled-environment study, coupled with a time-of-season weight function and a dispersal index (based on total rain per rain episode), predicted the trend in spore density over time reasonably well, although the total magnitude of spore density could not be predicted because the density of lesions was not known. Results can be used for improving the accuracy of a disease warning system that currently only considers infection of grapes by P. viticola.

Temporal Patterns of Sporulation Potential of Phomopsis viticola on Infected Grape Shoots, Canes, and Rachises

Phomopsis cane and leaf spot on Vitis spp. (grape) is currently understood to be monocyclic, with primary inoculum only being produced early in the growing season. However, of the few published studies pertaining to sporulation of Phomopsis viticola, none specifically examined rachises, and none were designed to determine when infected tissues become capable of sporulation. The objective of these studies was to determine when grape shoots, canes, and rachises infected with P. viticola develop the capacity to sporulate, and to determine the time period during which those tissues remain capable of sporulation. Starting in 2009 and 2010, infected first-year shoots and rachises were collected biweekly throughout the growing season, into the dormant season, and into the following growing season. Tissues were collected from 'Catawba,' 'Concord,' and 'Reliance' vineyards. Samples were observed for sporulation after 48 h of incubation in a moist chamber at 23°C; the magnitude of the conidia production under these optimal conditions was considered the sporulation potential. For infections that occurred in 2009 and 2010, the production of conidia was not observed until after harvest. In the year following infection, sporulation potential was found from about bud break until shortly after the end of bloom. There was a generally consistent temporal pattern to relative sporulation potential across sampled vineyards, years, and grape tissues (rachises and canes), described by a modified β model, with peak sporulation potential occurring around 16 May. These results confirmed that Phomopsis cane and leaf spot is a monocyclic disease and support control recommendations for use of fungicides in spring.

Characterizing heterogeneity of disease incidence in a spatial hierarchy: a case study from a decade of observations of fusarium head blight of wheat

A multilevel analysis of heterogeneity of disease incidence was conducted based on observations of Fusarium head blight (caused by Fusarium graminearum) in Ohio during the 2002-11 growing seasons. Sampling consisted of counting the number of diseased and healthy wheat spikes per 0.3 m of row at 10 sites (about 30 m apart) in a total of 67 to 159 sampled fields in 12 to 32 sampled counties per year. Incidence was then determined as the proportion of diseased spikes at each site. Spatial heterogeneity of incidence among counties, fields within counties, and sites within fields and counties was characterized by fitting a generalized linear mixed model to the data, using a complementary log-log link function, with the assumption that the disease status of spikes was binomially distributed conditional on the effects of county, field, and site. Based on the estimated variance terms, there was highly significant spatial heterogeneity among counties and among fields within counties each year; magnitude of the estimated variances was similar for counties and fields. The lowest level of heterogeneity was among sites within fields, and the site variance was either 0 or not significantly greater than 0 in 3 of the 10 years. Based on the variances, the intracluster correlation of disease status of spikes within sites indicated that spikes from the same site were somewhat more likely to share the same disease status relative to spikes from other sites, fields, or counties. The estimated best linear unbiased predictor (EBLUP) for each county was determined, showing large differences across the state in disease incidence (as represented by the link function of the estimated probability that a spike was diseased) but no consistency between years for the different counties. The effects of geographical location, corn and wheat acreage per county, and environmental conditions on the EBLUP for each county were not significant in the majority of years.

Efficacy and Stability of Integrating Fungicide and Cultivar Resistance to Manage Fusarium Head Blight and Deoxynivalenol in Wheat

Integration of host resistance and prothioconazole + tebuconazole fungicide application at anthesis to manage Fusarium head blight (FHB) and deoxynivalenol (DON) in wheat was evaluated using data from over 40 trials in 12 U.S. states. Means of FHB index (index) and DON from up to six resistance class-fungicide management combinations per trial (susceptible treated [S_TR] and untreated [S_UT]; moderately susceptible treated [MS_TR] and untreated [MS_UT]; moderately resistant treated [MR_TR] and untreated [MR_UT]) were used in multivariate meta-analyses, and mean log response ratios across trials were estimated and transformed to estimate mean percent control ( ) due to the management combinations relative to S_UT. All combinations led to a significant reduction in index and DON (P < 0.001). MR_TR was the most effective combination, with a of 76% for index and 71% for DON, followed by MS_TR (71 and 58%, respectively), MR_UT (54 and 51%, respectively), S_TR (53 and 39%, respectively), and MS_UT (43 and 30%, respectively). Calculations based on the principle of treatment independence showed that the combination of fungicide application and resistance was additive in terms of percent control for index and DON. Management combinations were ranked based on percent control relative to S_UT within each trial, and nonparametric analyses were performed to determine management combination stability across environments (trials) using the Kendall coefficient of concordance (W). There was a significant concordance of management combinations for both index and DON (P < 0.001), indicating a nonrandom ranking across environments and relatively low variability in the within-environment ranking of management combinations. MR_TR had the highest mean rank (best control relative to S_UT) and was one of the most stable management combinations across environments, with low rank stability variance (0.99 for index and 0.67 for DON). MS_UT had the lowest mean rank (poorest control) but was also one of the most stable management combinations. Based on Piepho's nonparametric rank-based variance homogeneity U test, there was an interaction of management combination and environment for index (P = 0.011) but not for DON (P = 0.147), indicating that the rank ordering for index depended somewhat on environment. In conclusion, although the magnitude of percent control will likely vary among environments, integrating a single tebuconazole + prothioconazole application at anthesis with cultivar resistance will be a more effective and stable management practice for both index and DON than either approach used alone.

Variability in fusarium head blight epidemics in relation to global climate fluctuations as represented by the El Niño-Southern Oscillation and other atmospheric patterns

Cross-spectral analysis was used to characterize the relationship between climate variability, represented by atmospheric patterns, and annual fluctuations of Fusarium head blight (FHB) disease intensity in wheat. Time series investigated were the Oceanic Niño Index (ONI), which is a measure of the El Niño-Southern Oscillation (ENSO), the Pacific-North American (PNA) pattern and the North Atlantic Oscillation (NAO), which are known to have strong influences on the Northern Hemisphere climate, and FHB disease intensity observations in Ohio from 1965 to 2010 and in Indiana from 1973 to 2008. For each climate variable, mean climate index values for the boreal winter (December to February) and spring (March to May) were utilized. The spectral density of each time series and the (squared) coherency of each pair of FHB-climate-index series were estimated. Significance for coherency was determined by a nonparametric permutation procedure. Results showed that winter and spring ONI were significantly coherent with FHB in Ohio, with a period of about 5.1 years (as well as for some adjacent periods). The estimated phase-shift distribution indicated that there was a generally negative relation between the two series, with high values of FHB (an indication of a major epidemic) estimated to occur about 1 year following low values of ONI (indication of a La Niña); equivalently, low values of FHB were estimated to occur about 1 year after high values of ONI (El Niño). There was also limited evidence that winter ONI had significant coherency with FHB in Indiana. At periods between 2 and 7 years, the PNA and NAO indices were coherent with FHB in both Ohio and Indiana, although results for phase shift and period depended on the specific location, climate index, and time span used in calculating the climate index. Differences in results for Ohio and Indiana were expected because the FHB disease series for the two states were not similar. Results suggest that global climate indices and models could be used to identify potential years with high (or low) risk for FHB development, although the most accurate risk predictions will need to be customized for a region and will also require use of local weather data during key time periods for sporulation and infection by the fungal pathogen.

Comments regarding the binary power law for heterogeneity of disease incidence

The binary power law (BPL) has been successfully used to characterize heterogeneity (overdispersion or small-scale aggregation) of disease incidence for many plant pathosystems. With the BPL, the log of the observed variance is a linear function of the log of the theoretical variance for a binomial distribution over the range of incidence values, and the estimated scale (?) and slope (b) parameters provide information on the characteristics of aggregation. When b = 1, the interpretation is that the degree of aggregation remains constant over the range of incidence values observed; otherwise, aggregation is variable. In two articles published in this journal in 2009, Gosme and Lucas used their stochastic simulation model, Cascade, to show a multiphasic (split-line) relationship of the variances, with straight-line (linear) relationships on a log-log scale within each phase. In particular, they showed a strong break point in the lines at very low incidence, with b considerably >1 in the first line segment (corresponding to a range of incidence values usually not observed in the field), and b being ?1 in the next segment (corresponding to the range of incidence values usually observed). We evaluated their findings by utilizing a general spatially explicit stochastic simulator developed by Xu and Ridout in 1998, with a wide range of median dispersal distances for the contact distribution and number of plants in the sampling units (quadrats), and through an assessment of published BPL results. The simulation results showed that the split-line phenomenon can occur, with a break point at incidence values of ?0.01; however, the split is most obvious for short median dispersal distances and large quadrat sizes. However, values of b in the second phase were almost always >1, and only approached 1 with extremely short median dispersal distances and small quadrat sizes. An appraisal of published results showed no evidence of multiple phases (although the minimum incidence may generally be too high to observe the break), and estimates of b were almost always >1. Thus, it appears that the results from the Cascade simulation model represent a special epidemiological case, corresponding primarily to a roughly nearest-neighbor population-dynamic process. Implications of a multiphasic BPL property may be important and are discussed.

Meta-analysis of yield response of hybrid field corn to foliar fungicides in the U.S. Corn Belt

The use of foliar fungicides on field corn has increased greatly over the past 5 years in the United States in an attempt to increase yields, despite limited evidence that use of the fungicides is consistently profitable. To assess the value of using fungicides in grain corn production, random-effects meta-analyses were performed on results from foliar fungicide experiments conducted during 2002 to 2009 in 14 states across the United States to determine the mean yield response to the fungicides azoxystrobin, pyraclostrobin, propiconazole + trifloxystrobin, and propiconazole + azoxystrobin. For all fungicides, the yield difference between treated and nontreated plots was highly variable among studies. All four fungicides resulted in a significant mean yield increase relative to the nontreated plots (P < 0.05). Mean yield difference was highest for propiconazole + trifloxystrobin (390 kg/ha), followed by propiconazole + azoxystrobin (331 kg/ha) and pyraclostrobin (256 kg/ha), and lowest for azoxystrobin (230 kg/ha). Baseline yield (mean yield in the nontreated plots) had a significant effect on yield for propiconazole + azoxystrobin (P < 0.05), whereas baseline foliar disease severity (mean severity in the nontreated plots) significantly affected the yield response to pyraclostrobin, propiconazole + trifloxystrobin, and propiconazole + azoxystrobin but not to azoxystrobin. Mean yield difference was generally higher in the lowest yield and higher disease severity categories than in the highest yield and lower disease categories. The probability of failing to recover the fungicide application cost (p(loss)) also was estimated for a range of grain corn prices and application costs. At the 10-year average corn grain price of $0.12/kg ($2.97/bushel) and application costs of $40 to 95/ha, p(loss) for disease severity <5% was 0.55 to 0.98 for pyraclostrobin, 0.62 to 0.93 for propiconazole + trifloxystrobin, 0.58 to 0.89 for propiconazole + azoxystrobin, and 0.91 to 0.99 for azoxystrobin. When disease severity was >5%, the corresponding probabilities were 0.36 to 95, 0.25 to 0.69, 0.25 to 0.64, and 0.37 to 0.98 for the four fungicides. In conclusion, the high p(loss) values found in most scenarios suggest that the use of these foliar fungicides is unlikely to be profitable when foliar disease severity is low and yield expectation is high.

Perceptions of disease risk: from social construction of subjective judgments to rational decision making

Many factors influence how people form risk perceptions. Farmers' perceptions of risk and levels of risk aversion impact on decision-making about such things as technology adoption and disease management practices. Irrespective of the underlying factors that affect risk perceptions, those perceptions can be summarized by variables capturing impact and uncertainty components of risk. We discuss a new framework that has the subjective probability of disease and the cost of decision errors as its central features, which might allow a better integration of social science and epidemiology, to the benefit of plant disease management. By focusing on the probability and cost (or impact) dimensions of risk, the framework integrates research from the social sciences, economics, decision theory, and epidemiology. In particular, we review some useful properties of expected regret and skill value, two measures of expected cost that are particularly useful in the evaluation of decision tools. We highlight decision-theoretic constraints on the usefulness of decision tools that may partly explain cases of failure of adoption. We extend this analysis by considering information-theoretic criteria that link model complexity and relative performance and which might explain why users reject forecasters that impose even moderate increases in the complexity of decision making despite improvements in performance or accept very simple decision tools that have relatively poor performance.

Modelling virus- and host-limitation in vectored plant disease epidemics

Models of plant virus epidemics have received less attention than those caused by fungal pathogens. Intuitively, the fact that virus diseases are systemic means that the individual diseased plant can be considered as the population unit which simplifies modelling. However, the fact that a vector is required in the vast majority of cases for virus transmission, means that explicit consideration must be taken of the vector, or, the involvement of the vector in the transmission process must be considered implicitly. In the latter case it is also important that within-plant processes, such as virus multiplication and systemic movement, are taken into account. In this paper we propose an approach based on the linking of transmission at the population level with virus multiplication within plants. The resulting models are parameter-sparse and hence simplistic. However, the range of model outcomes is representative of field observations relating to the apparent limitation of epidemic development in populations of healthy susceptible plants. We propose that epidemic development can be constrained by virus limitation in the early stages of an epidemic when the availability of healthy susceptible hosts is not limiting. There is an inverse relationship between levels of transmission in the population and the mean virus titre/infected plant. In the case of competition between viruses, both virus and host limitation are likely to be important in determining whether one virus can displace another or whether both viruses can co-exist in a plant population. Lotka-Volterra type equations are derived to describe density-dependent competition between two viruses multiplying within plants, embedded within a population level epidemiological model. Explicit expressions determining displacement or co-existence of the viruses are obtained. Unlike the classical Lotka-Volterra competition equations, the co-existence requirement for the competition coefficients to be both less than 1 can be relaxed.

Multiple treatment meta-analysis of products evaluated for control of fire blight in the eastern United States

The aim of this analysis was to estimate the effect sizes and consistency of products evaluated for fire blight control in the eastern United States over the last decade. Because only 3% of the 69 studies published from 2000 to 2008 explicitly presented a measure of within-study variability, a method for estimating the least significant difference (LSD) and, hence the sampling variance, for studies with at least two significant mean separations in the presented mean multiple comparisons was developed. Lin's concordance analysis indicated that the estimated LSD was an accurate predictor of the actual LSD based on 35 studies in a calibration evaluation (ρ(c) = 0.997). Separate multi-treatment random-effects meta-analyses were performed for three control categories: antibiotics, biological control, and plant defense-activating products and mean log response ratios relative to the nontreated controls ([Formula: see text]) were computed for each treatment and then back-transformed to obtain the mean percent disease control. None of the products evaluated performed as well as streptomycin, the standard product for fire blight control, for which the mean disease control was 68.6%. As a group, experimental antibiotics provided the best fire blight control with mean effect sizes ranging from 59.7 to 61.7%. Among the biological controls, the best control was noted for treatments combining the antibiotic streptomycin with a product based on Pantoea agglomerans (55.0% mean disease reduction) or Bacillus subtilis (53.9%). Mean disease control was 31.9, 25.7, and 22.6%, respectively, for products based on B. subtilis, Pantoea agglomerans, and Pseudomonas fluorescens without an antibiotic, suggesting that the higher efficacy of the combination treatments was due to the antibiotic. Among the plant defense-activating products, prohexadione calcium had the highest and most consistent effect size (50.7% control), while other products provided modest mean disease control of between 6.1 and 25.8%. Percent control values were significantly moderated by study location and cultivar used in the study, and were smaller, but more variable, when products were tested under high disease intensity compared with low disease intensity. Results indicate that wide-scale use of biological control and plant defense-activating products in the eastern United States is likely to remain low.

Meta-analysis for evidence synthesis in plant pathology: an overview

Meta-analysis is the analysis of the results of multiple studies, which is typically performed in order to synthesize evidence from many possible sources in a formal probabilistic manner. In a simple sense, the outcome of each study becomes a single observation in the meta-analysis of all available studies. The methodology was developed originally in the social sciences by Smith, Glass, Rosenthal, Hunter, and Schmidt, based on earlier pioneering contributions in statistics by Fisher, Pearson, Yates, and Cochran, but this approach to research synthesis has now been embraced within many scientific disciplines. However, only a handful of articles have been published in plant pathology and related fields utilizing meta-analysis. After reviewing basic concepts and approaches, methods for estimating parameters and interpreting results are shown. The advantages of meta-analysis are presented in terms of prediction and risk analysis, and the high statistical power that can be achieved for detecting significant effects of treatments or significant relationships between variables. Based on power considerations, the fallacy of naïve counting of P values in a narrative review is demonstrated. Although there are many advantages to meta-analysis, results can be biased if the analysis is based on a nonrepresentative sample of study outcomes. Therefore, novel approaches for characterizing the upper bound on the bias are discussed, in order to show the robustness of meta-analysis to possible violation of assumptions.

An Assessment of Mixed-Modeling Approaches for Characterizing Profiles of Time-Varying Response and Predictor Variables

A general statistical modeling approach was tested for characterizing the relationship between pathogen inoculum density (or other biological response variables) and environmental variables when the data are collected as temporal profiles of observations within multiple locations or years. The approach, based on the use of linear mixed models, simultaneously accounts for serial correlations of the observations within each time profile, the random effects of location–year (or other grouping factors), and the cross-correlation of the environmental variables, and is appropriate when the environmental effects on the response variable or its transformation (Y) are distributed over several times (e.g., days). Stability and precision of parameter estimates for environmental effects over multiple time lags were achieved through the use of polynomial constraints within a likelihood-based full mixed-model fit; from the parameter estimates, marginal effects of environmental variables and weights for individual time lags were determined. The mixed model was directly expanded, through the incorporation of smoothing functions, to potentially account for possible longer-term trends in the temporal profiles unrelated to the environmental variables being considered. The new approach described here (with or without a smoothing function) generalizes a previously used—and computationally less demanding—two-stage (composite) approach. In the previous approach, constrained parameter estimates and associated weights were first determined without consideration of serial correlation, cross-correlation of environmental variables, and the random effects of location–year; then, a mixed-model fit was accomplished using the fixed time-lag weights derived in the first step. Using data for inoculum density of Gibberella zeae on wheat spikes from 27 location–years, similar results were achieved with the full mixed model and the two-stage approaches, in terms of both the calculated parameters and predictions of Y. With the use of smoothing functions, the precision of the predictions was improved but the general conclusions regarding environmental effects on Y were not affected. Thus, in the particular example data set, previously derived conclusions regarding environmental effects on inoculum density were robust in terms of the statistical methodology used in analysis; most researchers will find the two-stage approach much easier to implement for the analysis of multiple profiles of time-varying observations.

Relationship between yearly fluctuations in Fusarium head blight intensity and environmental variables: a window-pane analysis

Window-pane methodology was used to determine the length and starting time of temporal windows where environmental variables were associated with annual fluctuations of Fusarium head blight (FHB) intensity in wheat. Initial analysis involved FHB intensity observations for Ohio (44 years), with additional analyses for Indiana (36 years), Kansas (28 years), and North Dakota (23 years). Selected window lengths of 10 to 280 days were evaluated, with starting times from approximate crop maturity back to the approximate time of planting. Associations were quantified with Spearman rank correlation coefficients. Significance for a given variable (for any window starting time in a collection of starting times) was declared using the Simes' multiplicity adjustment; at individual time windows, significant correlations were declared when the individual (unadjusted) P values were <0.005. In all states, moisture- or wetness-related variables (e.g., daily average relative humidity [RH] and total daily precipitation) were found to be positively correlated with FHB intensity for multiple window lengths and starting times; however, the highest correlations were primarily for shorter-length windows (especially 15 and 30 days) at similar starting times during the final 60 days of the growing season, particularly near the time of anthesis. This period encompasses spore production, dispersal, and fungal colonization of wheat spikes. There was no evidence of significant correlations between FHB and temperature-only variables for any time window; however, variables that combined aspects of moisture or wetness with temperature (e.g., duration of temperature between 15 and 30 degrees C and RH > or = 80%) were positively correlated with FHB intensity. Results confirm that the intensity of FHB in a region depends, at least in part, on environmental conditions during relatively short, critical time periods for epidemic development.

Meta-analysis of the effects of triazole-based fungicides on wheat yield and test weight as influenced by Fusarium head blight intensity

ABSTRACT Multivariate random-effects meta-analyses were conducted on 12 years of data from 14 U.S. states to determine the mean yield and test-weight responses of wheat to treatment with propiconazole, prothioconazole, tebuconazole, metconazole, and prothioconazole+tebuconazole. All fungicides led to a significant increase in mean yield and test weight relative to the check (D; P < 0.001). Metconazole resulted in the highest overall yield increase, with a D of 450 kg/ha, followed by prothioconazole+ tebuconazole (444.5 kg/ha), prothioconazole (419.1 kg/ha), tebuconazole (272.6 kg/ha), and propiconazole (199.6 kg/ha). Metconazole, prothioconazole+tebuconazole, and prothioconazole also resulted in the highest increases in test weight, with D values of 17.4 to 19.4 kg/m(3), respectively. On a relative scale, the best three fungicides resulted in an overall 13.8 to 15.0% increase in yield but only a 2.5 to 2.8% increase in test weight. Except for prothioconazole+tebuconazole, wheat type significantly affected the yield response to treatment; depending on the fungicide, D was 110.0 to 163.7 kg/ha higher in spring than in soft-red winter wheat. Fusarium head blight (FHB) disease index (field or plot-level severity) in the untreated check plots, a measure of the risk of disease development in a study, had a significant effect on the yield response to treatment, in that D increased with increasing FHB index. The probability was estimated that fungicide treatment in a randomly selected study will result in a positive yield increase (p(+)) and increases of at least 250 and 500 kg/ha (p(250) and p(500), respectively). For the three most effective fungicide treatments (metconazole, prothioconazole+tebuconazole, and prothioconazole) at the higher selected FHB index, p(+) was very large (e.g., >/=0.99 for both wheat types) but p(500) was considerably lower (e.g., 0.78 to 0.92 for spring and 0.54 to 0.68 for soft-red winter wheat); at the lower FHB index, p(500) for the same three fungicides was 0.34 to 0.36 for spring and only 0.09 to 0.23 for soft-red winter wheat.

Assessing heterogeneity in the relationship between wheat yield and Fusarium head blight intensity using random-coefficient mixed models

Seventy-seven studies reporting Fusarium head blight disease index (Y; mean percentage of diseased spikelets per spike) and wheat yield (W; MT/ha) were analyzed to determine the relationship between W and Y, and to assess the degree of variation for the relationship among studies. A linear random-coefficient model-comprising a population-average intercept and slope, a random residual term, and random effects of study on the intercept and slope (best linear unbiased predictors; BLUPs)-was successfully fitted to the data using maximum likelihood. From the predicted random effects, study-specific intercepts and slopes were obtained, and both population-average and subject-specific predictions of yield were determined. The estimated population-average intercept (expected yield when disease symptoms were not present) was 4.10 MT/ha, and the population-average slope was 0.038 MT/ha per unit increase of disease index. Wheat class had a significant effect on the intercept but not on the slope, with soft-red winter wheat having, on average, 0.85 MT/ha higher yield than spring wheat. Based on the estimates of the among-study variances, there was high variation in the effects of study on the intercept, but substantially lower variation in the effects of study on the slope. Thus, although one cannot predict with accuracy the actual wheat yield in a field or plot based on disease index using population-average results, one can predict with accuracy the decline in yield at a given level of disease index using the population-average slope. Through the modeling results, predicted relative yield (as a percentage of yield when disease is not present) can be determined, as well as predicted disease index at which a prespecified level of yield (or yield loss) is expected to occur. The predicted reduction in yield on a percentage scale was greater for spring than for soft-red winter wheat, on average, because of the lower estimated intercept in absolute units for spring wheat.

Efficacy of triazole-based fungicides for fusarium head blight and deoxynivalenol control in wheat: a multivariate meta-analysis

The effects of propiconazole, prothioconazole, tebuconazole, metconazole, and prothioconazole+tebuconazole (as a tank mix or a formulated premix) on the control of Fusarium head blight index (IND; field or plot-level disease severity) and deoxynivalenol (DON) in wheat were determined. A multivariate random-effects meta-analytical model was fitted to the log-transformed treatment means from over 100 uniform fungicide studies across 11 years and 14 states, and the mean log ratio (relative to the untreated check or tebuconazole mean) was determined as the overall effect size for quantifying fungicide efficacy. Mean log ratios were then transformed to estimate mean percent reduction in IND and DON relative to the untreated check (percent control: C(IND) and C(DON)) and relative to tebuconazole. All fungicides led to a significant reduction in IND and DON (P < 0.001), although there was substantial between-study variability. Prothioconazole+tebuconazole was the most effective fungicide for IND, with a C(IND) of 52%, followed by metconazole (50%), prothioconazole (48%), tebuconazole (40%), and propiconazole (32%). For DON, metconazole was the most effective treatment, with a [Formula: see text](DON) of 45%; prothioconazole+tebuconazole and prothioconazole showed similar efficacy, with C(DON) values of 42 and 43%, respectively; tebuconazole and propiconazole were the least effective, with C(DON) values of 23 and 12%, respectively. All fungicides, with the exception of propiconazole, were significantly more effective than tebuconazole for control of both IND and DON (P < 0.001). Relative to tebuconazole, prothioconazole, metconazole, and tebuconzole+prothioconzole reduced disease index a further 14 to 20% and DON a further 25 to 29%. In general, fungicide efficacy was significantly higher for spring wheat than for soft winter wheat studies; depending on the fungicide, the difference in percent control between spring and soft winter wheat was 5 to 20% for C(IND) and 7 to 16% for C(DON). Based on the mean log ratios and between-study variances, the probability that IND or DON in a treated plot from a randomly selected study was lower than that in the check by a fixed margin was determined, which confirmed the superior efficacy of prothioconazole, metconazole, and tebuconzole+prothioconzole for Fusarium head blight disease and toxin control.

Variation in Disease Incidence of Phomopsis Cane and Leaf Spot of Grape in Commercial Vineyards in Ohio

A statewide survey for incidence of Phomopsis cane and leaf spot of grape (caused by Phomopsis viticola) was conducted during the 2002 to 2004 growing seasons. Over the 3 years, disease was observed in all surveyed vineyards, and mean disease incidence for leaves and internodes was 42 and 50%, respectively. A hierarchical linear mixed model was used to evaluate effects of region, farm within region, vineyard within farm, sampling site (i.e., vine) within vineyard, and shoot (i.e., cane) within vine on disease incidence. Region of the state did not have a significant effect on incidence but there was significant variation at all other levels of the hierarchy (P < 0.05); the greatest variation was at the lowest scale (shoots within vines). The potential effects of weather and management practices on disease risk at the vineyard scale were determined by using nonparametric correlation and binary logistic analyses after first classifying mean incidence per vineyard as being below or above 20% (D20 = 0,1) and 40% (D40 = 0,1). Overall results indicated that variables for predicted number of moderate infection events (DM; based on ambient temperature and hours when either there was measured rainfall or relative humidity above 90%), the extent of fungicide application (C) during early- and mid-May (M1 and M2, respectively), and the use of a dormant-period application of fungicide (DOR) were the key factors in predicting disease risk (for either D20 or D40). Accuracy (percentage of high and low disease vineyards correctly predicted) and area under the receiver operating characteristic curve (an overall measure of the accuracy of a model) for a generic model combining these predictor variables were 74 and 0.84, respectively, for D40 and 87 and 0.97, respectively, for D20. Models based on management practices were as accurate as those that incorporated weather variables. Although the degree of control of this disease is inadequate in Ohio, based on the survey results for incidence, the results from the risk-model analysis showed that improved management might be obtained by applying fungicide early during the growing season.

The basic reproduction number of plant pathogens: matrix approaches to complex dynamics

The basic reproduction number, R0, is defined as the total number of infections arising from one newly infected individual introduced into a healthy (disease-free) host population. R0 is widely used in ecology and animal and human epidemiology, but has received far less attention in the plant pathology literature. Although the calculation of R0 in simple systems is straightforward, the calculation in complex situations is challenging. A very generic framework exists in the mathematical and biomathematical literature, which is difficult to interpret and apply in specific cases. In this paper we describe a special case of this general framework involving the use of matrix population models. Leading by example, we explain the existing mathematical literature on this subject in such a way that plant pathologists can apply the method for a wide range of pathosystems.

Baseline and Differential Sensitivity to Two QoI Fungicides Among Isolates of Phytophthora cactorum That Cause Leather Rot and Crown Rot on Strawberry

Sensitivities of 89 isolates of Phytophthora cactorum, the causal agent of crown rot and leather rot on strawberry plants, from seven states (Florida, Maine, North Carolina, Ohio, Oregon, South Carolina, and New York) to the QoI fungicide azoxystrobin were determined based on mycelium growth and zoospore germination. Radial growth of mycelia on lima bean agar amended with azoxystrobin at 0.001, 0.01, 0.1, 1.0, 10, and 30 μg/ml and salicylhydroxamic acid (SHAM) at 100 μg/ml was measured after 6 days. Effect on zoospore germination was evaluated in aqueous solutions of azoxystrobin at 0.005, 0.01, 0.05, 0.1, 0.5, and 1.0 μg/ml in 96-well microtiter plates by counting germinated and nongerminated zoospores after 4 h at room temperature. SHAM was not used to evaluate zoospore sensitivity. The effective dose to reduce mycelium growth by 50% (ED₅₀) ranged from 0.16 to 12.52 μg/ml for leather rot isolates and 0.10 to 15 μg/ml for crown rot isolates. The Kolmogorov-Smirnov test showed significant differences (P < 0.001) between the two distributions. Zoospores were much more sensitive to azoxystrobin than were mycelia. Differences between sensitivity distributions for zoospores from leather rot and crown rot isolates were significant at P = 0.05. Estimated ED₅₀ values ranged from 0.01 to 0.24 μg/ml with a median of 0.04 μg/ml. Experiments with pyraclostrobin, another QoI fungicide, demonstrated that both mycelia and zoospores of P. cactorum were more sensitive to pyraclostrobin than to azoxystrobin. Sensitivities to azoxystrobin and pyraclostrobin were moderately but significantly correlated (r = 0.60, P = 0.0001).

A Distributed Lag Analysis of the Relationship Between Gibberella zeae Inoculum Density on Wheat Spikes and Weather Variables

ABSTRACT In an effort to characterize the association between weather variables and inoculum of Gibberella zeae in wheat canopies, spikes were sampled and assayed for pathogen propagules from plots established in Indiana, North Dakota, Ohio, Pennsylvania, South Dakota, and Manitoba between 1999 and 2005. Inoculum abundance was quantified as the daily number of colony forming units per spike (CFU/spike). A total of 49 individual weather variables for 24-h periods were generated from measurements of ambient weather data. Polynomial distributed lag regression analysis, followed by linear mixed model analysis, was used to (i) identify weather variables significantly related to log-transformed CFU/spike (the response variable; Y), (ii) determine the time window (i.e., lag length) over which each weather variable affected Y, (iii) determine the form of the relationship between each weather variable and Y (defined in terms of the polynomial degree for the relationship between the parameter weights for the weather variables and the time lag involved), and (iv) account for location-specific effects and random effects of years within locations on the response variable. Both location and year within location affected the magnitude of Y, but there was no consistent trend in Y over time. Y on each day was significantly and simultaneously related to weather variables on the day of sampling and on the 8 days prior to sampling (giving a 9-day time window). The structural relationship corresponded to polynomial degrees of 0, 1, or 2, generally showing a smooth change in the parameter weights and time lag. Moisture- (e.g., relative humidity-) related variables had the strongest relationship with Y, but air temperature- and rainfall-related variables also significantly affected Y. The overall marginal effect of each weather variable on Y was positive. Thus, local weather conditions can be utilized to improve estimates of spore density on wheat spikes around the time of flowering.

Consideration of Nonparametric Approaches for Assessing Genotype-by-Environment (G × E) Interaction with Disease Severity Data

Determination of host genotype-by-environment (G × E) interaction is needed to assess the stability of cultivar traits such as plant disease resistance and to reveal differences in aggressiveness or virulence of pathogen strains among locations. Here we explored the use of rank-based methodology to quantify the concordance (or discordance) of disease responses of host genotypes across environments, based on the Kendall coefficient of concordance (W) and ancillary test statistics, in order to determine the extent to which environment affected rankings of genotypes. An analysis of four data sets for disease severity of gray leaf spot of maize (with genotypes planted in as many as 11 locations in a given year) revealed highly significant concordance (P ≤ 0.001) overall, indicating that genotypes varied little in within-environment rankings. This suggests that the G × E interaction was small or nonexistent (in terms of rankings). A novel rank-based method by Piepho was evaluated to further elucidate the interaction (if any) through a test for variance homogeneity. The Piepho test statistic was not significant (P > 0.25) for any of the gray leaf spot data sets, confirming the stability of genotypes across environments for this pathosystem; however, analysis of published data sets for other pathosystems indicated significant results. The relationship shown by Hühn, Lotito, and Piepho between the ratio of genotype and residual variances of the original data and the rank-based W statistic was evaluated using Monte Carlo simulations. A more general functional relationship was developed that is applicable over a wide range of number of genotypes and environments in the analyzed studies. This confirms previously shown linkages between rankings of genotypes within environments and variability in the original (unranked) data, thus permitting ease of interpretation of parametric and nonparametric results.

Pre- and Post-Infection Activity of Azoxystrobin, Pyraclostrobin, Mefenoxam, and Phosphite Against Leather Rot of Strawberry, Caused by Phytophthora cactorum

Pre- and post-infection activity of azoxystrobin, pyraclostrobin, mefenoxam, and phosphite against leather rot of strawberry, caused by Phytophthora cactorum, was determined under greenhouse conditions. Strawberry plants (cv. Honeoye) were grown in pots, and attached fruit at the green-to-white stage of development were used in evaluations. Plants and fruit were sprayed to runoff with the above-mentioned fungicides either before (protectant) or after (curative) inoculation with a zoospore suspension (10⁵ zoospores/ml) of P. cactorum. Inoculated plants with fruit were placed in a mist chamber for 12 h to ensure infection. Fungicides were applied at either 2, 4, or 7 days before inoculation or 13, 24, 36, or 48 h after inoculation. Incidence (proportion of diseased fruit) was recorded 6 days after inoculation. Azoxystrobin and pyraclostrobin provided protectant activity for up to 7 days before inoculation, but only slight curative activity when applied 13 h after inoculation. Phosphite and mefenoxam also provided protection for up to 7 days, as well as curative activity of at least 36 h. There were no significant differences in protectant activity among the QoI fungicides azoxystrobin and pyraclostrobin, phosphite and mefenoxam.

Systemic Modulation of Gene Expression in Tomato by Trichoderma hamatum 382

ABSTRACT A light sphagnum peat mix inoculated with Trichoderma hamatum 382 consistently provided a significant (P = 0.05) degree of protection against bacterial spot of tomato and its pathogen Xanthomonas euvesicatoria 110c compared with the control peat mix, even though this biocontrol agent did not colonize aboveground plant parts. To gain insight into the mechanism by which T. hamatum 382 induced resistance in tomato, high-density oligonucleotide microarrays were used to determine its effect on the expression pattern of 15,925 genes in leaves just before they were inoculated with the pathogen. T. hamatum 382 consistently modulated the expression of genes in tomato leaves. We identified 45 genes to be differentially expressed across the replicated treatments, and 41 of these genes could be assigned to at least one of seven functional categories. T. hamatum 382-induced genes have functions associated with biotic or abiotic stress, as well as RNA, DNA, and protein metabolism. Four extensin and extensin-like proteins were induced. However, besides pathogenesis-related protein 5, the main markers of systemic acquired resistance were not significantly induced. This work showed that T. hamatum 382 actively induces systemic changes in plant physiology and disease resistance through systemic modulation of the expression of stress and metabolism genes.

A quantitative review of tebuconazole effect on fusarium head blight and deoxynivalenol content in wheat

ABSTRACT A meta-analysis of the effect of tebuconazole (e.g., Folicur 3.6F) on Fusarium head blight and deoxynivalenol (DON) content of wheat grain was performed using data collected from uniform fungicide trials (UFTs) conducted at multiple locations across U.S. wheat-growing regions. Response ratios (mean disease and DON levels from tebuconazole-treated plots, divided by mean disease and DON levels from untreated check plots) were calculated for each of 139 studies for tebuconazole effect on Fusarium head blight index (IND; field or plot-level disease severity, i.e., mean proportion of diseased spikelets per spike) and 101 studies for tebuconazole effect on DON contamination of harvested grain. A random-effects meta-analysis was performed on the log-transformed ratios, and the estimated mean log ratios were transformed to estimate the mean (expected) percent control for IND ( C(IND) ) and DON ( C(DON)). A mixed effects meta-analysis was then done to determine the effects of wheat type (spring versus winter wheat) and disease and DON levels in the controls on the log ratios. Tebuconazole was more effective at limiting IND than DON, with C(IND) and C(DON) values of 40.3 and 21.6%, respectively. The efficacy of tebuconazole as determined by the impact on both IND and DON was greater in spring wheat than in winter wheat (P < 0.01), with a 13.2% higher C(IND) and a 12.4% higher C(DON) in spring wheat than in winter wheat. In general, C(IND) and C(DON) were both at their lowest values (and not significantly different from 0) when mean IND and DON in the controls, respectively, were low (</=2% for IND and <1 ppm for DON). C(IND) was 25% higher in studies with mean IND between 2 and 15% than in studies with mean IND </= 2%, whereas C(DON) was 28.8% higher in studies with mean DON between 1 and 10 ppm than in studies with mean DON < 1 ppm. The between-study variance was significantly greater than 0 (P < 0.01), indicating considerable (unexplained) variability in percent control.

Meta-analysis of regression coefficients for the relationship between fusarium head blight and deoxynivalenol content of wheat

ABSTRACT A total of 126 field studies reporting deoxynivalenol (DON; ppm) content of harvested wheat grain and Fusarium head blight index (IND; field or plot-level disease severity) were analyzed to determine the overall mean regression slope and intercept for the relationship between DON and IND, and the influence of study-specific variables on the slope and intercept. A separate linear regression analysis was performed to determine the slope and intercept for each study followed by a meta-analysis of the regression coefficients from all studies. Between-study variances were significantly (P < 0.05) greater than 0, indicating substantial variation in the relationship between the variables. Regression slopes and intercepts were between -0.27 and 1.48 ppm per unit IND and -10.55 to 32.75 ppm, respectively. The overall mean regression slope and intercept, 0.22 ppm per unit IND and 2.94 ppm, respectively, were significantly different from zero (P < 0.001), and the width of the 95% confidence interval was 0.07 ppm per unit IND for slope and 1.44 ppm for intercept. Both slope and intercept were significantly affected by wheat type (P < 0.05); the overall mean intercept was significantly higher in studies conducted using winter wheat cultivars than in studies conducted using spring wheat cultivars, whereas the overall mean slope was significantly higher in studies conducted using spring wheat cultivars than in winter wheat cultivars. Study location had a significant effect on the intercept (P < 0.05), with studies from U.S. winter wheat-growing region having the highest overall mean intercept followed by studies from Canadian wheat-growing regions and U.S. spring wheat-growing regions. The study-wide magnitude of DON and IND had significant effects on one or both of the regression coefficients, resulting in considerable reduction in between-study variances. This indicates that, at least indirectly, environment affected the relationship between DON and IND.

Effects of Application of Fungicide During the Dormant Period on Phomopsis Cane and Leaf Spot of Grape Disease Intensity and Inoculum Production

Efficacy of application of the fungicides calcium polysulfide or fixed copper during the dormant period on control of Phomopsis cane and leaf spot of grape (Vitis spp.), caused by Phomopsis viticola, was examined under field conditions during the 2003 and 2004 growing seasons in Ohio. Dormant-period fungicide applications were made either in the fall (after leaf drop and periderm tissue formation on the first-year canes, mid-November), or spring (at bud-swell, mid-April), or both. Disease incidence and severity on leaves and internodes were examined. In addition, effects of dormant-period application on sporulation of P. viticola were determined by examining the number of conidia in rain-splashed water in the spring and formation of mature pycnidia on cane sections in the winter. Fall-and-spring and spring applications of calcium poly-sulfide provided 12 to 88% reduction in disease intensity (incidence or severity), whereas calendar-based protectant mancozeb applications reduced overall disease intensity by 47 to 100%. Fixed-copper applications did not provide a consistent reduction of the disease. Fall applications of dormant-period fungicide provided little or no effect by itself. There was a significantly lower number of conidia observed in collected splashed rain water from vines treated with fall-and-spring applications of calcium polysulfide than in rain water from nonsprayed vines. Fall-and-spring and spring applications of calcium polysulfide provided a significant reduction in the number of mature pycnidia formed on incubated cane sections compared with the nonsprayed control (5 versus 10 pycnidia/cm²), whereas fixed copper did not provide a significant reduction.

Evaluation of a Disease Warning System for Phomopsis Cane and Leaf Spot of Grape: A Field Study

A field evaluation of a warning system for Phomopsis cane and leaf spot of grape (Vitis spp.), caused by Phomopsis viticola, was conducted in Ohio over 3 years (2002 to 2004) by applying fungicides and fungicide-adjuvant combinations based on predicted infection events. Three different criteria for risk-light, moderate, and high-were evaluated with the warning system. The warning system is based on measured weather conditions (temperature and wetness duration following rain) and a model for risk of leaf and internode infection. Vines were sprayed with fungicides based on either the warning system or a calendar-based 7-day protectant program, from 2.5-cm shoot growth (Eichhorn-Lorenz [E-L] stage 7) to the end of the broom (E-L stage 27). Fungicides were tested with or without an adjuvant (JMS Stylet-Oil or Regulaid). In the controls, the mean percentage of leaves and internodes with infections ranged from 36 to 100%, the number of lesions per leaf ranged from 1 to 28, and percentage of internodes covered by lesions ranged from 1 to 12%. Both the calendar-based protectant treatment (based on use of mancozeb) and the warning system treatment based on spraying in response to light or moderate predicted infection events (especially with mancozeb + Regulaid) resulted in significantly less disease incidence and severity compared with the controls. The mean percent control (relative difference in disease between a treatment and the control) was higher for the protectant schedule (˜55% and ˜80% for incidence and severity, respectively, based on application of mancozeb) than for the warning system (˜36% and ˜60% for incidence and severity, respectively, based on application of mancozeb + Regulaid), but there were two to three times more fungicide applications with the protectant schedule than with the warning system.

An assessment model for rating high-threat crop pathogens

ABSTRACT Natural, accidental, and deliberate introductions of nonindigenous crop pathogens have become increasingly recognized as threats to the U.S. economy. Given the large number of pathogens that could be introduced, development of rapid detection methods and control strategies for every potential agent would be extremely difficult and costly. Thus, to ensure the most effective direction of resources a list of high-threat pathogens is needed. We address development of a pathogen threat assessment model based on the analytic hierarchy process (AHP) that can be applied world-wide, using the United States as an illustrative example. Previously, the AHP has been shown to work well for strategic planning and risk assessment. Using the collective knowledge of subject matter expert panels incorporated into commercial decision-making software, 17 biological and economic criteria were determined and given weights for assessing the threat of accidental or deliberately introduced pathogens. The rating model can be applied by experts on particular crops to develop threat lists, especially those of high priority, based on the current knowledge of individual diseases.

Role of Temperature and Moisture in the Production and Maturation of Gibberella zeae Perithecia

Fusarium graminearum (teleomorph Gibberella zeae) is the most common pathogen of Fusarium head blight (FHB) in North America. Ascospores released from the perithecia of G. zeae are a major source of inoculum for FHB. The influence of temperature and moisture on perithecial production and development was evaluated by monitoring autoclaved inoculated cornstalk sections in controlled environments. Perithecial development was assessed at all combinations of five temperatures (12, 16, 20, 24, and 28°C) and four moisture levels with means (range) -0.45 (-0.18, -1.16), -1.30 (-0.81, -1.68), -2.36 (-1.34, -3.53) and -4.02 (-2.39, -5.88) MPa. Moisture levels of -0.45 and -1.30 MPa and temperatures from 16 to 24°C promoted perithecial production and development. Temperatures of 12 and 28°C and moisture levels of -2.36 and -4.02 MPa either slowed or limited perithecial production and development. The water potential of -1.30 MPa had mature perithecia after 10 days at 20°C, but not until after 15 days for 24°C. In contrast, few perithecia achieved maturity and produced ascospores at lower moisture levels (-2.36 and -4.02 MPa) and low (12°C) and high (28°C) temperatures. In the future, it may be possible to use the information gathered in these experiments to improve the accuracy of FHB forecasting systems.

Systemic Resistance Induced by Trichoderma spp.: Interactions Between the Host, the Pathogen, the Biocontrol Agent, and Soil Organic Matter Quality

ABSTRACT Several factors affect the ability of Trichoderma spp. to provide systemic disease control. This paper focuses on the role of the substrate in which plants are grown, resistance of the host to disease, and the ability of introduced Trichoderma inoculum to spread under commercial conditions. Several reports reveal that foliar disease control provided by Trichoderma spp. is more effective on plants grown in compost-amended media compared with in lower-in-microbial-carrying-capacity sphagnum peat media. In Rhododendron spp., host resistance affects control of Phytophthora dieback provided by Trichoderma spp. For example, T. hamatum 382 (T382) significantly (P = 0.05) suppressed the disease on susceptible cv. Roseum Elegans while plant vigor was increased. The disease was not suppressed, however, on highly susceptible cvs. Aglo and PJM Elite even though the vigor of these plants was increased. Using a strain-specific polymerase chain reaction assay under commercial conditions, it was demonstrated that introduced inoculum of T382 did not spread frequently from inoculated to control compost-amended media. Other Trichoderma isolates typically are abundant in control media within days after potting unless inoculated with a specific Trichoderma isolate. Thus, the low population of isolates that can induce systemic resistance in composting and potting mix environments may explain why most compost-amended substrates do not naturally suppress foliar diseases.

Suppression of Botrytis Blight of Begonia by Trichoderma hamatum 382 in Peat and Compost-Amended Potting Mixes

Inoculation of an industry standard light sphagnum peat potting mix with Trichoderma hamatum 382 (T382) significantly (P = 0.05) reduced the severity of Botrytis blight, caused by Botrytis cinerea, on begonia plants grown in a greenhouse. In data combined from three experiments, the degree of control provided by T382 did not differ significantly (P = 0.05) from that provided by weekly topical sprays with chlorothalonil. In addition, T382 significantly (P = 0.05) increased shoot dry weight and salability of flowering plants. Incorporation of composted cow manure (5%, vol/vol) into the light peat mix also significantly (P = 0.05) decreased blight severity while shoot dry weight and salability were increased. Blight severity on plants in this compost mix did not differ significantly (P = 0.05) from that on those in the light peat mix inoculated with T382. Finally, T382 and chlorothalonil did not significantly (P = 0.05) affect blight severity, shoot dry weight, or salability of plants grown in the compost mix. Spatial separation was maintained in begonias between the biocontrol agent T382 and the pathogen. It was concluded, therefore, that the decrease in disease severity provided by inoculation of the peat mix with T382 most likely was due to systemic resistance induced in begonia against Botrytis blight. The suppressive effect of the compost mix against Botrytis blight was unusual because composts typically do not provide such effects unless inoculated with a biocontrol agent capable of inducing systemic resistance in plants to disease.

Relationship between visual estimates of fusarium head blight intensity and deoxynivalenol accumulation in harvested wheat grain: a meta-analysis

ABSTRACT The association between Fusarium head blight (FHB) intensity and deoxynivalenol (DON) accumulation in harvested grain is not fully understood. A quantitative review of research findings was performed to determine if there was a consistent and significant relationship between measures of Fusarium head blight intensity and DON in harvested wheat grain. Results from published and unpublished studies reporting correlations between DON and Fusarium head blight "index" (IND; field or plot-level disease severity), incidence (INC), diseased-head severity (DHS), and Fusarium-damaged kernels (FDK) were analyzed using meta-analysis to determine the overall magnitude, significance, and precision of these associations. A total of 163 studies was analyzed, with estimated correlation coefficients (r) between -0.58 and 0.99. More than 65% of all r values were >0.50, whereas less that 7% were <0. The overall mean correlation coefficients for all relationships between DON and disease intensity were significantly different from zero (P < 0.001). Based on the analysis of Fisher-transformed r values ( z(r) values), FDK had the strongest relationship with DON, with a mean r of 0.73, followed by IND (r = 0.62), DHS (r = 0.53), and INC (r = 0.52). The mean difference between pairs of transformed z(r) values (z(d) ) was significantly different from zero for all pairwise comparisons, except the comparison between INC and DHS. Transformed correlations were significantly affected by wheat type (spring versus winter wheat), study type (fungicide versus genotype trials), and study location (U.S. spring- and winter-wheat-growing regions, and other wheat-growing regions). The strongest correlations were observed in studies with spring wheat cultivars, in fungicide trials, and in studies conducted in U.S. spring-wheat-growing regions. There were minor effects of magnitude of disease intensity (and indirectly, environment) on the transformed correlations.