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.

Cool, moist conditions in combination with minimum tillage, earlier planting, and recent shifts in commercial fungicide seed-treatment active ingredients have led to an increase in corn (Zea mays) and soybean (Glycine max) seedling establishment problems. This situation resulted in an investigation of Pythium spp. associated with seed and seedling diseases. Samples of diseased corn and soybean seedlings were collected from 42 production fields in Ohio. All isolates of Pythium recovered were identified to species using morphological and molecular techniques and evaluated in an in vitro pathogenicity assay on both corn and soybean seed, and a subset of the isolates was tested for sensitivity to fungicides currently used as seed treatments. Eleven species and two distinct morphological groups of Pythium were identified, of which six species were moderately to highly pathogenic on corn seed and nine species were highly pathogenic on soybean seed. There was significant variation (P < 0.05) in sensitivity to mefenoxam, azoxystrobin, trifloxystrobin, and captan both across and within species. Multiple species of Pythium had the capacity to reduce germination of both corn and soybean seed. Results indicated that mefenoxam, azoxystrobin, trifloxystrobin, or captan, when used individually, may not inhibit all pathogenic species of Pythium found in Ohio soils.

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.

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 ( zr 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 zr values (zd ) 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.

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.

To ensure future food security, it is crucial to understand how potential climate change scenarios will affect agriculture. One key area of interest is how climatic factors, both in the near- and the long-term future, could affect fungal infection of crops and mycotoxin production by these fungi. The objective of this paper is to review the potential impact of climate change on three important mycotoxins that contaminate maize in the United States, and to highlight key research questions and approaches for understanding this impact. Recent climate change analyses that pertain to agriculture and in particular to mycotoxigenic fungi are discussed, with respect to the climatic factors – temperature and relative humidity – at which they thrive and cause severe damage. Additionally, we discuss how climate change will likely alter the life cycles and geographic distribution of insects that are known to facilitate fungal infection of crops.

Fusarium graminearum is an important pathogen of cereal crops in Ohio causing primarily head blight in wheat and stalk and ear rot of corn. During the springs of 2004 and 2005, 112 isolates of F. graminearum were recovered from diseased corn and soybean seedlings from 30 locations in 13 Ohio counties. These isolates were evaluated in an in vitro pathogenicity assay on both corn and soybean seed, and 28 isolates were tested for sensitivity to the seed treatment fungicides azoxystrobin, trifloxystrobin, fludioxonil, and captan. All of the isolates were highly pathogenic on corn seed and moderately to highly pathogenic on soybean seed. Fludioxonil was the only fungicide that provided sufficient inhibition of mycelial growth; however, several fludioxonil-resistant mutants were identified during the sensitivity experiments. These results indicate that F. graminearum is an important pathogen of both corn and soybean seed and seedlings in Ohio, and that continued use of fludioxonil potentially may select for less sensitive isolates of F. graminearum.

Shah, D. A., Molineros, J. E., Paul, P. A., Willyerd, K. T., Madden, L. V., and De Wolf, E. D. 2013. Predicting Fusarium head blight epidemics with weather-driven pre-and post-anthesis logistic regression models. Phytopathology 103:906-919.Our objective was to identify weather-based variables in pre-and postanthesis 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-daylong 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 weatherbased 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.Additional keywords: additive logistic regression, data mining, multiple imputation.In the United States, Fusarium head blight (FHB) of wheat (Triticum aestivum L. em. Thell) is caused primarily by Fusarium graminearum sensu stricto of the F. graminearum species complex (44). Major FHB epidemics have occurred somewhere in the United States in every decade since the disease was formally described by W. G. Smith in 1884 (60) although, in any given location, epidemics tend to occur sporadically. During the last two decades, U.S. wheat experienced large direct production losses because of FHB (35,36) and even larger indirect losses in other sectors of the economy (43), contributing to the characterization of FHB as a reemerging disease of importance (36,53). Increased corn (Zea mays) production in wheat-growing regions, concurrent with wider adoption of reduced tillage for soil conservation, were likely contributory factors to severe epidemics beginning in the latter part of the 19th century (36,60), as pathogen survival in corn residue is an acknowledged FHB risk factor (13,27). FHB epidemiological research includes (i) basic documentation of epidemics and observed weather conditions at the time, a mainly descriptive effort, followed by quantification of optimal (usually controlle...