Search

A high-throughput baiting and identification process identified more than 7,000 isolates of Pythium from 88 locations in Ohio. Isolates were identified using direct-colony polymerase chain reaction followed by single-strand conformational polymorphism, and communities were assembled using the Jaccard similarity coefficient and cluster analysis. Both univariate and multivariate statistics were used to evaluate differences in soil properties between communities, and canonical discriminant analysis (CDA) was used to assess the strength of the association of soil variables within communities from 83 of the locations. In all, 21 species of Pythium were identified but only 6 were recovered from >40% of the locations. Five communities were formed using the cluster analysis, and significant differences were observed in disease incidence, as well as soil pH, calcium, magnesium, and cation exchange capacity between communities. Stepwise multiple discriminant analysis and CDA identified pH, calcium, magnesium, and field capacity as contributing the most to the separation of the five Pythium communities. There was a strong association between abiotic soil components and the structure of Pythium communities, as well as diversity of Pythium spp. collected from agronomic production fields in Ohio.

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (CIND) and DON (CDON). 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 CIND) and CDON 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 CIND) and a 12.4% higher CDON in spring wheat than in winter wheat. In general, CIND and CDON 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). CIND was 25% higher in studies with mean IND between 2 and 15% than in studies with mean IND ≤ 2%, whereas CDON 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 II 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. [ABSTRACT FROM AUTHOR]

Avenacinase activity has been shown to be a key factor determining the host range of Gaeumannomyces graminis on oats (Avena saliva). G. graminis var. avenae produces avenacinase, which detoxifies the oat root saponin avenacin, enabling it to infect oats. G. graminis var. tritici does not produce avenacinase and is unable to infect oats. G. graminis var. avenae is also reported to incite take-all patch on creeping bentgrass (Agrostis stolonifera). It is unknown whether creeping bentgrass produces avenacin and if the avenacin-avenacinase interaction influences G. graminis pathogenicity on creeping bentgrass. The root extracts of six creeping bentgrass cultivars were analyzed by fluorimetry, thin-layer chromatography, and high performance liquid chromatography for avenacin content. Avenacin was not detected in any creeping bentgrass cultivars, and pathogenicity assays confirmed that both C. graminis var. avenae and G. graminis var. tritici can infect creeping bentgrass and wheat (Triticum aestivum), but only G. graminis var. avenae incited disease on oats. These results are consistent with the root analyses and confirm that avenacinase activity is not required for creeping bentgrass infection by G. graminis. [ABSTRACT FROM AUTHOR]

To determine the relationship between incidence (1; proportion of diseased spikes) and severity (S; mean proportion of diseased spikelets per spike) for Fusarium head blight of wheat and to determine if severity could be predicted reliably from incidence data, disease assessments were made visually at multiple sample sites in artificially and naturally inoculated research and production fields between 1999 and 2002. Ten distinct data sets were collected. Mean disease intensity ranged from 0.023 to 0.975 for incidence and from 0.0003 to 0.808 for severity. A model based on complementary log-log transformation of incidence and severity performed well for all data sets, based on calculated coefficients of determination and random residual plots. The I-S relationship was consistent among years and locations, with similar slopes for all data sets. For 7 of the 10 data sets and for the pooled data from all locations and years, the estimated slope from the fit of the model ranged from 1.03 to 1.26. Time of disease assessment affected the relationship between incidence and severity; however, the estimated slopes from each assessment time were also close to I. Based on the width of the 95% prediction interval, severity was estimated more precisely at lower incidence values than at higher values. The number of sampling units and the index of dispersion of disease incidence had only minor effects on the precision with which S was predicted from 1. The estimation of mean S from 1 would substantially reduce the time required to assess Fusarium head blight in field surveys and treatment comparisons, and the observed relationship between I and S could be used to identify genotypes with some types of disease resistance. [ABSTRACT FROM AUTHOR]