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The impact of Gibberella ear rot (GER; caused by Fusarium graminearum) on deoxynivalenol (DON) contamination of grain and yield components in maize were investigated using data from 30 environments in Ohio (3 years by 10 locations). Fifteen hybrids, later classified as susceptible (SU), moderately susceptible (MS), or moderately resistant (MR), based on the magnitude of differences in mean arcsine square-root-transformed GER severity (arcSEV) and log-transformed DON (logDON) relative to a reference SU check, were planted in each environment, and 10 ears per hybrid were inoculated with a spore suspension of F. graminearum. Relationships between GER severity and DON were well described by a Kono-Sugino-type nonlinear equation. Estimated parameters representing height (A) and steepness (β) of the curves were significantly higher for SU than MS and MR hybrids but A was not significantly different between MS and MR. Results from a surrogacy analysis showed that GER was a moderate trial- and individual-level surrogate for DON. Both grain weight per ear and ear diameter decreased with increasing arcSEV but the regression slopes varied among resistance classes. The rates of reduction in both yield components per unit increase in arcSEV were significantly greater for SU than for MS and MR. An estimated 50% reduction in grain weight occurred at 62% GER severity for SU, compared with 77% severity for MS and 83% for MR. These results show that GER severity can be used as a surrogate for early estimation of DON contamination and yield loss to help guide grain handling and marketing decisions.

Constructing models that accurately predict Fusarium head blight (FHB) epidemics and are also amenable to large-scale deployment is a challenging task. In the US, the emphasis has been on simple logistic regression (LR) models which are easy to implement but may suffer from lower accuracies when compared to more complicated, harder-to-deploy (over large geographies) model frameworks such as functional or boosted regressions. This article examined the plausibility of random forests (RF) for the binary prediction of FHB epidemics as a possible mediation between model simplicity and complexity without sacrificing accuracy. A minimalist set of predictors was also desirable rather than having the RF model use all 90 candidate variables as predictors. The input predictor set was filtered with the aid of three RF variable selection algorithms (Boruta, varSelRF and VSURF), using resampling techniques to quantify the variability and stability of selected variable sets. Post-selection filtering produced 58 competitive RF models with no more than 14 predictors each. One variable representing temperature stability in the 20 days before anthesis was the most frequently selected predictor. This was a departure from the prominence of relative humidity-based variables previously reported in LR models for FHB. The RF models had overall superior predictive performance than the LR models and may be suitable candidates for use by the Fusarium Head Blight Prediction Center.

Forrest W. Nutter, Jr., had a distinguished career in plant disease epidemiology and crop loss assessment, starting as a graduate student at the University of New Hampshire and North Dakota State University, and then as a faculty member at the University of Georgia and Iowa State University. This article reviews his pioneering contributions in phytopathometry over a nearly 40-year period, in which he developed and evaluated the tenets of disease assessment, and explored ways of improving assessment using visual, photographic, and electronic means. He initiated research on the measurement of disease and other crop traits using multispectral radiometers in 1983, and continued this work for decades, providing early evidence of the wavelengths often associated with disease severity, and demonstrating multiple applications for radiometric measurements. This work led to research on the reliability and accuracy of electronic and visual methods for assessing disease intensity and the relationships between different types of assessments; evaluations of the underlying principles of visual disease estimation; and the development of computerized training programs for disease assessment (e.g., Disease.Pro and Severity.Pro), which have been used in more than 25 countries and 100 universities. Among other things, his research provided substantial evidence to refute the Horsfall-Barratt scale for accurate or reliable visual disease assessment. His contributions have given a strong foundation for all researchers who wish to improve measurements of disease, or use measurements to evaluate controls or understand disease progress and crop losses.

Fusarium head blight (FHB) of wheat, caused by the fungus Fusarium graminearum, is associated with grain contamination with mycotoxins such as deoxynivalenol (DON). Although FHB is often positively correlated with DON, this relationship can break down under certain conditions. One possible explanation for this could be the conversion of DON to DON-3-glucoside (D3G), which is typically missed by common DON testing methods. The objective of this study was to quantify the effects of temperature, relative humidity (RH), and preharvest rainfall on DON, D3G, and the D3D/DON relationship. D3G levels were higher in grain from spikes exposed to 100% RH than to 70, 80, or 90% RH at 20 and 25°C across all tested levels of mean FHB index (percentage of diseased spikelets per spike). Mean D3G contamination was higher at 20°C than at 25 or 30°C. There were significantly positive linear relationships between DON and D3G. Rainfall treatments resulted in significantly higher mean D3G than the rain-free check and induced preharvest sprouting, as indicated by low falling numbers (FNs). There were significant positive relationships between the rate of increase in D3G per unit increase in DON (a measure of conversion) and sprouting. As FN decreased, the rate of D3G conversion increased, and this rate of conversion per unit decrease in FN was greater at relatively low than at high mean DON levels. These results provide strong evidence that moisture after FHB visual symptom development was associated with DON-to-D3G conversion and constitute valuable new information for understanding this complex disease-mycotoxin system.

Fusarium head blight (FHB), caused by the fungus Fusarium graminearum, is associated with grain contamination with mycotoxins such as deoxynivalenol (DON) and zearalenone (ZEA). Unlike DON, less is known about factors affecting ZEA production during FHB epidemics. The objective of this study was to quantify ZEA contamination of wheat grain as influenced by temperature, relative humidity, FHB index (IND), grain maturation, simulated late-season rainfall, and harvest timing. Mean ZEA concentrations were low (

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.

Field experiments were conducted to investigate the efficacy of fungicide treatments in combination with genetic resistance against Fusarium head blight (FHB) and its associated mycotoxins under persistently wet pre- and postanthesis conditions in plots inoculated with Fusarium graminearum-colonized corn spawn. Treatments consisted of a single application of prothioconazole + tebuconazole at early anthesis (PA), or at 3 (P3), 6 (P6), or 9 (P9) days after early anthesis, or PA followed by a single application of metconazole at 3 (PA+C3), 6 (PA+C6), or 9 (PA+C9) days after early anthesis. PA and P3 were the most efficacious of the single-application treatments in terms of mean percentage control of FHB index (IND), deoxynivalenol (DON), zearalenone (ZEA), and mean increase in grain yield and test weight (TW) relative to the nontreated susceptible check (S_CK). The double-application treatments (PA+C3, PA+C6, and PA+C9) were the most effective of all tested fungicide programs. However, relative to S_CK, the highest overall mean percentage reduction in IND, DON, and ZEA and increase in grain yield and TW were observed when the double-application fungicide programs were integrated with genetic resistance. The estimated net cash income (NCI) of the integrated management (IM) programs was consistently higher than the NCI of other tested programs across different grain prices and fungicide application costs. Thus, the benefits of the two-treatment IM programs under highly favorable conditions for FHB development were enough to offset the cost of two applications, making these programs profitable.

Sometimes plant pathologists assess disease intensity when they are primarily interested in other response variables, such as yield loss or toxin concentration in harvested products. In these situations, disease intensity potentially could be considered a surrogate of yield or toxin. A surrogate is a variable which can be used instead of the variable of interest in the evaluation of experimental treatments or in making predictions. Surrogates can be measured earlier, more conveniently, or more cheaply than the variable of primary interest, but the reliability or validity of the surrogate must be shown. We demonstrate ways of quantifying two facets of surrogacy by using a protocol originally developed by Buyse and colleagues for medical research. Coefficient-of-determination type statistics can be used to conveniently assess the strength of surrogacy on a unitless scale. As a case study, we evaluated whether field severity of Fusarium head blight (i.e., FHB index) can be used as a surrogate for yield loss and deoxynivalenol (DON) toxin concentration in harvested wheat grain. Bivariate mixed models and corresponding approximations were fitted to data from 82 uniform fungicide trials conducted from 2008 to 2013. Individual-level surrogacy—for predicting the variable of interest (yield or DON) from the surrogate (index) in plots with the same treatment—was very low. Trial-level surrogacy—for predicting the effect of treatment (e.g., mean difference) for the variable of interest based on the effect of the treatment on the surrogate (index)—was moderate for yield, and only low for DON. Challenges in using disease severity as a surrogate for yield and toxin are discussed.

Network meta-analysis is a popular method to synthesize the information obtained in a systematic review of studies (e.g., randomized clinical trials) involving subsets of multiple treatments of interest. The dominant method of analysis employs within-study information on treatment contrasts and integrates this over a network of studies. One advantage of this approach is that all inference is protected by within-study randomization. By contrast, arm-based analyses have been criticized in the past because they may also recover inter-study information when studies are modeled as random, which is the dominant practice, hence violating the principle of concurrent control, requiring treated individuals to only be compared directly with randomized controls. This issue arises regardless of whether analysis is implemented within a frequentist or a Bayesian framework. Here, we argue that recovery of inter-study information can be prevented in an arm-based analysis by adding a fixed study main effect. This simple device means that it is possible to honor the principle of concurrent control in a two-way analysis-of-variance approach that is very easy to implement using generalized linear mixed model procedures and hence may be particularly welcome to those not well versed in the more intricate coding required for a contrast-based analysis.

Because Fusarium head blight (FHB) intensity is usually highly variable within a plot, the number of spikes rated for FHB index (IND) quantification must be considered when designing experiments. In addition, quantification of sources of IND heterogeneity is crucial for defining sampling protocols. Field experiments were conducted to quantify the variability of IND (“field severity”) at different spatial scales and to investigate the effects of sample size on estimated plot-level mean IND and its accuracy. A total of 216 7-row × 6-m-long plots of a moderately resistant and a susceptible cultivar were spray-inoculated with different Fusarium graminearum spore concentrations at anthesis to generate a range of IND levels. A one-stage cluster sampling approach was used to estimate IND, with an average of 32 spikes rated at each of 10 equally spaced points per plot. Plot-level mean IND ranged from 0.9 to 37.9%. Heterogeneity of IND, quantified by fitting unconditional hierarchical linear models, was higher among spikes within clusters than among clusters within plots or among plots. The projected relative error of mean IND increased as mean IND decreased, and as sample size decreased to

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.

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.

Models were developed to quantify the risk of deoxynivalenol (DON) contamination of maize grain based on weather, cultural practices, hybrid resistance, and Gibberella ear rot (GER) intensity. Data on natural DON contamination of 15 to 16 hybrids and weather were collected from 10 Ohio locations over 4 years. Logistic regression with 10-fold cross-validation was used to develop models to predict the risk of DON ≥1 ppm. The presence and severity of GER predicted DON risk with an accuracy of 0.81 and 0.87, respectively. Temperature, relative humidity, surface wetness, and rainfall were used to generate 37 weather-based predictor variables summarized over each of six 15-day windows relative to maize silking (R1). With these variables, least absolute shrinkage and selection operator (LASSO) followed by all-subsets variable selection and logistic regression with 10-fold cross-validation were used to build single-window weather-based models, from which 11 with one or two predictors were selected based on performance metrics and simplicity. LASSO logistic regression was also used to build more complex multiwindow models with up to 22 predictors. The performance of the best single-window models was comparable to that of the best multiwindow models, with accuracy ranging from 0.81 to 0.83 for the former and 0.83 to 0.87 for the latter group of models. These results indicated that the risk of DON ≥1 ppm can be accurately predicted with simple models built using temperature- and moisture-based predictors from a single window. These models will be the foundation for developing tools to predict the risk of DON contamination of maize grain.

summary A model of splash dispersal of point-source pathogens is developed and parameterized from experimental data of Colletotrichum acutatum, the cause of strawberry anthracnose. Starting with analysis on a probability density function of a sum of random displacement vectors, the model turned out to be the fundamental solution of the classic diffusion equation. Model parameters were determined for three ground covers (plastic, soil, and straw) and two rain intensities (15 and 30 mm h−1). The nonlinear coefficient of determination varied from 0.80 to 0.96, indicating that the application of the diffusion equation is promising in modelling of splash dispersal processes from a point source over a homogeneous ground cover.

The binary power law (BPL) is often used to characterize spatial heterogeneity of disease incidence. A hierarchical mixed model, coupled with multiple imputation to randomly generate any missing standard errors, was used to conduct a meta-analysis of >200 published values of the estimated aggregation (b) parameter of the BPL. Approximately 50% of estimated b values ranged from 1.1 to 1.3. Moderator variable analysis showed that the number of individuals per sampling unit (n) had a strong positive effect on b, with a linear relation between estimated b and ln(n). Estimated expected value of b for the population of published regressions at a reference n of 15 was 1.22. The increase in the variance due to the imputations was only 0.03, and the efficiency exceeded 0.98. Results were confirmed with an alternative mixed model that considered a range of possible within-trial correlations of the estimated b values and with a random-coefficient mixed model fitted to the subset of the data. Cropping system, dispersal mode, and pathogen type all had significant effects on b, with annuals having larger expected value than woody perennials, soilborne and rain-splashed dispersed pathogens having the largest expected values for dispersal mode, and bacteria and oomycetes having the largest expected values for pathogen type. However, there was considerable variation within each of the levels of the moderators, and the differences of expected values from smallest to largest were small, ≤0.16. Results are discussed in relation to previously published findings from stochastic simulations.

Epidemics of wheat blast, caused the Triticum pathotype of Magnaporthe oryzae, were studied in the Santa Cruz del la Sierra region of Bolivia to quantify and compare the temporal dynamics of the disease under different growing conditions. Six plots of a susceptible wheat cultivar were planted at Cuatro Cañadas (CC), Okinawa 1 (OK1), and Okinawa 2 (OK2) in 2015. Spike blast incidence (INC) and severity (SEV) and leaf blast severity (LEAF) were quantified in each plot at regular intervals on a 10 × 10 grid (n = 100 clusters of spikes), beginning at head emergence (Feekes growth stage 10.5), for a total of nine assessments at CC, six at OK1, and six at OK2. Spike blast increased over time for 20 to 30 days before approaching a mean INC of 100% and a mean SEV of 60 to 75%. The logistic model was the most appropriate for describing the temporal dynamics of spike blast. The highest absolute rates of disease increase occurred earliest at OK1 and latest at OK2, and in all cases it coincided with major rain events. Estimated y0 values (initial blast intensity) were significantly (P < 0.05) higher at OK1 than at CC or OK2, whereas rL values (the logistic rate parameter) were significantly higher at OK2 than at CC or OK1. It took about 10 fewer days for SEV to reach 10, 15, or 20% at OK1 compared with OK2 and CC. Based on survival analyses, the survivor functions for time to 10, 15 and 20% SEV (ts) were significantly different between OK1 and the other locations, with the probabilities of SEV reaching the thresholds being highest at OK1. LEAF at 21 days after Feekes 10.5 had a significant effect on ts at OK1. For every 5% increase in LEAF, the chance of SEV reaching the thresholds by day 21 increased by 30 to 55%.

The

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.

The Florida Strawberry Advisory System (StAS) forecasts the need for fungicide applications for control of Botrytis fruit rot (BFR) and anthracnose fruit rot (AFR) based on leaf wetness and temperature during the moist period. StAS was evaluated in Ohio for four seasons from 2011 to 2014 and compared with the calendar-based system currently used by growers. BFR was severe in 2011 and 2013 and nearly absent in the other years, whereas AFR was not a problem in any of the four years. Using the calendar-based system, five to six applications were made each year for BFR, whereas only two or fewer were recommended using StAS. For AFR control, six to seven applications were made based on the calendar system, whereas three or fewer were recommended by StAS. There was no case in which the calendar program outperformed the StAS in disease management. Use of StAS would greatly reduce the number of sprays needed for control of fruit rot diseases and result in significant savings for Ohio strawberry growers.

Sudden oak death, caused by the invasive pathogen Phytophthora ramorum Werres, de Cock & Man in't Veld, can be deadly for Quercus agrifolia Neé (coast live oak, CLO). However, resistant trees have been observed in natural populations. The objective of this study was to examine if pre-attack (constitutive) levels of phenolic compounds can be used as biomarkers to identify trees likely to be resistant. Naïve trees were selected from a natural population and phloem was sampled for analysis of constitutive phenolics. Following P. ramorum inoculation, trees were phenotyped to determine disease susceptibility and constitutive phenolic biomarkers of resistance were identified. Seasonal variation in phloem phenolics was also assessed in a subset of non-inoculated trees. Four biomarkers, including myricitrin and three incompletely characterized flavonoids, together correctly classified 80% of trees. Biomarker levels were then used to predict survival of inoculated CLO and the proportion of resistant trees within a subset of non-inoculated trees from the same population. Levels of five phenolics were significantly affected by season, but with no pronounced variation in average levels among seasons. These results suggest that pre-infection levels of specific phenolic compounds (i.e., biomarkers) can identify trees naturally resistant to this invasive forest pathogen. Knowledge of resistant trees within natural populations may be useful for conserving and breeding resistant trees and for disease management.

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’.

Chemical and cultural controls for strawberry leather rot, caused by Phytophthora cactorum, were compared in replicated plots on a commercial farm near Wooster, Ohio. Straw mulch, applied uniformly for winter protection, was removed in early spring from between the rows of all check and fungicide treatment plots. Supplemental straw mulch was applied to other plots to achieve a level of approximately 9 t/ha. Overhead irrigation, which caused the soil to puddle, was provided on alternate days during rain-free periods to promote disease development (58, 66, and 73% incidence in check plots for 1993, 1994, and 1995, respectively). Foliar sprays of fosetyl-Al, applied weekly from bloom through preharvest at rates (a.i.) of 2.24 and 4.48 kg/ha, provided 88 to 96% control, with no significant difference between fungicide rates. Metalaxyl (1.17 kg/ha), applied either once or twice as a soil drench from early growth through fruit set, provided 82 to 94% control. Straw mulch alone provided 95 to 99% control. Thus, straw mulch between the rows was equally or more effective than fungicides for controlling leather rot.

Spatial pattern of the incidence of strawberry leaf spot, caused by Mycosphaerella fragariae (Ramularia brunnea), was quantified on commercial strawberry farms in Ohio. For each planting of strawberry, one or two transects were randomly chosen, and the proportion of leaflets (out of 15) with leaf spot was determined from N = 29 to 87 evenly spaced sampling units. Based on a likelihood ratio test, the beta-binomial distribution described the frequency of diseased leaflets better than the binomial in 93% of the 59 data sets over 3 years. Estimates of mean incidence ranged from 0.0009 to 0.82, with a median of 0.05. Estimates of the beta-binomial aggregation parameter, θ, ranged from 0 to 1.06, with a median of 0.20. Moreover, the estimate of the slope of the binary power law, fitted to the variance data for the 59 data sets, was significantly (P < 0.01) greater than one, indicating that heterogeneity, and hence the pattern of disease incidence at the spatial scale of the sampling units or smaller, was dependent on mean incidence. Spatial autocorrelation and Spatial Analysis by Distance IndicEs (SADIE) analyses detected significant positive association of disease incidence among sampling units in approximately 40% of the data sets, indicating that disease clusters extended beyond the borders of the sampling units in these fields. Collectively, the results show that strawberry leaf spot was characterized by relatively tight clusters of disease (based on θ) that extended beyond the borders of the sampling units in a little under half of the data sets (based on correlations). The information on heterogeneity was used to develop fixed and sequential sampling curves to precisely estimate disease incidence. The sequential-estimation procedure was evaluated using statistical bootstrap methods and performed well over the range of disease incidences encountered.

An electronic warning system for grape downy mildew— based on models for the infection of leaves of Vitis lambrusca, production of sporangia by Plasmopara viticola in lesions, and sporangial survival—was tested over 7 years in Ohio. Grapevines were sprayed with metalaxyl plus mancozeb (Ridomil MZ58) when the warning system indicated that environmental conditions were favorable for sporulation and subsequent infection. Over the 7 years, plots were sprayed from one to four times according to the warning system, and from four to 10 times according to the standard calendar-based schedule (depending on the date of the initiation of the experiment). The warning system resulted in yearly reductions of one to six sprays (with median of three sprays). Disease incidence (i.e., proportion of leaves with symptoms) in unsprayed plots at the end of the season ranged from 0 to 86%, with a median of 68%. Incidence generally was very similar for the warning-system and standard-schedule treatments (median of 7% of the leaves with symptoms), and both of these incidence values were significantly lower (P < 0.05) than that found for the unsprayed control, based on a generalized-linear-model analysis. Simplifications of the disease warning system, where sprays were applied based only on the infection or sporulation components of the system, were also effective in controlling the disease, although more fungicide applications sometimes were applied. Effective control of downy mildew, therefore, can be achieved with the use of the warning system with fewer sprays than a with a standard schedule.

During spring (May and June) 1999, ≈30% of the fruit in a 0.5-ha commercial planting of strawberry (Fragaria × ananassa ‘Allstar’) was lost to an unknown fruit rot. The planting was established on black plastic (plasticulture) during summer 1998. Plasticulture is a relatively new system of perennial strawberry production in Ohio that is rapidly gaining popularity among growers (2). It was observed that the plastic beneath the plants in the plasticulture planting was covered with a layer of dead leaves from the previous season's growth, and virtually all rotted fruits were in contact with dead leaves. Fruit rot was rarely observed on fruits that were not in direct contact with dead leaves, and fruits of the same cultivar grown in the traditional matted-row system in an adjacent field did not show rot symptoms. It was postulated that infested dead leaf material could serve as an inoculum source for infection. Fruit rot symptoms were identical to those described for Phomopsis soft rot (1). Isolations were made from infected berries. Berries were soaked in 70% ethanol for 60 s, and tissue sections were placed on potato dextrose agar and incubated at room temperature (≈20 to 22°C). A fungus was isolated consistently from infected tissue. The fungus produced pycnidia in culture, and the fruiting structure and conidia conformed to the description of Phomopsis obscurans (Ellis & Everh.) Sutton (synamorph Dendrophoma obscurans (Ellis & Everh.) H.W. Anderson). Pathogenicity studies were conducted by placing one drop (20 μl) of a conidial suspension (9 × 106 conidia per ml) obtained from 2-week-old cultures on each of five ripe (red) and five immature (pink) detached strawberry fruits. Inoculated fruits were placed on screens in plastic moisture chambers. Five uninoculated fruits served as controls. Within 3 days, whitish lesions appeared on all inoculated fruit; within 8 days, the symptoms observed in the field were reproduced, and lesions were covered by pycnidia. No fruit rot developed on control fruit. The fungus was reisolated from infected fruit to complete Koch's postulates. Although the incidence of Phomopsis leaf blight is increasing in many Ohio strawberry plantings and is becoming a concern to growers, this is the first observation and report of Phomopsis fruit rot in Ohio. Unlike leaf blight, for which it is difficult to directly relate economic loss to disease incidence or severity, losses due to fruit rot can be high, as in this field. As the number of strawberry plantings under plasticulture continues to increase, the importance of Phomopsis leaf blight and fruit rot also may increase. References: (1) J. L. Maas, ed. 1998. Compendium of Strawberry Diseases, 2nd ed. The American Phytopathological Society, St. Paul, MN. (2) E. B. Poling. HortTechnology 3:384, 1993.

Six potato fields were randomly sampled using quadrat areas ranging from 10 m2 to 4,000 m2. A composite soil sample consisting of 20 soil cores was taken from each of 20 quadrats of each unit area. All samples were assayed for Verticillium dahliae and nematodes. The area in which a composite sample was taken had little effect on estimated mean and variance of V. dahliae until quadrat size was ≥1,000 m2. Then variances dramatically increased with size. With Pratylenchus spp., there was no systematic relationship between quadrat size and variance. Seven additional fields were divided into 2,000 or 4,000 m2 grids, and each grid was sampled in both spring and fall for V. dahliae and plant-parasitic nematodes (Pratylenchus spp. and Meloidogyne hapla). Density of V. dahliae was lower, and in some cases, degree of aggregation was higher, in the spring than in the fall. Sampling for V. dahliae in the fall required 2 to 7 composite samples to estimate mean density within 25% of the true mean, while in the spring, 10 to 19 samples were required. Assays of Pratylenchus spp. required a similar number of samples in spring and fall surveys, while M. hapla required fewer samples with spring than with fall sampling when estimating densities >20 second-stage juveniles per 100 cm3 of soil.

Diseased individuals (e.g., leaves, plants) typically are clustered in nature, resulting in greater heterogeneity or variability of disease incidence than would be expected for a random pattern. To account for this variability, as well as the binary nature of disease incidence and the multiple sources of variation in designed experiments, a generalized linear mixed model (GLMM) can be used to analyze collected data. GLMMs are becoming more common in many disciplines and may be preferred over analysis of variance for non-normally distributed data. We evaluated several GLMMs for analyzing the incidence of Phomopsis leaf blight of strawberry in relation to fungicide treatments in five experiments which varied greatly in mean incidence and the differences in incidence between treatments. The first model form accounted for heterogeneity through the residual variance (i.e., the overdispersion parameter), which was assumed to be either fixed for the experiment, or dependent on either treatment or incidence. The second model form accounted for heterogeneity explicitly through a within-plot sampling variance, which was assumed to be either constant or dependent on treatment. All GLMMs could be successfully fitted to the data in each experiment, but there was weak evidence based on the conditional deviance and residual plots that the residual-variance models were more appropriate than the sampling-variance models. Model choice had only a minor effect on F tests for treatment effects and significant differences between treatment means. Based on ease of use and evaluation results, we recommend that the simplest (fixed residual variance) model be used as the first choice in analyzing disease incidence data using GLMMs.

In 1998 and 1999, controlled-environment studies were conducted in growth chambers to determine the temperature and wetness-duration parameters required for leaf and cane infection of grape by Phomopsis viticola. Greenhouse-grown ‘Catawba’ (Vitis labrusca) and ‘Seyval’ (French hybrid) grapes were inoculated with P. viticola and incubated at constant temperatures of 5, 10, 15, 20, 25, 30, and 35°C and at wetness durations of 5, 10, 15, and 20 h for each temperature. Data from each cultivar were analyzed by nonlinear regression analysis to determine the relationship between disease severity and temperature and wetness duration. A generalized form of the Analytis Beta model was found to provide the best fit to the data. Disease severity on leaves and canes increased with increasing wetness duration at most temperatures. Minimum and maximum temperatures for infection were around 5 and 35.5°C, respectively. Optimum temperatures for leaf and cane infection were between 16 and 20°C. In the 2000 and 2001 growing seasons, the generalized Beta model was validated in ‘Catawba’ and ‘Seyval’ vineyards by inoculating vines during natural rain events. Average temperature and hours of wetness for each event and inoculation were recorded and used in the model equation to predict disease severity on leaves and internodes. Correlation coefficients between observed disease severities following field inoculations and predicted disease severities for both cultivars were between 0.71 and 0.81 and always significant (P < 0.01). These results indicate that the model reliably predicted leaf and cane infection on both cultivars over a wide range of wetness durations and temperatures. The model may be useful in developing disease-forecasting systems for Phomopsis cane and leaf spot on grapes.

The dynamics of spread of Hop latent virus (HpLV), Hop mosaic virus (HpMV), and Apple mosaic virus (ApMV) in an Australian hop garden were characterized by fitting a stochastic spatiotemporal model, using the Monte Carlo Markov Chain (MCMC) method, to the changes in patterns of virus-infected plants. The model has parameters for background infection rate (from virus reservoirs outside the gardens) and local infection rate (spread from infected plants to virus-free plants). The HpLV epidemic could be explained by very short range spread within the garden, suggesting mechanical transmission through cultural operations. This is consistent with the absence of the only known aphid vector of HpLV from Australia. The HpMV epidemic could be explained by local spread coupled with background infection. This suggests that HpMV may be introduced by aphids, and subsequent localized spread may occur by aphids or mechanical means. The ApMV epidemic was also consistent with localized spread coupled with background infection, but the possibility of longer-range spread could not be rejected for this virus. Although intra-garden transmission of ApMV may occur through contact or cultural operations, these results suggest that other mechanisms could contribute to transmission of ApMV in Australian hop gardens. Join-count statistics for the patterns of infected plants supported the conclusion from the spatiotemporal modeling that short-range spread was the primary determinant of disease increase.

Temperature, leaf wetness, and leaflet age effects on infection of strawberry foliage by Phomopsis obscurans were examined in controlled-environment experiments. A mid-season (‘Honeoye’) and early-season (‘Earliglow’) cultivar were used. Tested temperatures were 10, 15, 20, 25, 30, and 35°C, and tested wetness periods were 5, 10, 15, 20, 25, and 35 h. Leaflets were labeled based on age: 0 to 1, 2 to 6, and 7 to 14 days old. Effects of temperature, wetness duration, and leaflet age on the logit of disease incidence and severity were quantified using a linear mixed model analysis of variance (ANOVA). Age, wetness duration, and their interaction significantly affected these measures of disease. Disease intensity decreased dramatically with increasing leaflet age at the time of infection, indicating a decrease in susceptibility with maturation of foliage, and increased with increasing wetness duration. Temperature only affected disease incidence with ‘Honeoye’. A prediction model was developed for leaflet infection based on ANOVA results. Coefficients of determination were approximately 0.8 for both cultivars and measures of disease (incidence and severity), indicating that disease could be described accurately based on environmental conditions and leaflet age.

Reliable greenhouse assays are needed to differentiate types of resistance in wheat to Fusarium graminearum. Genotypes with known field reactions were evaluated for resistance type using four greenhouse inoculation techniques. Percentage of spikelets with symptoms per spike (severity) and percentage of inoculated spikelets per spike developing symptoms were assessed 7, 10, and 14 days after inoculation (DAI). Genotypes were evaluated using disease assessments 14 DAI and area under the disease progress curve (AUDPC). Significant genotype-inoculation technique interactions for disease assessments indicated that genotypes responded differently to greenhouse inoculation techniques. The central floret injection technique used to assess resistance to spread within the spike (type II resistance) did not indicate a resistant response in genotypes with low field disease severity (putative type II). Atomizing macroconidia onto spikes, used to assess resistance to primary infection (type I resistance), indicated a resistant response in one of five genotypes with low field disease incidence (percentage of spikes with symptomatic spikelets) (putative type I). The inoculation techniques in the greenhouse were unable to differentiate between different types of partial resistance. Results indicated disparity in the ability of greenhouse inoculation methods to identify genotypes with partial resistance as observed in the field. It was concluded that atomizing conidia onto the spikes and assessing disease severity did not differentiate between resistance types. Additionally, a one-time assessment 14 DAI was determined to be as informative as multiple assessments and calculating the AUDPC.

Horst, L. E., Locke, J., Krause, C. R., McMahon, R. W., Madden, L. V., and Hoitink, H. A. J. 2005. Suppression of Botrytis blight of begonia by Trichoderma hamatum 382 in peat and compost-amended potting mixes. Plant Dis. 89:1195-1200. 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.

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.

Rebollar-Alviter, A., Madden, L. V., and Ellis, M. A. 2007. Pre- and post-infection activity of azoxystrobin, pyraclostrobin, mefenoxam, and phosphite against leather rot of strawberry, caused by Phytophthora cactorum. Plant Dis. 91:559-564. 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.

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.

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.

Bean pod mottle virus (BPMV) infection reduces yield and seed quality in soybean. To test the hypothesis that virus incidence and movement within plots would be reduced in soybean with resistance to feeding by the virus' bean leaf beetle (Cerotoma trifurcata) vector, BPMV spread was evaluated in five soybean genotypes at two inoculum levels over 2 years at two locations in Ohio. Soybean genotypes included two insect-feeding-susceptible genotypes (Williams 82 and Resnik), two insect-feeding-resistant, semidwarf genotypes (HC95-15 and HC95-24), and an insect-feeding-susceptible, semidwarf genotype (Troll). BPMV incidence was assessed in individual plants at growth stages R5/R6 and R7/R8 using enzyme-linked immunosorbent assay. Beetle feeding was visually assessed in 2004. Data for infection of individual plants were analyzed using a generalized linear mixed model, with a binomial distribution and logit-link. Within plots, BPMV incidence was highest in Resnik and Williams 82 and significantly lower in Troll. Incidence in HC95-15 was not significantly different than in Williams 82 and Resnik but incidence in HC95-24 was lower than in Resnik. BPMV incidence was also significantly (P < 0.05) affected by year, location, inoculum level and sampling date, with increasing incidence over time and higher incidence at the higher inoculum level. Beetle feeding damage was affected by the interaction of location–genotype. Significant spatial aggregation of infected plants was found for most plots but aggregation was independent of host genotype and inoculum level. Although the results indicate that BPMV infection varied by genotype, they do not support the hypothesis that insect-feeding resistance is sufficient to reduce the incidence and spread of BPMV.

Nagle, A. M., Long, R. P., Madden, L. V., and Bonello, P. 2010. Association of Phytophthora cinnamomi with white oak decline in southern Ohio. Plant Dis. 94:1026-1034. A decline syndrome and widespread mortality of mature white oak tree (Quercus alba) associated with wet and low-lying areas has been recently observed in southern Ohio forests. Previous studies have isolated Phytophthora cinnamomi from white oak rhizospheres. In 2008 and 2009, P. cinnamomi population densities in two healthy and two declining white oak stands at Scioto Trail State Forest were quantified and potential roles of three environmental drivers of Phytophthora spp.–induced decline were assessed: soil texture, soil moisture, and topography. Significantly higher P. cinnamomi propagule densities were found in declining stands in both years but propagule densities were not associated with soil moisture content. Trends in population densities were not correlated with soil moisture or topographic position within field sites. There was a positive, exponential relationship between overall P. cinnamomi population levels and soil moisture on a seasonal scale in 2008 but not 2009. Sites with greater soil clay content were associated with greater decline. Effects of P. cinnamomi inoculum and periodic flooding on root health of 1-year-old potted white oak trees grown in native soil mixes in the greenhouse were examined. Root systems of potted oak were significantly damaged by soil inoculation with P. cinnamomi, especially under flooding conditions. Results of these studies support the hypothesis that P. cinnamomi is a contributing agent to white oak decline in southern Ohio.

Fusarium head blight (FHB) reduces wheat grain yield and quality, leading to price discounts due to Fusarium-damaged kernels (FDK), deoxynivalenol (DON) contamination of grain, and reduced test weight (weight per unit volume of grain). Experiments were conducted to determine whether changing combine harvester configurations to differentially remove diseased kernels affected the yield and quality of grain harvested from plots with different mean levels of FHB index (IND, mean proportion of diseased spikelets per spike), achieved with inoculations at different spore densities. Plots were harvested using four combine configurations, with C1 being the standard, set at a fan speed of 1,375 rpm and a shutter opening of 70 mm, and C2, C3, and C4 regulated to fan speeds and shutter openings of 1,475 rpm and 70 mm, 1,475 rpm and 90 mm, and 1,375 rpm and 90 mm, respectively. C3 and C4 consistently had significantly lower mean arcsine-transformed FDK and log-transformed DON and higher mean test weight than did C1. However, C3 and C4 also resulted in significantly lower mean amounts of harvested grain than did C1. The estimated mean responses to combine configuration were consistent across a range of mean IND levels (5 to 35%). Using a common price discount schedule based on the incidence of FDK, DON, and test weight, and the mean values found in the current investigation for these grain-quality variables, configurations C2, C3, and C4 resulted in between $10 and 40/t lower estimated grain price discounts than C1, with the lowest discounts corresponding to C3 and C4. Using the discount values, a range of grain prices, and the mean yield values from this investigation, estimated gross cash income (GCI; mean estimated yield × grain price adjusted for discounts due to inferior quality) was generally higher for grain harvested with C2 and C4 than with C1 or C3, with C4 being the most consistent across a range of IND levels (5 to 35%) and grain prices ($118 to 276/t). For all modified configurations, the greatest increases in GCI over C1 were observed at the lowest tested grain price, and the improvement of GCI over C1 increased with increasing IND up to the highest disease level tested. Thus, these results showed that, when harvesting grain from FHB-affected fields, the improvement in grain quality and reduction in price discounts with a combine adjustment could be great enough to counteract the reduction in harvested grain that results from the adjustment.

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.

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.

Salgado, J. D., Madden, L. V., and Paul, P. A. 2014. Efficacy and economics of integrating in-field and harvesting strategies to manage Fusarium head blight of wheat. Plant Dis. 98:1407-1421. Fusarium head blight (FHB), a fungal disease of wheat caused by Fusarium graminearum, and its associated toxins, particularly deoxynivalenol (DON), are best managed by integrating multiple strategies. Experiments were established in 2011 and 2013 to evaluate the effects of integrating cultivar resistance, fungicide application, and grain harvesting strategy on FHB index (IND; field severity), DON, grain yield (YLD), and grain test weight (TW; weight per unit volume). Plots of two moderately resistant and two susceptible cultivars were either treated with 19% tebuconazole + 19% prothioconazole or left untreated, and then inoculated with F. graminearum. IND was rated as the mean percentage of diseased spikelets per spike. Separate subsets of the plots of each cultivar–treatment combination were harvested with one of two combine harvester configuration: C1 (the default, set at a fan speed of 1,375 rpm and a shutter opening of 70 mm) and C4 (modified, with the same fan speeds but a wider shutter opening of 90 mm). YLD and TW data were collected, and grain samples were rated for percent Fusarium-damaged kernels (FDK) and tested for DON. Results from linear mixed-model analyses showed that the cultivar– treatment interaction was significant for all FHB-related responses, with the magnitude of the difference in mean arcsine-square-root-transformed IND and FDK (arcIND and arcFDK) and log-transformed DON (logDON) between treated and untreated being higher for susceptible than moderately resistant cultivars. Plots harvested with the C4 combine configuration had significantly higher mean TW than those harvested with C1. Treated plots had significantly higher YLD and TW than untreated plots, regardless of cultivar and configuration. Relative to the reference management program (untreated, susceptible cultivar, harvested with C1), the greatest percent reduction in FDK and DON and increase in YLD was observed for programs that included moderate resistance and fungicide treatment. The greatest percent increase in TW relative to the reference was observed when C4 adjusted combine setting was integrated with resistance and fungicide. Overall, the most effective management programs all included fungicide treatment, two included moderate resistance, and two included C4 combine setting. Relative to the reference management program, these programs resulted in 30 to 51% reduction in total estimated price discount, $127 to 312 ha –1 increase in gross cash income, and economic benefit of $31 to 272 ha–1, depending on the level of FHB IND (5 to 15%), grain price ($118 to 276 metric ton –1 ), and fungicide application cost ($40 to 96 ha–1).

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

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.

Ensembling combines the predictions made by individual component base models with the goal of achieving a predictive accuracy that is better than that of any one of the constituent member models. Diversity among the base models in terms of predictions is a crucial criterion in ensembling. However, there are practical instances when the available base models produce highly correlated predictions, because they may have been developed within the same research group or may have been built from the same underlying algorithm. We investigated, via a case study on Fusarium head blight (FHB) on wheat in the U.S., whether ensembles of simple yet highly correlated models for predicting the risk of FHB epidemics, all generated from logistic regression, provided any benefit to predictive performance, despite relatively low levels of base model diversity. Three ensembling methods were explored: soft voting, weighted averaging of smaller subsets of the base models, and penalized regression as a stacking algorithm. Soft voting and weighted model averages were generally better at classification than the base models, though not universally so. The performances of stacked regressions were superior to those of the other two ensembling methods we analyzed in this study. Ensembling simple yet correlated models is computationally feasible and is therefore worth pursuing for models of epidemic risk., Author summary Ensembling takes a set of predictions from individual models and combines them such that the performance of the ensemble is ideally better than that of any one of the constituent models. Ensembling requires diversity among the individual models in terms of their predictions. However, models developed within the same research group may in fact be interrelated, and high levels of correlation among their predictions could theoretically negate any ensembling benefit. We examined, using a case study on predicting epidemics of Fusarium head blight of wheat, whether ensembling could still be beneficial when the individual models were simple but highly correlated. Even in this situation ensembling led to improvements in prediction without a high computational cost and was therefore profitable even when the diversity in model predictions was low.

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.