E. D. De Wolf

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

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

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

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

Neural network classification of Tan spot and Stagonospora blotch infection periods in a wheat field environment.

ABSTRACT Tan spot and Stagonospora blotch of hard red spring wheat served as a model system for evaluating disease forecasts by artificial neural networks. Pathogen infection periods on susceptible wheat plants were measured in the field from 1993 to 1998, and incidence data were merged with 24-h summaries of accumulated growing degree days, temperature, relative humidity, precipitation, and leaf wetness duration. The resulting data set of 202 discrete periods was randomly assigned to 10 modeldevelopment or -validation (n = 50) data sets. Backpropagation neural networks, general regression neural networks, logistic regression, and parametric and nonparametric methods of discriminant analysis were chosen for comparison. Mean validation classification of tan spot incidence was between 71% for logistic regression and 76% for backpropagation models. No significant difference was found between methods of modeling tan spot infection periods. Mean validation prediction accuracy of Stagonospora blotch incidence was 86 and 81% for backpropagation and logistic regression, respectively. Prediction accuracies of other modeling methods were </=78% and were significantly different (P = 0.01) from backpropagation, but not logistic regression, results. The best backpropagation models of tan spot and Stagonospora blotch incidences correctly classified 82 and 84% of validation cases, respectively. High classification accuracy and consistently good performance demonstrate the applicability of neural network technology to plant disease forecasting.

Occurrence and Distribution of Triticum mosaic virus in the Central Great Plains

Wheat curl mite (WCM)-transmitted viruses-namely, Wheat streak mosaic virus (WSMV), Triticum mosaic virus (TriMV), and the High Plains virus (HPV)-are three of the wheat-infecting viruses in the central Great Plains of the United States. TriMV is newly discovered and its prevalence and incidence are largely unknown. Field surveys were carried out in Colorado, Kansas, Nebraska, and South Dakota in spring and fall 2010 and 2011 to determine TriMV prevalence and incidence and the frequency of TriMV co-infection with WSMV or HPV in winter wheat. WSMV was the most prevalent and was detected in 83% of 185 season-counties (= s-counties), 73% of 420 season-fields (= s-fields), and 35% of 12,973 samples. TriMV was detected in 32, 6, and 6% of s-counties, s-fields, and samples, respectively. HPV was detected in 34, 15, and 4% of s-counties, s-fields, and samples, respectively. TriMV was detected in all four states. In all, 91% of TriMV-positive samples were co-infected with WSMV, whereas WSMV and HPV were mainly detected as single infections. The results from this study indicate that TriMV occurs in winter wheat predominantly as a double infection with WSMV, which will complicate breeding for resistance to WCM-transmitted viruses.

First Report of Pyrenophora tritici-repentis Race 5 from North America

Tan spot, caused by Pyrenophora tritici-repentis, is an important foliar disease of wheat worldwide. The fungus produces two distinct symptoms, necrosis (nec) and chlorosis (chl), on susceptible wheat genotypes. Fungal isolates have been grouped into five races based on their ability to induce necrosis and/or chlorosis on differentials Glenlea, Katepwa, 6B365, and Salamouni (1). Moreover, the isolates were designated on their ability to induce necrosis and chlorosis as follows: nec+chl+ (necrosis and chlorosis), nec+chl- (necrosis only), nec-chl+ (chlorosis only), and nec-chl- (neither symptom). Races 3 and 5 induce extensive chlorosis (nec-chl+) on 6B365 and Katepwa, respectively. Race 5 was reported on durum from North Africa. Races 1 to 4 were described from North America (1,2). During 1998, a survey of durum fields was conducted in the primary durum-growing area of North Dakota to assess the virulence pattern of P. tritici-repentis. Fifty-two single-spore isolates were obtained from diseased leaves. The isolates were evaluated for their virulence by inoculating them individually onto 15 seedlings of each wheat differential in the greenhouse. Forty-nine of 52 isolates were grouped as race 1 (nec+chl+) and three isolates, obtained from the Langdon Experiment Research Station, were grouped as race 5 (nec-chl+). Race 5 isolates were evaluated three times and consistently induced extensive chlorosis on Katepwa. This is the first report of the occurrence of race 5 outside of North Africa. This race may threaten wheat in the United States, so cultivars and germplasm should be evaluated for resistance. More isolates are under investigation to obtain a comprehensive virulence pattern of the pathogen population in the United States. References: L. Lamari and C. C. Bernier. Can. J. Plant Pathol. 11:49, 1989; (2) L. Lamari et al. Can. J. Plant Pathol. 17:312, 1995.

Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment

ABSTRACT Tan spot of wheat, caused by Pyrenophora tritici-repentis, provided a model system for testing disease forecasts based on an artificial neural network. Infection periods for P. tritici-repentis on susceptible wheat cultivars were identified from a bioassay system that correlated tan spot incidence with crop growth stage and 24-h summaries of environmental data, including temperature, relative humidity, wind speed, wind direction, solar radiation, precipitation, and flat-plate resistance-type wetness sensors. The resulting data set consisted of 97 discrete periods, of which 32 were reserved for validation analysis. Neural networks with zero to nine processing elements were evaluated 20 times each to identify the model that most accurately predicted an infection event. The 200 models averaged 74 to 77% accuracy, depending on the number of processing elements and random initialization of coefficients. The most accurate model had five processing elements and correctly predicted 87% of the infection p...

Predicting Fusarium Head Blight Epidemics With Weather-Driven Pre- and Post-Anthesis Logistic Regression Models

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

Relationships Between Weather Conditions, Agronomic Practices, and Fermentation Characteristics with Deoxynivalenol Content in Fresh and Ensiled Maize

The deoxynivalenol (DON) content of maize silage was determined in samples collected at harvest and after ensiling in 2001 and 2002 from 30 to 40 Pennsylvania dairies. Information on cultural practices, hybrid maturity, planting, and harvest date was collected from each site. Site-specific weather data and a corn development model were used to estimate hybrid development at each site. Correlation analysis was used to assess the relationship between weather data, hybrid development, cultural practices and preharvest DON. Fermentation characteristics (moisture, pH, and so on) of ensiled samples were measured to study their relationship to postharvest DON contamination. No significant difference (P ≤ 0.05) was noted between the numbers of samples containing DON in 2001 and 2002, although concentration was higher in 2002 samples. A positive correlation was observed between DON concentration of harvest samples and daily average temperature, minimum temperature, and growing degree day during tasselling, silking, and milk stages. A negative correlation was observed between daily average precipitation at blister stage and DON concentration in harvest samples. Samples from no-till or minimum-till locations had higher DON concentrations than moldboard or mixed-till locations. Harvest samples had higher DON concentration than ensiled samples, suggesting that some physical, chemical, or microbiological changes, resulting from ensiling, may reduce DON in storage.

Predicting Fusarium head blight epidemics with boosted regression trees

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