Predicting plant disease epidemics from functionally represented weather series.

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

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

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

Predicting Fusarium head blight epidemics with boosted regression trees.

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

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

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

The Effect of Solar Irradiance on the Mortality of Phakopsora pachyrhizi Urediniospores.

Soybean rust, caused by Phakopsora pachyrhizi, may be the most important foliar disease of soybean. Within the last 10 years, the fungus has moved to many new geographical locations via spread of airborne urediniospores. The objective of this study was to determine the relationship between urediniospore viability and exposure to solar radiation. Urediniospores of P. pachyrhizi were exposed in Capitán Miranda, Paraguay, to determine the deleterious effects of sunlight. Concomitant total solar (0.285 to 2.8 µm) and ultraviolet (0.295 to 0.385 µm) irradiance measurements were used to predict urediniospore germination. Urediniospores exposed to doses of solar and ultraviolet (UV) radiation ≥27.3 MJ/m2 and ≥1.2 MJ/m2, respectively, did not germinate. The proportions of urediniospores that germinated, normalized with respect to the germination proportion for unexposed urediniospores from the same collections, were a linear function of solar irradiance (R2 = 0.83). UV measurements predicted normalized germination proportions equally well. Results of inoculation experiments with exposed P. pachyrhizi urediniospores supported the results of the germination trials, although the effects of moderate levels of irradiance varied. The relationship between urediniospore viability and exposure to solar radiation has been incorporated into the U.S. Department of Agriculture's soybean rust aerobiological model that provides North American soybean growers with decision support for managing soybean rust.

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.

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.

Risk assessment models for wheat fusarium head blight epidemics based on within-season weather data.

ABSTRACT Logistic regression models for wheat Fusarium head blight were developed using information collected at 50 location-years, including four states, representing three different U.S. wheat-production regions. Non-parametric correlation analysis and stepwise logistic regression analysis identified combinations of temperature, relative humidity, and rainfall or durations of specified weather conditions, for 7 days prior to anthesis, and 10 days beginning at crop anthesis, as potential predictor variables. Prediction accuracy of developed logistic regression models ranged from 62 to 85%. Models suitable for application as a disease warning system were identified based on model prediction accuracy, sensitivity, specificity, and availability of weather variables at crop anthesis. Four of the identified models correctly classified 84% of the 50 location-years. A fifth model that used only pre-anthesis weather conditions correctly classified 70% of the location-years. The most useful predictor variables were the duration (h) of precipitation 7 days prior to anthesis, duration (h) that temperature was between 15 and 30 degrees C 7 days prior to anthesis, and the duration (h) that temperature was between 15 and 30 degrees C and relative humidity was greater than or equal to 90%. When model performance was evaluated with an independent validation set (n = 9), prediction accuracy was only 6% lower than the accuracy for the original data sets. These results indicate that narrow time periods around crop anthesis can be used to predict Fusarium head blight epidemics.

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

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 periods in the validation set. In comparison, stepwise logistic regression correctly predicted 69% of the validation cases, and multivariate discriminant analysis distinguished 50% of the validation cases. When wetness-sensor inputs were withheld from the models, both the neural network and logistic regression models declined 6% in prediction accuracy. Thus, neural networks were more accurate than statistical procedures, both with and without wetness-sensor inputs. These results demonstrate the applicability of neural networks to plant disease forecasting.