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

Shah, Denis A.; Molineros, Julio E.; Paul, Pierce A.; Willyerd, K. T.; Madden, L. V.; Wolf, E. D. De

doi:10.1094/phyto-11-12-0304-r

Cited by 65 publications

(79 citation statements)

References 64 publications

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“…Weather conditions from heading/flowering to harvest gave the highest correlations with DON accumulation in our study, while weather conditions during early growth stages were less associated with DON accumulation. This is in agreement with most studies of FHB in wheat (Kriss et al 2010;Shah et al 2013). Associations between weather conditions during plant development (pre-, around and post-flowering) and DON accumulation in grains are often evaluated in models developed to examine DON in wheat (Hooker et al 2002;De Wolf et al 2003;Klem et al 2007).…”

Section: Summarizing Comments On the Relationship Between Weather Consupporting

confidence: 83%

“…In addition, high air temperatures may accelerate plant development and reduce the length of the flowering period, thereby reducing the risk of F. graminearum infection and subsequent DON accumulation. Some FHB models have avoided using air temperature as an explanatory variable alone (De Wolf et al 2003;Shah et al 2013). Crop debris is recognized as a source of F. graminearum inoculum (Pereyra et al 2004).…”

Section: Association Between Don Content and Weather Conditions At DImentioning

confidence: 99%

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DON content in oat grains in Norway related to weather conditions at different growth stages

et al. 2016

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High concentrations of the mycotoxin deoxynivalenol (DON), produced by Fusarium graminearum have occurred frequently in Norwegian oats recently. Early prediction of DON levels is important for farmers, authorities and the Cereal Industry. In this study, the main weather factors influencing mycotoxin accumulation were identified and two models to predict the risk of DON in oat grains in Norway were developed: (1) as a warning system for farmers to decide if and when to treat with fungicide, and (2) for authorities and industry to use at harvest to identify potential food safety problems. Oat grain samples from farmers' fields were collected together with weather data (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013).A mathematical model was developed and used to estimate phenology windows of growth stages in oats (tillering, flowering etc.). Weather summarisations were then calculated within these windows, and the Spearman rank correlation factor calculated between DONcontamination in oats at harvest and the weather summarisations for each phenological window. DON contamination was most clearly associated with the weather conditions around flowering and close to harvest. Warm, rainy and humid weather during and around flowering increased the risk of DON accumulation in oats, as did dry periods during germination/seedling growth and tillering. Prior to harvest, warm and humid weather conditions followed by cool and dry conditions were associated with a decreased risk of DON accumulation. A prediction model, including only pre-flowering weather conditions, adequately forecasted risk of DON contamination in oat, and can aid in decisions about fungicide treatments.

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Section: Summarizing Comments On the Relationship Between Weather Consupporting

confidence: 83%

Section: Association Between Don Content and Weather Conditions At DImentioning

confidence: 99%

DON content in oat grains in Norway related to weather conditions at different growth stages

et al. 2016

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“…The most relevant climatic factors are believed to be temperature, humidity and precipitation Nazari et al, 2014;Shah et al, 2013;Van Asselt et al, 2012;. Those factors directly affect fungal infection and growth (Nazari et al, 2014).…”

Section: Impact Of Climate Changementioning

confidence: 99%

Modelling climate change impacts on mycotoxin contamination

Fels‐Klerx

Liu

Battilani

2016

WMJ

110

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Projected climate change effects will influence primary agricultural systems and thus food security, directly via impacts on yields, and indirectly via impacts on its safety, with mycotoxins considered as crucial hazards. Mycotoxins are produced by a wide variety of fungal species, each having their own characteristics and requirements. The geographic distribution of toxigenic fungi reflects their ecological needs, with thermophilic fungi prevalent at lower latitudes and psychrophiles at the higher latitudes. A resulting gradient of mycotoxin contamination has been repeatedly stressed. Changes in climatic conditions will lead to shifts in the fungal population and the mycotoxin patterns. In general, climate change is expected to increase mycotoxin contamination of crops, but due to the complexity of mycoflora associated to each crop and its interaction with the environment, it appears rash to draw conclusions without specific studies. Very recently first quantitative estimations of impacts of climate change on mycotoxin occurrence have been made. Two studies each applied models of different disciplines including climate projection, crop phenology and fungal/mycotoxin prediction to cereals cultivated in Europe. They were followed by a case study on climate change effects on Alternaria moulds and their mycotoxins in tomato. Results showed that DON contamination of wheat grown in Europe was, in general, expected to increase. However, variation was large, and in some years and some regions a decrease in DON contamination was expected. Regarding aflatoxin contamination of maize grown in Europe, an increase was estimated, mainly in the +2 °C scenario. Two main research gaps were identified related to the (limited) number of existing quantitative models taking into account climate change and their validation in limited areas. Efforts are therefore mandatory to be prepared for future changes and challenges on model validation and limited mycotoxin-crop combinations.

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“…The forecasts are based on logistic regression models developed by De Wolf et al (13), with subsequent revisions (37,38). We recently reexamined those models with more up-to-date data and analytical tools, in the process developing 15 new logistic regression models with improved predictive performance on a test data set (51). That latter effort was not without some statistical challenges, however.…”

mentioning

confidence: 99%

Predicting Fusarium Head Blight Epidemics with Boosted Regression Trees

Shah¹,

Wolf²,

Paul³

et al. 2014

Phytopathology®

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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 weatherbased 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.Additional keywords: disease modeling, disease forecasting, machine learning, plant disease epidemiology, wheat scab.Major epidemics of Fusarium head blight (FHB), caused primarily by Fusarium graminearum sensu stricto (59) of the F. graminearum species complex (43,53), are a recurring obstacle to successful wheat (Triticum aestivum L. em. Thell) production worldwide. Epidemics are responsible for large direct (35,36) and indirect (42) economic losses. Foliar fungicide applications timed to coincide with anthesis, if the environment is conducive for FHB, are one component of an effective disease management strategy (56). Accurate forecasts help growers recognize when their wheat crops are at a high risk of a major FHB epidemic, and most likely to benefit from a fungicide. When the risk of a major epidemic is low, growers could forgo inessential fungicide applications. Management decisions (and, hence, forecasts) need to be made by anthesis so that producers have sufficient time to spray should they choose that option.The Fusarium Head Blight Risk Assessment Tool (http://www. wheatscab.psu.edu) is a publicly funded service that provides local-level, empirical FHB predictions across several of the wheat-growing regions of the United States which have historically experienced FHB epidemics (35). The forecasts are based on logistic regression models developed by De Wolf et al. (13), with subsequent revisions (37,38). We recently reex...

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

Cited by 65 publications

References 64 publications

DON content in oat grains in Norway related to weather conditions at different growth stages

DON content in oat grains in Norway related to weather conditions at different growth stages

Modelling climate change impacts on mycotoxin contamination

Predicting Fusarium Head Blight Epidemics with Boosted Regression Trees

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