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.

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

“…The choice of a 10% discriminatory point between major and non-major epidemics was consistent with past logistic regression models (12,40), and is also used in the current models of the Risk Assessment Tool. The response variable of interest was a binary categorization of S:…”

“…Increased corn (Zea mays) production in wheat-growing regions, concurrent with wider adoption of reduced tillage for soil conservation, were likely contributory factors to severe epidemics beginning in the latter part of the 19th century (36,60), as pathogen survival in corn residue is an acknowledged FHB risk factor (13,27). FHB epidemiological research includes (i) basic documentation of epidemics and observed weather conditions at the time, a mainly descriptive effort, followed by quantification of optimal (usually controlled) conditions for various epidemiologically relevant processes (7,14,45,59,62,63); (ii) synthesis of basic epidemiological results into generalized, qualitative risk algorithms predicting FHB epidemics (47,59,63); and (iii) translation of the generalized risk algorithms into quantitative risk models (10,12,18,25,32,34,39,41,58), several of which were reviewed elsewhere (55). Within the United States, models originally developed by De Wolf and colleagues (12) and subsequently refined (39,40) are the central risk algorithms behind the Fusarium Head Blight Risk Assessment Tool (http://www.wheatscab.psu.edu), a publicly funded service providing local-level, empirical FHB predictions across 31 states (35).…”

“…The objective of the present investigation was to identify, within a parsimonious logistic regression framework, weather-based predictors in pre-and post-anthesis windows of fixed length that were associated with FHB epidemics in this observational data set, and which could be used to improve the Risk Assessment Tool. Hence, this work expanded on previous modeling efforts (12,39,40) by considering a larger data set, an expanded set of potential predictors summarizing weather conditions in ways not considered in the previous work, and some improved analytical techniques that have been shown to be of value in the development and testing of risk models in other disciplines (23,24,30). …”

“…The models driving the Risk Assessment Tool were created with a relatively small set of observations collected prior to 2006 (12,40), which possibly limits inference to new data from a wider set of environmental permutations. In this article, we describe a larger data set of FHB observations, available because of active collaborator participation in U.S.…”

Introgression and gene family contraction drive the evolution of lifestyle and host shifts of hypocrealean fungi

“…Generally, different families display distinct host associations. Nectriaceae includes numerous important plant pathogens such as Fusarium that cause serious plant diseases and economical losses (De Wolf et al 2003; Summerell et al 2011). Cordycipitaceae represented by Cordyceps spp.…”