Post-Anthesis Moisture Increased Fusarium Head Blight and Deoxynivalenol Levels in North Carolina Winter Wheat

Abstract

Current models for forecasting Fusarium head blight (FHB) and deoxynivalenol (DON) levels in wheat are based on weather near anthesis, and breeding for resistance to FHB pathogens often relies on irrigation before and shortly after anthesis to encourage disease development. The effects of post-anthesis environmental conditions on FHB are poorly understood. We performed a field experiment at Kinston, NC, to explore the effects of increasing duration of post-anthesis moisture on disease incidence, disease severity, Fusarium-damaged kernels (FDK), percent infected kernels, and DON. The experiment had a split-plot design, and one trial was conducted in each of two successive years. Main plots consisted of post-anthesis mist durations of 0, 10, 20, or 30 days. Subplots were of eight cultivars in the first year and seven in the second year, two being susceptible to FHB and the remainder each with varying degrees of apparent type I and type II resistance. Plots were inoculated by spraying Fusarium graminearum macroconidia at mid-anthesis. Averaging across years and cultivars, 10 or 20 days of post-anthesis mist had the same effect (P > or = 0.198) and were associated with an approximately fourfold increase in mean disease incidence and eightfold increase in disease severity compared with 0 days of mist (P < or = 0.0002). In both years, mean FDK percentages at 0 and 10 days post-anthesis mist were the same and significantly lower than FDK percentages under 20 or 30 days of post-anthesis mist. Mist duration had a significant effect on percent kernels infected with Fusarium spp. as detected by a selective medium assay of 2007 samples. Averaging across all cultivars, in both years, DON levels increased significantly for 10 days compared with 0 days of mist, and increased again with 20 days of mist (P < or = 0.04). This is the first investigation to show that extended post-flowering moisture can have a significant enhancing effect on FHB, FDK, DON, and percent infected kernels of wheat. For all disease and toxin assays, cultivar rankings were significantly noncorrelated among mist durations in at least 1 year, suggesting that FHB screening programs might rank genotypes differently under extended post-anthesis moisture than without it. Our findings also imply that accurate forecasts of DON in small grains must take account of post-anthesis weather conditions.

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

“…Weather conditions during the post-flowering period, from milk development to harvest, had a major impact on DON accumulation in our study, in agreement with findings on winter wheat (Cowger et al 2009). We detected an increased DON content in oat grains harvested from fields with cool and moist weather during this period.…”

“…The mycotoxin content in grain at harvest is variable and depends largely on the interaction of plant growth stage and weather (Hooker et al 2002;Culler et al 2007;Klem et al 2007;Cowger et al 2009;Franz et al 2009;Gourdain et al 2011;Landschoot et al 2012b;Xu et al 2013Xu et al , 2014. Weather summarisations are therefore frequently calculated within different temporal time intervals (Coakley et al 1982), fixed for the number of days (7 days, 10 days etc.…”

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

“…10 . Fixed-length windows of 5, 7, 10, 14, and 15 days pre-and post-anthesis were created, the decision being influenced by previous research (6,7,12,28,29,40,41,50,71). The windows were discrete, a subset of all possible windows, and were nested on either side of fdate.…”

“…Previous research (7,12,28,29,40,41,50,71) guided the creation of 380 weather-based predictors from w pre.5 , w pre.7 , w pre.10 , w pre.14 , w pre.15 , w post.5 , w post.7 , w post.10 , w post.14 , and w post. 15 (Table 1).…”

“…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).…”