Variability in fusarium head blight epidemics in relation to global climate fluctuations as represented by the El Niño-Southern Oscillation and other atmospheric patterns.

Cross-spectral analysis was used to characterize the relationship between climate variability, represented by atmospheric patterns, and annual fluctuations of Fusarium head blight (FHB) disease intensity in wheat. Time series investigated were the Oceanic Niño Index (ONI), which is a measure of the El Niño-Southern Oscillation (ENSO), the Pacific-North American (PNA) pattern and the North Atlantic Oscillation (NAO), which are known to have strong influences on the Northern Hemisphere climate, and FHB disease intensity observations in Ohio from 1965 to 2010 and in Indiana from 1973 to 2008. For each climate variable, mean climate index values for the boreal winter (December to February) and spring (March to May) were utilized. The spectral density of each time series and the (squared) coherency of each pair of FHB-climate-index series were estimated. Significance for coherency was determined by a nonparametric permutation procedure. Results showed that winter and spring ONI were significantly coherent with FHB in Ohio, with a period of about 5.1 years (as well as for some adjacent periods). The estimated phase-shift distribution indicated that there was a generally negative relation between the two series, with high values of FHB (an indication of a major epidemic) estimated to occur about 1 year following low values of ONI (indication of a La Niña); equivalently, low values of FHB were estimated to occur about 1 year after high values of ONI (El Niño). There was also limited evidence that winter ONI had significant coherency with FHB in Indiana. At periods between 2 and 7 years, the PNA and NAO indices were coherent with FHB in both Ohio and Indiana, although results for phase shift and period depended on the specific location, climate index, and time span used in calculating the climate index. Differences in results for Ohio and Indiana were expected because the FHB disease series for the two states were not similar. Results suggest that global climate indices and models could be used to identify potential years with high (or low) risk for FHB development, although the most accurate risk predictions will need to be customized for a region and will also require use of local weather data during key time periods for sporulation and infection by the fungal pathogen.