Using long-term research station data to assess and predict disease response to climate change


Abstract: Plant disease response to climate change ideally should be evaluated with long-term disease records for specific cultivars and locations, and predicted with models operating at seasonal and regional scales. Such records are uniquely available for apple trials at research stations. We compiled nearly 40 years disease data on unsprayed ‘Rome Beauty’ apples at Fletcher, North Carolina and Biglerville, Pennsylvania, USA, spanning a latitudinal gradient from 35.4°N to 39.9°N. Disease incidence response to broad seasonal patterns was initially evaluated by categorizing all seasons into terciles based on average temperature or total precipitation, or by El Niño Southern Oscillation (ENSO) cycle during or immediately prior to the growing season. Patterns were revealed that suggested predictive power at this scale for some pathogens. For example, sooty blotch-fly speck incidence at Fletcher was 41% and 47% less in the driest tercile of growing seasons than in the mesic or wettest terciles, respectively (p < 0.01). Powdery mildew had a similar response but differences were not significant. ENSO, however, appeared to have little influence on disease. Chronological trends in disease and phenology are also considered, all of which can inform future models which are useful at the spatial and temporal scales of climate change.

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