Real-time PCR detection of Erwinia amylovoraon blossoms correlates with subsequent fire blight incidence

Abstract

Fire blight is the most devastating bacterial disease of rosaceous plants. Forecasting fire blight infections is important to allow for countermeasures that reduce economic damage in pome fruit production. Current computerized forecasting models are solely based on physical factors such as temperature and moisture, but not on the actual presence of the pathogen Erwinia amylovora. Although the inoculum concentration is considered to be crucial for infection and disease outbreak, most current approaches used for identification of fire blight inoculum including morphological, biochemical, serological, and DNA-based methods are nonquantitative. Based on a real-time PCR approach previously published, an improved protocol to be used directly on whole bacteria in the field is described. The method allows for early detection and quantification of the pathogen prior to the occurrence of first symptoms. There is a clear correlation between bacterial abundance and subsequent disease development. However, in some cases, no disease symptoms could be observed despite a pathogen load of up to 3·4 × 106 cells per blossom. Integration of the amount of pathogen detected into refined prediction algorithms may allow for the improvement of applied forecasting models, finally permitting a better abatement of fire blight.