Effect of Temperature on Sporulation and Infectivity of Podosphaera macularis on Humulus lupulus

Hop powdery mildew, caused by Podosphaera macularis, can result in complete crop loss and requires numerous fungicide applications for effective management. To assess the impact of temperature on the production of infective conidia, 10-day-old sporulating colonies were exposed to 18, 30, 33, 36, 39,...

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Veröffentlicht in:Plant disease. - 1997. - 93(2009), 3 vom: 11. März, Seite 281-286
1. Verfasser: Peetz, Amy B (VerfasserIn)
Weitere Verfasser: Mahaffee, Walter F, Gent, David H
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2009
Zugriff auf das übergeordnete Werk:Plant disease
Schlagworte:Journal Article
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520 |a Hop powdery mildew, caused by Podosphaera macularis, can result in complete crop loss and requires numerous fungicide applications for effective management. To assess the impact of temperature on the production of infective conidia, 10-day-old sporulating colonies were exposed to 18, 30, 33, 36, 39, and 42°C for 6 h, and then incubated at 18°C for 18 h. Conidia were harvested, inoculated onto hop plants, incubated at 18°C for 10 days, and then lesions/cm2 of leaf area was determined. Disease was significantly reduced at temperatures ≥30°C with a nonlinear response in the production of infective conidia (P < 0.0001). Temperature effects on sporulation of P. macularis were examined using a custom impaction conidia sampler in growth chambers programmed at constant temperatures of 5, 10, 15, 20, 25, 30, and 35°C, or 18°C before and after ramping to 18, 22, 26, 30, 34, and 38°C for 6 h. The effect of constant temperature on sporulation was best described by a nonlinear thermodynamic model (P = 0.0001) with maximal production near 25°C. Exposure to fluctuating temperatures produced a curvilinear response in sporulation (P = 0.0122) with maximum production near 25°C. These data indicate that inoculum availability is reduced when ambient temperature exceeds 30°C and that modeling inoculum availability could help further refine current disease forecasting models 
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