Quantitative Models for Germination and Infection of Pseudoperonospora cubensis in Response to Temperature and Duration of Leaf Wetness

The influence of temperature and leaf wetness duration on germination of sporangia and infection of cantaloupe leaves by Pseudoperonospora cubensis was examined in three independent controlled-environment experiments by inoculating plants with a spore suspension and exposing them to a range of leaf wetness durations (2 to 24 h) at six fixed temperatures (5 to 30°C). Germination of sporangia was assessed at the end of each wetness period and infection was evaluated from assessments of disease severity 5 days after inoculation. Three response surface models based on modified forms of the Weibull function were evaluated for their ability to describe germination of sporangia and infection in response to temperature and leaf wetness duration. The models estimated 15.7 to 17.3 and 19.5 to 21.7°C as the optimum temperature (t) range for germination and infection, respectively, with little germination or infection at 5 or 30°C. For wetness periods of 4 to 8 h, a distinct optimum for infection was observed at t = 20°C but broader optimum curves resulted from wetness periods >8 h. Model 1 of the form f(w,t) = f(t) × (1 – exp{–[B × w]D}) resulted in smaller asymptotic standard errors and yielded higher correlations between observed and predicted germination and infection data than either model 2 of the form f(w,t) = A{1 – exp[– f(t) × (w – C)]D} or model 3 of the form f(w,t) = [1 – exp{–(B × w)2}]/cosh[(t – F)G/2]. Models 1 and 2 had nonsignificant lack-of-fit test statistics for both germination and infection data, whereas a lack-of-fit test was significant for model 3. The models accounted for ≈87% (model 3) to 98% (model 1) of the total variation in the germination and infection data. In the validation of the models using data generated with a different isolate of P. cubensis, slopes of the regression line between observed and predicted germination and infection data were not significantly different (P > 0.2487) and correlation coefficients between observed and predicted values were high (r2 > 0.81). Models 1 and 2 were used to construct risk threshold charts that can be used to estimate the potential risk for infection based on observed or forecasted temperature and leaf wetness duration.