Rhizoctonia Web Blight Development on Container-Grown Azalea in Relation to Time and Environmental Factors

Abstract

Rhizoctonia web blight, caused by binucleate Rhizoctonia spp., is an annual problem in the southern United States on container-grown azaleas (Rhododendron spp.) that receive daily irrigation. Disease progress was assessed weekly from mid-May to mid-September on nursery-grown plants at three locations in Mississippi and Alabama in 2006, 2007, and 2008. Disease onset, defined as the appearance of blighted leaves at the exterior canopy of at least one plant, occurred on average on 20 July, and calendar date was a more precise predictor of disease onset than several combined time–weather variables. Disease progress curves exhibited weekly fluctuations around a typically exponential increase in the mean number of symptomatic leaves per plant until early to mid-September, after which web blight severity leveled off or declined due to disease-induced leaf dehiscence and the appearance of new, asymptomatic leaves. Based on the relative increase in the log-transformed number of infected leaves per plant, weekly assessment periods were classified as having slow (≤0%), intermediate (>0 to <10%), or rapid (≥10% increase) disease progress. Three-day moving averages (MA) of various weather variables were calculated, and lagged values (by 5 days) of the MA were used in an attempt to predict disease progress as slow, intermediate, or rapid. Of the periods assessed as having slow disease progress in the 2006–2007 data set (model development data), 90.6% (29 of 32) met at least one of the following heuristically derived criteria for the lagged MA: min. temperature < 20.0°C, max. temperature > 35.0°C, avg. vapor pressure deficit < 2.50 hPa, or day of the year > 240 (28 August). One or more of these same criteria were met in 5 of 16 (31.2%) assessment periods with rapid disease progress, indicating that periods with slow versus rapid disease progression could be distinguished reasonably well based on weather. Results were similar for the 2008 validation data. However, weather variables were not useful in separating periods with either slow or rapid disease progress from those having intermediate progress. Instead, weather variables were most useful when used in a negative-prognosis approach to predict disease progression as being “not rapid” (which includes slow and intermediate periods) or “not slow” (including intermediate and rapid periods). The data set was further analyzed using Classification and Regression Tree (CART) analysis to relate weekly disease progress periods to weather variables. The resulting CART model agreed with the heuristic approach in that temperature variables were more prominent than moisture variables in classifying disease progress periods. With both approaches, satisfactory accuracy was accomplished only with negative-prognoses that classified disease progress periods as not rapid or not slow based on temperature and moisture limits.