1. Environmental Influences on Rhizoctonia Web Blight of Azalea Harald Scherm University of Georgia, Athens, GA Warren Copes USDA-ARS, Poplarville, MS Idiosyncrasies

2. Rhizoctonia Web Blight Binucleate Rhizoctonia AG-P or AG-U Common in Deep South during mid- summer and fall Reduces plant attractiveness, causes defoliation, in some cases death Some differences in cv. susceptibility Managed with summer fungicide sprays

3. Rhizoctonia Web Blight Binucleate Rhizoctonia AG-P or AG-U

4. Previous research documented predictable effect of plant spacing on microclimate… Cumulative evaporation (mid-Jul. - mid-Sept.) Hours/day with T between 25 and 30 o C 1-gal ‘Gumpo’ plants Artificially inoculated with Rhizoctonia grown on barley grain Measured total length of blighted stems from mid-July to mid-Sept. Copes & Scherm (2005)

5. …but not on web blight development Frequent rainfall and daily overhead irrigation negate plant spacing effect (increased evaporation) in this production system Copes & Scherm (2005) Regardless of plant spacing, 90% of days between June and early Sept. had RH ≥ 95% for ≥ 8 h per day Leaves wet for ≥ 6 h per day

6. Follow-Up Epidemiological Study 2006-2008 How do environmental variables affect disease onset and disease progression? Would there be any use for weather-based models? 3 locations (2× MS, AL), 3 years 1-gal ‘Gumpo’ plants with standard spacing Natural inoculum 180 to 506 plants per site monitored weekly for web blight development

7. Analysis of Disease Onset Disease onset defined operationally as day of year when disease was first visible on  1 plant by exterior visual assessment Calculated time (or combined weather-time variable) from a weather-based biofix to disease onset Day of year when 3-day moving average of Tmin first reached 20 o C used as biofix Identify the weather-time variable that minimizes coefficient of variation (CV) across the 8 data sets

8. Analysis of Disease Onset Day of year of disease onset Days from biofix to onset Hrs T 20- 30 o C from biofix to onset Hrs LW from biofix to onset Hrs T 20- 30 o C and LW from biofix to onset Mean200.654.8997.8621.6536.7 Range1522357.8273.2238.5 CV (%)2.6516.214.416.219.4 Fixed day of year ~200 (20 July) best predictor of disease onset?! Weather information does not improve accuracy of onset prediction Situation more complicated in real nurseries

9. Analysis of Disease Progress Curves  Disease progress classes based on percent change between weekly values of log 10 -transformed disease severity values (number of diseased leaves/plant) % ChangeCategory ≤ 0SLOW > 0 and < 10INTERMEDIATE ≥ 10FAST

10. Analysis of Disease Progress Curves  Goal: relate actual disease progress classes to weather risk factors occurring prior to disease increase (3-day moving averages lagged by 5 days)  Visual inspection of disease progress classes revealed that slow progress associated with:  Tmin < 20 o C  Tmax > 35 o C  Avg. VPD < 2.50 hPa (excessively wet)  Or day of year > 240 (late-season)  One or more of these criteria applied to >90% of slow progress periods in 2006-07 (development data set)  Allowed us to define low vs. high weather-based risk

11. How good is cross-classification of disease progress classes vs. weather-based disease risk? Good in predicting the extremes (low vs. high), but not intermediate disease progress; low overall accuracy “Negative prognosis” approach (“not high” vs. “not low”) much more accurate Copes & Scherm (2010)

12. Heuristic approach validated with Classification and Regression Tree (CART) analysis CART model resulted in different cut- offs, but also emphasized T variables over moisture variables and yielded similar accuracy Copes & Scherm (2010)

13. Conclusions Azalea plant spacing influences T and evaporation but not wetness periods or web blight intensity Disease onset more accurately predicted by day of year than by weather-related variables Slow and fast disease progress periods may be predicted reasonably well by weather, but intermediate disease progress is not This makes a “negative prognosis” classifying disease progress as “not fast” or “not slow” most useful

14. Conclusions Conclusions from the informal (heuristic) and CART analysis were similar Some idiosyncrasies in this system Abundant moisture (frequent rain and daily irrigation) reduces overall value of moisture variables for disease prediction Three simultaneously occurring subprocesses in web blight cycle (mycelium growth along limb, infection cushion development, leaf necrotization) may make prediction inherently more difficult than for foliar fungi with more discrete cycles