Environmental Influences on Rhizoctonia Web Blight of Azalea Harald Scherm University of Georgia,...
Transcript of Environmental Influences on Rhizoctonia Web Blight of Azalea Harald Scherm University of Georgia,...
Environmental Influences on Rhizoctonia Web Blight
of Azalea
Harald SchermUniversity of Georgia, Athens, GA
Warren CopesUSDA-ARS, Poplarville, MS
Idiosyncrasies
Rhizoctonia Web BlightBinucleate 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
Rhizoctonia Web BlightBinucleate Rhizoctonia AG-P or AG-U
Previous research documented
predictable effect of plant spacing on microclimate…
0 6 12 18 24
Cum
ulat
ive
evap
orat
ion
(mm
)
0
200
400
600
800
1000
1200
2002 2003
Plant spacing (cm)
0 6 12 18 24
Num
ber
of h
ours
bet
wee
n 25
and
30o C
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
A
B
Cumulative evaporation
(mid-Jul. - mid-Sept.)
Hours/day with T between
25 and 30oC
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)
…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)
Plant spacing (cm)
0 6 12 18 24
Cum
ulat
ive
leng
th o
f blig
hted
ste
ms
(cm
)
2000
3000
4000
5000
6000
7000
8000 2002 2003
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
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
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 20oC used as biofix
Identify the weather-time variable that minimizes coefficient of variation (CV) across the 8 data sets
Analysis of Disease Onset
Day of year of disease onset
Days from biofix to onset
Hrs T 20-30oC from biofix to onset
Hrs LW from biofix to onset
Hrs T 20-30oC and LW from biofix to onset
Mean 200.6 54.8 997.8 621.6 536.7
Range 15 22 357.8 273.2 238.5
CV (%) 2.65 16.2 14.4 16.2 19.4
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
Analysis of Disease Progress Curves
Disease progress classes based on percent change between weekly values of log10-transformed disease severity values (number of diseased leaves/plant)
% Change Category
≤ 0 SLOW
> 0 and < 10 INTERMEDIATE
≥ 10 FAST
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 < 20oC Tmax > 35oC 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
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)
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)
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
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