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