Meta-analysis of Response of Yellow Spot and Stagonospora Nodorum Blotch (SNB)

in Wheat to Fungicides in Western Australia

ACPP-APPS, Darwin, 26-29 April 2011

Kawsar P. Salam, Geoff J. Thomas,Moin U. Salam, Ciara Beard,

William J. MacLeod & Robert LoughmanDepartment of Agriculture & Food, Western

Australia (DAFWA)

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Yellow spot and SNB in wheat in Western Australia

Source: NDSU Extension Service; PP-1249 (Revised), 2011

Yellow spot / tan spot(Pyrenophora tritici-repentis)

Stagonospora Nodorum Blotch (SNB)(Stagonospora nodorum (previously Septoria nodorum))

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Response of the YS to fungicides is usually not quiet evident from individual experiments, especially when questions needed to answer, such as,

does the response vary between the• number of sprays?• seasons?• cultivars of different resistance rating?• fungicides?• and so on …

Is meta-analysis a better technique to address those questions?

Why meta-analysis?

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“Garbage-in, garbage-out” - “Mixing apples and oranges” (ci. Madden & Paul 2011)

• New Applications of Statistical Tools in Plant Pathology (Garrett et al. 2002)• Meta-Analysis in Plant Pathology: Synthesizing Research Results (Rosenberg et al. 2004)• Relationship between visual estimates of Fusarium head blight intensity and deoxynivalenol accumulation in

harvested wheat grain: A meta-analysis (Paul et al. 2005)• Biological and application-oriented factors influencing plant disease suppression by biological control: A meta-

analytical review (Ojiambo & Scherm 2006)• Yield loss in sweet corn caused by Puccinia sorghi: A meta-analysis (Shah & Dillard 2006)• A quantitative review of tebuconazole effect on Fusarium head blight and deoxynivalenol content in wheat (Paul

et al. 2007)• Meta-analysis of the effects of triazole-based fungicides on wheat yield and test weight as influenced by

Fusarium head blight intensity (Paul et al. 2010)• Meta-analysis todetermine the effects of plant disease management measures: Review and case studies on

soybean and apple (Ngugi et al. 2011)• Multiple Treatment Meta-Analysis of Products Evaluated for Control of Fire Blight in the Eastern United States

(Ngugi et al. 2011)• Meta-Analysis for Evidence Synthesis in Plant Pathology: An Overview (Madden & Paul 2011)• A meta-analysis of severity and yield loss from ascochyta blight on field pea in Western Australia (Salam et al.

2011)

“It is obvious that the new scientific discipline of meta-analysis is here to stay” (Chalmers & Lau 1993)

Meta-analysis is the analysis of experiments

It is built on the principle that science is meant to be a cumulative process, where individual studies contribute to the overall total knowledge base.

Results of individual studies can contribute something to the total, but it is the collection of results from many sources that matter in moving science forward or

in informing our decision-making process.

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Meta-analysis calculates “Effect size” ~ an index of how much change is evident across all the studies

Presentation of meta-analysis

R2 = 0.7524

-200

0

200

400

600

800

1000

1200

-10 0 10 20 30 40

Yield gain or loss (%)

Yiel

d ga

in o

r los

s (k

g ha

-1)

Effect size is presented here as,I. kg ha-1 gain or loss over no fungicide treatment from each

experimentII. number of effects (~data-points) used to create the estimate &III. 95% confidence intervals (CI) to determine the consistency

and reliability of the mean estimated effect size

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347 data-points; 88 Experiments; 16 growing seasons during 1982 to 201024 cultivars (YS resistance rating 2-5); sprayed 1-3 (few 4/5) with 13

fungicides, predominantly Folicur [tebuconazole](46% data-points & Tilt [propiconazole] 31% data-points)

90% data-points from northern agricultural region of Western Australian’s grain-belt

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What response did we derive?

0 0 0 0 0 0 0 0

17

10

20

27

36

30

39

25

32

10

18

9

1915

116 8

3 3 1 2 14

10

10

20

30

40-5

00-4

50-4

00-3

50-3

00-2

50-2

00-1

50-1

00 -50 0 50 100

150

200

250

300

350

400

450

500

550

600

650

700

750

800

850

900

950

1000

1050

Category of yield loss or gain (kg ha-1)

Freq

uenc

y226 - 278 kg ha-1 (CI 95%); Average 252 kg ha-1; Median = 200 kg ha-1

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0

100

200

300

400

1 2 3 4 5

Number of fungicide application

Yiel

d ga

in o

r los

s (k

g ha

-1)

Did the response vary between the numbers of fungicide application?

2 or 3 application > 1; Parallel lines = envelope of 95% CI of avg response

Folicur 50%Tilt 28%Z39 45%Z43 12%N=228

Folicur 72%

Z39/41/47Z39/61/65

N=43

Folicur 22%Tilt 56%

Z32, Z39, Z60

N=71 N=2 N=1

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Did the response vary between the growing seasons?

10/16 = similar; 3/16 = low; 3/16 = high; High response 1999 onwards

0

200

400

60019

82

1983

1991

1992

1993

1995

1996

1997

1998

1999

2000

2003

2004

2008

2009

2010

Year

Yiel

d ga

in o

r los

s (k

g ha

-1)

N=29

N=44

N=2

N=3

N=31

N=24

N=5

N=40

N=78

N=25

N=27

N=19

N=4

N=10

N=18

N=7

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Did the response vary between the cultivar resistance rating?

High response to very susceptible cultivars

0

100

200

300

400

1 2 3 4 5 6 7 8 9

Yellow spot resistance scale

Yiel

d ga

in o

r los

s (k

g ha

-1)

Resistance rating scale1:Extremely susceptible2:Very susceptible3:Susceptile4:Moderately susceptible5:Intermediate6:Moderately resistant7:Resistant8:Highly resistant9:Immune

N=89

N=172

N=35

N=42

N=6

N=3

10

0

100

200

300

400

Folicur Tilt

Fungicide

Yiel

d ga

in o

r los

s (k

g ha

-1)

Did the response vary between the fungicides?

Tilt appears to be responded better than Folicur

N=190

N=281

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0

10

20

30

40

50

60

1 Spray 2 Spray 3 Spray

Scenarios

A$

gain

or l

oss

(ha-1

) ove

r fu

ngic

ide

cost

s (in

clud

ing

spra

ying

, cro

p da

mag

e et

c)

“A difference, to be a difference, must make a difference” $ return makes the difference

Costs per spray (fungicide, application, crop damage etc): $20 ha-1

Farmgate price of wheat: 250 t-1

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N=228 N=72N=43

S

U

M

M

A

R

Y

252 kg ha-1

[226–277 kg ha-1 CI 95%]88 Experiments; 347 data-points; 16 seasons (1982 to 2010); 24 cultivars

(YS rating 2–5); sprayed 1-3 times with 13 fungicides, predominantly Folicur (46% data-points) & Tilt (31% data-points)

FUNGICIDE APPLICATION (NUMBER OF SPRAY)

S1: 195 kg ha-1 [N=227][168–222 kg ha-1 CI 95%]S2: 347 kg ha-1 [N=43][279–415 kg ha-1 CI 95%]S3: 368 kg ha-1 [N=72][302–434 kg ha-1 CI 95%]

GROWING SEASON

20%: 451 kg ha-1 [N=50][385–518 kg ha-1 CI 95%]60%: 281 kg ha-1 [N=168][245–318 kg ha-1 CI 95%]20%: 73 kg ha-1 [N=86][49–97 kg ha-1 CI 95%]

CULTIVAR RESISTANCE

YS 2: 319 kg ha-1 [N=89][266–372 kg ha-1 CI 95%]YS 3-5: 229 kg ha-1 [N=258][200–258 kg ha-1 CI 95%]

FUNGICIDE

Folicur: 227 kg ha-1 [N=281][200–254 kg ha-1 CI 95%]Tilt: 304 kg ha-1 [N=190][268–340 kg ha-1 CI 95%]

Between categories: same colour ~ similar at 95% CIWithin a category: double arrow ~ different at 95% CI

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Meta-analysis of Response of Yellow Spot and Stagonospora Nodorum Blotch (SNB)

in Wheat to Fungicides in Western Australia

ACPP-APPS, Darwin, 26-29 April 2011

We thank Australian Grains Research and Development Corporation (GRDC) for funding for this research through projects DAW00207

and DAW00210

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