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 Loughman Department of Agriculture & Food, Western Australia (DAFWA) 1
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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
Kawsar P. Salam, Geoff J. Thomas,Moin U. Salam, Ciara Beard,
William J. MacLeod & Robert LoughmanDepartment of Agriculture & Food, Western
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
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0
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-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