Skip to main content
ARS Home » Plains Area » Manhattan, Kansas » Center for Grain and Animal Health Research » Hard Winter Wheat Genetics Research » Research » Publications at this Location » Publication #166986
Title: META-ANALYSIS IN PLANT PATHOLOGY: SYNTHESIZING RESEARCH RESULTS

Author
item ROSENBERG, M - BIOLOGY - ARIZONA ST. UN
item GARRETT, K - PLANT PATH - KSU
item SU, Z - PLANT PATH - KSU
item Bowden, Robert

Submitted to: Phytopathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/16/2004
Publication Date: 8/1/2004
Citation: Rosenberg, M.S., Garrett, K.A., Su, Z., Bowden, R.L. 2004. Meta-analysis in plant pathology: synthesizing research results. Phytopathology. 94:1013-1017.

Interpretive Summary: In studies of such things as field treatments or management practices designed to prevent various types of plant diseases or insect infestations in agricultural crops, one often finds that several different investigators have tested similar treatments using different experimental conditions. Meta-analysis is a set of mathematical procedures for combining the results from several individual studies to obtain a more complete picture of the overall impact of a particular treatment or management practice. For example, certain fungicide treatments have been tested by different investigators over many years for control of leaf rust in wheat. Meta-analysis can be used to combine the results from these studies and examine the relationships between treatments, disease severity, and yield losses. This paper provides a general review of meta-analysis and gives a specific example of how it can be applied.

Technical Abstract: Meta-analysis is a set of mathematical procedures for synthesizing research results from a number of different studies. An estimate of a statistical effect, such as the difference in disease severity for plants with or without a management treatment, is collected from each study along with a measure of the variance of the estimate of the effect. Combining results from different studies will generally result in increased statistical power so that it is easier to detect small effects. Combining results from different studies may also make it possible to compare the size of the effect as a function of other predictor variables such as geographic region or pathogen species. We present a review of the basic methodology for meta-analysis. We also present an example meta-analysis of the relationship between disease severity and yield loss for foliar wheat diseases based on data collected from a decade of Fungicide and Nematicide Tests results.