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ARS Home » Pacific West Area » Corvallis, Oregon » Forage Seed and Cereal Research Unit » Research » Publications at this Location » Publication #175656
Title: NEW APPLICATIONS OF STATISTICAL TOOLS IN PLANT PATHOLOGY

Author
item GARRETT, K - KANSAS STATE UNIVERSITY
item MADDEN, L - OHIO STATE UNIVERSITY
item HUGHES, G - UNIV OF EDINBURGH
item Pfender, William

Submitted to: Phytopathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/15/2004
Publication Date: 9/1/2004
Citation: Garrett, K.A., Madden, L.V., Hughes, G., Pfender, W.F. 2004. New applications of statistical tools in plant pathology. Phytopathology.v 94. p.999-1003.

Interpretive Summary: This paper is an introduction to a series of papers presented as a symposium on statistical applications in plant pathology. The topics addressed in the symposium include survival analysis, nonparametric analysis of disease associations, multivariate analyses, neural networks, meta-analysis, and Bayesian statistics. In this introductory paper, we present an overview of additional applications of statistics in plant pathology. These include methods that were formerly too complex for routine use but which are now easily accomplished, due to advances in computing power. Also, we briefly review some statistical tools from other fields that are finding use in plant disease epidemiology.

Technical Abstract: The series of papers introduced by this one addresses a range of statistical applications in plant pathology, including survival analysis, nonparametric analysis of disease associations, multivariate analyses, neural networks, meta-analysis, and Bayesian statistics. Here we present an overview of additional applications of statistics in plant pathology. These include the use of generalized linear models to appropriately deal with discrete responses, and new nonparametric approaches for analysis of ordinal data such as disease ratings. New or expanded computing packages, such as SAS PROC MIXED, coupled with extensive advances in statistical theory, allow for appropriate analyses of discrete data with generalized linear mixed models. Decision theory offers a framework in plant pathology for contexts such as the decision about whether to apply or withhold a treatment. Plant pathologists studying pathogens at the population level have traditionally been the main consumers of statistical approaches in plant pathology, but statistical applications to the study of the landscape of the field and the landscape of the genome share several issues, including the problem of pseudoreplication.