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Sanogo2004_Overview - Symposium New Applications of...

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1004 PHYTOPATHOLOGY Symposium New Applications of Statistical Tools in Plant Pathology Overview of Selected Multivariate Statistical Methods and Their Use in Phytopathological Research S. Sanogo and X. B. Yang First author: Department of Entomology, Plant Pathology, and Weed Science, New Mexico State University, Las Cruces 88003; and second author: Department of Plant Pathology, Iowa State University, Ames 50011. Accepted for publication 16 May 2004. ABSTRACT Sanogo, S., and Yang, X. B. 2004. Overview of selected multivariate statistical methods and their use in phytopathological research. Phyto- pathology 94:1004-1006. To disentangle the nature of a pathosystem or a component of the system such as disease epidemics for descriptive or predictive purposes, mensuration is conducted on several variables of the physical and chemi- cal environment, pathogenic populations, and host plants. For instance, it may be desired to (i) distinguish pathogenic variation among several isolates of a pathogen based on disease severity; (ii) identify the most im- portant variables that characterize the structure of an epidemic; and (iii) assess the potential of developing regional scale versus site-specific post- management schemes using weather and site variation. In all these cases, a simultaneous handling of several variables is required, and entails the use of multivariate statistics such as discriminant analysis, multivariate analysis of variance, correspondence analysis, and canonical correlation analysis. These tools have been used to varying degree in the phyto- pathological literature. A succinct overview of these tools is presented with cited examples. The dogma of plant pathology, which states that disease is the result of interactive effects of environment, pathogen, and host plant, provides upfront a stage for handling several variables to characterize disease epidemics. Plant pathologists routinely under- take various activities in order to describe and predict (i) disease risks based on climatic variables or changes in host plant and/or pathogen populations, (ii) the impact of anthropogenic activities (farming practices such as cropping systems and management schemes) on the magnitude of diseases, and (iii) preference or perception of producers on new products and technologies for disease management. These activities entail collection of data on many variables. With the development of computers and auto- matic digital data recorders, the amount of data and number of variables are increasing dramatically. Plant pathologists working in areas such as epidemiology, pathogen ecology, pathogen popu- lation biology, and disease management are challenged in un- covering patterns in multivariable data. Statistical methods are available for analyzing data comprised of multiple variables (7,9,11,18), and encompass three major tools (Table 1): (i) ordination, which aims at describing data by identifying a reduced data dimension of a few variables that ac- count for the greatest amount of variability in the data; (ii) dis- crimination, which aims at delineating experimental groups or
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