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Multivariate analysis in weed science research

Published online by Cambridge University Press:  20 January 2017

N. C. Kenkel ,
D. A. Derksen ,
A. G. Thomas  and
P. R. Watson
D. A. Derksen
Affiliation:
Agriculture and Agri-Food Canada, P.O. Box 1000F, R.R. #3, Brandon, Manitoba, Canada R7A 5Y3
A. G. Thomas
Affiliation:
Agriculture Canada Research Station, 107 Science Cres., Saskatoon, Saskatchewan, Canada S7N 0X2
P. R. Watson
Affiliation:
Agriculture and Agri-Food Canada, P.O. Box 1000F, R.R. #3, Brandon, Manitoba, Canada R7A 5Y3
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Abstract

Data containing many variables are often collected in weed science research, but until recently few weed scientists have used multivariate statistical methods to examine such data. Multivariate analysis can be used for both descriptive and predictive modeling. This paper provides an intuitive geometric introduction to the more commonly used and relevant multivariate methods in weed science research, including ordination, discriminant analysis, and canonical analysis. These methods are illustrated using a simple artificial data set consisting of abundance measures of six weed species and two soil variables over 12 sample plots.

Keywords

Canonical correlation analysis (CANCOR)canonical correspondence analysis (CCA)canonical discriminant analysis (CDA)correspondence analysis (CA)nonmetric multidimensional scaling (NMDS)ordination analysis, principal component analysis (PCA)principal coordinate analysis (PCoA)redundancy analysis (RDA)statistical methods

Information

Type
Review
Information
Weed Science , Volume 50 , Issue 3 , June 2002 , pp. 281 - 292
Copyright
Copyright © Weed Science Society of America 

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