Application of canonical discriminant analysis, principal component analysis, and canonical correlation analysis as tools for evaluating differences in pasture botanical composition

Abstract

Abstract The application of three multivariate analysis techniques (canonical discriminant analysis (CDA), principal component analysis (PCA), and canonical correlation analysis (CCA)) for evaluation of pasture botanical composition data is illustrated and discussed. CDA and PCA were used to describe differences in pasture botanical composition for different microsites within a pasture near Palmerston North, New Zealand. CCA could not be validly applied to this data set because a sampling strategy inappropriate for CCA had been used to collect the data. However, CCA is conceptually ideal for determining association between two groups of variables and CCA was used for a second data set from the Hawkes Bay region to establish association between differences in pasture botanical composition and differences in environmental variables. CCA identified a transition from white clover (Trifolium repens L.) to subterranean clover {Trifolium subterraneum L.) presence associated with decreasing rainfall, and a similar transition from ryegrass presence to browntop presence associated with altitude. There are practical difficulties in obtaining a suitable data set for canonical correlation, but with attention to sampling strategy a more precise definition of effect of environmental factors on pasture botanical composition would be expected.