For users of germplasm collections, the purpose of measuring characterization and evaluation descriptors, and subsequently using statistical methodology to summarize the data, is not only to interpret the relationships between the descriptors, but also to characterize the differences and similarities between accessions in relation to their phenotypic variability for each of the measured descriptors. The set of descriptors for the accessions of most germplasm collections consists of both numerical and categorical descriptors. This poses problems for a combined analysis of all descriptors because few statistical techniques deal with mixtures of measurement types. In this article, nonlinear principal component analysis was used to analyze the descriptors of the accessions in the Australian groundnut collection. It was demonstrated that the nonlinear variant of ordinary principal component analysis is an appropriate analytical tool because subspecies and botanical varieties could be identified on the basis of the analysis and characterized in terms of all descriptors. Moreover, outlying accessions could be easily spotted and their characteristics established. The statistical results and their interpretations provide users with a more efficient way to identify accessions of potential relevance for their plant improvement programs and encourage and improve the usefulness and utilization of germplasm collections.

Key Words
Genetic diversity; Mixture of data types; Ordinal data; Oleic-linoleic ratio; Ordination; Arachis hypogaea L.

P.M. Kroonenberg is Associate Professor, Department of Education, Leiden University, Wassenaarseweg 52, Leiden, The Netherlands. B.D. Harch is Statistician, CSIRO, Mathematical & Information Sciences, Private Bag 2, Glen Osmond, SA 5064, Australia. K.E. Basford is Associate Professor, Department of Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia. A. Cruickshank is Peanut Breeder, J. Bjelke-Petersen Research Station, P.O. Box 23, Kingaroy Qld 4610, Australia.