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Assessment of Genetic Variation in Hairy Vetch Using Canonical Discriminant Analysis


This article in CS

  1. Vol. 44 No. 1, p. 185-189
    Received: Oct 28, 2002

    * Corresponding author(s): gbollero@uiuc.edu
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  1. Kathleen M. Yeater,
  2. German A. Bollero *,
  3. Donald G. Bullock,
  4. A. Lane Rayburn and
  5. Sandra Rodriguez-Zas
  1. Dep. of Animal Sciences, Univ. of Illinois, 1102 S. Goodwin Ave., Urbana, IL 61801


For improvement of hairy vetch (Vicia villosa Roth) as a winter cover crop, it is necessary to gain insight into the magnitude of variability present in the species. This study was conducted to assess the sources of genetic and phenotypic variability of V. villosa accessions. Thirteen morphological and agronomic traits were measured on 42 populations of V. villosa and two populations of V. pannonica Crantz in field studies at Urbana, IL, for 2 yr. These measurements include initial seed weight, germination, stem length, stem width, leaf length, and leaf width in the fall and spring, winter survival, biomass, and a ratio of organic C to total N (C:N ratio) of the plants. The multivariate data set was analyzed by canonical discriminant analysis (CDA) in combination with a clustering procedure. In this analysis, the first two canonical variates were significant and accounted for 94% of the among-accession variability. The canonical variates indicated that fall and spring measurements of the leaf and stem, and C:N ratio are the most differentiating traits among the accessions. The canonical variates were used to cluster the accessions into four subgroups on the basis of the differentiating traits. Canonical discriminant analysis was useful in identifying the genetic variation and the traits that better describe the variation among hairy vetch populations. Cluster analysis was successful in differentiating the accessions into similar subgroups on the basis of the measured traits. Plant breeders can use the information on variation among Vicia accessions and focus on traits of particular significance.

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