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Mark K. Ehlenfeldt, James J. Polashock, Allan W. Stretch and Matthew Kramer

understanding of the data. The method we developed produces stable rankings that match our intuitive understanding of the data and also produces estimates of uncertainty about the ranking. The method is based on principal component analyses and resampling

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Amy F. Iezzoni and Marvin P. Pritts

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R.G. Fjellstrom, D.E. Parfitt and G.H. McGranahan

RFLP markers were used to investigate genetic diversity among California walnut (Juglans regia) cultivars and germplasm collected worldwide. Sixteen of 21 RFLP markers were polymorphic in the 48 walnut accessions tested. RFLP markers were useful for identifying walnut cultivars. All genotypes were heterozygous at ≈20% of the loci for both California and worldwide germplasm. California walnut germplasm contained 60% of the worldwide allelic diversity. Cluster analysis of genetic distance between accessions and principal component analysis of allelic genotypes showed two major groups of walnut domestication. California germplasm was associated with germplasm from France, central Europe, and Iran and had less genotypic similarity with germplasm from Nepal, China, Korea, and Japan.

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Md. Aktar Hossain, Sooah Kim, Kyoung Heon Kim, Sung-Joon Lee and Hojoung Lee

was set to 60,000 to measure the masses of the compounds. Data sets organized in matrix form were subsequently exported to SIMCA-P software (Version 11.5; Umetrics, Umea, Sweden) for principal component analysis (PCA). PCA is an unsupervised

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Esther Giraldo, Margarita López-Corrales and José Ignacio Hormaza

selected variable categories was also reduced by the Pearson correlation coefficient and by principal components analysis (PCA) ( Iezzoni and Pritts, 1991 ). These analyses were performed on the correlation matrix obtained with the frequencies, turning the

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Michel Génard and Claude Bruchou

Abbreviation: PCA, principal component analysis. 1 Station de Biométrie. We thank F. Lescourret, L. Pages, and reviewers for helpful comments on this paper, and S. Hamilton and M. Jones for improving the English translation. The PACA Region is also

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Omar Franco-Mora, Edgar Jesús Morales-Rosales, Andrés González-Huerta and Juan Guillermo Cruz-Castillo

with the method of Pearson; then, only the highly correlated descriptors were selected and used to perform a cluster analysis by the unweighted pair group method with arithmetic mean. Finally, a principal component analysis was performed, and the

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Hrvoje Rukavina, Harrison Hughes and Randy Johnson

trait of interest presented a function of three random factors: source locations, clones nested within locations, and blocks or replicates. Principal component analysis (PROC PRINCOMP) was performed on the data set for leaf length, canopy height, and

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S. Pérez, S. Montes and C. Mejía

weight ratio; GH, growth habit; GP, germination period; HS, harvest season; La, leaf area; NW, nodes with zero buds: Nl b, nodes with one bud; N2b. nodes with two buds; N3b. nodes with three buds; P, productivity; PCA, principal component analysis: Prec

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Timothy K. Broschat

being severe and 5 being completely free of deficiency symptoms for that element. Because plant size variables, such as height, width, and stem caliper, are typically highly intercorrelated, principal component analysis was performed on the data to