Multivariate analysis of phenotypic diversity in Polish spring wheat collection
Marcin Studnicki
marcin_studnicki@sggw.edu.plKatedra Doświadczalnictwa i Bioinformatyki, SGGW w Warszawie (Poland)
Wiesław Mądry
Katedra Doświadczalnictwa i Bioinformatyki, SGGW w Warszawie (Poland)
Tadeusz Śmiałowski
Instytut Hodowli i Aklimatyzacji Roślin, Zakład Roślin Zbożowych w Krakowie (Poland)
Abstract
The success of crop improvement programs depends among others on the knowledge of both phenotypic and genetic diversity within the maintained plant genetic resources and from the degree of their utilization. The use of multivariate statistical methods is an important strategy for assessment of within- collection diversity, classification and typology of accessions. The purpose of the paper was to evaluate phenotypic diversity for selected quantitative agronomic traits in a spring wheat collection (so-called “working” collection) maintained by the Department Unit of Cereal Breeding and Quality Evaluation of the Plant Breeding and Acclimatization Institute in Kraków. A field trial including 149 accessions (released cultivars and advanced breeding lines) was conducted across three seasons (1996–1999) in Kończewice, Kujawy-Pomerania province. In each year 12 agronomic quantitative traits were evaluated. The data were put into incomplete two-way accession × year classification for each trait. Two complementary multivariate methods including cluster analysis and principal component analysis were used. The first four principal components captured 67% of the total multivariate variability among the accessions. The first principal component accounted for 24% of the total variability and it was mostly correlated with grain yield and weight of grain per ear as the traits with the largest contribution to the multivariate variability among the accessions. The accessions were grouped into nine clusters based on Ward’s clustering method. The clusters (groups) were then characterized with regard to all the studied traits, and multivariate similarities among the groups were described using the biplot in principal component analysis.
Keywords:
BLUP, agronomic traits, cluster analysis, genetic resources, phenotypic diversity, principal component analysis, spring wheatReferences
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Authors
Marcin Studnickimarcin_studnicki@sggw.edu.pl
Katedra Doświadczalnictwa i Bioinformatyki, SGGW w Warszawie Poland
Authors
Wiesław MądryKatedra Doświadczalnictwa i Bioinformatyki, SGGW w Warszawie Poland
Authors
Tadeusz ŚmiałowskiInstytut Hodowli i Aklimatyzacji Roślin, Zakład Roślin Zbożowych w Krakowie Poland
Statistics
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