Multivariate distinguishing of phenotypically similar groups of genotypes in a winter wheat (Triticum aestivum L.) working germplasm collection
Joanna Ukalska
joanna_ukalska@sggw.edu.plZakład Biometrii, Katedra Ekonometrii i Statystyki, Szkoła Główna Gospodarstwa Wiejskiego (Poland)
Krzysztof Ukalski
Zakład Biometrii, Katedra Ekonometrii i Statystyki, Szkoła Główna Gospodarstwa Wiejskiego (Poland)
Tadeusz Śmiałowski
Zakład Roślin Zbożowych, Instytut Hodowli i Aklimatyzacji Roślin w Krakowie (Poland)
Abstract
The investigations were conducted on part of winter wheat working collection from the Plant Breeding and Acclimatization Institute, the Department of Cereals Crops in Cracow. Fifty-one genotypes (cultivars and clones) were evaluated in the years 1999–2002. Yield structure traits and susceptibility to the most important winter wheat diseases were assessed. The aim of the study was to classify the genotypes into homogeneous groups (clusters), identify the traits having the highest discriminative power in separating these groups, and characterize the genotypic diversity of the distinguished clusters for the examined traits. Hierarchical cluster analysis was carried out using Ward’s procedure and squared Euclidean distance. The final number of groups (clusters) was obtained on the basis of the pseudo t2 statistic which indicating a possibility to classify the genotypes in 3, 5 or 8 groups. For each division the MANOVA and Canonical Variable Analysis (CVA) were carried out on the basis of Mahalanobis distance. Finally, the genotypes were classified into five homogeneous groups, which including 4 to 22 objects. The three first canonical variables accounted for 82% of the total variation between groups. The following traits were found to have the highest discriminative power: plant height, lodging score, 1000-grain weight, weight per spike, number of grains per spike, number of days to heading, powdery mildew score and protein content.
Keywords:
Triticum aestivum L., canonical variable analysis, cluster analysis, germplasm collection, multivariate analysis of variance, phenotypic diversityReferences
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Authors
Joanna Ukalskajoanna_ukalska@sggw.edu.pl
Zakład Biometrii, Katedra Ekonometrii i Statystyki, Szkoła Główna Gospodarstwa Wiejskiego Poland
Authors
Krzysztof UkalskiZakład Biometrii, Katedra Ekonometrii i Statystyki, Szkoła Główna Gospodarstwa Wiejskiego Poland
Authors
Tadeusz ŚmiałowskiZakład Roślin Zbożowych, Instytut Hodowli i Aklimatyzacji Roślin w Krakowie Poland
Statistics
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