TITLE:
Multivariate Cluster and Principle Component Analyses of Selected Yield Traits in Uzbek Bread Wheat Cultivars
AUTHORS:
Shokista Sh. Adilova, Dilafruz E. Qulmamatova, Saidmurad K. Baboev, Tohir A. Bozorov, Aleksey I. Morgunov
KEYWORDS:
Bread Wheat, Principal Component Analysis, Dispersion, Cluster Analysis, Grain Yield, Spike Number Per Square Meter, Drought Stress, Thousand-Kernel Weight
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.11 No.6,
June
30,
2020
ABSTRACT: Investigation of genetic
diversity of geographically distant wheat genotypes is a useful approach in wheat breeding providing
efficient crop varieties. This article presents multivariate cluster and
principal component analyses (PCA) of some yield traits of wheat, such as
thousand-kernel weight (TKW), grain number, grain yield and plant height. Based
on the results, an evaluation of economically valuable attributes by
eigenvalues made it possible to determine the components that significantly
contribute to the yield of common wheat genotypes. Twenty-five genotypes were
grouped into four clusters on the basis of average linkage. The PCA showed four
principal components (PC) with eigenvalues > 1, explaining approximately 90.8% of the total
variability. According to PC analysis, the variance in the eigenvalues was the greatest (4.33) for PC-1, PC-2 (1.86) and PC-3
(1.01). The cluster analysis revealed the classification of 25 accessions into
four diverse groups. Averages, standard deviations and variances for clusters
based on morpho-physiological traits showed that the maximum average values for
grain yield (742.2), biomass (1756.7), grains square meter (18,373.7), and grains per spike (45.3) were higher in
cluster C compared to other clusters. Cluster D exhibited the maximum
thousand-kernel weight (TKW) (46.6).