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Research Article Open access CC BY 4.0

Genotypes X Traits Biplot for Multivariate Analysis of Yield and Contributing Traits Wheat (Triticum aestivum L.) Genotypes

Divya Phougat, Ajay Verma, Sheetal Choudhary

International Journal of Environment and Climate Change · pp. 3543–3553 · Published 2 Nov 2022

10.9734/ijecc/2022/v12i111403

Abstract

Eighty two diverse wheat genotypes have been evaluated in Research area of Wheat and Barley Section, CCS HAU trials at CCSHAU during 2021-22 cropping seasons in augmented design with four established checks replicated five times each. Highest average value recorded for grain yield per plot followed by days to maturity while least value expressed by spike length (cm). Of course lowest error also reported by spike length, whereas extreme large by grain yield per plot. Number of wheat genotypes in non-significant groups had observed 54, 12 8, 41, 6, 48, 6 genotypes  for days to heading, days to maturity, plant height, tillers per meter row length, grains per spike, thousands grains weight, spike length, grain yield per plot respectively. First two principal components explained 51.6% of the total phenotypic variation. Biplot analysis expressed high positive correlation of days to heading with spike length, grains per spike, plant height. Positive association also observed between days to maturity with thousands grains weight, grains per spike and grain filling period, as plant height showed with spike length grain yield per plot, TGW. DWAP1108, GW1348, DWAP1608, HS 628, MACS 5052 observed far from other wheat genotypes.  Multivariate hierarchical clustering of genotypes based on Ward’s method observed nine different clusters based on important morphological traits. First group consisted of three genotypes (G1, G8, G82) while the last one contained nine genotypes (G70, G81, G74, G76, G77, G78, G73, G80, G75). Only three genotypes G4, G6, G19 placed in third cluster whereas largest cluster of twenty three genotypes was the fifth one.

Divergence yield contributing traits multivariate clustering biplot analysis

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