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PCA Based Multivariate Statistical Diversity Analysis for Grain Yield and its Components in Wheat (Triticum aestivum L.)

AKSHAY HULLE, NIDHI DUBEY, HARSHAL AVINASHE*, J. PRANAY REDDY, SAGAR CHORMULE AND SHARAD SACHAN
Department of Genetics and Plant Breeding, Lovely Professional University, Phagwara-144 411 (Punjab), India
*(e-mail: havinashe@gmail.com; Mobile: 90567 87045)
(Received: February 21, 2023; Accepted: March 25, 2023)

ABSTRACT

The research work investigated the genetic diversity of 20 lines seeded in RCBD with three replications and were evaluated for 15 phenotypic characteristics at the Agriculture Research Farm, Lovely Professional University, Phagwara (Punjab). According to principal component analysis (PCA), six of the 15 (PC1 to PC6) PCs had eigen values above 1.0 and a cumulative variance of around 87.1%. These were number of productive tillers per plant, peduncle length, number of ears per plant, biological yield per plant, plant height and grain yield per plant. On this foundation of high PC1 scores, the most notable lines, MACS-6145, HI-1500 and CHIRYA-3 were selected for yield components. The outcomes of this investigation might be used as a foundation for defining and implementing subsequent wheat breeding initiatives.
Key words : Bread wheat, PCA, eigen value