Cluster and principal component analysis for the selection of maize (Zea mays L.) genotypes
Int. J. Exp. Res. Rev., Vol. 9: 5-10 (2017)
Author: Pratima Pahadi, Manoj Sapkota, Dhruba Bahadur Thapa and Shreena Pradhan
Abstract: Breeding for high yield crop requires information on the nature and magnitude of variation in the available materials, relationship of yield with other agronomic characters and the degree of environmental influence on the expression of these components characters.This study was conducted with the aim of identifying better performing maize genotypes and related traits with the help of principal component analysis and cluster analysis of major quantitative traits of the crop. Six genotypes of maize were tested and observed for days to tasseling, days to silking, days to pollen shed anthesis, ear height, silk length, plant height, ear length, ear circumference, number of kernel row per ear, number of kernel per row, five hundred kernel weight and grain yield.The first two components that explained 73.7% of the total variation were determined from Principal component analysis and were used for clustering genotypes. Second cluster comprising of four genotypes namely Rampur Yellow, CP808, Khumal Yellow and Rajkumar, had higher value of traits like number of kernel row per ear, number of kernel per row, and grain yield. The selection from the second cluster can be considered worthwhile as it has genotypes performing better in terms of yield and yield attributing characters and can be used for breeding purpose of hybrids.
Key words: Maize, PCA, Cluster analysis, selection, yield.
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