PRINCIPAL COMPONENT ANALYSIS IN SWEET CORN (ZEA MAYS L. SACCHARATA.)
A. THANGA HEMAVATHY*
Department of Pulses,
TNAU, Coimbatore-641 003 (Tamil Nadu), India
*(e-mail : email@example.com)
(Received : 21 March 2020; Accepted : 30 March 2020)
An experiment was conducted in 26 genotypes of sweet corn to study Principal Component Analysis at Department of Millets, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore during kharif, 2014. Eight quantitative and five qualitative characters were taken under observation to estimate substantial variation and relationship among sweet corn genotypes to identify the best performing lines. Analysis of variation for such quantitative traits in diverse line showed considerable and dissimilar level of variability. Green cob yield was highly significant and positively correlated with green cob length (0.410), green cob girth (0.579) and number of kernel rows per cob (0.421) however total sugar, sucrose and starch was non-significant negatively correlated with green cob yield. Principal component analysis showed the amount of variation by principal components as 1 to 6. Clustering analysis based on various morphological traits assorted 26 sweet corn genotypes into eight clusters. Dendrogram based on hierarchal clustering grouped genotypes based on their morphological traits rather than geographic origin. The diverse genotypes will be used for future breeding programme.
Key words:Sweet corn, principal component analysis, clustering, diversity