Vol. 7, Special Issue 1 (2018)
Principal component analysis in Turmeric (Curcuma longa .L)
Author(s): Rahul Kumar Verma, Preeti Kumari, Vijay kumar, RB Verma, Nisha Rani and Rajesh Kumar
Abstract: Turmeric is one of most important and common spice crop all over the world. It is a cross-pollinated, triploid species, which can be propagated through vegetative mean using its underground rhizomes. Since hybridization is ineffective in most cases, genetic improvement is often limited to germplasm selection and mutation breeding. Principal component analysis (PCA) is a useful tool in analyzing genetic variation among the accessions and determining the most important variables contributing to this variation in diversity analysis. This is an important step in evaluating the materials as genetic resources for breeding programs. In the current study, Eighty three geonotypes of turmaric collected from different areas were analyzed for phenotypic traits using PCA, and then they were grouped by cluster analysis based on principal components from PCA. The total genotypes were grouped into 10 different clusters on the basis of principle component analysis. Among the different clusters, cluster III consists the maximum number of genotypes followed by cluster IV. The highest intra–cluster distance was recorded for cluster 7 (8941.217) followed by cluster IV (6392.287), while highest inter cluster distance is found between cluster IX and VII. The genotypes of the distant cluster could be used as potential source for obtaining wide range of variation among the segregates and crop improvement programmes to produce populations with wider variability with transgressive segregates possessing high yielding
How to cite this article:
Rahul Kumar Verma, Preeti Kumari, Vijay kumar, RB Verma, Nisha Rani and Rajesh Kumar. Principal component analysis in Turmeric (Curcuma longa .L). 2018; 7(1S): 1097-1101.