Affiliation(s)
1. Institute of Agricultural Research for Development, Specialized Centre for Oil Palm Research (CEREPAH-IRAD), P.O. Box 243, Douala, Cameroon
2. Department of Agronomy and Horticulture, Bogor Agricultural University (IPB), Bogor 16680, Indonesia
ABSTRACT
In this study, the multivariate
tools, namely principal
component analysis (PCA) and cluster analysis, were used to classify
and measure the pattern of genetic diversity and evaluate the correlation of
nine oil palm traits in 25 progenies. Fresh
fruit bunch weight (FFB), kernel to
fruit (K/F) and kernel to bunch (K/B) ratios showed significant variance, while
bunch number (BN), kernel yield (KY) and oil
yield (OY) showed little variance. Positive significant correlation between
these traits and yield was appreciated through PCA, where 90.55% of the variation was explained by the first three principal
components. Progeny grouping was
performed and revealed three clusters of oil
palm progenies. Cluster I contained progenies with high production of FFB, BN, OY and KY, while low height
increment (HI) of palm trees was
found in cluster II. However, most of
progenies with high mean values of bunch spikelet weight (SpW), average fruit weight
(AFW), K/F and K/B were grouped in cluster III. This
grouping could help oil palm breeders to identify progenies with the traits of
interest for breeding and commercial seed production.
KEYWORDS
Oil palm, Elaeis guineensis Jacq., cluster analysis, correlation, genetic diversity, principal component
analysis.
Cite this paper
References