Selection of Superior Genotypes of Coffea Canephora Pierre on ControlledHybrid Population Using Cluster Analysis Method

Selection of superior genotypes of robusta coffee (Coffea canephora) to improve its important agronomic characters should be conducted continuously to get better planting productivity. The aim of this research was to select superior genotypes of Robusta coffee for high yield and high proportion of...

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Bibliographic Details
Main Authors: Ucu Sumirat, Priyono, Surip Mawardi
Format: Article
Language:English
Published: Indonesian Coffee and Cocoa Research Institute 2007-05-01
Series:Coffee and Cocoa Research Journal
Online Access:http://www.ccrjournal.com/index.php/ccrj/article/view/90
Description
Summary:Selection of superior genotypes of robusta coffee (Coffea canephora) to improve its important agronomic characters should be conducted continuously to get better planting productivity. The aim of this research was to select superior genotypes of Robusta coffee for high yield and high proportion of large bean. Selection was conducted on controlled hybrid populations, developed from three crossing parental clones, i.e. BP 961 x Q 121 (A), BP 409 x Q 121 (B) and BP 961 x BP 409 (C). Selection was done by applying cluster analysis with complete linkage and Euclidean distance as the clustering method. The result of the research showed that the selection was successful to identify superior genotypes of Robusta coffee for high yield and high proportion of large bean. The parameters used (cherries weight/tree, bean weight/tree, bean size percentage > 6.5 mm and 100 cherries weight) were effective in clustering the superior genotypes, indicated by increased minimum and average value of population. Yield potential and percentage of bean size > 6.5 mm of those genotypes were having better performance than the control genotype and its parent. The selection code A 95, B 28, B 62, B 66, B 74 and C 38 were considered  as promising superior genotypes of Robusta coffee, respectively. Key words: Coffea canephora, selection, bean size, yield, cluster analysis
ISSN:0215-0212
2406-9574