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Selection for bean and liquor qualities within related hybrids of Arabica coffee in multilocal field trials

Agwanda C.O., Baradat P., Eskes A., Cilas C., Charrier A.. 2003. Euphytica, 131 (1) : p. 1-14.

DOI: 10.1023/A:1023071815109

The success of a new variety of Arabica coffee (Coffea arabica L.) depends to an important extent on its liquor and bean qualities. Selection for these traits is however constrained by the prevalence of large genotype-by-environment (GxE) interactions in conjunction with the low genetic variability characteristic of this species. The aim of this study was to assess the extent to which key bean and liquor traits are affected by the environments and could be combined in order to improve the efficiency of selection for coffee quality in a narrow genetic basis set of hybrids obtained from a set of related lines and collectively representing the Ruiru 11 cultivar. Twenty-one full-sib families representative of this hybrid cultivar grown in Kenya in five sites exhibiting strong edaphic and climatic differences were used for the study. Rainfall amounts during various phases of berry development were used to explain the differences observed in the discriminating abilities of the locations for bean and liquor traits. The results showed that these families were best differentiated for bean sizes in the site where moisture supply was optimal throughout berry expansion and tilling stages, whereas discrimination on the basis of liquor traits were best observed in the site where moderate moisture stress occurred during bean filling stage. The overall precision in prediction of family values was low for liquor qualities; but a much more efficient selection for large bold beans, optimally based on the AA grade was shown to be possible. Selection indices for family selection were computed to realize a trade-off between genetic gains in bean size and liquor flavour.

Mots-clés : coffea arabica; hybride; intéraction génotype environnement; fève de café; café arabica; qualité; critère de sélection; technique de prévision; facteur climatique; facteur lié au site; kenya

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