Título:	Yield, maturation cycle, and estimates of genetic parameters of Robusta coffee genotypes under irrigation in the Cerrado
Autor(es):	Santin, Mateus Rollemberg Coelho, Mateus Costa Sayd, Ricardo Meneses Peixoto, José Ricardo Amabile, Renato Fernando
ORCID:	http://orcid.org/0000-0001-5861-8004
Assunto:	Repetibilidade Variância fenotípica Café - cultivo Café
Data de publicação:	2019
Editora:	Crop Breeding and Applied Biotechnology
Referência:	SANTIN, Mateus Rollemberg et al. Yield, maturation cycle, and estimates of genetic parameters of Robusta coffee genotypes under irrigation in the Cerrado. Crop Breeding and Applied Biotechnology, v. 19, n. 4, p. 387-394, 2019. DOI: https://doi.org/10.1590/1984-70332019v19n4a55. Disponível em: http://scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332019000400387. Acesso em: 23 jan. 2020.
Abstract:	Eighty-five Coffea canephora genotypes were evaluated for three crop seasons under irrigation in the Cerrado of the Distrito Federal, Brazil, to characterize the population regarding phenotype, yield and maturation cycle. The experiment was conducted through the Basic Model of Repeatability without Design methodology. Regarding the cycles, genotypes were divided into four groups with the following periods, in days, from resumption of irrigation to the cherry stage: very early (243-255), early (256-267), medium (268-280), and semi-late (281-293). For yield, the repeatability obtained was approximately 33%, a medium value that represents the proportion of the permanent phenotypic variance in relation to the total phenotypic variance. The average repeatability obtained surpassed 59%, and accuracy, with three crop seasons, reached 77%. The selection gains obtained may be higher than 38%. There are promising genotypes for cultivation in the region under study. The mean repeatability and the accuracy obtained favor selection based on phenotypes.
Unidade Acadêmica:	Faculdade de Agronomia e Medicina Veterinária (FAV)
Licença:	(CC BY) - This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOI:	https://doi.org/10.1590/1984-70332019v19n4a55
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