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http://www.alice.cnptia.embrapa.br/alice/handle/doc/1121764
Título: | New strategy for evaluating grain cooking quality of progenies in dry bean breeding programs. |
Autor: | CARVALHO, B. L.![]() ![]() RAMALHO, M. A. P. ![]() ![]() VIEIRA JÚNIOR, I. C. ![]() ![]() ABREU, A. de F. B. ![]() ![]() |
Afiliación: | BRUNA LINE CARVALHO, UFLA; MAGNO ANTONIO PATTO RAMALHO, UFLA; INDALÉCIO CUNHA VIEIRA JUNIOR, UFLA; ANGELA DE FATIMA BARBOSA ABREU, CNPAF. |
Año: | 2017 |
Referencia: | Crop Breeding and Applied Biotechnology, v. 17, n. 2, p. 115-123, Apr./June 2017. |
Descripción: | The methodology available for evaluating the cooking quality of dry beans is impractical for assessing a large number of progenies. The aims of this study were to propose a new strategy for evaluating cooking quality of grains and to estimate genetic and phenotypic parameters using a selection index. A total of 256 progenies of the 13thcycle of a recurrent selection program were evaluated at three locations for yield, grain type, and cooked grains. Samples of grains from each progeny were placing in a cooker and the percentage of cooked grains was assessed. The new strategy for evaluating cooking quality was efficient because it allowed a nine-fold increase in the number of progenies evaluated per unit time in comparison to available methods. The absence of association between grain yield and percentage of cooked grains or grain type indicated that it is possible to select high yielding lines with excellent grain aspect and good cooking properties using a selection index. |
Thesagro: | Feijão Phaseolus Vulgaris Melhoramento Genético Vegetal Índice de Seleção Seleção Recorrente |
NAL Thesaurus: | Beans Plant breeding Selection index Quantitative genetics Recurrent selection |
ISSN: | 1984-7033 |
DOI: | 10.1590/1984-70332017v17n2a18 |
Tipo de Material: | Artigo de periódico |
Acceso: | openAccess |
Aparece en las colecciones: | Artigo em periódico indexado (CNPAF)![]() ![]() |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
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CNPAF2017cbab.pdf | 593.31 kB | Adobe PDF | ![]() Visualizar/Abrir |