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Selection of inbred soybean progeny ( Glycine max ): an approach with population effect
Author(s) -
Volpato Leonardo,
Simiqueli Guilherme Ferreira,
Alves Rodrigo Silva,
Rocha João Romero do Amaral Santos de Carvalho,
Del Conte Murilo Viotto,
Resende Marcos Deon Vilela de,
Carneiro Pedro Crescêncio Souza,
Silva Felipe Lopes da
Publication year - 2018
Publication title -
plant breeding
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.583
H-Index - 71
eISSN - 1439-0523
pISSN - 0179-9541
DOI - 10.1111/pbr.12648
Subject(s) - biology , best linear unbiased prediction , selection (genetic algorithm) , population , index selection , genetic gain , microbiology and biotechnology , statistics , genetics , genetic variation , mathematics , demography , gene , artificial intelligence , sociology , computer science
To meet the growing demand of the soybean consumer market, cultivars increasingly early, productive and resistant to biotic and abiotic stress are sought. Several populations are obtained in soybean breeding programmes, but progeny are selected without being weighted for their respective population effect. As a consequence, progeny originating from high‐merit populations may be discarded too early. Given this scenario, this study proposes to employ the selection index with progeny and population effect via best linear unbiased prediction ( SIPP ‐ BLUP ) for the genetic selection of early and productive soybean progeny. A total of 180 progeny derived from three populations were evaluated for yield‐related traits. Genetic gains from selection, Spearman correlation and coincidence index were used to check the efficiency of the models with and without the population effect. The SIPP ‐ BLUP index achieved greater selection accuracy and was efficient in the identification and future selection of early soybean progeny. Therefore, this study demonstrates that soybean breeding programmes should consider the population effect via SIPP ‐ BLUP in progeny selection to obtain future lines that really contribute to genetic gain.