Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/43336
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dc.creatorMelo, Vinícius Lopes de-
dc.creatorMarçal, Tiago de Souza-
dc.creatorRocha, João Romero Amaral Santos de Carvalho-
dc.creatorAnjos, Rafael Silva Ramos dos-
dc.creatorCarneiro, Pedro Crescêncio Souza-
dc.creatorCarneiro, José Eustáquio de Souza-
dc.date.accessioned2020-10-06T21:21:40Z-
dc.date.available2020-10-06T21:21:40Z-
dc.date.issued2020-04-
dc.identifier.citationMELO, V. L. de et al. Modeling (co)variance structures for genetic and non-genetic effects in the selection of common bean progenies. Euphytica, [S.I.], v. 216, 2020. DOI: https://doi.org/10.1007/s10681-020-02607-9.pt_BR
dc.identifier.urihttps://doi.org/10.1007/s10681-020-02607-9pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/43336-
dc.description.abstractIn common bean breeding programs, experiments are conducted in different environments to select plants with high potential for inbred lines extraction and/or recombination. The occurrence of genetic and/or statistical unbalance is common in these experiments. Moreover, there may be (co)variance between genetic and non-genetic effects when treatments are assessed in different environments. Our aim was to (1) test different (co)variance structures between seasons for genetic and non-genetic effects; (2) choose the model with the highest predictive capacity of the genotypic value; and (3) select the superior progenies to mitigate the effects of genotype-by-environment interactions. To this end, two experiments were conducted in the 2015 drought and winter seasons. The grain yield and grain aspect were assessed. Model 4, with an unstructured (co)variance for genetic effects, homogeneous block variance, and heterogeneous residual diagonal variance, was the model that best fit the data. The heritability estimates and their accuracy differed between the different adjusted models, with the most accurate estimates observed in model 4. The genetic correlation between the drought and winter seasons was of low magnitude (− 0.04) for grain yield, which corroborates the strong genotype by environment interaction. The average gain predicted with the recombination of the selected progenies in model 4 was 2.97% for grain yield. The modeling of different (co)variance structures for genetic and non-genetic effects could be applicable for analyses involving statistical unbalance and the assessment of progenies in different environments, with the aim of selecting those with high potential for recombination.pt_BR
dc.languageenpt_BR
dc.publisherSpringer Naturept_BR
dc.rightsrestrictAccesspt_BR
dc.sourceEuphyticapt_BR
dc.subjectMixed modelspt_BR
dc.subjectPhaseolus vulgarispt_BR
dc.subjectG × E interactionpt_BR
dc.subjectRecurrent selectionpt_BR
dc.subjectFeijão - Progêniept_BR
dc.subjectModelos mistospt_BR
dc.subjectInteração gene-ambientept_BR
dc.subjectSeleção recorrentept_BR
dc.titleModeling (co)variance structures for genetic and non-genetic effects in the selection of common bean progeniespt_BR
dc.typeArtigopt_BR
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