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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
02/05/2022 |
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Data da última atualização: |
14/06/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OLIVEIRA, L. A. de; SILVA, C. P. da; SILVA, A. Q. da; MENDES, C. T. E.; NUVUNGA, J. J.; NUNES, J. A. R.; PARRELLA, R. A. da C.; BALESTE, M.; BUENO FILHO, J. S. de S. |
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Afiliação: |
LUCIANO ANTONIO DE OLIVEIRA, Universidade Federal da Grande Dourados; CARLOS PEREIRA DA SILVA, Universidade Federal de Lavras; ALESSANDRA QUERINO DA SILVA, Universidade Federal da Grande Dourados; CRISTIAN TIAGO ERAZO MENDES, Universidade Federal de Lavras; JOEL JORGE NUVUNGA, Universidade Eduardo Mondlane; JOSÉ AIRTON RODRIGUES NUNES, Universidade Federal de Lavras; RAFAEL AUGUSTO DA COSTA PARRELLA, CNPMS; MARCIO BALESTE, Universidade Federal de Lavras; JÚLIO SÍLVIO DE SOUSA BUENO FILHO, Universidade Federal de Lavras. |
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Título: |
Bayesian GGE model for heteroscedastic multienvironmental trials. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Crop Science, v. 62, p. 982-996, 2022. |
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DOI: |
https://doi.org/10.1002/csc2.20696 |
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Idioma: |
Inglês |
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Conteúdo: |
The dissection of genotype×environment interaction (GEI) is a crucial aspect ofthe final stages of plant breeding pipelines and recommendation of cultivars. Linear-bilinear models used to analyze this interaction, such as the additive main effectsand multiplicative interaction (AMMI) and genotype plus GEI (GGE), often assumehomogeneity of the residual variances across environments which affects the esti-mates and therefore, interpretations and conclusions. Our main objective was topropose a GGE model that considers heteroscedasticity across environments usingBayesian inference and to evaluate its implications in the interpretation of real andsimulated data. The GGE model assuming common variance was also fitted for com-parison purposes. The great flexibility of the Bayesian inference is transferred to thebiplots, allowing the construction of credible regions for genotypic and environmen-tal scores. The inference on the stability and adaptability of genotypes might changewhen heteroscedasticity is ignored. When real data are used, different patterns of cor-relations between environments also affect the representativeness and discriminationof the target environment. The modeling of heteroscedasticity allowed the clusteringof environments into subgroups, with similar effects for GEI. The proposed GGEmodel was more adequate and realistic to deal with scenarios of heterogeneous vari-ance in multienvironment trials, which can be useful for exploiting the GEI. |
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Palavras-Chave: |
Ensaio de cultivar; Ensaio de rendimento; Estabilidade; Interação meio ambiente; Modelo misto. |
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Thesagro: |
Genótipo; Melhoramento Vegetal; Variedade. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/236108/1/Bayesian-GGE-model-for-heteroscedastic.pdf
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Marc: |
LEADER 02417naa a2200325 a 4500
001 2142572
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008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1002/csc2.20696$2DOI
100 1 $aOLIVEIRA, L. A. de
245 $aBayesian GGE model for heteroscedastic multienvironmental trials.$h[electronic resource]
260 $c2022
520 $aThe dissection of genotype×environment interaction (GEI) is a crucial aspect ofthe final stages of plant breeding pipelines and recommendation of cultivars. Linear-bilinear models used to analyze this interaction, such as the additive main effectsand multiplicative interaction (AMMI) and genotype plus GEI (GGE), often assumehomogeneity of the residual variances across environments which affects the esti-mates and therefore, interpretations and conclusions. Our main objective was topropose a GGE model that considers heteroscedasticity across environments usingBayesian inference and to evaluate its implications in the interpretation of real andsimulated data. The GGE model assuming common variance was also fitted for com-parison purposes. The great flexibility of the Bayesian inference is transferred to thebiplots, allowing the construction of credible regions for genotypic and environmen-tal scores. The inference on the stability and adaptability of genotypes might changewhen heteroscedasticity is ignored. When real data are used, different patterns of cor-relations between environments also affect the representativeness and discriminationof the target environment. The modeling of heteroscedasticity allowed the clusteringof environments into subgroups, with similar effects for GEI. The proposed GGEmodel was more adequate and realistic to deal with scenarios of heterogeneous vari-ance in multienvironment trials, which can be useful for exploiting the GEI.
650 $aGenótipo
650 $aMelhoramento Vegetal
650 $aVariedade
653 $aEnsaio de cultivar
653 $aEnsaio de rendimento
653 $aEstabilidade
653 $aInteração meio ambiente
653 $aModelo misto
700 1 $aSILVA, C. P. da
700 1 $aSILVA, A. Q. da
700 1 $aMENDES, C. T. E.
700 1 $aNUVUNGA, J. J.
700 1 $aNUNES, J. A. R.
700 1 $aPARRELLA, R. A. da C.
700 1 $aBALESTE, M.
700 1 $aBUENO FILHO, J. S. de S.
773 $tCrop Science$gv. 62, p. 982-996, 2022.
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Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
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| 1. |  | OLIVEIRA, L. A. de; SILVA, C. P. da; SILVA, A. Q. da; MENDES, C. T. E.; NUVUNGA, J. J.; NUNES, J. A. R.; PARRELLA, R. A. da C.; BALESTE, M.; BUENO FILHO, J. S. de S. Bayesian GGE model for heteroscedastic multienvironmental trials. Crop Science, v. 62, p. 982-996, 2022.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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