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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
19/03/2024 |
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Data da última atualização: |
19/03/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ARAÚJO, M. S.; CHAVES, S. F. S.; DIAS, L. A. S.; FERREIRA, F. M.; PEREIRA, G. R.; BEZERRA, A. R. G.; ALVES, R. S.; HEINEMANN, A. B.; BRESEGHELLO, F.; CARNEIRO, P. C. S.; KRAUSE, M. D.; COSTA-NETO, G.; DIAS, K. O. G. |
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Afiliação: |
MAURICIO S. ARAUJO, UFV; SAULO F. S. CHAVES, UFV; LUIZ A. S. DIAS, UFV; FILIPE M. FERREIRA, UNESP, Botucatu-SP; GUILHERME R. PEREIRA, UFV; ANDRE R. G. BEZERRA, LIMAGRAIN BRAZIL, Jataí-GO; RODRIGO S. ALVES, UFV; ALEXANDRE BRYAN HEINEMANN, CNPAF; FLAVIO BRESEGHELLO, CNPAF; PEDRO C. S. CARNEIRO, UFV; MATHEUS D. KRAUSE, IOWA STATE UNIVERSITY; GERMANO COSTA-NETO, CORNELL UNIVERSITY; KAIO O. G. DIAS, UFV. |
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Título: |
GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Theoretical and Applied Genetics, v. 137, 80, Mar. 2024. |
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ISSN: |
0040-5752 |
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DOI: |
https://doi.org/10.1007/s00122-024-04579-z |
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Idioma: |
Inglês |
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Conteúdo: |
Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps. |
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Thesagro: |
Sistema de Informação Geográfica. |
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Thesaurus Nal: |
Cultivars; Environmental indicators. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02413naa a2200325 a 4500
001 2162951
005 2024-03-19
008 2024 bl uuuu u00u1 u #d
022 $a0040-5752
024 7 $ahttps://doi.org/10.1007/s00122-024-04579-z$2DOI
100 1 $aARAÚJO, M. S.
245 $aGIS-FA$ban approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.$h[electronic resource]
260 $c2024
520 $aParsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.
650 $aCultivars
650 $aEnvironmental indicators
650 $aSistema de Informação Geográfica
700 1 $aCHAVES, S. F. S.
700 1 $aDIAS, L. A. S.
700 1 $aFERREIRA, F. M.
700 1 $aPEREIRA, G. R.
700 1 $aBEZERRA, A. R. G.
700 1 $aALVES, R. S.
700 1 $aHEINEMANN, A. B.
700 1 $aBRESEGHELLO, F.
700 1 $aCARNEIRO, P. C. S.
700 1 $aKRAUSE, M. D.
700 1 $aCOSTA-NETO, G.
700 1 $aDIAS, K. O. G.
773 $tTheoretical and Applied Genetics$gv. 137, 80, Mar. 2024.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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| Registros recuperados : 129 | |
| 4. |  | BRESEGHELLO, F. Semeadura do arroz. In: BRESEGHELLO, F.; STONE, L. F. (Ed.). Tecnologia para o arroz de terras altas. Santo Antônio de Goiás: Embrapa Arroz e Feijão, 1998. p. 55-58.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 19. | | FAGERIA, N. K.; BRESEGHELLO, F.; STONE, L. F. Diagnóstico nutricional da produção de arroz de terras altas na Chapada dos Parecis, Estado de Mato Grosso. In: CONGRESSO DA CADEIA PRODUTIVA DE ARROZ, 1.; REUNIÃO NACIONAL DE PESQUISA DE ARROZ - RENAPA, 7., 2002, Florianópolis. Anais... Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2002. p. 540-542. (Embrapa Arroz e Feijão. Documentos, 134).| Biblioteca(s): Embrapa Arroz e Feijão. |
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| Registros recuperados : 129 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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