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| Registros recuperados : 8 | |
| 1. |  | FREIRE, A. I.; DIAS, K. O. G.; OLIVEIRA, L. B. V.; NALIN, R. S.; GUEDES, F. L.; SOUZA, J. C. Genetic control of the number of leaves above the ear in maize. Genetics and Molecular Research, Ribeirao Preto, v. 14, n. 1, p. 1318-1323, Mar. 2015.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 3. | | DIAS, K. O. G.; SANTOS, J. P. R. dos; KRAUSE, M. D.; PIEPHO, H.-P.; GUIMARAES, L. J. M.; PASTINA, M. M.; GARCIA, A. A. F. Leveraging probability concepts for cultivar recommendation in multi?environment trials. Theoretical and Applied Genetics, v. 135, n. 4, p. 1385-1399, 2022.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 4. | | CHAVES, S. F. S.; EVANGELISTA, J. S. P. C.; ALVES, R. S.; FERREIRA, F. M.; DIAS, L. A. S.; ALVES, R. M.; DIAS, K. O. G.; BHERING, L. L. Application of linear mixed models for multiple harvest/site trial analyses in perennial plant breeding. Tree Genetics & Genomes, v. 18, n. 6, Article number: 44, Dec. 2022.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 5. | | CHAVES, S. F. S.; EVANGELISTA, J. S. P. C.; TRINDADE, R. dos S.; DIAS, L. A. S.; GUIMARAES, P. E. de O.; GUIMARAES, L. J. M.; ALVES, R. S.; BHERING, L. L.; DIAS, K. O. G. Employing factor analytic tools for selecting high-performance and stable tropical maize hybrids. Crop Science, v. 63, n. 3, p. 1114-1125, 2023.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 6. | | DIAS, K. O. G.; PIEPHO, H. P.; GUIMARAES, L. J. M.; GUIMARAES, P. E. de O.; PARENTONI, S. N.; PINTO, M. de O.; NODA, R. W.; MAGALHAES, J. V. de; GUIMARÃES, C. T.; GARCIA, A. A. F.; PASTINA, M. M. Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data. Theoretical and Applied Genetics, v. 133, p. 443-455, 2020. Publicado online em 22 nov. 2019.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 7. | | 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. GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting. Theoretical and Applied Genetics, v. 137, 80, Mar. 2024.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 8. | | FERREIRA, F. M.; CHAVES, S. F. S.; BHERING, L. L.; ALVES, R. S.; TAKAHASHI, E. K.; SOUSA, J. E.; RESENDE, M. D. V. de; LEITE, F. P.; GEZAN, S. A.; VIANA, J. M. S.; FERNANDES, S. B.; DIAS, K. O. G. A novel strategy to predict clonal composites by jointly modeling spatial variation and genetic competition. Forest Ecology and Management, v. 548, Article 121393, 2023. 10 p.| Biblioteca(s): Embrapa Café. |
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| Registros recuperados : 8 | |
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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. |
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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Circulação/Nível: |
A - 1 |
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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 |
|
Marc: |
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001 2162951
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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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