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| Registros recuperados : 19 | |
| 9. |  | SANTANA, F. M.; LAU, D.; SBALCHEIRO, C. C.; SUSSEL, A. A. B.; VENANCIO, W. S.; PÁDUA, J. M. V.; COSTA, A. A.; OLIVEIRA, C. R. R. de. Eficiência de fungicidas para controle de brusone de trigo: resultados dos ensaios cooperativos, safra 2020. Passo Fundo, RS: Embrapa Trigo, 2021. 19 p. (Embrapa Trigo. Circular Técnica, 66)| Biblioteca(s): Embrapa Trigo. |
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| 11. | | SANTANA, F. M.; LAU, D.; SBALCHEIRO, C. C.; GUTERRES, C. W.; KUHNEM JUNIOR, P. R.; PADUA, J. M. V.; VENÂNCIO, W. S.; SCHIPANSKI, C. A.; CHAGAS, D. F.; CASAROTTO, G.; CAPITANIO, C. G.; SENGER, M. Eficiência de fungicidas para controle de giberela do trigo: resultados dos ensaios cooperativos, safra 2020. Passo Fundo: Embrapa Trigo, 2022. 22 p. (Embrapa Trigo. Circular técnica, 74). ODS 2, ODS 12| Biblioteca(s): Embrapa Trigo. |
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| 12. | | SANTANA, F. M.; LAU, D.; SBALCHEIRO, C. C.; GUTERRES, C. W.; KUHNEM JUNIOR, P. R.; PADUA, J. M. V.; VENÂNCIO, W. S.; SCHIPANSKI, C. A.; CHAGAS, D. F.; CASAROTTO, G.; CAPITANIO, C. G.; SENGER, M.; MACHADO, F. J. Eficiência de fungicidas para controle de giberela do trigo: resultados dos ensaios cooperativos, safra 2021. Passo Fundo: Embrapa Trigo, 2023. 24 p. (Embrapa Trigo. Circular técnica, 79). ODS 2, ODS 12.| Biblioteca(s): Embrapa Clima Temperado; Embrapa Trigo. |
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| 13. | | SANTANA, F. M.; THEISEN, G.; SBALCHEIRO, C. C.; SENGER, M.; CHAGAS, D. F.; SCHIPANSKI, C. A.; CASAROTTO, G.; VENANCIO, W. S.; PIZOLOTTO, C.; UTIAMADA, C. M.; COSTA, A. A.; PADUA, J. M. V.; GUTERRES, C. W. Eficiência de fungicidas para controle de manchas foliares do trigo: resultados da Rede de Ensaios Cooperativos do Trigo, safras 2020 e 2021. Pelotas: Embrapa Clima Temperado, 2024. 17 p. (Embrapa Clima Temperado. Boletim de Pesquisa e Desenvolvimento, 372).| Biblioteca(s): Embrapa Clima Temperado. |
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| 14. | | FERREIRA, A.; SBALCHEIRO, C. C.; SANTANA, F. M.; DEL PONTE, E. M.; CASAROTTO, G.; CHAGAS, D. F.; SCHIPANSKI, C. A.; PIZOLOTTO, C.; VENANCIO, W. S.; GUTERRES, C. W.; SENGER, M.; FORCELINI, C. A.; ROEHRIG, R.; ZANATTA, M.; PÁDUA, J. M. V.; UTIAMADA, C. M.; SUSSEL, A. A. B.; CÓL, M. C. de. Eficiência de fungicidas para controle de manchas foliares do trigo: resultados da Rede de Ensaios Cooperativos do Trigo - safra 2022. Passo Fundo: Embrapa Trigo, 2023. 16 p. (Embrapa Trigo. Circular técnica, 82). ODS 2. ODS 12.| Biblioteca(s): Embrapa Trigo. |
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| 15. | | SANTANA, F. M.; LAU, D.; SBALCHEIRO, C. C.; GUTERRES, C. W.; VENÂNCIO, W. S.; PADUA, J. M. V.; COSTA, A. A.; OLIVEIRA, C. R. R. de; SCHIPANSKI, C. A.; CHAGAS, D. F.; CASAROTTO, G.; CAPITANIO, C. G.; REIS, E. M.; ZANATTA, M.; SENGER, M. Eficiência de fungicidas para controle de oídio do trigo: resultados dos ensaios cooperativos, safra 2020. Passo Fundo: Embrapa Trigo, 2022. 24 p. (Embrapa Trigo. Circular técnica, 73) ODS 2, ODS 12.| Biblioteca(s): Embrapa Trigo. |
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| 16. | | DIAS, K. O. das G.; GEZAN, S. A.; GUIMARÃES, C. T.; NAZARIAN, A.; SILVA, L. da C. e; PARENTONI, S. N.; GUIMARAES, P. E. de O.; ANONI, C. de O.; PÁDUA, J. M. V.; PINTO, M. de O.; NODA, R. W.; RIBEIRO, C. A. G.; MAGALHAES, J. V. de; GARCIA, A. A. F.; SOUZA, J. C. de; GUIMARAES, L. J. M.; PASTINA, M. M. Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. Heredity, London, v. 121, n. 1, p. 24-37, 2018.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 17. | | PADUA, J. M. V.; DIAS, K. O. das G.; PASTINA, M. M.; SOUZA, J. C. de; QUEIROZ, V. A. V.; COSTA, R. V. da; SILVA, M. B. P. da; RIBEIRO, C. A. G.; GUIMARAES, C. T.; GEZAN, S. A.; GUIMARAES, L. J. M. A multi-environment trials diallel analysis provides insights on the inheritance of fumonisin contamination resistance in tropical maize. Euphytica, Dordrecht, v. 211, n. 3, p. 277-285, 2016| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 18. | | MACIEL, J. L. N.; CHAGAS, J. H.; PÁDUA, J. M. V.; GOUSSAIN, R. de C. S.; EIDES, J. R.; MORAIS, L. K. de; KOVALESKI, M.; CASTRO, R. L. de; SBALCHEIRO, C. C.; FRONZA, V.; COELHO, M. A. de O.; AITA, V. Resultados da rede de ensaios cooperativos para a resistência à brusone da espiga de trigo (RECORBE), safras 2020 e 2021. Passo Fundo: Embrapa Trigo, 2022. 20 p. (Embrapa Trigo. Circular técnica, 69). ODS-2| Biblioteca(s): Embrapa Tabuleiros Costeiros; Embrapa Trigo. |
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| 19. | | ASCARI, J. P.; BARRO, J. P.; SANTANA, F. M.; PADUA, J. M. V.; MACIEL, J. L. N.; LAU, D.; TORRES, G. A. M.; SBALCHEIRO, C. C.; SEIXAS, C. D. S.; GOULART, A. C. P.; SUSSEL, A. A. B.; SCHIPANSKI, C. A.; CHAGAS, D. F.; COELHO, M. A. O.; MONTECELLI, T. D. N.; AMARAL, D. R.; CUSTÓDIO, A. A. P.; MOREIRA, L. S. O.; UTIAMADA, C. M.; VENÂNCIO, W. S.; GOUSSAIN, R. C. S.; ALVES, K. S.; DEL PONTE, E. M. Sequential post-heading applications for controlling wheat blast: a nine-tear summary of fungicide performance in Brazil. Plant Disease, PDIS-06-21-1183-RE Jul. 2021.| Biblioteca(s): Embrapa Agropecuária Oeste; Embrapa Cerrados; Embrapa Trigo. |
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| Registros recuperados : 19 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
24/07/2018 |
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Data da última atualização: |
05/02/2019 |
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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: |
DIAS, K. O. das G.; GEZAN, S. A.; GUIMARÃES, C. T.; NAZARIAN, A.; SILVA, L. da C. e; PARENTONI, S. N.; GUIMARAES, P. E. de O.; ANONI, C. de O.; PÁDUA, J. M. V.; PINTO, M. de O.; NODA, R. W.; RIBEIRO, C. A. G.; MAGALHAES, J. V. de; GARCIA, A. A. F.; SOUZA, J. C. de; GUIMARAES, L. J. M.; PASTINA, M. M. |
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Afiliação: |
Kaio Olímpio das Graças Dias, Universidade Federal de Lavras; Salvador Alejandro Gezan, School of Forest Resources & Conservation, University of Florida, Gainesville.; CLAUDIA TEIXEIRA GUIMARAES, CNPMS; Alireza Nazarian, School of Forest Resources & Conservation, University of Florida, Gainesville.; Luciano da Costa e Silva, JMP Division, SAS Institute Inc., Cary.; SIDNEY NETTO PARENTONI, CNPMS; PAULO EVARISTO DE O GUIMARAES, CNPMS; Carina de Oliveira Anoni, Escola Superior de Agricultura “Luiz de Queiroz”; José Maria Villela Pádua, Universidade Federal de Lavras; MARCOS DE OLIVEIRA PINTO, CNPMS; ROBERTO WILLIANS NODA, CNPMS; Carlos Alexandre Gomes Ribeiro, Universidade Federal de Viçosa; JURANDIR VIEIRA DE MAGALHAES, CNPMS; Antonio Augusto Franco Garcia, Escola Superior de Agricultura “Luiz de Queiroz”; João Cândido de Souza, Universidade Federal de Lavras; LAURO JOSE MOREIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS. |
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Título: |
Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Heredity, London, v. 121, n. 1, p. 24-37, 2018. |
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DOI: |
10.1038/s41437-018-0053-6 |
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Idioma: |
Inglês |
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Conteúdo: |
Breeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids. MenosBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS a... Mostrar Tudo |
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Thesagro: |
Milho; Resistência a Seca. |
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Categoria do assunto: |
-- |
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Marc: |
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024 7 $a10.1038/s41437-018-0053-6$2DOI
100 1 $aDIAS, K. O. das G.
245 $aImproving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials.$h[electronic resource]
260 $c2018
520 $aBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids.
650 $aMilho
650 $aResistência a Seca
700 1 $aGEZAN, S. A.
700 1 $aGUIMARÃES, C. T.
700 1 $aNAZARIAN, A.
700 1 $aSILVA, L. da C. e
700 1 $aPARENTONI, S. N.
700 1 $aGUIMARAES, P. E. de O.
700 1 $aANONI, C. de O.
700 1 $aPÁDUA, J. M. V.
700 1 $aPINTO, M. de O.
700 1 $aNODA, R. W.
700 1 $aRIBEIRO, C. A. G.
700 1 $aMAGALHAES, J. V. de
700 1 $aGARCIA, A. A. F.
700 1 $aSOUZA, J. C. de
700 1 $aGUIMARAES, L. J. M.
700 1 $aPASTINA, M. M.
773 $tHeredity, London$gv. 121, n. 1, p. 24-37, 2018.
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