|
|
Registro Completo |
Biblioteca(s): |
Embrapa Milho e Sorgo. |
Data corrente: |
25/03/2024 |
Data da última atualização: |
25/03/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
BARRETO, C. A. V.; DIAS, K. O. das G.; SOUSA, I. C. de; AZEVEDO, C. F.; NASCIMENTO, A. C. C.; GUIMARAES, L. J. M.; GUIMARÃES, C. T.; PASTINA, M. M.; NASCIMENTO, M. |
Afiliação: |
CYNTHIA APARECIDA VALIATI BARRETO, UNIVERSIDADE FEDERAL DE VIÇOSA; KAIO OLIMPIO DAS GRAÇAS DIAS, UNIVERSIDADE FEDERAL DE VIÇOSA; ITHALO COELHO DE SOUSA, UNIVERSIDADE FEDERAL DE RONDÔNIA; CAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; ANA CAROLINA CAMPANA NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; LAURO JOSE MOREIRA GUIMARAES, CNPMS; CLAUDIA TEIXEIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS; MOYSÉS NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA. |
Título: |
Genomic prediction in multi-environment trials in maize using statistical and machine learning methods. |
Ano de publicação: |
2024 |
Fonte/Imprenta: |
Scientific Reports, v. 14, 1062, 2024. |
DOI: |
https://doi.org/10.1038/s41598-024-51792-3 |
Idioma: |
Inglês |
Conteúdo: |
In the context of multi-environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non-additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in specific environments. The best methodology is case-dependent, specifically, to explore the potential of GBLUP, it is important to perform accurate modeling of the variance components to optimize the prediction of new hybrids. On the other hand, machine learning methodologies can capture non-additive effects without making any assumptions at the outset of the model. Overall, predicting the performance of new hybrids that were not evaluated in any field trials was more challenging than predicting hybrids in sparse test designs. |
Palavras-Chave: |
Predição genômica. |
Thesagro: |
Hibrido; Milho; Produtividade. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1163114/1/Genomic-prediction-in-multi-environment-trials-in-maize.pdf
|
Marc: |
LEADER 02163naa a2200277 a 4500 001 2163114 005 2024-03-25 008 2024 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1038/s41598-024-51792-3$2DOI 100 1 $aBARRETO, C. A. V. 245 $aGenomic prediction in multi-environment trials in maize using statistical and machine learning methods.$h[electronic resource] 260 $c2024 520 $aIn the context of multi-environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non-additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in specific environments. The best methodology is case-dependent, specifically, to explore the potential of GBLUP, it is important to perform accurate modeling of the variance components to optimize the prediction of new hybrids. On the other hand, machine learning methodologies can capture non-additive effects without making any assumptions at the outset of the model. Overall, predicting the performance of new hybrids that were not evaluated in any field trials was more challenging than predicting hybrids in sparse test designs. 650 $aHibrido 650 $aMilho 650 $aProdutividade 653 $aPredição genômica 700 1 $aDIAS, K. O. das G. 700 1 $aSOUSA, I. C. de 700 1 $aAZEVEDO, C. F. 700 1 $aNASCIMENTO, A. C. C. 700 1 $aGUIMARAES, L. J. M. 700 1 $aGUIMARÃES, C. T. 700 1 $aPASTINA, M. M. 700 1 $aNASCIMENTO, M. 773 $tScientific Reports$gv. 14, 1062, 2024.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
Registros recuperados : 200 | |
23. | | WANG, S. H.; MAIA, L. H.; CABRAL, L. C.; GERMANI, R.; BORGES, J. T. S. Influencia da proporcao arroz: soja sobre a solubilidade e as propriedades espumantes dos mingaus desidratados. Ciencia e Tecnologia de Alimentos, Campinas, v. 20, n. 1, p .83-89, 2000.Biblioteca(s): Embrapa Agroindústria de Alimentos. |
| |
24. | | COSTA, C. M. C.; MAIA, L. C.; CAVALCANTE, U. M. T.; NOGUEIRA, R. J. M. C. Influência de fungos micorrízicos arbusculares sobre o crescimento de dois genótipos de aceroleira (Malpighia emarginata D.C.). Pesquisa Agropecuária Brasileira, Brasília, DF, v. 36, n. 6, p. 893-901, jun. 2001 Título em inglês: Effect of arbuscular mycorrhizal fungi on growth of two genotypes of Malpighia emarginata D.C.Biblioteca(s): Embrapa Unidades Centrais. |
| |
33. | | WANG, S.; CABRAL, L. C.; ARAUJO, F. B.; MAIA, L. H. Características sensoriais de leites de soja reconstituídos. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 34, n. 3, p. 467-72, mar. 1999 Título em inglês: Sensory characteristics of reconstituted soybean milk.Biblioteca(s): Embrapa Unidades Centrais. |
| |
36. | | BORGES, G. G.; WANG, S. H.; CABRAL, L. C.; MAIA, L. H.; ASCHERI, J. L. R. Chemical characterization of dehydrated corn grits-soybean porridges. In: AACC Annual Meeting, 84., 1999, Seattle, WA. Abstracts... Seattle, 1999. p.211.Biblioteca(s): Embrapa Agroindústria de Alimentos. |
| |
38. | | AVANSI, W.; MENDONÇA, V. R.; MAIA, L. J. Q.; RIBEIRO, C.; LONGO, E. Estudo das propriedades óticas e atividade fotocatalítica de nanoestruturas de V2O5nH2O. In: WORKSHOP DA REDE DE NANOTECNOLOGIA APLICADA AO AGRONEGÓCIO, 6., 2012, Fortaleza. Anais... São Carlos: Embrapa Instrumentação; Fortaleza: Embrapa Agroindústria Tropical, 2012. p. 393-395. Editores: Maria Alice Martins, MOrsyleide de Freitas Rosa, Men de Sá Moreira de Souza Filho, Nicodemos Moreira dos Santos Junior, Odílio Benedito Garrido de Assis, Caue Ribeiro, Luiz Henrique Capparelli Mattoso.Tipo: Artigo em Anais de Congresso |
Biblioteca(s): Embrapa Instrumentação. |
| |
Registros recuperados : 200 | |
|
Nenhum registro encontrado para a expressão de busca informada. |
|
|