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Registro Completo |
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
10/05/2004 |
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Data da última atualização: |
28/10/2009 |
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Autoria: |
GUERRA, A. F.; RODRIGUES, G. C.; NAZARENO, R. B. |
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Título: |
Uso do tanque Classe A para irrigacao do feijao Perola no cerrado. |
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Ano de publicação: |
2002 |
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Fonte/Imprenta: |
Planaltina: Embrapa Cerrados, 2002. |
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Páginas: |
4 p. |
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Série: |
(Embrapa Cerrados. Comunicado Tecnico, 84). |
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Idioma: |
Português |
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Thesagro: |
Cerrado; Evapotranspiração; Feijão; Irrigação; Manejo de Água; Phaseolus Vulgaris. |
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Thesaurus Nal: |
beans; evapotranspiration; irrigation; water management. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/CPAC-2009/24555/1/comtec_84.pdf
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Marc: |
LEADER 00728nam a2200265 a 4500
001 1557507
005 2009-10-28
008 2002 bl uuuu u0uu1 u #d
100 1 $aGUERRA, A. F.
245 $aUso do tanque Classe A para irrigacao do feijao Perola no cerrado.
260 $aPlanaltina: Embrapa Cerrados$c2002
300 $a4 p.
490 $a(Embrapa Cerrados. Comunicado Tecnico, 84).
650 $abeans
650 $aevapotranspiration
650 $airrigation
650 $awater management
650 $aCerrado
650 $aEvapotranspiração
650 $aFeijão
650 $aIrrigação
650 $aManejo de Água
650 $aPhaseolus Vulgaris
700 1 $aRODRIGUES, G. C.
700 1 $aNAZARENO, R. B.
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Registro original: |
Embrapa Cerrados (CPAC) |
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Registro Completo
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
25/03/2024 |
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Data da última atualização: |
25/03/2024 |
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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: |
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. |
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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. |
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Título: |
Genomic prediction in multi-environment trials in maize using statistical and machine learning methods. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Scientific Reports, v. 14, 1062, 2024. |
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DOI: |
https://doi.org/10.1038/s41598-024-51792-3 |
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Idioma: |
Inglês |
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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. |
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Palavras-Chave: |
Predição genômica. |
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Thesagro: |
Hibrido; Milho; Produtividade. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1163114/1/Genomic-prediction-in-multi-environment-trials-in-maize.pdf
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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.
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Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
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