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Registro Completo |
Biblioteca(s): |
Embrapa Recursos Genéticos e Biotecnologia. |
Data corrente: |
17/10/2019 |
Data da última atualização: |
30/03/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
CASTRO, M. T. de; MONTALVÃO, S. C. L.; MONNERAT, R. G. |
Afiliação: |
MARCELO TAVARES DE CASTRO, CENTRO UNIVERSITÁRIO ICESP; SANDRO COELHO LINHARES MONTALVÃO; ROSE GOMES MONNERAT SOLON DE PONTES, Cenargen. |
Título: |
Control of mahogany shoot borer, Hypsipyla grandella (Lepidoptera: Pyralidae), with Bacillus thuringiensis in a systemic way. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
Nativa, Sinop, v. 7, n. 4, p. 426-430, jul./ago. 2019. |
DOI: |
10.31413/nativa.v7i4.6567 |
Idioma: |
Inglês |
Palavras-Chave: |
Controle da broca-do-mogno; Cry toxins; Endofítico; Entomologia florestal; Forest entomology; Forest pest; Hendophytic; Praga florestal; Toxinas cry. |
Thesagro: |
Bacillus Thuringiensis; Controle Biológico; Hypsipyla Grandella. |
Thesaurus Nal: |
Biological control. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00960naa a2200301 a 4500 001 2113185 005 2020-03-30 008 2019 bl uuuu u00u1 u #d 024 7 $a10.31413/nativa.v7i4.6567$2DOI 100 1 $aCASTRO, M. T. de 245 $aControl of mahogany shoot borer, Hypsipyla grandella (Lepidoptera$bPyralidae), with Bacillus thuringiensis in a systemic way.$h[electronic resource] 260 $c2019 650 $aBiological control 650 $aBacillus Thuringiensis 650 $aControle Biológico 650 $aHypsipyla Grandella 653 $aControle da broca-do-mogno 653 $aCry toxins 653 $aEndofítico 653 $aEntomologia florestal 653 $aForest entomology 653 $aForest pest 653 $aHendophytic 653 $aPraga florestal 653 $aToxinas cry 700 1 $aMONTALVÃO, S. C. L. 700 1 $aMONNERAT, R. G. 773 $tNativa, Sinop$gv. 7, n. 4, p. 426-430, jul./ago. 2019.
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Embrapa Recursos Genéticos e Biotecnologia (CENARGEN) |
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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 |
Circulação/Nível: |
A - 1 |
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
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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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