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Registros recuperados : 17.119 | |
2. | | MATSUNAGA, W. K.; SALES, E. S. G.; ASSIS JÚNIOR, G. C.; SILVA, M. T.; LACERDA, F. F.; LIMA, E. de P.; SANTOS, C. A. C. dos; BRITO, J. I. B. de. Application of ERA5-Land reanalysis data in zoning of climate risk for corn in the state of Bahia-Brazil. Theoretical and Applied Climatology, v. 155, p. 945-963, 2024. Biblioteca(s): Embrapa Solos. |
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3. | | LOPES, A. da S.; ANDRADE JUNIOR, A. S. de; BASTOS, E. A.; SOUSA, C. A. F. de; CASARI, R. A. das C. N.; MOURA, M. S. B. de. Assessment of maize hybrid water status using aerial images from an unmanned aerial vehicle. Revista Caatinga, Mossoró, v. 37, e11701, 2024. Biblioteca(s): Embrapa Meio-Norte; Embrapa Semiárido. |
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4. | | VASCONCELOS, M. J. V. de; JAIN, A.; FIGUEIREDO, J. E. F.; OLIVEIRA, M. F. de; TRINDADE, R. dos S.; YUGANDHAR, P.; RAGHOTHAMA, K. G. Gaspé Flint corn as a seed-to-seed model to study the effect of phosphorus on maize growth and development. Genetics and Molecular Research, v. 23, n. 1, gmr19213, 2024. Biblioteca(s): Embrapa Milho e Sorgo. |
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6. | | 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. Genomic prediction in multi-environment trials in maize using statistical and machine learning methods. Scientific Reports, v. 14, 1062, 2024. Biblioteca(s): Embrapa Milho e Sorgo. |
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7. | | SANTOS, L. R.; BARROS, P. S. do R.; MONTEIRO, D. A.; TABOSA, J. N.; MELO, A. F. de; LYRA, M. do C. C. P. de; OLIVEIRA, J. R. de S.; FERNANDES JUNIOR, P. I.; FREITAS, A. D. S. de; RACHID, C. T. C. da C. Influences of plant organ, genotype, and cultivation site on the endophytic bacteriome of maize (Zea mays L.) in the semi‑arid region of Pernambuco, Brazil. Brazilian Journal of Microbiology, v. 55, n. 1, p. 789-797, 2024. Biblioteca(s): Embrapa Semiárido. |
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11. | | BINI, D.; MATTOS, B. B.; FIGUEIREDO, J. E. F.; SANTOS, F. C. dos; MARRIEL, I. E.; SANTOS, C. A. dos; OLIVEIRA-PAIVA, C. A. Parameter evaluation for developing phosphate-solubilizing Bacillus inoculants. Brazilian Journal of Microbiology, v. 55, n. 1, p. 737-748, 2024. Biblioteca(s): Embrapa Milho e Sorgo; Embrapa Solos. |
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12. | | SOBRAL, G. de C.; OLIVEIRA, J. S. de; SANTOS, E. M.; ARAUJO, G. G. L. de; SANTOS, F. N. de S.; CAMPOS. F. S.; CAVALCANTI, H. S.; VIEIRA, D. de S.; LEITE, G. M.; COELHO, D. F. O.; SANTANA, L. P.; GOMES, P. G. B.; TORRES JÚNIOR, P. da C.; SANTOS, M. A. C.; VIANA, N. B. Optimizing silage quality in drylands: corn stover and forage cactus mixture on nutritive value, microbial activity, and aerobic stability. Journal of Arid Environments, v. 220, 105123, 2024. Biblioteca(s): Embrapa Semiárido. |
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16. | | SENABIO, J. A.; PEREIRA, F. de C.; PIETRO-SOUZA, W.; SOUSA, T. F.; SILVA, G. F. da; SOARES, M. A. Enhanced mercury phytoremediation by Pseudomonodictys pantanalensis sp. nov. A73 and Westerdykella aquatica P71. Brazilian Journal of Microbiology, v. 54, n. 2, p. 949-964, jun. 2023. Biblioteca(s): Embrapa Amazônia Ocidental. |
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17. | | CUSTÓDIO, A. A. P.; SILVA, D. D. da; UTIAMADA, C. M.; CAMPOS, H. D.; COSTA, R. V. da; FANTIN, L. H.; YADA, I. F. U.; DUARTE, A. P.; DIAS, A. R.; COSTA, A. A.; MOCHKO. A. C.; CHAGAS, D. F.; SICHOCKO, D.; LUJAN, D. W.; MOREIRA, E. N.; MEDEIROS, F. C. L.; JULIATTI, F. C.; JULIATTI, F. C.; FANTIN, G. M.; ARAÚJO, I. P.; ROY, J. M. T.; GRIGOLLI, J. F. J.; NUNES JÚNIOR, J.; FONTANA, L. F.; BELUFI, L. M. R.; CARNEIRO, L. C.; BRAGA, K.; KUDLAWIEC, K.; SOUSA, M. V.; SENGER, M.; STEFANELLO, M. S.; MÜLLER, M. A.; TORMEN, N. R.; BRAND, S. C.; CARLIN, V. J. Eficiência de fungicidas DMI e MBC no controle das manchas de Bipolaris e túrcicum do milho safrinha em 2021 e 2022. In: SEMINÁRIO NACIONAL DE MILHO SAFRINHA, 17., 2023, Campo Grande, MS. Preservar e produzir: anais. Maracaju: Fundação MS, 2023. p. 77-78. Biblioteca(s): Embrapa Milho e Sorgo. |
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18. | | 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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19. | | HERNANDES-LOPES, J.; PINTO, M. S.; VIEIRA, L. R.; MONTEIRO, P. B.; GERASIMOVA, S. V.; NONATO, J. V. A.; BRUNO, M. H. F.; VIKHOREV, A.; FERNANDES, F. R.; GERHARDT, I. R.; PAUWELS, L.; ARRUDA, P.; DANTE, R. A.; YASSITEPE, J. E. de C. T. Enabling genome editing in tropical maize lines through an improved, morphogenic regulator-assisted transformation protocol. Frontiers in Genome Editing, v. 5, 1241035, 2023. Na publicação: Fernanda Rausch-Fernandes. Biblioteca(s): Embrapa Agricultura Digital. |
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Registros recuperados : 17.119 | |
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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
|
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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