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| Registros recuperados : 204 | |
| 1. |  | DIAS, M. C.; ZIDANES, U. L.; MASCARENHAS, A. R. P.; SETTER, C.; SCATOLINO, M. V.; MARTINS, M. A.; MORI, F. A.; BELGACEM, M. N.; TONOLI, G. H. D.; FERREIRA, S. R. Mandacaru cactus as a source of nanofibrillated cellulose for nanopaper production. International Journal of Biological Macromolecules, v. 235, 123850, 2023. 1-11| Biblioteca(s): Embrapa Instrumentação. |
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| 2. | | SETTER, C.; MASCARENHAS, A. R. P.; DIAS, M. C.; MEIRA, A. C. F. O.; CARVALHO, N. T. S.; LOURENÇO, M. S.; MARTINS, M. A.; TONOLI, G. H. D.; OLIVEIRA, T. J. P. de. Surface modification of cellulosic nanofibrils by spray drying: Drying yield and microstructural, thermal and chemical characterization. Industrial Crops & Products, v. 201, 116899, 2023. 1 - 15| Biblioteca(s): Embrapa Instrumentação. |
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| 3. | | MASCARENHAS, A. R. P.; SCATOLINO, M. V.; DIAS, M. C.; MARTINS, M. A.; MELO, R. R. de; MENDONDÇA, M. C.; TONOLI, G. H. D. Association of cellulose micro/nanofibrils and silicates for cardboard coating: Technological aspects for packaging. Industrial Crops & Products, v. 188, e115667, 2022.| Biblioteca(s): Embrapa Instrumentação. |
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| 5. | | MASCARENHAS, A. R. P.; SCATOLINO, M. V.; DIAS, M. C.; MARTINS, M. A.; MELO, R. R. de; DAMÁSIO, R. A. P.; MENDONÇA, M. C.; TONOLI, G. H. D. Fibers pre-treatments with sodium silicate afect the properties of suspensions, flms, and quality index of cellulose micro/nanofbrils. Nordic Pulp & Paper Research Journal, v. 37, n. 3, 2022. 534-552| Biblioteca(s): Embrapa Instrumentação. |
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| 6. | | MASCARENHAS, A. R. P.; MENDONÇA, M. C.; DIAS, M. C.; MARTINS, M. A.; MELO, R. R. de; DAMASIO, R. A. P.; TONOLI, G. H. D. Production of cellulose micro/nanofbrils with sodium silicate: impact on energy consumption, microstructure, crystallinity and stability of suspensions. Nordic Pulp & Paper Research Journal, v. 37, n. 4, 2022. 686-701| Biblioteca(s): Embrapa Instrumentação. |
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| 9. | | SOUZA. L. O.; LESSA, O. A.; DIAS, M. C.; TONOLI, G. H. D.; REZENDE, D. V. B.; MARTINS, M. A.; NEVES, I. C. O.; RESENDE, J. V.; CARVALHO, E. E. N.; VILAS BOAS, E. V. B.; OLIVEIRA, J. R.; FRANCO, M. Study of morphological properties and rheological parameters of cellulose nanofibrils of cocoa shell (Theobroma cacao L.). Carbohydrate Polymers, [S. l.], v. 207, 2019. 152-158| Biblioteca(s): Embrapa Instrumentação. |
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| 10. | | SCATOLINO, M. V.; DIAS, M. C.; SILVA, D. W.; BUFALINO, L.; MARTINS, M. A.; PICCOLI, R. H.; TONOLI, G. H. D.; LONDERO, A. A.; OENNING NETO, V.; MENDES, L. M. Tannin-stabilized silver nanoparticles and citric acid added associated to cellulose nanofibrils: effect on film antimicrobial properties. SN Applied Science, n. 1, art. 1243, 2019 Published online: 20 September 2019.| Biblioteca(s): Embrapa Instrumentação. |
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| 16. | | OLIVEIRA, I. J. de; ATROCH, A. L.; DIAS, M. C.; GUIMARAES, L. J. M.; GUIMARAES, P. E. de O. Selection of corn cultivars for yield, stability, and adaptability in the state of Amazonas, Brazil. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 52, n. 6, p. 455-463, jun. 2017. Título em português: Seleção de cultivares de milho quanto à produtividade, estabilidade e adaptabilidade no Amazonas.| Biblioteca(s): Embrapa Amazônia Ocidental; Embrapa Milho e Sorgo; Embrapa Unidades Centrais. |
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| Registros recuperados : 204 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
05/07/2019 |
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Data da última atualização: |
30/10/2019 |
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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: |
VOLPATO, L.; ALVES, R. S.; TEODORO, P. E.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; LUDKE, W. H.; SILVA, F. L. da; BORÉM, A. |
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Afiliação: |
Leonardo Volpato, Universidade Federal de Viçosa; Rodrigo Silva Alves, Universidade Federal de Viçosa; Paulo Eduardo Teodoro, Universidade Federal de Mato Grosso do Sul; MARCOS DEON VILELA DE RESENDE, CNPF; Moysés Nascimento, Universidade Federal de Viçosa; Ana Carolina Campana Nascimento, Universidade Federal de Viçosa; Willian Hytalo Ludke, Universidade Federal de Viçosa; Felipe Lopes da Silva, Universidade Federal de Viçosa; Aluízio Borém, Universidade Federal de Viçosa. |
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Título: |
Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
PLoS ONE, v. 14, n. 4, e0215315, Apr. 2019. 22 p. |
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DOI: |
10.1371/journal.pone.0215315 |
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Idioma: |
Inglês |
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Conteúdo: |
At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of h2 prog. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny. MenosAt present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided... Mostrar Tudo |
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Palavras-Chave: |
Bayesian-inference; Breeding values; Genomic selection; Inferência Bayesian; Mixed models; Modelo misto; Seed protein; Seleção genômica. |
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Thesagro: |
Soja. |
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Thesaurus NAL: |
Agronomic traits; Prediction; Soybeans. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/199239/1/2019-M.Deon-PO-Multi-trait.pdf
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Marc: |
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100 1 $aVOLPATO, L.
245 $aMulti-trait multi-environment models in the genetic selection of segregating soybean progeny.$h[electronic resource]
260 $c2019
520 $aAt present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of h2 prog. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.
650 $aAgronomic traits
650 $aPrediction
650 $aSoybeans
650 $aSoja
653 $aBayesian-inference
653 $aBreeding values
653 $aGenomic selection
653 $aInferência Bayesian
653 $aMixed models
653 $aModelo misto
653 $aSeed protein
653 $aSeleção genômica
700 1 $aALVES, R. S.
700 1 $aTEODORO, P. E.
700 1 $aRESENDE, M. D. V. de
700 1 $aNASCIMENTO, M.
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700 1 $aLUDKE, W. H.
700 1 $aSILVA, F. L. da
700 1 $aBORÉM, A.
773 $tPLoS ONE$gv. 14, n. 4, e0215315, Apr. 2019. 22 p.
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Embrapa Florestas (CNPF) |
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