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Registro Completo |
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
13/11/1996 |
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Data da última atualização: |
23/03/2018 |
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Autoria: |
GAUDENCIO, C. de A.; PHILIPOVSKY, J. F.; SILVA, A. C. da; RIEDE, C. R.; VIEIRA, L. G. E.; ALCOVER, M.; BALBINOT, I.; GIBLER, J. W.; BASSOI, M. C.; MAROSTICA, N.; PAN, W. |
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Afiliação: |
CNPSO. |
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Título: |
Ensaios de cultivares de trigo para recomendações Paraná zonas Norte e Oeste 1978. |
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Ano de publicação: |
1979 |
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Fonte/Imprenta: |
[S.l.]: EMBRAPA / IAPAR / OCEPAR, [1979]. |
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Páginas: |
55 p. |
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Idioma: |
Português |
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Palavras-Chave: |
Brasil; Parana; Recomendacao de cultivar; Recommended variety. |
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Thesagro: |
Trigo. |
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Thesaurus Nal: |
Brazil; wheat. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/174446/1/ID3970.pdf
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Marc: |
LEADER 00836nam a2200313 a 4500
001 1447405
005 2018-03-23
008 1979 bl uuuu 00u1 u #d
100 1 $aGAUDENCIO, C. de A.
245 $aEnsaios de cultivares de trigo para recomendações Paraná zonas Norte e Oeste 1978.
260 $a[S.l.]: EMBRAPA / IAPAR / OCEPAR, [1979].$c1979
300 $a55 p.
650 $aBrazil
650 $awheat
650 $aTrigo
653 $aBrasil
653 $aParana
653 $aRecomendacao de cultivar
653 $aRecommended variety
700 1 $aPHILIPOVSKY, J. F.
700 1 $aSILVA, A. C. da
700 1 $aRIEDE, C. R.
700 1 $aVIEIRA, L. G. E.
700 1 $aALCOVER, M.
700 1 $aBALBINOT, I.
700 1 $aGIBLER, J. W.
700 1 $aBASSOI, M. C.
700 1 $aMAROSTICA, N.
700 1 $aPAN, W.
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Registro original: |
Embrapa Soja (CNPSO) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
21/06/2016 |
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Data da última atualização: |
21/06/2016 |
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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: |
OLIVEIRA, H. R.; SILVA, F. F.; SIQUEIRA, O. H. G. B. D.; SOUZA, N. O.; JUNQUEIRA, V. S.; RESENDE, M. D. V. de; BORQUIS, R. R. A.; RODRIGUES, M. T. |
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Afiliação: |
H. R. Oliveira, UFV; F. F. Silva, UFV; O. H. G. B. D. Siqueira, UFV; N. O. Souza, UFV; V. S. Junqueira, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; R. R. A. Borquis, UNESP; M. T. Rodrigues, UFV. |
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Título: |
Combining different functions to describe milk, fat, and protein yield in goats using Bayesian multiple-trait random regression models. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Journal of Animal Science, v. 94 n. 5, p. 1865-1874, May 2016. |
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DOI: |
10.2527/jas2015-0150 |
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Idioma: |
Inglês |
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Conteúdo: |
We proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using Bayesian inference. A total of 3,856 MY, FP, and PP test-day records, measured between 2000 and 2014, from 535 first lactations of Saanen and Alpine goats, including their cross, were used in this study. The initial analyses were performed using the following single-trait random regression models (STRRM): third- and fifth-order Legendre polynomials (Leg3 and Leg5), linear B-splines with 3 and 5 knots, the Ali and Schaeffer function (Ali), and Wilmink function. Heterogeneity of residual variances was modeled considering 3 classes. After the selection of the best STRRM to describe each trait on the basis of the deviance information criterion (DIC) and posterior model probabilities (PMP), the functions were combined to compose the MTRRM. All combined MTRRM presented lower DIC values and higher PMP, showing the superiority of these models when compared to other MTRRM based only on the same function assumed for all traits. Among the combined MTRRM, those considering Ali to describe MY and PP and Leg5 to describe FP (Ali_Leg5_Ali model) presented the best fit. From the Ali_Leg5_Ali model, heritability estimates over time for MY, FP. and PP ranged from 0.25 to 0.54, 0.27 to 0.48, and 0.35 to 0.51, respectively. Genetic correlation between MY and FP, MY and PP, and FP and PP ranged from −0.58 to 0.03, −0.46 to 0.12, and 0.37 to 0.64, respectively. We concluded that combining different functions under a MTRRM approach can be a plausible alternative for joint genetic evaluation of milk yield and milk constituents in goats. MenosWe proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using Bayesian inference. A total of 3,856 MY, FP, and PP test-day records, measured between 2000 and 2014, from 535 first lactations of Saanen and Alpine goats, including their cross, were used in this study. The initial analyses were performed using the following single-trait random regression models (STRRM): third- and fifth-order Legendre polynomials (Leg3 and Leg5), linear B-splines with 3 and 5 knots, the Ali and Schaeffer function (Ali), and Wilmink function. Heterogeneity of residual variances was modeled considering 3 classes. After the selection of the best STRRM to describe each trait on the basis of the deviance information criterion (DIC) and posterior model probabilities (PMP), the functions were combined to compose the MTRRM. All combined MTRRM presented lower DIC values and higher PMP, showing the superiority of these models when compared to other MTRRM based only on the same function assumed for all traits. Among the combined MTRRM, those considering Ali to describe MY and PP and Leg5 to describe FP (Ali_Leg5_Ali model) presented the best fit. From the Ali_Leg5_Ali model, heritability estimates over time for MY, FP. and PP ranged from 0.25 to 0.54, 0.27 to 0.48, and 0.35 to 0.51, respectively. Genetic correlation between MY and FP, MY and PP, and FP and PP ranged fro... Mostrar Tudo |
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Palavras-Chave: |
Ali and Schaeffer function; B-splines; Deviance information criterion; Legendre polynomials; Posterior model probabilities; Wilmink function. |
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Thesagro: |
Cabra leiteira; Leite; Método estatístico. |
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Thesaurus NAL: |
Dairy goats; Milk yield; Statistical analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02889naa a2200361 a 4500
001 2047576
005 2016-06-21
008 2016 bl uuuu u00u1 u #d
024 7 $a10.2527/jas2015-0150$2DOI
100 1 $aOLIVEIRA, H. R.
245 $aCombining different functions to describe milk, fat, and protein yield in goats using Bayesian multiple-trait random regression models.$h[electronic resource]
260 $c2016
520 $aWe proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using Bayesian inference. A total of 3,856 MY, FP, and PP test-day records, measured between 2000 and 2014, from 535 first lactations of Saanen and Alpine goats, including their cross, were used in this study. The initial analyses were performed using the following single-trait random regression models (STRRM): third- and fifth-order Legendre polynomials (Leg3 and Leg5), linear B-splines with 3 and 5 knots, the Ali and Schaeffer function (Ali), and Wilmink function. Heterogeneity of residual variances was modeled considering 3 classes. After the selection of the best STRRM to describe each trait on the basis of the deviance information criterion (DIC) and posterior model probabilities (PMP), the functions were combined to compose the MTRRM. All combined MTRRM presented lower DIC values and higher PMP, showing the superiority of these models when compared to other MTRRM based only on the same function assumed for all traits. Among the combined MTRRM, those considering Ali to describe MY and PP and Leg5 to describe FP (Ali_Leg5_Ali model) presented the best fit. From the Ali_Leg5_Ali model, heritability estimates over time for MY, FP. and PP ranged from 0.25 to 0.54, 0.27 to 0.48, and 0.35 to 0.51, respectively. Genetic correlation between MY and FP, MY and PP, and FP and PP ranged from −0.58 to 0.03, −0.46 to 0.12, and 0.37 to 0.64, respectively. We concluded that combining different functions under a MTRRM approach can be a plausible alternative for joint genetic evaluation of milk yield and milk constituents in goats.
650 $aDairy goats
650 $aMilk yield
650 $aStatistical analysis
650 $aCabra leiteira
650 $aLeite
650 $aMétodo estatístico
653 $aAli and Schaeffer function
653 $aB-splines
653 $aDeviance information criterion
653 $aLegendre polynomials
653 $aPosterior model probabilities
653 $aWilmink function
700 1 $aSILVA, F. F.
700 1 $aSIQUEIRA, O. H. G. B. D.
700 1 $aSOUZA, N. O.
700 1 $aJUNQUEIRA, V. S.
700 1 $aRESENDE, M. D. V. de
700 1 $aBORQUIS, R. R. A.
700 1 $aRODRIGUES, M. T.
773 $tJournal of Animal Science$gv. 94 n. 5, p. 1865-1874, May 2016.
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