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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
17/12/2013 |
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Data da última atualização: |
23/09/2019 |
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Autoria: |
SEIXAS, B. L. S. |
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Título: |
Não plante ladeira abaixo. |
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Ano de publicação: |
1985 |
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Fonte/Imprenta: |
Cruz das Almas: UFBA, 1985. |
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Páginas: |
10 p. |
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Série: |
(UFBA. Boletim Informativo, 20). |
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Idioma: |
Português |
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Conteúdo: |
Plantar as suas lavouras, cortando as águas da enxurrada; Fazer faixas de plantas que sustentam o chão. |
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Palavras-Chave: |
Ladeira. |
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Thesagro: |
Erosão; Serra; Solo. |
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Thesaurus Nal: |
Soil. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 00547nam a2200193 a 4500
001 1974137
005 2019-09-23
008 1985 bl uuuu u0uu1 u #d
100 1 $aSEIXAS, B. L. S.
245 $aNão plante ladeira abaixo.
260 $aCruz das Almas: UFBA$c1985
300 $a10 p.
490 $a(UFBA. Boletim Informativo, 20).
520 $aPlantar as suas lavouras, cortando as águas da enxurrada; Fazer faixas de plantas que sustentam o chão.
650 $aSoil
650 $aErosão
650 $aSerra
650 $aSolo
653 $aLadeira
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Registro original: |
Embrapa Semiárido (CPATSA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
04/10/2011 |
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Data da última atualização: |
23/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
COBUCI, J. A.; COSTA, C. N.; BRACCINI NETO, J.; FREITAS, A. F. de. |
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Afiliação: |
JAIME ARAÚJO COBUCI, UFRGS / CNPq; CLAUDIO NAPOLIS COSTA, CNPGL; JOSÉ BRACCINI NETO, UFRGS.; ARY FERREIRA DE FREITAS, Pesquisador aposentado do CNPGL. |
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Título: |
Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
Revista Brasileira de Zootecnia, v. 40, n. 3, p. 557-567, 2011. |
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DOI: |
https://doi.org/10.1590/S1516-35982011000300013 |
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Idioma: |
Inglês |
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Conteúdo: |
Records of test-day milk yields of the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters for milk yield by using two alternatives of definition of fixed regression of the random regression models (RRM). Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve (defined based on averages of the populations or multiple sub-populations formed by grouping animals which calved at the same age and in the same season of the year) or random lactation curves (additive genetic and permanent enviroment). Akaike information criterion (AIC) and Bayesian information criterion (BIC) indicated that the models which used multiple regression of fixed lactation curves of lactation multiple regression model with fixed lactation curves had the best fit for the first lactation test-day milk yields and the models which used a single regression of fixed curve had the best fit for the second and third lactations. Heritability for milk yield during lactation estimates did not vary among models but ranged from 0.22 to 0.34, from 0.11 to 0.21, and from 0.10 to 0.20, respectively, in the first three lactations. Similarly to heridability estimates of genetic correlations did not vary among models. The use of single or multiple fixed regressions for fixed lactation curves by RRM does not influence the estimates of genetic parameters for test-day milk yield across lactations. |
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Palavras-Chave: |
Legendre polynomial; Selection; Test-day milk yield. |
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Thesaurus NAL: |
genetic correlation; heritability. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/54198/1/Genetic-parameters-for-milk.pdf
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Marc: |
LEADER 02208naa a2200229 a 4500
001 1902188
005 2023-01-23
008 2011 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1590/S1516-35982011000300013$2DOI
100 1 $aCOBUCI, J. A.
245 $aGenetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling.$h[electronic resource]
260 $c2011
520 $aRecords of test-day milk yields of the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters for milk yield by using two alternatives of definition of fixed regression of the random regression models (RRM). Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve (defined based on averages of the populations or multiple sub-populations formed by grouping animals which calved at the same age and in the same season of the year) or random lactation curves (additive genetic and permanent enviroment). Akaike information criterion (AIC) and Bayesian information criterion (BIC) indicated that the models which used multiple regression of fixed lactation curves of lactation multiple regression model with fixed lactation curves had the best fit for the first lactation test-day milk yields and the models which used a single regression of fixed curve had the best fit for the second and third lactations. Heritability for milk yield during lactation estimates did not vary among models but ranged from 0.22 to 0.34, from 0.11 to 0.21, and from 0.10 to 0.20, respectively, in the first three lactations. Similarly to heridability estimates of genetic correlations did not vary among models. The use of single or multiple fixed regressions for fixed lactation curves by RRM does not influence the estimates of genetic parameters for test-day milk yield across lactations.
650 $agenetic correlation
650 $aheritability
653 $aLegendre polynomial
653 $aSelection
653 $aTest-day milk yield
700 1 $aCOSTA, C. N.
700 1 $aBRACCINI NETO, J.
700 1 $aFREITAS, A. F. de
773 $tRevista Brasileira de Zootecnia$gv. 40, n. 3, p. 557-567, 2011.
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Embrapa Gado de Leite (CNPGL) |
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