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Titolo:
The use of covariance functions and random regressions for genetic evaluation of milk production based on test day records
Autore:
Van der Werf, JHJ; Goddard, ME; Meyer, K;
Indirizzi:
Univ351, England, Div Anim Sci, Anim Genet & Breeding Unit, Armidale, NSW 2 Univ New England Armidale NSW Australia 2351 eeding Unit, Armidale, NSW 2
Titolo Testata:
JOURNAL OF DAIRY SCIENCE
fascicolo: 12, volume: 81, anno: 1998,
pagine: 3300 - 3308
SICI:
0022-0302(199812)81:12<3300:TUOCFA>2.0.ZU;2-A
Fonte:
ISI
Lingua:
ENG
Soggetto:
RESTRICTED MAXIMUM-LIKELIHOOD; TEST DAY YIELDS; TEST DAY MODEL; ANIMAL-MODELS; TRAITS; PARAMETERS; SELECTION;
Keywords:
covariance functions; random regression; test day yields; genetic evaluation;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Citazioni:
16
Recensione:
Indirizzi per estratti:
Indirizzo: Van der Werf, JHJ Univ351, England, Div Anim Sci, Anim Genet & Breeding Unit, Armidale, NSW 2 Univ New England Armidale NSW Australia 2351 ale, NSW 2
Citazione:
J.H.J. Van der Werf et al., "The use of covariance functions and random regressions for genetic evaluation of milk production based on test day records", J DAIRY SCI, 81(12), 1998, pp. 3300-3308

Abstract

In the analysis of test day records for dairy cattle, covariance functionsallow a continuous change of variances and covariances of test day yields on different lactation days. The equivalence between covariance functions as an infinite dimensional extension of multivariate models and random regression models is shown in this paper. A canonical transformation procedure is proposed for random regression models in large-scale genetic evaluations. Two methods were used to estimate covariance function coefficients for first parity test day yields of Holsteins: 1) a two-step procedure fitting covariance functions to matrices with estimated genetic and residual covariances between predetermined periods of lactation and 2) REML directly from data with a random regression model. The first method gave more reliable estimates, particularly for the periphery of the trajectory. The goodness of fitof a random regression model based on covariables describing the shape of the lactation curve was nearly the same as random regression on Legendre polynomials. In the latter model, two and three regression coefficients were sufficient to fit the covariance structure for additive genetic and permanent environment, respectively. The eigenfunction pattern revealed the possibility of selection for persistency. Covariance functions can be usefully implemented in large-scale test day models by means of random regressions.

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Documento generato il 22/09/20 alle ore 16:44:25