Literature DB >> 11814038

Genetic evaluation of dairy cattle using test-day models.

J Jensen1.   

Abstract

Recently there has been considerable interest in modeling individual test-day records (TDR) for genetic evaluation of dairy cattle as a replacement for the traditional use of estimated accumulated 305-d yields. Some advantages of test-day models (TDM) include the ability to account for environmental effects of each test day, the ability to model the trajectory of the lactation for individual genotypes or groups of animals, and the possibility of genetic evaluations for persistency of production. Also, the use of test-day models avoids the necessity of extending short lactations on culled animals and animals with records in progress. The disadvantages of TDM include computational difficulties associated with analyzing much larger datasets and the need to estimate many more parameters than in a traditional 305-d lactation model. Several different models have been proposed to model the trajectory of the lactation, including so-called "biological functions," various polynomials and character process models. At present, there is not universal agreement on which models to use in routine prediction of breeding values and better methods to compare models are desirable. Obtaining accurate estimates of the dispersion parameters to use in TDM remains a challenge. Methods used include a two-step procedure in which the dispersion parameters are estimated in a series of multivariate models followed by a reduction in order of fit using covariance functions, and a one-step procedure in which the parameters of TDM are estimated using restricted maximum likelihood or Bayesian methods in a random regression model. Further research should focus on including multiple lactation data and accounting for heterogeneity variance.

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Year:  2001        PMID: 11814038     DOI: 10.3168/jds.S0022-0302(01)74736-4

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  5 in total

1.  Mapping quantitative trait loci for longitudinal traits in line crosses.

Authors:  Runqing Yang; Quan Tian; Shizhong Xu
Journal:  Genetics       Date:  2006-06-04       Impact factor: 4.562

2.  Use of test-day records to predict first lactation 305-day milk yield using artificial neural network in Kenyan Holstein-Friesian dairy cows.

Authors:  D M Njubi; J W Wakhungu; M S Badamana
Journal:  Trop Anim Health Prod       Date:  2009-10-10       Impact factor: 1.559

3.  Genetic parameters for milk, fat and protein yields in Murrah buffaloes (Bubalus bubalis Artiodactyla, Bovidae).

Authors:  Rusbel Raúl Aspilcueta-Borquis; Roberta Cristina Sesana; Milthon Honorio Munoz Berrocal; Leonardo de Oliveira Seno; Annaiza Braga Bignardi; Lenira El Faro; Lucia Galvão de Albuquerque; Gregório Miguel Ferreira de Camargo; Humberto Tonhati
Journal:  Genet Mol Biol       Date:  2010-03-01       Impact factor: 1.771

4.  Application of random regression models for genetic analysis of 305-d milk yield over different lactations of Iranian Holsteins.

Authors:  Mahdi Elahi Torshizi; Homayoun Farhangfar; Mojtaba Hosseinpour Mashhadi
Journal:  Asian-Australas J Anim Sci       Date:  2017-04-21       Impact factor: 2.509

5.  Prediction of random-regression coefficient for daily milk yield after 305 days in milk by using the regression-coefficient estimates from the first 305 days.

Authors:  Takeshi Yamazaki; Hisato Takeda; Koichi Hagiya; Satoshi Yamaguchi; Osamu Sasaki
Journal:  Asian-Australas J Anim Sci       Date:  2018-03-13       Impact factor: 2.509

  5 in total

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