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      Genetic evaluation of dairy cattle using test-day models.

      Journal of dairy science
      Algorithms, Animals, Cattle, genetics, Environment, Female, Lactation, Likelihood Functions, Male, Models, Genetic, Models, Statistical

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          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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          Author and article information

          Journal
          11814038
          10.3168/jds.S0022-0302(01)74736-4

          Chemistry
          Algorithms,Animals,Cattle,genetics,Environment,Female,Lactation,Likelihood Functions,Male,Models, Genetic,Models, Statistical

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