Estimates of Genetic Parameters for a Test Day Model with Random Regressions for Yield Traits of First Lactation Holsteins

The objective of this study was to describe the genetic and phenotypic relationship between milk urea nitrogen (MUN) and reproductive traits in Iranian Holstein dairy cows. Test-day MUN data obtained from 57 301 dairy cows on 20 large dairy herds in Iran between January 2005 and June 2009. Genetic parameters for MUN and reproductive traits were estimated with a five-trait model using ASREML program. Random regression test-day models were used to estimate heritabilities separately for MUN from first, second and third lactations. Regression curves were modeled using Legendre polynomials of order 3. Herd-year-season along with age at calving was included as fixed effects in all models for reproductive traits. Heritabilities for MUN and reproductive traits were estimated separately for first lactation, second lactation and third lactation. The estimated heritabilities for MUN varied from 0.18 to 0.22. The heritability estimate was low for reproductive traits, which ranged from 0.02 to 0.06 for different traits and across parities. Except for days open, phenotypic and genetic correlations of MUN with reproductive performance traits were close to zero. Genetic correlations between MUN and days open were 0.23, 0.35 and 0.45 in first, second and third lactation, respectively. However, the phenotypic correlation between MUN at different parities was moderate (0.28 to 0.35), but the genetic correlation between MUN at different parities was high and ranged from 0.84 to 0.97. This study shows a limited application of MUN for use in selection programs to improve reproductive performance.

Section: Methodsmentioning

confidence: 99%

Genetic relationship between milk urea nitrogen and reproductive performance in Holstein dairy cows

Hossein-Zadeh

Ardalan

2011

“…In such a context, Jamrozik and Schaeffer (1997) reported very high heritability estimates using TDM, but negative genetic correlations among test days in early and late lactation when using Ali and Schaeffer (1987) curve to adjust the additive genetic effect, assuming the non-hereditary animal effect constant. Later, Jamrozik et al (1997a and b), using the Wilmink (1987) curve reported more realistic heritability estimates at the beginning and end of lactation, indicating the importance of adequate modeling of the non-hereditary animal effect in random regression models.…”

Section: Introductionmentioning

confidence: 99%

Genetic parameters for test day milk yields of first lactation Holstein cows by random regression models

Melo

Packer²,

Costa³

et al. 2007

Covariance components for test day milk yield using 263 390 first lactation records of 32 448 Holstein cows were estimated using random regression animal models by restricted maximum likelihood. Three functions were used to adjust the lactation curve: the five-parameter logarithmic Ali and Schaeffer function (AS), the three-parameter exponential Wilmink function in its standard form (W) and in a modified form (W*), by reducing the range of covariate, and the combination of Legendre polynomial and W (LEG þ W). Heterogeneous residual variance (RV) for different classes (4 and 29) of days in milk was considered in adjusting the functions. Estimates of RV were quite similar, rating from 4.15 to 5.29 kg 2 . Heritability estimates for AS (0.29 to 0.42), LEG þ W (0.28 to 0.42) and W* (0.33 to 0.40) were similar, but heritability estimates used W (0.25 to 0.65) were highest than those estimated by the other functions, particularly at the end of lactation. Genetic correlations between milk yield on consecutive test days were close to unity, but decreased as the interval between test days increased. The AS function with homogeneous RV model had the best fit among those evaluated.

“…Residual variance seems to vary in the course of lactation. Jamrozik and Schaeffer (1997) clearly found higher residual variances for milk yield in the first lactation days in contrast to further lactation. This seems to be similar in the present study and can explain the higher residual variance estimated for the whole lactation (ŝ 2 e 5 5.44) in contrast to the separate sections of lactation (ŝ 2 e 5 4.00, 3.65 and 3.05, respectively), because the first lactation days are not included in the first section (days in milk 30 to 70), but they are included for the whole lactation (days in milk 5 to 230).…”

Section: Lactation Curvesmentioning

confidence: 86%

“…Wood, 1967;Grossman and Koops, 1988;Kirkpatrick et al, 1994;Jamrozik and Schaeffer, 1997;van der Werf, 2001;Sylvestre et al, 2006). Ló pez- Romero and Carabañ o (2003) noted that functions frequently used for the modelling of lactation curves are those proposed by Ali and Schaeffer (1987) or Wilmink (1987).…”

Section: Model I (Fr)mentioning

confidence: 99%

Analysis of water intake, dry matter intake and daily milk yield using different error covariance structures

Kramer

Stamer²,

Spilke

et al. 2008

The aim of the present study was to investigate the daily measured traits milk yield, water intake and dry matter intake with fixed and random regression models added with different error covariance structures. It was analysed whether these models deliver better model fitting in contrast to conventional fixed and random regression models. Furthermore, possible autocorrelation between repeated measures was investigated. The effect of model choice on statistical inference was also tested. Data recording was performed on the Futterkamp dairy research farm of the Chamber of Agriculture of SchleswigHolstein. A dataset of about 21 000 observations from 178 Holstein cows was used. Average milk yield, water intake and dry matter intake were 34.9, 82.4 and 19.8 kg, respectively. Statistical analysis was performed using different linear mixed models. Lactation number, test day and the parameters to model the function of lactation day were included as fixed effects. Different structures were tested for the residuals; they were compared for their ability to fit the model using the likelihood ratio test, and Akaike's and Bayesian's information criteria. Different autocorrelation patterns were found. Adjacent repeated measures of daily milk yield were highest correlated (p 1 5 0.32) in contrast to measures further apart, while for water intake and dry matter intake, the measurements with a lag of two units had the highest correlations with p 2 5 0.11 and 0.12. The covariance structure of TOEPLITZ was most suitable to indicate the dependencies of the repeated measures for all traits. Generally, the most complex model, random regression with the additional covariance structure TOEPLITZ(4), provided the lowest information criteria. Furthermore, the model choice influenced the significance values of one fixed effect and therefore the general inference of the data analysis. Thus, the random regression 1 TOEPLITZ(4) model is recommended for use for the analysis of equally spaced datasets of milk yield, water intake and dry matter intake.