Genetic parameters for milkability from the first three lactations in Fleckvieh cows

Dodenhoff, J.; Emmerling, Reiner

doi:10.1017/s1751731108003716

Cited by 11 publications

(17 citation statements)

References 11 publications

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“…The heritability obtained by the 2 models showed no important oscillations until week 21, after which it decreased until the end of lactation. The tendencies of the heritability estimates were similar to those reported by Zwald et al (2005), Dodenhoff and Emmerling (2009), Gray et al (2011, and Laureano et al (2012). Smaller oscillations in the heritability estimates after week 7 of lactation were observed for the models using Legendre polynomials.…”

Section: Resultssupporting

confidence: 82%

“…Repeatability models have been used to investigate milk flow in primiparous Holstein cows using either a scale of visual scores ranging from 1 to 3 (Erf et al, 1992) or objective measures (milking time, kg/min) (Rensing and Ruten, 2005). Although milk flow is the data obtained subjectively during lactation usually, this parameter tends to be analyzed by repeatability or ordinary multi-trait models (König et al, 2006;Dodenhoff and Emmerling, 2009;Gray et al, 2011Gray et al, , 2012Samoré et al, 2011;Laureano et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Random regression models using different functions to model milk flow in dairy cows

Laureano¹,

Bignardi²,

Faro³

et al. 2014

Genet. Mol. Res.

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ABSTRACT. We analyzed 75,555 test-day milk flow records from 2175 primiparous Holstein cows that calved between 1997 and 2005. Milk flow was obtained by dividing the mean milk yield (kg) of the 3 daily milking by the total milking time (min) and was expressed as kg/min. Milk flow was grouped into 43 weekly classes. The analyses were performed using a single-trait Random Regression Models that included direct additive genetic, permanent environmental, and residual random effects. In addition, the contemporary group and linear and quadratic effects of cow age at calving were included as fixed effects. Fourth-order orthogonal Legendre polynomial of days in milk was used to model the mean trend in milk flow. The additive genetic and permanent environmental covariance functions were estimated using random regression Legendre polynomials and B-spline functions of days in milk. The model using a third-order Legendre polynomial for additive genetic effects and a sixth-order polynomial for permanent environmental effects, which contained 7 residual classes, proved to be the most adequate to describe variations in milk flow, and was also the Random regression models and milk flow in dairy cows most parsimonious. The heritability in milk flow estimated by the most parsimonious model was of moderate to high magnitude.

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Section: Resultssupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Random regression models using different functions to model milk flow in dairy cows

Laureano¹,

Bignardi²,

Faro³

et al. 2014

Genet. Mol. Res.

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show abstract

“…The lactation profiles for the different milking characteristics are generally in line with previous studies in lactating dairy cows (Zwald et al, 2005;Tan in et al, 2006;McCarthy et al, 2007;Walsh et al, 2007;Dodenhoff and Emmerling, 2009;Prendiville et al 2010); Samoré et al (2011) also reported an increased Significances of the associations are in Table 2. percentage of bimodal milk profiles later in lactation. Furthermore, the greater milking duration in older animals reported in the present study corroborate analyses of other international populations (Tan in et al, 2006;McCarthy et al, 2007;Sandrucci et al, 2007;Walsh et al, 2007).…”

supporting

confidence: 88%

“…The RMD lactation profile of the younger animals differed from older animals, with the speed of milking in younger animals increasing with stage of lactation, possibly reflecting the acclimatization of younger animals to the milking process. In contrast, however, to the present study, others (Tan in et al, 2006;Antalík and Strapák, 2011) detected no significant parity effects on either AMF or MMF rate; some studies (McCarthy et al, 2007;Sandrucci et al, 2007;Walsh et al, 2007;Dodenhoff and Emmerling, 2009) did nonetheless report a significant effect of parity on AMF and MMF rate, with mean parity trends in agreement with those observed in the present study.…”

supporting

confidence: 78%

Factors associated with milking characteristics in dairy cows

Berry

Coughlan²,

Enright³

et al. 2013

Journal of Dairy Science

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Milking characteristics, and in particular milking duration, are a known contributor to costs in dairy production systems. Results from previous studies suggest that higher-yielding animals, on average, milk for a longer duration. Culling or selection for reduced milking duration alone may, therefore, reduce milk yield. Here, we propose 2 new traits, residual milking duration (RMD) and residual milking duration including somatic cell score (RMDS). Residual milking duration is represented by the residuals from a least squares regression of milking duration on milk yield; RMDS is represented by the residuals from a least squares regression of milking duration on both milk yield and somatic cell score [i.e., logarithm (base 10) of somatic cell count]. The mathematical properties of least squares regression ensure than the residual traits are independent from the regressor variables, or, in other words, RMDS is not correlated with either milk yield or somatic cell score. Both RMD and RMDS were defined using electronically measured individual cow milking duration from 235,036 part-day milking events from 74,607 cows from 1,075 Irish dairy herds. Twenty-four percent of the variation in milking duration was explained by the multiple regression model containing both milk yield and somatic cell score. The phenotypic standard deviation of RMD and RMDS was 102.2 and 98.2s, respectively, suggesting large variation in milking duration independent of milk yield (and somatic cell score). The correlation of RMD and RMDS with average milk flow rate, which may also be considered a measure of milking efficiency, was -0.74 and -0.75, respectively. Neither RMD nor RMDS was correlated with somatic cell score. However, average milk flow rate was correlated with milk yield (0.57) and milking duration (-0.38). Both RMD and RMDS are useful traits, which exhibit considerable variation and, therefore, can be used by farmers to identify phenotypically slower milking animals irrespective of milk yield (and somatic cell score). However, because of the lack of a correlation between RMD and somatic cell score in the sample population used in the present study, RMD and RMDS values per milking were almost identical.

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“…Sørensen et al (2000) estimated heritability of values of 0.35, 0.27, and 0.17 for Danish Red, Danish Holstein, and Danish Jersey breeds, respectively. Dodenhoff and Emmerling (2009) estimated test-day heritability for average flow rate from 0.21 to 0.40, for different lactations. To date, the literature contains no estimates of genetic correlation between MS and MT.…”

mentioning

confidence: 99%

Short communication: Genetic parameters of milking temperament and milking speed in Canadian Holsteins

Sewalem

Miglior

Kistemaker³

2011

Journal of Dairy Science

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The objectives of this study were to estimate genetic parameters of milking temperament (MT) and milking speed (MS) in Canadian Holsteins and to examine associations of bull proofs of MT and MS with other economically important traits. First-lactation data consisted of 1,940,092 and 2,620,175 cows for MT and MS, respectively. Milking temperament and MS were recorded on a scale from 1 to 5 from very nervous to very calm and from very slow to very fast, respectively. The linear animal model included the fixed effects of herd-year-season of calving, stage of lactation, age at first calving, and the random effects of animal and residual. Both single-trait and bivariate analyses were carried out to estimate genetic parameters of MT and MS. For genetic parameter estimation, 20,000 records from randomly selected herds were used. However, for breeding value estimation, all records were included. Heritability values were 0.128 and 0.139 for MT and MS, respectively. The genetic correlation between MT and MS was 0.247. Analysis of bull proof correlation of MT and MS with other traits indicated that these traits were lowly correlated with a wide range of traits such as production, reproduction, conformation, and auxiliary traits.

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Genetic parameters for milkability from the first three lactations in Fleckvieh cows

Cited by 11 publications

References 11 publications

Random regression models using different functions to model milk flow in dairy cows

Random regression models using different functions to model milk flow in dairy cows

Factors associated with milking characteristics in dairy cows

Short communication: Genetic parameters of milking temperament and milking speed in Canadian Holsteins

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