Estimates of Genetic Parameters for Daily Somatic Cell Count of Australian Dairy Cattle

Haile‐Mariam, M.; Goddard, Michael E.; Bowman, P.J.

doi:10.3168/jds.s0022-0302(01)74587-0

Cited by 61 publications

(91 citation statements)

References 19 publications

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“…For log SCC, the heritabilities, phenotypic and genetic correlations estimated in the current study are higher than those reported by Haile-Mariam et al (2001) based on the first 305 days of lactation.…”

Section: --contrasting

confidence: 89%

Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations)

Haile‐Mariam¹,

Goddard

2008

Animal

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Test-day milk yield and somatic cell count data over extended lactation (lactation to 540-600 days) were analysed considering part lactations as different traits and fitting random regression (RR) models. Data on Australian Jersey and Holstein Friesian (HF) were used to demonstrate the shape of the lactation curve and data on HF were used for genetic study. Test-day data from about 100 000 cows that calved between 1998 and 2005 were used for this study. In all analyses, a sire model was used.When part lactations were considered as different traits, protein yield early in the lactation (e.g. first 2 months) had a genetic correlation of about 0.8 with protein yield produced after 300 days of lactation. Genetic correlations between lactation stages that are adjacent to each other were high (0.9 or more) within parity. Across parities, genetic correlations were high for both protein and milk yield if they are within the same stage of lactation. Phenotypic correlations were lower than genetic correlations. Heritability of milk-yield traits estimated from the RR model varied from 0.15 at the beginning of the lactation to as high as 0.37 by the 4th month of lactation. All genetic correlations between different days in milk were positive, with the highest correlations between adjacent days in milk and decreasing correlations with increasing time-span. The pattern of genetic correlations between milk yield in the second 300 days (301 to 600 days of lactation) do not markedly differ from the pattern in the first 300 days of lactation. The lowest estimated genetic correlation was 0.15 between milk yield on days 45 and 525 of lactation. The result from this study shows that progeny of bulls with high estimated breeding values for yield traits and those that produce at a relatively high level in the first few months are the most likely candidates for use in herds favouring extended lactations.

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Section: --contrasting

confidence: 89%

Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations)

Haile‐Mariam¹,

Goddard

2008

Animal

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“…The permanent environmental effect was much higher than the heritabilities for all lactations of both breeds. Heritability estimates from the ML-model from this study are on the lower range of those reported in the literature, while the permanent environmental estimates correspond better (Reents et al, 1995a, Reents et al, 1995b, Haile-Mariam et al, 2000. The RM-model estimated heritabilities for SCS of 0.19 for the Holsteins and 0.18 for the Jerseys, while the permanent environmental effects are in the same range (0.19 for the Holsteins) or slightly higher (0.21 for the Jerseys).…”

Section: Resultsmentioning

confidence: 67%

“…In order to ensure a well-linked data structure for variance component estimation, the data were strictly selected according to the study of Haile-Mariam et al (2000). Somatic Cell Count (1000's/ml) (SCC) was transformed to ln (SCC) for each testday to achieve normality and homogeneity of variances.…”

Section: Methodsmentioning

confidence: 99%

Breeding value estimation for somatic cell score in South African dairy cattle

Mostert¹,

Banga²,

Groeneveld³

et al. 2004

SA J. An. Sci.

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Two fixed regression testday models were applied for variance component estimation and prediction of breeding values for somatic cell score, using testday records of the first three lactations of South African Holstein and Jersey cows. The first model (ML-model) considered the testdays of the different lactations as different traits in a multiple-trait animal model and the second analysis (RM-model) treated later lactation records as repeated measures of the first lactation. Heritabilities from the RM-model were more in the range of literature estimates compared to that of the ML-model, i.e. 0.19 ± 0.003 for the Holstein breed and 0.18 ± 0.003 for the Jersey breed. Rank correlations indicated that minor changes occur in the ranking of proven sires between breeding values obtained from the ML-and RM-models. Although genetic correlations between parities are not unity, the RM-model estimates more competitive variances and requires extensively less computer time to predict breeding values compared to the ML-model and are therefore recommended for breeding value estimation on a national basis. _______________________________________________________________________________________

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“…Genetic variances estimated by de Roos et al (2003) were within a range of values similar to or slightly lower than the genetic variances in this paper. Haile-Mariam et al (2001b) presented an opposite pattern of changes of genetic variances; they obtained low values at the beginning and higher ones in the second part of lactation, with much lower values (0.04-0.10), but these authors transformed SCC values using a natural logarithm and twoparity RR sire model.…”

Section: Discussionmentioning

confidence: 99%

Genetic parameters for somatic cell score for Polish Black-and-White cattle estimated with a random regression model

Ptak

Brzozowski²,

Jagusiak³

et al. 2007

J. Anim. Feed Sci.

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Genetic parameters for somatic cell score (SCS) for Polish Black-and-White cattle were estimated using 52,599 test day records from the first six lactations of 8,418 cows, and the Bayesian method via Gibbs sampling. The two-trait random regression (RR) model included fixed herd-yearmonth of test effect, fixed regressions within genetic group by age at calving by season of calving subclasses, and random regressions for additive genetic and permanent environment effects. First and subsequent lactations were treated as separate traits. Fourth-order Legendre polynomials were used for regressions. There were 200,000 samples of (co)variances for random effects generated with the first 20,000 samples as the burn-in period.Genetic variances were in the range of 0.37 to 0.76 throughout first parity and 0.43 to 0.91 throughout later parities, with averages of 0.41 and 0.48, respectively. Variances for permanent environment increased from 1.04 to 1.37 throughout the first, and from 1.11 to 2.00 throughout subsequent parities. Estimates of heritabilities were from 0.11 to 0.20 for first and 0.13 to 0.22 for subsequent lactations, indicating stronger genetic resistance to mastitis in later parities. The highest values of heritability were observed at peripheries. Genetic correlations for SCS between first and later parities for different days in milk (DIM) were all less than 0.5, with the highest values in midlactation.

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Estimates of Genetic Parameters for Daily Somatic Cell Count of Australian Dairy Cattle

Cited by 61 publications

References 19 publications

Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations)

Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations)

Breeding value estimation for somatic cell score in South African dairy cattle

Genetic parameters for somatic cell score for Polish Black-and-White cattle estimated with a random regression model

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