Estimating variance components for test day milk records by restricted maximum likelihood with a random regression animal model

Olori, V E; Hill, William G.; McGuirk, Brian; Brotherstone, S.

doi:10.1016/s0301-6226(99)00052-4

Cited by 133 publications

(212 citation statements)

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“…Differently from observations in other studies (Olori et al, 1999;Rekaya et al, 1999;Brotherstone et al, 2000;Jamrozik et al, 2001;Lopez-Romero and Carabañ o, 2003), in which higher RV estimates were obtained for the beginning and end of lactation, RV estimates in the present study were similar among the DIM classes, as well as Table 5 Estimates of covariance and correlations between the random regression coefficients of the non-hereditary animal effect for AS models and eigenvalues (l) associated to matrix k p among the fitted models. Thus, assuming RV homogeneity does not appear to compromise the accuracy of the estimates of the other variance components and genetic parameters (Tables 3, 4 and 5).…”

Section: Discussioncontrasting

confidence: 99%

“…A slight increase was observed at the end of lactation, when heritability estimates reached the value 0.40. Olori et al (1999) reported similar trends when fitting a quadratic polynomial curve. With respect to the convergence problems observed, heritability estimates in the W model ( Figure 4c) were higher than those obtained from the other models (except at the beginning of lactation).…”

Section: Discussionmentioning

confidence: 59%

“…Similar results were reported in other studies (Rekaya et al, 1999;Brotherstone et al, 2000). Table 7 Estimates of heritability (diagonal), genetic (below the diagonal) and non-hereditary correlations (above the diagonal) between test-day milk yields for selected days in milk (DIM) using the ASME1 model Table 9 Estimates of heritability (diagonal), genetic (below the diagonal) and non-hereditary correlations (above the diagonal) between test-day milk yields for selected days in milk (DIM) using the W* model The present study was focused on the evaluation of the parametric functions of Ali and Schaeffer and Wilmink and a combination of Legendre and Wilmink function, but other studies report satisfactory results obtained from using the Legendre orthogonal polynomials to fit random regression models (Olori et al, 1999;Brotherstone et al, 2000;Pool et al, 2000). Brotherstone et al (2000) observed that parametric functions resulted in higher values for the maximum likelihood functions, but produced negative genetic correlations between test days in the beginning and end of lactation, which were not observed when using Legendre polynomials.…”

Section: Discussionmentioning

confidence: 99%

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

Melo

Packer²,

Costa³

et al. 2007

Animal

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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.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 59%

Section: Discussionmentioning

confidence: 99%

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

Melo

Packer²,

Costa³

et al. 2007

Animal

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“…The current heritability estimates for milk yield are higher than those reported earlier based on Australian HF data (Meyer et al, 1989;Veerkamp and Goddard, 1998;Van der Werf et al, 1998). On the other hand, the current heritability estimates are slightly lower than those reported by Olori et al (1999) and Pool et al (2000) in the first 305 days.…”

Section: Discussioncontrasting

confidence: 85%

“…For example, Wilmink (1988) estimated genetic correlations of 0.76, 0.64 and 0.67 for milk, fat and protein yield in the 2nd month of lactation and with the corresponding yield recorded between 91 to 180 days, respectively. Others have shown similar results (Meyer et al, 1989;Van der Werf et al, 1998;Veerkamp and Goddard, 1998;Olori et al, 1999;Pool et al, 2000). The results in this study suggest that the pattern of relationships among days of lactation to 540 days is, in general, similar to that observed in the first 305 days.…”

Section: --supporting

confidence: 88%

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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Impact of different numbers of milk test-day records during lactation on the reliability of estimated breeding values

Feltes

Michelotti

Oliveira

et al. 2022

Trop Anim Health Prod

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Estimating variance components for test day milk records by restricted maximum likelihood with a random regression animal model

Cited by 133 publications

References 19 publications

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

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

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

Impact of different numbers of milk test-day records during lactation on the reliability of estimated breeding values

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