Effect of milk fat to protein ratio on genetic variance for milk yield in Thai tropical Holstein cattle

The objective of this research was to investigate the optimum fat to protein ratio (FPR) in Thai tropical Holstein dairy cattle. First parity data consisting of 20 492 milk yields (MY) records for 24 891 cows for the period 2001 and 2011, were used in the analysis. The analysis used a random regression test-day animal model of third-order Legendre polynomials through the creation of a covariance function based on different FPRs. Variance components were estimated using the Bayesian method via the Gibbs sampling. The estimated heritability of MY in relation to FPR ranged from 0.19 to 0.27 with the pattern being similar to the genetic variances. Genetic correlations of MY at different FPRs were high at consecutive FPRs and then declined to negative in response to greater differences in FPR. Based on the results, it is concluded that the optimum FPR is in the range of 0.9 to 1.9, corresponding to the genetically controlled energy balance for MY in tropical Holsteins.

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Genetic parameters for test day milk yield of first lactation Holstein cows estimated by random regression using Legendre polynomials

Revista Brasileira de Zootecnia ◽

10.1590/s1516-35982008000400003 ◽

2008 ◽

Vol 37 (4) ◽

pp. 602-608 ◽

Cited By ~ 26

Author(s):

Claudio Napolis Costa ◽

Claudio Manoel Rodrigues de Melo ◽

Irineu Umberto Packer ◽

Ary Ferreira de Freitas ◽

Nilson Milagres Teixeira ◽

...

Keyword(s):

Variance Components ◽

Legendre Polynomials ◽

Genetic Parameters ◽

Genetic Correlations ◽

Random Regression ◽

Holstein Cows ◽

Residual Variance ◽

Environmental Variance ◽

Environmental Correlations ◽

Lp Estimates

Data comprising 263,390 test-day (TD) records of 32,448 first parity cows calving in 467 herds between 1991 and 2001 from the Brazilian Holstein Association were used to estimate genetic and permanent environmental variance components in a random regression animal model using Legendre polynomials (LP) of order three to five by REML. Residual variance was assumed to be constant in all or in some classes of lactation periods for each LP. Estimates of genetic and permanent environmental variances did not show any trend due to the increase in the LP order. Residual variance decreased as the order of LP increased when it was assumed constant, and it was highest at the beginning of lactation and relatively constant in mid lactation when assumed to vary between classes. The range for the estimates of heritability (0.27 - 0.42) was similar for all models and was higher in mid lactation. There were only slight differences between the models in both genetic and permanent environmental correlations. Genetic correlations decreased for near unity between adjacent days to values as low as 0.24 between early and late lactation. A five parameter LP to model both genetic and permanent environmental effects and assuming a homogeneous residual variance would be a parsimonious option to fit TD yields of Holstein cows in Brazil.

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Genetic parameters for milk production and persistency in the Iranian Holstein population by the multitrait random regression model

Archives Animal Breeding ◽

10.7482/0003-9438-57-012 ◽

2014 ◽

Vol 57 (1) ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Khabat Kheirabadi ◽

Sadegh Alijani

Keyword(s):

Milk Yield ◽

Milk Fat ◽

Genetic Correlations ◽

Random Regression ◽

Genetic Dissection ◽

Production Traits ◽

Data Set ◽

Random Regression Model ◽

Regression Test ◽

Fat Yield

Abstract. For genetic dissection of milk, fat, and protein production traits in the Iranian primiparous Holstein dairy cattle, records of these traits were analysed using a multitrait random regression test-day model. Data set included 763 505 test-day records from 88 204 cows calving since 1993. The (co)variance components were estimated by Bayesian method. The obtained results indicated that as in case of genetic correlations within traits, genetic correlations between traits decrease as days in milk (DIM) got further apart. The strength of the correlations decreased with increasing DIM, especially between milk and fat. Heritability estimates for 305-d milk, fat, and protein yields were 0.31, 0.29, and 0.29, respectively. Heritabilities of test-day milk, fat, and protein yields for selected DIM were higher in the end than at the beginning or the middle of lactation. Heritabilities for persistency ranged from 0.02 to 0.24 and were generally highest for protein yield (0.05 to 0.24) and lowest for fat yield (0.02 to 0.17), with milk yield having intermediate values (0.06 to 0.22). Genetic correlations between persistency measures and 305-d production were higher for protein and milk yield than for fat yield. The genetic correlation of the same persistency measures between milk and fat yields averaged 0.76, and between milk and protein yields averaged 0.82.

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Estimation of genetic parameters for test-day milk yield in Girolando cows using a random regression model

Arquivo Brasileiro de Medicina Veterinária e Zootecnia ◽

10.1590/1678-4162-12071 ◽

2021 ◽

Vol 73 (1) ◽

pp. 18-24

Author(s):

E.P.B. Santos ◽

G.L. Feltes ◽

R. Negri ◽

J.A. Cobuci ◽

M.V.G.B. Silva

Keyword(s):

Regression Model ◽

Milk Yield ◽

Legendre Polynomials ◽

Genetic Parameters ◽

Genetic Correlations ◽

Random Regression ◽

Third Order ◽

Good Tool ◽

Components Of Variance ◽

Random Regression Model

ABSTRACT The objective of this study was to estimate the components of variance and genetic parameters of test-day milk yield in first lactation Girolando cows, using a random regression model. A total of 126,892 test-day milk yield (TDMY) records of 15,351 first-parity Holstein, Gyr, and Girolando breed cows were used, obtained from the Associação Brasileira dos Criadores de Girolando. To estimate the components of (co) variance, the additive genetic functions and permanent environmental covariance were estimated by random regression in three functions: Wilmink, Legendre Polynomials (third order) and Linear spline Polynomials (three knots). The Legendre polynomial function showed better fit quality. The genetic and permanent environment variances for TDMY ranged from 2.67 to 5.14 and from 9.31 to 12.04, respectively. Heritability estimates gradually increased from the beginning (0.13) to mid-lactation (0.19). The genetic correlations between the days of the control ranged from 0.37 to 1.00. The correlations of permanent environment followed the same trend as genetic correlations. The use of Legendre polynomials via random regression model can be considered as a good tool for estimating genetic parameters for test-day milk yield records.

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Genetic association between test-day milk fat to protein ratio and fertility traits in dairy cows: A random regression model analyses

Suomen Maataloustieteellisen Seuran Tiedote ◽

10.33354/smst.75923 ◽

2008 ◽

pp. 1-7

Author(s):

E. Negussie ◽

I. Strandén ◽

E. A. Mäntysaari

Keyword(s):

Genetic Association ◽

Milk Fat ◽

Genetic Correlations ◽

Random Regression ◽

Ovarian Activity ◽

Early Lactation ◽

Genetic Associations ◽

Protein Ratio ◽

Negative State ◽

Fertility Traits

In early lactating cows changes in energy balance (EB) and subsequent mobilisation of body reserves result changes in milk yield (MY) and milk composition. These variations could be used as indicators of changes or problems in feeding, health and fertility. Recently it is postulated that changes in milk fat to protein ratio (FPR) may be associated with a negative EB. A negative EB, typical of the early phase of lactation impairs cows fertility whereas a recovery in EB from its most negative state, signals the initiation of ovarian activity indicating a direct relationship. Therefore, since measuring EB in large populations is difficult and expensive, assessing the genetic association between ratios of milk components and fertility traits, especially at different stages of lactation may provide an inexpensive indicator of EB. Besides, in selection programs, such information could be used to identify sires and cow families that have chronic energy deficiency and poor fertility in early lactation. The objectives of this study were to estimate covariance components for test-day FPR, MY and fertility traits and to assess the genetic associations between these traits during lactation using random regression models (RRM). Genetic parameters of test-day FPR, MY and fertility were estimated using bivariate RRM that combine traits with different data structures employing a meta-model analyses. Fertility traits considered were days from calving to insemination (DFI), days open (DO), number of inseminations (NI), non-return rate to 56 days (NRR). Data was from a total of 22422 first lactation Finnish Ayrshire cows. The sire pedigree file had 638 males of which 509 sires had daughters with data. Heritability of test-day FPR during lactation ranged from 0.08 to 0.17 while the heritability of DFI, DO, NI and NRR were 0.06, 0.03, 0.01 and 0.02, respectively. Genetic correlations between test-day FPR and MY during early lactation ranged from 0.10-0.28. The positive genetic correlation between these traits indicates that genetically high producing cows tend to have high FPR during early lactation. Genetic correlations between test-day FPR and DFI, DO, NI, and NRR during early lactation were from 0.05-0.28, 0.03-0.24, 0.01-0.03, and -0.01-0.03, respectively. Of the fertility traits, the strongest genetic association was between test-day FPR and DFI or DO. The relatively low correlations between test-day FPR and the other fertility traits (NI and NRR) could be due to they are measures of fertility that are recorded after cows recovered from the most negative state of EB and started cycling. The positive and relatively higher genetic correlations between test-day FPR and DFI or DO indicate cows with high test-day FPR in early lactation tends to take longer from calving to first insemination and successful conception. The results from this study indicate that high FPR in early lactation could be used as an indicator of negative EB and cows of poor fertility, which take longer time from calving to first insemination and successful conception.

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Genetic parameters for production traits in primiparous Holstein cows estimated by random regression models

Revista Brasileira de Zootecnia ◽

10.1590/s1516-35982011000100012 ◽

2011 ◽

Vol 40 (1) ◽

pp. 85-94 ◽

Cited By ~ 6

Author(s):

Igor de Oliveira Biassus ◽

Jaime Araújo Cobuci ◽

Claudio Napolis Costa ◽

Paulo Roberto Nogara Rorato ◽

José Braccini Neto ◽

...

Keyword(s):

Milk Fat ◽

Legendre Polynomials ◽

Regression Models ◽

Genetic Parameters ◽

Genetic Correlations ◽

Random Regression ◽

Holstein Cows ◽

Lactation Period ◽

Production Traits ◽

Residual Variances

The objective of this study was to estimate genetic parameters for milk, fat and protein yields of Holstein cows using 56,508; 35,091 and 8,326 test-day milk records from 7,015, 4,476 and 1,114 cows, calves of 359, 246 and 90 bulls, respectively. The additive genetic and permanent environmental effects were estimated using REML. Random regression models with Legendre polynomials from order 3 to 6 were used. Residual variances were considered homogeneous over the lactation period. The estimates of variance components showed similar trends, with an increase of the polynomial order for each trait. The heritability estimates ranged from 0.14 to 0.31; 0.03 to 0.21 and 0.09 to 0.33 for milk, fat and protein yield, respectively. Genetic correlations among milk, fat and protein yields ranged from 0.02 to 1.00; 0.34 to 1.00 and 0.42 to 1.00, respectively. Models with higher order Legendre polynomials are the best suited to adjust test-day data for the three production traits studied.

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Relationship between Temperament and Stage of Lactation, Productivity and Milk Composition of Dairy Cows

Animals ◽

10.3390/ani11071840 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1840

Author(s):

Ramūnas Antanaitis ◽

Vida Juozaitienė ◽

Vesta Jonike ◽

Vytenis Čukauskas ◽

Danguolė Urbšienė ◽

...

Keyword(s):

Milk Fat ◽

Genetic Correlations ◽

Subclinical Mastitis ◽

Milk Composition ◽

Cow Milk ◽

Phenotypic Correlation ◽

Protein Ratio ◽

Stage Of Lactation ◽

Milk Urea ◽

Milk Performance

The aim of this study was to assess the relationship between temperament and milk performance in cows at different stages of lactation, describing their productivity, metabolic status and resistance to mastitis. This study showed that with increasing lactation, cows’ temperament indicators decreased (p < 0.001) and they became calmer. The highest temperament score on a five-point scale was found in cows between 45 and 100 days of lactation. In the group of pregnant cows, we found more cows (p = 0.005) with a temperament score of 1–2 compared with non-pregnant cows A normal temperament was usually detected in cows with lactose levels in milk of 4.60% or more and when the somatic cell count (SCC) values in cow milk were <100,000/mL and 100,000–200,000/mL, with a milk fat-to-protein ratio of 1.2. A larger number of more sensitive and highly aggressive cows was detected at a low milk urea level. In contrast to a positive phenotypic correlation (p < 0.05), this study showed a negative genetic correlation between the temperament of cows and milk yield (p < 0.001). Positive genetic correlations between temperament scores and milk somatic cells (p < 0.001) and milk fat-to-protein ratio (p < 0.05) were found to indicate a lower genetic predisposition in cows with a calmer temperament to subclinical mastitis and ketosis. On the other hand, the heritability of temperament (h2 = 0.044–0.100) showed that only a small part of the phenotypic changes in this indicator is associated with genetic factors.

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Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods

Archives Animal Breeding ◽

10.5194/aab-59-243-2016 ◽

2016 ◽

Vol 59 (2) ◽

pp. 243-248 ◽

Cited By ~ 3

Author(s):

Hafedh Ben Zaabza ◽

Abderrahmen Ben Gara ◽

Hedi Hammami ◽

Mohamed Amine Ferchichi ◽

Boulbaba Rekik

Keyword(s):

Dairy Cattle ◽

Bayesian Methods ◽

Milk Fat ◽

Variance Components ◽

Genetic Parameters ◽

Genetic Correlations ◽

Bayesian Procedure ◽

Milk Traits ◽

Residual Variances ◽

Estimation Of Variance

Abstract. A multi-trait repeatability animal model under restricted maximum likelihood (REML) and Bayesian methods was used to estimate genetic parameters of milk, fat, and protein yields in Tunisian Holstein cows. The estimates of heritability for milk, fat, and protein yields from the REML procedure were 0.21 ± 0.05, 0.159 ± 0.04, and 0.158 ± 0.04, respectively. The corresponding results from the Bayesian procedure were 0.273 ± 0.02, 0.198 ± 0.01, and 0.187 ± 0.01. Heritability estimates tended to be larger via the Bayesian than those obtained by the REML method. Genetic and permanent environmental variances estimated by REML were smaller than those obtained by the Bayesian analysis. Inversely, REML estimates of the residual variances were larger than Bayesian estimates. Genetic and permanent correlation estimates were on the other hand comparable by both REML and Bayesian methods with permanent environmental being larger than genetic correlations. Results from this study confirm previous reports on genetic parameters for milk traits in Tunisian Holsteins and suggest that a multi-trait approach can be an alternative for implementing a routine genetic evaluation of the Tunisian dairy cattle population.

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Estimation of genetic parameters and comparison of random regression animal and sire models of production traits in the first three lactations of Iranian Holsteins

Biotechnology in Animal Husbandry ◽

10.2298/bah1402261m ◽

2014 ◽

Vol 30 (2) ◽

pp. 261-279 ◽

Cited By ~ 1

Author(s):

A. Mohammadi ◽

S. Alijani

Keyword(s):

Milk Fat ◽

Genetic Parameters ◽

Additive Genetic Variance ◽

Genetic Correlations ◽

Random Regression ◽

Lactation Period ◽

Production Traits ◽

Environmental Variance ◽

In The Beginning ◽

Residual Variances

This study was conducted to compare of random regression (RR) animal and sire models for estimation of the genetic parameters for production traits of Iranian Holstein dairy cows. For this purpose, the test day records were used belonged to first three lactations of cows and for, milk, fat and protein yields traits where, collected from 2003 to 2010, by the national breeding center of Iran. The genetic parameters were estimated using restricted maximum likelihood algorithm. To compare the model, different criterion -2logL value, AIC, BIC and RV were used for considered traits. Residual variances were considered homogeneous over the lactation period. Obtained results showed that additive genetic variance was highest in the beginning and end lactation and permanent environmental variance was highest in beginning of lactation than other lactation period. Heritabilities estimate for milk, fat and protein yields by RR animal and sire models were found to be lowest during early lactation (0.05, 0.04 and 0.07; 0.05, 0.19 and 0.13; 0.14, 0.19 and 0.15, for milk, fat and protein yields and in first, second and third lactation respectively). However, estimated heritabilities during lactation did not vary among different order Legendre polynomials, and also between RR animal and sire models. The variation in genetic correlations estimate in the RR animal and sire models was larger in the first lactation than in the second and third lactations. Thus, based on the results obtained, it can be inferred that the RR animal model is better for modeling yield traits in Iranian Holsteins.

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Definition of subgroups for fixed regression in the test-day animal model for milk production ofHolsteincattle in theCzech Republic

Czech Journal of Animal Science ◽

10.17221/3976-cjas ◽

2011 ◽

Vol 50 (No. 1) ◽

pp. 7-13 ◽

Cited By ~ 3

Author(s):

L. Zavadilová ◽

E. Němcová ◽

J. Přibyl ◽

J. Wolf

Keyword(s):

Animal Model ◽

Milk Production ◽

Legendre Polynomials ◽

Environmental Effect ◽

Random Regression ◽

Breeding Values ◽

Regression Test ◽

Days Open ◽

Calving Season ◽

Definition Of

The investigation was based on roughly 3.9, 2.7 and 1.7 million test-day records from first, second and third lactation, respectively, sampled from 596 200 Czech Holstein cows between the years 1991 and 2002. Breeding values were estimated from multi-lactation random-regression test-day models which contained the fixed effect of herd-test day, fixed regression on days in milk and random regressions on the animal level and the permanent environmental effect. Third degree Legendre polynomials (with four coefficients) were used for both the fixed and random regressions. The models differed in fixed regression. In Analysis I, 96 subclasses were defined according to age at calving, season and year of calving within lactation. In Analysis II, days open were additionally included as a grouping factor resulting in 480 subclasses. Rank correlations over 0.98 between both analyses were observed for breeding values for sires. Grouping according to Analysis I was recommended.  

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Random regression models to estimate genetic parameters for test-day milk yield and composition in Iranian buffaloes

Archives Animal Breeding ◽

10.7482/0003-9438-56-027 ◽

2013 ◽

Vol 56 (1) ◽

pp. 276-284 ◽

Cited By ~ 1

Author(s):

M. Madad ◽

N. Ghavi Hossein-Zadeh ◽

A. A. Shadparvar ◽

D. Kianzad

Keyword(s):

Milk Yield ◽

Milk Fat ◽

Milk Protein ◽

Genetic Parameters ◽

Genetic Correlations ◽

Random Regression ◽

Production Traits ◽

Environmental Correlations ◽

Protein Percentage ◽

Heritability Estimates

Abstract. The objective of this study was to estimate genetic parameters for milk yield and milk percentages of fat and protein in Iranian buffaloes. A total of 9,278 test-day production records obtained from 1,501 first lactation buffaloes on 414 herds in Iran between 1993 and 2009 were used for the analysis. Genetic parameters for productive traits were estimated using random regression test-day models. Regression curves were modeled using Legendre polynomials (LPs). Heritability estimates were low to moderate for milk production traits and ranged from 0.09 to 0.33 for milk yield, 0.01 to 0.27 for milk protein percentage and 0.03 to 0.24 for milk fat percentage, respectively. Genetic correlations ranged from −0.24 to 1 for milk yield between different days in milk over the lactation. Genetic correlations of milk yield at different days in milk were often higher than permanent environmental correlations. Genetic correlations for milk protein percentage ranged from −0.89 to 1 between different days in milk. Also, genetic correlations for milk percentage of fat ranged from −0.60 to 1 between different days in milk. The highest estimates of genetic and permanent environmental correlations for milk traits were observed at adjacent test-days. Ignoring heritability estimates for milk yield and milk protein percentage in the first and final days of lactation, these estimates were higher in the 120 days of lactation. Test-day milk yield heritability estimates were moderate in the course of the lactation, suggesting that this trait could be applied as selection criteria in Iranian milking buffaloes.

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