scholarly journals Estimation of genetic parameters and trends for production traits of dairy cattle in Thailand using a multiple-trait multiple-lactation test day model

2020 ◽  
Vol 33 (9) ◽  
pp. 1387-1399
Author(s):  
Sayan Buaban ◽  
Somsook Puangdee ◽  
Monchai Duangjinda ◽  
Wuttigrai Boonkum
Keyword(s):  
Dairy Cattle ◽  
Milk Fat ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Breeding Value ◽  
Production Traits ◽  
Linear Quadratic ◽  
Multiple Trait

Objective: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,- 3-lactation random regression test-day model.Methods: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients.Results: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively.Conclusion: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.

scholarly journals Estimation of genetic parameters for daily milk yields of primiparous Iranian Holstein cows

2013 ◽  
Vol 56 (1) ◽  
pp. 455-466
Author(s):  
K. Kheirabadi ◽  
S. Alijani ◽  
L. Zavadilová ◽  
S. A. Rafat ◽  
G. Moghaddam
Keyword(s):  
Milk Fat ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
National Level ◽  
Genetic Correlations ◽  
Protein Yield ◽  
Random Regression ◽  
Production Traits ◽  
Multiple Traits ◽  
Multiple Trait

Abstract. Applying a multiple trait random regression (MT-RR) in national level and for whole test day records of a country is a great advance in animal breeding context. Having reliable (co) variance components is a critical step in applying multiple traits genetic evaluation especially in developing countries. Genetic parameters of milk, fat and protein yields were estimated for Iranian Holstein dairy cows. Data included 276 692 test day (TD) production traits records collected of 30 705 primiparous cows belonging to 619 sires. An animal multi-trait random regression model was employed in the analyses using the restricted maximum likelihood (REML) method. The model included herd-test-date, age-season of calving (by applying a fixed regression for each subclass of this effect) and year of calving as fixed effects and random regression (RR) coefficients for additive genetic (AG) and permanent environmental (PE) effects. Obtained results showed that daily heritabilities ranged from 0.10 to 0.21 for milk, from 0.05 to 0.08 for fat and from 0.08 to 0.18 for protein yield. Estimated heritability for 305-d milk, fat and protein yields were 0.25, 0.20 and 0.25, respectively. Correlations between individual test day records within traits were high for adjacent tests (nearly 1) and decreased as the interval between tests increased. Correlations between yields of milk, fat and protein on a given test day are also high and greater during late lactation than during early or mid-lactation. Genetic correlations between 305-d yield traits ranged from 0.75 to 0.92. The largest genetic correlation, as well as permanent environmental correlation, was observed between milk and protein yield.

scholarly journals Genetic parameters for test-day model with random regressions for production traits of Czech Holstein cattle

2011 ◽  
Vol 50 (No. 4) ◽  
pp. 142-154 ◽  
Author(s):  
L. Zavadilová ◽  
J. Jamrozik ◽  
Schaeffer LR
Keyword(s):  
Milk Fat ◽  
Sampling Method ◽  
Random Regression ◽  
Model Parameters ◽  
Residual Variance ◽  
Posterior Distributions ◽  
Production Traits ◽  
Multiple Trait ◽  
Holstein Breed ◽  
Random Regression Model

Multiple-lactation random regression model was applied to test-day records of milk, fat and protein yields in the first three lactations of the Czech Holstein breed. Data included 9 583 cows, 89 584, 44 207 and 11 266 test-day records in the first, second and third lactation, respectively. Milk, fat and protein in the first three lactations were analysed separately and in a multiple-trait analysis. Linear model included herd-test date, fixed regressions within age-season class and two random effects: animal genetic and permanent environment modelled by regressions. Gibbs sampling method was used to generate samples from marginal posterior distributions of the model parameters. The single- and multiple-trait models provided similar results. Genetic and permanent environmental variances and heritability for particular days in milk were high at the beginning and at the end of lactation. The residual variance decreased throughout the lactation. The resulting heritability ranged from 0.13 to 0.52 and increased with parity.  

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

2014 ◽  
Vol 30 (2) ◽  
pp. 261-279 ◽  
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.

Linear and threshold model genetic parameters for disease, fertility and milk production in dairy cattle

2000 ◽  
Vol 71 (3) ◽  
pp. 411-419 ◽  
Author(s):  
H. N. Kadarmideen ◽  
R. Thompson ◽  
G. Simm
Keyword(s):  
Dairy Cattle ◽  
Milk Production ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Threshold Model ◽  
Production Traits ◽  
Milk Production Traits ◽  
Model Framework ◽  
Data Set ◽  
Fertility Traits

AbstractThis study provides estimates of genetic parameters for various diseases, fertility and 305-day milk production traits in dairy cattle using data from a UK national milk recording scheme. The data set consisted of 63891 multiple lactation records on diseases (mastitis, lameness, milk fever, ketosis and tetany), fertility traits (calving interval, conception to first service, number of services for a conception, and number of days to first service), dystocia and 305-day milk, fat and protein yield. All traits were analysed by multi-trait repeatability linear animal models (LM). Binary diseases and fertility traits were further analysed by threshold sire models (TM). Both LM and TM analyses were based on the generalized linear mixed model framework. The LM included herd-year-season of calving (HYS), age at calving and parity as fixed effects and genetic, permanent environmental and residual effects as random. The TM analyses included the same effects as for LM, but HYS effects were treated as random to avoid convergence problems when HYS sub-classes had 0 or 100% incidence. Because HYS effects were treated as random, herd effects were fitted as fixed effects to account for effect of herds in the data. The LM estimates of heritability ranged from 0•389 to 0•399 for 305-day milk production traits, 0•010 to 0•029 for fertility traits and 0•004 to 0•038 for diseases. The LM estimates of repeatability ranged from 0•556 to 0•586 for 305-day milk production traits, 0•029 to 0•086 for fertility traits and 0•004 to 0•100 for diseases. The TM estimates of heritabilities and repeatabilities were greater than LM estimates for binary traits and were in the range 0•012 to 0•126 and 0•013 to 0•168, respectively. Genetic correlations between milk production traits and fertility and diseases were all unfavorable: they ranged from 0•07 to 0•37 for milk production and diseases, 0•31 to 0•54 for milk production and poor fertility and 0•06 to 0•41 for diseases and poor fertility. These results show that future selection programmes should include disease and fertility for genetic improvement of health and reproduction and for sustained economic growth in the dairy cattle industry.

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

2013 ◽  
Vol 56 (1) ◽  
pp. 276-284 ◽  
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.

scholarly journals Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes

2013 ◽  
Vol 96 (9) ◽  
pp. 5923-5932 ◽  
Author(s):  
Rusbel Raul Aspilcueta Borquis ◽  
Francisco Ribeiro de Araujo Neto ◽  
Fernando Baldi ◽  
Naudin Hurtado-Lugo ◽  
Gregório M.F. de Camargo ◽  
...  
Keyword(s):  
Milk Fat ◽  
Regression Models ◽  
Genetic Parameters ◽  
Protein Yield ◽  
Random Regression ◽  
Multiple Trait ◽  
Estimation Of Genetic Parameters

scholarly journals Comparison of different polynomial functions in Random Regression Model for milk production traits of Iranian Holstein dairy cattle

2014 ◽  
Vol 14 (1) ◽  
pp. 55-68 ◽  
Author(s):  
Ali Mohammadi ◽  
Sadegh Alijani ◽  
Hossein Daghighkia
Keyword(s):  
Dairy Cattle ◽  
Regression Model ◽  
Milk Production ◽  
Genetic Parameters ◽  
Random Regression ◽  
Polynomial Functions ◽  
Early Lactation ◽  
Production Traits ◽  
Milk Production Traits ◽  
Random Regression Model

Abstract The aim of this research was to compare different polynomial functions including Legendre polynomials (LP), Wilmink (WRR) and Ali-Schaeffer (ARR) functions, in random regression model (RRM) for estimation of genetic parameters for milk production traits of Iranian Holstein dairy cattle. For this purpose the performance records obtained from test-day (TD) regarding milk yield, fat and protein contents of the cows calving for the first time were used. The numbers of records for the above mentioned traits were 701212, 657004, and 560775, respectively. These records were collected from the years 2006 to 2010 by the National Breeding Center of Iran. The genetic parameters were estimated using Restricted Maximum Likelihood (REML) method by applying RRM. Residual variances were considered homogeneous over the lactation period. To compare the model, different criteria (-2Logl, AIC, BIC and RV) were used for considered traits. Based on the results obtained, for all traits, RRM with LP function (2,5) were chosen as the best model. Considering residual variance (RV), LP (2,2) was proved to be a model which has the lowest performance, while using -2Logl, AIC, BIC criteria, RRM with ARR function was the worst model. According to the results, it is recommended to use LP with low orders for the additive genetic effects and with more orders for the permanent environment effects in the RRM for Iranian Holstein cattle. Permanent environment variance was higher in early lactation than during lactation and additive genetic variance in the early lactation was lower than at the end of lactation. Heritability range of milk yield, fat and protein contents was estimated to be from 0.08 to 0.23, 0.05 to 0.20 and 0.08 to 0.14, respectively. Phenotypic variance of the considered traits during lactation was not constant and it was higher at the beginning and the end of lactation. The additive genetic correlation between adjacent test days was higher than between distant test days.

scholarly journals Genetic analysis of productive and reproductive traits in multiple-breed dairy cattle populations

2017 ◽  
Vol 52 (11) ◽  
pp. 1109-1117 ◽  
Author(s):  
Virgínia Mara Pereira Ribeiro ◽  
Fernanda Albuquerque Merlo ◽  
Gabriela Canabrava Gouveia ◽  
Larissa Kretli Winkelstroter ◽  
Luíza Rodrigues Alves Abreu ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Genetic Parameters ◽  
Reproductive Traits ◽  
Genetic Group ◽  
Random Regression ◽  
Multiple Trait ◽  
Genetic Merit ◽  
Lactation Length ◽  
Random Regression Model ◽  
Residual Variances

Abstract: The objective of this work was to determine whether the random regression model using linear splines (RRMLS) is suitable to estimate the genetic parameters for productive and reproductive traits of a multiple-breed dairy cattle population, as well as to investigate the effect of the genetic group of the progeny on the genetic merit of the sire. The multiple-trait model (MTM) and the RRMLS with one knot fitted for every genetic group were used to obtain the genetic parameters. Records of 1/2 Holstein + 1/2 Gyr (1/2HG), 5/8 Holstein + 3/8 Gyr (5/8HG), and 3/4 Holstein + 1/4 Gyr (3/4HG) crossbreed dams were considered. The RRMLS showed better fitting. The additive and residual variances estimated by the MTM and the RRMLS were similar. Heritability varied from 0.20 to 0.33 for age at first calving, from 0.09 to 0.22 for first lactation length, and from 0.15 to 0.35 for first lactation 305-day milk yield, according to the genetic composition of the dams. The RRMLS is suitable to estimate the genetic parameters for productive and reproductive traits of multiple-breed dairy cattle populations. The genetic merit of the sires is affected by the genetic group of the progeny by which they are evaluated.

scholarly journals Estimation of (co)variance components and breeding values for test-day milk production traits of Holstein dairy cattle via Bayesian approach

2014 ◽  
Vol 30 (1) ◽  
pp. 15-28 ◽  
Author(s):  
R. Mosharraf ◽  
J. Shodja ◽  
M. Bohlouli ◽  
S. Alijani ◽  
S.A. Rafat
Keyword(s):  
Dairy Cattle ◽  
Regression Model ◽  
Milk Production ◽  
Milk Fat ◽  
Variance Components ◽  
Random Regression ◽  
Production Traits ◽  
Milk Production Traits ◽  
Breeding Values ◽  
Random Regression Model

Genetic parameters of milk, fat, and protein yields were estimated in the first lactation of Holstein dairy cattle. The records were collected during the period 2006 to 2011 and analyzed fitting the random regression model. The data included 41178, 25397 and 18716 test-day records of milk, fat and protein yields, respectively that produced by 4746, 3437 and 2525 cows respectively. Fixed effects in model included herd-year-month of test day and age-season of calving. The fixed and random regressions were modeled with normalized Legendre polynomials and (co)variance components were estimated by Bayesian method and Gibbs sampling was used to obtain posterior distributions. Estimates of heritability for milk, fat and protein yields ranged from 0.18 to 0.26; 0.06 to 0.11 and 0.09 to 0.22, respectively. Heritabilities for 305-d milk, fat and protein yields were 0.36, 0.23 and 0.29, respectively. For milk and protein yields, heritabilities were lower at the early of lactation due to the trends of lower additive genetic variance, higher permanent environmental variance. Genetic correlations for milk, fat and protein yields ranged from 0.14 to 1.00; 0.39 to 1.00 and 0.27 to 1.00, respectively. Ranges of estimated breeding values for 305-d yield of milk, fat and protein yields were from -1194.48 to 1412.44; -210.57 to 271.22 and -194.08 to 203.25, respectively. According to the results of this study, random regression model seems to be a flexible and reliable procedure for the genetic evaluation of milk production traits and it can be useful in the breeding programs for Iranian dairy cattle.

Contemporary group alternatives for genetic evaluation of milk yield in small populations of dairy cattle

2019 ◽  
Vol 59 (6) ◽  
pp. 1022 ◽  
Author(s):  
R. J. Pereira ◽  
F. S. Schenkel ◽  
R. V. Ventura ◽  
D. R. Ayres ◽  
L. El Faro ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Milk Yield ◽  
Random Effect ◽  
Genetic Evaluation ◽  
Fixed Effect ◽  
Random Regression ◽  
Period Effect ◽  
Production Traits ◽  
Breeding Values ◽  
Contemporary Group

In the present study, different random regression models, focussed on the nature of the effect of the contemporary group, fixed or random, were compared for genetic evaluation of test-day milk yield in dairy Gir breed, whose herds are characterised by relatively reduced numbers of cows. Cows were assigned to the same contemporary group if they were tested in the same herd, year and month. In one of the evaluated models, the contemporary group was investigated as a fixed effect and a clustering procedure was adopted to group herd-year subclasses in order to, at the end of the process, all contemporary groups contained at least three cows. The models were compared by the quality of fit, the accuracy of the predicted breeding values and their estimates of genetic parameters. The clustering procedure did not significantly improve the accuracy of predicted breeding values. Moreover, the fit to the data for this model was negatively affected. Therefore, this strategy should not be further implemented. The model including the herd-calving period effect and contemporary group treated as random effect showed similar characteristics to its equivalent, where the contemporary group was modelled as a fixed effect. However, the fit to the data for this model was slightly worse. Thus, the results suggest a random regression model including the herd-calving period effect and the fixed effect of contemporary group for the genetic evaluations of production traits in dairy Gir cattle. These findings could be extended to small dairy cattle populations whose herds are small-sized.

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