The relationship between temperature-humidity index and test-day milk yield of Iranian Holstein dairy cattle using random regression model

Abstract: The objective of this work was to compare genetic evaluations of milk yield in the Gir breed, in terms of breeding values and their accuracy, using a random regression model applied to test-day records or the traditional model (TM) applied to estimates of 305-day milk yield, as well as to predict genetic trends for parameters of interest. A total of 10,576 first lactations, corresponding to 81,135 test-day (TD) records, were used. Rank correlations between the breeding values (EBVs) predicted with the two models were 0.96. The percentage of animals selected in common was 67 or 82%, respectively, when 1 or 5% of bulls were chosen, according to EBVs from random regression model (RRM) or TM genetic evaluations. Average gains in accuracy of 2.7, 3.0, and 2.6% were observed for all animals, cows with yield record, and bulls (sires of cows with yield record), respectively, when the RRM was used. The mean annual genetic gain for 305-day milk yield was 56 kg after 1993. However, lower increases in the average EBVs were observed for the second regression coefficient, related to persistency. The RRM applied to TD records is efficient for the genetic evaluation of milk yield in the Gir dairy breed.

Download Full-text

Alternative parameterizations of the multiple-trait random regression model for milk yield and somatic cell score via recursive links between phenotypes

Journal of Animal Breeding and Genetics ◽

10.1111/j.1439-0388.2011.00918.x ◽

2011 ◽

Vol 128 (4) ◽

pp. 258-266 ◽

Cited By ~ 3

Author(s):

J. Jamrozik ◽

L.R. Schaeffer

Keyword(s):

Regression Model ◽

Somatic Cell ◽

Milk Yield ◽

Somatic Cell Score ◽

Random Regression ◽

Multiple Trait ◽

Random Regression Model

Download Full-text

Importance of genotype by environment interaction on genetic analysis of milk yield in Iranian Holstein cows using a random regression model

Animal Production Science ◽

10.1071/an17714 ◽

2019 ◽

Vol 59 (8) ◽

pp. 1438

Author(s):

Y. Fazel ◽

A. Esmailizadeh ◽

M. Momen ◽

M. Asadi Fozi

Keyword(s):

Genetic Analysis ◽

Regression Model ◽

Milk Yield ◽

Genetic Parameter ◽

Genotype By Environment Interaction ◽

Random Regression ◽

Holstein Cows ◽

Environment Interaction ◽

Genotype By Environment ◽

Random Regression Model

Changes in the relative performance of genotypes (sires) across different environments, which are referred to as genotype–environment interactions, play an important role in dairy production systems, especially in countries that rely on imported genetic material. Importance of genotype by environment interaction on genetic analysis of milk yield was investigated in Holstein cows by using random regression model. In total, 68945 milk test-day records of first, second and third lactations of 8515 animals that originated from 100 sires and 7743 dams in 34 herds, collected by the Iranian animal breeding centre during 2007–2009, were used. The different sires were considered as different genotypes, while factors such as herd size, herd milk average (HMA), herd protein average and herd fat average were used as criteria to define the different environments. The inclusion of the environmental descriptor improved not only the log-likelihood of the model, but also the Bayesian information criterion. The results showed that defining the environment on the basis of HMA affected genetic parameter estimations more than did the other environmental descriptors. The heritability of milk yield during lactating days reduced when sire × HMA was fitted to the model as an additional random effect, while the genetic and phenotypic correlations between lactating months increased. Therefore, ignoring this interaction term can lead to the biased genetic-parameter estimates, reduced selection accuracy and, thus, different ranking of the bulls in different environments.

Download Full-text

Estimation of genetic variation for macro- and micro-environmental sensitivities of milk yield and composition in Holstein cows using double hierarchical generalized linear models

Journal of Dairy Research ◽

10.1017/s0022029919000293 ◽

2019 ◽

Vol 86 (2) ◽

pp. 145-153 ◽

Cited By ~ 2

Author(s):

Jamshid Ehsaninia ◽

Navid Ghavi Hossein-Zadeh ◽

Abdol Ahad Shadparvar

Keyword(s):

Genetic Variation ◽

Regression Model ◽

Milk Yield ◽

Protein Yield ◽

Random Regression ◽

Residual Variance ◽

Variance Model ◽

Random Regression Model ◽

The Mean ◽

Fat Yield

AbstractThe aim of this study was to estimate genetic parameters for environmental sensitivities in milk yield and composition of Iranian Holstein cows using the double hierarchical generalized linear model (DHGLM) method. Data set included test-day productive records of cows which were provided by the Animal Breeding Center and Promotion of Animal Products of Iran during 1983 to 2014. In the DHGLM method, a random regression model was fitted which included two parts of mean and residual variance. A random regression model (mean model) and a residual variance model were used to study the genetic variation of micro-environmental sensitivities. In order to consider macro-environmental sensitivities, DHGLM was extended using a reaction norm model, and a sire model was applied. Based on the mean model, additive genetic variances for the mean were 38.25 for milk yield, 0.23 for fat yield and 0.03 for protein yield in the first lactation, respectively. Based on the residual variance model, additive genetic variances for residual variance were 0.039 for milk yield, 0.030 for fat yield and 0.020 for protein yield in the first lactation, respectively. Estimates of genetic correlation between milk yield and macro- and micro-environmental sensitivities were 0.660 and 0.597 in the first lactation, respectively. The results of this study indicated that macro- and micro-environmental sensitivities were present for milk production traits of Iranian Holsteins. High genetic coefficient of variation for micro-environmental sensitivities indicated the possibility of reducing environmental variation and increase in uniformity via selection.

Download Full-text

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

Annals of Animal Science ◽

10.2478/aoas-2013-0078 ◽

2014 ◽

Vol 14 (1) ◽

pp. 55-68 ◽

Cited By ~ 1

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.

Download Full-text