Estimation of Genetic Parameters for Milk Production Traits Using a Random Regression Test-day Model in Holstein Cows in Korea

The aim of this study was to estimate genetic parameters for female fertility and production traits in first-parity Czech Holstein cows and to quantify the effect of using this information on the accuracy of a selection index in seven different scenarios. In order to estimate genetic (co)variance components, the DMU software running an AI-REML algorithm was used. The analyses were made using a series of bivariate animal models. The pedigree included 164 125 animals and it was set up using a pruned animal model design. The present study included the following female fertility traits for the first lactations: calving to the first insemination (CF), days open (DO), calving from the first to the last insemination (FL), and milk production traits: milk production (MLK), kg of fat (FAT), and kg of protein (PROT). The heritability for all the investigated fertility traits was low and close to 0. Moderate heritabilities for production traits ranging from 0.20 (MLK) to 0.23 (PROT) were estimated. The strongest unfavourable correlation was found between PROT and DO (0.49). Other estimated correlations between fertility traits and production traits were moderate, ranging from 0.26 to 0.41. The results of this study evidence that cows with the poorest genetic potential for reproductive performance are those having high genetic potential for milk production and milk components. The results also show that the number of days from calving to new pregnancy depends on the production level. Seven investigated scenarios using selection index theory show a clear trend for increasing accuracy when more fertility traits were added as well as when higher numbers of daughters with information on reproduction traits per sire were available.  

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ESTIMATION OF GENETIC PARAMETERS FOR MILK PRODUCTION TRAITS IN A HERD OF HOLSTEIN FRIESIAN CATTLE IN EGYPT

Journal of Animal and Poultry Production ◽

10.21608/jappmu.2014.69537 ◽

2014 ◽

Vol 5 (5) ◽

pp. 267-278

Author(s):

H. Rushdi ◽

M. Ibrahim; ◽

N. Shaddad ◽

A. Nigm

Keyword(s):

Milk Production ◽

Genetic Parameters ◽

Production Traits ◽

Milk Production Traits ◽

Holstein Friesian ◽

Friesian Cattle ◽

Estimation Of Genetic Parameters

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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.

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