scholarly journals GENETIC PARAMETERS FOR BODY WEIGHT OF EGYPTAIN FARAFRA LAMBS WITH RANDOM REGRESSION MODEL

2006 ◽  
Vol 43 (1) ◽  
pp. 57-69 ◽  
Keyword(s):  
Body Weight ◽  
Regression Model ◽  
Genetic Parameters ◽  
Random Regression ◽  
Random Regression Model

scholarly journals Genetic Parameters for Tunisian Holsteins Using a Test-Day Random Regression Model

2008 ◽  
Vol 91 (5) ◽  
pp. 2118-2126 ◽  
Author(s):  
H. Hammami ◽  
B. Rekik ◽  
H. Soyeurt ◽  
A. Ben Gara ◽  
N. Gengler
Keyword(s):  
Regression Model ◽  
Genetic Parameters ◽  
Random Regression ◽  
Random Regression Model

scholarly journals Genetic Parameters for Litter Size in Pigs Using a Random Regression Model

2006 ◽  
Vol 20 (2) ◽  
pp. 160-165 ◽  
Author(s):  
Z. Lukovic ◽  
M. Uremovic ◽  
M. Konjacic ◽  
Z. Uremovic ◽  
D. Vincek
Keyword(s):  
Regression Model ◽  
Litter Size ◽  
Genetic Parameters ◽  
Random Regression ◽  
Random Regression Model

Genetic parameters for production traits of dairy Gyr (Bos indicus)×Holstein cattle estimated with a random regression model

2013 ◽  
Vol 158 (1-3) ◽  
pp. 24-31 ◽  
Author(s):  
Rodrigo Junqueira Pereira ◽  
Denise Rocha Ayres ◽  
Lenira El Faro ◽  
Aníbal Eugênio Vercesi Filho ◽  
Rui da Silva Verneque ◽  
...  
Keyword(s):  
Regression Model ◽  
Genetic Parameters ◽  
Bos Indicus ◽  
Random Regression ◽  
Holstein Cattle ◽  
Production Traits ◽  
Random Regression Model

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.

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