scholarly journals Genetic Parameters of Milk Production Traits in Crossbred Cows in Ethiopia

2021 ◽  
Vol 12 (2) ◽  
pp. 103-110
Author(s):  
K. Getahun ◽  
M. Tadesse ◽  
D. Hunde ◽  
Z. Lemma
Keyword(s):  
Milk Production ◽  
Genetic Parameters ◽  
Production Traits ◽  
Milk Production Traits ◽  
Crossbred Cows

scholarly journals Genetic parameters for female fertility and milk production traits in first-parity Czech Holstein cows

2012 ◽  
Vol 57 (No. 3) ◽  
pp. 108-114 ◽  
Author(s):  
V. Zink ◽  
J. Lassen ◽  
M. Štípková
Keyword(s):  
Milk Production ◽  
Genetic Parameters ◽  
Selection Index ◽  
Female Fertility ◽  
Holstein Cows ◽  
Production Traits ◽  
Milk Production Traits ◽  
Clear Trend ◽  
Genetic Potential ◽  
Fertility Traits

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.  

Prospects for genetic improvement of milk production traits of Sahiwal cattle in Kenya

2007 ◽  
Vol 2007 ◽  
pp. 69-69
Author(s):  
E.D. Ilatsia ◽  
T. K. Muasya ◽  
W. B. Muhuyi ◽  
A. K. Kahi
Keyword(s):  
Animal Model ◽  
Milk Production ◽  
Genetic Parameters ◽  
Genetic Relationships ◽  
Additive Genetic Variance ◽  
Parameter Estimates ◽  
Production Traits ◽  
Milk Production Traits ◽  
Phenotypic Parameter ◽  
Sahiwal Cattle

The primary emphasis of the long-term Sahiwal cattle breeding programme is to increase milk yield by selecting cows based on their performance in first three lactations. It is therefore important to have knowledge on the extend of additive genetic variance and genetic parameters for these traits. Genetic and phenotypic parameter estimates normally apply directly to the specific population and environment from which the data were collected. In the Sahiwal cattle in Kenya, very little is known about the genetic variation of milk production traits and their genetic relationships. Furthermore, genetic and phenotypic parameter estimates for the Sahiwal cattle based on multivariate animal model are scarce. This paper presents estimates of variance components and genetic parameters for milk production traits using trivariate animal model.

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.

Use of productive data to predict length of productive life in Iranian Holstein cattle

2005 ◽  
Vol 2005 ◽  
pp. 126-126
Author(s):  
A. Abdolmohammadi ◽  
M. Moradi Shahrebabak ◽  
S. R. M. Ashtiani
Keyword(s):  
Milk Production ◽  
Genetic Parameters ◽  
Selection Index ◽  
Direct Selection ◽  
Holstein Cattle ◽  
Production Traits ◽  
Milk Production Traits ◽  
Direct Evaluation ◽  
Productive Life ◽  
Selection Of

Improvement of longevity by direct selection of sires based on theirs daughters’ longevity measures is impractical because of a low heritability and generation intervals prolonged by waiting until all cows complete their productive life. As an alternative to direct evaluation of sires for longevity is indirect prediction from genetically correlated production traits measures in the first lactation. The objectives this study were 1) to estimate genetic parameters of longevity and production traits 2) to examine relationships between longevity and first lactation milk production traits and 3) to determine selection index for sires’ longevity based on production traits.

scholarly journals Genetic line comparisons and genetic parameters for endoparasite infections and test-day milk production traits

2017 ◽  
Vol 100 (9) ◽  
pp. 7330-7344 ◽  
Author(s):  
Katharina May ◽  
Kerstin Brügemann ◽  
Tong Yin ◽  
Carsten Scheper ◽  
Christina Strube ◽  
...  
Keyword(s):  
Milk Production ◽  
Genetic Parameters ◽  
Production Traits ◽  
Milk Production Traits ◽  
Genetic Line

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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