Genetic analysis of female fertility traits in Canadian Simmentals

2012 ◽  
Vol 150 (1-3) ◽  
pp. 302-309 ◽  
Author(s):  
J. Jamrozik ◽  
S. McGrath ◽  
R.A. Kemp ◽  
S.P. Miller
2012 ◽  
Vol 57 (No. 3) ◽  
pp. 108-114 ◽  
Author(s):  
V. Zink ◽  
J. Lassen ◽  
M. Štípková

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.  


2019 ◽  
Vol 4 (1) ◽  
pp. 423-441 ◽  
Author(s):  
Madison L Butler ◽  
Jennifer M Bormann ◽  
Robert L Weaber ◽  
David M Grieger ◽  
Megan M Rolf

Abstract Fertility is a critically important factor in cattle production because it directly relates to the ability to produce the offspring necessary to offset costs in production systems. Female fertility has received much attention and has been enhanced through assisted reproductive technologies, as well as genetic selection; however, improving bull fertility has been largely ignored. Improvements in bull reproductive performance are necessary to optimize the efficiency of cattle production. Selection and management to improve bull fertility not only have the potential to increase conception rates but also have the capacity to improve other economically relevant production traits. Bull fertility has reportedly been genetically correlated with traits such as average daily gain, heifer pregnancy, and calving interval. Published studies show that bull fertility traits are low to moderately heritable, indicating that improvements in bull fertility can be realized through selection. Although female fertility has continued to progress according to increasing conception rates, the reported correlation between male and female fertility is low, indicating that male fertility cannot be improved by selection for female fertility. Correlations between several bull fertility traits, such as concentration, number of spermatozoa, motility, and number of spermatozoa abnormalities, vary among studies. Using male fertility traits in selection indices would provide producers with more advanced selection tools. The objective of this review was to discuss current beef bull fertility measurements and to discuss the future of genetic evaluation of beef bull fertility and potential genetic improvement strategies.


BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jennifer N. Kiser ◽  
Elizabeth M. Keuter ◽  
Christopher M. Seabury ◽  
Mahesh Neupane ◽  
Joao G. N. Moraes ◽  
...  

2006 ◽  
Vol 89 (11) ◽  
pp. 4438-4444 ◽  
Author(s):  
O. González-Recio ◽  
R. Alenda ◽  
Y.M. Chang ◽  
K.A. Weigel ◽  
D. Gianola

2017 ◽  
Vol 100 (10) ◽  
pp. 8205-8219 ◽  
Author(s):  
Diana Sorg ◽  
Monika Wensch-Dorendorf ◽  
Kati Schöpke ◽  
Gunter Martin ◽  
Renate Schafberg ◽  
...  

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