Environmental and genetic variation factors of artificial insemination success in French dairy sheep
| Autor: | I. David, C. Robert-Granié, E. Manfredi, G. Lagriffoul, L. Bodin |
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| Jazyk: | angličtina |
| Rok vydání: | 2008 |
| Předmět: | |
| Zdroj: | Animal, Vol 2, Iss 7, Pp 979-986 (2008) |
| Druh dokumentu: | article |
| ISSN: | 1751-7311 17517311 |
| DOI: | 10.1017/S1751731108002152 |
| Popis: | Artificial inseminations (n = 678 168) recorded during 5 years in five French artificial insemination (AI) centres (2 ‘Lacaune’, 1 ‘Manech tête rousse’, 1 ‘Manech tête noire’ and 1 ‘Basco béarnaise’) were analysed to determine environmental and genetic factors affecting the insemination results. Analyses within centre-breed were performed using a linear model, which jointly estimates male and female fertility. This model combined four categories of data: the environmental effects related to the female, those related to the male, the non-sex-specific effects and finally the pedigree data of these males and females. After selection, the environmental female effects considered were age, synchronisation (0/1) on the previous year, total number of synchronisations during the female reproductive life, time interval between previous lambing and insemination, already dry or still lactating (0/1) when inseminated, and milk quantity produced during the previous year expressed as quartiles intra herd * year. The environmental male effects were motility and concentration of the semen. The non-sex-specific effects were the inseminator, the interaction herd * year nested within the inseminator, considered as random effects and the interaction year * season considered as a fixed effect. The main variation factors of AI success were relative to non-sex-specific effects and to female effects. Heritability estimates varied from 0.001 to 0.005 for male fertility and from 0.040 to 0.078 for female fertility. Repeatability estimates varied from 0.007 to 0.015 for male fertility and from 0.104 to 0.136 for female fertility. These parameters indicate that genetic improvement of AI results through a classical polygenic selection would be difficult. Moreover, in spite of the large quantity of variation factors fitted by the joint model, a very large residual variance remained unexplained. |
| Databáze: | Directory of Open Access Journals |
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