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BMC Genetics  2011 

Optimized diffusion of buck semen for saving genetic variability in selected dairy goat populations

DOI: 10.1186/1471-2156-12-25

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Abstract:

The whole procedure was tested on two French goat populations (Alpine and Saanen breeds) and the results presented in the abstract were based on the average of the two breeds. The procedure induced an immediate acceleration of genetic gain in comparison with the current annual genetic gain (0.15 genetic standard deviation unit), as shown by two facts. First, the genetic level of replacement natural service (NS) bucks was predicted, 1.5 years ahead at the moment of reproduction, to be equivalent to that of the progeny-tested bucks in service, born from the current breeding scheme. Second, the genetic level of replacement goats was much higher than that of their dams (0.86 unit), which represented 6 years of selection, although dams were only 3 years older than their replacement daughters. This improved genetic gain could be achieved while decreasing inbreeding coefficients substantially. Inbreeding coefficients (%) of NS bucks was lower than that of the progeny-tested bucks (-0.17). Goats were also less inbred than their dams (-0.67).It was possible to account for complex operational constraints while developing goat selection schemes, both efficient and sustainable. Therefore, the recommended selection and mating decisions might receive attention from goat breeders using both AI and NS.It is increasingly accepted that selection procedures should be as sustainable as possible, both to avoid a fast loss of variability in the genetic pool of selected populations and to avoid inbreeding effects on performances. The principles of these procedures have become quite clear, based on extensive research and validation work [1,2]. The major challenge will be to implement these procedures in the field. For any selection scheme, the appropriate approach is to first identify the detailed selection steps and corresponding operational constraints, then to accordingly develop optimization algorithms. This approach is much more demanding for large domestic animals than for poultry or

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