EXPECTED EFFICIENCY OF SELECTION FOR GROWTH IN A FRENCH BEEF-CATTLE BREEDING SCHEME .1. MULTISTAGE SELECTION OF BULLS USED IN ARTIFICIAL-INSEMINATION

Genetic improvement of beef cattle for growth traits implies selection on both direct and maternal effects through on-farm and station indiv idual and progeny performance tests. To optimize the use of these tool s, a French selection scheme of artificial insemination (AI) bulls is modelled, including its main components, ie 2 kinds of station perform ance tests and 2 kinds of progeny tests (farm and station). Three bree ding objectives are derived in order to represent the heterogeneity of production systems: Hs for suckler herds, Hf for suckler-fattening he rds and an average objective Hg considered as the most realistic for t he whole breed. These objectives include direct and maternal genetic e ffects on weaning weight and direct effects on final weight. Economic, demographic and genetic parameters are derived for the Limousin breed . Multistage selection procedures are algebraically optimised by findi ng selection thresholds which maximize response for the breeding objec tives. The current scheme appears to be more efficient for Hf than for Hs. However, whatever the objective, maternal genetic response is exp ected to be slightly negative, due to a negative correlation between d irect and maternal genetic effects. Standard deviations of genetic res ponses are calculated to take into account some uncertainty on estimat es of genetic parameters. With a 95% confidence interval, maternal gen etic response could be positive. An alternative to this complex scheme is considered, using only one kind of station performance test and th e on-farm progeny test. The increase of on-farm progeny test capacity reduces the value of station progeny test for selecting AI bulls, at l east when only direct and maternal effects on growth traits are consid ered. For the simplified scheme, maternal response is expected to be p ositive, though uncertain due to a large standard deviation.