Expected efficiency of selection for growth in a French beef cattle breeding scheme. I. Multistage selection of bulls used in artificial insemination

Abstract

Genetic improvement of beef cattle for growth traits implies selection on both direct and maternal effects through on-farm and station individual and progeny performance tests. To optimize the use of these tools, a French selection scheme of artificial insemination (AI) bulls is modelled, including its main components, ie 2 kinds of station performance tests and 2 kinds of progeny tests (farm and station). Three breeding objectives are derived in order to represent the heterogeneity of production systems: Hs for suckler herds, Hf for suckler-fattening herds and an average objective H g considered as the most realistic for the whole breed. These objectives include direct and maternal genetic effects 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 optimized by finding selection thresholds which maximize response for the breeding objectives. The current scheme appears to be more efficient for Hf than for Hs. However, whatever the objective, maternal genetic response is expected to be slightly negative, due to a negative correlation between direct and maternal genetic effects. Standard deviations of genetic responses are calculated to take into account some uncertainty on estimates of genetic parameters. With a 95% confidence interval, maternal genetic response could be positive. An alternative to this complex scheme is considered, using only one kind of station performance test and the on-farm progeny test. The increase of on-farm progeny test capacity reduces the value of station progeny test for selecting AI bulls, at least when only direct and maternal effects on growth traits are considered. For the simplified scheme, maternal response is expected to be positive, though uncertain due to a large standard deviation. © 1995 Elsevier/INRA.