Genetic evaluation strategies for multiple traits and countries

Genetic evaluation strategies were studied using simulated data for three lactation traits in each of two importing and two exporting countries, each running a typical progeny test program. Conversion (CNV), multiple-trait across-country evaluation (MACE) and global animal model (GAM) strategies were considered. Base populations were either unselected, or all males were above average, and exporting countries had higher genetic means than importing countries. With unselected base populations, errors of prediction (ERP) for top (highest predicted merit) bulls were equivalent (P>0.10) for MACE and GAM strategies and higher for CNV (P<0.01), while GAM had lower ERP (P<0.01) for all bulls. With selected base populations, MACE strategies had lower ERP than GAM for top bulls (P<0.01) and lower but similar ERP (P>0.05) for all bulls. Strategies to evaluate multiple traits per country consistently had lower ERP than strategies evaluating one trait per country. Evaluations were biased, favouring bulls from importing countries on foreign scales of evaluation, for all strategies and with either selected or unselected base populations. True merits of top bulls selected with MACE or GAM were similar (P>0.05) and greater than CNV (P<0.01), and 90-95% of the top 1% selected bulls were the same for multiple-trait MACE and GAM strategies.