Probability of Success and Predicted Returns of Sires in Progeny Test Programs

Abstract

Probability theory was used to predict the probability of young AI sires achieving a successful (marketable) progeny test evaluation and to predict their present value of net returns. The population was assumed to have reached a constant variance with corresponding accuracies under moderate selection. Probabilities of success were determined from the percentile rank of a sire for a total merit index within a national cohort. Heritability in the base population had little effect on probabilities of success. Based on estimated breeding values with an accuracy of .6, young bulls from the 95th percentile were 11.7 and 6.3 times more likely to enter the top 5% (sires of bulls) and top 15% (sires of cows) of progeny-tested sires than a young bull from the 5th percentile. Within a cohort of 300 young North American bulls tested annually, $282,000 higher returns are expected from a young bull that ranked in the top percentile than from an average young bull within the national cohort and $75,000 higher returns from an average sire than from a bull in the lowest percentile. Based on expected returns, AI studs should cull the lowest 10% of bulls, after sib test results, for savings of up to $7000 per bull.