Predictive ability of models for calving difficulty in US Holsteins

Abstract

The performance of alternative threshold models for analyzing calving difficulty (CD) in Holstein cows was evaluated in terms of predictive ability. Four models were considered, with CD classified into either three or four categories and analysed either as a single trait or jointly with gestation length (GL). The data contained GL and CD records from 90 393 primiparous cows, sired by 1122 bulls and distributed over 935 herd-calving year classes. Predictive ability of each model was evaluated using four criteria: mean squared error of the difference between observed and predicted CD scores; a Kullback-Leibler divergence measure between the observed and predicted distributions of CD scores; Pearson's correlation between observed and predicted CD scores and ability to correctly classify bulls as above or below average for incidence of CD. In general, the four models had similar predictive abilities. The joint analysis of CD with GL produced little, if any, improvement in predictive ability over univariate models. In light of the small difference in predictive ability between models treating CD with three or four categories and considering that a greater number of categories can provide more information, analysis of CD classified into four categories seems warranted.