Comparison of Poisson, probit and linear models for genetic analysis of number of inseminations to conception and success at first insemination in Iranian Holstein cows

Abstract

The goals of this study were to estimate genetic parameters and to assess alternative models for genetic evaluation of number of inseminations to conception (INS) and success at first insemination (SF) in Iranian Holstein cows. Two models were considered for each trait: linear and probit models for SF, and linear and Poisson models for INS. Data consisted of 72,124 records of parities 1 to 6 from 27,113 cows having lactation between 1981 and 2007 and distributed over 15 large Holstein herds. Genetic parameters and goodness of fit statistics were estimated using the whole data set and predictive ability of models was assessed via a 4-fold cross-validation based on mean squared error of prediction (MSEP) and correlation between observed and fitted values. Estimates of heritability ranged from 0.039 to 0.062 for SF and 0.040 to 0.165 for INS. The performance of linear and probit models was very similar for SF. Predictions of random effects from these models were highly correlated, and both models exhibited similar predictive ability. For INS, the linear model performed better than the Poisson model according to goodness of fit statistics, but these two models showed the same predictive ability. Overall, nonlinear models did not outperform linear models for genetic evaluations of SF and INS in Iranian Holstein cows.