Comparison of fixed and random regression models for the analysis of milk production traits in South African Holstein dairy cattle under two production systems

Fixed regression model (FRM) analyses that consider only fixed, non-genetic effects to vary over the lactation are currently used for genetic evaluation of production traits in South African Holstein. With random regression models (RRM), the random animal and permanent environmental effects are allowed to also vary over the lactation. Hence, RRM can account for an individual component representing changes during the lactation i.e., its persistency (PERS), enabling selection for more persistent cows. Also, test-day (TD) records used for genetic evaluations come from cows in contrasted production systems. The main ones rely on full pasture (PAST) or a total mixed ration (TMR), a choice often depending on local average rainfall where herds are situated. TD records from herds were divided into 2 datasets based on the production system (PAST or TMR). REML was used to analyze production for each of the first 3 lactations under different multiple-lactation models for milk, butterfat and protein production, as well as butterfat and protein percentage. Various FRM were compared to the current FRM officially used for genetic evaluation in South Africa (saFRM). A FRM that cumulates different curves over the lactation for different fixed effects was retained based on results in the PAST dataset and was also applied to the TMR dataset. This model was then broadened to an alternative RRM (aRRM) combining for each lactation an average production and a PERS effect, after which it was compared to the current saFRM under both production systems. The aRRM for both PAST and TMR had a better goodness of fit than the current saFRM for all traits except protein percentage. The mean squared error of aRRM was lower for all traits. Generally, aRRM heritability estimates (h2) were higher than with the saFRM at the beginning and end of lactation for most traits in PAST while being mostly higher during late lactation in TMR. Overall, the h2 in PAST were mostly higher than in TMR for all traits. Estimates of between-lactations genetic correlations for average production from the aRRM were generally higher. Within-lactations genetic correlations between average production and PERS for TMR from the aRRM were negative and stronger than for PAST. The extra source of information from the aRRM enables a genetic prediction of PERS and is expected to increase accuracy of genetic predictions. Different genetic parameters between the 2 production systems may denote a genotype x environment interaction.