Comparison of fixed and random regression models for the analysis of milk production traits in South African Holstein dairy cattle under two production systems
Comparaison de modèles de régression fixes et aléatoires pour l'analyse de caractères de production laitière des vaches laitières de race Holstein en Afrique du Sud sous deux systèmes de production
Abstract
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.