Abstract. The aims of this study were to estimate the genetic parameters of the
test day milk yield (TDMY) of the White Maritza sheep breed population and to
choose the most appropriate linear models for genetic-parameter estimation
of test day milk yield. The White Maritza sheep breed is a multipurpose
native sheep breed in Bulgaria. Test day milk yield data were collected from
1992 to 2015 (24 years). Milk yield recordings were made in 18 flocks
according to the AC method (official milk recording by ICAR regulations). The database includes 8768 test day milk yield records
belonging to 987 ewes. The pedigree file includes 1937 animals. Nine test
day models (TDMs) were formulated and tested for the estimation of the genetic
parameters of milk yield. The first three models were repeatability models
(REP models), the second three were random regression models (RRMs), and the
last three models were also random regression models with an added Ali and
Schaeffer regression to describe the lactation curve using first-, second-
and third-order polynomials. The average TDMY was 764.47 mL. There were no
significant differences in the values of heritability (h2) calculated by the three REP
models: REP1 0.355 ± 0.060, REP2 0.344 ± 0.047 and REP3 0.347 ± 0.060. The same applied to the repeatability coefficients,
which, for the three REP models, were 0.384 ± 0.065, 0.376 ± 0.051
and 0.378 ± 0.065, respectively. Based on REP model 1, three models
with random regression RRM1, RRM2 and RRM3 were constructed, which is
associated with the use of first-, second- and third-order polynomials (for the random effects of both the animal and the permanent environment). The
trajectories of h2 calculated by the three RRMs were not similar and
demonstrated some differences, both at the beginning and in the middle of
the milking period. The RRM with third-order polynomials demonstrated more
genetic diversity until the 165th day of lactation, but Akaike information criterion (AIC), Bayesian information criterion (BIC) and log-likelihood (LogL)
estimates were higher. The regression models with first- and second-degree
polynomials were insufficient to reveal genetic diversity to a higher degree
than REP model 1. The trend in the trajectories of h2 calculated by the
three random regression models with Ali and Schaeffer regression models
(ASRMs) was similar to that of random regression models without
the Ali and Schaeffer regression incorporated. Although the noted advantages of
the random regression models revealed, to a greater extent, the genetic
diversity of test day milk yield, AIC, BIC and LogL estimates indicated that
repeatability models achieved a better balance between complexity and
fitness and a smaller prediction error compared to random regression models.