The genetic gains were estimated for milk
production and persistency, derived from random
regression models, using eigenvector indices, and
they were compared with the traditional selection
index. The data set contained 4971 test day milk
yield recorded for 691 buffalo cows, daughters
of 120 sires and 532 dams. The model included
the random effects of direct additive genetic,
permanent environment and error, whereas the
fixed effects were herd test day, year and season of
calving and parity, and as a covariate, it was milk
days. The first and the 2nd eigenvalues explained
73.1 and 22.9% of the variation of the random
regression coefficients, respectively, suggesting
that the use of the first two eigenvectors is sufficient.
Genetic responses in total milk yield (TMY) based
on the first eigenvector index (Ie1) and that based
on the conventional selection (IMY) have close
gain of about 171 kg in each index. The second
eigenvector index (Ie2) showed an increase in TMY
(9.91 kg), and thus an increase in the persistency
(0.86 kg). The TMY and persistency are the two
economically important traits in dairy production,
additional genetic gains in persistency and high genetic gain for TMY could be obtained using the 2nd eigenvector index (I*2).