to have the highest )DTEM values, but the correlation
between a.rg and the rate of financial loss per
percentage of under- or overestimation of REV was
only 0.38 and 0.29 respectively. Thus, a.h2 or a.rg is
an imperfect measure of sensitivity of REVs for
multi-trait dairy cattle selection indexes because of
the variance–covariance structure of traits.
The sensitivity of the TEM response differs
depending on whether the REV has been under- or
overestimated, i.e. is asymmetric. The correlation
between the rate of change in TEM and trait REV
when under- or overestimated was only 0.50 overall
for all traits. The calculations here, the first such
analysis for UK Holsteins, suggest that TEM is most
sensitive to proportional underestimates and overestimates
of the REV of protein, fat, milk and lifespan
and least sensitive to calving interval, lameness,
non-return and mastitis REVs. Non-linear and nonsymmetrical
bias effects on TEM were also found in
the earlier study of Vandepitte & Hazel (1977), who
simulated a vector of biased REVs for seven breeding
objective traits in pigs, as well as studied single trait
bias. They found that feed efficiency had the largest
percentage effect when REVs were underestimated,
whereas dressing percentage had the highest effect
when REVs were overestimated, REV overestimates
being lower in TEM effect generally. They also found
that the effects of errors in REV on TEM increased
rapidly as the level of error in the REV increased.