J.B. Cole and D.J. Null
Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
2009 J. Dairy Sci. (?)
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Cows with high lactation persistency tend to produce
less milk than expected at the beginning of lactation
and more than expected at the end. Best prediction of
lactation persistency is calculated as a function of trait-specific
standard lactation curves and linear regressions
of test-day deviations on days in milk. Because
regression coefficients are deviations from a tipping
point selected to make yield and lactation persistency
phenotypically uncorrelated it should be possible to use
305-d actual yield and lactation persistency to predict
yield for lactations with later endpoints. The objectives
of this study were to calculate (co)variance components
and breeding values for best predictions of lactation
persistency of milk (PM), fat (PF), protein (PP), and
somatic cell score (PSCS) in breeds other than Holstein,
and to demonstrate the calculation of prediction
equations for 400-d actual milk yield. Data included
lactations from Ayrshire, Brown Swiss, Guernsey (GU),
Jersey (JE), and Milking Shorthorn (MS) cows calving
since 1997. The number of sires evaluated ranged
from 86 (MS) to 3,192 (JE), and mean sire estimated
breeding value for PM ranged from 0.001 (Ayrshire)
to 0.10 (Brown Swiss); mean estimated breeding value
for PSCS ranged from −0.01 (MS) to −0.043 (JE).
Heritabilities were generally highest for PM (0.09 to
0.15) and lowest for PSCS (0.03 to 0.06), with PF and
PP having intermediate values (0.07 to 0.13). Repeatabilities
varied considerably between breeds, ranging
from 0.08 (PSCS in GU, JE, and MS) to 0.28 (PM in
GU). Genetic correlations of PM, PF, and PP with
PSCS were moderate and favorable (negative), indicating
that increasing lactation persistency of yield traits
is associated with decreases in lactation persistency of
SCS, as expected. Genetic correlations among yield and
lactation persistency were low to moderate and ranged
from −0.55 (PP in GU) to 0.40 (PP in MS). Prediction
equations for 400-d milk yield were calculated for each
breed by regression of both 305-d yield and 305-d yield
and lactation persistency on 400-d yield. Goodness-of-fit
was very good for both models, but the addition of lactation
persistency to the model significantly improved
fit in all cases. Routine genetic evaluations for lactation
persistency, as well as the development of prediction
equations for several lactation end-points, may provide
producers with tools to better manage their herds.
(Key words: best prediction, genetic evaluation, persistency)