1
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2
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- At the same level of production cows with high persistency milk more at
the end than the beginning of lactation.
- Best prediction of persistency is calculated as a function of
trait-specific standard lactation curves and the linear regression of a
cow’s test day deviations on days in milk.
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3
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- Selection Index
- Predict missing yields from measured yields
- Condense daily into lactation yield and persistency
- Only phenotypic covariances are needed
- Mean and variance of herd assumed known
- Reverse prediction
- Daily yield predicted from lactation yield and persistency
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4
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- Definition
- Squared correlation of estimated with true persistency
- Same as Rel (breeding value)
- Single-trait or multi-trait (M, F, P, SCS)
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5
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- Definition
- 305 daily yield deviations (DIM - DIMo)
- Uncorrelated with yield by choosing DIMo
- DIMo were 161, 159, 166, and 155 for M, F, P, and SCS
- DIM0 have increased over time
- Standardized estimate
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6
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7
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- Calculate (co)variance components and breeding values for persistency of
milk (PM), fat (PF), protein (PP), and SCS (PSCS) in Holsteins
- Estimate genetic correlations among persistency and yield traits
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8
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- 8,682,138 lactation records from 4,375,938 Holstein cows calving since
January 1, 1997
- Best prediction of persistency
- Milk (M), fat (F), protein (P), SCS
- Floor and ceiling of ± 4.0
- Phenotypic reliability ≥ 50%
- 1st - 5th lactations (1st required)
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9
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10
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11
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12
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- Repeatability animal model:
- yijkl = hysi + lacj + ak +
pek + β(dojk) + eijkl
- yijkl = persistency of milk,fat, protein, or SCS
- hysi = fixed effect of herd-year-season of calving I
- lacj = fixed effect of lactation j
- ak = random additive genetic effect of animal k
- pek = random permanent environmental effect of animal k
- dojk = days open for lactation j of animal k
- eijkl = random residual error
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13
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14
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15
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16
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17
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18
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- Heritabilities and repeatabilities are low to moderate
- Routine genetic evaluations for persistency are feasible
- The shape of the lactation curve may be altered without affecting
production
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