Presentation from the 2007 ADSA meeting describing enhancements to best prediction to accommodate long lactations.

1. J. B. ColeJ. B. Cole1,*1,*
, P. M. VanRaden, P. M. VanRaden11
, and C. M. B., and C. M. B.
DematawewaDematawewa22
1
Animal Improvement Programs Laboratory, Agricultural
Research Service, USDA, Beltsville, MD
2
Department of Dairy Science, Virginia Polytechnic Institute
and State University, Blacksburg
2007
Estimation of yields for long
lactations using best prediction

2. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Best PredictionBest Prediction
VanRaden JDS 80:3015-3022 (1997), 6VanRaden JDS 80:3015-3022 (1997), 6thth
WCGALP XXIII:347-350 (1998)WCGALP XXIII:347-350 (1998)
• Selection IndexSelection Index
− Predict missing yields from measured yields.Predict missing yields from measured yields.
− Condense test days into lactation yield andCondense test days into lactation yield and
persistency.persistency.
− Only phenotypic covariances are needed.Only phenotypic covariances are needed.
− Mean and variance of herd assumed known.Mean and variance of herd assumed known.
• Reverse predictionReverse prediction
− Daily yield predicted from lactation yield andDaily yield predicted from lactation yield and
persistency.persistency.
• Single or multiple trait predictionSingle or multiple trait prediction

3. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
HistoryHistory
• Calculation of lactation records for
milk (M), fat (F), protein (P), and
somatic cell score (SCS) using best
prediction (BP) began in November
1999.
• Replaced the test interval method
and projection factors at AIPL.
• Used for cows calving in January 1997
and later.

4. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
AdvantagesAdvantages
• Small for most 305-d lactations but
larger for lactations with infrequent
testing or missing component
samples.
• More precise estimation of records
for SCS because test days are
adjusted for stage of lactation.
• Yield records have slightly lower SD
because BP regresses estimates
toward the herd average.

5. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
UsersUsers
• AIPL: Calculation of lactation yields
and data collection ratings (DCR).
− DCR indicates the accuracy of lactation
records obtained from BP.
• Breed Associations: Publish DCR on
pedigrees.
• DRPCs: Interested in replacing test
interval estimates with BP.
− Can also calculate persistency.
− May have management applications.

6. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Restrictions of Original SoftwareRestrictions of Original Software
• Limited to 305-d lactations used since
1935.
• Changes to parameters requires
recompilation.
• Uses simple linear interpolation for
calculation of standard curves.
• It is not possible to obtain BP for
individual days of lactation.

7. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Enhancements in New SoftwareEnhancements in New Software
• Lactations of any length can be modeled.
− Lactation-to-date and projected yields.
• The autoregressive function used to
model correlations among test day yields
was updated.
• Program options set in a parameter file.
• Diagnostic plots available for all traits.
• BP of individual daily yields, test day
yields, and standard curves now output.

8. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Data and EditsData and Edits
• Holstein TD data were extracted from
the national dairy database.
• The edits of Norman et al. (1999)
were applied to the data set used by
Dematawewa et al. (2007).
− 1st through 5th parities were included.
− Lactation lengths were at least 250 d for
the 305 d group and 800 d for the 999 d
group.
− Records were made in a single herd.
− At least five tests were reported.
− Only twice-daily milking was reported.

9. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Summary StatisticsSummary Statistics
First Later
Records 171,970 176,153
Length (d) 362 369
Pct > 305-d 23.9 27.5
Pct > 500-d 3.3 3.4

10. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Correlations among test day yieldsCorrelations among test day yields
Norman et al. JDS 82:2205-2211 (1999)Norman et al. JDS 82:2205-2211 (1999)
• An autoregressive matrix accounts for
biological changes, and an identity
matrix models daily measurement
error.
• Autoregressive parameters (r) were
estimated separately for first-
(r=0.998) and later-parity (r=0.995)
cows.
• These r were slightly larger than
previous estimates due to the
inclusion of the identity matrix.

11. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Modeling Long LactationsModeling Long Lactations
• Dematawewa et al. (2007) recommend
simple models, such as Wood's (1967)
curve, for long lactations.
• Curves were developed for M, F, and P
yield, but not SCS.
− Little previous work on fitting lactation
curves to SCS (Rodriguez-Zas et al., 2000).
• BP also requires curves for the standard
deviation (SD) of yields.

12. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Modeling SCS and SDModeling SCS and SD
• Test day yields were assigned to 30-d
intervals and means and SD were
calculated for each interval.
− First, second, and third-and-later parities.
• Curves were fit to the resulting means
(SCS) and SD (all traits).
• SD of yield modeled with Woods curves.
• SCS means and SD modeled using curve
C4 from Morant and Gnanasankthy
(1989).

13. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Mean Milk Yield (1Mean Milk Yield (1stst
parity) (kg)parity) (kg)

14. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
SD of Milk Yield (first parity) (kg)SD of Milk Yield (first parity) (kg)

15. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Mean Somatic Cell Score (1Mean Somatic Cell Score (1stst
parity)parity)

16. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Mean Somatic Cell Score(3+ parity)Mean Somatic Cell Score(3+ parity)

17. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
SD of Somatic Cell Score (1SD of Somatic Cell Score (1stst
parity)parity)

18. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
SD of Somatic Cell Score (3+ parity)SD of Somatic Cell Score (3+ parity)

19. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Uses of Daily EstimatesUses of Daily Estimates
• Daily yields can be adjusted for
known sources of variation.
− Example: Daily loss from clinical mastitis
(Rajala-Schultz et al., 1999).
• This could lead to animal-specific
rather than group-specific
adjustments.
• Research into optimal management
strategies.
• Management support in on-farm
computer software.

20. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Mean Milk Yield (kg)Mean Milk Yield (kg)

21. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Accounting for Mastitis LossesAccounting for Mastitis Losses

22. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
ConclusionsConclusions
 Correlations among successive test days
may require periodic re-estimation as
lactation curves change.
 Many cows can produce profitably for
>305 days in milk, and the revised BP
program provides a flexible tool to
model those records.
 Daily BP of yields may be useful for on-
farm management.