1


2

 Selection Index
 Predict missing yields from measured yields.
 Condense test days 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.
 Single or multiple trait prediction

3

 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

 Small for most 305d 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

 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

 Limited to 305d 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

 Lactations of any length can be modeled.
 Lactationtodate 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

 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 twicedaily milking was reported.

9


10

 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 laterparity (r=0.995) cows.
 These r were slightly larger than
previous estimates due to the inclusion of the identity matrix.

11

 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 (RodriguezZas
et al., 2000).
 BP also requires curves for the standard deviation (SD) of yields.

12

 Test day yields were assigned to 30d intervals and means and SD were
calculated for each interval.
 First, second, and thirdandlater 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


14


15


16


17


18


19

 Daily yields can be adjusted for known sources of variation.
 Example: Daily loss from clinical mastitis (RajalaSchultz et al.,
1999).
 This could lead to animalspecific rather than groupspecific
adjustments.
 Research into optimal management strategies.
 Management support in onfarm computer software.

20


21


22

 Correlations among successive test days may require periodic
reestimation 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 onfarm management.
