# Presentation: 167370

## Data Collection Ratings and Best Prediction of Lactation Yields

## Best Prediction

**Selection Index**
**Use measured yields to predict missing yields**
**Condense test days into lactation yield and persistency**
**Only phenotypic covariances are needed; herd means and variances assumed known**
**Reverse prediction**
**Daily yield predicted from lactation yield and persistency**
**Single or multiple trait prediction**

## History

**Calculation of lactation records for milk (****M****), fat (****F****), and protein (****P****) yields  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 on ****January 1, 1997**** and later**

## Advantages

**Small for most ****305 d**** lactations but larger for lactations with infrequent testing or missing components**
**More precise estimation of records for ****SCS**** because test days are adjusted for stage of lactation**
**Yields have slightly lower SD because BP regresses estimates toward the herd average**

## Users

**AIPL:**** Calculation of lactation yields and data collection ratings (****DCR****)****‏**
**Breed Associations:**** Publish DCR on pedigrees**
**DRPCs:**** Interested in replacing test interval estimates with BP**
**Can also calculate persistency**
**May have management applications**

## How does BP work?

**Inputs:**** TD and herd averages**
**Computations:**
**Standard curve calculated for each herd-breed-parity group**
**Cow’s lactation curve based on her TD deviations from standard curve**
**Outputs:**** Actual and ME yields, persistency, daily yields, DCR, REL**

## Specific curves

**Breeds:**** AY, BS, GU, HO, JE, MS**
**Traits:**** M, F, P, SCS**
**Parity:**** 1****st**** versus later**

## Modeling long lactations

**Dematawewa et al. (2007) recommend simple models for long lactations**
**Curves developed for ****M****, ****F****, and ****P**** yield, but not ****SCS**
**Little previous work on lactation curves for SCS (Rodriguez-Zas et al., 2000)****‏**
**BP also requires curves for the standard deviation (****SD****) of yields**

## Slide 9

## Slide 10

## Data and edits

**Holstein TD data from the NDDB**
**Edits of Dematawewa et al. (2007)****‏**
**1st through 5th parities**
**Lactations were ****≤****500 d**** for M, F, and P and ****≤****800 d**** for SCS**
**Records were made in a single herd**
**At least five tests reported**
**Only twice-daily milking reported**

## Modeling SCS and SD

**TD yields were assigned to ****30-**** or ****15-d**** intervals, and means and SD were calculated for each interval**
**Curves were fit to the resulting means (SCS) and SD (all traits)****‏**
**M****,**** ****F****, and ****P**** modeled with Woods curves**
**SCS**** modeled using curve C4 from Morant and Gnanasakthy (1989)****‏**

## Slide 13

## Slide 14

## Uses of daily estimates

**Daily yields can be adjusted for known sources of variation**
**Example:**** ****Losses from clinical mastitis**
**Animal-specific rather than group-specific adjustments**
**Optimal management strategies**
**Management support in software**

## Bar et al. JDS 90:4643-4653 (2007)‏

- Bar et al. JDS 90:4643-4653 (2007)‏

## Slide 17

## Validation: old versus new

## Validation: first 3 versus all 10 TD

## Validation: daily yields

**7-d averages of daily M yield from on-farm systems in ****4**** university herds were compared to daily BP**
**Correlations:**
**First parity:**** 0.927**
**Later parities:**** 0.956**
**Quist et al. (2007) reported that actual yields are overestimated with the Canadian equivalent of BP**

## Data Collection Ratings

**Used to compare the accuracy of lactation records from test plans**
**Squared correlation of estimated and true yields multiplied by ****104**
**Separate DCR are calculated for milk yield, components yield, and somatic cell score**

## Factors affecting DCR

**Averaging of several daily yields, as in labor efficient records (****+****)****‏**
**Collection of only a fraction of daily yields, as in AM-PM testing (****-****)****‏**
**Owner reporting rather than supervisor reporting of records (****-****)****‏**
**The number and pattern of tests within the lactation (****±****)****‏**

## High-frequency testing plans

## Monthly testing plans

## Bi-monthly testing plans

## Use in the Animal Model

**The ratio of the ****error variance from daily testing**** to the ****error variance from less-complete testing**** is used to weight lactation records**
**Weights increase with sampling frequency**
**The variance ratio does not include genetic and permanent environmental effects**

## Implementation

**Mature equivalent yields in the AIPL test database have been updated**
**Genetic evaluations have been calculated using the updated values**
**Data will be submitted to the Interbull test run in August**

## Enhancements to BP software

**Can accommodate lactations of any reasonable length (tested to ****999 d****)****‏**
**Lactation-to-date and projected yields calculated**
**BP of daily yields, test day yields, and standard curves now output**
**Covariance modeling functions have simple biological interpretations**

## Best Prediction programs

**Source code in the public domain**
**May be freely downloaded, changed, and redistributed**
**Written in Fortran 90 and C**
**Tested on Linux and Windows XP using free and commercial compilers**
**Programs and documentation are available on the AIPL website**
_**[www.aipl.arsusda.gov/software/bestpred/](http://www.aipl.arsusda.gov/software/bestpred/)**_

## Conclusions

**BP is a flexible tool for accurately modeling M, F, and P yields and SCS in lactations of any reasonable length**
**Almost all data from the field is used for genetic evaluation**
**More frequent sampling with supervision results in higher DCR**
**Daily BP of yields may be useful for on-farm management**

## Acknowledgments

**Computing:**** Dan Null and Lillian Bacheller at AIPL**
**Testing: ****Brad Heins at University of Minnesota**