# Presentation: 711471

## Best prediction of actual lactation yields

## My credentials

## Best PredictionVanRaden JDS 80:3015-3022 (1997), 6th WCGALP XXIII:347-350 (1998)‏

**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**

## History

**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.**

## Advantages

**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.**

## Users

**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.**

## Restrictions of Original Software

**Limited to 305-d lactations used since 1935.**
**Used simple linear interpolation for calculation of standard curves.**
**Could not obtain BP for individual days of lactation.**
**Difficult to change parameters.**

## Enhancements in New Software

**Can accommodate lactations of any length (tested to 999 d).**
**Lactation-to-date and projected yields calculated.**
**BP of daily yields, test day yields, and standard curves now output.**
**The function which models correlations among test day yields was updated.**

## How does BP work?

**Inputs:**** TD and herd averages in**
**Statistical wizardry**
**Standard curve calculated**
**Cow’s lactation curve based on TD deviations from standard curve**
**Outputs:**** Actual lactation yields, persistency, and daily BP of yield**

## Specific curves

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

## Modeling 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.**

## Data and Edits

**Holstein TD data were extracted from the national dairy database.**
**The data of Dematawewa et al. (2007) were used.**
**1st through 5th parities**
**Lactations 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 reported.**
**Only twice-daily milking reported.**

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## Modeling SCS and SD

**Test day yields were assigned to 30-d intervals and means and SD were calculated for each interval.**
**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 Gnanasakthy (1989).**

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## SD of Somatic Cell Score (1st parity)‏

## SD of Somatic Cell Score (3+ parity)‏

## SD of Milk Yield (first parity) (kg)‏

## Correlations among test day yieldsNorman et al. JDS 82:2205-2211 (1999)‏

**Rather than calculate each correlation separately we use a formula to approximate them.**
**Our model accounts for biological changes and daily measurement error.**
**The programs were updated to use a simpler formula with similar accuracy.**

## Supervised records

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## Example – HO 2nd versus JE 2nd

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## Sampling in odd months:ST versus MT

## ST versus MT estimation

**MT uses information on 3 or 4 traits simultaneously.**
**Provides more accurate estimates of components yield.**
**Advantage greatest with infrequent testing or missing component samples.**
**Canavesi et al. (2007)**

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## Uses 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.**

## Accounting for Mastitis Losses

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

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

## Validation

## Validation: new versus old programs (ST)‏

## Validation: new versus old programs (MT)‏

## Validation: first 3 versus all 10 TD

## Validation: sums of 7-d averages and daily BP

**Daily milk yields from the on-farm system (7-d averages) were summed and compared to 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.**

## Implementation

**When testing is complete:**
**Yields in the AIPL database will be updated.**
**Data will be submitted to Interbull for a test run.**
**The BP programs have been sent to 4 DRPCs for testing.**

## Conclusions

**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.**