# Presentation: 705092

## Estimation of yields for long lactations using best prediction

## 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.
- 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.

## Enhancements 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.

## Data 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.

## Summary 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 |

## Correlations among test day yields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.

## 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.

## Modeling 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).

## Mean Milk Yield (1st parity) (kg)

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

## Mean Somatic Cell Score (1st parity)

## Mean Somatic Cell Score(3+ parity)

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

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

## 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.

## Mean Milk Yield (kg)

## Accounting for Mastitis Losses

## 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.