Estimation of yields for long lactations using best prediction

1. Estimation of yields for long lactations using best prediction

2. Best PredictionVanRaden 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

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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 and800 dfor the999 dgroup. Records were made in a single herd. At least five tests were reported. Only twice-daily milking was reported.

9. Summary Statistics

10. Correlations among test day yieldsNorman 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. 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.

12. 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).

13. Mean Milk Yield (1st parity) (kg)

14. SD of Milk Yield (first parity) (kg)

15. Mean Somatic Cell Score (1st parity)

16. Mean Somatic Cell Score(3+ parity)

17. SD of Somatic Cell Score (1st parity)

18. SD of Somatic Cell Score (3+ parity)

19. 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.

20. Mean Milk Yield (kg)

21. Accounting for Mastitis Losses

22. 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.