Best prediction of actual lactation yields

1. Best prediction of actual lactation yields

2. My credentials

3. 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

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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

10. Specific curves • Breeds: AY, BS, GU, HO, JE, MS • Traits: M, F, P, SCS • Parity: 1st and later

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

16. 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).

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

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

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

22. Correlations among test day yieldsNorman 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.

23. Supervised records

25. Example – HO 2nd versus JE 2nd

28. Sampling in odd months:ST versus MT

29. 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)

33. 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.

34. Accounting for Mastitis Losses

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

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

37. Validation

38. Validation: new versus old programs (ST)‏

39. Validation: new versus old programs (MT)‏

40. Validation: first 3 versus all 10 TD

41. 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.

42. 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.

43. 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.