2004 2007 John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Best prediction.

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Presentation transcript:

John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Best prediction of actual lactation yields

ATA 2007: Best prediction of yield (2)Cole My credentials

ATA 2007: Best prediction of yield (3)Cole Best Prediction VanRaden JDS 80: (1997), 6 th WCGALP XXIII: (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

ATA 2007: Best prediction of yield (4)Cole History  Calculation of lactation records for milk (M), fat (F), protein (P), and somatic cell score (SCS) using best prediction (BP) began in November  Replaced the test interval method and projection factors at AIPL.  Used for cows calving in January 1997 and later.

ATA 2007: Best prediction of yield (5)Cole 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.

ATA 2007: Best prediction of yield (6)Cole 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.

ATA 2007: Best prediction of yield (7)Cole Restrictions of Original Software  Limited to 305-d lactations used since  Used simple linear interpolation for calculation of standard curves.  Could not obtain BP for individual days of lactation.  Difficult to change parameters.

ATA 2007: Best prediction of yield (8)Cole 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.

ATA 2007: Best prediction of yield (9)Cole 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

ATA 2007: Best prediction of yield (10)Cole Specific curves  Breeds: AY, BS, GU, HO, JE, MS  Traits: M, F, P, SCS  Parity: 1 st and later

ATA 2007: Best prediction of yield (11)Cole 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.

ATA 2007: Best prediction of yield (12)Cole 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.

ATA 2007: Best prediction of yield (13)Cole

ATA 2007: Best prediction of yield (14)Cole

ATA 2007: Best prediction of yield (15)Cole

ATA 2007: Best prediction of yield (16)Cole 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).

ATA 2007: Best prediction of yield (17)Cole

ATA 2007: Best prediction of yield (18)Cole

ATA 2007: Best prediction of yield (19)Cole SD of Somatic Cell Score (1 st parity) ‏

ATA 2007: Best prediction of yield (20)Cole SD of Somatic Cell Score (3+ parity) ‏

ATA 2007: Best prediction of yield (21)Cole SD of Milk Yield (first parity) (kg) ‏

ATA 2007: Best prediction of yield (22)Cole Correlations among test day yields Norman et al. JDS 82: (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.

ATA 2007: Best prediction of yield (23)Cole Supervised records

ATA 2007: Best prediction of yield (24)Cole

ATA 2007: Best prediction of yield (25)Cole Example – HO 2 nd versus JE 2 nd

ATA 2007: Best prediction of yield (26)Cole

ATA 2007: Best prediction of yield (27)Cole

ATA 2007: Best prediction of yield (28)Cole Sampling in odd months: ST versus MT

ATA 2007: Best prediction of yield (29)Cole 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)

ATA 2007: Best prediction of yield (30)Cole

ATA 2007: Best prediction of yield (31)Cole

ATA 2007: Best prediction of yield (32)Cole

ATA 2007: Best prediction of yield (33)Cole 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.

ATA 2007: Best prediction of yield (34)Cole Accounting for Mastitis Losses

ATA 2007: Best prediction of yield (35)Cole Bar et al. JDS 90: (2007)

ATA 2007: Best prediction of yield (36)Cole Bar et al. JDS 90: (2007)

ATA 2007: Best prediction of yield (37)Cole Validation

ATA 2007: Best prediction of yield (38)Cole Validation: new versus old programs (ST) ‏

ATA 2007: Best prediction of yield (39)Cole Validation: new versus old programs (MT) ‏

ATA 2007: Best prediction of yield (40)Cole Validation: first 3 versus all 10 TD

ATA 2007: Best prediction of yield (41)Cole 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:  Later parities:  Quist et al. (2007) reported that actual yields are overestimated with the Canadian equivalent of BP.

ATA 2007: Best prediction of yield (42)Cole 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.

ATA 2007: Best prediction of yield (43)Cole 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.