J. B. Cole 1,*, P. M. VanRaden 1, and C. M. B. Dematawewa 2 1 Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD 2 Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 2007 Estimation of yields for long lactations using best prediction

ADSA 2007 – Best prediction and long lactations Cole et al Best Prediction VanRaden JDS 80: (1997), 6 th WCGALP XXIII: (1998) ‏ Selection Index 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 Reverse prediction  Daily yield predicted from lactation yield and persistency. Single or multiple trait prediction Single or multiple trait prediction

ADSA 2007 – Best prediction and long lactations Cole et al 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.

ADSA 2007 – Best prediction and long lactations Cole et al 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.

ADSA 2007 – Best prediction and long lactations Cole et al 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.

ADSA 2007 – Best prediction and long lactations Cole et al Restrictions of Original Software Limited to 305-d lactations used since 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.

ADSA 2007 – Best prediction and long lactations Cole et al 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.

ADSA 2007 – Best prediction and long lactations Cole et al 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.

ADSA 2007 – Best prediction and long lactations Cole et al Summary Statistics FirstLater Records171,970176,153 Length (d) Pct > 305-d Pct > 500-d3.33.4

ADSA 2007 – Best prediction and long lactations Cole et al Correlations among test day yields Norman et al. JDS 82: (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.

ADSA 2007 – Best prediction and long lactations Cole et al 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.

ADSA 2007 – Best prediction and long lactations Cole et al 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).

ADSA 2007 – Best prediction and long lactations Cole et al Mean Milk Yield (1 st parity) (kg)

ADSA 2007 – Best prediction and long lactations Cole et al SD of Milk Yield (first parity) (kg)

ADSA 2007 – Best prediction and long lactations Cole et al Mean Somatic Cell Score (1 st parity)

ADSA 2007 – Best prediction and long lactations Cole et al Mean Somatic Cell Score(3+ parity)

ADSA 2007 – Best prediction and long lactations Cole et al SD of Somatic Cell Score (1 st parity)

ADSA 2007 – Best prediction and long lactations Cole et al SD of Somatic Cell Score (3+ parity)

ADSA 2007 – Best prediction and long lactations Cole et al 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.

ADSA 2007 – Best prediction and long lactations Cole et al Mean Milk Yield (kg)

ADSA 2007 – Best prediction and long lactations Cole et al Accounting for Mastitis Losses

ADSA 2007 – Best prediction and long lactations Cole et al 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.