Estimation of yields for long lactations using best prediction

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Presentation from the 2007 ADSA meeting describing enhancements to best prediction to accommodate long lactations.

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Estimation of yields for long lactations using best prediction

  1. 1. J. B. ColeJ. B. Cole1,*1,* , P. M. VanRaden, P. M. VanRaden11 , and C. M. B., and C. M. B. DematawewaDematawewa22 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
  2. 2. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Best PredictionBest Prediction VanRaden JDS 80:3015-3022 (1997), 6VanRaden JDS 80:3015-3022 (1997), 6thth WCGALP XXIII:347-350 (1998)WCGALP XXIII:347-350 (1998) • Selection IndexSelection Index − Predict missing yields from measured yields.Predict missing yields from measured yields. − Condense test days into lactation yield andCondense test days into lactation yield and persistency.persistency. − Only phenotypic covariances are needed.Only phenotypic covariances are needed. − Mean and variance of herd assumed known.Mean and variance of herd assumed known. • Reverse predictionReverse prediction − Daily yield predicted from lactation yield andDaily yield predicted from lactation yield and persistency.persistency. • Single or multiple trait predictionSingle or multiple trait prediction
  3. 3. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 HistoryHistory • 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. 4. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 AdvantagesAdvantages • 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. 5. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 UsersUsers • 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. 6. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Restrictions of Original SoftwareRestrictions 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. 7. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Enhancements in New SoftwareEnhancements 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. 8. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Data and EditsData 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.
  9. 9. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Summary StatisticsSummary 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
  10. 10. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Correlations among test day yieldsCorrelations among test day yields Norman et al. JDS 82:2205-2211 (1999)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.
  11. 11. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Modeling Long LactationsModeling 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. 12. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Modeling SCS and SDModeling 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. 13. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Mean Milk Yield (1Mean Milk Yield (1stst parity) (kg)parity) (kg)
  14. 14. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 SD of Milk Yield (first parity) (kg)SD of Milk Yield (first parity) (kg)
  15. 15. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Mean Somatic Cell Score (1Mean Somatic Cell Score (1stst parity)parity)
  16. 16. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Mean Somatic Cell Score(3+ parity)Mean Somatic Cell Score(3+ parity)
  17. 17. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 SD of Somatic Cell Score (1SD of Somatic Cell Score (1stst parity)parity)
  18. 18. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 SD of Somatic Cell Score (3+ parity)SD of Somatic Cell Score (3+ parity)
  19. 19. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Uses of Daily EstimatesUses 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. 20. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Mean Milk Yield (kg)Mean Milk Yield (kg)
  21. 21. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 Accounting for Mastitis LossesAccounting for Mastitis Losses
  22. 22. ADSA 2007 – Best prediction and long lactations Cole et al. 2007 ConclusionsConclusions  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.

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