2004
2004
2007
John B. Cole
Animal Improvement Programs Laboratory
Agricultural Research Service, USDA, Beltsville, MD
john.cole@ars.usda.gov
Best prediction of actuallactation yields
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My credentials
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Best PredictionVanRaden JDS 80:3015-3022 (1997), 6th WCGALP XXIII:347-350 (1998)
Selection IndexSelection Index
Predict missing yields from measuredyields.Predict missing yields from measuredyields.
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 yieldand persistency.Daily yield predicted from lactation yieldand persistency.
Single or multiple trait predictionSingle or multiple trait prediction
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History
Calculation of lactation records formilk (M), fat (F), protein (P), andsomatic cell score (SCS) using bestprediction (BP) began in November1999.
Replaced the test interval methodand projection factors at AIPL.
Used for cows calving in January1997 and later.
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Advantages
Small for most 305-d lactations butlarger for lactations with infrequenttesting or missing componentsamples.
More precise estimation of recordsfor SCS because test days areadjusted for stage of lactation.
Yield records have slightly lower SDbecause BP regresses estimatestoward the herd average.
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Users
AIPL: Calculation of lactation yieldsand data collection ratings (DCR).
DCR indicates the accuracy oflactation records obtained from BP.
Breed Associations: Publish DCR onpedigrees.
DRPCs: Interested in replacing testinterval estimates with BP.
Can also calculate persistency.
May have management applications.
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Restrictions of Original Software
Limited to 305-d lactations used since1935.
Used simple linear interpolation forcalculation of standard curves.
Could not obtain BP for individual daysof lactation.
Difficult to change parameters.
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Enhancements in New Software
Can accommodate lactations of anylength (tested to 999 d).
Lactation-to-date and projected yieldscalculated.
BP of daily yields, test day yields, andstandard curves now output.
The function which models correlationsamong test day yields was updated.
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How does BP work?
Inputs: TD and herd averages in
Statistical wizardry
Standard curve calculated
Cow’s lactation curve based on TDdeviations from standard curve
Outputs: Actual lactation yields,persistency, and daily BP of yield
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Specific curves
Breeds: AY, BS, GU, HO, JE, MS
Traits: M, F, P, SCS
Parity: 1st and later
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Modeling Long Lactations
Dematawewa et al. (2007) recommendsimple models, such as Wood's (1967)curve, for long lactations.
Curves were developed for M, F, and Pyield, but not SCS.
Little previous work on fitting lactationcurves to SCS (Rodriguez-Zas et al.,2000).
BP also requires curves for thestandard deviation (SD) of yields.
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Data and Edits
Holstein TD data were extractedfrom the national dairy database.
The data of Dematawewa et al.(2007) were used.
1st through 5th parities
Lactations were at least 250 d for the305 d group and 800 d for the 999 dgroup.
Records were made in a single herd.
At least five tests reported.
Only twice-daily milking reported.
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Modeling SCS and SD
Test day yields were assigned to 30-dintervals and means and SD werecalculated for each interval.
Curves were fit to the resulting means(SCS) and SD (all traits).
SD of yield modeled with Woodscurves.
SCS means and SD modeled usingcurve C4 from Morant andGnanasakthy (1989).
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SD of Somatic Cell Score (1st parity)
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SD of Somatic Cell Score (3+ parity)
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SD of Milk Yield (first parity) (kg)
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Correlations among test day yieldsNorman et al. JDS 82:2205-2211 (1999)
Rather than calculate eachcorrelation separately we use aformula to approximate them.
Our model accounts for biologicalchanges and daily measurementerror.
The programs were updated to usea simpler formula with similaraccuracy.
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Supervised records
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Example – HO 2nd versus JE 2nd
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Sampling in odd months:ST versus MT
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ST versus MT estimation
MT uses information on 3 or 4 traitssimultaneously.
Provides more accurate estimates ofcomponents yield.
Advantage greatest with infrequenttesting or missing component samples.
Canavesi et al. (2007)
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Uses of Daily Estimates
Daily yields can be adjusted forknown sources of variation.
Example: Daily loss from clinicalmastitis (Rajala-Schultz et al., 1999).
This could lead to animal-specificrather than group-specificadjustments.
Research into optimal managementstrategies.
Management support in on-farmcomputer software.
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Accounting for Mastitis Losses
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Bar et al. JDS 90:4643-4653 (2007)
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Bar et al. JDS 90:4643-4653 (2007)
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Validation
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Validation: new versus oldprograms (ST)
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Validation: new versus oldprograms (MT)
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Validation: first 3 versus all 10 TD
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Validation: sums of 7-d averagesand daily BP
Daily milk yields from the on-farmsystem (7-d averages) were summedand compared to BP.
Correlations:
First parity: 0.927
Later parities: 0.956
Quist et al. (2007) reported thatactual yields are overestimated withthe Canadian equivalent of BP.
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Implementation
When testing is complete:
Yields in the AIPL database will beupdated.
Data will be submitted to Interbullfor a test run.
The BP programs have been sent to4 DRPCs for testing.
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Conclusions
Correlations among successive testdays may require periodic re-estimation as lactation curves change.
Many cows can produce profitably for>305 days in milk, and the revised BPprogram provides a flexible tool tomodel those records.
Daily BP of yields may be useful foron-farm management.