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USDA Dairy Goat Genetic Evaluation Program
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USDA Dairy Goat Evaluations
Evaluations for milk, fat, protein, and type Yield evaluations in July Type evaluations in December Evaluations provided to ADGA, DRPC, and publicly via the Internet
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Data Flow FARM DHIA DRPC ADGA AIPL COMPONENT TEST LAB INTERNET
Milk Data collected monthly COMPONENT TEST LAB FARM DHIA Center Data Sent to AIPL DRMS – NC Daily DHI-Provo – UT Agri-Tech – CA 2x/week AgSource – WI Weekly Langston - OK Monthly DRPC ADGA INTERNET AIPL
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Does Contributing Data from Test Day Nearest June 30th, 2003 By Processing Center
Herds Does Percent of Does DRMS 142 2529 35.0 DHI-Provo 162 2449 33.8 Agri-Tech 21 835 11.5 AgSource 26 727 10.0 Langston 90 694 9.6 Total 441 7234
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Alpine Milk Production Lactation Curve
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Alpine Fat Percentage Lactation Curve
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Alpine Protein Percentage Lactation Curve
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Alpine and Nubian Milk Production Second Lactation
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Nubian Fat and Protein Percentage Second Lactation
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Genetic Improvement Program
Phenotype = Genotype + Environment Genetic improvement programs only change genotype Heritability is the portion of total variation due to genetics Rate of genetic improvement determined by generation interval selection intensity heritability
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Factors Affecting Value of Data
Completeness of ID and parentage reporting Years herd on test Size of herd Frequency of testing and component determination
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Evaluation Calculation
Goal predict productivity of progeny Method separate genetic component from other factors influencing evaluated traits All relationships are considered bucks receive evaluations from the records on their female relatives
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Yield Evaluation Model
MODEL: y = hys + hs + pe + a + e y = yield of milk, fat, or protein during a lactation hys = herd-year-season - accounts for environmental effects common to does kidding in the same herd in the same season hs = herd-sire - effect common to daughters of a buck in the same herd pe = permanent environment - effect common to all a doe's lactations that is not genetic a = animal genetic effect (breeding value) e = unexplained residual
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Index of Yield Evaluations
Milk-Fat-Protein Dollars (MFP$) combines evaluations into a single number MFP$ = (0.010 PTAM) + (1.15 PTAF) + (2.55 PTAP)
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Breeding Value Trend for Milk
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Breeding Value Trend for Fat
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Breeding Value Trend for Protein
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Type Traits Measure of similarity to ideal
Final Score (overall assessment) scored 50-99 Linear traits (13 defined traits) scored 1-50
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Linear Traits Stature - Height at withers
Strength - Width and depth of chest, width of muzzle Dairyness - Sharpness and flatness of bone, etc. Teat diameter - Measured at base Rear Legs - Angle of the hock, side view
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Linear Traits Rump Angle - Angle from hips to pins
Rump Width - Width of pelvis Fore Udder Attachment - Strength of attachment Rear Udder Height - Distance from vulva to top of udder
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Linear Traits Rear Udder Arch - Width and shape of rear udder attachment Udder Depth - Measured relative to hocks Medial Suspensory Ligament - Measure of udder cleft Teat Placement - Distance between teats
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Type Evaluation Model MODEL: y = h + a + p + e
y = adjusted type record h = herd appraisal date a = animal genetic effect (breeding value) p = permanent environment - effect common to all a doe's lactations that is not genetic e = unexplained residual Multi-trait evaluation allows scores from one trait to affect the evaluation of another trait through the genetic correlations among the traits.
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Recent Type Appraisal Data
1998 1999 2000 2001 2002 Alpine 1293 1050 1072 1341 1038 Nubian 1393 1286 1319 1299 1081 Toggenburg 592 587 473 672 474 LaMancha 875 635 719 667 627 Saanen 426 459 389 659 502 Oberhasli 370 216 254 323 301 Experimental 253 198 191 269 210
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Type Trait Genetic Correlations
Final Score Strength Dairyness Fore Udder Attachment 1.00 .30 -.15 .66 -.51 .15 -.16 F. Udder Att.
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Breeding Value Trend for Type
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Accuracy of Evaluations
Number of does kidding in same hys more records better estimate of hys effect Number of bucks with daughters having records in same hys more direct comparisons better ranking of bucks Number of lactation records Number of daughters Completeness of pedigree data
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Selection is a Continuous Decision Making Process
Which does to breed Which bucks to use Which specific matings Avoiding inbreeding Correction of faults Which kids to raise Which kids to breed Which does to milk
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Selection is a Continuous Decision Making Process
The greatest impact on progress is from selection of bucks
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Program for Genetic Improvement
Dairy cattle program based on: Artificial insemination (AI) Allows for many progeny from superior males Allows semen to be used in geographically diverse locations Progeny testing (PT) Use young males to get a representative group of daughters Wait until those daughters are milking Based on the evaluations, return the best males to heavy use
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Dairy Cattle Improvement Program
About 1 in 10 PT bulls become active Bulls remain active only a few years Young bulls waiting for daughter records are not active Intensive selection program
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Steps to Increase Rate of Improvement in Goats
Employ AI to use better bucks in more herds Focus on larger herds to improve accuracy Identify young bucks for PT
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Alternative to Waiting for PT
Use young bucks for most breedings Replace bucks quickly Bank semen of young bucks Use frozen semen from superior proven bucks as sires of next generation of young bucks
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AIPL Web Services Queries provide display of:
Queries provide display of: pedigree information yield records herd test characteristics genetic evaluations of does & bucks yield Type Access information using: ID number animal name herd code
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http://aipl. arsusda. gov/cgi-bin/general/Qpublic/do. Q. cgi
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http://aipl. arsusda. gov/cgi-bin/general/Qpublic/do. Q. cgi
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http://aipl. arsusda. gov/cgi-bin/general/Qpublic/do. Q. cgi
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http://aipl. arsusda. gov/cgi-bin/general/Qpublic/do. Q. cgi
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Recent Changes New web query for accessing data by animal name
Yield data since 1998 extracted from the master file each run incorporates corrections, deletions, and ID changes Standardized yields back to 1974 available
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Possible Enhancements
Add evaluations for more traits Productive Life Somatic Cell Score Daughter Pregnancy Rate Switch to test day model Provides better accounting for environment Accounts for genetic differences in shape of lactation curve
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