2003 George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD USDA Dairy Goat Genetic Evaluation Program

ADGA 2003 (2) G.R. Wiggans 2003 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

ADGA 2003 (3) G.R. Wiggans 2003 Data Flow FARM COMPONENT TEST LAB DRPC Center Data Sent to AIPL DRMS – NCDaily DHI-Provo – UTDaily Agri-Tech – CA2x/week AgSource – WIWeekly Langston - OKMonthly AIPL ADGA INTERNET Milk Data collected monthly DHIA

ADGA 2003 (4) G.R. Wiggans 2003 Does Contributing Data from Test Day Nearest June 30 th, 2003 Does Contributing Data from Test Day Nearest June 30 th, 2003 By Processing Center CenterHerdsDoesPercent of Does DRMS DHI-Provo Agri-Tech AgSource Langston Total

ADGA 2003 (5) G.R. Wiggans 2003 Alpine Milk Production Lactation Curve Lactation 1 Lactation 2 Lactation 3

ADGA 2003 (6) G.R. Wiggans 2003 Alpine Fat Percentage Lactation Curve Lactation 1 Lactation 2 Lactation 3

ADGA 2003 (7) G.R. Wiggans 2003 Alpine Protein Percentage Lactation Curve Lactation 1 Lactation 2 Lactation 3

ADGA 2003 (8) G.R. Wiggans 2003 Alpine and Nubian Milk Production Second Lactation AlpineNubian

ADGA 2003 (9) G.R. Wiggans 2003 Nubian Fat and Protein Percentage Second Lactation FatProtein

ADGA 2003 (10) G.R. Wiggans 2003 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

ADGA 2003 (11) G.R. Wiggans 2003 Factors Affecting Value of Data  Completeness of ID and parentage reporting  Years herd on test  Size of herd  Frequency of testing and component determination

ADGA 2003 (12) G.R. Wiggans 2003 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

ADGA 2003 (13) G.R. Wiggans 2003 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

ADGA 2003 (14) G.R. Wiggans 2003 Index of Yield Evaluations Milk-Fat-Protein Dollars (MFP$) combines evaluations into a single number MFP$ = (0.010  PTA M ) + (1.15  PTA F ) + (2.55  PTA P )

ADGA 2003 (15) G.R. Wiggans 2003 Breeding Value Trend for Milk

ADGA 2003 (16) G.R. Wiggans 2003 Breeding Value Trend for Fat

ADGA 2003 (17) G.R. Wiggans 2003 Breeding Value Trend for Protein

ADGA 2003 (18) G.R. Wiggans 2003 Type Traits Measure of similarity to ideal Final Score (overall assessment) scored Linear traits (13 defined traits) scored 1-50

ADGA 2003 (19) G.R. Wiggans 2003 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

ADGA 2003 (20) G.R. Wiggans 2003 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

ADGA 2003 (21) G.R. Wiggans 2003 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

ADGA 2003 (22) G.R. Wiggans 2003 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.

ADGA 2003 (23) G.R. Wiggans 2003 Recent Type Appraisal Data Alpine Nubian Toggenburg LaMancha Saanen Oberhasli Experimental

ADGA 2003 (24) G.R. Wiggans 2003 Type Trait Genetic Correlations Final ScoreStrengthDairyness Fore Udder Attachment Final Score Strength Dairyness F. Udder Att

ADGA 2003 (25) G.R. Wiggans 2003 Breeding Value Trend for Type

ADGA 2003 (26) G.R. Wiggans 2003 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

ADGA 2003 (27) G.R. Wiggans 2003 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

ADGA 2003 (28) G.R. Wiggans 2003 Selection is a Continuous Decision Making Process The greatest impact on progress is from selection of bucks

ADGA 2003 (29) G.R. Wiggans 2003 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

ADGA 2003 (30) G.R. Wiggans 2003 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

ADGA 2003 (31) G.R. Wiggans 2003 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

ADGA 2003 (32) G.R. Wiggans 2003 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

ADGA 2003 (33) G.R. Wiggans 2003 AIPL Web Services  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

http :// aipl.arsusda.gov/cgi-bin/general/Qpublic/do.Q.cgi?qname=goatherderr&single

ADGA 2003 (43) G.R. Wiggans 2003 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

ADGA 2003 (44) G.R. Wiggans 2003 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