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Dairy Cattle Breeding in the United States

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Presentation made to the Department of Animal Science at Colorado State University.

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Dairy Cattle Breeding in the United States

  1. 1. 200420042006 John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD jcole@aipl.arsusda.gov Dairy Cattle Breeding in the United States
  2. 2. CSU 2006 – Departmental Seminar (2) Cole 20042006 U.S. dairy statistics (2004)  9.0 million cows  67,000 herds  135 cows/herd  19,000 lb (8600 kg)/cow  ~93% Holsteins, ~5% Jerseys  ~75% bred AI  46% milk recorded through Dairy Herd Improvement (DHI)
  3. 3. CSU 2006 – Departmental Seminar (3) Cole 20042006 U.S. dairy population and yield 0 5 10 15 20 25 30 40 50 60 70 80 90 00 Year Cows(millions) 0 2,000 4,000 6,000 8,000 10,000 Milkyield(kg/cow)
  4. 4. CSU 2006 – Departmental Seminar (4) Cole 20042006 DHI statistics (2004)  4.1 million cows  97% fat recorded  93% protein recorded  93% SCC recorded  25,000 herds  164 cows/herd  21,250 lb (9640 kg)/cow  3.69% fat  3.09% (true) protein
  5. 5. CSU 2006 – Departmental Seminar (5) Cole 20042006 U.S. progeny-test bulls (2000)  Major and marketing-only AI organizations plus breeder-proven  Breeds Ayrshire 10 bulls Brown Swiss 53 bulls Guernsey 15 bulls Holstein 1436 bulls Jersey 116 bulls Milking Shorthorn 1 bull
  6. 6. CSU 2006 – Departmental Seminar (6) Cole 20042006 National Dairy Genetic Evaluation Program AIPL CDCB NAAB PDCA DHI Universities AIPL Animal Improvement Programs Lab., USDA CDCB Council on Dairy Cattle Breeding DHI Dairy Herd Improvement (milk recording organizations) NAAB National Association of Animal Breeders (AI) PDCA Purebred Dairy Cattle Association (breed registries)
  7. 7. CSU 2006 – Departmental Seminar (7) Cole 20042006 AIPL mission  Conduct research to discover, test, and implement improved genetic evaluation techniques for economically important traits of dairy cattle and goats  Genetically improve efficiency of dairy animals for yield and fitness
  8. 8. CSU 2006 – Departmental Seminar (8) Cole 20042006 AIPL research objectives  Maintain a national database with animal identification, production, fitness, reproduction, and health traits to support research on dairy genetics and management  Provide data to others researchers submitting proposals compatible with industry needs
  9. 9. CSU 2006 – Departmental Seminar (9) Cole 20042006 AIPL research objectives (cont.)  Increase accuracy of genetic evaluations for traits through improved methodology and through inclusion and appropriate weighting of deviant data  Develop bioinformatic tools to automate data processing in support of quantitative trait locus detection, marker testing, and mapping methods
  10. 10. CSU 2006 – Departmental Seminar (10) Cole 20042006 AIPL research objectives (cont.)  Improve genetic rankings for overall economic merit by evaluating appropriate traits and by determining economic values of those traits in the index  Improved profit functions are derived from reviewing incomes and expenses associated with each trait available for selection
  11. 11. CSU 2006 – Departmental Seminar (11) Cole 20042006 AIPL research objectives (cont.)  Characterize dairy industry practices in milk recording, breed registry, and artificial-insemination to document status and changes in data collection and use and in observed and genetic trends in the population
  12. 12. CSU 2006 – Departmental Seminar (12) Cole 20042006 Traits evaluated  Yield (milk, fat, protein volume; component percentages)  Type/conformation  Productive life/longevity  Somatic cell score/mastitis resistance  Fertility  Daughter pregnancy rate (cow)  Estimated relative conception rate (bull)  Dystocia and stillbirth (service sire, daughter)
  13. 13. CSU 2006 – Departmental Seminar (13) Cole 20042006 Evaluation methods  Animal model (linear)  Yield (milk, fat, protein)  Type (Ayrshire, Brown Swiss, Guernsey, Jersey)  Productive life  SCS  Daughter pregnancy rate  Sire – maternal grandsire model (threshold)  Service sire calving ease  Daughter calving ease  Service sire stillbirth  Daughter stillbirth Heritability 25 – 40% 7 – 54% 8.5% 12% 4% 8.6% 3.6% 3.0% 6.5%
  14. 14. CSU 2006 – Departmental Seminar (14) Cole 20042006 Genetic trend – Milk Phenotypic base = 11,638 kg -3500 -3000 -2500 -2000 -1500 -1000 -500 0 500 1000 1960 1970 1980 1990 2000 Holstein birth year Breedingvalue(kg) sires cows
  15. 15. CSU 2006 – Departmental Seminar (15) Cole 20042006 Genetic trend – Fat -125 -100 -75 -50 -25 0 25 1960 1970 1980 1990 2000 Holstein birth year Breedingvalue(kg) Phenotypic base = 424 kg sires cows
  16. 16. CSU 2006 – Departmental Seminar (16) Cole 20042006 Genetic trend – Protein -125 -100 -75 -50 -25 0 25 1975 1980 1985 1990 1995 2000 Holstein birth year Breedingvalue(kg) Phenotypic base = 350 kg sires cows
  17. 17. CSU 2006 – Departmental Seminar (17) Cole 20042006 Genetic trend – Productive life (mo) -7 -6 -5 -4 -3 -2 -1 0 1 1960 1970 1980 1990 2000 Holstein birth year Breedingvalue(months) Phenotypic base = 24.6 months sires cows
  18. 18. CSU 2006 – Departmental Seminar (18) Cole 20042006 Genetic trend – Somatic cell score -.15 -.10 -.05 .00 .05 .10 1985 1990 1995 2000 Holstein birth year Breedingvalue(logbase2) Phenotypic base = 3.08 (log base 2) sires cows
  19. 19. CSU 2006 – Departmental Seminar (19) Cole 20042006 Genetic trend – Daughter pregnancy rate (%) -2 -1 0 1 2 3 4 5 1960 1970 1980 1990 2000 Holstein birth year Breedingvalue(%) Phenotypic base = 21.53% sires cows
  20. 20. CSU 2006 – Departmental Seminar (20) Cole 20042006 Genetic trend – calving ease 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 1990 1992 1994 1996 1998 2000 2002 Holstein birth year PTA%DBH (difficultbirthsinheifers) Phenotypic base = 8.47% DBH Phenotypic base = 7.99% DBH service sire daughter
  21. 21. CSU 2006 – Departmental Seminar (21) Cole 20042006 Genetic trend – stillbirth 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 1980 1985 1990 1995 2000 Holstein birth year PTA%Stillbirths Phenotypic base = 8% SBH service sire daughter
  22. 22. CSU 2006 – Departmental Seminar (22) Cole 20042006 Genetic-economic indices Trait Relative value (%) Net merit Cheese merit Fluid merit Milk (lb) 0 -12 24 Fat (lb) 23 18 23 Protein (lb) 23 28 0 Productive life (mo) (PL) 17 13 17 Somatic cell score (log2) (SCS) –9 –7 -9 Udder composite (UDC) 6 5 6 Feet/legs composite (FLC) 3 3 3 Body size composite (BSC) –4 –3 -4 Daughter pregnancy rate (%) (DPR) 9 7 8 Calving ability ($) (CA$) 6 4 6
  23. 23. CSU 2006 – Departmental Seminar (23) Cole 20042006 Index changes Trait Relative emphasis on traits in index (%) PD$ (1971) MFP$ (1976) CY$ (1984) NM$ (1994) NM$ (2000) NM$ (2003) NM$ (2006) Milk 52 27 –2 6 5 0 0 Fat 48 46 45 25 21 22 23 Protein … 27 53 43 36 33 23 PL … … … 20 14 11 17 SCS … … … –6 –9 –9 –9 UDC … … … … 7 7 6 FLC … … … … 4 4 3 BDC … … … … –4 –3 –4 DPR … … … … … 7 9 SCE … … … … … –2 … DCE … … … … … –2 … CA$ … … … … … … 6
  24. 24. CSU 2006 – Departmental Seminar (24) Cole 20042006 Persistency
  25. 25. CSU 2006 – Departmental Seminar (25) Cole 20042006 Introduction  At the same level of production cows with high persistency milk more at the end than the beginning of lactation  Best prediction of persistency is calculated as a function of trait- specific standard lactation curves and the linear regression of a cow’s test day deviations on days in milk
  26. 26. CSU 2006 – Departmental Seminar (26) Cole 20042006 Best Prediction  Selection IndexSelection Index  Predict missing yields from measured yieldsPredict missing yields from measured yields  Condense daily into lactation yield andCondense daily into lactation yield and persistencypersistency  Only phenotypic covariances are neededOnly phenotypic covariances are needed  Mean and variance of herd assumed knownMean and variance of herd assumed known  Reverse predictionReverse prediction  Daily yield predicted from lactation yieldDaily yield predicted from lactation yield and persistencyand persistency
  27. 27. CSU 2006 – Departmental Seminar (27) Cole 20042006 Persistency Cole and VanRaden 2006 JDS 89:2722-2728  DefinitionDefinition 305 daily yield deviations (DIM - DIM305 daily yield deviations (DIM - DIMoo)) Uncorrelated with yield by choosingUncorrelated with yield by choosing DIMDIMoo DIMDIMoo werewere 161161,, 159159,, 166166, and, and 155155 forfor M, F, P, and SCSM, F, P, and SCS • DIMDIM00 have increased over timehave increased over time Standardized estimateStandardized estimate
  28. 28. CSU 2006 – Departmental Seminar (28) Cole 20042006 Cow with Average Persistency 0 5 10 15 20 25 0 30 60 90 120 150 180 210 240 270 300 Days in Milk Kilograms Best Prediction Standard Curve Test Days
  29. 29. CSU 2006 – Departmental Seminar (29) Cole 20042006 Highest Cow Persistency 0 10 20 30 40 50 60 70 80 90 100 0 30 60 90 120 150 180 210 240 270 300 Days in Milk Kilograms Best Prediction Standard Curve Test Days
  30. 30. CSU 2006 – Departmental Seminar (30) Cole 20042006 Lowest Cow Persistency 0 10 20 30 40 50 60 70 80 90 100 0 30 60 90 120 150 180 210 240 270 300 Days in Milk Kilograms Best Prediction Standard Curve Test Days
  31. 31. CSU 2006 – Departmental Seminar (31) Cole 20042006 Model Repeatability animal model: yijkl = hysi + lacj + ak + pek + β(dojk) + eijkl yijkl = persistency of milk, fat, protein, or SCS hysi = fixed effect of herd-year-season of calving I lacj = fixed effect of lactation j ak = random additive genetic effect of animal k pek = random permanent environmental effect of animal k dojk = days open for lactation j of animal k eijkl = random residual error ijkljkkkjiijkl e)β(dopealachysy +++++= ijkljkkkjiijkl e)β(dopealachysy +++++=
  32. 32. CSU 2006 – Departmental Seminar (32) Cole 20042006 (Co)variance Components σa 2 σpe 2 σe 2 h2 rept PM 0.10 0.09 0.85 0.10 0.18 PF 0.07 0.08 0.79 0.07 0.15 PP 0.08 0.07 0.70 0.09 0.17 PSCS 0.02 0.03 0.64 0.03 0.07
  33. 33. CSU 2006 – Departmental Seminar (33) Cole 20042006 Correlations Among Persistency Traits PM PF PP PSCS PM 0.83 0.87 -0.48 PF 0.72 0.82 -0.41 PP 0.91 0.72 -0.58 PSCS -0.19 -0.11 -0.14 1 Genetic correlations above diagonal, residual correlations below diagonal.
  34. 34. CSU 2006 – Departmental Seminar (34) Cole 20042006 Genetic Correlations Among Persistency and Yield M F P SCS PM 0.05 0.10 0.03 -0.04 PF 0.12 0.12 0.00 0.00 PP -0.02 0.08 -0.09 -0.11 PSCS -0.23 -0.28 -0.20 0.41
  35. 35. CSU 2006 – Departmental Seminar (35) Cole 20042006 Factors Affecting Persistency  Parity: 1st lactation cows tend to have flatter lactation curves than later lactation cows  Nutrition: underfeeding energy will reduce peak yield, leading to higher persistency  Stress: low persistency in cows under handling or heat stress  Diseases?  Breed differences?
  36. 36. CSU 2006 – Departmental Seminar (36) Cole 20042006 Summary  Heritabilities and repeatabilities are low to moderate  Routine genetic evaluations for persistency are feasible  The shape of the lactation curve may be altered without affecting production
  37. 37. CSU 2006 – Departmental Seminar (37) Cole 20042006 Diseases and Persistency Appuhamy, Cassell, and Cole 2006  Other measures may improve disease resistance through indirect selection, e.g. productive life (PL), body condition scores, and persistency  Studies of the effect of diseases on milk yield is abundant in literature  Investigations of relationships between diseases and other traits are lacking (Muir et al., 2004)
  38. 38. CSU 2006 – Departmental Seminar (38) Cole 20042006 Objectives  Investigate the effect of common health disorders on persistency  Estimate phenotypic correlations among diseases and persistency  Measure breed effects (Holstein and Jersey) on these relationships
  39. 39. CSU 2006 – Departmental Seminar (39) Cole 20042006 Materials and Methods Daily milk yield records of Holstein and Jersey cows at the Virginia Tech Dairy Complex from 07/18/2004 to 06/07/2006 Holstein Jersey First lactation (L1) 41 10 Second lactation (L2) 34 08 Third and later (L3+) 40 15 Total Lactations 115 33 Total cows 93 33
  40. 40. CSU 2006 – Departmental Seminar (40) Cole 20042006 Definition of Disease Variables Mastitis (MAST) : All causes of udder infections MAST1 : in first 100 days (stage1) MAST2 : after 100th DIM (stage2) Post Partum Metabolic Diseases (METAB): Milk fever and/or ketosis Other diseases: LAME, DA, MET, PNEU, DIARR
  41. 41. CSU 2006 – Departmental Seminar (41) Cole 20042006 Statistical Analysis where: Yijklm = Lactation persistency of cow m Li = Effect of ith lactation (i = 1, 2, & 3) YSj = Effect of jth calving year-season ( j=1, 2, 3, 4, 5 & 6) Dk = Effect of kth status of the disease ( k =1 or 0) Ol = Effect of lth status of other diseases (l=1 or 0) eijklm = residual effect (Other diseases includes all diseases beside the disease of interest.) Pijklm = Li + Yj +Dk + Ol + eijklm
  42. 42. CSU 2006 – Departmental Seminar (42) Cole 20042006 Disease incidence rates in Holstein (H) & Jersey (J) cows 0 10 20 30 MAST1 MAST2 METAB OTHER Disease Incidencerate(%) H J
  43. 43. CSU 2006 – Departmental Seminar (43) Cole 20042006 Diseases and Breed on Persistency ** Significant (p<0.05)  Factor  Levels  LS Mean  Correlation  MAST1** 0 -0.18 -0.241 -0.76  MAST2 0 -0.3 -0.091 -0.55 METAB  0 -0.35 -0.081 0.37 BREED**  H -0.11   J -0.74  
  44. 44. CSU 2006 – Departmental Seminar (44) Cole 20042006 Conclusions  Mastitis in early lactation has a significant, negative effect on persistency  Mastitis in late lactation and post partum metabolic diseases have non- significant, but negative, effects on persistency  Persistency differs significantly between Holstein and Jersey cows
  45. 45. CSU 2006 – Departmental Seminar (45) Cole 20042006 All-Breeds Evaluation
  46. 46. CSU 2006 – Departmental Seminar (46) Cole 20042006 Goals  Evaluate crossbred animals without biasing purebred evaluations  Accurately estimate breed differences  Compute national evaluations and examine changes  PTA of purebreds and crossbreds  Changes in reliability  Display results without confusion
  47. 47. CSU 2006 – Departmental Seminar (47) Cole 20042006 Methods  All-breed animal model Purebreds and crossbreds together Unknown parents grouped by breed Variance adjustments by breed Age adjust to 36 months, not mature 1988 software, good convergence  Within-breed-of-sire model examined but not used
  48. 48. CSU 2006 – Departmental Seminar (48) Cole 20042006 Unknown Parent Groups  Groups formed based on Birth year (flexible) Breed (must have >10,000 cows) Path (dams of cows, sires of cows, parents of bulls) Origin (domestic vs other countries)  Paths have >1000 in last 15 years  Groups each have >500 animals
  49. 49. CSU 2006 – Departmental Seminar (49) Cole 20042006 Data  Numbers of cows of all breeds  22.6 million for milk and fat  16.1 million for protein  22.5 million for productive life  19.9 million for daughter pregnancy rate  10.5 million for somatic cell score  Type evaluated in separate breed files  Calving ease joint HO, BS, and HO x BS  Goats in all-breed model since 1988
  50. 50. CSU 2006 – Departmental Seminar (50) Cole 20042006 Crossbred Cows with 1st parity records Year F1 (%) F1 cows Back- cross Het > 0 XX cows 2005 1.3 8647 2495 12621 4465 2004 1.2 7863 1983 11191 3947 2003 .9 6248 1492 9051 3111 2002 .7 4689 1467 7338 2564 2001 .5 3491 1330 5878 2081
  51. 51. CSU 2006 – Departmental Seminar (51) Cole 20042006 Reliability  Crossbred cows Will have PTA, most did not before Accurate PTA from both parents  Purebred animals Information from crossbred relatives More contemporaries
  52. 52. CSU 2006 – Departmental Seminar (52) Cole 20042006 All- vs Within-Breed Evaluations Correlations of PTA Milk Breed 99% REL bulls Recent bulls Recent cows Holstein >.999 .994 .989 Jersey .997 .988 .972 Brown Swiss .990 .960 .942 Guernsey .991 .988 .969 Ayrshire .990 .963 .943 Milking Shorthorn .997 .986 .947
  53. 53. CSU 2006 – Departmental Seminar (53) Cole 20042006 Display of PTAs  Genetic base  Convert all-breed base back to within- breed-of-sire bases  Each animal gets just one PTA  PTAbrd = (PTAall – meanbrd) SDbrd/SDall  Heterosis and inbreeding  Both effects removed in the animal model  Heterosis added to crossbred animal PTA  Expected Future Inbreeding (EFI) and merit differ with mate breed
  54. 54. CSU 2006 – Departmental Seminar (54) Cole 20042006 Schedule  Interbull test run Feb. 1, 2006 Trend validation Convert all-breed PTA back to within-breed bases  Scientific publication (JDS)  Implementation Expected May 2007
  55. 55. CSU 2006 – Departmental Seminar (55) Cole 20042006 Conclusions  All breed model accounts for: General heterosis Unknown parent groups by breed Heterogeneous variance by breed  PTA converted back to within breed bases, crossbreds to breed of sire  PTA changes more in breeds with fewer animals
  56. 56. CSU 2006 – Departmental Seminar (56) Cole 20042006 Genomics
  57. 57. CSU 2006 – Departmental Seminar (57) Cole 20042006 SNP Project Outcomes  Genome-wide selection  Parentage verification & traceability panels  Enhanced QTL mapping & gene discovery
  58. 58. CSU 2006 – Departmental Seminar (58) Cole 20042006 Linkage disequilibrium (LD)  Non-random association of alleles at two or more loci, not necessarily on the same chromosome  Not the same as linkage, which describes the association of two or more loci on a chromosome with limited recombination between them
  59. 59. CSU 2006 – Departmental Seminar (59) Cole 20042006 The population is young enough that large segments of the genome are not disrupted by recombination (LD) Concept of a HapMap Many Generations
  60. 60. CSU 2006 – Departmental Seminar (60) Cole 20042006 Genome Selection  Animals are genotyped at birth  Genomic EBV calculated for many traits Even those not typically recorded (e.g. semen quality)  Accuracy is predicted to be similar to progeny test evaluation
  61. 61. CSU 2006 – Departmental Seminar (61) Cole 20042006 Advantages of Genome Selection  Generation intervals can be reduced  Costs of progeny testing can be decreased  More accurate selection among full sibs Decreased risk in selection program
  62. 62. CSU 2006 – Departmental Seminar (62) Cole 20042006 Low-cost parentage verification  SNP tests may make parentage validation cheap enough for widespread adoption  Develop a database and software to check parentage and suggest alternatives for invalid IDs  Determine rate of parentage errors in a sample of herds
  63. 63. CSU 2006 – Departmental Seminar (63) Cole 20042006 In Conclusion
  64. 64. CSU 2006 – Departmental Seminar (64) Cole 20042006 Ongoing Work  New traits  Stillbirth (HOL)  Milking speed (BSW)  Rear legs/rear view (BSW, GUE)  Bull fertility (transferred from DRMS)  Improved online tools  Fully buzzword-compliant  Web services for data delivery  Choice of scales
  65. 65. CSU 2006 – Departmental Seminar (65) Cole 20042006 Senior research staff

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