1. Japie van der Westhuizen & Colleagues SA Stud Book
Die toekoms: Nuwe tegnologie en moderne hulpmiddels in stoetteling The future: New technology & modern aids in stud breeding
Japie van der Westhuizen & Colleagues
SA Stud Book

2. Big effects from small decisions & actions

3. P = G + E

4. P = G + E Total variation of P in a population
Variation of P in each contemporary group

5. P = G + E Total variation of P in each contemporary group
Variation of P in each contemporary group due to genetic differences
Variation of P in each contemporary group due to additive genetic differences

6. Principles: PHENOTYPE (P)
PHENOTYPES that are TRUSTED and USEFUL
Compliance to international standards of quality
Conformity and harmonised to expected recording practices and international bodies
Automated testing according to biological and logical norms
Scrutiny and Rectification at source
Expansion of phenotypes (traits) to include all of economic importance

7. Principles: ENVIRONMENT (E)
Proper description and definition of ENVIRONMENTAL factors causing differences in performance
Identification and quantifying non-genetic factors causing differences in performance among animals for each trait
Correct definition of contemporary groups
Defining common environments
Linking environments with link-sires and related animals
Standardisation of some specific test environments

8. Principles: GENETIC (G)
Prediction of GENETIC merit making a difference in economic value of progeny in selection candidates
Model specification based on scientific principles and global practices
Isolating additive genetic differences among selection candidates
Accounting for other random effects causing phenotypic differences
Optimising prediction accuracy and frequency
Incorporating of GENOMIC information in prediction of additive genetic merit

9. Using the Dairy Example
Using PHENOTYPIC information in making a difference in the bottom line of livestock farmers
Phenotypic expression assist in:
Assessing management levels
Benchmarking
Animal response to management practices
Suitability and profitability of farming system
Needs for management interventions
Using the Dairy Example

10. Logix Milk Automated Herd Health monitoring system
Developed with Vets
Benchmarking
Producer’s choice of participation level
Immediate action lists for animals to be treated
Specialised Animal Science advice
Interactive web based portal on logix.org.za

11. Automated diagnostics in dairy herds – a game changer

12. Restriction of financial loss in dairy herds

13. Action lists

14. Benchmarking against the breed and other herds
Own herd
Chosen herds
Breed

15. Comparison and benchmarking against biological and economic norms
Desired
range
Ideal for maximum profit

16. Saving on input costs
Feeding excess energy

17. Long term sustainability is “all about GENETICS”
Starts with proper recording & trait definition
Continue with proper contemporary allocation
Knowing what breeding value prediction means
Using breeding values and their derivatives for selection
Combining genomic information appropriately for prediction accuracy

18. GOAL Genetic merit prediction Selection index (value) Pedigree
Economic value each trait
Genetic covariance structure
Objectives & Criteria
Genetic merit of each animal for each trait
Genetic Economic Merit (Each Animal)
Selection & Mating plans
Evaluation of effect
GOAL
Return on investment
Environmental constraints
Market needs
Recordability of traits
Payment systems
Risk factors
Other constraints
Labour
Infrastructure
Other
Genetic merit prediction
Selection index
(value)
Pedigree
Performance
Genomic info

19. Genetic merit tools – a few examples
Achieving GOALS
Mating plans
Ranking on Objectives
Available selection candidates

20. Total herd analysis
Economic values
Individual traits

21. Genetic Windows, easy assessment of selection and mating strategies
Herd 1:
Top female fertility
High wean, heavy birth weights
Herd 2:
Medium framed animals
Milk low
Herd 4: Medium framed, lower fertility
Herd 3: Bigger framed animals

22. Combining genetic merit with profitability
What makes a beef cow more profitable
Early calving in season
Regular calving
Easy calving
Milk
Progeny growth rate
Low maintenance
Logix Cow Value
(profit per Ha)

23. Logix Cow value distribution of cows and the impact when improved
Stars Logix cow value
Average Breeding Values:
Rand per Ha 4½
R58.70 4
R46.86 3½
R34.85 3
R25.32 2½
R13.07 2
R2.02 1½
-R9.68
R44.66
R22.75
2 -> 4
1½ -> 2 ½

24. Less profitable (R/Ha) More profitable (R/Ha)
LOWER PROFIT ENVIRONMENT
HIGER PROFIT ENVIRONMENT
Environment 1
Environment 2
Mate with star bulls
Star
Cow herd
Star
Cow herd
Less profitable (R/Ha)
More profitable (R/Ha)
Environmental effect on profitability

25. Less profitable (R/Ha) More profitable (R/Ha)
LOWER PROFIT ENVIRONMENT
HIGHER PROFIT ENVIRONMENT
Environment 1
Environment 2
Progeny of star bulls
Star
Cow herd
Star
Cow herd
Star
Cow herd
Star
Cow herd
R22.50 difference
R22.50 difference
Less profitable (R/Ha)
More profitable (R/Ha)
Environmental effect on profitability

26. Genetic change for PROFIT in beef cattle
Profit per Hectare

27. Informative Sales catalogues
PROFIT breeding Cows
Balanced PROFIT
Components of PROFIT
PROFIT Finishing progeny

28. Informative decision making from catalogues – Example 1
Corrective mating

29. Informative decision making from catalogues – Example 2
Mating best with best

30. Optimal matings – completing the plan
Achieving GOALS
Mating plans
Ranking on Objectives
Available selection candidates

31. Optimal achieving of goals

32. Real time ultra SOUND scanning as a predictor of carcass properties

33. RTU Locations scanned

34. Determining fat Subcutaneous fat Marbling RTU Image Eye Muscle
(carcass)
RTU Image

35. Marbling

36. Muscle on the carcass
Eye muscle
area
Eye Muscle
(carcass)
RTU Image

37. GENOMICS Dairy cattle Beef Cattle
Stud Book service via Interbull conversions
Correct way = combining Parent Average with DGVs
Dairy Genomic Program (DGP) onset
Beef Cattle
Initial steps by Bonsmara
Beef Genomics Program (BGP)

38. Reliability of prediction
Impact of Genomic Selection on reliability of genetic merit prediction in cattle. Example of MILK or Daughter fertility
Age (Years)
Reliability of prediction 2 4 6 8 10 12
0.2
0.4
0.6
0.8
1.0
DGV
EBV
GEBV
1st crop daughters
2nd crop daughters

39. How will Genomic Selection make a practical difference in genetic progress.
The major advantage of genome based breeding values is for young animals without measurements or measured progeny.
Many traits of economic importance are either sex limited, can only be verified at a late age or can only be measured on the carcass.
Examples: Milk production contribution to birth and weaning weight, Calving Ease, Female Fertility, Productive herd life, Progeny retention, Carcase, Meat and other product characteristics.

40. Genetic Merit of Each Animal
Breeding Value?
Genetic Merit of Each Animal
Difference
Population Mean
Progeny Mean
Comparing each animal’s PROGENY PERFORMANCE relative to the population (breed)
BREED Performance
Progeny Performance

41. What would be the change from EBVs to GEBVs?
EBVs of PARENTS
Exact EBV from BLUP when no recordings from progeny (eg. too young or sex limited trait)
Expected spread of EBVs of PROGENY
Direct Genomic Value (DGV) relate the animal at DNA level to others with similar genomic building blocks
GEBV (Genomic EBV) combine EBV and DGV according to information accuracy of both

42. Closing remarks

43. Tools of the trade Record correctly Set objectives and criteria
Mate best with best and cull non profitable animals
Scan the population
Use phenotypes for the right reasons
Management decisions
Benchmarking
Profitability of enterprise
Use wisely

44. Embrace technology Draw direct lines to profitability
No compromise to objective recording
Start with breeding objective and goals
Optimise mating plans
Use all the relevant aids available
Can still keep it simple

45. DANKIE Japie van der Westhuizen Binnekort! japie@studbook.co.za
Binnekort!
DANKIE