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

Big effects from small decisions & actions

P = G + E

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

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

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

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

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

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

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

Automated diagnostics in dairy herds – a game changer

Restriction of financial loss in dairy herds

Action lists

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

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

Saving on input costs Feeding excess energy

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

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

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

Total herd analysis Economic values Individual traits

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

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)

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

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

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

Genetic change for PROFIT in beef cattle Profit per Hectare

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

Informative decision making from catalogues – Example 1 Corrective mating

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

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

Optimal achieving of goals

Real time ultra SOUND scanning as a predictor of carcass properties

RTU Locations scanned

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

Marbling

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

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)

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

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.

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

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

Closing remarks

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

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

DANKIE Japie van der Westhuizen Binnekort! japie@studbook.co.za www.studbook.co.za www.SADairyBulls.com www.logix.org.za Binnekort! www.SABeefBulls.com DANKIE