Genetics of feed efficiency in dairy and beef cattle

1. Genetics of feed efficiency in dairy and beef cattle Donagh Berry1 & John Crowley2 1Teagasc, Moorepark, Ireland 2University of Alberta, Canada donagh.berry@teagasc.ie American Society of Animal Science, Cell Biology Symposium, Phoenix July 2012

2. Motivation • World food demand is increasing …. • Land-base is decreasing ….. • More from less!!! • Genetics is cumulative and permanent • Good • ….and bad!!!

3. Objective of talk • To challenge the current dogma (Daily) feed efficiency is the most important trait ever!! Feed is the largest variable cost Agree that feed is the largest variable cost but is addressing daily feed efficiency the best use of resources?

4. Objective of talk • To challenge the current dogma We need to collect lots of feed intake data (for breeding) Really? (for breeding!!)

5. (Feed) efficiency – growing animals • Feed conversion ratio • Kleiber ratio • Relative growth rate • Residual feed intake • Residual average daily gain • FCR - traditional measure but: • Ratio trait (breeding) • can be linearised • anyway would you recommend selecting on it? • Correlated with growth – mature size • Breeding goal can restrict cow size • Most variation explained by growth • More or less the same for other traits • ….

6. (Feed) efficiency – growing animals • Feed conversion ratio • Kleiber ratio • Relative growth rate • Residual feed intake • Residual average daily gain • FCR - traditional measure because: • Easy to calculate • The dog on the street knows what it is • Correlated with growth • Poor animals will unlikely have good FCR • Never going to recommend single trait selection anyway

7. (Feed) efficiency – growing animals • Feed conversion ratio • Kleiber ratio • Relative growth rate • Residual feed intake (RFI) • Residual average daily gain (RG)

8. A few points – RFI & RG • Byerly (1941) actually first suggested • RFI & RG are (restricted) selection indexes • Never more efficient than an optimal selection index • Is this why it is difficult to explain variation in RFI?? • Is all the heritability we see true heritability in feed efficiency? • Re-ranking on index versus component traits • Koch et al. (1963) actually favoured RG • Issues with how RFI/RG is modelled

9. National breeding objective • Goal = Growth rate + fertility ADG ADG ADG Fert. Fert. Fert. Goal Goal Goal Would you go for the goal or the individual traits?

10. Residual Feed Intake (RFI) DMI = ADG + LWT + … + e

11. Residual Feed Intake (RFI) DMI = ADG + LWT + … + RFI More efficient animals “under the line”

12. Residual Feed Intake (RFI) High ADG What the producer wants Low ADG

13. Residual Daily Gain (RDG) ADG = DMI + LWT + … + RDG Daily Gain (kg/d) More efficient animals “over the line” Daily Gain (kg/d)

14. So….. • RFI is independent of live-weight & growth • RG is independent of live-weight & feed intake • -1*RFI + RG must still be independent of live-weight (apparently a favourable characteristic but I’m not sure why given we recommend using selection indexes) • But negative correlation with feed intake and a positive correlation with gain

15. An alternative • 2,605 performance test bulls from Ireland • Calculated RFI and RG • Residual intake & gain (RIG) = -1*RFI+RG Genetic above diag. Berry and Crowley, (2012)

16. Back of the envelope calculations John Crowley PhD Thesis Top 10% of animals ranked on RFI, RG and RIG 300 kg weight to gain Assumed constant ADG and DMI throughout … ridiculous I know!

17. (Feed) efficiency –lactating animals • Milk solids per kg live-weight • Milk solids per kg intake (FCE) • Intake per kg live-weight • Residual feed intake • Residual solids production Ratios Simple Same “(dis)advantages” as FCR Principle from beef Not common

18. Is RFI/RSP really useful? RFIt = DMIt – ([Milk]t + BWt0.75 + ΔBWt + BCSt) RSPt = MSt – (DMIt + BWt0.75 + ΔBWt + BCSt) DMI: 15.6 kg/d LWT: 452 kg Milk Yld: 24.83 kg/d Similar elsewhere DMI: 20.6 kg/d LWT: 602 kg Milk Yld: 24.89 kg/d Similar elsewhere RFI:-1.386 kg/d RSP:0.174 kg RFI:-1.386 kg/d RSP:0.194 kg

19. However …. • Systems efficiency is key (nationally!) Where can we make the most gains??

20. However …. • Systems efficiency is key (nationally!) Fertility?

21. Genetics of feed efficiency

22. Heritability (h2) • One of the most mis-interpreted concepts in quantitative genetics • Proportion of the differences in performance among contemporaries that is due to additive (i.e. transmitted) genetic differences • Growth rate, milk yield ~35% • Fertility, health <0.05% • Remaining variation is not all management!!

23. Heritability – growing animals Most performance traits are around 35% heritable Meta-analysis of 45 studies/ populations

24. Accuracy Intensity Variation Of course variation is (arguably) more important Information h2 CVgRFI = 1-3% Genetic gain CVgDMI = 3-6%

25. Heritability – lactating animals Coefficient of genetic variation 4-7% Meta-analysis of 11 studies/ populations

26. Genetic correlations among measures

27. Genetic correlations with performance

28. Feed intake / efficiency in a breeding program

29. Feed efficiency or not feed efficiency….that is the question • RFI is uncorrelated with weight and ADG • …or is it!!!! • RFI is derived at the phenotypic level • Does not imply genetic independence • Simulated feed intake with a phenotypic correlation structure with weight and ADG • h2 RFI = 0.06 ± 0.03 • “Picking up” genetic correlations with weight and ADG

30. So would you put it in a breeding goal • No! It is a breeding goal in itself! • Why not? • Confusing term • Feed intake economic weight placed on individual performance traits – transparency, customized indexes • Selection bias is genetic evaluations – “uncorrelated” with selection traits • Not optimal adjustment for fixed effects Put feed intake in the breeding goal

31. Put feed intake in the breeding goal We need to collect lots of feed intake data (for breeding) Really? (for breeding!!) Selection index theory

32. Selection index theory • Using information on genetic merit of animals for individual traits to predict genetic merit of a composite • Analogous to multiple-regression; PROC GLM, PROC MIXED, PROC REG • Confounding factors already removed • Used in all breeding objectives • Especially useful for low heritability traits • Also useful in difficult to measure traits

33. Goal = feed intake (Growing animals) Meta-analysis of up to 20 studies C’G-1C = 69.8%

34. Goal = feed intake (Growing animals) Meta-analysis of up to 20 studies C’G-1C = 71.1%

35. Goal = feed intake (Growing animals) Meta-analysis of up to 20 studies C’G-1C = 89.6%

36. Goal = feed intake (Lactating animals) Veerkamp & Brotherstone, 1994 Is it worth going after the remaining 10% C’G-1C = 89.4%

37. Gaps in knowledge • Is researching daily feed efficiency the best use of resources to improve system efficiency • We have the parameters to investigate • Personally I would focus on feed intake • Prediction of feed intake • Phenotypic ≠ genetic • Do not forget selection index theory • KISS • Water efficiency, methane efficiency

38. Straying a bit….. • Methane researchers ≈ Feed efficiency researchers • Feed efficiency • Ratio rates are bad • Environment • Ratio traits are no longer bad • Phenotype = CH4/kg DMI • Random simulation of CH4 (h2=0); h2 DMI = 0.49 • h2 CH4/kg DMI = 0.19 ± 0.05

39. What I want to know…residual methane production (RMP) Any genetic variation?? CH4= milk + maintenance + intake + body tissue change + e

40. Conclusions • We now know a lot about the feed intake complex • Time to take stock, evaluate, and prioritise

41. Acknowledgements • Financial support: • ASAS • EAAP