1. Genetics of feed efficiency in dairy and beef cattle
Donagh Berry1 & John Crowley2
1Teagasc, Moorepark, Ireland 2University of Alberta, Canada
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. We need to collect lots of feed intake data (for breeding)
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 + BWt ΔBWt + BCSt)
RSPt = MSt – (DMIt + BWt ΔBWt + BCSt)
DMI: 15.6 kg/d
LWT: 452 kg
Milk Yld: kg/d
Similar elsewhere
DMI: 20.6 kg/d
LWT: 602 kg
Milk Yld: kg/d
Similar elsewhere
RFI: kg/d
RSP: kg
RFI: kg/d
RSP: 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. Of course variation is (arguably) more important
Information h2
Accuracy
Intensity
Variation
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
Trait
FCR
RFI RG
DMI
0.39
[-0.57 to 0.90]
0.72
[-0.34 to 0.85]
-0.03
[-0.03 to 0.00]
ADG
-0.62
[-0.89 to 0.75]
0.02
[-0.15 to 0.53]
0.82 WT
[-0.62 to 0.88]
-0.01
[-0.40 to 0.33]
0.07
-0.89
-0.46
0.75
[-0.21 to 0.93]

27. Genetic correlations with performance
Trait
FCR
RFI RG
Lean
-0.47
[-0.72 to 0.54]
-0.18
[-0.52 to 0.52]
0.03
Fat
0.08
[-0.29 to 0.49]
0.20
[-0.79 to 0.48]
-0.44
Carcass conf
[-0.6 to -0.02]
-0.30
[-0.56 to 0.29]
0.35
Carcass fat
-0.23
[-0.61 to 0.11]
0.06
[-0.37 to 0.33]
-0.10
Carcass wt
[-0.69 to -0.26]
-0.11
[-0.60 to 0.26]
0.32
Mature weight
-0.62
[-0.62 to -0.54]
[-0.23 to -0.22]
0.67
Milk
0.57

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)
Traits
DMI
ADG
0.78
LWT
0.75
0.68
Meta-analysis of up to 20 studies
C’G-1C = 69.8%

34. Goal = feed intake (Growing animals)
Traits
DMI
ADG
LWT
0.78
0.75
0.68
Fat
0.28
0.09
0.21
Meta-analysis of up to 20 studies
C’G-1C = 71.1%

35. Goal = feed intake (Growing animals)
Traits
DMI
ADG
LWT
Fat
0.78
0.75
0.68
0.28
0.09
0.21
Muscle
0.01
0.19
0.23
0.72
Meta-analysis of up to 20 studies
C’G-1C = 89.6%

36. Goal = feed intake (Lactating animals)
Traits
DMI
Milk
LWT
Stature
0.59
0.27
-0.09
0.13
0.42
0.52
Chest width
0.28
0.24
0.79
0.37
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
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