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Genetics of feed efficiency in dairy and
beef cattleDonagh Berry1 & John
Crowley2
1Teagasc, Moorepark, Ireland 2University of Alberta, Canada
American Society of Animal Science, Cell Biology Symposium, Phoenix July 2012
Motivation
• World food demand is increasing ….
• Land-base is decreasing …..• More from less!!!
• Genetics is cumulative and permanent• Good• ….and bad!!!
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?
Objective of talk
To challenge the current dogma
We need to collect lots of feed intake
data (for breeding)
Really? (for breeding!!)
(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
•….
(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
• Feed conversion ratio
• Kleiber ratio
• Relative growth rate
• Residual feed intake (RFI)
• Residual average daily gain (RG)
(Feed) efficiency – growing animals
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
National breeding objective
Goal = Growth rate + fertility
ADG
Fert.
ADG
Fert.
Goal Goal
ADG
Fert.
Goal
Would you go for the goal or the individual traits?
Residual Feed Intake (RFI)
6
7
8
9
10
11
12
13
6 8 10 12 14 16
Predicted Feed I ntake (kg DM/d)
Act
ual
Fee
d I
ntak
e (k
g D
M/d
)
DMI = ADG + LWT + … + e
Residual Feed Intake (RFI)
6
7
8
9
10
11
12
13
6 8 10 12 14 16
Predicted Feed I ntake (kg DM/d)
Act
ual
Fee
d I
ntak
e (k
g D
M/d
)
More efficient animals “under the
line”
DMI = ADG + LWT + … + RFI
Residual Feed Intake (RFI)
6
7
8
9
10
11
12
13
6 8 10 12 14 16
Predicted Feed I ntake (kg DM/d)
Act
ual
Fee
d I
ntak
e (k
g D
M/d
)
High ADG
Low ADGWhat the producer
wants
6
7
8
9
10
11
12
13
6 8 10 12 14 16
Predicted Feed I ntake (kg DM/d)
Act
ual
Fee
d I
ntak
e (k
g D
M/d
)Residual Daily Gain (RDG)
Daily Gain (kg/d)
Daily G
ain
(kg
/d)
More efficient animals “over the
line”
ADG = DMI + LWT + … + RDG
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
An alternative• 2,605 performance test bulls from
Ireland
• Calculated RFI and RG
• Residual intake & gain (RIG) = -
1*RFI+RG
Trait DMI ADG LWT RFI RG RIG
DMI 0.55 0.73 0.59 - 0.03 - 0.35
ADG 0.38 0.37 0.01 0.82 0.47
LWT 0.59 0.34 - 0.17 0.06 0.11
RFI 0.58 0.00 0.00 - 0.46 - 0.87
RG 0.00 0.70 0.00 - 0.40 0.83
RIG - 0.37 0.41 0.00 - 0.85 0.85
Berry and Crowley, (2012)
Genetic
above diag.
Back of the envelope calculations
John Crowley PhD Thesis Top 10% of animals ranked on RFI, RG and RIG
DMI ADGRFI 9.2 1.71RG 10.7 2.18RIG 9.9 2.06
300 kg weight to
gain
Age to slaughter Total DMIRFI 176 1619RG 137 1474RIG 146 1446
Assumed constant ADG and DMI throughout … ridiculous I know!
(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
RatiosSimple
Principle from beefNot common
Same “(dis)advantages” as FCR
Is RFI/RSP really useful?RFIt = DMIt – ([Milk]t + BWt
0.75 + ΔBWt + BCSt)
RSPt = MSt – (DMIt + BWt0.75 + ΔBWt + BCSt)
DMI: 15.6 kg/dLWT: 452 kgMilk Yld: 24.83 kg/dSimilar elsewhere
DMI: 20.6 kg/dLWT: 602 kgMilk Yld: 24.89 kg/dSimilar elsewhere
RFI: -1.386 kg/dRSP: 0.174 kg
RFI: -1.386 kg/dRSP: 0.194 kg
However ….
• Systems efficiency is key (nationally!)
Where can we make the most gains??
BeefBeefReplaceReplaceCowCowDAIRY DMInDMInDMIn
valuebeefvalueMilkFCEHerd
OffReplaceReplaceCowCow
OffBEEF DMIweanDMInDMIn
loss)(weanVALUEFCEHerd
BeefBeefReplaceReplaceCowCowDAIRY DMInDMInDMIn
valuebeefvalueMilkFCEHerd
However ….
• Systems efficiency is key (nationally!)
OffReplaceReplaceCowCow
OffBEEF DMIweanDMInDMIn
loss)(weanVALUEFCEHerd
Fertility?
Genetics of feed
efficiency
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!!
Heritability – growing animals
Meta-analysis of 45 studies/ populations
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
ADG WT DMI FCR RFI RG KR RGR RI G
Trait
Her
itab
ility
Most performance traits are around 35% heritable
Of course variation is (arguably) more important
σriΔG Genetic gain
IntensityAccuracy
Variation
CVgRFI = 1-3%CVgDMI = 3-6%
h2Information
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
WT DMI FCR RFI
Trait
Her
itab
ility
Heritability – lactating animals
Meta-analysis of 11 studies/ populations
Coefficient of genetic variation 4-7%
Genetic correlations among measuresTrai
t FCR RFI RG
DMI0.39
[-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-0.03
[-0.62 to 0.88]-0.01
[-0.40 to 0.33] 0.07
RG-0.89 -0.46
RFI0.75
[-0.21 to 0.93]
Genetic correlations with performanceTrait FCR RFI RG
Lean-0.47
[-0.72 to 0.54]-0.18
[-0.52 to 0.52]
0.03
Fat0.08
[-0.29 to 0.49]0.20
[-0.79 to 0.48] -0.44
Carcass conf
-0.47[-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-0.44
[-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[-0.23 to -0.22]
0.67
Milk 0.03 0.57
Feed intake / efficiency
in a breeding program
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
So would you put it in a breeding goal• No! It is a breeding goal in itself!
• Why not?1.Confusing term2.Feed intake economic weight placed
on individual performance traits – transparency, customized indexes
3.Selection bias is genetic evaluations – “uncorrelated” with selection traits
4.Not optimal adjustment for fixed effects
Put feed in
take in
the
breeding goal
Put feed intake in the breeding goal
We need to collect lots of feed intake data (for breeding)
Really? (for breeding!!)Selection index theory
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
Goal = feed intake (Growing animals)
Traits DMI ADG
ADG 0.78
LWT 0.75 0.68
C’G-1C = 69.8%
Meta-analysis of up to 20 studies
Goal = feed intake (Growing animals)
Traits DMI ADG LWT
ADG 0.78
LWT 0.75 0.68
Fat 0.28 0.09 0.21
C’G-1C = 71.1%
Meta-analysis of up to 20 studies
Goal = feed intake (Growing animals)
Traits DMI ADG LWT Fat
ADG 0.78
LWT 0.75 0.68
Fat 0.28 0.09 0.21
Muscle 0.01 0.19 0.23 0.72
C’G-1C = 89.6%
Meta-analysis of up to 20 studies
Goal = feed intake (Lactating animals)
C’G-1C = 89.4%
Veerkamp & Brotherstone, 1994
Traits DMI Milk LWTStatur
e
Milk 0.59
LWT 0.27 -0.09
Stature 0.13 0.42 0.52
Chest width 0.28 0.24 0.79 0.37
Is it worth going after the remaining
10%
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
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
What I want to know…residual methane production (RMP)
CH4= milk + maintenance + intake + body tissue change + e
Any genetic
variation??
Conclusions
• We now know a lot about the feed intake complex
• Time to take stock, evaluate, and prioritise
Acknowledgements
• Financial support:
• ASAS
• EAAP