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
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
FCR - traditional measure but:
• Kleiber
•Ratio
traitratio
(breeding)
•can be linearised
• Relative
growth
•anyway would
you rate
recommend selecting on it?
•Correlated with growth – mature size
• Residual
feed
intake
•Breeding goal can restrict cow size
•Most variation explained by growth
• Residual
average
dailyfor
gain
•More or less
the same
other traits
•….
(Feed) efficiency – growing animals
• Feed conversion ratio
FCR - traditional measure because:
• Kleiber
ratio
•Easy
to calculate
•The dog on the street knows what it is
• Relative with
growth
rate
•Correlated
growth
•Poor animals will unlikely have good FCR
•
Residual
feed
intake
•Never going to recommend single trait
selection anyway
• Residual average daily gain
(Feed) efficiency – growing animals
• Feed conversion ratio
• Kleiber ratio
• Relative growth rate
• Residual feed intake (RFI)
• Residual average daily gain (RG)
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
ADG
ADG
Fert.
Fert.
Fert.
Goal
Goal
Goal
Would you go for the goal or the individual traits?
Actual Feed Intake (kg DM/d)
Residual Feed Intake (RFI)
13
DMI = ADG + LWT + … + e
12
11
10
9
8
7
6
6
8
10
12
Predicted Feed Intake (kg DM/d)
14
16
Actual Feed Intake (kg DM/d)
Residual Feed Intake (RFI)
13
DMI = ADG + LWT + … + RFI
12
11
10
9
More efficient animals
“under the line”
8
7
6
6
8
10
12
Predicted Feed Intake (kg DM/d)
14
16
Actual Feed Intake (kg DM/d)
Residual Feed Intake (RFI)
13
High ADG
12
11
10
9
Low ADG
8
What the
producer wants
7
6
6
8
10
12
Predicted Feed Intake (kg DM/d)
14
16
13
ADG = DMI + LWT + … + RDG
Daily Gain (kg/d)
Actual Feed Intake (kg DM/d)
Residual Daily Gain (RDG)
12
11
10
9
More efficient animals
“over the line”
8
7
6
6
8
10
12
Daily Gain (kg/d)
Predicted Feed Intake (kg DM/d)
14
16
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
DMI
ADG
LWT
RFI
RG
RIG
0.55
0.73
0.59
-0.03
-0.35
0.37
0.01
0.82
0.47
-0.17
0.06
0.11
-0.46
-0.87
ADG
0.38
LWT
0.59
0.34
RFI
0.58
0.00
0.00
RG
0.00
0.70
0.00
-0.40
RIG
-0.37
0.41
0.00
-0.85
Genetic
above
diag.
0.83
0.85
Berry and Crowley, (2012)
Back of the envelope calculations
John Crowley PhD Thesis
Top 10% of animals ranked on RFI, RG and RIG
DMI
RFI 9.2
RG 10.7
RIG 9.9
ADG
1.71
2.18
2.06
300 kg weight
to gain
Assumed constant
ADG and DMI
throughout …
ridiculous I know!
Age to slaughter Total DMI
RFI
176
1619
RG
137
1474
RIG
146
1446
(Feed) efficiency –lactating animals
• Milk solids per kg live-weight
• Milk solids per kg intake (FCE)
• Intake per kg live-weight
Ratios
Simple
Same
“(dis)advantages”
as from
FCR
Principle
• Residual feed
intake
beef
• Residual solids production Not common
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
However ….
• Systems efficiency is key (nationally!)
He rdFCEBEEF
VALUEOff (we an loss)

nCow  DMICow  n Replace  DMIReplace  we an DMIOff
Where can we make the most gains??
He rdFC EDAIRY
Milk value be e f value

nCow  DMICow  n Replace  DMIReplace  nBeef  DMIBeef
However ….
• Systems efficiency is key (nationally!)
He rdFCEBEEF
VALUEOff (we an loss)

nCow  DMICow  n Replace  DMIReplace  we an DMIOff
Fertility?
He rdFC EDAIRY
Milk value be e f value

nCow  DMICow  n Replace  DMIReplace  nBeef  DMIBeef
Genetics of feed
efficiency
Heritability
2
(h )
• 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
Most performance traits are
around 35% heritable
0.8
Heritability
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
ADG
WT
DMI
FCR
RFI
Trait
RG
KR
RGR
RIG
Meta-analysis of 45 studies/ populations
Of course variation is (arguably) more
important
Information
Intensity
Accuracy
h2
Variation
ΔG  i  r  σ
Genetic gain
CVgRFI = 1-3%
CVgDMI = 3-6%
Heritability – lactating animals
0.9
0.8
Coefficient of genetic
variation 4-7%
Heritability
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
WT
FCR
DMI
RFI
Trait
Meta-analysis of 11 studies/ populations
Genetic correlations among measures
Trait
DMI
ADG
WT
RG
RFI
FCR
0.39
RFI
0.72
[-0.57 to 0.90]
[-0.34 to 0.85]
-0.62
0.02
[-0.89 to 0.75]
[-0.15 to 0.53]
-0.03
-0.01
[-0.62 to 0.88]
[-0.40 to 0.33]
-0.89
-0.46
0.75
[-0.21 to 0.93]
RG
-0.03
[-0.03 to 0.00]
0.82
0.07
Genetic correlations with performance
Trait
Lean
Fat
Carcass conf
Carcass fat
Carcass wt
FCR
-0.47
RFI
-0.18
[-0.72 to 0.54]
[-0.52 to 0.52]
0.08
0.20
[-0.29 to 0.49]
[-0.79 to 0.48]
-0.47
-0.30
[-0.6 to -0.02]
[-0.56 to 0.29]
-0.23
0.06
RG
0.03
-0.44
0.35
[-0.61 to 0.11]
[-0.37 to 0.33]
-0.10
-0.44
0.32
[-0.69 to -0.26]
-0.11
[-0.60 to 0.26]
-0.62
-0.23
0.67
0.03
0.57
Mature weight [-0.62 to -0.54]
Milk
[-0.23 to -0.22]
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 term
2.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 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
LWT
0.78
0.75 0.68
C’G-1C = 69.8%
Meta-analysis
of up to 20
studies
Goal = feed intake
(Growing animals)
Traits
DMI
ADG
ADG
LWT
Fat
0.78
0.75 0.68
0.28 0.09 0.21
C’G-1C = 71.1%
LWT
Meta-analysis
of up to 20
studies
Goal = feed intake
(Growing animals)
Traits
DMI
ADG
ADG
LWT
Fat
Muscle
0.78
0.75 0.68
0.28 0.09 0.21
0.01 0.19 0.23 0.72
C’G-1C = 89.6%
LWT
Fat
Meta-analysis
of up to 20
studies
Goal = feed intake
(Lactating animals)
Traits
Milk
LWT
Stature
Chest width
DMI
0.59
0.27
0.13
0.28
Milk
-0.09
0.42
0.24
LWT Stature
0.52
0.79
0.37
Veerkamp & Brotherstone, 1994
C’G-1C
Is it worth going after
= 89.4%
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)
Any genetic
CH4= milk + maintenance + intake variation??
+ body tissue change + e
Conclusions
• We now know a lot about the feed
intake complex
• Time to take stock, evaluate, and
prioritise
Acknowledgements
• Financial support:
• ASAS
• EAAP