Transcript 2003 BIF RFI - Welcome to Angus Journal coverage of the

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The Genetics of Feed Efficiency in Cattle
Dr. D.H. “Denny” Crews, Jr.
Research Scientist, Beef Quantitative Genomics
National Study Leader, Livestock Genetics & Genomics
AAFC Research Centre, Lethbridge, Alberta
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Many Measures of Efficiency
• Probably two dozen measures of efficiency have
been described in beef cattle
• Feed conversion ratio is a gross measure of
efficiency
– Genetic trend has been positive along with growth
• Rg (FCR, growth): -0.61 to -0.95
– Related to increased mature weights and therefore,
maintenance energy requirements
– Lends poorly to selection
• Most selection pressure on growth rate
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Reducing Inputs
• Very little genetic improvement has been aimed at reducing
input costs:
– Feed costs are the largest non-fixed cost of beef production
– >70% of total variable costs
• Daily feed intake (dDMI) is heritable (h2 = 0.34 based on 23
studies [Koots et al., 1994a]) and therefore likely to respond to
selection
Trait
Rg (dDMI)
BWT
0.77
WT205
0.67
WT365
0.79
MKTWT
0.92
Reference
Koots et al. (1994b)
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Reducing Inputs: Feed Efficiency
• Gross efficiency (Archer et al., 1999; gain/feed) and
feed conversion ratio (FCR, feed/gain) have been
discussed for more than 30 years, along with at
least 20 other so-called efficiency measurements
• Most have at least moderate heritability (> 0.32 0.37) and strong genetic correlation with growth
ΔGFCR | WWT = (RgFCR,WWT) (h2
WWT)
(i
WWT)
(σg(FCR)) = -0.21 kgd-1/gen
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Selection: FCR
• Adding feed conversion ratio to breeding objectives would
have the following implications:
– Additional ΔG for growth; the most immediate concern is that
with mature size (RgFCR,MWT > 0.50)
– Disproportionate selection on dDMI versus ADG. Gunsett (1984)
discussed the problems associated with selection for ratio traits
– Negative genetic trend in FCR does not translate to incremental
improvement in feed efficiency
• Changes in FCR can be made without changing efficiency (+ ADG)
• Selection response is usually unpredictable (Gunsett, 1984)
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RFI Definition
• Residual feed intake (syn. net feed efficiency) is defined as the
difference between actual feed intake and that predicted by
regression accounting for requirements of production and body
weight maintenance
– dDMI = CG + ADG + BWT + “other production” + RFI
– Regression can be either phenotypic or genetic
– “Forced” independence with growth rate, stage of production and
weight alleviates problems with correlated response
– RFI phenotypes are independent of age, stage of production, and
previous plane of nutrition
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Figure 2. Relationship between residual
feed intake and average daily gain.
2
2
1.5
1.5
Res idual feed intake, kg/day
Res idual feed intake, kg/day
Figure 1. Relationship between residual
feed intake and metabolic body weight
1
0.5
0
-0.5
-1
-1.5
1
0.5
0
-0.5
-1
-1.5
r squared=0.000, n=75, P=0.9936
r squared=0.000, n=75, P<0.9969
-2
-2
60
70
80
90
100
110
M etabolic mid-weight, kg
Figures courtesy J. A. Basarab
120
0.8
1
1.2
1.4
1.6
1.8
2
Average daily gain, kg/day
2.2
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An Expensive Phenotype
• Cost of data collection is high
– $150-175 per head for equipment alone
• Intensive 70-90 d test period
– Limited numbers of animals with phenotypes
• Technology is still developing
– Reduction in altered feeding behavior: Individual intake on
group-fed cattle
• Commercial test facilities largely unavailable
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Potential Returns
• Most agree RFI is moderately heritable (0.30 to 0.40)
• Can force independence with any production trait
– Typical RFI generally uncorrelated with body composition
• Preliminary research reports
– Uncorrelated with mature size
– Highly positive genetic correlation with mature cow efficiency
– No evidence of antagonism with reproductive merit
• Phenotypic and genetic variance
– 5-7 lb per day phenotypic difference among yearling bulls
– Similar variability among crossbred steers during finishing
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Differences in RFI groups
RFI < 0.00
RFI > 0.00
Efficient
Inefficient
P-value
21.7
25.5
<0.001
2121.9
2511.0
<0.001
Total 84-d gain, lb
264
270
>0.470
Feeding Events per d
5.56
6.22
<0.001
Carcass fat, in
0.28
0.30
<0.110
Lean Yield, %
59.93
59.47
>0.240
Marbling score
Select 80
Select 75
>0.640
Intake per d, lb
Total 84-d intake, lb
Crews et al., 2003
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Potential Industry Impact
• Our results show that the more efficient half of
steers gained the same amount of weight, produced
carcasses with the same yield and quality grades
with the same amount of time on feed but
consumed 390 pounds less feed than the less
efficient half.
• In a region with 2+ million head processed per year,
that 780 million pounds of feed costs almost $40
million.
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RFI Genetic Variability
• Several studies have estimated genetic variance and
heritability for RFI
Var(G)
0.149
0.220
0.267
h2
Reference
0.28
0.14
Koch et al. (1963)
Fan et al. (1995)*
0.44
0.39
0.39
0.30
Arthur et al. (1997)
Arthur et al. (2001a)
Arthur et al. (2001b)
Crews et al. (2003a,b)
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RFI Adjusted for Body Composition
Trait
% of DFI variance
Rank Correlation, RFI-1
MWT + ADG
67.9 + 8.6
1.00
Gain in Empty Body Fat
3.9
0.92
Gain in Empty Body Water
1.1
0.90
Basarab et al. (2003)
• Adding gain in RTU rib fat and(or) RTU intramuscular fat
provided similarly small increases in model R2
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RFI Genetic Correlations
Trait
Rg(RFIp)
Feed Conversion Ratio
0.70
Feed Conversion Ratio
0.85
Feed Intake
0.64
Feed Intake
0.79
Back Fat
0.17
Live weight
0.32
ADG
0.10
Carcass REA
-0.17
Carcass marbling score
-0.44
Reference
Herd and Bishop (2000)
Arthur et al. (2001a,b)
Arthur et al. (2001b)
Schenkel et al. (2004)
Crews et al. (2003a)
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Phenotypic Regression RFI and Production
• RFI is defined as the component of feed intake that is
phenotypically independent of production
• Recent studies have shown significant non-zero genetic
correlation of RFIp with production, body weight, etc.
• RFIp usually contains a genetic component due to production
• The phenotypic variance of RFIp is completely described by
– Heritability of feed intake and production
– Genetic and environmental correlations of feed intake with
production
– (Kennedy et al., 1993)
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Repeatability of RFIp
•
•
•
Archer et al. (2002) measured intake and derived RFIp on heifers
postweaning and then on open cows following weaning of their
second calf
dDMI, ADG, MWT, FCR and RFI considered different traits between
cows and heifers to estimate genetic correlations
Rg > 0.85 strongly indicates genetic equivalence:
Trait
Rg (cow x heifer)
DFI
0.94
ADG
0.72
MWT
FCR
RFIp
0.82
0.20
0.98
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RFI and Multiple Trait Selection
• Single trait selection is not advisable
• Few attempts have been made to
incorporate RFI into selection schemes
• An example multiple trait index was
developed by Crews et al. (2006)
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Index Values
0.342
b =
SYM
0.000
0.066
-1.127
6.048
1791.2
-1
0.156
0.000
0.000
-21.49
0.049
0.017
3.708
183.73
8.664
2.019
709.4
-0.27
-10.12
=
24.79
-0.09
I = -10.12 (RFI) + 24.79 (ADG) – 0.09 (YWT) ~ N ( 100 , 7.812 ; range: 80.1 – 115.7)
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Correlations of Index Value with Component Traits
Bull Trait
r (I)
P - value
RFI
-0.74
0.01
dDMI
-0.22
0.03
ADG
0.53
0.01
YWT
0.01
0.90
YSC
0.16
0.12
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Summary
• RFI may be a candidate for genetic evaluation
and improvement systems
• Independence with growth, body weight, and
any identifiable source of dDMI covariance can
be forced
• Heritability is at least as high as early growth
but genetic variance is limited
– Probably enough to make substantial economic
improvement
• Multiple trait selection schemes still required
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Summary
“Genetic improvement in efficiency of feed
utilization is higher-hanging fruit”
John Pollak, BIF 2002
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Thank you
[email protected]
403-317-2288