The genetics of feed efficiency in cattle
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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. Many Measures of Efficiency.

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The genetics of feed efficiency in cattle l.jpg

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


Many measures of efficiency l.jpg
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


Reducing inputs l.jpg

Trait

Rg (dDMI)

Reference

Koots et al. (1994b)

BWT

0.77

WT205

0.67

WT365

0.79

MKTWT

0.92

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


Reducing inputs feed efficiency l.jpg
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) (h2WWT) (i WWT) (σg(FCR)) = -0.21 kgd-1/gen


Selection fcr l.jpg
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)


Rfi definition l.jpg
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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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


Differences in rfi groups l.jpg

RFI < 0.00

RFI > 0.00

P-value

Efficient

Inefficient

21.7

25.5

<0.001

Intake per d, lb

2121.9

2511.0

<0.001

Total 84-d intake, lb

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

Select 80

Select 75

>0.640

Marbling score

Differences in RFI groups

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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Reference

Var(G)

h2

Koch et al. (1963)

0.28

Fan et al. (1995)*

0.14

Arthur et al. (1997)

0.44

Arthur et al. (2001a)

0.149

0.39

Arthur et al. (2001b)

0.220

0.39

Crews et al. (2003a,b)

0.267

0.30

RFI Genetic Variability

  • Several studies have estimated genetic variance and heritability for RFI


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RFI Adjusted for Body Composition

  • Adding gain in RTU rib fat and(or) RTU intramuscular fat provided similarly small increases in model R2

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

1.1

0.90

Gain in Empty Body Water

Basarab et al. (2003)


Rfi genetic correlations l.jpg

Trait

Rg(RFIp)

Reference

Feed Conversion Ratio

0.70

Herd and Bishop (2000)

Arthur et al. (2001a,b)

Feed Conversion Ratio

0.85

Feed Intake

0.64

Feed Intake

0.79

Back Fat

0.17

Arthur et al. (2001b)

Live weight

0.32

ADG

0.10

Carcass REA

-0.17

Schenkel et al. (2004)

Crews et al. (2003a)

Carcass marbling score

-0.44

RFI Genetic Correlations


Phenotypic regression rfi and production l.jpg
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)


Repeatability of rfip l.jpg

Trait

Rg (cow x heifer)

DFI

0.94

ADG

0.72

MWT

0.82

FCR

0.20

RFIp

0.98

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:


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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)


Index values l.jpg

0.342

SYM

-1

0.156

0.000

0.000

-21.49

-10.12

b =

0.000

0.066

0.049

0.017

3.708

183.73

=

24.79

8.664

2.019

709.4

-1.127

6.048

1791.2

-0.27

-0.09

Index Values

I = -10.12 (RFI) + 24.79 (ADG) – 0.09 (YWT) ~ N ( 100 , 7.812 ; range: 80.1 – 115.7)


Correlations of index value with component traits l.jpg

Bull Trait

P - value

r (I)

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

Correlations of Index Value with Component Traits


Summary l.jpg

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

Summary


Summary21 l.jpg

“Genetic improvement in efficiency of feed utilization is higher-hanging fruit”

Summary

John Pollak, BIF 2002


Thank you l.jpg

Thank you higher-hanging fruit”

[email protected]

403-317-2288


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