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Dissection of Koch's residual feed intake: Implications for selection

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For 50 yr, residual feed intake (RFI) has remained a black box even though many researchers have touted it as a more biological estimate of efficiency of feed utilization than feed conversion ratio (FCR). We successfully dissected the efficiency of feed utilization by decomposing the components of RFI and ascertained the contributions of its components. Currently, a fixed effect model is used to predict RFI, which we term RFIF. We used a random effect model to predict RFIR, which allowed a separate estimation of RFI for maintenance (RFIM) and for growth (RFIG) and also ascertained their respective efficiencies. Judged by residual variance, R(2) and deviance information criterion, the random effect model was superior to the traditional fixed effect model used to generate RFIF. Under the traditional method, the h(2) of RFIF was 0.13 but h(2) of RFIR was 0.35. The heritability of RFIM and RFIG were moderate (~0.50), but the genetic correlation between them was highly negative (-0.95), suggesting that these 2 efficiencies contribute in an opposing way toward RFI. As a result, there should be caution in ascribing a biological basis to RFI. Under the current methodology, a biological basis can be ascribed to RFIM and RFIG. Selecting on RFIM will lead to smaller but efficient birds. The genetic gains in feed efficiency will be achieved by reductions in feed required for maintenance. The RFIG is not an efficiency parameter and should not be used as a sole criterion for selection. The ability of the current method to estimate efficiency values for metabolic BW and BW gain provides geneticists with additional parameters to use to discriminate between animals with similar RFIR. It also provides the flexibility to impose weights on RFIM and RFIG to meet a desired objective.
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INTRODUCTION
Feed costs constitute the largest single expense in
poultry and livestock production operations. Increasing
corn prices as a result of drought and use of corn for
ethanol production has made improvement in feed ef-
ficiency a major factor in breeding programs. Defining
a trait accurately is a necessity for understanding the
genetic variation that underlies that trait and its im-
plication for genetic improvement. To date, the 2 main
measures of feed efficiency are feed conversion ratio
(FCR) and residual feed intake (RFI). Feed conver-
sion ratio is defined as the ratio of feed intake (FI) to
BW gain (BWG). Feed conversion ratio is a ratio trait
and as such is difficult to improve through direct selec-
tion because of the disproportionate manner by which
selection pressure is imposed on the component traits.
In addition, FCR is not normally distributed and has
no real mean and variance, and the nonnormality of
a ratio trait is also increased when the magnitude of
the coefficient of variation of the denominator increases
(Atchley and Anderson, 1978). In addition to its un-
attractive statistical properties, variability in mainte-
nance requirements and also in the composition of gain
makes it difficult for FCR to reflect an accurate mea-
sure of efficiency of feed conversion.
Koch et al. (1963) proposed the concept of RFI, which
is defined as the difference between actual and pre-
dicted FI after taking into account variability in main-
tenance requirement and growth. Selection to improve
feed efficiency through RFI has been suggested because
of the phenotypic independence of RFI from metabolic
BW and BWG. However, this phenotypic independence
is rather the direct result of the distributing properties
of the regression procedure used to obtain RFI (Netter
et al., 2004). Kennedy et al. (1993) showed that genetic
variability of RFI is not independent of metabolic BW
and BWG. In the literature, several biological bases
have been ascribed to RFI (Zhang and Aggrey, 2003;
Richardson and Herd, 2004; Herd et al., 2004; Herd and
Arthur, 2009; Hoque and Suzuki, 2009; Moore et al.,
2009; Aggrey et al., 2010; Boddicker et al., 2011). How-
ever, the current measure of RFI for growth is a com-
Dissection of Koch’s residual feed intake: Implications for selection
Samuel E. Aggrey *1 and Romdhane Rekaya †
* NutriGenomics Laboratory, Department of Poultry Science,
and Department of Animal and Dairy Science, University of Georgia, Athens 30602
ABSTRACT For 50 yr, residual feed intake (RFI) has
remained a black box even though many researchers
have touted it as a more biological estimate of efficien-
cy of feed utilization than feed conversion ratio (FCR).
We successfully dissected the efficiency of feed utiliza-
tion by decomposing the components of RFI and ascer-
tained the contributions of its components. Currently,
a fixed effect model is used to predict RFI, which we
term RFIF. We used a random effect model to predict
RFIR, which allowed a separate estimation of RFI for
maintenance (RFIM) and for growth (RFIG) and also
ascertained their respective efficiencies. Judged by re-
sidual variance, R2 and deviance information criterion,
the random effect model was superior to the traditional
fixed effect model used to generate RFIF. Under the
traditional method, the h2 of RFIF was 0.13 but h2
of RFIR was 0.35. The heritability of RFIM and RFIG
were moderate (~0.50), but the genetic correlation
between them was highly negative (−0.95), suggest-
ing that these 2 efficiencies contribute in an opposing
way toward RFI. As a result, there should be caution
in ascribing a biological basis to RFI. Under the cur-
rent methodology, a biological basis can be ascribed to
RFIM and RFIG. Selecting on RFIM will lead to smaller
but efficient birds. The genetic gains in feed efficien-
cy will be achieved by reductions in feed required for
maintenance. The RFIG is not an efficiency parameter
and should not be used as a sole criterion for selection.
The ability of the current method to estimate efficiency
values for metabolic BW and BW gain provides geneti-
cists with additional parameters to use to discriminate
between animals with similar RFIR. It also provides
the flexibility to impose weights on RFIM and RFIG to
meet a desired objective.
Key words: residual feed intake , feed conversion ratio , random effect , fixed effect , chicken
2013 Poultry Science 92 :2600–2605
http://dx.doi.org/ 10.3382/ps.2013-03302
GENETICS
Received May 9, 2013.
Accepted June 12, 2013.
1 Corresponding author: saggrey@uga.edu
© 2013 Poultry Science Association Inc.
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bination of 2 efficiencies: 1) efficiency of maintenance
(RFIM) and 2) efficiency of gain (RFIG), for which
the biological bases may be different. The individual
contribution of RFIM and RFIG toward RFI, their ge-
netic bases, and interrelationship with other compo-
nents of RFI are all unknown. In addition, the current
fixed effect regression model uses average metabolic
BW (MBW) and average BWG because adjustment
parameters for each individual in the population and
an individual’s maintenance and gain efficiency cannot
be determined. This also implies that animals with BW
greater than the population mean at the beginning of
the feeding trail are intrinsically inefficient and are pe-
nalized, and smaller animals are assumed to be efficient
and are favored.
The objectives of this study are to 1) estimate the
components of RFI for individuals using a random ef-
fect model and 2) determine the genetic interrelation-
ship between components of RFI and other feed-related
traits.
MATERIALS AND METHODS
Population
The experimental population and animal husbandry
used in the current study has been reported on by Ag-
grey et al. (2010). A pedigreed population composed of
24 sires and 72 dams was used to generate 2,289 chicks
in 8 hatches with complete data. The BW and FI mea-
sured at d 35 and 42 were used in the current analysis.
Metabolic BW (BW0.75) at d 35, FI at d 35 to 42, and
FCR were calculated.
Estimation of RFI
Two methods were used to estimate RFI using FI,
MBW, and BWG data. The first one is based on the
classical model first proposed by (Koch et al., 1963)
and consists in using a fixed regression of FI on MBW
and BWG. The RFI was calculated as the deviation
between the observed FI and the expected FI for each
bird with a given weight and BWG. The statistical
model used was
yaaae
iiii
=+ ++
01 2
MBWBWG ,
[1]
where yi is the FI for bird i, aj is a fixed regression (j =
0, 1, 2), and ei is the error term assumed to be nor-
mally distributed with zero mean and variance equal to
σe
2.
Using this model, estimated RFI for animal i is given
by
RFIMBW BWG
ii ii
yaaa=− ++( ).
ˆˆ ˆ
01 2
[2]
Using equations [1] and [2], it is clear that the esti-
mated RFI includes the residual term, which is not due
to the bird effect. Thus, this estimated RFI is biased
and the magnitude of this bias depends on the variance
of the error terms. With white noise and grouped data
(multiple records per bird), the estimated RFI as pre-
sented in [2] could be robust. However, for individual
birds with single FI and BWG record, the estimated
RFI could be grossly biased.
To deal with this intrinsic issue in estimating RFI
using the classical method, we propose a new model
that tries to separate true RFI from the residual terms,
thus eliminating the likely bias present in the classi-
cal method. Furthermore, it is clear both biologically
and statistically that the true RFI has 2 components:
metabolic efficiency (maintaining BW) and growth effi-
ciency. Statistically, the proposed model is an extension
of [1] by assuming bird-specific regression coefficients
rather than population level parameters and could be
written as
ya au au
iiiiii
=+ ++++
01122
() () ,MBWBWG ε
[3]
where aj is a fixed regression (j = 0, 1, 2) as in model
[1], and uji (j = 1, 2) is the random regression specific
to bird i and ei is the error term assumed to be nor-
mally distributed with zero mean and variance equal to
σε
2.
And after rearranging terms
yaaauu
iiii ii ii
=+ ++ ++
01 21 2
MBWBWG MBWBWG ε,
[4]
where aj is a fixed regression (j = 0, 1, 2) as in model
[1], and uji (j = 1, 2) is bird j specific random regres-
sions. Based on the model in [4], estimated RFI is cal-
culated as
RFIMBW BWG
MBWBWG
RFIRFI RF
ii ii i
ii
i
iMi
uu
ya
aa
=+
−+ +
=+
=
ˆˆ
()
12
01 2
II
Gi
,
[5]
where RFIM and RFIG are the metabolic and growth
RFI, respectively.
When only one FI observation is collected per bird,
no pedigree information between measured birds is
available, the residual and random regressions variance
components are unknown. In that case, the model in
[4] is not identifiable and u1i and u2i are not separable
from each other or from the residual terms. In the cur-
rent case, pedigree information between measured birds
is available and hence the model is identifiable.
Both models in equations 1 and 3 were implement-
ed using a Bayesian approach via Gibbs sampler. Flat
bounded priors were assumed for the fixed regressions
and the residual variances. For the u1 and u2, the fol-
lowing prior was assumed:
p Nuu GAG
12
00
0,,~(,),|A
()
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where A is the known additive relationship matrix, and
G0 is a 2 × 2 matrix of genetic covariances.
Implementation via Gibbs sampler was straightfor-
ward for both models. All needed conditional distribu-
tions were in closed form, being normal for the position
parameters (a0, a1, a2, u1, and u2), scaled inverted chi-
squared for σ
e
2 and
σε
2 with (n-2) degrees of freedom, and
scaled inverted Wishart for genetic (co)variance ma-
trix. For model 1, a chain of 50,000 iterations was im-
plemented with the first 10,000 rounds discarded as
burn-in period. For model 2 (equation [3]), a chain of
100,000 rounds with the first 20,000 discarded as burn-
in was implemented. The RFI estimated under equa-
tions [1] and [3] will be designated RFIF and RFIR,
respectively. The fits of equations [1] and [3] were eval-
uated by (1) the residual variance, coefficient of deter-
mination (R2), and the deviance information criterion
(DIC). The DIC is a hierarchical modeling generaliza-
tion of the Akaike information criteria and Bayesian
information criterion. For further information on DIC,
see Rekaya et al. (2003).
Variance Component Estimation
To investigate the genetic relationships between
RFIF (estimated using the classical fixed effect meth-
od), the RFIR (estimated using random effect method),
FI, BWG, and FCR, the following mixed linear model
was employed for the multiple trait analysis:
ijk ijkijk
where yijk was the observed phenotype (RFIF, RFIR,
FI, BWG, and FCR); Hi was the fixed effect of hatch
class i, Sexj was the fixed effect of sex class j, Ak was
the random additive effect of bird k, and eijk was the
random residual term. In a second analysis, RFIM,
RFIG, FI, BWG, and FCR were also run with a multi-
variate analysis.
In matrix notation, the model could be expressed as
yXZue=++β,
where
y(y',y' ,y',y' ,y')'
12345
=
is the vector observa-
tions, β was the vector of systematic effects of order p,
u was the vector of animal effects with order q, and e
was the vector of residual effects. The X and Z were
corresponding incidence matrices with the appropriate
dimensions.
Statistical Analysis and Computations
A Bayesian implementation via Gibbs sampling was
adopted. Conditionally on the position parameter vec-
tor, ϴ, and the residual (co)variance matrix, R, the
observed responses were assumed to be normally dis-
tributed:
py| ,R ~N XZu, IRΘβ
()
+⊗
()
.
[6]
To ensure proper posterior distribution, the following
prior distributions were assumed for the parameters in
the model.
pβ
()
()
~ N0, 106
;
[7]
p u |~AG NAG,0,
()
()
,
[8]
where G is a 5 × 5 matrix of additive genetic (co)vari-
ances, A is a known matrix of relationships between
birds, and I is the identity matrix with the appropriate
dimensions.
For all parameters included in G and R, uniform
bounded priors were assumed. The joint posterior den-
sity was obtained by the product of densities in ex-
pressions [6] through [8] and the dispersion parameters.
The resulting joint posterior distribution was in closed
form, and the conditional posterior distribution of all
the parameters of the model was derived as described
by Jensen et al. (1994), with normal and scaled-invert-
ed Wishart distributions for the position and dispersion
parameters, respectively. Convergence diagnostics were
assessed using visual inspection of the trace plots. A
unique chain of 100,000 iterations of the Gibbs sampler
was run with a conservative burn-in of 25,000 itera-
tions. The remaining 75,000 iterations were retained
without thinning for post-Gibbs analysis.
RESULTS AND DISCUSSION
The means and SD of feed and growth traits are pre-
sented in Table 1. The fits of the fixed effect and ran-
dom effect models are presented in Table 2. All the 3
model comparison criteria
(,
σ
e
2
R2, and DIC) clearly
Table 1. Means and SD of feed efficiency and growth param-
eters in meat-type chickens
Trait1Mean SD
MBW, g 206.74 20.84
FI, g 887.25 146.33
BWG, g 455.58 90.50
RFI, g 0.00 114.07
FCR, g/g 2.00 0.42
1MBW = metabolic BW; FI = feed intake; BWG = BW gain; RFI =
residual feed intake; FCR = feed conversion ratio.
Table 2. Residual variance, coefficient of determination (R2),
and deviance information criterion (DIC) for fixed effect model
(M1) and random effect model (M2)
Item M1 M2
Residual variance 12,816.60 8,483.90
R20.41 0.63
DIC 47,862.12 44,767.85
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showed that the random effect model in equation [3]
explained FI data better than the traditional model in
equation [1]. The significantly reduced residual vari-
ance from the random effect model indicates that the
RFI is grossly biased by using the traditional model. If
the distribution of the biased results are not random
and equally applied to each individual in the popula-
tion, this bias will lead to errors in the ranking of ani-
mals.
The heritability and genetic correlation among feed
efficiency parameters are shown in Table 3. Under the
traditional method, the h2 of RFIF was 0.13. However,
the h2 of RFIR was 0.35, which is an improvement of
260% over the traditional method. Surprisingly, the ge-
netic correlation between RFIF and RFIR is low (0.4)
which suggests that genetic correlations associated with
RFIF are equally biased and should be used with great
caution in predicting correlated responses. Also, the
reranking of animals for selection will affect selection
differential and, subsequently, the expected response to
selection based on RFIF compared with using RFIR.
Considering the fact that feed efficiency measured
through this approach is residual, errors due to mea-
surement of both FI, MBW, and BWG affect RFI. It
is therefore necessary to remove any foreseeable bias
especially that from a statistical model; as a result,
RFIR should be preferred over the traditional RFIF.
Nevertheless, selection on both RFI measures will lead
to a reduction in FI. Selection on RFIR will most likely
maintain MBW and keep growth constant while reduc-
ing FI because the genetic correlations between RFIR
and MBW, and RFIR and BWG are close to zero. On
the other hand, selection on RFIF will reduce MBW
and to a lesser extent BWG. It is interesting to note
that RFIF is biased upwards and as a result correla-
tions with RFIF are equally biased upwards. Most of
this bias is removed in RFIR as shown in Table 2.
However, selection on FCR will increase BWG and
simultaneously reduce FI, but the reduction in FI will
not be in the same magnitude as selecting on RFI. The
results clearly show that selection on either FCR or
RFI will lead to efficiency of feed utilization, however,
through different means. Selecting on FCR will achieve
efficiency primarily by increasing growth with a slight
reduction in FI, whereas selecting on RFI will achieve
efficiency by substantially reducing FI and maintaining
or slightly reducing growth.
The appeal of the current method to estimate RFI
is obvious because 1) it allows for efficiencies of main-
tenance (u1) and of weight gain (u2) to be calculated,
and subsequently separates RFI for maintenance and
weight gain; and 2) it has the flexibility to include vari-
ance-covariance structure between RFIM and RFIG.
The genetic variances-covariance were 1.47, 0.15, and
−0.46 for u1, u2, and u1u2, respectively. The genetic
parameters of RFIM, RFIG, MBW, BWG, FI, and FCR
are presented in Table 4. Both RFIM and RFIG were
moderately heritable (h2~0.50) and can respond to se-
lection, but the genetic correlation between RFIM and
RFIG was highly negative (−0.95), indicating that these
2 efficiencies contribute in an opposing way toward RFI.
Under the current approach, we have deciphered the
“black box” of RFI and ascertained that RFIM is the
main contributing factor toward efficient animals and
RFIG is a feed-inefficient factor. Thus, the response to
selecting for RFI is achieved mainly through reducing
FI for maintenance. The genetic antagonist relationship
between RFIM and RFIG is very consistent with their
Table 3. Heritability (bold), residual (above diagonal), and genetic correlations (below diagonal) of
feed efficiency traits1 in meat-type birds
Item MBW BWG FCR RFIRRFIFFI
MBW 0.412 0.177 0.041 −0.163 −0.054 0.270
BWG 0.060 0.143 −0.641 0.150 −0.008 0.492
FCR 0.095 −0.545 0.104 0.224 0.676 0.291
RFIR−0.056 −0.018 0.272 0.349 0.504 0.468
RFIF−0.223 −0.089 0.477 0.400 0.134 0.835
FI 0.324 0.448 0.184 0.290 0.700 0.125
1MBW = metabolic BW; BWG = BW gain; FCR = feed conversion ratio; RFIR = residual feed intake from
fixed model; RFIF = residual feed intake from random model; FI = feed intake.
Table 4. Heritability (bold), residual (above diagonal), and genetic correlations (below diagonal) of
components of feed efficiency traits1 in meat-type birds
Item MBW BWG FCR RFIMRFIGFI
MBW 0.436 0.152 0.039 −0.159 0.127 0.286
BWG 0.177 0.086 −0.686 −0.028 0.113 0.492
FCR 0.347 −0.082 0.144 0.268 −0.277 0.237
RFIM0.225 0.225 0.635 0.492 −0.973 0.301
RFIG−0.237 −0.208 −0.636 −0.950 0.489 −0.209
FI 0.475 0.397 0.633 0.867 −0.856 0.219
1MBW = metabolic BW; BWG = BW gain; FCR = feed conversion ratio; RFIM = residual feed intake for
maintenance; RFIG = residual feed intake for gain; FI = feed intake.
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relationships with MBW, BWG, FI, and FCR. Select-
ing on RFIM will lead to smaller but efficient animals.
The genetic gains in feed efficiency will be achieved by
reductions in feed required for maintenance. The RFIG
is not an efficiency parameter and should not be used
as a sole criterion for selection.
Despite the inefficient contribution of RFIG toward
RFIR, to maintain growth in a selection program, RFIR
can be used as a selection criterion, but the biologi-
cal (nutritional, physiological, molecular, proteomic,
and metabolomic) basis ascribed to RFIR should be
done with great caution and hesitation because RFIR
is a compound feed efficiency estimate with genetically
negative correlated components. In spite of that, we
now have the opportunity to assign a biological basis
to RFIM and RFIG. Estimates on the genetic relation-
ship between RFI and BWG have ranged from almost
zero (Arthur et al., 2001; Pakdel et al., 2005; Aggrey et
al., 2010) to positive (Cai et al., 2008). The covariance
between RFI and weight gain cannot be consistent, but
will depend on the contribution of RFIG toward RFIR,
which will be unique for the population. In addition,
the stage of growth and other contributing factors to-
ward growth such as skeletal material, lean tissue, fat
tissue, or feathers will dictate the nature of the genetic
covariance because composition of BWG has a huge
effect on feed efficiency. Lean tissue is mostly water,
whereas adipose tissue is mostly DM. A unit of lean
tissue takes less energy than a unit of adipose tissue.
A major selection conundrum for using FCR as a
selection criterion is when a geneticist is confronted
with 2 animals with similar values (e.g., 2/1 and 4/2).
The ability to estimate u1 and u2 in RFIR eliminates
this dilemma. For example, animals A and B have the
same RFIR of −167 and MBW of 204 g. The u1 and u2
values of A are −2.316 and 0.761, respectively. Similar
values for B are −1.588 and 0.508, respectively. Assum-
ing they accrue the same weight gain over time, both
animals would have similar MBW, but animal A will
be more efficient and will have a better RFI over time
compared with animal B. This is illustrated in Figure 1.
This implies that a geneticist has additional tools in u1
and u2 when confronted with 2 animals with the same
RFIR. Also, depending on the breeding objectives of a
breeder, different weights can be applied to u1 and u2
to achieve the desired objective.
ACKNOWLEDGMENTS
This work was supported by USDA National Re-
search Initiative grant 2009-35205-05208 and Georgia
Food Industry Partnership grant 10.26KR696-110. We
appreciate the assistance of Arthur Bob Karnuah of the
Department of Poultry Science, University of Georgia,
Athens, and Christopher McKenzie of the Poultry Re-
search Center of University of Georgia, Athens.
REFERENCES
Aggrey, S. E., A. B. Karnuah, B. Sebastian, and N. B. Anthony.
2010. Genetic properties of feed efficiency parameters in meat-
type chickens. Genet. Sel. Evol. 42:25.
Arthur, P. F., J. A. Archer, D. J. Johnston, R. M. Herd, E. C. Rich-
ardson, and P. F. Parnell. 2001. Genetic and phenotypic vari-
ance and covariance components for feed intake, feed efficiency
and other postweaning traits in Angus cattle. J. Anim. Sci.
79:2805–2811.
Atchley, W. R., and D. Anderson. 1978. Ratios and the statistical
analysis of biological data. Syst. Zool. 27:71–78.
Boddicker, N., N. K. Gabler, M. E. Spurlock, D. Nettleton, and J. C.
M. Dekkers. 2011. Effects of ad libitum and restricted feed intake
on growth performance and body composition of Yorkshire pigs
selected for residual feed intake. J. Anim. Sci. 89:40–51.
Figure 1. Relationship between maintenance (u1) and growth (u2) efficiencies, and metabolic BW (MBW) and residual feed intake (RFI).
Color version available in the online PDF.
2604 AGGREY AND REKAYA
at University of Georgia on December 19, 2016http://ps.oxfordjournals.org/Downloaded from
Cai, W., D. S. Casey, and J. C. M. Dekkers. 2008. Selection response
and genetic parameters for residual feed intake in Yorkshire
swine. J. Anim. Sci. 86:287–298.
Herd, R. M., and P. F. Arthur. 2009. Physiological basis of residual
feed intake. J. Anim. Sci. 87(Suppl.):E64–E71.
Herd, R. M., V. H. Oddy, and E. C. Richardson. 2004. Biological
basis for variation in residual feed intake in beef cattle. 1. Review
of potential mechanisms. Aust. J. Exp. Agric. 44:423–430.
Hoque, M. A., and K. Suzuki. 2009. Genetics of residual feed in-
take in cattle and pigs: A review. Asian-australas. J. Anim. Sci.
22:747–755.
Jensen, J., C. S. Wang, D. A. Sorensen, and D. Gianola. 1994. Mar-
ginal inferences of variance and covariance components for traits
influenced by maternal and direct genetic effects using the Gibb
sampler. Acta Agric. Scand. A Anim. Sci. 44:193–201.
Kennedy, B. W., J. H. J. van der Werf, and T. H. E. Meuwissen.
1993. Genetic and statistical properties of residual feed intake. J.
Anim. Sci. 71:3239–3250.
Koch, R. M., L. A. Swiger, D. Chambers, and K. E. Gregory. 1963.
Efficiency of feed use in beef cattle. J. Anim. Sci. 22:486–494.
Moore, S. S., F. D. Mujibi, and E. L. Sherman. 2009. Molecu-
lar basis for residual feed intake in beef cattle. J. Anim. Sci.
87(Suppl.):E41–E47.
Netter, J., W. Wasserman, and M. H. Kutner. 2004. Applied Linear
Statistical Models. 5th ed. McGraw-Hill, New York, NY.
Pakdel, A., J. A. van Arendonk, A. L. Vereijken, and H. Bovenhuis.
2005. Genetic parameters of ascites-related traits in broilers: Cor-
relations with feed efficiency and carcase traits. Br. Poult. Sci.
46:43–53.
Rekaya, R., K. A. Weigel, and D. Gianola. 2003. Bayesian estima-
tion of parameters of a structural model for genetic covariances
between milk yield in five regions of the United States. J. Dairy
Sci. 86:1837–1844.
Richardson, E. C., and R. M. Herd. 2004. Biological basis for varia-
tion in residual feed intake in beef cattle. 2. Synthesis of results
following divergent selection. Aust. J. Exp. Agric. 44:431–440.
Zhang, W., and S. E. Aggrey. 2003. Genetic variability in feed uti-
lization efficiency of meat-type birds. World’s Poult. Sci. J.
59:328–339.
2605
DISSECTION OF KOCH’S RESIDUAL FEED INTAKE
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... FCR is defined as the ratio of feed intake to weight gain, with lower FCR values indicating higher efficiency. On the other hand, RFI is defined as the difference between actual measured feed intake and expected feed intake of an animal accounting for its maintenance requirement, where expected feed intake is calculated based on average feed intake and weight grain of a group of animals [2,3]. Similar to FCR, a lower RFI value indicates higher efficiency. ...
... A difference in RFI among animals is most likely due to a variation in the maintenance energy expenditure. Long-term selection of animals based on FCR often leads to larger animals that consume more feed, while RFI selection results in comparable animal sizes and production levels with reduced feed intake [2][3][4]. RFI is, therefore, becoming a method of choice for measuring feed efficiency [2][3][4]. ...
... Long-term selection of animals based on FCR often leads to larger animals that consume more feed, while RFI selection results in comparable animal sizes and production levels with reduced feed intake [2][3][4]. RFI is, therefore, becoming a method of choice for measuring feed efficiency [2][3][4]. ...
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Background: Intestinal microbiota plays a key role in nutrient digestion and utilization with a profound impact on feed efficiency of livestock animals. However, the intestinal microbes that are critically involved in feed efficiency remain elusive. Methods: To identify intestinal bacteria associated with residual feed intake (RFI) in chickens, male Cobb broiler chicks were individually housed from day 14 to day 35. Individual RFI values were calculated for 56 chickens. Luminal contents were collected from the ileum, cecum, and cloaca of each animal on day 35. Bacterial DNA was isolated and subjected to 16S rRNA gene sequencing. Intestinal microbiota was classified to the feature level using Deblur and QIIME 2. High and low RFI groups were formed by selecting 15 and 17 chickens with the most extreme RFI values for subsequent LEfSe comparison of the difference in the microbiota. Spearman correlation analysis was further performed to identify correlations between the intestinal microbiota composition and RFI. Results: No significant difference in evenness, richness, and overall diversity of the microbiota in the ileum, cecum, or cloaca was observed between high and low RFI chickens. However, LEfSe analysis revealed a number of bacterial features being differentially enriched in either high or low RFI chickens. Spearman correlation analysis further identified many differentially enriched bacterial features to be significantly correlated with RFI (P < 0.05). Importantly, not all short-chain fatty acid (SCFA) producers showed a positive association with RFI. While two novel members of Oscillibacter and Butyricicoccus were more abundant in low-RFI, high-efficiency chickens, several other SCFA producers such as Subdoligranulum variabile and two related Peptostreptococcaceae members were negatively associated with feed efficiency. Moreover, a few closely-related Lachnospiraceae family members showed a positive correlation with feed efficiency, while others of the same family displayed an opposite relationship. Conclusions: Our results highlight the complexity of the intestinal microbiota and a need to differentiate the bacteria to the species, subspecies, and even strain levels in order to reveal their true association with feed efficiency. Identification of RFI-associated bacteria provides important leads to manipulate the intestinal microbiota for improving production efficiency, profitability, and sustainability of poultry production.
... Feed intake and BW were recorded on days 21 and 39 for each pen after a 12 h feed withdrawal. The average daily gain (ADG), average daily feed intake (ADFI), and feed conversion ratio (FCR) was computed for each group using the following formula: FCR = feed intake/weight gain [91]. Mortality was recorded daily, and ADG, ADFI, and FCR were corrected by mortality. ...
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This study aimed to investigate the effects of Bacillus amyloliquefaciens LFB112 on the growth performance, carcass traits, immune response, and serum biochemical parameters of broiler chickens. A total of 396 1 day old, mixed-sex commercial Ross 308 broilers with similar body weights were allotted into six treatment groups. The assigned groups were the CON group (basal diet with no supplement), AB (antibiotics) group (basal diet + 150 mg of aureomycin/kg), C+M group (basal diet + 5 × 10 8 CFU/kg B. amyloliquefaciens LFB112 powder with vegetative cells + metabolites), C group (basal diet + 5 × 10 8 CFU/kg B. amyloliquefaciens LFB112 vegetative cell powder with removed metabolites), M group (basal diet + 5 × 10 8 CFU/kg B. amyloliquefaciens LFB112 metabolite powder with removed vegetative cells), and CICC group (basal diet + 5 × 10 8 CFU/kg Bacillus subtilis CICC 20179). Results indicated that chickens in the C+M, C, and M groups had higher body weight (BW) and average daily gain (ADG) (p < 0.05) and lower feed conversion ratio (FCR) (p = 0.02) compared to the CON group. The C+M group showed the lowest abdominal fat rate compared to those in the CON, AB, and CICC groups (p < 0.05). Compared to the CON group, serum IgA and IgG levels in the C+M, C, and M groups significantly increased while declining in the AB group (p < 0.05). B. amyloliquefaciens LFB112 supplementation significantly reduced the serum triglyceride, cholesterol, urea, and creatinine levels, while increasing the serum glucose and total protein (p < 0.05). In conclusion, B. amyloliquefaciens LFB112 significantly improved the growth performance, carcass traits, immunity, and blood chemical indices of broiler chickens and may be used as an efficient broiler feed supplement.
... Only when energy gain is not different between RFI phenotypes as in the Bos Indicus cattle, does the high RFI phenotype have lesser gross energy efficiency. In support of this idea, Aggrey and Rekaya (74) reported that RFI computed from random effect model in poultry resulting in RFIM (maintenance based on mid-test body weight) and RFIG (growth based on average daily gain); these traits had a high, negative genetic correlation (-0.95). This negative correlation would be expected if animals that consume more feed have greater maintenance energy requirement (larger is less desirable), but greater efficiency of metabolizable energy use (larger is more desirable). ...
Research Proposal
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This is grant proposal to adjust the calculation of residual feed intake to reduce dependency on feed intake and increase association with maintenance energy requirements and efficiency of metabolizable energy use for maintenance and gain. This proposal was not funded; however, there are some important ideas that may stimulate further research in this area.
... Classical RFI allows identification of animals that are more, or less, efficient (assuming that the errors described in the section "Nature of the residual" are properly dealt with), but cannot shed light on which biological components of the efficiency complex contribute to differences in feed efficiency. The paper of Fischer et al. (2018), building on previous studies (Aggrey and Rekaya, 2013;on birds;and Savietto et al., 2014;on beef), explored a new method to isolate the cow-specific part of residual energy intake from the residual. It consists of including a random component to the coefficients in the RFI equation, and thus capturing the interindividual variation in efficiency. ...
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Residual feed intake (RFI) is an increasingly used trait to analyze feed efficiency in livestock, and in some sectors such as dairy cattle, it is one of the most frequently used traits. Although the principle for calculating RFI is always the same (i.e., using the residual of a regression of intake on performance predictors), a wide range of models are found in the literature, with different predictors, different ways of considering intake, and more recently, different statistical approaches. Consequently, the results are not easily comparable from one study to another as they reflect different biological variabilities, and the relationship between the residual (i.e., RFI) and the underlying true efficiency also differs. In this review, the components of the RFI equation are explored with respect to the underlying biological processes. The aim of this decomposition is to provide a better understanding of which of the processes in this complex trait contribute significantly to the individual variability in efficiency. The intricacies associated with the residual term, as well as the energy sinks and the intake term, are broken down and discussed. Based on this exploration as well as on some recent literature, new forms of the RFI equation are proposed to better separate the efficiency terms from errors and inaccuracies. The review also considers the time period of measurement of RFI. This is a key consideration for the accuracy of the RFI estimation itself, and also for understanding the relationships between short-term efficiency, animal resilience, and long-term efficiency. As livestock production moves toward sustainable efficiency, these considerations are increasingly important to bring to bear in RFI estimations.
... In our study, the heritability of AFW and of AFP were 0.33 and 0.30, respectively, which was close to the values reported by Chen et al. (2008), but lower than the results of Zerehdaran et al. (2004Zerehdaran et al. ( ), N'dri et al. (2006, and Chabault et al. (2012). The heritability was 0.36 and 0.38 for the FCR and RFI, respectively, which was in accordance with many reported estimations (Pakdel et al., 2005;Aggrey et al., 2010Aggrey et al., , 2013Xu et al., 2016;Sell-Kubiak et al., 2017). Results by us and others confirmed that body weight, abdominal fat, and feed efficiency are selectable in broilers. ...
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Feed consumption represents a major cost in poultry production and improving feed efficiency is one of the important goals in breeding strategies. The present study aimed to analyze the relationship between feed efficiency and relevant traits and find the proper selection method for improving feed efficiency by using the Northeast Agricultural University High and Low Fat broiler lines that were divergently selected for abdominal fat content. A total of 899 birds were used to measure the feed intake (FI), abdominal fat weight (AFW), and body weight traits. The abdominal fat percentage (AFP), feed conversion ratio (FCR), and the residual feed intake (RFI) were calculated for each individual broiler. The differences in the AFW, AFP, and in traits relevant to feed efficiency, such as FCR and RFI, between the fat line and the lean line were analyzed, and the genetic parameters were estimated for AFW, AFP, and feed efficiency relevant traits. The results showed that AFW, AFP, body weight gain (BWG), FI, FCR, and RFI were significantly higher in the fat line compared with the lean line. The heritability of FI, BWG, FCR, RFI, AFW, and AFP were 0.45, 0.28, 0.36, 0.38, 0.33, and 0.30, respectively. Both FCR and RFI showed high positive genetic correlations with FI, AFW, and AFP and relatively low, negative genetic correlations with BWG. The RFI showed much higher positive genetic correlation with the abdominal fat traits than FCR. In addition, the FCR showed negative genetic correlation with body weight of 4 wk (BW4) and 7 wk (BW7), whereas RFI showed positive genetic correlation with BW4 and BW7. The results showed that both RFI and FCR could be used for improving feed efficiency. When selecting against RFI, the AFP could be significantly reduced, and by selecting against FCR, the body weight could be improved simultaneously.
... Thus, there is growing interest among producers with respect to using RFI as a tool for genetic improvement, with a greater experience in swine [8] and poultry [9], numerous research efforts have investigated the effectiveness of selecting for feed efficiency using RFI in beef cattle [10][11][12], dairy cattle [3,13,14], or sheep [15][16][17][18]. The sheep industry, however, has yet to fully investigate the potential impacts associated with selecting for RFI on carcass merit, growth traits, reproduction traits, and fleece characteristics [15]. ...
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Simple Summary: Few, if any, reference is available in residual feed intake in dairy sheep. In this study, carried out during more than two months with French Lacaune dairy ewes in mid-lactation, we demonstrated an intraflock variability in feed efficiency determined by, beyond litter size and daily milking frequency, evident differences between the individuals in their efficiency of using the available total mixed rations. Abstract: This study examined the intraflock variability of feed efficiency in dairy ewes, through monitoring residual feed intakes (RFI). Primiparous lactating ewes (n = 43; 57.7 ± 0.91 kg body weight [BW] at lambing), representative of a French Lacaune dairy flock, were allocated in an equilibrated 2 × 2 factorial design experiment, lasting for 63 days during mid-lactation and combining 2 litter sizes (singletons, SING or twins, TWIN) and 2 daily milking frequencies (once, ONE or twice, TWO). Weaning occurred, and milking started, at 35 days after lambing (DIM). Ewes were individually fed a diet based on ryegrass silage, local hay, and supplements. Individual DMI was recorded daily and further used to evaluate (and compare) differences in RFI between ewes at 42, 49, 56, 63, 70, 77, 84, 91, and 98. Average individual RFI were calculated weekly since the first week (i.e., 35-42 DIM). Total (BW) and metabolic (BW 0.75) body weight, body condition score (BCS), milk yield, and plasma non-esterified fatty acids (NEFA) were monitored weekly. Differences in DMI were mainly due to the lactation stage and litter size and were 11% higher in ewes with TWIN compared to SING. This was positively correlated to milk yield and consistent with differences in RFI which varied due to litter size and to the milking frequency × lactation stage interaction. Ewes that lambed SING showed higher feed efficiency (−0.08 ± 0.018 vs. 0.13 ± 0.014 kg DM/ewe/d of RFI in SING vs. TWIN, respectively), whereas there were no differences in BW or BCS. Milking frequency did not affect DMI but milk yields were higher in TWO, which was related to a higher feed efficiency in this group (0.115 ± 0.016 vs. −0.07 ± 0.016 kg DM/ewe/d of RFI in ONE vs. TWO, respectively). Average RFI was affected (p < 0.0001) by the ewe, thus allowing a ranking among individuals to be established. High (n = 22) or low (n = 21) feed efficiency ewes averaged −0.17 ± 0.09 or 0.18 ± 0.09 kg DM/d RFI, respectively. Estimates of RFI were not correlated to the individual milk production potential. Even if no differences in BW, BW 0.75 , or BCS were detected, high-efficiency ewes mobilized 1.5 times their body reserves (0.30 vs. 0.20 mmol NEFA/L of plasma) when compared to the low-efficiency group. The observed intraflock variability in feed efficiency of this dairy ewes' flock was affected by litter size and milking frequency but also by evident differences between individuals' physiologies.
... Thus, there is growing interest among producers with respect to using RFI as a tool for genetic 55 improvement, with a greater experience in swine ( Patience et al., 2015) and poultry; (Aggrey and 56 Rekaya, 2013), numerous research efforts have investigated the effectiveness of selecting for feed 57 efficiency using RFI in beef cattle ( Fitzsimons et al., 2014;Gomes et al., 2012), dairy cattle (Green 58 et al., Potts et al., 2015;Pryce et al., 2014) or sheep ( Cockrum et al., 2013;Meyer et al., 2015;59 Redden et al., 2013). The sheep industry, however, has yet to fully investigate the potential impacts 60 associated with selecting for RFI on carcass merit, growth traits, reproduction traits, and fleece 61 characteristics ( Cockrum et al., 2013). ...
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This study examined the intraflock variability of feed efficiency in dairy ewes, through monitoring residual feed intakes (RFI). Primiparous lactating ewes (n=43; 57.7±0.91 kg body weight [BW] at lambing), representative of a French Lacaune dairy flock, were allocated in an equilibrated 2 × 2 factorial design experiment, lasting for 63 days during mid-lactation and combining 2 litter sizes (singletons, SING or twins, TWIN) and 2 daily milking frequencies (once, ONE or twice, TWO). Ewes were individually fed a diet based on ryegrass silage, local hay and supplements. Individual DMI was recorded daily and further used to evaluate (and compare) differences in RFI between ewes at 35, 42, 49, 56, 63, 70, 77, 84, 91 and 98 days relative to lambing (DIM). Total (BW) and metabolic (BW0.75) body weight, BCS, milk yield and plasma NEFA were monitored weekly. Differences in DMI were mainly due to the lactation stage and litter size and were 11% higher in ewes with TWIN compared to SING. This was positively correlated to milk yield and consistent with differences in RFI which varied due to litter size and to the milking frequency × lactation stage interaction. Ewes that lambed SING showed higher feed efficiency (−0.13±0.020 vs. 0.08±0.015 kg DM/ewe/d of RFI in SING vs. TWIN, respectively), whereas there was no differences in BW or BCS. Milking frequency did not affect DMI but milk yields were higher in TWO, which was related to a higher feed efficiency in this group (0.04±0.017 vs. −0.10±0.018 kg DM/ewe/d of RFI in ONE vs. TWO, respectively). Average RFI was affected (P <0.0001) by the ewe, thus allowing a ranking among individuals to be established. High (n=22) or low (n=21) feed efficiency ewes averaged −0.17±0.09 or 0.18±0.09 kg DM/d RFI, respectively. Estimates of RFI were not correlated to the individual milk production potential. Even if no differences in BW, BW0.75 or BCS were detected, high efficiency ewes mobilised almost two-fold their body reserves when compared to the low efficiency group. The observed intraflock variability in feed efficiency of this dairy ewes flock was affected by litter size and milking frequency but also by evident differences between individuals physiologies.
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Improving feed efficiency is an important breeding target for the poultry industry; to achieve this, it is necessary to understand the molecular basis of feed efficiency. We compared the jejunal transcriptomes of low- and high-feed conversion ratio (FCR) slow-growing Korat chickens (KRs). Using an original sample of 75 isolated 10-week-old KR males, we took jejunal samples from six individuals in two groups: those with extremely low FCR (n = 3; FCR = 1.93 ± 0.05) and those with extremely high FCR (n = 3; FCR = 3.29 ± 0.06). Jejunal transcriptome profiling via RNA sequencing revealed 56 genes that were differentially expressed (p < 0.01, FC > 2): 31 were upregulated, and 25 were downregulated, in the low-FCR group relative to the high-FCR group. Functional annotation revealed that these differentially expressed genes were enriched in biological processes related to immune response, glutathione metabolism, vitamin transport and metabolism, lipid metabolism, and neuronal and cardiac maturation, development, and growth, suggesting that these are important mechanisms governing jejunal feed conversion. These findings provide an important molecular basis for future breeding strategies to improve slow-growing chicken feed efficiency.
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A greater number of dairy economic selection indexes are incorporating a measure of feed efficiency (FE) as a key trait. Definitions of FE traits have ranged from dry matter intake (DMI) to residual feed intake (RFI), noting that RFI is effectively DMI adjusted for various energy sink traits such as body weight (BW) and milk energy (MilkE). Other definitions of FE fall between these 2 extremes such as feed saved (FS), which combines RFI and the portion of DMI required to maintain BW. The choice between different FE traits can create confusion as to how to meaningfully compare their heritabilities, estimated breeding values (EBV) and their corresponding reliabilities, and how to differentially incorporate these EBV into selection indexes. If RFI and FS are merely linear functions of DMI, BW, and MilkE with known genetic variances and covariances between these 3 traits, there may be no need to directly compute RFI or FS phenotypes to determine their heritabilities, genetic correlations, EBV, and respective reliabilities for individual animals. We demonstrate how the estimated total genetic merit is invariant to the specification of a FE trait within a selection index. That is, economic weights for a selection index involving one particular FE trait readily convert into the economic weights for a selection index involving a different linear function of that FE trait. We use these different specifications of FE to provide insight as to the effect of the degree of missingness (e.g., paucity of DMI relative to milk yield records) on the EBV accuracies of the various derivative FE traits. We particularly highlight that the generally observed higher EBV accuracies for DMI, then for FS, and lastly for RFI are partly driven by the greater genetic correlations of DMI with BW and MilkE and of FS with BW. Finally, we advocate a genetic regression approach to deriving FS and RFI recognizing that genetic versus residual relationships between FE component traits may differ substantially from each other.
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The feed resource for animals is a major cost determinant for profitability in livestock production enterprises, and thus any effort at improving the efficiency of feed use will help to reduce feed cost. Feed conversion ratio, expressed as feed inputs per unit output, is a traditional measure of efficiency that has significant phenotypic and genetic correlations with feed intake and growth traits. The use of ratio traits for genetic selection may cause problems associated with prediction of change in the component traits in future generations. Residual feed intake, a linear index, is a trait derived from the difference between actual feed intake and that predicted on the basis of the requirements for maintenance of body weight and production. Considerable genetic variation exists in residual feed intake for cattle and pigs, which should respond to selection. Phenotypic independence of phenotypic residual feed intake with body weight and weight gain can be obligatory. Genetic residual feed intake is genetically independent of its component traits (body weight and weight gain). Genetic correlations of residual feed intake with daily feed intake and feed conversion efficiency have been strong and positive in both cattle and pigs. Residual feed intake is favorably genetically correlated with eye muscle area and carcass weight in cattle and with eye muscle area and backfat in pigs. Selection to reduce residual feed intake (excessive intake of feed) will improve the efficiency of feed and most of the economically important carcass traits in cattle and pigs. Therefore, residual feed intake can be used to replace traditional feed conversion ratio as a selection criterion of feed efficiency in breeding programs. However, further studies are required on the variation of residual feed intake during different developmental stage of production.
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Genetic variations for feed utilization efficiency widely exist in meat-type chickens, even in populations that have undergone selection for the trait. The trait has moderate heritability and is moderately correlated with growth rate. The genetic basis for the trait was thought to be different from that for growth even though they may be related. As a result, the improvement in feed efficient through selection for body weight should be limiting. Fast growing birds require more nutrients and energy for maintenance. When age-fixed measure is used, part of the genetic variation of feed efficiency for growth may be masked. For this reason, adjusted age-fixed feed efficiency should be used in commercial breeding. The combination of restricted maximum likelihood (REML) method with an animal model can greatly account for the effect of selection of breeding population, leading to more accurate estimates of genetic parameters. The identification of quantitative trait loci (QTL) for feed efficiency in chickens has begun. The incorporation QTL in the prediction of breeding value will further improve selection efficiency for feed efficiency. While it was generally assumed that there is little variation in digestibility and metabolizability of feed within and between different strains or breeds, selection for growth rate has resulted in increased digestive enzyme activity. This suggests that the improvement in digestive ability could not be ignored in investigating new approach for increasing feed efficiency in meat-type chickens. Selection for improved FCR makes birds deposit less of the retained energy as fat and convert more energy and nitrogen into body protein. A negative effect of selection for feed efficiency is that, the combination of fast growth and improved FCR is related to increased occurrence of ascites. Index selection based on weight gain and feed intake could be better for improving FCR.
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A method for analyzing traits influenced by both maternal and direct genetic effects is presented in a Bayesian setting. A Bayesian analysis requires full marginalization of the joint posterior density. The necessary multidimensional integrations were carried out using the Gibbs sampler. This gives the possibility of exact marginal inference on (co)variance components of interest as opposed to results of REML analysis, where only joint inferences are possible. The method is illustrated by an example on growth in sheep.
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Experiments on Angus steer progeny following a single generation of divergent selection for residual feed intake suggest that there are many physiological mechanisms contributing to variation in residual feed intake. Difference in energy retained in protein and fat accounted for only 5% of the difference in residual feed intake following divergent selection. Differences in digestion contributed (conservatively) 10% and feeding patterns 2% to the variation in residual feed intake. The heat increment of feeding contributed 9% and activity contributed 10%. Indirect measures of protein turnover suggest that protein turnover, tissue metabolism and stress contributed to at least 37% of the variation in residual feed intake. About 27% of the difference in residual feed intake was due to variation in other processes such as ion transport, not yet measured. It is hypothesised that susceptibility to stress is a key driver for many of the biological differences observed following divergent selection for residual feed intake in beef cattle. Further research is required to accurately quantify the effect of selection for improved residual feed intake on protein turnover, tissue metabolism and ion transport, and to confirm the association between stress susceptibility and residual feed intake in beef cattle.
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IFFERENCES among animals in con- verting feed into body tissue are im- portant in determining net income from beef cattle operations. However, measuring in- dividual feed consumption is costly because of increased equipment and labor require- ments. The heritability of efficiency of feed use and its genetic relationship with other measurable traits need to be examined care- fully before recommendations concerning individual feeding can be made. The problem of measuring efficiency of feed use is dis- cussed in this paper and various measures are evaluated. The genetic and phenotypic variation and covariation among efficiency, gain and feed consumption are examined.