Small Ruminant Research 66 (2006) 201–208
In-vivo composition of carcass regions in lambs of
two genetic lines, and selection of CT positions
for estimation of each region
T. Kvame ∗ , O. Vangen
Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences,
Arboetveien 6, P.O. Box 5003, N-1432 Aas, Norway
Received 15 March 2005; received in revised form 19 July 2005; accepted 7 September 2005
Available online 13 October 2005
Abstract
Data on 155 lambs computer tomography (CT) scanned at a mean age of 118 days and 41.5 kg were included in this study. The
object was to examine in vivo composition in three major carcass regions, the leg, the mid-region and the shoulder of lambs of
two genetic lines, a meat line (ML) and a control line (CL). Additionally, the study aimed to determine the best scan positions for
estimating tissue weights in each region. Images were taken at 40 mm intervals, on average 23 scans per animal. Each CT scan was
located to an anatomical position and to one of the three regions. Weight of lean, fat and bone was found for each region from all
images located to a region. Tissue weights, and proportion of lean, fat and bone relative to the total weight of the region, was found
by GLM adjusted for fixed effects and live weight (covariate). Stepwise regression procedure was used to find the best combination
of scan sites for estimation of composition in each region.
The ML had a significantly heavier weight and larger proportion of lean, and a lower proportion of bone in all regions compared
to the CL. Furthermore, the ML had a lower proportion of fat in the leg and the mid-region than the CL (P < 0.05). Moreover, the
ML had a larger weight of lean in the leg than in the shoulder compared to CL (P < 0.05). The latter indicated that selection for lean
and introduction of Texel into the ML had increased the development of lean in the leg compared to the shoulder. Both lines had
largest percentage lean (78–80%) in the leg and lowest in the shoulder (70–72%), whereas smallest percentage fat was found in the
leg.
A minimum of two scan sites should be recorded for estimations of the shoulder and the mid-region, and three for the leg. The
best combination of scan positions for the shoulder was the 6th thoracic and the 7th cervical vertebra, for the mid-region the 4th
lumbar and the 8th thoracic vertebra, and for the leg the 3rd and 4th caudal and the 4th sacral vertebra. Using these scan sites
for estimation of tissue weights in each region, the R2 ranged from 0.894 to 0.978 for lean and fat and from 0.689 to 0.833 for
bone.
© 2005 Elsevier B.V. All rights reserved.
Keywords: Lamb; CT scanning; Meat line; Composition of carcass regions; Scan sites
1. Introduction
∗
Corresponding author. Tel.: +47 22 09 21 72;
fax: +47 22 15 59 08.
E-mail address: [email protected] (T. Kvame).
0921-4488/$ – see front matter © 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.smallrumres.2005.09.014
Improvement in carcass quality of lamb is needed in
view of consumer’s preference for leaner meat (Gilde
Norsk Kjøtt, 2003). The recent use of advanced
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T. Kvame, O. Vangen / Small Ruminant Research 66 (2006) 201–208
technology such as ultrasound and computer tomography (CT) for more accurate measure of carcass traits in
live animals, has increased the rate of genetic improvement in body composition traits in meat sheep populations. Traits measured by ultrasound and especially
CT are measured accurately (Sehested, 1986) and are
moderate to highly heritable (Jones et al., 2004; Kvame
and Vangen, 2005). Though, the literature for use of CT
to describe composition of carcass regions is limited
to results reported by Kvame et al. (2004) for composition of lamb cuts for New Zealand (NZ) standard
cuts and that of Jones et al. (2002) for muscularity
measures in three terminal sire breeds common in Scotland.
Data of lambs from a nucleus flock of a sheep including a meat line (ML) of Norwegian White Sheep (NWS)
crossed in with Texel and a control line (CL) of NWS
were analysed by CT for differences in carcass composition. The ML was selected for lean weight determined
by ultrasound and CT (Kvame and Vangen, 2005) while
the CL was bred in agreement with the National Sheep
Recording Schemes (NSG) (Olesen et al., 1995). Kvame
et al. (2005) showed that lambs of the two lines developed body tissues differently from 42 to 118 days of age,
especially for muscle weight. This was further supported
by the significant response in selection for lean weight
within the ML (Kvame and Vangen, 2005). The results
from these two studies raised further questions. Firstly,
had selection for lean and introduction of Texel within
the ML also altered the deposition of carcass tissues
within the three major carcass regions: the shoulder,
the mid-region and the leg? Secondly, was there a
difference in the ratios of tissues between the different
regions?
Also, as EUROP classification system is based on subjective assessment of the shoulder, the mid-region and
the leg (Fagsenteret for Kjøtt, 2005), reference scans for
estimation of composition of major carcass regions was
needed. As reported for lamb cuts by Kvame et al. (2004),
prediction of scan sites for estimation of composition of
carcass regions could be of great value if selection could
be for lean and fat in each region rather than for total lean
in the carcass as at present. Adding extra emphasis on
the two most economically valued parts of the carcass,
the mid-region and the leg could increase carcass
value.
Hence, the aim of this study was to (i) examine
the weight and proportion of lean, fat and bone in the
shoulder, the mid-region and the leg of lambs of a ML
and a CL, and (ii) find the best combination of scan
sites for estimation of composition in each carcass
region.
2. Material and methods
2.1. Data
A total of 155 lambs CT scanned in the period from
2002 to 2004 were included in this study. The lambs
represented a sample of animals recorded for a growth
study (Kvame et al., 2005) and a selection study (Kvame
and Vangen, 2005). In brief, the lambs came from two
genetic lines, a meat line (ML) of Norwegian White
Sheep (NWS) with Texel influence, and a control line
(CL) of NWS. The ML was selected for ultrasound muscle depth from 1993 until 2001, and for lean weight
determined by ultrasound and CT from 2001, while the
CL was bred for an over-all breeding goal in agreement with the National Sheep Breeding Scheme (NSG)
(Olesen et al., 1995). All animals were managed as one
flock and provided the same treatment throughout the
research period. At CT scanning, lambs of the ML were
on average 119 (7.9) days and 42.2 (5.3) kg, and the CL
115 (10.7) days and 38 (5.3) kg. The majority of the
lambs were born and raced as a twin lamb. All lambs
included in the study went with their mothers from birth
until they were weaned in one day up to a week prior to
CT scanning.
2.2. CT procedure and defining scan position
CT scanning procedure is described in Kvame and
Vangen (2005). In short, lambs were scanned lying on
their back with their fore- and hind-limbs extended using
a Siemens Somatom Emotion. CT scans were recorded
at 40 mm interval (Cavalieri CT, Gundersen et al., 1988)
throughout the body starting at a fixed position on proximal tibia. An average of 23 images were taken per
animal.
In agreement with the current score for conformation of lamb carcasses in Norway (Fagsenteret for Kjøtt,
2005), three carcass regions were defined; the shoulder (from the 1st cervical vertebra to the beginning of
scapula, 6th thoracic vertebra), the mid-region (from the
7th thoracic vertebra to the 7th lumbar vertebra) and the
leg (from the 1st sacral vertebra to the middle of the
hindlimbs). Each image was analysed for its anatomical position according to Davies et al. (1987), given a
code (Table 1) and located to one of the three regions.
Non-carcass components were then removed from the
images.
Tissue areas from each scan for each region were
numerically integrated to estimate tissue volume for each
depot (Gundersen et al., 1988). These were then corrected for density to provide estimates of tissue weight
T. Kvame, O. Vangen / Small Ruminant Research 66 (2006) 201–208
203
Table 1
CT scan site, code, body region (shoulder, mid-region, leg) and number (n) of images located to each position
Code
CT scan position
Carcass region
CT scan (n)
CV1–CV7
T1–T6
T7–T13
L1–L7
S1–S4
CA1–CA4
DF1–DF2
PT1–PT2
1st–7th Cervical vertebrae
1st–6th Thoracic vertebrae
7th–13th Thoracic vertebrae
1st–7th Lumbar vertebrae
1st–4th Sacral vertebrae
1st–4th Caudal vertebrae
Distal femur
Proximal tibia
Shoulder
Shoulder
Mid-region
Mid-region
Leg
Leg
Leg
Leg
73, 97, 88, 80, 114, 121, 76
84, 77, 68, 71, 90, 59
22, 128, 50, 114, 118, 128, 146
130, 142, 56, 128, 103, 131, 61
142, 55, 104, 65
87, 113, 111, 139
143, 66
58, 15
(Fullerton, 1980). Weight of lean, fat and bone in each
region for each animal was then found using AutoCAT
(Jopson et al., 1995). Boundaries for fat, lean and bone
were set to 30–115, 116–200, and 201–256, respectively.
The weight of each region was found from the sum of
lean, fat and bone in each region. The average number
of CT scans located to the leg, the mid-region and the
shoulder for each lamb was 7, 9 and 7, respectively.
The different scan positions and the corresponding
codes and carcass regions, as well as the number of CT
scans recorded for each position, are presented in Table 1.
In total, 39 different positions were defined. As an average of 23 images were recorded per animal, no animals
had images taken at all positions defined.
In addition to the weight of each tissue in each region,
proportion of lean (lean%), fat (fat%) and bone (bone%)
in each region was estimated. Also, tissue weights and
total weight of each region relative to the weights found
for all the same traits for the two other regions were
examined.
2.3. Statistical analysis
Weight and proportion of lean, fat and bone in each
region, and tissue weights in one region relative to
the other two was found from a GLM model in SAS
(1999). The model (model 1) included fixed effect of
line (i = CL, ML), year (j = 2002, 2003, 2004), dam age
(k = 1, 2, 3–4, 5–6), birth (l = 1, 2, 3), and live weight at
weaning (linear covariate).
Model 1:
yijklmn = mean + linei + yearj + dam agek + birthl + live
weight + eijklm
yijklmn = trait analysed (tissue weights/tissue proportions and tissue ratios)
mean = the general mean of Yijklmn
eijklm = random residual effect
Stepwise regression procedure was used to find the
best anatomical position for estimations of lean in each
region (Model 2). For each region, only scan sites that
were a component of the region analysed were included
in the estimation of a specific region. The scan site with
the highest R2 for lean was selected and used in the further estimation of the best combination of two scan sites
for estimation of a region. Again, the criteria for a subset
of two positions to be defined as ‘best’ were the largest
R2 for lean. This procedure was further used to find the
best combination of three and four anatomical positions
for each region. Though, it was required that for any
combination of scans to be analysed, a minimum of 30
animals should be included. Given the latter criteria,
the design of the data and the use of stepwise regression allowed up to four scans to be analysed for each
region.
Model 2:
Y = tissue area scan 1 + tissue area scan 2 + tissue area
scan 3 + tissue area scan 4
Y = weight of lean, fat or bone in a specific region, or
the total weight of a region
Predictors in brackets in Model 2 (tissue area scans
2–4) were added to the model one by one.
Degree of variation described in the shoulder, the
mid-region and the leg for each trait including fixed
effects, live weight and tissue area of the two best scan
sites for the shoulder and the mid-region, and the three
best for the leg were then analysed by GLM in SAS
(Model 3) (1999). Tissue areas were fitted as linear
covariates in the model (Model 3).
Model 3:
yijklmn = mean + linei + yearj + dam agek + birthl + tissue
area scan 1 + tissue area scan 2 + tissue area scan 3
(included for trait of the leg only) + live weight + eijklm
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yijklmn = trait analysed (weight of lean, fat and bone in a
specific region, and the total weight of a specific region)
For Models 1 and 3, effects were removed from the
model when they had small impact on the results. Accuracy of the estimates was expressed in terms of the
coefficient of determination (R2 ).
3. Results
Table 2 present coefficients of determination (R2 ),
least squares means and standard error for weight of lean,
fat and bone in the three carcass regions analysed for the
two genetic lines. The R2 value for lean ranged from
0.69 in the shoulder to 0.74 in the leg. Bone was the
least accurately estimated tissue (R2 = 0.46–0.60), while
highest R2 was found for the total weight of each region
(R2 = 0.78–0.80). The ML had larger weight of lean in
each region compared to the CL (P < 0.05), especially
in the mid-region (+31 g) and in the leg (+52 g). Both
lines had largest weight of lean in the leg and lowest in
the shoulder, and weight of fat lowest for the leg and of
similar weight for the mid-region and the shoulder.
Least squares mean weight of lean, fat and bone in
one region relative to the weight of each tissue in the
two other regions are shown in Fig. 1a–c. The two lines
had similar distribution of tissues in the shoulder relative
to composition of the mid-region (Fig. 1a). In contrary,
the CL had a higher ratio for lean in shoulder than in the
Table 2
Coefficient of determination (R2 ), least square mean weight and standard error of lean, fat, bone and total weight of the shoulder, the
mid-region and the leg for lambs of a meat line (ML) and a control line
(CL) (Model 1)
Variable
R2
ML
CL
Shoulder (seven images)
Lean
0.69
Fat
0.68
Bone
0.60
Total weight
0.78
5.76
1.42
0.91
8.11
±
±
±
±
0.07a
0.04
0.01
0.09
5.50
1.48
0.92
7.94
±
±
±
±
0.13
0.07
0.02
0.17
Mid-region (nine images)
Lean
0.71
Fat
0.58
Bone
0.46
Total weight
0.79
4.21
0.92
0.56
5.69
±
±
±
±
0.05b
0.04
0.01
0.06
3.90
1.03
0.58
5.51
±
±
±
±
0.09
0.07
0.02
0.12
Leg (seven images)
Lean
Fat
Bone
Total weight
5.69
0.76
0.65
7.05
±
±
±
±
0.06b
0.01
0.01
0.05b
5.11
0.78
0.64
6.48
±
±
±
±
0.15
0.03
0.01
0.11
a
b
0.74
0.65
0.60
0.80
Line means significant different within row; P < 0.05.
Line means significant different within row; P < 0.0001.
Fig. 1. Least square mean weight of lean, fat, bone and total weight
of: (a) the shoulder relative to the mid-region, (b) the shoulder relative
to the leg, and (c) for the mid-region relative to the leg for lambs
of a meat line (ML) and a control line (CL). Lean S-MR, fat S-MR,
bone S-MR = lean, fat and bone in the shoulder relative to the midregion, lean S-L, fat S-L, bone S-L = lean, fat and bone in the shoulder
relative to the leg, lean MR-L, fat MR-L, bone MR-L = lean, fat and
bone in the mid-region relative to the leg.
leg compared to the ML (Fig. 1b), analogous to the high
weight of lean in the leg reported for ML in Table 2.
Fig. 1a–c also illustrate the difference in composition
of tissues between regions. The weight of fat was, for
instance, significantly larger in the shoulder and in the
mid-region than in the leg, shown by the high ratio for
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205
Table 3
Results from stepwise regression analysis for the best scan site(s)a for estimation of weight of lean, fat, bone and in total of the shoulder, the
mid-region and the leg (Model 2)
Variable
R2
R2
R2
Shoulder (seven images)
Lean
Fat
Bone
Total weight
T6 (n = 57)
0.693
0.862
0.342
0.758
T6 + CV7 (n = 32)
0.828
0.958
0.459
0.856
T6 + CV7 + T4 (n = 31)
0.845
0.963
0.576
0.870
Mid-region (9 images)
Lean
Fat
Bone
Total weight
L4 (n = 126)
0.755
0.924
0.152
0.817
L4 + T8 (n = 104)
0.830
0.969
0.497
0.883
L4 + T8 + L7 (n = 39)
0.885
0.988
0.642
0.941
L4 + T8 + L7 + T11 (n = 31)
0.910
0.993
0.786
0.970
Leg (seven images)
Lean
Fat
Bone
Total weight
CA3 (n = 111)
0.785
0.854
0.066
0.836
S4 + CA3 (n = 57)
0.856
0.871
0.773
0.884
S4 + CA3 + CA4 (n = 47)
0.915
0.936
0.738
0.940
S4 + CA3 + CA4 + S1 (n = 40)
0.950
0.954
0.822
0.965
R2
a
T4, T6, T8, T11: 4th, 6th, 8th and 11th thoracic vertebrae; CV7: 7th cervical vertebrae; L4, L7: 4th and 7th lumbar vertebrae; CA3, CA4: 3rd
and 4th caudal vertebrae; S1, S4: 1st and 4th sacral vertebrae.
fat in these two regions, especially for fat in the shoulder
relative to fat in the leg (Fig. 1b and c).
Table 3 presents the coefficient of determination estimated from the single best and from combinations of
the best two, three and four scan sites. For each trait
analysed for each region, the accuracy of estimation (R2
value) increased by adding additional CT scans to the
estimations. Though, largest increase in the R2 value
was found when adding a second position to the single best scan site, especially for bone and less for fat.
Including two scans, high R2 value was found for all
traits (R2 = 0.828–0.969) but bone (R2 = 0.459–0.773).
For lean, the R2 value ranged from 0.828 for the shoulder to 0.856 for the leg. Adding a third scan, the increase
in the R2 for lean was largest for the leg (R2 = 0.915)
and smallest for the shoulder (R2 = 0.845). Though, for
the mid-region and the leg, including four CT scans
the accuracy of estimation was high for all traits for
both regions (R2 ranging from 0.786 to 0.993). The best
subset of two scans was the 7th cervical and the 6th
thoracic vertebrae for the shoulder, the 8th thoracic vertebra and the 4th lumbar vertebra for the mid-region,
and the 4th sacral and the 3rd caudal vertebrae for the
leg.
Including tissue area from the two (shoulder and midregion) or three (leg) best scan sites, and correcting for
fixed effects and live weight (Model 2) as shown in
Table 4, all traits were more accurately estimated than
reported for the corresponding variable in Table 3. For
lean, the R2 increased from 0.828 to 0.927 for the shoul-
der, from 0.830 to 0.894 for the mid-region, and from
0.915 to 927 for the leg. For all regions, largest improvement in the R2 value was found for bone (increase in
the R2 from 0.459–0.738 to 690–833). For fat, the R2
increased from 0.871–0.969 from stepwise regression
analysis to 0.932–0.978 from GLM. Live weight was
Table 4
Coefficient of determination (R2 ) for weight of lean, fat and bone and
in total for the leg, the mid-region and the shoulder estimated from the
two best scan sitesa found for each region adjusted for fixed effects
and live weight (Model 3)
Variable
R2
Shoulder (T6 + CV7, n = 32)
Lean
Fat
Bone
Total weight
0.927
0.964
0.716
0.922
Mid-region (L4 + T8, n = 104)
Lean
Fat
Bone
Total weight
0.894
0.978
0.689
0.920
Leg (CA3 + S4 + CA4, n = 47)
Lean
Fat
Bone
Total weight
0.928
0.952
0.833
0.947
a T6,
T8: 6th and 8th thoracic vertebrae; CV7: 7th cervical vertebrae;
L4: 4th lumbar vertebrae; CA3, CA4: 3rd and 4th caudal vertebrae;
S4: 4th sacral vertebrae.
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significant for fat in the mid-region, lean in the shoulder
and for total weight of the leg.
4.2. Estimation of reference scans for the leg, the
mid-region and the shoulder
4. Discussion
Results reported in Tables 3 and 4 showed that composition of carcass regions could be estimated from two to
three scans located to each carcass region. Two scan sites
should be recorded for the shoulder (T6 and CV7) and
the mid-region (L4 and T8), while three scans should
be included for estimation of the leg (CA3, CA4 and
S4). Kvame et al. (2004) reported comparable results
from a study performed in NZ for especially the hindleg (S3 and CA2) and the forequarter (T5 and CV7),
while only T9 reported for the rack was analogous to T8
found for the mid-region in the present study, including
two predictors for each cut. Kvame et al. (2004) reported
similar R2 values for the forequarter (0.928) as found for
the shoulder in the present study (0.927). Though, they
reported a higher accuracy of predictions for lean in the
loin (0.969) and in the rack (0.985) than estimated for
the mid-region reported from the current study (0.894,
Table 4). Including three scans for the leg, this region
was more accurately estimated than the comparable cut
(hindleg) reported by Kvame et al. (2004) (0.928 versus
0.802).
The components analysed in Kvame et al. (2004)
were cuts as defined for the NZ standard cuts (Kirton
et al., 1999), hence images of especially the loin and
rack (comparable to the mid-region in the present study)
were trimmed so that only the longissimus dorsi and
the vertebras with ribs to the outer edge of this muscle
remained in the images for analysis for this part of the
carcass. The trimming of these two cuts made them very
uniform, and therefore very accurate predictions were
obtained for these two cuts. No trimming after removal
of non-carcass components was done for any images
of the three carcass regions analysed in the present
study.
4.1. Composition of carcass tissues in different
carcass regions
In the present study, ML was found to have a larger
weight and proportion of lean, and a lower proportion of
bone in all regions compared to the CL (Table 2). Moreover, current findings indicated that the ML deposited
more lean in the leg than in the shoulder compared to
the CL, supported by findings of Hopkins et al. (1997)
and Hopkins and Fogarty (1998). They analysed the
leg shape of several breeds including the Texel breed,
and found genotype differences for conformation using
the EUROP classification system. Carcasses from Texel
sires had significantly (P < 0.05) more muscle in the
hindleg than carcasses from Poll Dorset sires.
Wolf et al. (2001) reported from a study of carcass
composition of the Texel breed that leg shape score and
carcass composition were clearly related, but they could
not determine whether lean weight increased linearly
with shape or if the advantages were seen only in the
highest leg shape scores. On the contrary, Kirton et al.
(1997) reported that despite breed differences in the proportion of muscle found in the carcasses from different
breeds and strains, they found no evidence for differences among breed groups in muscle distribution when
related to the total weight of dissected muscle in the cuts
they came from. Genotypes had a similar leg muscle cut
distribution, breed differences were only found for the
relative proportion of muscle to fat and bone. Kirton et
al. (1997) analysed lamb carcasses of 19.5 kg (similar to
estimated carcass weight of lambs in the present study)
of different breeds including the Texel.
Though, present findings agreed with Kirton et al.
(1997) for relative composition of different carcass
regions. Kirton et al. (1997) reported heavier weight in
the forequarter (shoulder) than in the hindleg (leg). Further, both studies reported largest proportion of lean and
smallest proportion of fat in the hindleg/leg, whereas
largest proportion of fat was reported for the forequarter/shoulder. Lambs analysed in the present study had a
higher proportion of lean and a lower proportion of fat
in the leg than reported by Kirton et al. (1997) (lean: 79
versus 64%; fat: 11 versus 18%). However, the trend of
tissue distribution was similar for both studies for the
two carcass regions; less lean, more fat and a tendency
of more bone in the shoulder than in the leg. Both lines
in the current study showed this trend.
4.3. Accuracy of estimation including one or more
scan sites for each region
Since 2001, ML animals have been selected based
on lean weight determined by CT (Kvame and Vangen, 2005). Consequently, high accuracy for lean was
important in the selection of the best scan site and the
combination of the best positions for each region. The
best subset of two scan sites found for a region was
not the best combination for all traits for the region
(Tables 3 and 4). This is possibly because the distribution of lean, fat and bone vary for different parts of
the carcass as also supported by Kirton et al. (1997) and
Hopkins and Fogarty (1998).
T. Kvame, O. Vangen / Small Ruminant Research 66 (2006) 201–208
The results presented in Table 3 showed that the
effect of adding a second predictor for estimation of tissue and total weight of each component analysed was
largest for weight of bone. Bone is the least variable
carcass tissue (Kvame et al., 2004; Afonso, 1992) and
the least accurately measured depot using CT (Young
et al., 1996; Kvame et al., 2004). This is because of
the variation in bone is depending on the positioning
of the animal on the bed and the random positioning
of CT scans recorded. This is especially so for using
Cavalieri CT procedure. Because of the random recording of images, some images were taken at the defined
vertebras, whereas others were recorded between two
anatomical positions defined. Hence, the area of bone in
images located to a specific anatomical position varied,
especially for scans taken in the shoulder with a large
proportion of bone.
In the present study, the additional value of adding
more than two scan sites for each region was evaluated
by the increase in the R2 for lean obtained from including
a larger number of CT scans and the number of animals included in the analysis. Also, a limited number
of scan sites was needed because of the cost of scanning additional scan sites for each animal. For instance,
for the shoulder, the R2 for lean increase from 0.828 to
0.845 (Table 3) when adding a third scan position, while
the number of animals used in the estimations was only
reduced by one. Further, for the mid-region the number
of animals used in the estimation based on three scan
sites was reduced from 104 to 39. Hence, further studies should confirm whether the higher accuracy obtained
including a third scan will outweigh the additional cost
of including a third scan for this region. On the other
hand, for the leg being one of the most valued parts of
the carcass, three scans should be taken. This is because
of the relatively high increase in the R2 for lean when
adding a third scan site (0.856–0.915). From the above,
it was concluded that two positions should be included
for the shoulder (T6 and CV7) and the mid-region (L4
and T8), while three scan sites (CA3, CA4 and S4) should
be included for the leg.
4.4. Practical application of the results and further
work
This study has shown that CT can be used to examine
and describe composition of carcass regions. Also, that
carcass regions can be estimated with sufficient accuracies from two and three scans located to each region,
comparable to results reported for cuts by Kvame et al.
(2004). They also reported that estimates for composition of carcass cuts could be incorporated into selection
207
indices and be selected on for greater carcass value,
recently supported by Jopson et al. (2004). However, one
should be careful with single-trait selection for yield in
specific regions because of interaction with other traits
that might result in severe negative effects. For example, if selection was for lean in the leg largely, negative
side effects as reported for the double muscling ‘Belgian
Blue’ cattle breed (Noakes, 1997) might be found. Selection for high muscling in this breed has resulted in severe
birth difficulties (dystocia) due to feto-maternal disproportion (Noakes, 1997) and reduced fitness (Webster,
2002). Norwegian producers want sheep that are fit and
capable to utilize hill and mountain pasture, an important nutritional recourse in Norway. Further, weight and
distribution of lean and fat in the carcass are just two
factors with influence on the profitability of lamb production. Growth rates and the number of lambs produced
per ewe must be put in balance with yield of lean and
proportions in the different parts of the body.
If selection for composition of carcass regions will be
an alternative in Norway in the future, it should be practised within a system of terminal sire line mated to ewes
of a maternal line where the emphasis is for reproduction
and maternal traits. Jones et al. (2002) suggested from
a study of muscularity measures determined by CT that
at least two measures should be included in the index,
for example one for the leg and on for the mid-region.
A maternal line should be maintained in order to retain
a high rate of reproduction important for efficient sheep
production in Norway. Though, high mature weight or
too large emphasis of muscling in one region should be
avoided in order to minimize birth difficulties.
Further studies should be done to confirm present
findings for the best scan site for each region. All lambs
should be CT scanned at all defined positions to ensure
records for all animals for all positions. The best positions may vary between genotypes or breeds because of
variation in body shape and body composition.
5. Conclusion
The ML had a significant larger weight and proportion of lean and a lower proportion of bone in all regions
compared to the CL. Further, the ML had a lower proportion of fat in the leg and the mid-region and a larger
weight of lean in the leg than in the shoulder compared
to the CL. The latter indicate that selection for lean has
increased the deposition of lean in preference for the leg
relative to the shoulder.
This study also showed that weight of lean and fat
in each region can be made from two scan sites for
the shoulder and the mid-region, while three scan sites
208
T. Kvame, O. Vangen / Small Ruminant Research 66 (2006) 201–208
should be taken for estimations of the leg. For the shoulder, scans should be taken at CV7 and T6, for the midregion at L4 and T8, and for the leg at CA3, CA4 and S4.
Further studies should confirm present findings. The data
should include lambs CT-scanned at all defined anatomical positions.
Acknowledgements
Thanks to K.S. Dalen and C.G. Fristedt for running
the CT scanner and providing high-quality images for
this study. Thanks are also to the ‘Animal Production
Experimental Centre’ for management of the animals
and for lamb recordings. Finally, we are grateful to ‘Gilde
Norwegian Meat’ and the ‘Norwegian Research Counsel’ for funding the project.
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