In-vivo composition of carcass regions in lambs of two genetic lines, and selection of CT positions for estimation of each region

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1 Small Ruminant Research 66 (2006) 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 R 2 ranged from to for lean and fat and from to for bone Elsevier B.V. All rights reserved. Keywords: Lamb; CT scanning; Meat line; Composition of carcass regions; Scan sites 1. Introduction Corresponding author. Tel.: ; fax: address: turi.kvame@gilde.no (T. Kvame). 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 /$ see front matter 2005 Elsevier B.V. All rights reserved. doi: /j.smallrumres

2 202 T. Kvame, O. Vangen / Small Ruminant Research 66 (2006) 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 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

3 T. Kvame, O. Vangen / Small Ruminant Research 66 (2006) 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 1st 7th Cervical vertebrae Shoulder 73, 97, 88, 80, 114, 121, 76 T1 T6 1st 6th Thoracic vertebrae Shoulder 84, 77, 68, 71, 90, 59 T7 T13 7th 13th Thoracic vertebrae Mid-region 22, 128, 50, 114, 118, 128, 146 L1 L7 1st 7th Lumbar vertebrae Mid-region 130, 142, 56, 128, 103, 131, 61 S1 S4 1st 4th Sacral vertebrae Leg 142, 55, 104, 65 CA1 CA4 1st 4th Caudal vertebrae Leg 87, 113, 111, 139 DF1 DF2 Distal femur Leg 143, 66 PT1 PT2 Proximal tibia Leg 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 , , and , 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 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: y ijklmn = mean + line i + year j + dam age k + birth l +live weight + e ijklm y ijklmn = trait analysed (tissue weights/tissue proportions and tissue ratios) mean = the general mean of Y ijklmn e ijklm = 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 R 2 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 R 2 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: y ijklmn = mean + line i + year j + dam age k + birth l + tissue area scan 1 + tissue area scan 2 + tissue area scan 3 (included for trait of the leg only) + live weight + e ijklm

4 204 T. Kvame, O. Vangen / Small Ruminant Research 66 (2006) y ijklmn = 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 (R 2 ). 3. Results Table 2 present coefficients of determination (R 2 ), 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 R 2 value for lean ranged from 0.69 in the shoulder to 0.74 in the leg. Bone was the least accurately estimated tissue (R 2 = ), while highest R 2 was found for the total weight of each region (R 2 = ). 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 (R 2 ), 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 R 2 ML CL Shoulder (seven images) Lean ± 0.07 a 5.50 ± 0.13 Fat ± ± 0.07 Bone ± ± 0.02 Total weight ± ± 0.17 Mid-region (nine images) Lean ± 0.05 b 3.90 ± 0.09 Fat ± ± 0.07 Bone ± ± 0.02 Total weight ± ± 0.12 Leg (seven images) Lean ± 0.06 b 5.11 ± 0.15 Fat ± ± 0.03 Bone ± ± 0.01 Total weight ± 0.05 b 6.48 ± 0.11 a Line means significant different within row; P < b Line means significant different within row; P < 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

5 T. Kvame, O. Vangen / Small Ruminant Research 66 (2006) 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 R 2 R 2 R 2 R 2 Shoulder (seven images) T6 (n = 57) T6 + CV7 (n = 32) T6 + CV7 + T4 (n = 31) Lean Fat Bone Total weight Mid-region (9 images) L4 (n = 126) L4 + T8 (n = 104) L4 + T8 + L7 (n =39) L4+T8+L7+T11(n = 31) Lean Fat Bone Total weight Leg (seven images) CA3 (n = 111) S4 + CA3 (n = 57) S4 + CA3 + CA4 (n = 47) S4 + CA3 + CA4 + S1 (n = 40) Lean Fat Bone Total weight 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 (R 2 value) increased by adding additional CT scans to the estimations. Though, largest increase in the R 2 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 R 2 value was found for all traits (R 2 = ) but bone (R 2 = ). For lean, the R 2 value ranged from for the shoulder to for the leg. Adding a third scan, the increase in the R 2 for lean was largest for the leg (R 2 = 0.915) and smallest for the shoulder (R 2 = 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 (R 2 ranging from 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 R 2 increased from to for the shoulder, from to for the mid-region, and from to 927 for the leg. For all regions, largest improvement in the R 2 value was found for bone (increase in the R 2 from to ). For fat, the R 2 increased from from stepwise regression analysis to from GLM. Live weight was Table 4 Coefficient of determination (R 2 ) 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 sites a found for each region adjusted for fixed effects and live weight (Model 3) Variable R 2 Shoulder (T6 + CV7, n = 32) Lean Fat Bone Total weight Mid-region (L4 + T8, n = 104) Lean Fat Bone Total weight Leg (CA3 + S4 + CA4, n = 47) Lean Fat Bone Total weight 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.

6 206 T. Kvame, O. Vangen / Small Ruminant Research 66 (2006) significant for fat in the mid-region, lean in the shoulder and for total weight of the leg. 4. Discussion 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 Estimation of reference scans for the leg, the mid-region and the shoulder 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 R 2 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 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).

7 T. Kvame, O. Vangen / Small Ruminant Research 66 (2006) 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 R 2 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 R 2 for lean increase from to (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 R 2 for lean when adding a third scan site ( ). 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 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 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

8 208 T. Kvame, O. Vangen / Small Ruminant Research 66 (2006) 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. References Afonso, J.J.M., Changes in body composition in sheep during growth, pregnancy and lactation. Dr. Thesis. University of New England, Armidale, Australia. Davies, A.S., Garden, K.L., Young, M.J., Reid, C.S.W., An atlas of X-ray tomographical anatomy of the sheep. Dep. Scientific and Industrial Research. N.Z. DSIR Bull. 243, 118. Fagsenteret for Kjøtt, Klassifiseringsprinsipper, småfe. Internet: document [ Klassifiseringsprinsipper Smaafe.doc]. Fullerton, G.D., Tissue imaging and characterisation. In: Fullerton, G.D., Zagzebski, J.A. (Eds.), Medical Physics of CT and Ultrasound. Medical Physics Monograph, vol. 6. American Institute of Physics, p Gilde Norsk Kjøtt, Kjøttnytt. Sau og Geit. 6, 70. Gundersen, H.J.G., Bendtsen, T.F., Korbo, L., Marcussen, N., Møller, A., Nielsen, K., Nyenaargd, J.R., Pakkenberg, B., Sørensen, F.B., Vesterby, A., West, M.J., Some new, simple and efficient sterological methods and their use in pathological research and diagnosis. Acta Pathol., Microbiol. Immunol. Scand. 96, Hopkins, D.L., Fogarty, N.M., Diverse Lamb Genotypes 1. Yield of saleable cuts and meat in the carcass and the prediction of yield. Meat Sci. 49 (4), Hopkins, D.L., Fogarty, N.M., Menzies, D.J., Differences in composition, muscularity, muscle:bone ratio and cut dimension between six lamb genotypes. Meat Sci. 45 (4), Jones, H.E., Lewis, R.M., Young, M.J., Simm, G., Genetic parameters for carcass composition and muscularity in sheep measured by X-ray computer tomography, ultrasound and dissection. Livest. Prod. Sci. 90 (2 3), Jones, H.E., Lewis, R.M., Young, M.J., Wolf, B.T., Warkup, C.C., Changes in muscularity with growth and its relationship with other carcass traits in three terminal sire breeds of sheep. Anim. Sci. 74, Jopson, N.B., Amer, P.R., McEwan, J.C., Comparison of twostage selection breeding programmes for terminal sire sheep. Proc. N.Z. Soc. Anim. Prod. 64, Jopson, N.B., Kolstad, K., Sehested, E., Vangen, O., Computed tomography as an accurate and cost effective alternative to carcass dissection. Proc. Aust. Assoc. Anim. Breed. Gen. 11, Kirton, A.H., Mercer, G.J.K., Duganzich, D.M., Clarke, J.N., Woods, E.G., Composition of lamb carcasses and cuts based on the October 1983 to 1998 export lamb carcass classification standards in New Zealand. N.Z. J. Agric. Res. 42 (1), Kirton, A.H., Duganzich, D.M., Mercer, G.J.K., Clarke, J.N., Dobbie, J.L., Wilson, J.A., Muscle distribution in lamb progeny from several breeds. Proc. N.Z. Soc. Anim. Prod. 57, 267. Kvame T., Vangen O., Selection for lean weight based on CT and ultrasound in a meat line of sheep. Livest. Prod. Sci., in press. Kvame T., Brenøe U.T., Vangen O., Body tissue development in lambs of two genetic lines analysed by X-ray computer tomography. Small Rum. Res., in press. Kvame, T.K., McEwan, J.C., Amer, P., Jopson, N.B., Economic benefits in selection for weight and composition of lamb cuts predicted by computer tomography. Livest. Prod. 90, Noakes, D.E., Dystocia in cattle. Vet. J. 153, Olesen, I., Svendsen, M., Klemetsdal, G., Steine, G., Application of a multiple trait model for genetic evaluation of maternal and lamb traits in Norwegian sheep. Anim. Sci. 60, SAS Institute Inc., SAS Technical Report p-229. SAS/STAT Software: Changes and Enhancements, v. 8. SAS Institute Inc., Cary, NC, 620 pp. Sehested, E., In-vivo prediction of lamb carcass composition by computerized tomography. Dr. Thesis. Agric. University, Norway, 81 pp. Webster, A.J.F., Rendering unto Caesar: welfare problems in Belgian blue cattle. Vet. J. 163, Wolf, B.T., Jones, D.A., Owen, M.G., Carcass composition, conformation and muscularity in Texel lambs of different breeding history, sex and leg shape score. Anim. Sci. 72, Young, M.J., Nsoso, S.J., Ogan, C.M., Beatson, P.R., Prediction of carcass tissue weight in vivo using live weight, ultrasound or X- ray CT-measurements. Proc. N.Z. Soc. Anim. Prod. 56,