Phenotypic and genetic variation in body weight,food intake and energy utilisation in Hereford cattle I. Performance test results

From body weight, food intake and carcass composition data on 542 Hereford bull calves, measuredfrom 200 to 400 days, several traits relating to the efficiency of beef cattle production were derived and analysed. Traits included body weight at various ages, weight gain, predicted carcass lean content, lean growth rate, food intake, food conversion ratio, lean food conversion ratio, food intake in relation to metabolic body weight, energy required for protein and fat deposition, and predicted maintenance expenditure.Maintenance expenditure and the costs of fat and protein deposition were calculated by two means,firstly from allometric equations describing fat and protein accretion, and secondly from a multiple regression of food intake on weight gain and predicted carcass lean content. The two methods gave different mean values, but the correlations between traits calculated by the two methods were almost all 1.00. Exponents for metabolic body weight derived from the two methods were 0.738 and 0.758, respectively.Genetic parameters were calculated using multivariate Restricted Maximum Likelihood techniques.Body weight, carcass composition and traits combining these measurements were moderately to strongly inherited whereas traits related to food intake and efficiency were weakly to moderately inherited. Energy used to deposit fat and lean was more strongly inherited than predicted maintenance expenditure, and these traits were genetically almost uncorrelated. Maintenance energy expenditure showed no genetic relationship with predicted carcass lean content. Efficiency and predicted maintenance expenditure were favourably correlated.     

The effect of selection for rapid lean growth on the dietary lysine and energy requirements of pigs fed to scale

A line of pigs (S line) selected for weight of ham lean, a measure of lean growth, was compared with an unselected control line (C line) of common origin on a series of food regimens ranging in average daily intake from 23.7 to 27.2 MJ digestible energy and from 13.3 to 23.4 g total lysine. The comparison was made over a 12-week test period starting at 25 kg liveweight and measurements were made of growth rate, fat depth by ultrasonics and, from these, predicted weight of lean in the ham at the end of test. As energy and lysine in the diet were increased, growth rate and ham lean rose at rates and reached limits which were higher in the S than the C line. As a result of 4.4 standard deviations (SD) of selection differential accumulated over five generations of selection, the superiority of the S over the C line in ham lean ranged from 0.5 (SD) on a low energy-lysine diet to 2.7 SD on a high energy-lysine diet. Maximum growth rate and ham lean were reached in the S line on a diet which provided 1 MJ day⁻¹ more digestible energy and 3 g day⁻¹ more total lysine than the diet at which the maxima were reached in the C line. Increasing dietary energy raised fat depths in the C line and increasing lysine lowered fat depths in the S line. Pigs from both lines were most profitable on diets lower in energy and lysine levels than those which gave maximum growth. Net monetary returns were most responsive to changes in energy in the C line and to changes in lysine in the S line.     

Estimates of genetic parameters and predicted selection responses for growth, fat and lean traits in mice

Heritabilities (?2) and genetic correlations (rG) were estimated by regression of offspring on sire in two replicate, unselected lines of mice. Traits were associated with growth, feed efficiency, fat deposition and lean tissue. The ?2 for growth traits ranged from .34 to .42, except for 3-wk body weight, which was only .05. The ?2 for feed efficiency was .28. Ranges in ?2 were .45 to .50 for fat deposition traits and .36 to .42 for lean tissue traits. The rG involving 3-wk to 6-wk feed efficiency with hind carcass and fat measurements at 12 wk were small. Antagonisms were found between the sign of rG and the direction of usual breeding goals for pairs of traits (e.g., rG greater than 0 between fat deposition and hind carcass weight and rG less than 0 between hind carcass as a percentage of body weight and body weight). Selection indexes were developed to counteract these antagonisms. Modified selection indexes were compared where responses in individual traits rather than the aggregate breeding value were of major importance. The aggregate breeding values and selection indexes included: 1) epididymal fat pad weight and body weight, 2) hind carcass weight and body weight, or 3) all three traits. Economic weights in retrospect were calculated for the modified selection indexes. In some cases, expected correlated responses in component traits were not influenced greatly over a wide range of ratios of economic weights, but in other cases the component traits changed sharply over a narrow range of ratios.     

Serum urea concentration as a predictor of dietary lysine requirement in selected lines of pigs

Serum urea concentrations were measured in Large White pigs from lines divergently selected for components of efficient lean growth rate and performance tested over three 14-d test periods starting at 30, 50, and 75 kg. Two methods of performance testing were used. Phase-fed pigs were fed to appetite isoenergetic diets differing in total lysine:energy ratio (0.58, 0.69, 0.81, 0.91, 1.01, 1.12, and 1.23 g/MJ of digestible energy), whereas diet-choice pigs were offered a choice of the 0.69 and 1.12 lysine:energy diets. Between test periods, all animals were fed one diet: 0.91 g of lysine/MJ of digestible energy. The study consisted of 230 boars and gilts with 150 pigs performance tested on phase-feeding and 80 pigs on diet-choice. The line selected for high lean food conversion had lower urea concentrations on each diet than the line selected for high lean growth rate, despite similar predicted lysine balances. Efficiency of lean growth rather than the rate of lean growth may be a better selection strategy in the context of nitrogen excretion. Urea concentrations at the end of each test period were correlated with lysine intake (0.33, 0.48 and 0.65; standard error, 0.08) and predicted lysine balance (0.39,0.44, and 0.64), but were uncorrelated with predicted lysine for protein deposition (0.01, 0.08, and 0.08) and maintenance. Urea concentration at the end of a test period was not a useful predictor of protein deposition, even after accounting for pretest variation in urea concentration and food intake during test. The expected response pattern of serum urea concentration to diets differing in total lysine:energy would be nonlinear, with the point of inflection occurring at the required dietary total lysine:energy for each genotype. However, there was no evidence of such an inflection point such that the prediction of lysine requirement from urea concentration was not possible for the selection lines in the study.     

Evaluation of alternative measures of pork carcass composition

Carcass and live measurements of 203 pigs representing seven genetic populations and four target live weights (100, 114, 128, and 152 kg) were used to evaluate alternative measures of carcass composition. Measures of carcass lean (fat tissue-free lean, FFLM; lipid-free soft tissue, LFSTIS; and dissected lean in the four lean cuts, DL), fat (total carcass fat tissue, TOFAT), and lipid mass (soft tissue lipid, STLIP) were evaluated. Overall, LFSTIS was 22.8% greater than FFLM (47.8 vs 38.9 kg) and TOFAT was 30% greater than STLIP (38.5 vs 29.6 kg). The allometric growth coefficients relative to carcass weight were different for the measures: b = 0.776, 0.828, 0.794, 1.37, and 1.49 for FFLM, LFSTIS, DL, TOFAT, and STLIP, respectively. At 90 kg carcass weight, the predicted growth of FFLM, LFSTIS, TOFAT, and STLIP was 0.314, 0.420, 0.553, and 0.446 kg/kg increase in carcass weight. The difference between FFLM and LFSTIS, representing nonlipid components of the carcass fat tissue, was greater for barrows than for gilts (9.2 vs 8.6 kg). Lipid-free soft tissue mass was predicted more accurately from carcass or live animal measurements than FFLM with smaller relative RSD (4.6 vs 6.5% of their mean values). The alternative measures of carcass composition were evaluated as predictors of empty body protein (MTPRO) and lipid (MTLIP) mass. Empty body protein was predicted with similar accuracy (R2 = 0.74 to 0.81) from either DL, FFLM, LFSTIS, or ribbed carcass measurements. Empty body lipid was predicted more accurately from TOFAT (R2 = 0.92) or STLIP (R2 = 0.93) than ribbed carcass measurements (R2 = 0.88). Although the alternative measures of lean mass (LFSTIS vs FFLM) and lipid mass (TOFAT vs STLIP) were highly related to each other (r = 0.93 to 0.98), they had different relative growth rates (allometric coefficients) and thus cannot be predicted as linear functions of the similar alternative variable without significant weight group biases. From the 100- to 152-kg target weight groups, gilts gained 12.9% greater FFLM and 12.1% greater MTPRO but only 4.4% greater LFSTIS than barrows. Fat-free lean mass is more precise as a measure of muscle growth and as a predictor of lysine requirements. Lipid-free soft tissue can be obtained more quickly and predicted more accurately from carcass or live animal measurements.     

Prediction of intramuscular fat by ultrasound in lean cattle

To study the accuracy and precision of intramuscular fat prediction by ultrasound (USIMF) in lean cattle, prediction models were developed based on 325 pure and crossbred beef and dual purpose bulls originating from I) commercial herds (n = 180) and II) a performance test station (n = 145). The bulls were scanned across the 12th thoracic vertebrae using a Pie 200 SLC scanner. Five images were collected per individual bull for later processing in the lab. After slaughter, a 2.5 cm cross-sectional sample located by the 13th thoracic vertebrae was used for chemical analysis to determine intramuscular fat % (CHIMF; Mean 1.25%, SD 0.6%) at the image site. The data were analysed sequentially involving 1) the Total Dataset, 2) Dataset I and 3) Dataset II. Prediction models were developed separately for each dataset, using stepwise regression procedures. The validation model R², RMSE, overall mean bias, SEP, r_P and rank correlations for CHIMF and USIMF based on the best prediction model (Dataset I) were 0.48, 0.46%, − 0.02%, 0.46%, 0.70 and 0.67, respectively. Further improvement in prediction accuracy can probably be achieved by increasing the size of datasets and the CHIMF variation. The results strongly indicate that ultrasound has potential for IMF prediction in lean cattle, although the value in indirect selection of breeding cattle for meat quality needs to be further evaluated.     

肉种鸡血浆极低密度脂蛋白质量浓度双向选择的效应

以父母代肉种鸡为材料，以血浆极低密度脂蛋白（very low density lipoprotein，VLDL）质量浓度为选择指标建立肉鸡高脂系和低脂系，个体测定一世代高、低脂系母鸡产蛋性能、种蛋受精率、孵化率、二世代6周龄体质量，观察血浆VLDL质量浓度选择效应。结果显示，一世代低脂系比高脂系早开产6．7d，低脂系40周龄和54周龄产蛋量显著高于高脂系零世代，一世代低脂系种蛋受精率、受精蛋孵化率、入孵蛋孵化率均优于高脂系；二世代高、低脂系6周龄体质量差异不显著。结果表明，对血浆VLDL质量浓度的低向选择使种鸡产蛋性能、种蛋受精率、孵化率产生了有益的间接反应，但对早期体质量没有影响。     

Ractopamine treatment biases in the prediction of pork carcass composition

Carcass and live measurements of 45 barrows were used to evaluate the magnitude of ractopamine (RAC) treatment prediction biases for measures of carcass composition. Barrows (body weight = 69.6 kg) were allotted by weight to three dietary treatments and fed to an average body weight of 114 kg. Treatments were: 1) 16% crude protein, 0.82% lysine control diet (CON); 2) control diet + 20 ppm RAC (RAC16); 3) a phase feeding sequence with 20 ppm RAC (RAC-P) consisting of 18% crude protein (1.08% lysine) during wk 1 and 4, 20% crude protein (1.22% lysine) during wk 2 and 3, 16% crude protein (0.94% lysine) during wk 6, and 16% crude protein (0.82% lysine) during wk 6. The four lean cuts from the right side of the carcasses (n = 15/treatment) were dissected into lean and fat tissue. The other cut soft tissue was collected from the jowl, ribs, and belly. Proximate analyses were completed on these three tissue pools and a sample of fat tissue from the other cut soft tissue. Prediction equations were developed for each of five measures of carcass composition: fat-free lean, lipid-free soft tissue, dissected lean in the four lean cuts, total carcass fat tissue, and soft-tissue lipid mass. Ractopamine treatment biases were found for equations in which midline backfat, ribbed carcass, and live ultrasonic measures were used as single technology sets of measurements. Prediction equations from live or carcass measurements underpredicted the lean mass of the RAC-P pigs and underpredicted the lean mass of the CON pigs. Only 20 to 50% of the true difference in fat-free lean mass or lipid-free soft-tissue mass between the control pigs and pigs fed RAC was predicted from equations including standard carcass measurements. The soft-tissue lipid and total carcass fat mass of RAC-P pigs was overpredicted from the carcass and live ultrasound measurements. Prediction equations including standard carcass measurements with dissected ham lean alone or with dissected loin lean reduced the residual standard deviation and magnitude of biases for the three measures of carcass leanmass. Prediction equations including the percentage of lipid of the other cut soft tissue improved residual standard deviation and reduced the magnitude of biases for total carcass fat mass and soft-tissue lipid. Prediction equations for easily obtained carcass or live ultrasound measures will only partially predict the true effect of RAC to increase carcass leanness. Accurate prediction of the carcass composition of RAC-fed pigs requires some partial dissection, chemical analysis, or alternative technologies.     

Use of bioelectrical impedance to predict leanness of Boston butts.

The objective of this research was to make available bioelectrical impedance technology for the prediction of kilograms of lean and kilograms of fat-free muscle of Boston butts. Seventy butts were removed from 70 pork carcasses according to standard procedures (NAMP, #406), with the exception that the fat was not removed. After the weight in kilograms (BUTT) and internal temperature in degrees centigrade (TEMP) were recorded, each butt was measured for resistance (Rs, ohms), reactance (Xc, ohms), and distance (L, centimeters) between detector terminals four different ways: parallel or perpendicular to the top of the carcass and on either lean surface or fat surface of the cut. Each cut was physically separated into lean, fat, and bone. Chemical composition (moisture, protein, and fat) was determined on the lean portion. Variable selection analysis was used to develop equations for predicting kilograms of lean and kilograms of fat-free muscle of Boston butts. Results of measurements of the four sites were quite similar; however, measuring perpendicularly on the lean surface is recommended. The prediction equation for kilograms of lean from measurements thus taken is as follows: .461-.0304 x TEMP + .576 x BUTT - .0118 x Rs + .00845 x Xc + .0630 x L. The respective coefficients of these independent variables for predicting kilograms of fat-free muscle are .537, -.0415, .479, -.0139, .00804, and .0764. In an industry application of these coefficients, recording temperature would not be imperative because the temperature range would be sufficiently narrow to render temperature of little practical influence when separating butts according to leanness.(ABSTRACT TRUNCATED AT 250 WORDS)     

Correlated physiological responses to selection forcarcass lean content in sheep

Correlated responses in physiological traits in lines of Texel-Oxford sheep selected for high or lowcarcass lean content were examined. Serum samples were taken at the end of performance test, 20 weeks of age, from 66 rams when fed ad libitum and every 24 h when fasted for 56 h. The lean line had higher serum concentrations of β-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA) and insulin-like growth factor- 1 (IGF-1) with lower concentrations of triglyceride (TRIG), creatinine (CREA) and UREA, but only the differences in UREA were statistically significant. There were substantial changes in serum concentrations of physiological traits in response to fasting as glucose and IGF-1 decreased while BHB, NEFA, TRIG, CREA and UREA increased. The correlated responses suggested that the lean line preferentially synthesises protein rather than deposits fat during normal feeding. When the animals are fasted, there may be relatively greater use of fat as an energy source in the lean line, rather than using products from protein catabolism as glucose precursors.The accuracy of selection was examined when physiological traits were incorporated into a selectionindex, which included the performance test traits: liveweight, ultrasonic backfat and muscle depths, to predict genetic merit for carcass lean content. UREA may be a useful predictor of genetic merit for lean meat production as it was correlated with estimated carcass lean content and there were substantial differences between the selection lines.     

Evaluation of procedures to predict fat-free lean in swine carcasses

The objectives were to develop equations for predicting fat-free lean in swine carcasses and to estimate the prediction bias that was due to genetic group, sex, and dietary lysine level. Barrows and gilts (n = 1,024) from four projects conducted by the National Pork Board were evaluated by six procedures, and their carcass fat-free lean was determined. Pigs of 16 genetic groups were fed within weight groups one of four dietary regimens that differed by 0.45% in lysine content and slaughtered at weights between 89 and 163 kg. Variables in equations included carcass weight and measures of backfat depth and LM. Fat-free lean was predicted from measures of fat and muscle depth measured with the Fat-O-Meater (FOM), Automated Ultrasonic System (AUS), and Ultrafom (UFOM) instruments, carcass 10th-rib backfat and LM area (C10R), carcass last-rib backfat (CLR), and live animal scan of backfat depth and LM area with an Aloka 500 instrument (SCAN). Equations for C10R (residual standard deviation, RSD = 2.93 kg) and SCAN (RSD = 3.06 kg) were the most precise. The RSD for AUS, FOM, and UFOM equations were 3.46, 3.57, and 3.62 kg, respectively. The least precise equation was CLR, for which the RSD was 4.04 kg. All procedures produced biased predictions for some genetic groups (P < 0.01). Fat-free lean tended to be overestimated in fatter groups and underestimated in leaner ones. The CLR, FOM, and AUS procedures overestimated fat-free lean in barrows and underestimated it in gilts (P < 0.01), but other procedures were not biased by sex. Bias due to dietary lysine level was assessed for the C10R, CLR, FOM, and SCAN procedures, and fat-free lean in pigs fed the lowlysine dietary regimen was overestimated by CLR, FOM, and SCAN (P < 0.05). Positive regressions of residuals (measured fat-free lean minus predicted fat-free lean) on measured fat-free lean were found for each procedure, ranging from 0.204+/-0.013 kg/kg for C10R to 0.605+/-0.049 kg/kg for UFOM, indicating that all procedures overestimated fat-free lean in fat pigs and underestimated it in lean pigs. The pigs evaluated represent the range of variation in pigs delivered to packing plants, and thus the prediction equations should have broad application within the industry. Buying systems that base fat-free lean predictions on measures of carcass fat depth and muscle depth or area will overvalue fat pigs and undervalue lean pigs.     

Predicting tissue distribution and partitioning in terminal sire sheep using x-ray computed tomography

The utility of x-ray computed tomography (CT) scanning in predicting carcass tissue distribution and fat partitioning in vivo in terminal sire sheep was examined using data from 160 lambs representing combinations of 3 breeds (Charollais, Suffolk, and Texel), 3 genetic lines, and both sexes. One-fifth of the lambs were slaughtered at each of 14, 18, and 22 wk of age, and the remaining two-fifths at 26 wk of age. The left side of each carcass was dissected into 8 joints with each joint dissected into fat (intermuscular and subcutaneous), lean, and bone. Chemical fat content of the LM was measured. Tissue distribution was described by proportions of total carcass tissue and lean weight contained within the leg, loin, and shoulder regions of the carcass and within the higher-priced joints. Fat partitioning variables included proportion of total carcass fat contained in the subcutaneous depot and intramuscular fat content of the LM. Before slaughter, all lambs were CT scanned at 7 anatomical positions (ischium, midshaft of femur, hip, second and fifth lumbar vertebrae, sixth and eighth thoracic vertebrae). Areas of fat, lean, and bone (mm(2)) and average fat and lean density (Hounsfield units) were measured from each cross-sectional scan. Areas of intermuscular and subcutaneous fat were measured on 2 scans (ischium and eighth thoracic vertebra). Intramuscular fat content was predicted with moderate accuracy (R(2) = 56.6) using information from only 2 CT scans. Four measures of carcass tissue distribution were predicted with moderate to high accuracy: the proportion of total carcass (R(2) = 54.7) and lean (R(2) = 46.2) weight contained in the higher-priced joints and the proportion of total carcass (R(2) = 77.7) and lean (R(2) = 55.0) weight in the leg region. Including BW in the predictions did not improve their accuracy (P > 0.05). Although breed-line-sex combination significantly affected fit of the regression for some tissue distribution variables, the values predicted were changed only trivially. Within terminal sire type animals, using a common set of prediction equations is justified. Tissue distribution and fat partitioning affect eating satisfaction and efficiency of production and processing; therefore, including such carcass quality measures in selection programs is increasingly important, and CT scanning appears to provide opportunities to do so.     

Prediction of swine carcass composition by total body electrical conductivity (TOBEC)

An experiment was conducted to determine prediction equations that used readings for total body electrical conductivity (TOBEC) in the model for estimation of total fat-free lean and total fat weight in the pork carcass. Ultrasound measurements of live hogs were used to select 32 gilts that represented a range in weight, muscling, and fatness. The TOBEC readings were recorded on warm carcass sides, chilled carcass sides, and the untrimmed ham from the left carcass side. Physical dissection and chemical analyses determined fat-free lean and fat weight of the carcass. All of the ham tissues were analyzed separately from the remainder of the carcass tissues to incorporate ham measurements for prediction of total fat-free lean and total fat weight in the entire carcass. Prediction equations were developed using stepwise regression procedures. An equation that used a warm carcass TOBEC reading in the model was determined to be the best warm TOBEC equation (R2 = 0.91; root mean square error = 0.81). A three-variable equation that used chilled carcass TOBEC reading, chilled carcass temperature, and carcass length in the model was determined to be the best chilled TOBEC equation (R2 = 0.93; root mean square error = 0.73). A four-variable equation that included chilled carcass side weight, untrimmed ham TOBEC reading, ham temperature, and fat thickness beneath the butt face of the ham in the model was determined to be the best equation overall (R2 = 0.95; root mean square error = 0.65). The TOBEC and the fat-free lean weight of the ham are excellent predictors of total carcass fat-free lean weight.     

Use of Real-Time Ultrasound in Pigs During the Early Finishing Phase to Predict Carcass Composition at Slaughter

The possibility of predicting kilograms of lean pork, the percentage of lean, and lean gain per day using measurements taken during the early finishing phase was studied using real-time ultrasound measurements on 164 barrows. Initial live weight (IWT), ultrasound backfat (IUBF), and ultrasound loin muscle area (IULA) were determined at 16 to 19 wk of age (70 kg). Final measures were obtained as the average pen weight reached 110 kg (22 to 24 wk of age). Initial kilograms of lean pork (IKOL) and the percentage of lean (ILEAN) were estimated with IWT and IUBF as the independent variables. Average daily gain, backfat accretion (BFACR), and lean gain per day (LGPD) were calculated from the initial and final measures. Pearson correlation coefficients were determined among the variables at the initial and final measures. The IULA and IKOL were correlated with the amount of lean tissue at slaughter (FKOL) (r=0.305 and 0.315, respectively; P<.001). Initial estimates of backfat (IUBF) and percentage lean (ILEAN) were correlated with FLEAN (r=−0.650 and −0.661, respectively; P<0.001). The IWT and IKOL were the only measurements taken in the early finishing phase related to LGPD r= −0.210 and −0.204, respectively; P<0.05). Prediction equations for FKOL, FLEAN, and LGPD were estimated from linear regression procedures. The IWT and IUBF were predictors of FKOL (R²=0.235; P<0.001). IUBF or ILEAN were predictive of FLEAN (R²=0.409 or 0.437, respectively; P<0.001). Measurements from the early finishing phase accounted for less than 10% of the variation in LGPD, ADG, or BFACR. This study suggests that carcass fat (FUBF or FLEAN) at slaughter can be predicted using real-time ultrasound in the early finishing phase with moderate confidence, accounting for 40 to 50% of the variation. Measures of lean tissue content or muscling (FKOL or FULA) can also be predicted, but with less confidence and accounting for less of the variation (20 to 30%).     

Computer image analysis for prediction of carcass composition from cross-sections of Japanese Black steers.

Carcasses from Japanese Black steers were used to obtain prediction equations for carcass composition from information derived by computer image analysis of carcass cross-section images. The total weights of lean, fat, and bone were obtained from the left sides of 55 carcasses (Data Set I) and 18 carcasses (Data Set II) by physical dissection. The information such as total lean, fat, and bone areas in the cross-sections; muscle area, muscle circumference, short and long radius axis lengths, and direction of long radius axis; and geometric distance between any two muscle centers of gravity was obtained by scanning and image analysis of pictures of the cross-sections of the beef side at the 6th/7th rib interface. The coefficients of determination of the multiple regression equations estimated from Data Set I for kilograms of lean, fat, and bone were 0.76, 0.82, and 0.69, respectively, whereas for the percentages of lean, fat, and bone they were 0.57, 0.66, and 0.42, respectively. The multiple regression equations from Data Set I was applied to Data Set II in order to test the applicability of the prediction equations obtained. The correlation coefficients between the value predicted by the multiple regression equation and the measurement obtained by physical dissection for kilograms of lean, fat, and bone were 0.71, 0.72, and 0.70, respectively, whereas those for the percentages of lean, fat, and bone were 0.63, 0.44, and 0.29, respectively. The results indicate that the information obtained from the carcass cross-sections by the computer image analysis method can be used to predict carcass composition in Japanese Black steers.     

Comparison of feed energy costs of maintenance, lean deposition, and fat deposition in three lines of mice selected for heat loss

Three replications of mouse selection populations for high heat loss (MH), low heat loss (ML), and a nonselected control (MC) were used to estimate the feed energy costs of maintenance and gain and to test whether selection had changed these costs. At 21 and 49 d of age, mice were weighed and subjected to dual x-ray densitometry measurement for prediction of body composition. At 21 d, mice were randomly assigned to an ad libitum, an 80% of ad libitum, or a 60% of ad libitum feeding group for 28-d collection of individual feed intake. Data were analyzed using 3 approaches. The first approach was an attempt to partition energy intake between costs for maintenance, fat deposition, and lean deposition for each replicate, sex, and line by multiple regression of feed intake on the sum of daily metabolic weight (kg(0.75)), fat gain, and lean gain. Approach II was a less restrictive attempt to partition energy intake between costs for maintenance and total gain for each replicate, sex, and line by multiple regression of feed intake on the sum of daily metabolic weight and total gain. Approach III used multiple regression on the entire data set with pooled regressions on fat and lean gains, and subclass regressions for maintenance. Contrasts were conducted to test the effect of selection (MH - ML) and asymmetry of selection [(MH + ML)/2 - MC] for the various energy costs. In approach I, there were no differences between lines for costs of maintenance, fat deposition, or protein deposition, but we question our ability to estimate these accurately. In approach II, selection changed both cost of maintenance (P = 0.03) and gain (P = 0.05); MH mice had greater per unit costs than ML mice for both. Asymmetry of the selection response was found in approach II for the cost of maintenance (P = 0.06). In approach III, the effect of selection (P < 0.01) contributed to differences in the maintenance cost, but asymmetry of selection (P > 0.17) was not evident. Sex effects were found for the cost of fat deposition (P = 0.02) in approach I and the cost of gain (P = 0.001) in approach II; females had a greater cost per unit than males. When costs per unit of fat and per unit of lean gain were assumed to be the same for both sexes (approach III), females had a somewhat greater estimate for maintenance cost (P = 0.10). We conclude that selection for heat loss has changed the costs for maintenance per unit size but probably not the costs for gain.     

Genetic correlations between live yearling bull and steer carcass traits adjusted to different slaughter end points. 1. Carcass lean percentage

We studied genetic relationships between age-constant live yearling beef bull growth and ultrasound traits and steer carcass traits with dissected steer carcass lean percentage adjusted to slaughter age-, HCW-, fat depth-, and marbling score-constant end points. Three measures of steer carcass lean percentage were used. Blue Tag lean percentage (BTLean) was predicted from HCW, fat depth, and LM area measurements. Ruler lean percentage (RulerLean) was predicted from carcass fat depth and LM depth and width measurements. Dissected lean percentage (DissLean) was based on dissection of the 10-11-12th rib section. Both BTLean (h2 = 0.30 to 0.44) and DissLean (h2 = 0.34 to 0.39) were more heritable than RulerLean (h2 = 0.05 to 0.14) at all end points. Genetic correlations among DissLean and RulerLean (rg = 0.61 to 0.70), DissLean and BTLean (rg = 0.56 to 0.72), and BTLean and RulerLean (rg = 0.59 to 0.90) indicated that these traits were not genetically identical. Adjusting Diss-Lean to different end points changed the magnitude, but generally not the direction, of genetic correlations with indicator traits. Ultrasound scan-age-constant live yearling bull lean percentage estimates were heritable (h2 = 0.26 to 0.42) and genetically correlated with each other (rg = 0.68 to 0.99) but had greater correlations with DissLean at slaughter age (rg = 0.24 to 0.48) and HCW (rg = 0.16 to 0.40) end points than at fat depth (rg = -0.08 to 0.13) and marbling score (rg = 0.02 to 0.11) end points. Scan-age-constant yearling bull ultrasound fat depth also had stronger correlations with DissLean at slaughter age (rg = -0.34) and HCW (rg = -0.25) than at fat depth (rg = -0.02) and marbling score (rg = -0.03) end points. Yearling bull scan-age-constant ultrasound LM area was positively correlated with DissLean at all endpoints (rg = 0.11 to 0.23). Genetic correlations between yearling bull LM method 1 width (rg = 0.38 to 0.56) and method 2 depth (rg = -0.17 to -0.38) measurements with DissLean suggested that LM shape may be a valuable addition to genetic improvement programs for carcass lean percentage at slaughter age, HCW, and fat depth constant end points. At all end points, steer carcass fat depth (rg = -0.60 to -0.64) and LM area (rg = 0.48 to 0.59) had stronger associations with DissLean than did corresponding live yearling bull measurements. Improved methods that combine live ultrasound and carcass traits would be beneficial for evaluating carcass lean percentage at fat depth or marbling score end points.     

Genetic Differences in Energy Partitioning in Growing Pigs

Energy partitioning was studied in pigs differing in potential for carcass lean growth and fatty tissue content from 25 to 105 kg body weight, by the means of repeated measures of detailed body composition and individual feed intake. In total, 141 pigs were included from the three genetic groups Norwegian Landrace (lean and efficient), Duroc and Landrace 2 LP (a fat and slow-growing selection line) (LLP). Individual feed consumption was registered, and detailed body composition measured repeatedly by computed tomography. Energy consumption [MJ metabolizable energy (ME) day -1 ] did not differ between the genetic groups. In general, about 40-50% of consumed energy was partitioned to growth. The genetic groups partitioned equal proportions of daily energy consumption to growth (ME GROWTH ) and maintenance (MEm) early and late in the growth period. From 50 to 85 kg body weight Landrace partitioned more to growth and less to maintenance compared with Duroc ( P <0.05). When considering partitioning of ME above maintenance, the genetically fat LLP had the highest net energy retention relative to the heat increment of feeding and was therefore the most efficient, Duroc was in an intermediate position while the lean Landrace had the lowest proportion. The partitioning of ME GROWTH to fatty tissue and carcass lean growth differed between the genetic groups ( P <0.001) according to genetic potential for carcass lean and fatty tissue gain. Increasing proportions were partitioned to fatty tissue growth with increasing body weight. The genetic groups partitioned equal proportions of ME GROWTH to non- fat visceral components (NFVC) growth. MEm varied between 0.65 and 0.68 MJ kg -0.75 day -1 . MEm increased with increasing weight of carcass lean and viscera ( P <0.01), more so in the modern breeds than in the LLP.     

Side effects of selection for growth in laboratory animalsEffets secondaires de la selection pour la croissance chez les animaux de laboratoireUnerwünschte Nebenwirkungen der Selektion auf Wachstum bei Labortieren

Correlated responses are examined from a biological rather than from a biometrical point of view. Selection for increased body weight in laboratory animals usually, though not always, leads to increases in food intake, gross efficiency and fat deposition, while some aspects of fertility are usually impaired. Increases in fatness are more pronounced in older animals, possibly because selection for early growth requires a high food intake which is not correspondingly reduced as the accretion of lean tissue slows down. Further, fat deposition may be an alternative to heat output, which explains why increases in both fat deposition and in gross efficiency may not be incompatible. It is concluded that many of the adverse side effects of selection for growth are the physiological consequences of increased fatness. In terms of applications to domestic livestock, it is suggested that the undesirable side effects should be controlled managementally by restricting food intake, on the grounds that the simultaneous avoidance of deleterious effects would unduly impair the efficiency of selection for increased growth.     

Estimated responses to eleven years of boar selection

Starting from ten Large White “founder” boars, put into service in November 1965, ten successive yearly boar generations were selected on a performance-test index, equal to 0.01 ADG – 0.5 BF, ADG being average daily gain (g) from 30 to 80 kg liveweight and BF being the average of six backfat measurements (mm) at 80 kg liveweight. Selection responses were estimated for growth rate, feed efficiency, carcass and meat quality traits and development of nasal turbinates. The data analyzed, which include 1604 female and 1284 castrated male progeny from 102 boars, show linear genetic trends which are, as a rule, larger over sire generations than over dam cohorts. A tentative and indirect estimation of the annual sire genetic trend in lean tissue growth rate is 6 g per day (2.6%), which is twice the dam trend. In lean tissue feed conversion, the annual genetic gain, which can only be estimated for sires, is 0.2 kg feed per kg lean tissue (1.9%). Correlated responses in meat quality traits indicate a tendency towards a paler meat colour, but conflicting sire and dam trends are observed for pH 24 and water-holding capacity. Unfavourable sire and dam trends are observed for development of nasal turbinates, which indicates a greater susceptibility to atrophic rhinitis as a consequence of the selection practised in this experiment.