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Ractopamine treatment biases in the prediction of pork carcass composition¹

Purdue University, West Lafayette, IN 47907-1151

² Correspondence:
1151 Lilly Hall (phone: 765-494-4836; fax: 765-494-9346; E-mail:
aschinck{at}purdue.edu).

	Abstract

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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 lean mass. 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.

Key Words: ß-Adrenergic Agonists • Carcass Composition • Pork • Prediction

	Introduction

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The implementation of lean value carcass pricing systems has led to the selection of pigs with increased lean growth rates, increased carcass lean percentages, and improved lean feed conversion (Schinckel, 1999). Ractopamine hydrochloride (RAC) is a feed additive that further increases the rate and efficiency of muscle tissue growth (Watkins et al., 1990; Moody et al., 2000).

Pork carcass composition research has been conducted to evaluate the effects of experimental treatment on model pig growth and to evaluate pork production systems (Schinckel and DeLange, 1996; Wagner et al., 1999). Traditionally, dissected lean in the four lean cuts has been evaluated as a measure of carcass value (Edwards et al., 1981; Siemens et al., 1989). Recently, two alternative methods of separating the soft tissue components have been used: 1) adjust dissected muscle tissue to a fat tissue-free tissue basis (Fahey et al., 1977; Wagner et al., 1999) and 2) adjust carcass soft-tissue mass for the chemically determined lipid content (Cisneros et al., 1996; Swensen et al., 1998). These two methods of determining and measuring carcass composition appear to be quite similar, but may have substantial differences (Schinckel et al., 2001).

In the majority of past RAC trials, carcass composition of the control and RAC-fed pigs was predicted from equations including standard carcass measurements. Using older genetic populations, prediction equations utilizing standardized carcass measurements underpredicted the impact of RAC to increase carcass lean mass (Mowery et al., 1991; Gu et al., 1992). The objectives of this study were 1) to evaluate the effect of RAC and dietary lysine-CP concentration on the prediction of alternative carcass composition endpoints from standard carcass measurements, and 2) to evaluate the ability of prediction equations that include partial carcass dissection or chemical analyses to minimize prediction biases.

	Materials and Methods

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The 45 barrows used were part of an experiment designed to evaluate the effects of dietary lysine-CP levels while feeding RAC on growth performance and carcass traits (Herr et al., 2001). Barrows (PIC 337 sires x C22 dams) were allotted at 69.6 kg of BW to three dietary treatments. The treatments were: 1) 16% CP (0.82% lysine) control diet (CON); 2) 16% CP, (0.82% lysine) with 20 ppm RAC (Paylean, Elanco Animal Health, Greenfield, IN; RAC16); and 3) a phase feeding sequence containing 20 ppm RAC (RAC-P) consisting of 18% CP (1.08% lysine) during wk 1 and 4, 20% CP (1.22% lysine) during wk 2 and 3, 16% CP (0.95% lysine) during wk 5; and 16% CP (0.82% lysine) during wk 6.

Slaughter Procedures
Pigs were removed when the mean of their experimental block reached 108.8 kg. The afternoon prior to slaughter, pigs were weighed (on farm) and live-animal B-mode ultrasound (Aloka model 500V Real-Time Ultrasound, Corometrics Medical Systems, Wallingford, CT) measurements were taken for backfat depth, 7 cm off-midline, at the 10th rib, and at the last rib. Ultrasonic measurements of the loin eye area were also taken at the 10th rib. One certified technician operated the ultrasound throughout the trial. Mineral oil was applied to ultrasound scan sites. Fifteen pigs per treatment were transported to the Purdue University Meat Science Laboratory. The pigs were stunned, immediately exsanguinated, and then scalded and mechanically dehaired.

The right sides were placed in a 2°C chilling unit for at least 24 h before further carcass measurements were taken. Backfat thickness, including skin, was measured with a ruler over the midline opposite the last rib. The right side of each carcass was ribbed between 10th- and 11th-rib positions prior to fabrication. Loin eye area and 10th-rib fat depth measurements (three-quarters of the length of the transverse section of the exposed longissimus muscle) were taken between the 10th and 11th ribs. Carcass measurements were taken by the same individual throughout the trial.

The right side of each carcass was fabricated into trimmed wholesale cuts as described by the Pork Carcass Evaluation Committee of the Reciprocal Meats Conference (RMC, 1952). The ham, loin, Boston butt, and picnic were individually dissected into lean, fat, bone, and skin. The dissected lean and fat tissue from the other cuts (belly, spare ribs, jowl, neckbone, tail, and lean and fat trimmings) were pooled. A 0.45-kg fat tissue sample was obtained from the other cuts (belly, spare ribs, jowl, neckbone, tail) proportional to their weight. All large pooled samples (lean, fat, other cut soft tissue) were ground through a 3-mm plate twice and then mixed in a puddle mixer (Leland Southwest, TX), at which time a 0.5-kg sample was taken and stored frozen. All samples were reground in a commercial stainless steel blender (Robot Coupe USA, Inc., Ridgeband, MS). Triplicate samples from each sample were analyzed for percentage of ether extractable lipid via the Soxhlet extraction procedure (AOAC, 1990). The lipid content of the dissectible lean from the four lean cuts (ham, loin, picnic and Boston butt), pooled dissected fat, other cut soft tissue, and other cut-fat sample were determined.

Determination of the Mass or Soft-Tissue Components
Two methods were used to divide the carcass soft tissue into two measures of carcass composition. The first method is to partition the soft-tissue mass into fat-free lean mass and total carcass fat tissue mass. The second method, based largely on chemical analysis, partitions the soft tissue into lipid-free soft tissue (LFSTIS) and soft tissue lipid (STLIP).

Fat-free lean mass (FFLM) is a measure of the dissected carcass lean muscle after accounting for the predicted amount of fat tissue remaining in the dissected lean. Thus, to determine FFLM, the total fat tissue mass, including connective tissue, water, and ash mass associated with adipose tissue, must be taken into account (Fahey et al., 1977; Orcutt et al., 1990; Schinckel et al., 2001). This method defines fat as a tissue containing adipose cells, connective tissue, cytoplasmic lipids, water, and other constituents (Allen et al., 1976). The percentage of inseparable fat tissue in the dissected lean of the four lean cuts and other cut soft tissue was predicted by dividing the percentage of lipid in the dissected lean of the four lean cuts and other cut soft tissue (CL%) by the percentage of lipid in the pooled dissected fat sample (CLF%) or other cut-fat sample. Calculation of FFLM of each of the two carcass components (dissected lean from the four lean cuts and other cut soft tissue) was determined with the following equation:

where DLM was dissected lean or other soft tissue mass. Total carcass FFLM was estimated as the sum of the FFLM of each of the four lean cuts and other cut soft tissue. Total carcass fat mass (TOFAT) is the fat tissue contained within the carcass soft tissue. Total carcass fat is the sum of the dissected fat plus the predicted amount of remaining fat in the four lean cuts and other cut soft tissue. The concentration of remaining fat tissue in the dissected lean or other soft tissue is predicted by the ratio of the percent of chemical lipid in the four lean cuts or other soft tissue divided by the percent lipid in the fat tissue.

Lipid-free soft tissue mass (LFSTIS) was calculated as the sum of the lipid-free mass of each of the four carcass components (dissected lean of the four primal cuts, other cut soft tissue, pooled dissected fat sample, and other cut fat sample). The lipid-free mass of each component = component mass x [1 - (CL%/100)] where CL% is the percentage lipid in each component.

Carcass soft-tissue lipid mass (STLIP) was calculated as the sum of the lipid mass of each of the four carcass components. The lipid mass of each component equals component mass x (CL%/100). Both sets of carcass composition variables sum to soft-tissue mass; that is, soft tissue mass = FFLM + TOFAT = LFSTIS + STLIP (Schinckel et al., 2001).

Statistical Analysis
Least squares means were calculated with the GLM procedure of SAS (SAS Inst., Inc., Cary, NC) for each dietary treatment. Simple correlation coefficients were calculated to determine the level of association between dependent and independent variables. Regression equations for predicting the mass of the carcass composition endpoint measures were developed using the GLM procedure of SAS. Independent variables, included in multiple regression equations, were grouped according to the type of measurements used (i.e., midline ruler, ribbed carcass, live ultrasonic scanning, and partial dissection).

The accuracy of each prediction equation was evaluated by the multiple coefficient of determination (R²), which is the proportion of the sums of squares of Y (dependent variable) attributable to the information obtained from the independent variables, and by the residual standard deviation (RSD), the standard deviation of differences between the actual and predicted values. Residual values from each prediction equation were fit as the dependent variable, and the effects of each treatment were used as independent variables. Least squares means of the residual values for the three treatments yield estimates of subpopulation biases (Gu et al., 1992). The residual values are the actual minus predicted value of the carcass component mass for each observation. Therefore, overprediction of a carcass component mass was indicated by negative residual values and underprediction was indicated by positive residual values. The residual variance for each prediction equation is comprised of the variance due to remaining treatment effects and a true residual variance. The RSD remaining after fitting the residual values for the treatment effects was calculated (RSD_R). The portion of the residual variance accounted for the treatment effects = [1 - (RSD_R/RSD)²], where RSD_R is the residual standard deviation of the second model (RSD = treatment) and RSD is the residual standard deviation of the prediction equation. The percentage of the true treatment difference accounted for by each regression equation was calculated as the difference between the mean values predicted by the equation for pigs of the two treatments divided by the true difference between the pigs of the two treatments

	Results

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Acronyms, definitions for variables, overall means, and diet treatment means are given in Table 1. The SD for live weight and carcass weight (4.85 and 4.19 kg, respectively) are smaller than in past pork carcass composition trials, as termination occurred when a mean block weight of 108.8 kg was achieved. In general, the SD for the mass or percentage of the carcass components is smaller than in previous dissection trials, which included barrows and gilts of multiple genetic sources (Orcutt et al., 1990; Schinckel et al., 2001).

The RAC-P barrows had a lower percentage of lipid in the other cut soft tissue and less other cut soft tissue lipid mass than the CON and RAC16 barrows. In addition, the percentage of lipid in the DL was less for the RAC-P barrows (4.44%) than the RAC16 (5.39%) and CON barrows (5.26%).

The correlations among the carcass composition mass and carcass percentage measures are presented in Table 2. The three measures of lean mass (FFLM, LFSTIS, and DL) were highly correlated with each other (r = 0.93 to 0.97). Fat-free lean percentage was negatively correlated with TOFAT% (-0.73) and STLIP% (-0.67). Lipid-free soft tissue percentage had similar correlations with TOFAT% (-0.63) and STLIP (-0.66). Because of the limited variation in carcass weight (CW), the correlations of each carcass component with its carcass percentage are high (0.81 to 0.97).

Correlations of the carcass composition mass and carcass percentage measures with the carcass live ultrasound, partial dissection, and other cut soft-tissue compositions are presented in Table 3. In this trial, CW, ultrasonic 10th-rib loin muscle area, and loin eye area had similar correlations (r = 0.56 to 0.72) with the three measures of carcass lean mass (FFLM, LFSTIS, and DL). The three measures of lean mass had higher correlations with off-midline measures of backfat depth (r = -0.35 to -0.48) than with midline last-rib backfat (-0.27 to -0.37) thickness. Off-midline measures of fat depth (fat depth at the 10th rib, ultrasonic 10th-rib backfat depth, ultrasonic last-rib backfat depth) had more negative relationships with lean percentage (r = -0.52 to -0.61) and more positive correlations with TOFAT% or soft tissue lipid percentage (0.63 to 0.72) than did midline last-rib backfat thickness (-0.35 to -0.44 and 0.37 to 0.45, respectively). These correlations are consistent with Orcutt et al. (1990), Gu et al. (1992), and Schinckel et al. (2001).

Dissected ham lean and dissected loin lean were more highly correlated (r = 0.76 to 0.90) to the three measures of lean mass than were the loin muscle measurements (r = 0.56 to 0.67). Gu et al. (1992) found a correlation of 0.93 between dissected ham lean and fat-standardized lean mass of RAC and CON barrows of five genetic populations and three target body weights (100, 114, and 127 kg). Lipid-free soft tissue mass had slightly higher correlations with CW (r = 0.72) and live weight (0.64) than did FFLM (0.64 and 0.55) or DL (0.59 and 0.49).

The mass of the dissected fat tissue from the ham and loin were correlated with TOFAT and STLIP (r = 0.70 to 0.83). The percentage of lipid of the other cut soft tissue was highly correlated with TOFAT and soft-tissue lipid mass (r = 0.79 and 0.82) and their carcass percentages (r = 0.85 and 0.86). The other cut STLIP was correlated with the TOFAT, STLIP, and their percentages (r = 0.83 to 0.88). These measures of lipid concentration or lipid mass of the other cut soft tissue had higher correlations with TOFAT and STLIP than did the backfat depth measurements. Other researchers have found TOFAT to be more highly correlated with dissected ham fat tissue mass (r = 0.91 to 0.93, Cross et al., 1970; Higbie et al., 2002) than with carcass fat-depth measurements (r = 0.81 to 0.85, Orcutt et al., 1990; Higbie et al., 2002).

Prediction equations for FFLM, LFSTIS, and DL are presented in Tables 4, 5, and 6 with corresponding summary statistics describing biases (residual values) associated with RAC treatment. As expected, partial regression coefficients for measures of backfat thickness were negative, whereas the coefficients for CW, live weight, loin muscle area, and dissected loin or ham lean were positive. The highest RSD values were produced by a combination of CW and midline last-rib backfat thickness measurements [Eq. 2]. The mean residual values and predicted values indicated that Eq. 2 predicted only 5 to 7% of the true difference between the RAC16 and CON treatments and 29 to 37% of the true difference between the CON and RAC-P treatments. For the three measures of carcass lean, approximately 50% of the residual variance (error mean square) of Eq. 2 was accounted for by the RAC treatment. Equation 3, based on ribbed carcass measurements (CW, loin eye area, and fat depth at the 10th rib), had slightly smaller RSD values than equations based on live weight and live animal ultrasonic measurements [Eq. 1]. Equation 1 predicted 53% of the increased FFLM and 55% of the LFSTIS produced by the RAC treatments. Equation 3 predicted 49% of the increased FFLM and 43% of the increased LFSTIS produced by RAC.

The inclusion of lipid analysis data of the other cut soft tissue in the prediction equations was also evaluated. The inclusion of the percentage of lipid in other cut soft tissue with fat depth at the 10th rib and CW improved the accuracy of prediction for LFSTIS and FFLM, but not for DL [Eq. 7 vs Eq. 3]. The inclusion of lipid-free or lipid mass of the other cut soft tissue improved the accuracy of the equation to a lesser amount than the percent lipid in the other cut soft tissue. Equation 7 predicted, on the average, 69.0% of the true differences between the RAC and control treatments (range 41.0 to 81.4%) for the three measures of lean mass.

Equation 7 for DL included CW, percentage of lipid in other cut soft tissue, dissected ham lean, dissected ham fat, fat depth at the 10th rib, and loin eye area percentage lipid in the other cuts was not significant in the prediction of DL. The inclusion of fat depth at the 10th rib and loin eye area were not significant for predicted FFLM or LFSTIS when the other four variables were included [Eq. 8]. Equation 8 accounted for 78 and 82% of the difference between the CON and RAC16 treatments, and 92% and 88% of the difference between the CON and RAC-P treatments for FFLM and LFSTIS, respectively. Only 11 and 8% of the residual variance was accounted for by the effect of treatment.

Prediction equations for TOFAT and STLIP and their corresponding summary statistics are presented in Tables 7 and 8. As expected, the partial regression coefficients for backfat thickness, live weight, and CW were positive, whereas the regression coefficient of dissected ham lean was negative. Equation 2 was the least accurate based on R² and RSD. The CON and RAC16 barrows had similar TOFAT (21.47, 21.29 kg) and STLIP (14.23 and 14.19 kg, respectively), whereas RAC-P barrows had 2.48 kg less TOFAT and 2.19 kg less STLIP than the CON pigs. Of this difference, Eq. 2 only predicted a 0.75 kg difference (30%) for TOFAT and a 0.44 kg (20%) difference in STLIP between the RAC-P pigs and CON pigs. The TOFAT and STLIP of the RAC-P were overpredicted (P < 0.0001).

The addition of dissected ham fat [Eq. 4] with fat depth at the 10th rib was significant (P < 0.05). Equation 4 tended to underpredict the TOFAT and STLIP of the RAC16 barrows and overpredict the TOFAT and STLIP of the RAC-P barrows. Equation 4 predicted 1.01 kg of the 1.34 difference in TOFAT between the RAC fed barrows in comparison to the CON barrows. Equation 4 predicted 1.01 kg of the 1.11-kg difference in STLIP between the RAC fed barrows in comparison to the CON barrows.

Dissection of the loins into fat and muscle tissue also resulted in increased accuracy and reduced biases compared to ribbed carcass measurements. Carcass weight, dissected loin fat, and dissected loin lean were all significant in the prediction of TOFAT [Eq. 5]. Equation 5 still tended (P = 0.13) to underpredict the TOFAT of the RAC16 barrows and underpredict the TOFAT of the RAC-P barrows. Equation 5 only included fat depth at the 10th rib and dissected loin fat for soft-tissue lipid. Again, the CON barrows were predicted without bias. On average, the RAC barrows were predicted with no bias (mean residual value of RAC16 and RAC-P barrows was close to zero). The STLIP of the RAC-P barrows tended to be overpredicted (0.44 kg), and the STLIP of the RAC16 barrows was underpredicted.

Equation 6, which included both ham and loin dissection data, was only slightly more accurate than Eq. 5. Using Eq. 6, the biases were reduced slightly in comparison to Eq. 5 for TOFAT and STLIP. The mean residual values of the CON and both RAC treatments were close to zero.

Equation 7 included measurements of carcass weight, fat depth at the 10th rib, and the percentage of lipid of the other cut soft tissue. The equation predicted 77 and 84% of the true difference between the RAC16 and CON treatments for TOFAT and STLIP. However, the prediction equations underpredicted the TOFAT and STLIP of the RAC-P barrows and overpredicted the TOFAT and STLIP of the RAC-P pigs (P = 0.041 and 0.022, respectively).

	Discussion

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The first objective of this research was to evaluate the magnitude of prediction biases of alternative carcass composition endpoints when RAC was fed. From a practical perspective, biases occur when different subpopulations (genetic populations, sexes, weight groups, or treatments) have different values of the dependent variables at the same values of the independent variables (Gu et al., 1992; Wagner et al., 1993; Hicks et al., 1998). Subpopulation differences in the proportional mass of lean and fat tissues and the chemical composition of the lean and fat tissues are partially responsible for subpopulation biases (Branscheid et al., 1988, Gu et al., 1992; Schinckel et al, 2001). The minimization of prediction biases should be a primary criterion in the development and use of prediction equations (Gu et al., 1992). Prediction biases could cause producers marketing RAC pigs to only receive partial payment of the increased carcass cut out value produced by RAC. Akridge et al. (1992) observed that technologies used varied among pork processors. Prediction biases will add additional "measurement method" variation on the predicted carcass value of RAC fed pigs.

It is important to realize that there may not be for a constant value that should be added for "RAC-fed" pigs. The impact of RAC to alter carcass composition is dependent on the RAC level fed (Watkins et al., 1990; Elanco, 1999; Schinckel et al., 2002), the duration of use (Anderson et al., 1988; Williams et al., 1994; Schinckel et al., 2002), and the lysine level fed (Anderson et al., 1987; Jones et al., 1988, Dunshea et al., 1993). Based on economic returns, most commercial producers are likely to feed 5 to 10 ppm of RAC (Kitts et al., 1991). All pigs at a finishing facility will likely start on RAC the same day, and the majority will be fed RAC from 1 to 4 wk (Elanco, 2001). The economic return for increased leanness of producers’ pigs will determine the optimal RAC and lysine level (Millar et al., 1990; Kitts et al., 1991).

Fat-free lean and carcass component tissue growth rates can be used as inputs to swine growth models (Schinckel and DeLange, 1996; DeLange et al., 2001). These models can predict farm-specific nutritional requirements and evaluate alternative marketing and management strategies. The predicted carcass component growth rates are sensitive to biased predictions of final carcass component mass. In this trial, the pigs were fed RAC for an average of 38 d. A 1% underestimation of FFLM (0.46 kg) or LFSTIS (0.53 kg) would result in a 2.3% decrease in daily FFLM or LFSTIS gain for the RAC feeding period. A 1.0% overprediction of TOFAT (0.19 kg) or STLIP (0.12 kg) results in a 2.8 and 3.3% overprediction of daily TOFAT and STLIP growth rates.

Fat-free lean gain has been extensively used to predict lysine requirements (Schinckel and DeLange, 1996; Dritz et al., 1997; NRC, 1998). Muscle tissue has high concentrations of lysine and other essential amino acids (Riis, 1983; Wünshe et al., 1983). The use of standard ribbed or live-animal ultrasound measurements in equations only predicted approximately 50% of the increased FFLM produced by RAC. Prediction equations developed from pigs not fed RAC would have underpredicted the FFLM of the RAC-P pigs by 3.36 kg based on real-time ultrasound measurements, and by 3.27 kg based on standard ribbed carcass measurements. These equations underpredicted FFLM gain by approximately 87 g/d. Essentially, the use of either real-time ultrasound or ribbed carcass measurements would predict approximately 50% of the increase in daily FFLM gain. This would result in diets being fed that would be expected to only allow approximately 50% of the RAC response to increase FFLM to be achieved.

Prediction equations including CW and last-rib midline backfat thickness had the lowest accuracy and greatest magnitude of biases for all measures of carcass composition. Other researchers have found that midline backfat measurements provide less precise prediction of carcass composition than midline measurements (Gu et al., 1992; Schinckel et al., 2001). Past research has found that RAC had little impact to reduce midline backfat thickness measurements, whereas RAC substantially increased dissected lean or fat-standardized lean and reduced carcass fat tissue percentage (Gu et al., 1991; Bark et al., 1992; Dunshea et al., 1993). In past research, only 15% of the RAC response to increase fat-standardized lean was predicted from equations with CW and midline backfat thickness measurements (Gu et al., 1992).

Researchers desiring a more precise prediction of carcass composition commonly use standard carcass measurements, including carcass weight, fat depth at the 10th rib, and loin eye area. In this study, prediction equations including standard carcass measurements only account for approximately 50% of the increased mass of the three measures of carcass lean composition. A prediction equation including these standard carcass measurements predicted 49.5% of the RAC response to increase fat-standardized lean mass (Gu et al., 1992). A similar equation predicted a 5.5-kg increase in carcass muscle mass when an 8.2-kg increase was actually observed in high lean-growth barrows (Bark et al., 1992). Although not statistically evaluated, a similar equation (Fahey et al., 1997) predicted increases of 0.5, 2.6, and 3.7 percentage units for fat-free lean percentage when 5, 10, and 20 ppm of RAC was fed, whereas 1.6, 4.0, and 6.0 percentage unit increases were obtained by dissection (Watkins et al., 1990).

The R² of the prediction equations for FFLM are lower than those previously reported by equations including standard carcass measurements. Typically the R² for predicting fat-free or fat-standardized lean mass from CW, fat depth at the 10th rib, and loin eye area range from 0.76 to 0.84 with RSD values ranging from 2.00 to 2.31 kg. Equations with CW and a single or average midline backfat depth had R² ranging from 0.56 to 0.68 and RSD values from 2.7 to 3.2 kg in similar weight pigs (Orcutt et al., 1990; Gu et al., 1992; Schinckel et al., 2001). The R² are likely lower as the standard deviation for BW (4.85 kg) and CW (4.10 kg) were also lower than in previous trials (range 8.0 to 12.0 kg for BW and 6.4 to 10.4 kg for CW). The RSD values in this trial were similar to previous trials and were substantially reduced after accounting for significant remaining treatment effects.

No previous research has been conducted evaluating the magnitude of prediction biases for total carcass fat tissue and soft tissue lipid mass in RAC-fed pigs. In this trial, only 20 to 30% of the reduction in total fat tissue or soft tissue lipid mass produced by the RAC-P diet could be predicted by equations including standard carcass measurements. Predicted lipid accretion is used to estimate daily energy intakes and subsequent optimal dietary essential AA concentrations (Schinckel and de Lange, 1996).

Researchers whose objective is to accurately evaluate the impact of RAC on carcass component mass and growth should consider additional measurements based on partial dissection or chemical analyses. Inclusion of dissected loin lean or dissected ham lean increased the accuracy of the prediction equations for DL, LFSTIS, and FFLM and reduced the magnitude of RAC treatment biases. The incorporation of dissected ham lean had a slightly greater impact than dissected loin lean to reduce prediction biases. Preliminary analyses of Gu et al. (1992) also found an advantage for dissected ham lean over dissected loin lean for equations including both RAC and CON barrows. The inclusion of dissected ham lean resulted in equations that accounted for 78 to 95% of the increased fat standardized lean mass produced by RAC (Gu et al., 1992). In this trial, neither dissected ham nor loin lean was as effective in reducing the magnitude of prediction biases for TOFAT or STLIP.

Another alternative to partial dissection is to identify a cut or tissue that is substantially altered in response to RAC and is representative of the overall carcass soft tissue composition. Bark et al. (1991) found a muscle mass (3.26 kg) to fat tissue mass (3.15 kg) ratio of 1.035 for the bellies of CON barrows vs. a ratio of 2.32 (4.63 kg of muscle to 2.00 kg of fat tissue) in the bellies of pigs fed RAC. Based on this data, the percentage of lipid, lipid mass, and lipid-free mass of the other cuts was considered. The lipid percentage of the other cut soft tissue increased the precision and reduced the magnitude of biases for equations predicting TOFAT and STLIP. Logically, it would seem that the lipid mass of the other cuts would be a better predictor of carcass fat tissue or STLIP than the lipid percentage of the other cuts. Correlation and regression analyses indicate that the mass of the other cuts or belly may not be as important as the collection and lipid analysis of a consistently defined sample. It is possible that a sample of the belly could be used with carcass weight and 10th-rib fat depth to predict TOFAT and LFSTIS in RAC-fed pigs.

The only other means found to precisely predict the response to RAC is the use of total-body electrical conductance (TOBEC) in combination with carcass weight and a measure of 10th-rib backfat depth (Gu et al., 1992). Past research has shown that TOBEC can accurately predict carcass composition (Forrest et al., 1989; Berg et al., 1994; Higbie et al., 2002) and carcass cutout value (Akridge et al., 1992; Berg et al., 2002). The TOBEC measurements have higher correlations with measures of lean mass than measures of longissimus muscle depth or area (Gu et al., 1992; Berg et al., 1994; Berg et al., 2002). The use of TOBEC to predict dissected ham lean and dissected ham fat with standard carcass measurements could also result in less biased prediction equations (Higbie et al., 2002).

The three measures of lean mass were highly correlated to each other and were predicted with similar precision. Dissected lean mass is not affected by the chemical composition of the dissected fat and muscle tissue. Lipid-free soft tissue mass is affected by the lipid analysis of the dissected lean and muscle tissue to a greater extent than FFLM (Schinckel et al., 2001). Ractopamine has been shown to reduce the lipid percentage of the dissected muscle tissue (Bark et al., 1992; Gu et al., 1992). In this trial, the feeding of RAC with diets formulated to maximize lean growth reduced the lipid percentage of both the dissected fat and muscle tissue. The decrease in lipid percentage of the dissected lean and fat tissues will increase LFSTIS and FFLM. The offsetting factor is that, in general, pigs with a decreased percentage of fat tissue have a lower percentage of lipid in their dissected fat tissue (Wood et al., 1989; Warriss et al., 1990; Schinckel et al., 2001) and muscle tissue (Bark et al., 1992; Schinckel et al., 2001). The percentage of lipid in the dissected fat tissue (63.05%) of the CON barrows was less than that of previous trials (75%; Orcutt et al., 1990; Schinckel et al., 2001).

In most cases, the defined measures of carcass composition will not be directly measured but will be predicted from less expensively collected carcass measurements. Overall, LFSTIS was predicted more accurately than FFLM. For the same prediction equation, LFSTIS was predicted with a smaller RSD, especially when expressed as a percentage of the mean (4.7 vs. 6.0%) for standard carcass measurements. The mean residual values of the treatments had similar patterns for LFSTIS and FFLM. However, the magnitude of the RAC treatment biases, both in absolute value and especially as a percentage of the mean, was greater for FFLM than LFSTIS. The results agree with those of Schinckel et al. (2001) that overall LFSTIS was predicted more accurately with smaller sex biases than FFLM.

The RSD values for STLIP were smaller than those for TOFAT, but were larger on a percentage basis for STLIP than TOFAT (13.5 vs 10.5% for an equation of CW and fat depth at the 10th rib). All prediction equations overpredicted the TOFAT and STLIP of the RAC-P pigs and underpredicted the TOFAT and STLIP of the RAC16 barrows. The absolute magnitude at which STLIP was overpredicted in the RAC-P barrows was 80 to 82% as great as the overprediction of TOFAT. When expressed as a percentage of the mean, STLIP was overpredicted to a greater extent than TOFAT.

The accurate evaluation of differences in TOFAT and STLIP accretion caused by feeding different dietary lysine and crude protein levels with RAC may be difficult. Equation 6 predicted 40% of the 2.14-kg difference in STLIP and 57% of the 2.28-kg difference in TOFAT between the RAC-P and RAC16 treatments. Equation 7, although the best equation in terms of RSD, only predicted 51.6% of the true difference in STLIP and 45.6% of the true difference in TOFAT between the RAC-P and RAC16 barrows. The RAC-P barrows had a significantly lower percentage of lipid in the dissected lean and fat tissues. Thus, some of the biases in predicting the STLIP of the RAC16 and RAC-P barrows are caused by the changes in chemical composition of the dissected tissues.

The prediction biases for TOFAT are likely caused by changes in the distribution of fat tissue between the RAC-P and RAC16 barrows. Had the relative proportion of dissected loin fat and dissected ham fat, or the relationships of fat depth at the 10th rib and percentage of lipid in other cut soft tissue to TOFAT, been consistent in the RAC-P and RAC16 treatments, then only small biases would have been expected (Gu et al., 1992).

	Implications

Top Abstract Introduction Materials and Methods Results Discussion Implications Literature Cited

 
Prediction equations from easily obtained carcass measures will only partially predict the true effect of RAC to increase carcass lean mass and reduce carcass lipid mass. The dietary lysine and crude protein levels affected the magnitude of the ractopamine response and biases. Researchers who want to accurately predict compositional growth of ractopamine-fed pigs should consider some partial carcass dissection, chemical analyses, or alternative technologies. Marketing systems using carcass measurements to predict lean mass will only partially account for the increased lean mass and value of ractopamine-fed pigs.

	Footnotes

 
¹ Purdue University Animal Research Program No. 16826.

Received for publication July 5, 2002. Accepted for publication September 17, 2002.

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