J. Anim Sci. 2008. 86:460-471. doi:10.2527/jas.2007-0625
© 2008 American Society of Animal Science
Growth of protein, moisture, lipid, and ash of two genetic lines of barrows and gilts from twenty to one hundred twenty-five kilograms of body weight1
A. P. Schinckel*,2,
D. C. Mahan
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T. G. Wiseman and
M. E. Einstein*
* Department of Animal Sciences, Purdue University, 915 West State Street, West Lafayette, IN 47907-2054; and
The Ohio State University and the Ohio Agricultural Research and Development Center, Columbus 43210-1095
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Abstract
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Two genetic lines of barrows and gilts with different lean growth rates were used to determine the BW and chemical composition growth from 23 to 125 kg of BW. The experiment was a 2 x 2 x 5 factorial arrangement of treatments in a completely randomized design conducted in 2 replicates. Six pigs from each sex and genetic line were killed at approximately 25-kg intervals from 23 kg to 125 kg of BW. At slaughter, tissues were collected and weighed. All components were ground and frozen until analyzed for water, protein, lipid, and ash. Serial BW data were fitted to alternative functions of day of age. Based on Akaike’s information criteria values, the random effects model, BWi, t = (1 + ci)(b0 + b1t + b2t2), was the best mixed model equation. The chemical component mass data were fitted to alternative functions of BW. The allometric function, chemical component mass = aBWb, provided the best fit to the data. Daily deposition rates of each chemical component were predicted by using the derivatives of the 2 functions. The overall ADG of the 2 genetic lines were not different. Barrows had 0.052 kg/d greater (P = 0.03) ADG than gilts. Allometric growth coefficients for all 4 chemical components were different (P < 0.01) for each genetic line. Allometric coefficients and predicted relative growth (g/kg of BW gain) for protein and moisture mass were greater (P < 0.01) for the high lean-gain pigs than the low lean-gain pigs. Allometric coefficients for lipid mass were smaller (P = 0.001) for the high lean-gain pigs than the low lean-gain pigs overall. Allometric coefficients and predicted relative growth rates for lipid mass were greater (P < 0.01) and for moisture and protein mass were lesser (P < 0.002) than the gilts. Compared with low lean-gain pigs, high lean-gain pigs had (1) 32.8% lesser predicted daily rates of lipid deposition (200 vs. 305 ± 80 g/d), with the difference increasing from 23 to 37% from 25 to 125 kg of BW; (2) 12.3% greater daily rates of protein deposition (118.7 vs. 106.0 ± 3.3 g/d); and (3) 18.8% greater predicted daily moisture accretion rates (423 vs. 356 ± 9 g/d). Overall, barrows had 21.3% greater lipid deposition (279 vs. 230 ± 78.2 g/d) than gilts. In this study, barrows and gilts had similar predicted daily moisture, protein, and ash accretion rates.
Key Words: chemical composition • genotype • growth • pig
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INTRODUCTION
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Swine growth models have the potential to allow evaluation of alternative management systems and optimize pork production systems relative to defined economic objectives (Moughan et al., 1995
; de Lange et al., 2001; Boys et al., 2007). Many of these models have been developed to improve the efficiency of N and P retention for growing-finishing pigs, thus minimizing the negative environmental impacts of pork production (de Lange et al., 2001; Kebreab et al., 2007).
Protein, lipid, water, and ash are the primary constituents of empty body mass (de Lange et al., 2003). Two of the four components, body ash and water content, are closely related to protein mass. For this reason, body protein and lipid mass and their deposition rates are key descriptive variables most often used to parameterize pig compositional growth models (Schinckel and de Lange, 1996; de Lange et al., 2003; Knap et al., 2003).
Protein deposition rates are used to predict daily essential amino acid and energy intake requirements (Schinckel and de Lange, 1996; Schinckel et al., 2002). Protein and lipid deposition rates are used in pig growth models to predict carcass muscle and fat tissue mass, the primary determinants of carcass cut-out value (Akridge et al., 1992; Schinckel et al., 2003a,b). Modeling the efficiency of energy utilization and heat production is largely based on protein and lipid deposition rates (van Milgen and Noblet, 2003).
Genetic selection for greater rate and efficiency of carcass muscle growth has substantially changed the absolute and relative rates of protein and lipid deposition, feed intakes, and nutrient requirements (Schinckel, 1994, 1999). The objective of this study was to model the chemical compositional growth of 2 genetic populations differing widely in carcass lean and fat tissue growth from 20 to 125 kg of BW.
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MATERIALS AND METHODS
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The experimental use of animals and procedures were approved by the College Animal Care and Use Committee at Ohio State University.
Two genetic lines of pigs having different herd histories of lean tissue development were evaluated in a previous experiment to estimate the pattern of lean and fat tissue development from 20 to 125 kg of BW (Wiseman et al., 2007a). The current experiment was a continuation of that study, for which various body tissues and organs were collected at 25-kg BW intervals from 23 to 125 kg of BW, and chemical composition of the major components was determined.
The experiment was conducted as a 2 (genetic line) x 2 (sex) x 5 (BW) factorial arrangement of treatments in 2 replicate groups with 3 pigs in each treatment group (n = 120 pigs total) in a completely randomized design. The low lean-gain genetic line had an estimated herd average of 280 g of fat-free lean/d, whereas the high lean-gain line had an estimated herd average of 375 g of fat-free lean/d. Barrows and gilts were evaluated within each genetic line. Phenotypic tissue weight and chemical composition of the pigs used for these analyses were reported earlier (Wiseman et al., 2007a,b). These measurements were taken from 23 to 125 kg of BW in 5 BW intervals. The genetic makeup of the pigs, management procedures, method of allotment, diets fed to each genetic line and sex, and slaughtering conditions were previously presented (Wiseman et al., 2007a). Details of tissue sampling and chemical analyses were presented in Wiseman et al. (2007b).
Body Weight Growth Functions
Three functions relating BW to age were considered. The BW data were fitted to age with Bridges function {WT = WTM[1 – e(–mt
)]} (Bridges et al., 1986), where WT equals BW minus birth weight (1.4 kg), WTM is an estimate of mature BW, m is an exponential growth decay parameter, is the kinetic order constant, and t is age in days, using PROC NLIN (SAS Inst. Inc., Cary, NC). The BW data were fitted to a generalized Michealis-Menten equation (Lopez et al., 2000; Schinckel et al., 2006). The equation has the form BW = [(WT0 KC) + (WF tC)]/(KC + tC), where WF is mean mature BW, WT0 is the mean birth BW, t is days of age, K is a parameter equal to the days of age in which one-half WF is achieved, and C is a unitless parameter related to changes in proportional growth and shape of the growth curves (Lopez et al., 2000). The BW data were also fitted to a less complex linear-quadratic function, BWi,t = b0 + b1 t + b2 t2, where t is days of age. The R2 values were calculated as the correlation coefficient between the predicted and the observed BW values. The residual SD (RSD) for all functions were calculated by the equation RSD = [
(ei)2/(n – p)]1/2, where ei is the residual value for the ith observation, n is the number of observations, and p is the degrees of freedom for the model. The functions that minimized the RSD values were utilized. Then alternate random effects models of the selected function were evaluated using the nonlinear mixed (NLMIXED) procedure of SAS. When BW are taken at specific target BW, the fitting of the BW to growth functions will result in biased prediction equations unless random effects for individual pigs are included (Schinckel and de Lange, 1996; Craig and Schinckel, 2001; Schinckel and Craig, 2002). Alternative single random effects models were evaluated based on the Akaike’s information criteria for each function (Craig and Schinckel, 2001; Schinckel and Craig, 2002). Two possible random effects models were evaluated for the linear-quadratic function: BWi,t = (1 + ci)(b0 + b1t + b2t2) and BWi,t = b0 + (b1 + ci) t + b2t2. Daily BW gain (ADG) was determined as the derivative of the BW function on time (ADG = 
BW/t).
Body Chemical Component Functions
Regression equations were developed to predict the mass of each chemical component as a function of BW and the real-time backfat and LM. Separate equations were developed for pigs of each target BW. The equations included the fixed effects of genetic line, sex, and group, and included backfat depth and LMA as covariates. Interactions among the fixed effects and the fixed effects and covariate variables were included at P < 0.10. The empty body chemical component mass of each pig not harvested at each target BW was predicted from these regression equations. The predicted and actual measured chemical component mass data were then fitted to a series of functions of BW. Empty body protein, moisture, and ash mass data were fitted to allometric (Y = aBWb), augmented allometric [Y = aBWb(700–BW)c], and generalized nonlinear (Y = C [1 – e(b0+ b1BW+ b2BW2)]) functions. Body lipid mass data were fitted to allometric, augmented allometric, and exponential functions of BW (Y = e(b0+ b1BW+ b2BW2)). The allometric and augmented allometric equations were transformed via log10 to log10 transformation into linear form. The allometric equation was transformed to the linear form, log10 Y = log10 a + b log10 BW. The augmented allometric equation was transformed to the form, log is Y = log10 a + b log10 BW + c log10 (BW-700). The exponential function was made linear in the form, loge Y = bo + b1 BW + b2 BW2. The linear form of these equations was evaluated for the significance of the interactions of the regression coefficients being different for the genetic lines, sex, and groups (Gu et al., 1992; Quiniou and Noblet, 1995). The R2 values were calculated based upon the correlation coefficient between the predicted and the observed values for each component. The RSD for all functions were calculated by the equation RSD = [ (ei)2/(n – p)]1/2, where ei is the residual value for the ith observation, n is the number of observations, and p is the degrees of freedom on the model. Functions that minimized the RSD were utilized. Then alternative random effects models of the selected function were evaluated, which included a random effect for each pig. This allowed prediction of the body component mass deposition of each pig. The body component deposition rates were determined as the product of the derivatives of 2 functions by C/t = [(C/BW) x (BW/t)], where C is the body component mass (Whittemore et al., 1988; Schinckel and de Lange, 1996).
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RESULTS
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Fitting of BW data to Bridges and generalized Michealis-Menten functions resulted in some extreme estimates of mature BW (over 5,000 kg). In some cases for group 2 pigs, the solution of the nonlinear functions failed to converge. These functions assume a sigmoidal pattern of growth with respect to age and required that sufficient BW data be collected periodically before, during, and after the period of maximal ADG (Schinckel and de Lange, 1996; Schinckel, 1999). Examination of the ADG for pigs with target BW of 100 and 125 kg provided no evidence that ADG increased, reached a maximal value (inflection point), and then decreased. Instead, ADG was essentially constant and marginally increased with respect to age for group 1 pigs and linearly increased with respect to age for group 2 pigs.
Analysis of BW data identified the linear-quadratic function of age as providing the most desirable goodness of fit. Based on Akaike’s information criteria values, the random effects model BWi, t = (1 + ci) (b0 + b1 t + b2 t2) was the best mixed model equation. Regression coefficients, R2, and RSD values for each sex-group-genetic line subclass are shown in Table 1. The ADG derived from the linear-quadratic functions are presented in Figures 1 and 2 for high and low lean-gain lines, respectively. Barrows in group 1 had greater ADG than gilts of group 1. However, predicted ADG for group 2 barrows and gilts were essentially identical. Pooled across both groups and sexes (Figure 3), high lean-gain pigs had greater ADG than low lean-gain pigs at 30 kg of BW (0.776 vs. 0.693 ± 0.02 kg/d) and had identical ADG (0.889 ± 0.02 kg/d) at 67 kg of BW. At 115 kg of BW, the low lean-gain pigs had greater predicted ADG (1.040 vs. 0.976 ± 0.03 kg/d) than the high lean-gain pigs. From 25 to 125 kg of BW, the high and low lean-gain pigs had identical predicted ADG (high lean-gain pigs 0.885 ± 0.02 kg/d vs. 0.882 ± 0.02 kg/d for the low lean-gain pigs). Pooled across genetic populations and groups (Figure 3), barrows had a consistent 0.049 to 0.054 kg/d greater predicted ADG (P = 0.03) than gilts from 25 to 125 kg of BW.
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Figure 3. Mean predicted ADG of the high and low lean-gain pigs (pooled across sexes and groups) and sexes (pooled across genetic populations). gx is the genetic line of sex subgroup.
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Fitting the chemical component mass data to the alternative functions of BW identified the allometric functions as providing the most desirable goodness of fit for each of the 4 chemical components (Tables 2 and 3). Allometric growth coefficients (the b values in the function Y = aXb) for moisture, lipid, and ash mass were different (P < 0.01) for each group and genetic line. Interactions of group with sex and group with genetic line were not detected (P > 0.25). High lean-gain pigs had greater (P < 0.01) allometric coefficients for protein mass and moisture mass than low lean-gain pigs. High lean-gain pigs had lesser (P = 0.001) allometric coefficients for lipid mass than the low lean-gain pigs. Allometric coefficients for moisture, lipid, and protein mass were also different (P < 0.01) for barrows and gilts, with gilts having greater allometric coefficients for protein and moisture and lesser for lipid mass than barrows.
Plotting predicted component mass of each genetic line-sex-group subclass resulted in a series of lines that overlaid and intertwined each other. To make component growth rates more evident, predicted marginal relative growth of each component was plotted with respect to BW. Predicted marginal value is growth of the chemical component per kg of BW gain at each BW and was calculated as the derivative of the allometric function at each BW.
High lean-gain pigs, pooled across groups and sexes, had overall greater (P < 0.07) predicted relative protein deposition rates than low lean-gain pigs (134.4 compared with 120.0 ± 3.1 g/kg of BW gain, Figure 4). Also, the relative protein deposition rates of high lean-gain pigs did not decrease as rapidly as BW increased compared with low lean-gain pigs. Allometric coefficients for protein mass were 0.023 greater (P < 0.07) for the high lean-gain pigs than the low lean-gain pigs, which results in the predicted relative protein deposition rates of the high lean-gain pigs to decrease less rapidly as BW increases than the low lean-gain pigs. The predicted relative protein deposition rates were approximately greater (P < 0.05) than the high lean-gain pigs at 30 kg of BW (135.5 compared with 121.3 ± 3.1 g/kg of BW gain). At 115 kg of BW, the relative protein deposition of the high lean-gain pigs was approximately 12.6% greater than the low lean-gain pigs (133.6 vs. 118.7 ± 3.8 g/kg).
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Figure 4. Mean predicted protein deposition per kilogram of BW gain at each BW for the high and low lean-gain pigs (pooled across sexes and groups) and sexes (pooled across genetic populations and groups). gx is the genetic line of sex subgroup.
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Overall, gilts had greater (P = 0.01) predicted relative protein deposition rates than barrows (130.9 vs. 124.2 g/kg of BW gain). The allometric coefficient for protein mass was greater (P = 0.007) for the gilts than the barrows, which results in the predicted relative protein deposition rates of the barrows to decrease more rapidly as BW increases than the gilts. Predicted relative protein deposition rates were approximately greater for the gilts at 30 kg of BW (131.7 vs. 127.8 ± 3.1 g/kg of BW gain). At 115 kg of BW, the relative predicted protein deposition of the gilts was approximately 6.8% greater than the barrows (130.3 vs. 122.0 ± 3.8 g/kg of BW gain).
Predicted marginal lipid deposition rates for each genetic population pooled across sexes and groups are depicted in Figure 5. Overall, relative lipid deposition rates substantially increased as BW increased, which is expected as allometric growth coefficients for lipid mass were substantially greater than 1. Overall, the predicted relative lipid deposition rates were 32.8% less for the high lean-gain pigs than the low lean-gain pigs (231 vs. 344 ± 6.8 g/kg of BW). Allometric coefficients for lipid mass are 0.12 greater for the low lean-gain pigs than the high lean-gain pigs, which results in the predicted lipid deposition rates of the low lean-gain pigs to increase more rapidly as BW increases than for the high lean-gain pigs. Predicted relative lipid deposition rates of the high lean-gain pigs were approximately 23% less than the low lean-gain pigs at 30 kg of BW (145 vs. 188 ± 3.7 g/kg of BW gain). At 115 kg of BW, the relative lipid deposition of the high lean-gain pigs was approximately 37% less than the low lean-gain pigs (303 vs. 481 g/kg of BW gain).
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Figure 5. Mean predicted lipid deposition per kilogram of BW gain at each BW for the high and low lean-gain pigs (pooled across sexes and groups) and sexes (pooled across genetic populations and groups). gx is the genetic line of sex subgroup.
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Overall, barrows had 14.4% greater predicted relative lipid deposition rates than gilts (307.3 vs. 268.3 ± 6.8 g/kg of BW gain, Figure 5
). Allometric coefficients were 0.06 greater for the barrows than the gilts, which resulted in the predicted relative lipid deposition rates to increase more rapidly for barrows than gilts. At 30 kg of BW, the relative lipid deposition rate of the barrows was nearly identical to the gilts (170 vs. 167 ± 3.7 g/kg of BW gain). At 115 kg of BW, the predicted relative lipid deposition of the barrows was 19.0% greater than the gilts (425 vs. 357 ± 10.0 g/kg of BW gain).
Relative moisture deposition rates decreased as BW increased, as the allometric coefficients for moisture mass were less than 1.0. Overall, high lean-gain pigs had 18.8% greater predicted relative moisture deposition rates than the low lean-gain pigs (480 vs. 404 ± 5.2 g/kg of BW gain, Figure 6). The absolute difference between the genetic lines for relative moisture deposition increased as BW increased because allometric growth coefficients were overall 0.074 greater for the high lean-gain pigs than the low lean-gain pigs. The high lean-gain pigs had 11.6% greater predicted relative moisture deposition than the low lean-gain pigs at 30 kg of BW (531 compared with 476 ± 9.1 g/kg of BW gain), which increased to 23.7% at 115 kg of BW (448 compared with 362 ± 11.0 g/kg of BW gain).
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Figure 6. Predicted moisture per kilogram of BW gain at each BW for the high and low lean-gain pigs (pooled across sexes and groups) and sexes (pooled across genetic populations and groups). gx is the genetic line of sex subgroup.
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Overall, gilts had 7.9% greater predicted relative moisture deposition rates than the barrows (458.5 compared with 424.9 ± 5.2 g/kg of BW gain, Figure 6). The difference between the gilts and the barrows increased as BW increased due to the fact that the allometric growth coefficients were 0.03 greater for gilts than the barrows.
Overall, low lean-gain pigs had slightly greater (P < 0.06) relative ash deposition rates than the high lean-gain pigs (Figure 7). The absolute difference between the genetic lines for relative ash deposition increased as BW increased because the allometric growth coefficients were overall 0.058 greater for low lean-gain pigs than high lean-gain pigs. Overall, gilts had slightly greater (P < 0.03) relative ash deposition than the barrows (228 vs. 218 ± 3.2 g/kg of BW gain, Figure 7).
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Figure 7. Predicted ash deposition per kilogram of BW gain at each BW for the high and low lean-gain pigs (pooled across sexes and groups) and sexes (pooled across genetic populations and groups). gx is the genetic line of sex subgroup.
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Predicted daily protein deposition rates are shown for the 2 genetic populations in Figure 8. Predicted protein deposition rates increased as BW increased for both genetic lines. These consistently increasing predicted daily protein deposition rates were produced as BW growth (ADG) overall increased as BW increased (small amount for group 1 pigs and substantially for group 2 pigs) and allometric growth coefficients for empty body protein were close to 1. Overall, high lean-gain pigs had 12.3% greater predicted protein deposition rates than the low lean-gain pigs (118.9 vs. 106.0 ± 2.3 g/d). The difference was approximately 19 g/d at 30 kg of BW (105 vs. 86 ± 2.7 g/d) and decreased to an approximate 5 g/d difference at 115 kg of BW (133 vs. 128 ± 3.7 g/d).
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Figure 8. Predicted daily protein deposition for the high and low lean-gain pigs (pooled across sexes and groups) and sexes (pooled across genetic populations and groups). gx is the genetic line of sex subgroup.
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Overall, gilts and barrows had similar protein deposition rates from 30 to 115 kg of BW (Figure 8, gilts 113 g/d compared with barrows 112 ± 2.2 g/d). Barrows were predicted to have slightly greater protein deposition rates than gilts from 25 to 50 kg of BW and lesser than the gilts after 90 kg of BW.
Predicted daily lipid deposition rates, pooled across sexes and groups, are depicted for the 2 genetic lines in Figure 9. Daily lipid deposition rates increased substantially as BW increased; the allometric coefficients for lipid mass are greater than 1. One of the largest differences between the 2 genetic lines was their predicted daily lipid deposition rates. Overall, high lean-gain pigs had 32.8% lesser predicted daily deposition rates than low lean-gain pigs (205 ± 7.0 vs. 305 ± 8.0 g/d). Lipid deposition of high lean-gain pigs increased from 110 ± 3.1 g/d at 30 kg of BW to 302 ± 7.1 g/d at 115 kg of BW. The lipid deposition of low lean-gain pigs increased from 129 ± 3.9 g/d at 30 kg of BW to 525 ± 14 g/d at 115 kg of BW. The growth functions predicted that low lean-gain pigs had a 17.3% greater daily lipid deposition rate at 30 kg, which increased to a 73.8% greater rate at 115 kg of BW than high lean-gain pigs.
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Figure 9. Mean predicted daily lipid deposition rates for the high and low lean-gain lines of pigs (pooled across sexes and groups) and sexes (pooled across groups and genetic populations). gx is the genetic line of sex subgroup.
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Overall predicted daily lipid deposition rates for barrows and gilts are depicted in Figure 9. Overall, barrows had 21.3% greater predicted daily lipid deposition rates than gilts (279 ± 5.1 compared with 230 ± 5.0 g/d). At 30 kg of BW, gilts had a mean predicted lipid deposition rate of 113 ± 4.2 g/d vs. 127 ± 14 g/d for barrows. At 115 kg of BW, gilts had a daily predicted lipid deposition rate of 327 ± 14 g/d and the barrows had a daily predicted lipid deposition of 425 ± 14 g/d. Thus, barrows had a 12.4% greater predicted lipid deposition than gilts at 30 kg of BW, which increased to 30.0% at 115 kg of BW. Overall, the difference between genetic lines for lipid deposition was approximately 2-fold greater than difference between the sexes.
Predicted daily moisture deposition rates, pooled across sexes and groups, are shown for the 2 genetic lines in Figure 10. Overall, high lean-gain pigs had 18.8% greater predicted daily moisture deposition rates than the low lean-gain pigs (423 ± 9.0 compared with 356 ± 9.0 g/d). Daily moisture deposition rates of high lean-gain pigs increased from 412 ± 14.0 g/d at 30 kg of BW to 438 ± 17 g/d at 115 kg of BW. The moisture deposition of low lean-gain pigs increased from 329 ± 14.0 to 382 ± 15.0 g/d from 30 to 115 kg of BW.
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Figure 10. Mean predicted daily moisture deposition rates for high and low lean-gain pigs (pooled across sexes and groups) and sexes (pooled across groups and genetic populations). gx is the genetic line of sex subgroup.
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Overall predicted daily moisture deposition rates for barrows and gilts are depicted in Figure 10. Over the entire growth period, gilts had a predicted moisture deposition of 392 ± 10.0 g/d compared with 387 ± 10.0 g/d for the barrows. At 30 kg of BW, gilts had a mean predicted moisture deposition rate of 366 ± 14 vs. 375 ± 14 g/d for the barrows. At 115 kg of BW, the gilts had a daily predicted moisture deposition rate of 429 ± 17 g/d and the barrows had a daily predicted moisture deposition of 411 ± 17 g/d.
Predicted daily ash deposition rates, pooled across sexes and groups, are depicted for the 2 genetic lines in Figure 11. Daily moisture deposition rates of high lean-gain pigs increased from 15.5 ± 0.8 g/d at 30 kg of BW to 22.9 ± 1.2 g/d at 115 kg of BW. The ash deposition of low lean-gain pigs increased from 13.2 ± 0.8 g/d at 30 kg of BW to 26.2 ± 1.2 g/d at 115 kg of BW. Pooled across genetic lines and groups, gilts had slightly less predicted daily ash deposition rates than barrows at 30 kg of BW (14.1 ± 0.8 g/d compared with 14.8 ± 1.2 g/d, Figure 11). At 115 kg of BW, gilts and barrows had similar predicted daily ash deposition rates (24.5 compared with 24.4 g/d for gilts and barrows, respectively).
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Figure 11. Mean predicted daily ash deposition for the high and low lean-gain pigs (pooled across sexes and groups) and sexes (pooled across genetic populations and groups). gx is the genetic line of sex subgroup.
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DISCUSSION
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Daily chemical component deposition rates were predicted from 2 functions, one that related BW to days of age and a second that related the mass of the chemical component to BW. The BW data of this study did not fit the nonlinear functions, which assume a sigmoidal response of BW to days of age in which ADG increases, reaches a plateau and then decreases as days of age increases. Instead, in this study ADG essentially remained constant for the group 1 pigs and increased with days of age for the group 2 pigs. Pigs reared under nearly ideal conditions, achieving greater overall growth rates, in general do have a sigmoidal response of BW to days of age (Schinckel, 1994; Schinckel et al., 1995; Schinckel and Craig, 2002). When pigs were reared under above average commercial conditions, ADG increased from 40 to 70 kg of BW and then essentially remained constant to 120 kg of BW (Hamilton et al., 2003). A recent study evaluated postweaning growth of pigs reared under 3 different lactation-weaning age treatments (Schinckel et al., 2007). The ADG of gilts increased from 70 d of age (32 to 35 kg of BW) to 133 to 144 d of age (84 to 90 kg of BW, ADG of 0.945 to 0.966 kg/d) and then remained essentially constant to 168 d of age (120 to 125 kg of BW). The ADG of barrows increased from 70 d of age (34 to 37 kg of BW), reached their maximal values (0.99 to 1.02 kg/d) at 120 to 128 d of age (93 to 100 kg of BW), and then decreased 0.02 to 0.05 kg/d to 168 d of age (129 to 130 kg of BW). It is possible that in modern high lean-gain pigs, their genetic potential for energy intake or the environment limits their growth rates from 25 to 60 kg of BW (Schinckel, 1994; Schinckel and de Lange, 1996; Weis et al., 2004). It is also possible that the BW growth curves of modern barrows and gilts have changed such that they achieve their maximal BW growth rates at increased BW and that to accurately identify the maximal ADG (inflection point), BW data will need to be collected greater than 125 kg.
In this study, pigs of the 2 replicate groups had different patterns of BW growth and different allometric coefficients for moisture, lipid, and ash mass. The genetic line by group and sex by group interactions were not significant for the allometric growth coefficients. This indicates overall that genetic line and sex differences in the compositional growth of the chemical component was similar for the 2 groups of pigs. Previous research has found that BW curves can vary for groups of pigs reared in the same pork production unit over time (Schinckel et al., 2002).
It is likely that some environmental factors limited growth, which resulted in the nonsigmoidal BW growth curves. Under the most ideal environmental conditions, enhanced health high lean-gain pigs have achieved ADG between 0.95 and 1.10 kg/d (Schinckel, 1994; 1999). Pigs of the greatest lean growth lines achieved ADG above 1.1 kg per day from 25 to 52 kg of BW and achieved their maximal BW growth rates at 60 to 80 kg of BW (Schinckel, 1994). The environment including health status, social stressors, stocking density, and temperature reduce pig growth rates (Holck et al., 1998; Wellock et al., 2004b). In general, the sigmoidal pattern of BW growth observed under more ideal conditions is less apparent when pigs are reared under environments that reduce pig growth rates (Schinckel, 1994; Smith et al., 1999; Schinckel et al., 2002). Also, in some cases, pigs that achieve relatively high ADG (0.96 kg/d for gilts) can express a nonsigmoidal pattern of growth (Moughan et al., 2006).
Protein deposition rates were predicted to increase as BW increased. Predicted protein deposition rates are a function of predicted ADG and predicted protein deposition per kilogram of BW gain. In this study, protein deposition relative to BW growth decreased as BW increased. However, BW growth (ADG) increased to a greater extent than the decrease in marginal rate of protein deposition relative to BW gain. Under nonlimiting conditions, maximal protein accretion rates are achieved at 50 to 80 kg of BW (Schinckel and de Lange, 1996; Schinckel, 1999; Wellock et al., 2004a). From a biological perspective, protein mass relative to age would be expected to fit sigmoidal function of age (Schinckel, 1999; Wellock et al., 2004a). Also, in this study, pigs may not have been evaluated to a heavy enough BW to find the expected decrease in ADG and move decreases in ADG and in protein deposition relative to BW found in previous studies (Schinckel and de Lange, 1996).
Pigs in this study were fed a series of diets formulated to slightly exceed the essential amino-acid requirements for the pigs of each genetic line based on prior estimates of their fat-free lean growth rates (NRC, 1998; Wiseman et al., 2007a). Although possible, it is not likely that the diets fed would limit protein deposition or lean tissue growth rates to any appreciable extent in this study.
The body mass component was fitted to allometric functions based on RSD values. Allometric functions (Y = axb) have been widely used, where Y is the weight of a tissue or compositional component and x represents the weight of the whole entity (empty BW, live weight, or carcass weight; Moughan et al., 1990; Gu et al., 1992). Allometric equations have several advantages including (1) simple stable linear solutions after the log to log transformation, (2) straightforward biological interpretation, and (3) simple, stable derivatives dY/dX = abxb –1. Researchers using allometric functions assume that the ratio of the relative growth rates of X and Y are constant throughout growth (Evans and Kempster, 1979; Walstra, 1980; Moughan et al., 1990). For every 1% change in empty BW, the body component changes b%. Thus, use of an allometric function considers that body component mass, expressed as a percentage BW, uniformly decreases (b < 1), remains constant (b = 1), or increases (b > 1) as BW increases. Protein percentages expressed as a percentage of empty BW did increase from 25 to 45 BW (P < 0.05). In the current study from 25 to 45 kg of BW, body protein percentages increased from 13.4 to 13.5% of BW (15.7 to 16.3% of empty BW) in high lean-gain pigs and from 12.5 to 12.6% of BW (14.5 to 15.1% of empty BW) in low lean-gain pigs (Wiseman et al., 2007b). In this study, protein percentages relative to BW did not increase from 25 to 45 kg of BW. Previous studies have provided evidence that body protein percentages increase from birth to approximately 45 to 65 kg of BW as percentage of water decreases (Shields et al., 1983; Ferrell and Cornelius, 1984; White et al., 1995; Wagner et al., 1999), whereas after 65 kg, percentage of protein decreases as percentage of lipid increases. Thus, these results do not support the use of allometric equations to relate body component mass to BW.
The augmented allometric, exponential, and other nonlinear functions evaluated in this and previous studies have reduced the RSD of the prediction equations but are less stable due to greater correlations between independent variables (Thompson et al., 1996; Schinckel, 1999; Wagner et al., 1999). Thus in smaller data sets, allometric functions which relate body component mass to BW have been commonly used (Whittemore et al., 1988; Moughan et al., 1990). The augmented allometric and nonlinear functions have resulted in nearly identical fit of empty body protein mass to BW, yet the predicted amount of protein deposited per kilogram of BW was different (Schinckel and de Lange, 1996; Wagner et al., 1999). Use of more complex functions has resulted in greater predicted relative and absolute protein deposition from 25 to 60 kg of BW and lesser predicted protein deposition rates after 90 kg of BW. In a recent study, pig compositional data collected from 20 to 140 kg of BW and body protein mass data were fitted to an allometric function of empty BW (Landgraf et al., 2006). Pigs had 15.91, 17.32, and 17.15% protein in their empty BW at 19.0, 30.6, and 60.1 kg of empty BW. The allometric equation [protein mass = 0.15136 (empty BW, kg)1.005] predicted that pigs deposited 1.79 and 4.57 kg of protein from 19.0 to 30.6 and from 30.6 to 60.1 kg of empty BW when the actual differences in protein mass measured for the 2 intervals were 2.28 and 5.01 kg (Landgraf et al., 2006). In this study, the allometric equations predicted that from the 20 to 45 kg of target BW groups, the high lean and low lean pigs deposited 3.73 and 3.32 kg of protein, compared with 3.79 and 3.46 kg of actual protein deposited, respectively (Wiseman et al., 2007b). Thus, allometric equations did not substantially underpredict the observed protein deposition rates in these studies during the BW range in which CP percent was actually increasing but predicted to be decreasing (13.66 to 13.57 and 12.61 to 12.48% of BW for high and low lean-gain lines) in this trial.
The objective of the current study was to evaluate chemical component growth of 2 genetic populations with different genetic potential for carcass muscle tissue growth. Protein and lipid accretion rates are the key descriptive variables used to parameterize genetic populations of pigs (Schinckel and de Lange, 1996; Schinckel, 1999, Knap et al., 2003) and are the primary variables that impact efficiency of energy utilization by growing pigs (Noblet et al., 1999; van Milgen and Noblet, 2003). Body moisture, the chemical component with the greatest rates of accretion in modern lines of pigs, is related to body protein mass (de Lange et al., 2003). Also, protein and lipid accretion rates are used to model carcass muscle and fat tissue mass (Quiniou and Noblet, 1995; Schinckel et al., 2003b) and ultimately predict carcass value (Schinckel et al., 2003a).
Pig growth models have predicted that pigs with increased protein deposition rates and decreased lipid deposition rates have substantially reduced cost and feed required per kg of carcass lean (Tess et al., 1983, 1986; Black et al., 1995). The selection objective (increased carcass muscle percentage, increased lean tissue feed conversion, or increased carcass muscle growth rates) impacts the relative emphasis placed on the measured performance traits (backfat thickness, ADG, or ADFI; Webb, 1989; Cameron and Curran, 1994; Schinckel, 1999). Selection for increased carcass lean tissue feed efficiency has resulted in pigs with reduced lipid deposition rates, reduced ADFI, and small changes in ADG (Cameron and Curran, 1994; Schinckel, 1999).
The largest differences observed between the 2 genetic populations evaluated in the current study were that in compared with low lean-gain pigs, high lean-gain pigs had: (1) 32.8% lesser predicted daily rates of lipid deposition, with the difference increasing from approximately 23 to 37% from 25 to 125 kg of BW, (2) 12.3% greater daily rates of protein deposition, and (3) 18.8% greater predicted daily moisture accretion rates. The slightly greater ratio of moisture to protein deposition for high lean-gain pigs may result from high lean-gain pigs being relatively less mature at the same BW than low lean-gain pigs. Differences between genetic lines for predicted daily chemical component deposition rates were almost entirely caused by differences in chemical composition of BW gain (relative growth rates of each component based on the allometric functions) and not differences in BW growth. High lean-gain pigs had greater relative rates of protein and moisture deposition and lesser rates of lipid deposition per kg of BW gain than low lean-gain pigs. Differences in ash accretion, the chemical component with the least rate of accretion, was similar for the 2 genetic lines. This result and the fact that the 2 genetic lines had nearly identical ADG indicate that greater protein and moisture deposition rates for high lean-gain pigs was essentially equal to their lesser lipid deposition rates compared with low lean-gain pigs.
Differences observed between barrows and gilts were smaller than the difference observed between the genetic lines. Allometric coefficients relating empty body protein to BW and predicted relative growth of empty body protein to BW are greater for gilts than barrows (Whittemore et al., 1988; Thompson et al., 1996). Compared with gilts, barrows had 0.052 kg/d greater ADG and overall 21.3% greater lipid deposition (approximately 0.049 kg/d). In the current study, barrows and gilts had similar predicted daily moisture, protein, and ash accretion rates. Thus in this study, differences in ADG between barrows and gilts was primarily accounted for by the difference in their lipid deposition rates. Barrows had slightly greater predicted protein accretion rates than gilts up to 80 kg of BW. After 80 kg of BW, gilts had slightly greater predicted protein deposition rates than barrows. Predicted lipid deposition rates of barrows were nearly identical to gilts at 30 kg of BW and 75 g/d greater than gilts at 115 kg of BW. Hamilton et al. (2003) found similar differences in predicted BW growth, protein deposition, and lipid deposition rates of barrows and gilts.
Data from this study indicate that substantial differences can exist between genetic populations of pigs in terms of relative growth rates of the 4 major chemical components. Data support the need for genetic population specific nutrient requirements (Schinckel and de Lange, 1996; NRC, 1998). Modeling of genetic population specific nutrient requirements is needed to optimize pork production systems (de Lange, 2001; Boys et al., 2007) and to improve the efficiency of N and P retention (de Lange et al., 2001; Kebreab et al., 2007).
Compositional growth models are used to predict daily essential amino acid and P requirements (Schinckel and de Lange, 1996; de Lange et al., 2001; Kebreab et al., 2007). The marginal growth rates of protein, ash, water, and lipid are needed to parameterize pig compositional growth and predict nutrient requirements (de Lange et al., 2003). Diets are usually formulated based on the ratio of grams of nutrient intake required to amount of ME or NE required (Schinckel, 1994; NRC, 1998; Schinckel et al., 2002). Grams of lysine or P required per unit ME or NE are essentially a function of relative growth of the body components (protein or ash) in relation to BW gain. In this study, predicted nutrient requirements of essential aminoacids, P, and other nutrients associated with lean (muscle, bone, viscera) growth are substantially less for low-lean growth pigs than high-lean growth pigs. The optimal diets to maximize daily returns above feed costs or to minimize N and P excretion (Boys et al., 2007; Kebreab et al., 2007) are substantially different for high and low lean-gain pigs. Results from this study indicate that although maximal protein deposition and BW growth were not likely achieved from 30 to 60 kg of BW, substantial differences in the nutrient requirements exist between genetic populations and sexes.
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Footnotes
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1 Salaries and research support were provided by state and federal funds appropriated to The Ohio Agric. Res. and Dev. Center and The Ohio State University. 
2 Corresponding author: aschinck{at}purdue.edu
Received for publication October 2, 2007.
Accepted for publication November 17, 2007.
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Abstract
INTRODUCTION
