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METABOLISM AND NUTRITION |
Department of Animal and Poultry Science, University of Guelph, Guelph, Ontario, Canada N1G 2W1
1 Corresponding author: sleeson{at}uoguelph.ca
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Key Words: broiler • partitioning • retained energy • growth rate
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The quantity of carcass fat is generally considered to be an unfavorable trait in the broiler industry (Remignon and Le Bihan-Duval, 2003) leading to studies in genetic selection (Whitehead and Griffin, 1984; Leenstra, 1988, Whitehead, 1990; Pym et al., 2004) and feeding programs (Bartov et al., 1974; MacLeod, 1990, 1991; Zubair and Leeson, 1994; Leeson and Zubair, 1997; Wiseman and Lewis, 1998; Morris, 2004; Leeson and Summers, 2005) aimed at reducing or limiting carcass fat content.
Mathematical models may be used to integrate knowledge of nutrient utilization for growth in broilers and to identify effective means to optimize nutrient utilization and carcass characteristics in different genotypes (Wiseman and Lewis, 1998; Eits et al., 2005). For such models to be effective, information is required on partitioning of daily retained energy (ER) in the body between fat (ERF) and protein (ERP), and the utilization of ME intake in broilers for these purposes (Van Milgen et al., 2001; Lopez and Leeson, 2005). Because fat deposition and protein accretion likely differ in their efficiencies of transfer of energy from feed to tissue (Buttery and Boorman, 1976; Pullar and Webster, 1977), changes in the proportion of both fat and protein during growth influence not only the total energy in the body, but also the efficiency of such gain. Today’s broilers reach commercial BW very early (Nicholson, 1998; Remignon and Le Bihan-Duval, 2003) at an immature BW and often without achieving maximum genetic potential for fat and protein deposition in terms of absolute quantities deposited each day.
Scarce information is available to improve our understanding of how modern broilers quantitatively deposit energy as fat, protein, or both; and this places limits on our understanding of energy metabolism of commercial broilers. The purpose of this study was to evaluate total energy retained in the body (TER) as fat (TERF) and protein (TERP) in growing broilers using a comparative slaughter technique. For this purpose, linear (linear regression) and nonlinear (allometric and the Gompertz) models were used as estimators of the potential rate of energy deposition as TERF and TERP. To gain insight into the effect of growth rate per se on TER, TERF, and TERP, 2 slower growing strains of birds were also used in this study.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Three strains of birds were used in this study that were selected in terms of anticipated differential growth rate. These strains were commercial broilers (Ross x Ross), Barred Plymouth Rock, and White Leghorn. Sixty males of each strain were hatched and housed at the Arkell Research Center facilities of the University of Guelph, and were managed and cared for according to guidelines established by the University of Guelph Animal Care Committee. Birds from each strain were housed in groups of 6 and randomly allocated to 1 of 9 growing cages (50 cm x 60 cm). Temperature was reduced according to brooding practices starting at 31°C and ending up at 22°C, and lighting was continuous. Feed was available ad libitum with a diet providing 3,100 kcal of AMEn/kg (NRC, 1994) and 20% CP, during the experimental period of 0 to 42 d (Table 1
). A single diet was used for simplicity and was deemed to represent the mean requirement, with no known deficiencies, for all 3 strains used in the study. Water was also available ad libitum.
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On d 0, 7, 10, 15, 19, 23, 28, 33, 37, and 42, 6 birds per strain were weighed and killed by cervical dislocation. Feed in the digestive tract was removed, and birds were reweighed and frozen for subsequent analyses. The frozen, feathered carcasses were ground in a meat grinder (Biro Mfg. Co., Marblehead, CA) to produce an homogeneous mince. The ground carcasses were individually freeze-dried, and 6 individual carcasses per strain and time period were analyzed for fat, N, and gross energy contents. Fat was determined using a Goldfish extraction apparatus (anhydrous ethyl ether); nitrogen was determined by Leco FP-428 Nitrogen Analyzer (Leco Instruments, Stockport, Cheshire, UK), and gross energy was determined by C5003 IKA adiabatic oxygen bomb calorimeter (GMBIT and Co. KG D-79219, Dresden, Germany). Body composition, total determined gross energy contained in the body (TERd), TERF, and TERP were calculated at each period of time (0, 7, 10, 15, 19, 23, 28, 33, 37, and 42 d of age). The energy retained as protein (6.25 x N gain) was also calculated as 5.70 kcal/g of protein, and the energy retained as fat calculated using the predictor 9.46 kcal/g (Znaniecka, 1967).
Statistical Analyses.
To study the changes of TERd, TERF, and TERP and empty BW (EBW) as a function of elapsed time, regression analysis was performed. The values of TERd, TERF, TERP, and EBW were obtained from sample determinations from body composition where the Yi was modeled as
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where Yi represented the response variable (TERd, TERF, TERP, or EBW) for strain of birdi; ß 0, ß 1, ß 2 the regression coefficients; Xi time in days; and 
I represented the residual error, which was assumed to have a normal distribution with mean 0 and variance 
2.
Separate analyses were conducted using the data from each of the 3 strains of birds. This allowed for study of 3 different growth patterns with the intent of modeling energy accretion relative to growth rate.
To study the behavior of TERF and TERP as a function of calculated total energy retention (TERc; calculated as TERF + TERP), the linear regression model was
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where Yi represented the response variable (TERF and TERP) for bird straini; ß 0, ß 1 the regression coefficients; Xi the TERc; and I represented the residual error, which was assumed to have a normal distribution with mean 0 and variance 2. A separate model was fitted for each of the 3 strains of birds.
To study whether there was a nonlinear relationship between EBW and time, the Gompertz model was used relating EBW to time (days). The model was
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where Y represented EBW of the bird at time t (days), a the asymptotic BW value, b decline in growth rate, M the age in days at which growth rate is at its maximum, and represented the residual error, which was assumed to have a normal distribution with mean 0 and variance 2. Estimates for the 3 regression coefficients (a, b, and M) were obtained using the NLIN procedure of SAS and for each of the bird strains.
Two additional models were considered to study the effect of EBW on TERF or TERP for each of the 3 strains of bird. In the first model, TERF and TERP were expressed as a function of EBW (kg) using the allometric model
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The parameters a and b were estimated for each variable (TERF and TERP) based on linear regression analyses of the logarithmic values for TERF and TERP vs. the logarithmic values for EBW (Gous et al., 1999).
In the second model, TERF or TERP were related to bird weight using a nonlinear regression model. The model corresponded to the Gompertz equation, given by
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where Y represented TERF or TERP, EBW, a represented the asymptotic value of TERF or TERP), b represented a measure of the decline in TERF or TERP growth rate, and M represent EBW at inflection. And represented the residual error, which was assumed to have a normal distribution with mean 0 and variance 2. Estimates for the 3 regression coefficients (a, b, and M) were estimated using the NLIN procedure of SAS.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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summarizes mean BW and composition of EBW, expressed in grams and as % of EBW for each bird type. For all strains EBW was consistently 95 to 96% of BW (Table 3). As expected, the broiler was the fastest growing strain, attaining 2.2 kg of EBW at 42 d compared with just 600 g for the Barred Plymouth Rock and 516 g for the Leghorn. At 42 d of age, all strains had a remarkably similar proportion of body protein, ranging between 17.0 and 18.6%. The broiler had proportionally more fat than the other strains at 42 d. For all strains and at all ages, the quantity and proportions of protein in the EBW was far greater than corresponding quantities of fat. This level of body protein is influenced by component feathers.
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to 3 detail quadratic relationships (P < 0.001) for deposition of energy in the body of growing birds vs. time. Details of these equations are summarized in Table 4. In terms of TERd, TERF, and TERP, the pattern of deposition was similar for the 2 slower growing strains. For broilers the deposition of total energy in the EBW (Figure 1) increased at an ever-increasing rate over time (P < 0.001, Figure 1). The rate of energy deposition as protein in broilers over time was slightly greater than for the corresponding coefficient for fat deposition (Figure 2). A simple linear model was sufficient to relate EBW to time (Table 4). For all 3 bird strains the quadratic regression coefficients were not significantly different from 0 (P > 0.05), and based on the linear regression coefficients growth was 69, 17.7, and 14.5 g per d for broilers, Barred Plymouth Rock, and Leghorns, respectively.
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, indicating that over 42 d, broilers deposited a constant proportion (about 50%) of body energy (
1 = 0.49, 1 = 0.50 for TERF and TERP, respectively) as fat and protein. The high R2 for both response variables indicates that most of the observed variability for TERF and TERP is explained by the simple linear regression model. There was close agreement between TERd, based on the determined gross energy content of the EBW and TERc. The TERc was calculated from consideration of the fat and protein content of EBW multiplied by the corresponding gross energy contents of fat and protein (Table 6). It is important to mention that a quadratic regression model was tested (Table 5); however, the quadratic regression coefficients were not significant (P > 0.05).
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show that growth of EBW vs. time is well represented using the Gompertz equation; for each of the bird strains the R2 are high and the estimates a, b, and M are significantly different from zero (
= 0.05). However, the estimated value for a (asymptotic value of EBW) was excessively high, especially for broilers (11.1 kg), and the estimate for M (age at which growth rate is at its maximum) is larger than the final slaughter age. The Gompertz equations also yielded high R2 values when relating TERF and TERP to BW, but for all 3 birds the quadratic functions resulted in higher R2 values (Table 4
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show that the allometric model fits the energy retention data well (R2 = 0.946 to 0.998 for TERF and TERP and all the strains). These results (Table 8) show that the estimates of b are close to 1; however, some of them are statistically different from 1 (P < 0.05).
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and 9 show the effects of bird strain and BW on TERF or TERP, and as established using either the allometric model or the Gompertz equation, respectively. The Gompertz equation (Table 9) fits the observed data, for all strains, with a high coefficient of determination (R2 from 0.965 to 0.996). According to the Gompertz equation, the asymptote of TERP was higher than for TERF for all 3 strains of birds. The shape of the relationship between TERF or TERP and BW varies with strain (Figures 4 and 5). In broilers selected for fast growth, the maximum rate of TERF and TERP is reached at 1.16 and 1.22 kg of EBW, respectively, subsequently declining as BW increases, whereas for the BPR and Leghorn strains, maximum values were achieved when the birds reached 0.30 to 0.35 kg for TERF and 0.32 to 0.38 for TERP of EBW. Table 10 shows daily estimates of accumulated energy in broilers and estimate of maintenance energy requirements.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The Gompertz equation has been traditionally adopted in broiler studies to appropriately describe growth over time (Wilson, 1977; Emmans, 1995; Hurwitz and Talpaz, 1997; Darmani Kuhi et al., 2002), growth of body components in broilers (Tzeng and Becker, 1981; Peter et al., 1997; Wiseman and Lewis, 1998; Gous et al., 1999), or both. This equation describes a sigmoidal biological growth pattern of broilers with a slow initial rate of growth followed by acceleration up to a certain age (the inflection point) followed by subsequent slowdown in the rate of growth as BW approaches its maximum value and birds reach maturity (Hurwitz and Talpaz, 1997). Tzeng and Becker (1981) fitted the nonlinear Gompertz equation to abdominal fat, intending to predict total carcass fat over time (Becker et al., 1979). Usually the methods for evaluating the growth of body components in broiler studies using the Gompertz equation have been based on data obtained from birds between 0 and 10 to 16 wk of age (Tzeng and Becker, 1981; Peter et al., 1997; Wiseman and Lewis, 1998; Gous et al., 1999). In these situations, measurements are obtained beyond the inflection point, and the asymptotic values for BW or body components are estimated with reasonable accuracy. However, today’s broilers reach slaughter BW earlier, at approximately 6 wk, and likely without achieving maximum genetic potentials for fat and protein deposition. Therefore, the asymptotic values for BW or body components are unlikely to be predicted accurately when measurements are not obtained beyond 42 to 49 d of age. This is reflected in the high SE values for estimates of these parameters. Based on the results presented in Table 4, it can be observed that the quadratic model fits the response TERd, TERF, TERP, and EBW reasonably well, with the degree-of-fit (R2) greater than 0.97 and numerically greater than those achieved when applying Gompertz equations (Table 9). Therefore when the use of models is restricted to predict growth performance of modern broilers up to 42 d of age, quadratic regression equations may be as effective as the Gompertz equation in representing growth patterns. A disadvantage of the positive quadratic regression model is the fact that there is no mathematical upper boundary for the response (TERF, TERP, and EBW) with increasing time, whereas in reality there will obviously be biological limits to growth and nutrient content of the body.
Although various linear and nonlinear modeling approaches can be used to represent TERF and TERP as a function of time, the Gompertz equation arises from theoretical considerations (Wilson, 1977; Emmans, 1995). The Gompertz equation could be considered a more biological model to describe growth patterns than the polynomial approach. Rather than relating growth of body components, such as TERF and TERP, to time, they may be related to BW or EBW. As illustrated in Table 8, the use of conventional allometric functions allows an accurate prediction of TERP and TERF from EBW. The difference among the estimates of the regression coefficient (â, 
) for the 3 strains of birds suggest the need to establish relationships between physical and chemical body composition in strains of birds that may have been altered through genetic selection. The Gompertz equation may also be used to relate body constituents such as TERF and TERP to EBW (Table 9). This is, however, a rather empirical application, because it implies that EBW gain can occur after TERF and TERP have reached their plateau values. Therefore, the equation as developed here is only applicable to BW ranges that were evaluated in the current study and cannot be used reliably to extrapolate values for birds of heavier weight.
This information showed, however, that all models (linear and quadratic regression, allometrics and the Gompertz equation) used in this research to quantify TER, TERF, TERP as a function of time (quadratic regression; Table 4) or BW (allometrics and the Gompertz equation; Tables 7 and 8) fit the data reasonably well within the first 42 d and could be used to predict TER, TERF, and TERP from time or BW.
Metabolizable energy intake is well documented to influence body composition (Hakansson and Svensson, 1984; Boekholt et al., 1994; Wiseman and Lewis, 1998) and therefore body ER. Boekholt et al. (1994), feeding broilers between 60 and 100% of their normal daily energy intake, reported that daily retention of fat and protein was linearly related to energy retention suggesting that in growing broilers, each additional unit of gain generated by energy intake over 43 kcal/kg W0.75 d, is composed of constant amounts of protein and fat with these proportions of the ER as fat and protein being 15 and 85%, respectively. These data suggest that at an ER of 43 kcal/kg W0.75 d, ERF is zero and only protein is retained (Boekholt et al., 1994). In the current experiment, and when broilers were fed ad libitum, the average increase of TERF and TERP was constant (about 0.50) per unit increase of TER, suggesting that during their early growth (d 0 to 42), broilers deposit constant proportions (50%) of body energy as fat and protein. It is calculated that within the commercial growing range of 0 to 42 d, broilers deposit body fat and protein that together represent 35 to 40% of their daily ME intake (Lopez and Leeson, 2005). There is renewed interest in investigating the utilization of ME intake and its partition between fat and protein (Van Milgen et al., 2001; Lopez and Leeson, 2005).
Previous studies indicate that the energetic efficiency of protein deposition is lower than that for fat deposition (Petersen, 1970; De Groote, 1974; Boekholt et al., 1994). De Groote (1974) reported that the efficiency of ME utilization above maintenance for lipid deposition ranges between 0.70 to 0.84 in adult birds and between 0.37 and 0.85 in growing birds. Petersen (1970), using White Plymouth Rock birds, estimated efficiencies of 0.51 and 0.78 for protein and fat deposition, respectively, indicating a need for 11.2 kcal of ME/g of protein deposition and 12.2 kcal of ME/g of fat deposion. More recent information in growing broilers suggests higher efficiencies for protein (0.66) and fat (0.86) deposition (Boekholt et al., 1994), indicating lower energy needs for protein (8.63 kcal of ME/g) and fat deposition (10.9 kcal of ME/g). Similar efficiencies (0.65 and 0.83) are reported in comparable studies with growing pigs (Noblet et al., 1999). Moreover, due to the close association between body water and body protein in lean meat, the ME requirements per gram of lean tissue gain are much lower than those per unit of fat tissue gain. Both increases in ERP/ERF and reductions in energy needs per gram of protein deposition contribute to increases in feed efficiency in modern broilers. Broilers may also have been selected for greater rate of protein synthesis, reduced protein degradation, or both (Urdaneta and Leeson, 2004), which will contribute to reductions in energy needs for protein deposition.
These data suggest that in immature birds fed ad libitum, total ME intake can be estimated with reasonable accuracy based on the actual TERF and TERP accretion rates and efficiency of utilization for fat and protein deposition. This approach also requires an estimate of maintenance energy requirements. For example, assuming maintenance energy requirements at 155/kcal kg of BW0.60 (Lopez and Leeson, 2005) and using efficiency values for kf (0.86) and kp (0.66) obtained by Boekholt et al. (1994), the energy cost for fat and protein deposition (Table 10), for a 42-d-old chick weighing 2.2 kg are 2,652 and 3,568 kcal of cumulative AME intake, respectively. Up to this BW the cumulative AME requirements for maintenance are estimated at 5,607 kcal. These estimates are similar when based on broilers weighing 2.2 kg (approximately 42 d) predicted by the estimated allometric or Gompertz model (Tables 8 and 11). The calculated total fat and protein deposition represents about 38 to 40% of AME intake. Unfortunately, AME intake was not measured in this experiment, although estimated AME requirements are similar to that observed in other experiments that were conducted under similar feeding conditions (Lopez and Leeson, 2005, 2007).
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ACKNOWLEDGMENTS |
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Thanks are extended to Jaap Van Milgen (INRA) for his valuable comments and detailed discussion of this research.
Received for publication March 15, 2007. Accepted for publication June 23, 2007.
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