Cross relationships between growth performance, growth composition ...

Cross relationships between growth performance, growth composition and feed composition in broiler chickens, calculated from published data B. Carr´e1 and B. M´eda Institut National de la Recherche Agronomique (INRA), Unit´e de Recherche 83, Recherches Avicoles, F-37380 Nouzilly, France ABSTRACT The study presented here used experimental data obtained from 42 articles to calculate the cross relationships between daily feed intake (DFI), feed composition (AMEn and CP), BW, daily weight gain (DG), mean age (A), and composition (FCG and PCG; fat and protein, respectively) of growth in broilers. All of the experiments selected were conducted at standard temperature and with ad libitum feeding. The articles in this database were published from 1980 to 2015, and represented a total of 12,277 broilers, 57 trials, and 384 basic treatments. Data ranged from 3 to 66, 0.077 to 3.322, 7.10 to 15.37, and 7.3 to 35.0 for A (d), mean BW (kg), AMEn (MJ/kg) and CP (%), respectively. Equations were established from regression calculations to calculate daily heat production as a function of BW, daily AME intake (MEI) as a function of BW, DG, FCG and PCG, PCG as a function of A and FCG, and FCG as a function of BW, protein efficiency (PE), AMEn, and CP. The combination of these equations

expressing MEI, PCG, or FCG with equations expressing the definitions of PE, AME (from AMEn), and DFI (from MEI) constituted a system of 6 equations which could be used to determine DFI values from the values of AMEn, CP, A, BW, and DG. Using the values of AMEn, CP, A, BW, and DG from the database, the DFI values calculated with this system of equations showed a reasonable correlation with the DFI values measured (R2 = 0.880). This system of 6 equations yielded values for DFI, PE, PCG, and FCG that were in agreement with the classical effects produced by increased CP values, or by reduced fat deposition associated with genetic selection for leanness. This system of 6 equations might thus be considered an interesting framework for future nutritional modelling systems. Regressions predicting feed efficiency as a function of AMEn, CP, DG, and BW, or as a function of AMEn, FCG, DG, and BW showed R2 values of 0.767 and 0.747, respectively.

Key words: energy partition, growth, feed intake, genetics, model 2015 Poultry Science 94:2191–2201 http://dx.doi.org/10.3382/ps/pev214

INTRODUCTION Modelling nutritional parameters in broilers is of critical importance for adjusting feed composition to advance in broiler genetics, and for optimizing broiler production. Several approaches are possible for such models. The use of relationships between growth, feed intake, growth composition, and feed composition in broilers constitutes one of these approaches. Experimental studies have been conducted in the past in order to calculate such relationships (Jackson et al., 1982; Nakhata and Anderson, 1982; Pesti and Fletcher, 1983; Gonzaleza and Pesti, 1993; Smith et al., 1998; Carr´e and Juin, 2015), and these calculations have provided valuable information about the general principles underlying these relationships. However, in most cases, precise quantitative information has only been related  C 2015 Poultry Science Association Inc. Received April 1, 2015. Accepted June 16, 2015. 1 Corresponding author: [email protected]

to a limited range of conditions defined by the experiments. With regard to the calculation of feed intake, a key variable in nutritional models, one powerful approach to obtaining a model that is acceptable in a wide range of situations consists of the use of an equation which partitions energy into energy for maintenance and energy deposited as fat or protein (Sakomura et al., 2005). However, such equations assume that the fat and protein contents of weight gain are known. Information about growth composition can be obtained from equations based on age or BW (Sakomura et al., 2005), but growth composition not only depends on age or BW, but also on feed composition (Fisher, 1984). Thus an understanding of growth composition is important, but it is difficult to evaluate because it depends on both animal and feed factors. Solving such a problem requires the analysis of large amounts of data combining both animal and feed factors in a wide range of situations. This approach was taken in the present study by pooling the data from 42 publications reporting experiments measuring growth, feed intake, and growth composition in broilers. Data were limited to

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experiments conducted in ad libitum fed chickens from broiler genotypes, reared in standard temperature conditions. Many of these experiments had been conducted to compare genetic fat and lean lines. In addition to information about the effects of genetics on nutrition parameters, these studies provided a large amount of data about relationships between feed intake and parameters of feed and growth, with, in particular, information on relationships between fat deposition and protein efficiency. As stated above, certain parameters may depend on both animal and feed factors, which may require the combination of both animal and feed factors as independent variables in regression models. For example, this approach was recently developed for calculation of the fat content of growth using a combination of AMEn, CP, and protein efficiency (PE) variables (Carr´e and Juin, 2015). Although the latter study was based on wide variations in feed composition, it was recognized that animal variability was not sufficient (Carr´e and Juin, 2015). A relationship calculating the protein content of growth was therefore not proposed, and it was not possible to introduce the BW parameter into the model of the fat content of growth (Carr´e and Juin, 2015). By combining many publications, the study reported here enabled us to undertake such calculations.

MATERIALS AND METHODS Analysis Design Publications reporting experiments measuring growth, feed intake, and body composition (nitrogen and fat) in ad libitum fed growing broilers reared in standard temperature conditions were selected (Table 1). Publications showing only mean values for pooled treatments were not retained. The search of publications was conducted with the “Web of Science” database using several combinations of key words such as “broiler”, “chicken”, “body”, “energy”, “growth”, “weight gain”, “fat”, and “lean”. A search based on authors’ names identified as being involved in genetic selection of lean and fat chicken lines was also performed. The variables investigated (Table 2) were the dietary AMEn value, the CP content, mean age (A), mean BW, mean BWi value, daily weight gain (DG), growth rate (GR) defined by the ratio of daily weight gain to mean BW0.634 , daily feed intake (DFI), feed efficiency (FE) (DG/DFI), PE defined as the ratio of total protein gain to protein intake, protein content of DG (PCG), fat content of DG (FCG), daily deposition of energy as protein or fat (DEprot or DEfat ) calculated with the gross energy coefficients of 23.69 MJ/kg and 39.18 MJ/kg for protein and fat, respectively (Znaniecka, 1967), daily AME intake (MEI), and daily heat production (DH), calculated as the difference between MEI and total energy deposition (DEprot

+ DEfat ). The AMEn values shown in publications were either measured or calculated (AMEnC) from dietary composition on the basis of feed composition tables. As AMEn values from feed composition tables generally come from values measured in adult cockerels, AMEnC values were transformed into “age adapted” values (AMEnA). AMEnA were obtained from a relationship between AMEn and age, derived from Zelenka’s experimental data (1997) calibrated on mean difference in AMEn between adult and young chickens reported in a study based on a large number of measurements (Bourdillon et al., 1990), as shown below: AMEnA = 0.9386 AMEnC, for A ≤ 14 days AMEnA = AMEnC (0.001465 A + 0.9181), for 14 days < A ≤ 56 days AMEnA = AMEnC, for A > 56 days. The MEI values measured were calculated from the measurements of daily AMEn (or AMEnA) intake corrected by the addition of uric acid energy equivalent to the amount of daily fixed nitrogen (0.0344 MJ/g nitrogen; Hill and Anderson, 1958). Daily requirement of AME for maintenance (AMEm ) was related to BW0.634 , according to the relationship between DH and BW found in the current study. All mean BWi values were calculated as means of initial and final BWi values [(BWiin + BWifi )/2] observed for a growth period. In some studies, body composition data were lacking or slightly biased for the calculation of PE, PCG, or FCG, which required certain data to be estimated or corrected. It was thus necessary to estimate the initial body composition at the beginning of the growth experiments for several studies (references 3, 5, 12, 22, 23, 26 in Table 1), which was achieved using data from other publications (Table 1). Other studies measured body protein content without feathers. For these studies (references 1, 2, 3, 5, 6, 10, 15, 18, 19 in Table 1), total body protein content was corrected by adding feather protein, using the feather protein proportions of whole protein reported by Larbier and Leclercq (1994) at various ages. For 2 studies (references 17, 30 in Table 1), whole protein deposition was calculated as a proportion of body growth, using data from other studies (Table 1). Treatment means were the individual data used in the regression calculations. A treatment was defined by one publication, one trial, one growth period defined by chicken ages, one diet, one sex (male, female, or a mixture of males and females), and one genetic chicken strain. A trial was defined by a publication and an experiment within a publication, or according to a variation in measurement methods within an experiment described in the “Materials and Methods” section. A trial fixed effect was introduced in some regression models to take account of variations originating from differences in methods. Introduction of this trial

2 1 3 1 1 1 1 1 1 2 1 1 1 1 2 1 1 2 1 1 1 3 1 1 1 1 1 1 2 2 2 1 1 1 1 2 1 2 1 1 1 3

T1 4; 12 8 6 6 8 4 4 2 4 12; 10 10 5 1 12 10 10 5 8 4 10 8 6; 8; 6 10 7 5 4 8 5 5; 4 6; 8 3 4 5 9 3 3; 2 10 4 15 12 2 10

t2 3; 10 / 31; 38; 45; 52; 59; 66 38 24 9; 17; 25; 33; 41; 49 24 38 35; 49 66 32 38 / 35 35 11 10 35 14 / 35 32 31 10; 32 42 37 9; 15; 20; 27; 35; 44 35 / 35 / 37 24 18 34 35 26; 33; 40; 47 18; 21 32 38 32 / 28 32 38 4 28 32 14 25 / 38 3; 10; 17 16 25 28

Mean ages (d) 1; 2 1 1; 2 2 1; 2 1 1 2 1+2 1 2 1 1 1 1; 2 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1

Sex F S F F F S S F F S S F M S F S F F S S F S F M S M F F; M F S F F F F F F F F; S F F F F

Growth types3 Unknown Fat; Lean Fat; Lean; Unknown W. Cornish Fat; Lean Fat; Lean Fat; Lean Fat; Lean W. Plymouth Rock Fat; Lean Unknown Ross Hubbard Fat; Lean Fat; Lean; FC; WN; WI Unknown Unknown Unknown Fat; Lean Fat; Lean Hybro Fat; Lean Ross Lohmann Unknown Anak FC; GL A; B; C; D; E Lohmann Fat; Lean Hybro G Ross Ross Ross Cobb Ross Ross QL; CL; Fat; Lean Unknown Ross Ross ISA IJV915

Names of strains

2

Number of trials. Number of treatments per trial. 3 F: fast growing; M: medium growing; S: slow growing broilers. Precise definitions for F, M, and S are shown in the “Materials and Methods” section. 4 Number of diets per trial.

Leeson and Summers, 1980 Leclercq 1983 Whitehead and Griffin, 1984 Chwalibog et al., 1985 Whitehead and Griffin, 1986 Geraert et al., 1987 Geraert et al., 1988 McLeod et al., 1988 Chwalibog and Egum, 1989 Geraert et al., 1990 MacLeod, 1990 Morris and Njuru, 1990 Yu et al., 1990 Leclercq and Guy, 1991 Leenstra and Cahaner, 1991 McLeod, 1991 Pinchasov and Nir, 1992 Summers et al., 1992 Geraert et al., 1993 Leclercq et al., 1993 Buyse et al., 1994 Leclercq et al., 1994 Deschepper and De Groote, 1995 Liebert, 1995 MacLeod, 1997 Nitsan et al., 1997 Buyse et al., 1998 Buys et al., 1999 Aletor et al., 2000 Alleman et al., 2000 Sanz et al., 2000 Aletor et al., 2002 Crespo and Esteve-Garcia, 2002 Noy and Sklan, 2002 Collin et al., 2003 Niess et al., 2003 Fatufe et al., 2004 Pym et al., 2004 Olukosi et al., 2008 Priyankarage et al., 2008 Conde-Aguilera et al., 2013 Carr´e and Juin, 2015

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

1

References

N◦ 1 4 1 1 2 2 2 1 1 6; 5 10 5 1 6 1 10 5 4 2 5 1 3; 4; 3 10 7 5 4 1 1 5; 4 3; 4 3 4 5 9 3 3; 2 10 2 5 12 2 10

d4

No No No No No Yes No No No No No No No Yes No No No Yes No Yes No Yes Yes No Yes No No No Yes Yes No Yes No No Yes Yes Yes No No Yes Yes No; Yes

Amino acid test

Table 1. Description of the database used for the regression computations predicting daily ME intake, protein and lipid contents of body gain, and feed efficiency, in broilers ad libitum fed in standard temperature conditions.

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fixed effect removed or reduced the possible bias due to the estimation or correction processes applied to some of the body composition values (see above). Moreover, in vivo methods for PCG and FCG measurements also differed between trials, 48 using the comparative slaughter technique based on body chemical analyses, and 9 using respiratory chambers measuring gas exchange. Only 10 trials however tested various age levels, while other trials tested only one age level. Thus, the introduction of a fixed trial effect markedly reduced the variability originating from age differences, which may have led to a significant reduction in the precision of the regression calculations. Regression calculations using A or BW as major dependent variables (PCG as a function of age and FCG, and FCG or DH as functions of BW) were undertaken without a fixed trial effect. GR values also showed small variations within trials (0.0075 kg/kg SD). Regressions calculating FE with GR as one of the independent variables were also obtained with no trial effect in model calculations. The values of a and i appearing in equations showing the form “Y = a[(Xiin + Xifi )/2]” were found by iterative regression calculations as follows: Ln Y = b1 + i1 Ln [(Xin + Xfi )/2], with “Ln Y” and “Ln [(Xin + Xfi )/2]” as dependent and independent variables of a simple regression calculation, respectively, which provided the values of i1 and b1. Then: Ln Y = b2 + i2 Ln ([(Xiin1 + Xifi1 )/2]1/ i1 ), with “Ln Y” and “Ln ([(Xin i1 + Xfi i1 )/2]1/ i1 )” as dependent and independent variables of a simple regression calculation, respectively, which provided the values of i2 and b2. Then: Ln Y = b3 + i3 Ln ([(Xin i2 + Xfi i2 )/2]1/ i2 ) Until in = in−1 in the following regression: Ln Y = bn + in Ln ([(Xin in−1 + Xfi in−1 )/2]1/ in−1 ). This gave: Ln Y = bn + Ln [(Xin in−1 + Xfi in−1 )/2]. Thus: Y = ebn [(Xin in−1 + Xfi in−1 )/2]. Thus: i = in = in-1 , and a = ebn The calculations described above were performed with the removal of the highest outlier at each step (see below for outlier removal procedure). Regression calculations were undertaken with both “SuperAnova” (1989 to 1991, version 1.11, Abacus Concepts, Inc., Berkeley, CA) (Aitkin et al., 1994) and “R” (2013, version 3.0.2, R Foundation for Statistical Computing, Vienna, Austria) (R Core Team, 2013) software packages. A measured value was considered an outlier when its absolute residual value exceeded 3 residual SD (RSD). Outliers were removed one by one by applying automatic iterative calculations. For regressions with no intercept, R2 values were calculated as [1 − RSD2 /σy2 ], with RSD and σy2 being the RSD value of the regression line, and the variance of the dependent variable, respectively. The solver function of the Excel software

Table 2. Variables used in regression and equation calculations. Variable name A CP AMEnC AMEnA1 AMEn BW BWi DG GR PE PCG FCG FCGcm FCGR2 DFI FE MEI DEprot 3 DEfat 3 DH MEIM DEMprot DEMfat

Definition

Units

mean age crude protein content of feed AMEn value of feed calculated from feedstuff composition tables AMEnC corrected for age effect measured AMEn or AMEnA mean body weight (BWinitial i + BWfinal i )/2 daily weight gain growth rate: (daily weight gain)/ BW0.634 protein efficiency: protein gain / protein intake protein content of weight gain fat content of weight gain FCG corrected for a minimum: FCG – 0.025 FCG / (0.025 + 0.137 BW0.418 ) daily feed intake feed efficiency: DG / DFI daily AME intake daily deposition of energy as protein: 0.02369 PCG × DG daily deposition of energy as fat: 0.03918 FCG × DG daily total heat production: MEI − DEprot − DEfat MEI / BW0.634 DEprot / BW0.634 DEfat / BW0.634

d % MJ/kg MJ/kg MJ/kg kg kg g kg/kg g/g g/g g/g g/g g/g g g/g MJ MJ MJ MJ MJ/kg MJ/kg MJ/kg

1

After Zelenka (1997) and Bourdillon et al. (1990) with: AMEnA = 0.93861 AMEnC, for A ≤ 14d AMEnA = AMEnC (0.001465 A + 0.9181), for 14d < A ≤ 56d AMEnA = AMEnC, for A > 56d. 2 (0.025 + 0.137 BW0.418 ) is derived from a regression calculating Ln(FCGcm ) (R3, Table 3). 3 Coefficients “0.02369” and “0.03918” come from Znaniecka (1967).

(version 15.0) was used to find the solutions in a system of several equations.

Description of the Database Table 1 shows publication references and some features of the trials and experimental treatments. The database contained 384 lines obtained from 42 publications, 57 trials, and 384 basic treatments. The total number of chickens involved in growth and feed intake measurements was 12,277. The total number for body analyses was lower (7,744), because body analyses were often carried out on only a portion of the animals used in a treatment. The mean numbers of chickens per treatment were 37 ± 41 (SD) and 21 ± 21 (SD) for growth performance and body analysis measurements,

2195

−2.090 −1.082 1.116 1.388 1.0 1.0 2.16 to 3.52

−0.209 0.680 −1.98 2.74 0.27 to 1.11

1

Ln(DH) MEIM Ln(FCGcm ) Ln(FCGR)

A trial fixed effect (57 levels) was added in regression model for R2 and R4. The number (n) of observations were 376, 366, 357, and 371 for R1, R2, R3, and R4, respectively. The difference between 384 and n represented the number of outliers (|Residual value of Y| > 3 RSD). Statistical significances for coefficients, trial effects, and models were all very high (P ≤ 0.0001). DEMprot : daily deposition of energy as protein, relative to metabolic BW. DEMfat : daily deposition of energy as fat, relative to metabolic BW. CP/AMEn: crude protein to AMEn values ratio for feed. PE: protein efficiency. DH: daily heat production (MJ). MEIM: daily AME intake relative to metabolic BW (MJ/kg BW0.634 ). FCGcm : fat content of weight gain, corrected for a minimum (0.025) (g/g); FCGcm = FCG – 0.025. FCGR: fat content of weight gain measured, relative to fat content of weight gain calculated according to the R3 regression (g/g); FCGR = FCG / (0.025 + 0.137 BW0.418 ). For more details about variables, see Table 2. RSD: residual SD. 2 Range of intercepts specific of each trial. 3 The intercept values shown for R2 and R4 were the means of intercepts specific of each trial.

0.194 0.0433 0.393 0.168 0.844 0.952 0.330 0.818

R2 PE (g/g) CP/AMEn [%/(MJ/kg)] DEMfat (MJ/kg BW0.634 ) DEMprot (MJ/kg BW0.634 ) Trial (range of intercepts)2

Intercept3

Ln(BW0.634 ) (kg)

Ln(BW0.418 ) (kg)

R1 R2 R3 R4

Regressions shown in Tables 3, 4 and 5 were developed to calculate heat production, partition of AME, fat and protein contents of growth, and FE.

Y variable

RESULTS AND DISCUSSION

Table 3. Regressions1 developed for calculating daily heat production, daily AME intake, and fat content of weight gain.

respectively. Articles had been published between 1980 and 2015 (mean: 1995) (Table 1). The most frequent journals in the database were British Poultry Science (43%), Poultry Science (24%), Archiv f¨ ur Gefl¨ ugelkunde (17%), and British Journal of Nutrition (7%). Many publications originated from research teams studying genetically selected fat and lean lines, which explains the geographical distribution of research teams (France, 26%; United Kingdom, 19%; Germany, 12%; Belgium, 12%). The mean age of chickens ranged between 3 and 66 days (treatment mean: 29 days) (Table 1), with growing periods ranging between 1 and 48 days (treatment mean ± SD: 18 ± 13 days). Males, females, or mixed sexes were used in 302, 78, and 4 treatments, respectively (Table 1). Broilers were from commercial genetic types in 223 treatments. Broilers in the other 161 treatments were from experimental genetic lines, most of them being fat or lean lines (140 treatments). The publications reporting experiments on fat and lean lines always examined these lines within a common trial. Broilers were considered “fast growing” (Table 1) for the results published with commercial broilers from 2000, or for mean [measured growth / calculated growth] ratios (R) above 0.75. Broilers were considered “medium” and “slow growing” (Table 1) for 0.5 < R < 0.75 and R < 0.5, respectively. These calculated growth values were based on age according to Sakomura et al. (2005). “Fast”, “medium”, and “slow” growing types were found in 244, 13, and 127 treatments, respectively. The numbers of treatments using male “fast growing” or male “slow growing” broilers were 182 and 107, respectively. Diets were given as pellets, mash, or unknown form in 207, 27, and 150 treatments, respectively. AMEn (or AMEnA) values ranged between 7.10 and 15.37 MJ/kg (treatment mean ± SD: 12.42 ± 1.080 MJ/kg), and CP values between 7.3 and 35.0 (treatment mean ± SD: 20.0 ± 3.52%). Different ratios of amino acid to CP were tested in 22 trials, 12 of them evaluating the effects of an ideal protein formulation. Mean BW, DG, GR, and DFI values varied from 0.077 to 3.322 kg, 3 to 114 g, 0.0053 to 0.1079, and 11 to 203 g, respectively (treatment means ± SD: 0.966 ± 0.4853, 47 ± 17.7, 0.054 ± 0.0140, 96 ± 35.9, respectively). Variation ranges were 0.093 to 0.859 and 0.150 to 0.753 for FE and PE, respectively (treatment means ± SD: 0.51 ± 0.122, 0.49 ± 0.091, respectively). Values of PCG and FCG ranged from 0.084 to 0.517 and from −0.002 to 0.646, respectively (treatment means ± SD: 0.192 ± 0.0304, 0.157 ± 0.0753, respectively).

RSD

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Table 4. Simple regressions calculating the protein content of defatted weight gain1 as a function of age, in different populations and ranges of ages. Population R5 R6 R7 R8

Range of mean ages (d)

Whole population Whole population Male fast growing Male fast growing

3 3 3 3

to to to to

66 52 59 52

Intercept

Age (d)

R2

RSD

n

0.179 0.174 0.160 0.157

0.00169 0.00189 0.00244 0.00259

0.342 0.373 0.523 0.554

0.0264 0.0256 0.0243 0.0235

380 374 180 178

1 Y variable (g/g): PCG / (1- FCG), with PCG and FCG being the protein and fat contents of weight gain, respectively. The numbers of outliers (|Residual value of Y| > 3 RSD) were 4, 4, 2 and 2 for R5, R6, R7, and R8, respectively. Statistical significances for age coefficients and models were all very high (P ≤ 0.0001). RSD: residual SD.

Table 5. Regressions1 calculating feed efficiency (FE)2 from feed composition (AMEn and CP), growth rate (GR)3 , and fat content of weight gain (FCG).

R9 R10

Intercept

AMEn (MJ/kg)

CP (%)

GR (kg/kg BW0.634 )

FCG (g/g)

R2

RSD

n

−0.273 −0.127

0.0227 0.0294

0.00908

5.90 6.01

−0.356

0.767 0.747

0.0575 0.0593

380 379

1 The numbers of outliers (|Residual value of Y| > 3 RSD) were 4 and 5 for R9 and R10, respectively. Statistical significances for coefficients and models were all very high (P ≤ 0.0001). RSD: residual SD. 2 Y variable (g/g): FE = weight gain / feed intake. 3 GR: daily weight gain / BW0.634 .

Heat Production and Partition of AME The first regression (R1) expressed the daily heat production as a function of metabolic BW using the common logarithmic transformation procedure (Table 3). The exponent found to express the metabolic BW was 0.634 (Table 3). According to this result, the metabolic BW of the current study was calculated as BW0.634 . This exponent value was close to the 0.66 found by Chwalibog et al. in 1985. The “DH = 0.811 BW0.634 ” equation could therefore be deduced from R1. This equation provides DH values that are about 9% higher than those provided by the formula reported by Chwalibog et al. in 1985 (DH = 0.720 BW0.66 ). This discrepancy may come from differences in the populations investigated or in the experimental methodology, since Chwalibog et al. conducted their experiment with respiratory chambers, while most experiments in the database were conducted with the comparative slaughter technique. The formula (DH = 0.92 BW0.75 ) reported in a previous review (Chepete, 2002) resulted in DH values that were substantially higher than the DH values calculated with the formula obtained in the current study for the grower and finisher periods. However, most experiments considered in the review reported by Chepete (2002) were conducted on the scale of a housing system, with the heat being produced by both animals and litter fermentation. DH relative to metabolic BW is often considered to be fairly independent of diet, a principle that can be used in model calculations. However, it is probable that DH relative to metabolic BW may be slightly affected by the quality of the diet, especially by the CP/AMEn ratio (Carr´e and Juin, 2015).

The regression expressing the energy partition is probably the most important in terms of impact on feed intake and feed efficiency. Such a regression (R2) calculated with the present database is shown in Table 3. Using Equation E4 (Table 6) deduced from R2, the relationship between measured and calculated MEI is shown in Figure 1 for each trial in the whole database. Coefficients obtained in R2 represent the inverse of the partial efficiencies (kp and kf ) of AME for deposition of energy as protein and fat, respectively (MacLeod, 1990). According to R2 (Table 3), the kp value (0.720) was lower than the kf value (0.896), as is usually found in the literature. These kp and kf values were close to values reported in the past (see Carr´e and Juin, 2015). The intercept of R2 (0.680, Table 3) represents the coefficient expressing AMEm (AMEm = 0.680 MJ/kg BW0.634 ). The AMEm values provided by this equation were about 14% higher than the AMEm values provided by the equation (AMEm = 0.550 MJ/kg BW0.7 ) recently proposed by Noblet et al. (2015). However, it must be remembered that the AMEm expression obtained by Noblet et al. (2015) corresponded to a maintenance requirement in fasting birds with zero physical activity, which differed from our calculations (no correction for physical activity), and from the conditions of studies of the database being used (ad libitum fed birds). Using the data of the present database, the MEI values calculated either by E4 (Table 6) or by the regression found by Carr´e and Juin (2015) were highly correlated (R2 = 0.9999) and differed on average by only 1.0%, which suggests good reliability of both the data and regressions. It should be noted that the correlation between the independent variables (considered values varying within trials) used in R2 was very low (R2 = 0.01),

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Table 6. A system of 6 equations1 derived from regressions and variable definitions shown in Tables 2, 3 and 4, relating components of feed and growth, and flows of energy and protein, in broilers ad libitum fed in standard temperature conditions. E12 E23 E34 E45 E5 E66

PE = (DG × PCG) / (CP × DFI / 100) PCG = (0.00259 A + 0.157) (1 – FCG) FCG = α (0.025 + 0.137 BW0.418 ) e(−1.082 CP / AMEn −2.090 PE) MEI = 0.680 BW0.634 + 0.03288 DG × PCG + 0.04372 DG × FCG DFI = 1000 MEI / AME AME = AMEn + 1000 (0.0344 DG × PCG / 6.25 / DFI)

1

Definitions and units of variables are shown in Table 2. E1 is derived from the definition of PE (see Table 2). 3 E2 is derived from R8 (Table 4). 4 E3 is derived from R4 (Table 3). For the whole population examined in the current publication, the α value was found to be 15.5 (R4, Table 3). According to the database, α values were found to be 14.7, 12.3, 13.0, and 15.2 for males from fast growing lines, from commercial Ross broilers, from experimental lean lines and from experimental fat lines, respectively. 5 E4 is derived from R2 (Table 3). 6 E6 is derived from Hill and Anderson (1958). 2

(Table 2). We therefore developed calculations in order to estimate FCG and PCG.

MEI (MJ) 3.0

Fat and Protein Contents of Weight Gain 2.5 2.0 1.5 1.0 0.5 0.0 0.0

0.5

1.0

1.5

2.0

2.5

3.0

Calculated MEI = 0.680 BW

0.634

+ 1.388 DEprot + 1.116 DEfat

Figure 1. Regression lines showing for each trial the relationship between the daily AME intakes (MEI; MJ) and the MEI values calculated on the basis of BW (kg) and daily energy depositions as protein (DEprot ; MJ) and fat (DEfat ; MJ). Covariance analysis: R2 = 0.994 (n = 366; 57 trials).

which reinforces the reliability of coefficients obtained in R2. However R2 cannot be used properly without information about its independent variables: daily deposition of energy as fat, relative to metabolic BW (DEMfat ) and daily deposition of energy as protein, relative to metabolic BW (DEMprot ) (Tables 2 and 3). Both these variables depend on FCG and PCG

In the calculation process for FCG, our aim was to express the various observations obtained in the past regarding FCG variations. According to Fisher (1984), fat deposition in growing broilers shows evidence of an asymptotic response tending towards a minimum value, reflecting the fact that body tissues physiologically contain a minimum of lipids, at least in cell membranes. Examination of the database led us to choose 0.025 as the minimum FCG value. It has also been reported in the past that the amount of body fat (ABF) increased with BW, as expressed by the allometric relationships calculated by Gous et al. (1999) and Sakomura et al. (2005). The whole database was therefore used to compute an allometric relationship relating FCG to BW. This calculation was performed on the FCG values corrected (FCGcm ) for a minimum (0.025, see above) (R3, Table 3), in order to take into account the principle of a minimum FCG value in growing broilers (see above). The classic logarithmic technique (R3, Table 3) was applied for fitting this allometric relationship. It should be remembered that FCG is different from ABF. When considering ABF a function of BW, FCG is the derivative of ABF. Thus, if “i” is the allometric exponent for ABF, the allometric exponent for FCG should be “i1”. The allometric exponent for FCGcm was found to be 0.418 (R3, Table 3), which was consistent with the range of allometric exponents usually found for ABF in broilers (of between 1 and 2; Fisher, 1984; Sakomura et al., 2005). The R2 value found with R3 (Table 3) was rather low (0.33, Table 3) and numerous outliers were detected (27, Table 3). This was to be expected, as the whole database mixed various sexes, genotypes, and diets, with a possible effect of each of these factors on fat deposition. Thus R3 was an initial rough calculation of

´ AND MEDA ´ CARRE

2198 FCG (g/g) 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.0

0.1

0.2

0.3

0.4

0.5

0.6

Calculated FCG = 15.5 (0.025 + 0.137 BW

0.418

)e

(-1.082 CP/AMEn – 2.090 PE)

Figure 2. Regression lines showing for each trial the relationship between the fat contents of weight gain (FCG; g/g) and the FCG values calculated on the basis of CP/AMEn [%/(MJ/kg)], BW (kg) and protein efficiency (PE; g/g). Covariance analysis: R2 = 0.87 (n = 371; 57 trials).

FCG. The next calculation R4 (Table 3) was conducted to improve the FCG calculation, by taking these factors into account. The dependent variable in R4 was the ratio of FCG measured to FCG calculated as an allometric response, in order to remove the effect of BW in the R4 calculation. The independent variables of R4 were PE and the CP/AMEn ratio. The CP/AMEn ratio is a classic variable that is negatively related to FCG (Fisher, 1984). PE is expected to represent another negative factor for FCG, as observed in the comparison of fat and lean lines (Leclercq and Guy, 1991), or in the comparison of diets that differed in the amino acid composition of proteins (MacLeod, 1997; Carr´e and Juin, 2015). It was not surprising to observe a significant correlation between PE and the CP/AMEn ratio (considered values varying within trials), but this was not very high (R2 = 0.14), which allowed these variables to be combined in the regression calculation. This expected correlation was probably “broken” by the test of both lean and fat lines, or by the test of various protein compositions, in common trials. The dependent variable in R4 was expressed as a logarithm (Table 3) in order to obtain a curvilinear relationship, as observed by Fisher (1984). Equation E3 expressing the calculation of FCG (Table 6) was deduced from R4 (Table 3). The relationship between measured and calculated FCG is shown in Figure 2 for each trial in the whole database. The relationships shown in Figure 2 indicate that the effects of the trial were important for FCG. In fact, Equation E3

did not take account of all the variations originating from the various genotypes. This could be seen from the ratio of measured to calculated FCG values (with an α value of 15.5 in E3) that was significantly higher (P = 0.0006) in males from fat lines than in males from lean lines (1.06 versus 0.89), which means that a specific α value is required for each sex and genotype. Calculating a regression without intercept between measured and calculated FCG (with an α value of 15.5 in E3) for a specific population was probably the best way to obtain the correcting factor, leading to an α value specific to the population under consideration. Using this procedure, the α values were found to be 14.7, 12.3, 13.0, and 15.2 for the males from fast growing strains, commercial Ross strains, experimental lean lines, and experimental fat lines, respectively. These results suggest that the commercial Ross broilers examined in the database were close to lean types. Regression calculations regarding PCG are shown in Table 4. PCG values were related to age, in agreement with the common finding of a positive relationship with age (Chwalibog et al., 1985). This relationship with age was made even more pronounced by expressing PCG as content relative to defatted weight gain (PCG / (1 - FCG)), Table 4). The advantage of using this expression of PCG was also to create a negative relationship between PCG and FCG, as observed in the comparison between lean and fat lines (Leclercq and Guy, 1991). Regressions were performed for all ages (up to 66 or 59 days; R5 and R7) or up to 52 days (R6 and R8), as it was observed that the few treatments based on ages above 52 days could have a pronounced effect on the regression calculations. Regressions did not show high R2 values (Table 4), probably because sex and genotype had some effect on the relationships. Regression calculations were therefore also performed on a more limited population (male fast growing broilers), which led to higher R2 values and different coefficients in the regression lines (Table 4) compared to calculations performed with the whole population.

DFI calculation based on Cross Relationships between Feed and Animal Characteristics Equations E2, E3, and E4 (Table 6) were derived from R8 (Table 4), R4, and R2 (Table 3), respectively. By adding Equations E1, E5, and E6 defining PE, the relationship of DFI with MEI, and that of AME with AMEn (Table 6), respectively, a system of 6 equations is proposed, characterizing the cross relationships between feed composition (AMEn and CP), feed intake, BW, weight gain, age, and deposition of fat and protein. This system of 6 equations (Table 6) can be used as a model for obtaining the values of the 6 variables PE, PCG, FCG, MEI, DFI, and AME from a knowledge of CP, AMEn, A, BW, and DG. Practical examples of such a calculation are shown in

2199

P = α (0.025 + 0.137 H) e(−1.082 B/A −2.09 N) , with α values of 15.5, 15.2 and 13.0 for the whole database (WD) population, males of fat lines (MF) and males of lean lines (ML), respectively. Q = (1 − I/O)2 ; I and J are found in order to obtain: Q + R = 0. 11 R = (1 − J/P)2 ; I and J are found in order to obtain: Q + R = 0. 10

Definition of variables are shown in Table 2. G = (D0.634 + E0.634 )/2. 3 H = (D0.418 + E0.418 )/2. 4 K = (0.179 + 0.00169 C) (1 − J). 5 L = A + 1000 (0.0344 F × K/6.25/I). 6 M = 0.680 G + 0.03288 F × K + 0.04372 F × J. 7 N = 100 F × K/I/B. 8 O = 1000 M/L. 2

1

9

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.136 0.124 0.132 0.102 119.6 117.1 118.7 112.7 0.519 0.503 0.525 0.572 1.632 1.601 1.621 1.544

FCG solver R11 DFI solver Q10

Defined values

FCG (g/g) P9 DFI (g) O8 PE (g/g) N7 MEI (MJ) M6

Calculated values

AME (MJ/kg) L5

13.64 13.67 13.65 13.71 0.195 0.198 0.196 0.203 0.136 0.124 0.132 0.102 119.6 117.1 118.7 112.7 1.055 1.055 1.055 1.055 1.099 1.099 1.099 1.099 71.43 71.43 71.43 71.43 1.700 1.700 1.700 1.700 0.700 0.700 0.700 0.700 28 28 28 28 22.5 24.0 22.5 22.5 13.0 13.0 13.0 13.0 WD WD MF ML

Table 7. The reliability of such a calculation is demonstrated in Figure 3, that shows the relationship between the measured and calculated DFI values for the whole database. It can be seen that the fairly precise relationship shown in Figure 3 was obtained with the calculated DFI values derived from equations with no adaptation of coefficients to various genotypes and sex. The calculations set out in Table 7 show results which are typical of the effects obtained with increased CP, or with a lean line compared to a fat line. Increasing CP from 22.5 to 24.0% resulted in decreased values for DFI, FCG, and PE (Table 7). When assessed by the decrease in α value in E3, comparison of a lean line to a fat line showed decreased values for DFI and FCG and increased values for PCG and PE (Table 7). This system of equations thus also shows good reliability from the point of view of classical nutritional responses. However, these equations cannot summarize all nutritional and genetic conditions. It is also necessary to find the α value in E3 (Table 6) adapted to the genetic features of broilers. Moreover, E4 (Table 6), the most important equation in the system, represents a mean response for the whole database. It cannot be excluded that this equation would have to be adapted to the genetic features of broilers, and also to their nutritional status, as a previous study (Carr´e and Juin, 2015) has suggested that broilers may be able to increase their fat deposition and their heat production in order to increase their feed intake, allowing them to meet their nutritional requirements in the case of nutrient deficiency. E2 (Table 6) probably also requires some specific adaptation to sex

Variable1 Symbol

Figure 3. Regression line (without intercept) showing the relationship between the daily feed intakes (DFI; g) and the DFI values calculated on the basis of feed composition (AMEn and CP), mean age, daily weight gain, and initial and final body weights, as described in Table 7 with an α value of 15.5 in E3 (Table 6). One point represents one treatment. RSD: residual SD.

PCG (g/g) K4

250

FCG (g/g) J

200

DFI (g) I

150

Proposed values

100

Calculated DFI

Mean BW0.418 (kg) H3

50

Mean BW0.634 (kg) G2

0

DG (g) F

0

Final BW (kg) E

y = 0.977x 2 R = 0.880 RSD = 12.5 n = 384

50

Known values

100

Initial BW (kg) D

150

Mean Age (d) C

200

CP (%) B

250

AMEn (MJ/kg) A

DFI (g)

Table 7. Examples of calculation of daily feed intake (DFI) from the feed composition (AMEn and CP), age, BW, and daily weight gain (DG) in broilers using the equations shown in Table 6, simulating a “mean” broiler from the whole database (WD) population, or from the males of fat lines (MF) or lean lines (ML) examined in the database.

NUTRITIONAL RELATIONSHIPS IN BROILERS

´ AND MEDA ´ CARRE

2200

and genotypes, as suggested in Table 4. This equation system, shown in Table 6, may thus represent a framework for future models and not a complete solution for a model. Regressions predicting FE are shown in Table 5. These regressions were developed using the same structures of calculation as those shown in a previous study (Carr´e and Juin, 2015), R9 and R10 (Table 5) being equivalent to equations 10 and 13 in the study reported by Carr´e and Juin (2015), respectively. In terms of RSD value, R9 and R10 were much less precise than equations 10 and 13, which was not surprising as the trials and genotypes were much more variable in the present database than in the previous study. According to R9, 1% CP was equivalent to 0.400 MJ/kg AMEn in terms of FE, while the previous publication (Carr´e and Juin, 2015) found 1% CP to be equivalent to 0.247 MJ/kg AMEn. The coefficients of R10 were also somewhat different from those of equation 13 (Carr´e and Juin, 2015). The negative relationship between GR and FCG (R2 = 0.21) in the present database could have introduced some bias in the coefficients of R10. In the same way as in the equation system shown in Table 6, R9 can supply an estimation of DFI on the basis of feed composition and growth rate. However, R9 is probably less precise than the equation system shown in Table 6, and more difficult to adapt to various nutritional or genetic situations. In conclusion, regressions calculated from the database resulted in a system of equations consisting of cross relationships between feed and growth characteristics. The 3 main equations expressed the partition of AME intake, the FCG values as a function of CP, AMEn, PE and BW, and the PCG values as a function of A and FCG. The equation system could be used to calculate DFI and PE from the knowledge of AMEn, CP, A, BW, and weight gain. Such a system represents a framework for future models, not a complete model for all conditions, as it was also shown that some equations required specific adaptations to genotypes. Moreover, some corrections to the equations are probably needed for adaptation to specific nutritional status, such as nutrient deficiency. With the database, linear regressions combining AMEn, CP, and GR, or combining AMEn, GR, and FCG were also obtained to predict FE with reasonable precision in a wide range of situations.

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