Estimation of the metabolizable energy content of meat … · Estimation of the metabolizable energy content of meat and bone meal for swine ... Estimation of the metabolizable energy

O. A. Olukosi and O. Adeola

Estimation of the metabolizable energy content of meat and bone meal for swine

doi: 10.2527/jas.2009-1775 originally published online Apr 24, 2009; 2009.87:2590-2599. J Anim Sci

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ABSTRACT: Experiments were conducted to deter-mine apparent ME (AME) and apparent nitrogen-cor-rected ME (AMEn) of 21 meat and bone meal (MBM) samples and to develop regression equations for predict-ing energy value of MBM. One hundred ninety-eight 32-kg of BW barrows were used for the study. The 22 treatments consisted of 1 corn-soybean meal reference diet and 21 test diets in which 21 MBM samples re-placed 100 g/kg of corn and soybean meal such that the ratio of corn and soybean meal was the same in the reference and test diets. The DE, AME, and AMEn of the MBM samples were determined by the difference method in a metabolism study consisting of 5-d adjust-ment and 5 d of total collection of feces and urine. On a DM basis, GE of MBM ranged from 3,895 to 5,193 kcal/kg, CP ranged from 491 to 641 g/kg, and ash ranged from 142 to 338 g/kg. The AME of the MBM samples ranged from 2,320 to 3,872 kcal/kg, whereas AMEn ranged from 2,212 to 3,767 kcal/kg. None of the proximate compositions explained >50% of the varia-tion in energy content of the MBM. Fat was positively correlated with GE, DE, AME, and AMEn (r ≤ 0.44), but CP, ash, Ca, and P were negatively correlated with

DE, AME, and AMEn. The ratios of the proximate compositions to each other were correlated with the energy contents of the MBM. Crude protein:fat and GE:fat were negatively correlated with DE, AME, and AMEn of the MBM (r ranged from −0.17 to −0.39), but fat:ash had the greatest positive correlation with AME and AMEn compared with other ratios tested. When the data from 1 MBM sample that was an outlier were removed from the analysis, R2 was 0.42 for AME and AMEn. The 4 variables that produced the best pre-diction equation for AME and AMEn were GE, CP, P, and ash. The prediction equation for AME using these variables was AME = 13,587 – (1.25 × GE, kcal/kg) – (3.51 × CP, g/kg) + (30.4 × P, g/kg) – (16.4 × Ash, g/kg), and for AMEn, the equation was AMEn = 13,547 – (1.25 × GE, kcal/kg) – (3.59 × CP, g/kg) + (31.0 × P, g/kg) – (16.5 × Ash, g/kg). It was concluded from this study that MBM is a good energy source for pigs and that, although other extrinsic factors may contribute to the variations in energy content of MBM, proximate compositions should be sufficient to predict the energy value of MBM for pigs.

Key words: energy content, meat and bone meal, prediction equation, proximate fraction, swine

©2009 American Society of Animal Science. All rights reserved. J. Anim. Sci. 2009. 87:2590–2599 doi:10.2527/jas.2009-1775

INTRODUCTION

Meat and bone meal (MBM) is the dried and ren-dered product of mammalian tissue and consists of ani-mal offal, bones, blood, heads, lean tissues, and fat and is generally regarded as a good source of CP and AA for swine (Traylor et al., 2005) and chickens (Huang et

al., 2005). However, there has been less emphasis on energy value of MBM for swine.

Batterham et al. (1980) reported a range of 2,247 to 3,322 kcal/kg for DE of 14 MBM for growing pigs. Shi and Noblet (1993) determined ME in sows and growing pigs; they reported the ME value of 3,009 and 2,175 kcal/kg of DM for sows and growing pigs, respectively. Recently, Adedokun and Adeola (2005) in a digestion trial determined the apparent ME (AME) and appar-ent nitrogen-corrected ME (AMEn) values of 12 MBM samples and reported a range of 1,569 to 3,308 kcal/kg of DM and 1,474 to 3,361 kcal/kg of DM, respectively. Dolz and De Blas (1992) observed that in chickens the differences in energy contents of MBM could be at-tributed to GE content and that GE tended to increase with an increase in the content of fat in the meal. Ad-edokun and Adeola (2005) noted that the variation in

Estimation of the metabolizable energy content of meat and bone meal for swine1,2

O. A. Olukosi and O. Adeola3

Department of Animal Sciences, Purdue University, West Lafayette, IN 47907-2054

1 The authors thank the staff of Purdue University Swine Research Unit and Jason Fields (Purdue University) for care of experimental animals. The able technical assistance of Pat Jaynes (Purdue Uni-versity) is also gratefully acknowledged.

2 This study was funded by Fats and Proteins Research Council Inc. (Alexandria, VA).

3 Corresponding author: [email protected] January 7, 2009.Accepted April 17, 2009.

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AME and AMEn were not related to any of the major chemical components, but that interactions among the components may be a factor.

Differences in species of origin, quantity of bones, and other factors produce variability in the proximate com-position and energy value of MBM. An assay of MBM from different sources that are different in composi-tion may provide a prediction equation that is robust enough to be used in the prediction of energy value of MBM. Therefore, the objective of this study was to determine the AME and AMEn of 21 MBM samples and establish equations for predicting energy values of MBM for swine.

MATERIALS AND METHODS

All animal experimentation procedures were approved by the Purdue Animal Care and Use Committee.

Animal and Diets

A total of 198 crossbred (Hampshire × Duroc × Yorkshire × Landrace) barrows with an average initial BW of 32.0 kg were used for this study. The barrows were allocated to 22 dietary treatments in a randomized complete block design with each treatment replicated 9 times. The barrows were housed in stainless-steel metabolism crates that allowed for total but separate collection of feces and urine using a 5-d adjustment period followed by 5 d of feeding the experimental di-ets according to the procedure of Adeola and Bajjalieh (1997). The 22 experimental diets included a corn-soy-bean meal reference diet (RD); the others were test diets (TD) in which each of the 21 MBM replaced part of corn and soybean meal in the RD such that the ra-tio of corn to soybean meal was the same in the RD and TD. Maintaining a constant ratio for the feedstuffs supplying energy in reference and test diets is a basic requirement to enable the use of the difference method for determining the ME content of a test ingredient. The ingredient compositions of RD and TD are pre-sented in Table 1.

Chemical Analyses

Dried fecal, orts, and feed samples were ground to pass through 0.5-mm screen using a mill grinder (Retsch ZM 100, GmbH & Co. K.C., Haan, Germany). Urine samples for each barrow were thawed and thor-oughly mixed, after which two 800-mL subsamples were filtered 3 times using glass wool and then dried in a forced-air oven. The dried urine was stored at −20°C before analysis. For DM determination, samples were dried at 100°C in a drying oven (Precision Scientific Co., Chicago, IL) for 24 h (AOAC, 2000). Gross en-ergy was determined in a bomb calorimeter (Parr 1261 bomb calorimeter, Parr Instruments Co., Moline, IL) using benzoic acid as a calibration standard. Nitrogen was determined by combustion method (Leco Model

2000 CHN analyzer, Leco Corp., St. Joseph, MI) using EDTA as a calibration standard.

The proximate and AA composition of the MBM samples were determined at the University of Missouri Experimental Station, Columbia. For AA analysis, MBM samples were hydrolyzed in 6 N HCl for 24 h at 110°C under N atmosphere. For Met and Cys, perfor-mic acid oxidation was carried out before acid hydro-lysis. The AA in the hydrolyzate were determined by HPLC after postcolumn derivatization [AOAC, 2000, method 982.30 E (a,b,c)].

Calculations

For each of the 22 diets, energy digestibility (ED, %) and energy metabolizability (EM, %) were calculated. Consequently, DE (kcal/kg) and ME (kcal/kg) of the diets were calculated by multiplying ED and ME (%), respectively, with GE (kcal/kg) of the diets.

To calculate the DE, AME, or AMEn of the test in-gredients (MBM), the following series of equations (ap-plicable to both DE and AME) were used:

Table 1. Ingredient composition (g/kg, as-fed basis) of the reference and test diets on as-fed basis

Item Reference diet Test diet

Ingredient Corn 717.0 661.0 Soybean meal 245.0 226.0 Dicalcium phosphate 16.0 0.0 Limestone 9.0 0.0 Salt 3.0 3.0 Vitamin premix1 3.0 3.0 Selenium premix2 0.5 0.5 Trace mineral premix3 1.5 1.5 Lysine·HCl 2.5 2.5 dl-Methionine 1.0 1.0 Threonine 0.7 0.7 Tryptophan 0.3 0.3 Antioxidant4 0.5 0.5 Meat and bone meal 0.0 100.0 Total 1,000 1,000Calculated nutrient and energy5

CP, g/kg 175.9 162.2 DE, kcal/kg 3,430 3,163 ME, kcal/kg 3,280 3,025 Ca, g/kg 7.2 11.0 P, g/kg 6.7 8.4

1Vitamin premix supplied per kilogram of diet: vitamin A, 3,635 IU; vitamin D3, 363 IU; vitamin E, 26.4 IU; vitamin K, 3.6 mg; menadione, 1,206 μg; vitamin B12, 21.2 μg; riboflavin, 4.2 mg; d-pantothenic acid, 13.5 mg; niacin, 19.5 mg.

2Provided 600 μg of Se (as sodium selenite) per g of premix.3Mineral premix supplied per kilogram diet: Cu (as copper sul-

fate), 9 mg; I (as Ca iodate), 0.34 mg; Fe (as ferrous sulfate), 97 mg; Mn (as manganese oxide), 12 mg; Zn (as zinc oxide), 97 mg; Fe (as FeSO4·H2O), 267 mg; Mn (as MnSO4), 90 mg; Zn (as ZnSO4), 225 mg; Cu (as CuSO4), 26.25 mg; I [as Ca(IO3)2], 4.5 mg.

4Provided 660 mg of ethoxyquin per g of premix (Spectrum Chemi-cal Mfg. Corp., Gardena, CA).

5Values for test diets depended on the characteristics of each meat and bone meal (MBM) sample. Calculations were based on 100 g/kg of Ca, 50 g/kg of P, and 90% P bioavailability (NRC, 1998) in the MBM sample used in the diet template.

Metabolizable energy of meat and bone meal 2591


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EM EM FC EM FCTD RD RD TD TI TI TD= ´( )+ ´( )/ / , [1]

where EMTD is the energy metabolizability (%) for the TD; EMRD is the energy metabolizability (%) for the RD; EMTI is the unknown energy metabolizability for the test ingredient (TI); FCRD/TD is the fractional con-tribution of GE (kcal/kg) in the RD to TD in the diet; and FCTI/TD is the fractional contribution of GE (kcal/kg) in the TI to the TD.

By definition, for energy or any other nutrient: FCTI/TD + FCRD/TD = 1. Therefore, FCRD/TD = 1 − FCTI/TD. [2] Hence, substituting [2] in [1] gives

EM EM FC EM FCTD RD TI TD TI TI TD= ´ -( )éëê

ùûú+ ´( )1 / / .

Solving for the only unknown, EMTI, gives

EMEM EM FC

FCTI

TD RD TI TD

TI TD

=- ´ -( )é

ëêùûú{ }1 /

/

.

The AME (kcal/kg) of TI was calculated as follows:

AME EM GETI TI TI= ´ ,

where AMETI and GETI are in kcal/kg. The AME cor-rected for retained nitrogen was calculated using a ca-loric value of 7.45 kcal/g of N (Harris et al., 1972).

Statistical Analyses

The data on DE, AME, and AMEn for the diets and MBM samples were analyzed using the GLM procedure (SAS Inst. Inc., Cary, NC). For the statistical analyses described below, the means for AME and AMEn for the 21 MBM samples were used along with their respective proximate analysis. To study the relationship among the proximate compositions and DE, AME, and AMEn of the MBM, the CORR procedure of SAS was used, whereas the INSIGHT procedure of SAS as well as re-gression diagnostics (DFFITS, DFBETA, and Cook’s distance) were used for the multiple regression analysis and for identifying possible influential points and out-lying observations. The measure of the influence of a particular case on each of the regression coefficients is denoted as DFBETA; a data point is influential if DFBETA >1. For multicolinearity diagnostics, toler-ance and variance inflation factors (VIF) were used. Excessive colinearity is indicated by tolerance >10 or VIF <0.1. It was assumed a priori that there is colin-earity among ash, P, and Ca; therefore, these were not considered in colinearity diagnostics.

To develop the optimum regression model, model se-lection criteria such as Mallows’ Cp (which compares full with reduced models for error sums of squares) and Akaike information criterion (AIC) were used along

with stepwise-regression analysis, forward selection, backward elimination, and maximum R2 improvement. The results obtained in these steps were compared with other model selection criteria listed above. The best 2 models for each subset of combination of explanatory variables were tested, and the optimum subset was cho-sen. The criterion Mallows’ Cp was used for model selec-tion such that the effect of multicolinearity on the fit of the regression model could be avoided. Multicolinearity may result from having many variables in the regres-sion model and may produce undesirable overfit of the model. Mallows’ Cp assigns values for each model, and the best model is the one that is approximately equal to the number of parameters (variables) in the model. Within each variable combination subsets, however, the best and unbiased model is the one with least Cp value or with Cp ≈p (where p is the number of parameters in the model). The full model is not considered in using Cp as a selection criterion because for the full model Cp is always equal to p. For the selection of the final model to be used in describing AME and AMEn of the MBM, AIC was used as the selection criterion. This model selection criterion is based on the log-likelihood of the model and penalizes for addition of parameters (model complexity); thus, it is capable of selecting a model that fits well but also has the minimum number of variables. Smaller AIC values indicate better fit.

RESULTS

The chemical composition of the 21 MBM samples is shown in Table 2. The GE content ranged from 3,895 to 5,193 with an average of 4,601 kcal/kg of DM. The nu-trient contents were, on average, 589, 113, 239, 38, and 77 g/kg of DM for CP, fat, ash, P, and Ca, respectively. In addition, the total AA content of the MBM ranged from 464 to 603 g/kg of DM with an average of 550 g/kg of DM (Table 3).

Generally, energy utilization responses, except AMEn, were least (P < 0.05) in the RD compared with the TD (data not shown). On average, DE, AME, and AMEn values for the diets were 4,045, 3,940, and 3,733 kcal/kg, respectively. Nitrogen and DM digestibility were on average 86.2 and 83.5%, respectively. Digestible energy, AME, and AMEn of the MBM are presented in Table 4. On average, DE, AME, and AMEn were 3,402, 3,069, and 2,963 kcal/kg, respectively.

Using DFBETA criterion, MBM 4 was an influential point because of the values of the DFBETA for CP and fat; hence, the data from this MBM were removed from further analyses. Correlation coefficients of the proxi-mate fractions with GE, DE, AME, and AMEn of the MBM are presented in Table 5. None of the correlations of the proximate fractions with the energy utilization responses were >0.50. Only fat was positively corre-lated with DE, AME, and AMEn; r values were 0.41, 0.42, 0.40, and 0.44 for AME, AMEn, DE, and GE, re-spectively. Calcium, ash, P, and CP were all negatively correlated with all the measures of energy utilization

Olukosi and Adeola2592

except for CP, which was positively correlated with GE. The greatest absolute r-values were observed for the correlations between proximate fractions and GE compared with the values for correlation of the proxi-mate fractions with DE, AME, and AMEn of the MBM. In Table 6, the correlations of the energy measures with the ratios of proximate fractions to each other are pre-sented. Except for CP:fat and GE:fat, all the ratios of proximate fractions were positively correlated with all the energy utilization measures. The greatest r of 0.91 was observed for correlation of CP + fat:ash with GE. In addition, GE:fat was positively correlated with GE of the MBM.

Table 7 shows the variables used for the model de-velopment. The variables used in the model building process were fat, ash, CP, Ca, P, and GE. Regression coefficient and Cp were the criteria used for model se-lection; the best 2 models within each subset of the variables from 1- to 5-variable models are presented. The variables entered in the models were the same for AME and AMEn. For AME and AMEn, fat was the best predictor in the 1-variable model because of its greater R2 value and lesser Cp compared with ash, the next best predictor in the 1-variable model. The best 2-vari-able models incorporated CP and ash, whereas the best 3-variable model used GE, P, and ash. The best 4-vari-able model included CP with the variables already in the 3-variable model, whereas the best 5-variable model used all the variables except Ca. As expected, R2-values increased as the number of variables in the model in-creased; however, there was not much improvement in the fit of the regression model when more than 4 vari-ables were in the model.

The regression equations for all the sets of regres-sion models are presented in Table 8. For AME, the full model (the equation that uses all the variables)

was AME = 15,190 – (1.22 × GE, kcal/kg) – (5.02 × CP, g/kg) + (33.2 × P, g/kg) – (2.60 × Ca, g/kg) – (5.88 × fat, g/kg) – (16.8 × ash, g/kg). For AMEn, the full model was AMEn = 15,071 (1.23 × GE, kcal/kg) – (5.02 × CP, g/kg) + (33.8 × P, g/kg) – (2.69 × Ca, g/kg) – (5.56 × fat, g/kg) – (16.9 × ash, g/kg). For AME prediction equation, AIC decreased as the number of variables in the model increased from 1 to 2 and then increased again when the number of variables in the model was >4. For AMEn, AIC only decreased when the number of variables in the model increased to 3, and then it increased again when more that 4 vari-ables were in the model. Therefore, on the basis of the values for AIC, the best models were those containing 4 variables, namely GE, CP, P, and ash. The 4-variable prediction equations for AME, therefore, was AME = 13,587 – (1.25 × GE, kcal/kg) – (3.51 × CP, g/kg) + (30.4 × P, g/kg) – (16.4 × ash, g/kg), and for AMEn, the equation was AMEn = 13,547 – (1.25 × GE, kcal/kg) – (3.59 × CP, g/kg) + (31.0 × P, g/kg) – (16.5 × ash, g/kg).

DISCUSSION

The objective of the current pig experiment was to determine the energy value of 21 MBM samples that differ in their chemical composition using digestibility assay and generate regression equations for predicting energy value of MBM. A previous study in our labora-tory designed to estimate AME and AMEn of MBM samples utilized 12 MBM samples (Adedokun and Ad-eola, 2005). The use of more samples (21 MBM sam-ples) in the current study was intended to allow for the development of a more robust prediction equation. Also, by using more MBM samples in the current study

Table 2. Proximate composition (g/kg of DM) of the meat and bone meal (MBM) samples

MBM sample DM GE, kcal/kg CP Phosphorus Calcium Fat Ash

1 960 4,269 548 47.2 94.8 104.9 284.72 939 4,657 641 37.9 70.9 100.7 239.73 952 4,167 571 51.2 102.9 105.3 285.34 946 4,605 601 37.2 80.2 119.4 247.75 939 4,270 612 46.8 93.4 92.1 280.46 956 3,895 491 56.0 116.2 120.4 338.27 965 3,968 514 51.8 107.1 120.9 328.28 961 4,722 631 36.8 66.8 101.3 217.19 965 4,769 629 32.4 66.2 106.8 221.610 964 4,761 632 35.8 64.5 102.5 226.611 966 4,734 554 37.5 76.9 108.8 241.312 963 4,720 626 38.5 72.5 112.3 224.713 967 4,789 621 38.8 72.6 110.9 242.014 967 4,702 619 38.0 77.8 115.1 231.115 942 5,077 570 23.1 38.0 135.5 142.016 944 5,106 569 23.9 40.5 135.1 148.017 945 5,193 573 22.5 37.0 141.2 147.318 971 4,640 570 44.2 97.0 110.8 263.919 953 4,627 626 41.7 79.3 105.3 227.120 969 4,247 567 29.5 86.6 106.0 263.921 967 4,697 594 35.4 69.8 116.0 210.7



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Tab

le 3

. A

min

o ac

id c

ompo

sition

(g/

kg o

f D

M)

of t

he m

eat

and

bone

mea

l

Sam

ple

num

ber

12

34

56

78

910

1112

1314

1516

1718

1920

21

Ess

ential

AA

Arg

inin

e35

.840

.539

.040

.040

.033

.534

.737

.637

.937

.737

.439

.138

.538

.034

.434

.034

.537

.741

.636

.038

.0 H

istidi

ne10

.616

.210

.013

.512

.18.

28.

514

.814

.915

.013

.914

.414

.414

.013

.713

.013

.711

.311

.112

.914

.2 I

sole

ucin

e16

.322

.615

.722

.418

.513

.213

.621

.621

.921

.020

.820

.920

.720

.621

.220

.221

.317

.420

.119

.620

.8 L

euci

ne33

.444

.033

.342

.437

.228

.129

.441

.241

.040

.139

.339

.839

.438

.639

.637

.939

.435

.239

.837

.740

.0 L

ysin

e30

.840

.629

.239

.133

.324

.724

.238

.138

.538

.737

.038

.238

.037

.431

.830

.732

.030

.729

.933

.335

.8 M

ethi

onin

e7.

310

.97.

711

.38.

46.

46.

110

.09.

99.

89.

89.

89.

69.

68.

98.

89.

18.

48.

29.

79.

8 P

heny

lala

nine

18.4

23.6

18.1

22.8

20.6

15.7

16.3

22.3

22.0

21.8

21.2

21.4

21.3

20.9

21.8

20.9

21.6

19.5

22.2

20.8

21.7

Thr

eoni

ne16

.920

.817

.421

.919

.014

.414

.321

.921

.121

.120

.520

.420

.219

.219

.919

.019

.317

.320

.018

.720

.1 T

rypt

opha

n4.

25.

03.

75.

34.

53.

33.

04.

84.

94.

94.

14.

64.

34.

43.

83.

84.

63.

63.

54.

64.

9 V

alin

e24

.131

.323

.629

.527

.419

.921

.128

.028

.426

.927

.227

.427

.227

.127

.626

.627

.624

.930

.326

.928

.0N

ones

sent

ial A

A A

lani

ne39

.345

.039

.041

.342

.835

.738

.441

.741

.642

.142

.143

.042

.642

.136

.335

.836

.241

.643

.337

.840

.7 A

spar

tic

acid

39.9

49.6

40.1

49.2

43.8

34.4

35.3

49.5

48.3

47.9

47.8

47.1

46.6

46.0

44.6

43.4

44.6

41.8

44.3

43.8

46.1

Cys

tein

e4.

74.

96.

86.

16.

64.

83.

84.

65.

65.

24.

55.

15.

14.

86.

46.

56.

74.

38.

45.

95.

4 G

luta

mic

aci

d64

.580

.468

.276

.871

.657

.159

.376

.976

.979

.374

.875

.274

.773

.876

.272

.874

.269

.072

.369

.774

.4 G

lyci

ne67

.264

.071

.060

.772

.966

.871

.860

.959

.561

.864

.365

.464

.464

.749

.551

.049

.969

.873

.856

.059

.7 H

ydro

xyly

sine

3.0

2.6

2.7

2.5

3.1

2.8

3.1

2.4

2.6

2.5

2.7

2.8

2.7

2.8

1.9

2.1

2.1

3.2

3.1

2.4

2.8

Hyd

roxy

prol

ine

26.3

21.0

27.5

20.5

27.4

27.3

29.3

21.5

21.1

23.3

23.3

24.4

23.8

23.4

14.5

15.7

15.0

26.5

25.8

18.6

20.1

Lan

thio

nine

0.1

0.1

3.3

0.1

2.3

0.7

0.1

0.0

0.1

0.2

0.1

0.1

0.1

0.1

0.1

0.2

0.1

2.2

3.6

0.1

0.1

Orn

ithi

ne0.

70.

90.

50.

40.

70.

40.

40.

90.

80.

61.

00.

80.

80.

80.

60.

81.

00.

81.

00.

60.

7 P

rolin

e42

.342

.545

.640

.347

.139

.242

.440

.939

.644

.546

.442

.141

.341

.432

.735

.634

.941

.249

.437

.437

.3 S

erin

e18

.419

.320

.921

.822

.217

.116

.221

.320

.621

.220

.119

.919

.818

.319

.618

.618

.417

.725

.418

.219

.3 T

auri

ne0.

81.

40.

70.

60.

70.

60.

50.

91.

00.

81.

01.

21.

11.

01.

31.

31.

40.

90.

81.

21.

0 T

yros

ine

12.9

16.2

12.6

16.5

12.9

10.1

10.3

15.2

15.2

15.0

14.4

14.7

14.4

14.0

14.9

14.2

14.6

13.5

14.8

13.7

14.6

Tot

al51

7.7

602.

853

6.8

585.

657

5.1

464.

448

1.9

577.

557

3.1

580.

957

3.5

577.

457

0.8

562.

652

1.2

512.

752

1.7

538.

659

2.9

525.

355

5.3



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than the number of samples used in Adedokun and Adeola (2005), the correlation between the proximate fractions and the energy value of the MBM samples would be more reliable. Although nothing appeared to be unique about MBM sample 4, regression model di-agnostics using DFBETA criterion revealed that data from this sample were influential points, and therefore, the AME and AMEn data from this sample were re-moved from further regression and correlation analyses. Removal of the data from this MBM sample increased R2 from 0.27 to 0.42.

The proximate composition of the MBM samples used in the current study was similar to what had been observed by others (Shi and Noblet, 1993; Vieites et al., 2000). In a previous work in our laboratory, Ad-edokun and Adeola (2005) reported average fat and P content that are similar to contents reported in the current study. Johnson and Parsons (1997) observed that high-ash MBM used in their study had decreased

GE and that low-ash MBM had greater GE, similar to the observation made with the samples used in the cur-rent study. In the current study, ash content explained about 95% of the variation in GE content of the MBM samples. High ash content could be because of high bone content. A greater proportion of bones may yield increased CP content in chemical analysis. However, this protein is primarily collagen, which is of little nu-tritional value to nonruminant animals (Eastoe and Long, 1960; Johnson and Parsons, 1997). This is one of the reasons why addition of an increased quantity of MBM (Peo and Hudman, 1962) or meat meal depressed growth performance in swine (Cromwell et al., 1991).

Batterham et al. (1980) reported that DE of 14 MBM for pigs ranged from 2,247 to 3,322 kcal/kg and Kara-kas et al. (2001) reported AMEn of porcine or bovine MBM in the range of 2,511 to 3,115 kcal/kg for broilers, Waring (1969) reported ME of MBM of 1,988 kcal/kg for colostomized roosters. Adedokun and Adeola (2005)

Table 4. Digestible energy, apparent ME (AME), and nitrogen-corrected apparent ME (AMEn; DM basis) contents of the meat and bone meal1

Meat and bone meal DE, kcal/kg AME, kcal/kg AMEn, kcal/kg

1 3,518 3,384 3,2832 3,267 2,901 2,7863 3,996 3,890 3,7944 4,120 3,842 3,7335 3,185 2,840 2,7296 2,669 2,611 2,5127 3,241 3,101 3,0018 3,387 3,001 2,8899 2,792 2,613 2,51010 3,125 2,714 2,60011 3,576 3,210 3,10212 3,534 3,267 3,17013 2,704 2,320 2,21214 3,367 2,988 2,88115 3,512 3,160 3,05316 3,863 3,442 3,34117 4,176 3,872 3,76718 3,130 2,627 2,51719 3,385 2,724 2,60920 3,353 2,860 2,74521 3,552 3,101 2,994SEM 207.7 219.7 222.7

1Data are means of 9 replicates per treatment.

Table 5. Correlation coefficients matrix of proximate fractions with energy content of meat and bone meals1

Item AME AMEn DE GE CP Phosphorus Calcium Fat Ash

AME — 0.99 0.92 0.15 −0.25 −0.20 −0.25 0.41 −0.29AMEn 0.93 0.15 −0.26 −0.19 −0.24 0.42 −0.29DE 0.34 −0.07 −0.38 −0.40 0.40 −0.48GE 0.50 −0.86 −0.94 0.44 −0.95CP −0.34 −0.42 −0.44 −0.42Phosphorus 0.93 −0.49 0.91Calcium −0.50 0.98Fat −0.52Ash —

1Correlation was conducted on 21 × 9 data points corresponding to the proximate components (9) values for the 21 meat and bone meal sam-ples; AME is apparent ME, kcal/kg of DM; AMEn is nitrogen-corrected apparent ME, kcal/kg of DM; DE, kcal/kg of DM; GE, kcal/kg of DM.



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reported AME ranging from 1,569 to 3,308 kcal/kg and AMEn ranging from 1,474 to 3,361 kcal/kg for swine.

It can be expected that CP utilization will affect energy utilization of any high-protein feedstuff includ-ing MBM. Interestingly, the correlation between GE and DE with the proximate fractions of the MBM was greater (exception was correlation between CP and DE) than the correlation between the same fraction with AME and AMEn. In addition, the greatest correlation was between GE and the proximate fractions. What this observation suggests is that it is probably less use-ful to relate energy utilizability (AME or AMEn) with proximate fractions than it is to relate proximate frac-tions with total content of energy (GE). The main con-tributors to GE in MBM are CP and fat; each gram of CP and fat contains 5.64 and 9.13 kcal, respectively (Larbier and Leclercq, 1992). Although the GE of fat is greater than that of CP, the CP content of MBM is much greater than its fat content. In the current study, the average proportions of CP and fat were 589

and 113 g/kg, respectively. On average, therefore, the proportional contribution of CP and fat to total GE in each kilogram of the MBM used in the current study was 3.2:1. This was likely why the correlation between CP and GE was greater than the correlation between fat and GE. Because of the greater contribution of CP to GE compared with fat, it is likely that any factor that affects CP utilization will have greater impact on energy utilization than factors affecting fat utilization. Interestingly, the current data showed that as the ra-tio of CP:fat in MBM increased, energy utilization re-sponses decreased and the correlation was especially stronger for postabsorptive energy utilization measures (i.e., AME and AMEn).

In view of the relationship between the data on en-ergy utilization and fat and CP content of MBM, it is of interest to investigate the factors that may affect the utilization of these proximate fractions and their pos-sible effects on energy utilization. These factors may in-clude AA balance, proportion of saturated:unsaturated

Table 7. Best model selection for prediction of apparent ME and nitrogen-corrected apparent ME contents of the meat and bone meal1

No. of variables R2 Mallows’ Cp Variables used

Apparent ME, kcal/kg 1 0.17 2.54 Fat 1 0.09 4.47 Ash 2 0.26 2.59 CP, ash 2 0.26 2.65 GE, ash 3 0.34 2.80 GE, CP, ash 3 0.32 2.23 GE, P, ash 4 0.41 3.14 GE, P, CP, ash 4 0.37 4.14 GE, P, fat, ash 5 0.42 5.02 GE, P, CP, fat, ash 5 0.41 5.13 GE, CP, P, Ca, ashNitrogen-corrected apparent ME, kcal/kg 1 0.17 2.51 Fat 1 0.08 4.55 Ash 2 0.26 2.66 CP, ash 2 0.26 2.68 GE, ash 3 0.34 2.83 GE, CP, ash 3 0.32 3.28 GE, P, ash 4 0.41 3.12 GE, P, CP, ash 4 0.37 4.12 GE, P, fat, ash 5 0.42 5.02 GE, P, CP, fat, ash 5 0.42 5.12 GE, CP, P, Ca, ash

1GE, kcal/kg; Mallows’ Cp = model selection criterion that compares full with reduced models for error sum of squares.

Table 6. Correlation coefficients of utilized energy in meat and bone meal and the ratio of nutrients in the feedstuff1

Item GE:Ash Fat:Ash CP + Fat:Ash CP:Fat GE:CP GE:Fat

AME 0.36 0.44 0.33 −0.39 0.39 −0.33AMEn 0.36 0.44 0.32 −0.39 0.39 −0.33DE 0.49 0.53 0.48 −0.29 0.43 −0.17GE 0.90 0.78 0.91 −0.05 0.58 0.29

1AME is apparent ME, kcal/kg; AMEn is nitrogen-corrected apparent ME, kcal/kg; DE, kcal/kg; GE, kcal/kg.



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fatty acids (Rustan et al., 1993), or ash content (John-son and Parsons, 1997) especially because ash may affect digesta passage rate or may potentiate inter-actions between minerals and other nutrients (Atteh and Leeson, 1984). Karakas et al. (2001) used MBM from swine or cattle but with different ash contents in a broiler study and reported that species of origin had no effect on AMEn of MBM, but rather, MBM samples with greater ash content (ash content ranged from 21 to 43%) at greater inclusion (20%) depressed AMEn. Shirley and Parsons (2001) noted that as the content of ash in MBM increased, there was also a decrease in the level of all essential AA (except Arg) as well as a reduc-tion in protein efficiency ratio of the feedstuff whereas there was no effect on CP digestibility. A decrease in CP utilization coupled with high CP intake will pro-duce increased N load on the animal with consequent increase in expenditure of energy for N excretion and hence a reduction in the amount of available energy to the animal.

Because the N status of an animal receiving a feed-stuff will influence the AME of that feedstuff, AME is usually corrected for N retention (AMEn). An animal in positive N balance will excrete less N and retain more of its dietary energy than an animal in negative N balance. Noblet et al. (1994) observed in growing pigs that the energy lost in DE to ME step was con-cerned mainly with digestible CP and digestible ADF and corresponded to the amount of urinary digestible CP energy losses. Urinary N loss is the main route for disposing of excess N arising from catabolism of AA. This N loss, if considerable, represents a significant en-ergy cost to the body.

For example, Diggs et al. (1965) observed that ME was about 82% of DE for pigs receiving high-CP feed-stuffs compared with the value of about 95% for the same measure in pigs receiving cereal grains. The use

of a NE system will likely be more advantageous than ME in describing the energy value of high-protein feed-stuffs. We have shown previously (Olukosi et al., 2008) that NE explained more of the variation in performance of broilers than ME. In addition, Just (1982a) noted that NE is the best measure of energy utilization in pigs because it takes into consideration the heat losses associated with digestion and nutrient metabolism. Pir-gozliev and Rose (1999) similarly observed in poultry that the efficiency of protein utilization might be the single most important variable to consider in adjusting ME to describe their NE values.

Taverner et al. (1983) pointed out that lysine digest-ibility of the MBM used in their study with pigs was approximately 50%. In a study with broilers (Karakas et al., 2001), ileal N and total AA digestibility were 55.8 and 56.2%, respectively, and CP digestibility de-creased with inclusion level of the MBM. In the same study, the authors reported high correlation (r = 0.9) between AMEn and CP content. In the current study, the average digestibility of MBM CP was 83% (data on N utilization are not presented), and N excretion was 18.1 g/d and was not different among the MBM. How-ever, there was a negative correlation between N output and DE, AME, and AMEn, and r was more negative (−0.37) for AME and AMEn than for DE (−0.14). Al-though the quantity of N output was also related to CP content (r = 0.48), a comparison of the relationship between N output and energy utilization suggests that the impact of N utilization on energy availability is very important. In fact, except for fat content, the correla-tion between N output and AME or AMEn was greater than the correlation for all the proximate fractions. In addition, the improvement in correlation (from 0.14 to 0.37) between N output and AME or AMEn compared with N output and DE further supports the suggestion that CP utilization had great influence on postabsorp-

Table 8. Parameters of the best regression models for apparent ME and nitrogen-corrected ME for the meat and bone meal

No. of variables in the model AIC1 R2 Intercept

Parameter estimate

GE, kcal/kg

CP, g/kg

Phosphorus, g/kg

Ca, g/kg

Fat, g/kg

Ash, g/kg

Apparent ME, kcal/kg of DM 1 240 0.17 1,530 13.3 2 239 0.26 13,037 −1.54 −12.2 3 239 0.34 12,727 −1.14 −3.24 −10.7 4 239 0.41 13,587 −1.25 −3.51 30.4 −16.4 5 241 0.42 14,994 −1.20 −4.90 31.6 −5.49 −16.1 6 242 0.42 15,190 −1.22 −5.02 33.2 −2.60 −5.88 −16.8Apparent nitrogen-corrected ME, kcal/kg of DM 1 240 0.17 1,401 13.5 2 240 0.26 6,451 −4.49 −3.72 3 239 0.34 12,670 −1.14 −3.31 −10.7 4 239 0.41 13,547 −1.25 −3.59 31.0 −16.5 5 240 0.42 14,870 −1.21 −4.90 32.2 −5.16 −17.5 6 242 0.42 15,071 −1.23 −5.02 33.8 −2.69 −5.56 −16.9

1Akaike information criterion (AIC) was used for model selection. The smaller the AIC, the better the model.



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tive energy utilization. Martosiswoyo and Jensen (1988) similarly reported decreased AMEn in broilers receiving greater inclusion level of MBM in their diet, and this was attributed to increased amount of excreted uric acid and N.

The proximate fractions generally explained <50% of the variations in DE, AME, and AMEn. However, there were greater correlations between the proximate frac-tions and GE. In the current experiment, there was a negative correlation between energy and CP and a posi-tive correlation between energy and fat. Adedokun and Adeola (2005) similarly reported negative correlations between CP with AME and AMEn of the MBM used in their study. However, the correlation coefficient of fat with AME or AMEn in Adedokun and Adeola (2005) was greater than observed in the current study, whereas the opposite was true for CP content of the MBM. The difference between the results obtained may be related to the MBM samples used. The MBM samples used in the current study had greater GE and CP and less Ca and ash than the ones used in Adedokun and Adeola (2005). However, results of the current study indicate that the proximate fractions did not explain much of the variation in energy utilization.

We are not aware of any study reporting the correla-tion of ratio of energy yielding fractions with one an-other or with ash on AME or AMEn of MBM. However, other studies have shown that the ratio of CP or pro-portions of calories contributions from energy-yielding nutrients can influence energy value of feedstuffs. For example, Hartsook et al. (1973) observed that propor-tional calories contribution from carbohydrate and fat decreased DE and ME of rat diets at a reduced CP level but had the opposite effect at an increased CP level. Just (1982b) observed that increasing the level of crude fiber in swine diet depressed ME by shifting digestion to the more distal portion of the digestive tract. However, Just (1982a) reported that increasing concentration of fat in diet for growing pigs increased the ME values of the diets because of greater efficiency of absorption of fatty acids as concentration of fats in the diets increased. Consequently, we were interested in examining how the ratios of energy-yielding nutrients in MBM may relate to AME and AMEn. However, in the current study, correlations of the ratio of proximate fractions did not explain more of the variations than were explained by the individual proximate fractions.

Because of the wide variation in the composition, species of origin, and processing techniques of MBM, chemical composition, energy content, and energy uti-lization will vary widely. It is not possible to determine the energy value of all possible types of MBM available, but it is hoped that an examination of MBM obtained from different species and having different chemical composition may yield a robust prediction equation that may be applicable in many situations. In the cur-rent study, to establish the best prediction equation, the full and reduced models were tested to establish which

of the predictors can be eliminated from the predic-tion equation. The P-values for the partial correlations did not reach significance (at 5% probability), indicat-ing that there was no substantial gain in precision by reducing the number of predictors used in the regres-sion equation. Consequently, the decision to eliminate some of the predictors was based on the result obtained from the model-building process using multiple layers of elimination criteria (R2, AIC, and Cp) to identify the best model candidate.

Batterham et al. (1980) noted that the best relation-ship between DE of MBM and the proximate composi-tions required inclusion of GE, fat, Ca, and P in the model. Adedokun and Adeola (2005) described a model that incorporated CP and ash for predicting AME and AMEn of MBM for pigs. As explained earlier, regression coefficient will increase as more variables are added to the model and so it is not a very useful criterion for choosing the optimum model. However, AIC penalizes for complexity of model and therefore ensures the use of the minimum number of variables. There is no per-fect model, and because the objective was to select the best model from a pool of possible model subsets, a combination of selection criteria was used in the cur-rent study.

In the current experiment, the best model for pre-dicting AME and AMEn of MBM for swine used 4 vari-ables, namely GE, P, CP, and ash. An examination of the information provided by R2, Cp, and AIC helped inform the choice of this model as previously explained in the Results section. Adedokun and Adeola (2005) noted that the best reduced model incorporated only CP and ash in MBM for predicting AMEn for swine, but the choice of the model in that study was based on R2 and SD values. Although the MBM used in the 2 studies were different, the chemical compositions were similar (the differences are noted earlier). However, in the 2 studies, ash and CP were included in the reduced model. It is likely that the use of more selection crite-ria in the current study allowed the choice of a model that is more precise and at the same time eliminated redundant variables that did not add significantly to the precision of the prediction equation.

In conclusion, the result of this study showed that MBM is, in addition to being a source of CP and miner-als, a good energy source with an average AME value of 3,070 kcal/kg. In addition, the current study high-lighted the potential of using proximate compositions for predicting the AME and AMEn of MBM for pigs. The study also established that in addition to the prox-imate compositions, factors that influence the utiliza-tion of these fractions, especially fat and CP, may have substantial impact on energy utilization of MBM. We believe that although other factors extrinsic to MBM may have influence on its energy utilization, the use of proximate fractions as well as the characteristics of these fractions should be sufficient for predicting the energy value of MBM for swine.



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