prediction of digestible energy for animal feed

8/10/2019 prediction of digestible energy for animal feed

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An imal F eed Science n d Technology, 39 1992) 39-59Elsevier Science Publishers B.V., Amsterdam

39

C. de Blasa, J. Wisemanb, .-J. Fraga and .-J. Villamide Departamento de Producci6n Ani mal, Escuela TPcnica Superi or de ngenieros Agr 6nomos.

Un iversidad Poli tPcnica, 28040 M adrid, SpainbVni versity of Notti ngham, F aculty ofA gri cultu ral and F ood Sciences, Sutton Bonington.

Loughborough, LE 12 M D, UK

(Received 23 April 1991; accepted 17 April 1992)

ABSTRACT

De Blas, C., Wiseman, J., Fraga, M.-J. and Villamide, M.-J., 1992. Prediction of the digestible energyand digestibility of gross energy of feeds for rabbits. 2. Mixed diets. An im. F eed Sci. Technol ., 39:39-59.

Mixed diets, for which information relating to various chemical measurements and rates of inclu-sion of certain raw materials (independent variables) and their nutritive value for rabbits (dependent

variables) as assessed in terms of digestible energy (DE) and the coefficient of digestibility of grossenergy (GEu) was available, were combined in a series of correlation matrices and step-wise linearregression analyses. GE,, removes the possible effect of variability in gross energy value on DE valuesand is reported. In general, GED was better correlated to acid detergent fibre (ADF) than crude fibre(CF ) . A general equation was derived: GEu - 0.867-0.0012 ADF (g kg- DM) R*=0.888. Closerinspection of individual points revealed that diets based on added fat, beet pulp, citrus pulp and strawat levels higher than 200 g kg-i behaved atypically. Separate prediction equations were derived foreach of these groups of diets. Multiple regression analyses considered more than one independentvariable, but gave only marginal improvements in terms of the accuracy of prediction of GEu. Addi-tion of rates of inclusion of feedstuffs as independent variables revealed that the chemical analysesemployed were insensitive to changes in nutritive value arising from additions of fat, pulps and straw.These feedstuffs need to be characterised further and general prediction equations should not be ap-plied to diets in which they are contained.

INTRODUCTION

Prediction of t e dietary energy vasurements is regarded as being of comore rapid than reliance on metaboli

f mixed diets from chemical mea-erable value. S an ap h isals, ahm for e effk ual-

Correspondence to: C. de Blas, Departamento de Production Animal, Escuela TCcnica Superiorde Ingenieros Agr6nomos, Universidad PolitCcnica, 28040 Madrid, Spain.

0 1992 Elsevier Science Publishers B.V. All rights reserved 0377-8401/92/ 05.00


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4 C. DE BLAS ET AL.

ity control and is of fundamental importance in legislation governing the dec-laration of values in diets supplied by the feed industry. In non-ruminants,equations have been derived for pigs (e.g. Wiseman and Cole, 1983; Morganet al,, 1987; Henry et al., 1988) and poultry (e.g. Fisher and McNab, 1987;Car&, 1990). There is a limited amount of data with respect to the derivationof prediction equations for diets fed to rabbits. Maertens et al. ( 1988 ) consid-ered a number of approaches to the prediction of the digestible energyDE )value of 3 diets, including those based on digested nutrients. However,

as was pointed out, this is of limited value unless reliable estimates of diges-tibility are available. In addition, the same balance procedures required todetermine the digestibility of nutrients are needed to determine DE. The lat-ter, in addition, requires only one laboratory analysis-that of gross energy.

Prediction of DE from chemical analyses would appear to be a more appro-priate procedure. Maertens et al. ( 1988) employed this approach, but someof the results were inconclusive in terms of, for example, the relative value ofthe different measurements of plant flbre. Furthermore, diets based on beetand citrus pulps had to be excluded from the analysis. Finally, the use of ni-trogen-free extract is not considered acceptable because it is determined bydifference and, accordingly, is not an independent variable. In addition, ow-ing to its method of determination, it contains all the accumulated errors as-sociated with other chemical determinations.

The objective of the current study was a comprehensive approach to thederivation of prediction equations for rabbits based on a number of diets ofdifferent chemical content and raw material composition.

MATERIALS AND METHODS

The data base for the regression analyses was that of the large number ofmixed diets that have been evaluated with rabbits at the Universidad Politec-nica, Madrid, Spain. Details of methodology and data have been presentedpreviously (De Blas et al., 198 1, 1986, 1989; Fraga et al., 1984, 1989, 199 1;Santoma et al., 1985,1987; Mendez et al., 1986; Carabafio et al., 1988; Ortizet al., 1989; Villamide et al., 1989, 199la,b); De Blas and Villamide, 1990;Motta, 1990; Femandez, 199 1; Garcia, 199 1) .

The analysis employed was a linear step-wise regression approach using theStatistical Analysis Systems Institute ( 1985) procedure. In addition to chem-ical measurements (see Table 1 for definitions), some analyses also includedsquared terms for acid detergent tibre ADF) together with neutral detergentfibre (NDF) and actual rates of inclusion (squared) of straw and pulps. Theseterms, either singly or in combination, were independent variables. Digesti-ble energy and the coeficient of digestibility of gross energy (GEP) were bothincluded in the analyses as estimates of nutritive value (dependent vari-


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FEEDS FOR RABBITS: MIXED DIETS 41

TABLE 1

Definitions of abbreviations used in the text

Chemical measurementsCP Crude proteinEE Ether extractCF Crude fibreADF Acid detergent fibreADF2 ADFxADFNDF Neutral detergent fibreADL Acid detergent Iignin

Biological measurementsDE Apparent digestible energyGE Gross energy

GED Coefficient of apparent digestibility of gross energy

Raw feeds@ materialsPU PulpsPU2 PUXPUST StrawST2 STxST

sis considered a number of groups ofon the absence or presence of unusual feedstuffs

taining added fat (n = 25 );

diets containing straw at rates of inclusion2OOgkg- (n=25).

RESULTS AND DISCUSSION

Correlations between individual measurements

Correlation matrices for AnalysesCeneraliyj high negative correlati

are presented in Tables 2 (Aere obtained between both


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GED and measurements of fibre. e majority of cases, Acorrelated than crude fibre (CF)ering added fat and beet pulp. For the dtion between DE together with GE,, anand - 0.244, respectively).

Regressi on nal y ses sing one ndependent ar i abl e

The results of the step-wise line ssion analyses for allare presented in Table 3 (A)-(H) . lyses considered bothas dependent variables, althoughvariables were chemical analyses t3 (G) and 3 (H), rates of inclusion of pulps and straw. No independent vari-

associated with a P value greater than 0.15 in any function was included.rlst it is accepted that P> 0.05 is the more conventional term to employ,

it was thought appropriate to consider all variables where there was evidencefo

ng only one independent vari re was a general su-periority of ADF over others in terms of R 2 valuesexpected, bearing in mind the original correlatioremoves the influence that any variability in diet ss energy values mighthave had on DE valcorrelations betweenthan when DE was included. Thus, eADF tended to be associated with hwas predicted.

Differences between groups of mixed feedsdiction of nutritive value from Aest figure for R2 (0.888, eqn. ( 1equation) was obtained from Dataand for which no measurements fortion was used subsequently to compa f GED to ADF levels forall the data sets. The relationships between GFigs. 1,2,3, and 4, respectively, forevident from this approach that the general equation gave a reasonable esti-mate of GED in all cases, although there were individual differences.values for Data Sets B and D had been lower, andindividual points revealed that in general thosefat and pulps deviated from the line of tillustrated in Figs. 5, 6 and 7 whbased on added fat (Analysis E)straw (Analysis er with the line for t


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51

0.81Mixed Diets

0.4150 75 100 125 150 175 200 225 250 275 300 325 3

ADF (g/kg DM)

Fig. 1. Relationship between ADF and the coefficient of digestibility of gross energy (GE,,).Analysis A: Diets excluding straw greater than at a rate of 200 g kg-, added fat and pulps, andfor which NDF and ADL were measured (n=41). y=O.863-0.0012 ADF (g kg- DM);R2=0.888 (eqn. (1), Table 3(C)).

Mixed Diets

0.7-

CJ 0.8-

0.5- n = 58

0.4s I 150 75 100 125 150 175 200 225 250 275 300 325 350

ADF (g/kg DM)

Fig. 2. Relationship between ADF and the coefficient of digestibility oe gross energy (GED).Analysis B: Diets including straw at all rates and pulps, and for which ISDF and ADL weremeasured (n=58).y=0.863-0.0012ADF (gkg-* DM);R2=0.888 (eqn. (l),Table3(C)).

bility of the general equation to predict the nutritive values of all diets withequal accuracy is due to the insensitivity oftployed (ADF) to estimate a ological value precisely.from one source does not hSuch insensitivity in themspecies of animals (e.g. with pigs, ng and Taverner, 1975; Just et al., 1984)


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52 C. DE BLAS ET AL.

Mixed Diets

0.7-

OS- n = 66

044* 50 75 100 125 150 175 200 225 250 275 300 325 3

ADF (@kg DM)

i0

Fig. 3. Relationship between ADF and the coefficient of digestibility of gross energy (GE,).&ralysis C: Diets excluding straw greater than at a rate of 200 g kg-, added fat and pulps, andfor which no measurements for NDF and ADL were taken (n = 66). y=O.863 -0.0012 ADF (gkg- DM); R*=0.888 (eqn. (l), Table 3(C)).

Mixed Diets

0.8

50 75 100 125 150 175 200 225 250 275 300 325 3ADF (g/kg DM)

i0

Fig. 4. Relationship between ADF and the coefficient of digestibility of gross energy (GE,).Analysis D: Diets including straw greater than at a rate of 200 g kg- and pulps, and for whichno measurements for NDF and ADL were taken (n = 93). y=O.863 - 0.0012 ADF (g kg- DM);R*=0.888 (eqn. (l), Table 3(C)).

and is the major reason why there is considerable interest in the derivation ofseparate prediction equations for individual classes of feeds (Wiseman et al.,1992).

.4ccordingly, it was considered appropriate to treat the diets containingadded fat, pulps and straw in this manner. The results are presented in Tables


15/21


Mixed Diets

0.7-

0.6.cl

g 0.5.

04

0.3,50

10; 75150 200 250 300 350 4

ADF (g/kg DM)0

I + 3Oglkg added fat 6Oglkg added fat 1

Fig. 5. Relationship between ADF and the coefficient of digestibility of gross energy (GE,,).Analysis E: Diets containing added fat (but excluding those in Tables 2(A) or 2(D) above)

n=25). y=O.863-0.0012 ADF (gkg- D ); R2=0.888 (eqn. (I), Table 3(C)).

Mixed Diets

w 0.6-(3

0.5.

n = 190.4-r I

50 75 100 125 150 175 209 225 250 275 300 325 3ADF (g/kg DM)

IO

I Beet pulp + citrus ulp 1

Fig. 6. Relationship between ADF and the coefficient of digestibility of gross energy (GE,,).Analysis G: Diets containing added beet and citrus pulp n= 19). y=O.863-0.0012 ADF (gkg- DM); R2=0.888 (eqn. (I), Table 3(C)).

3(E) (fat), 3(prediction equations relhigh figures fordent variable ( the use of CF resulted indiets containing beet pulp).


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54 C. DE BLA 3 ET AL.

Mixed Diets427

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S OS-

0.4-

0.d50 100 150 200 250 300 35: 4

ADF (g/kg DM)IO

Fig. 7. Relationship between ADF and the coefficient of digestibility of gross energy (GE,).Analysis H: Diets containing straw at rates greater than 200 g kg- (n = 25). y=O.863 -0.0012ADF (g kg- DM); R*=0.888 (eqn. (l), Table 3(C)).

generally poor predictors of GED in diets based on added fat. Although theaddition of fat could be regarded merely to dilute the other components ofthe diet, and thus maintain correlations between chemical measurements andnutritive value, fat is a variable commodity both in itself and in the mannerin which it may influence other dietary constituents (e.g. Santoma et al.,1987 ). Additions of a fixed amount of fat to a diet will accordingly have avariable response in terms of nutritive value. Furthermore, the addition offats to diets was invariably associated with changes in the content of otherchemical measurements (e.g. an increase in crude protein), which may alsohave influenced the nature of the regression.

Diets based on citrus pulp were also comparatively poorly correlated withmeasurements of fibre and, additionally, it was evident that the fibre fromthis raw material was behaving in a different way, nutritionally, than thatfrom other fibre sources. The general equation was a comparatively accuratepredictor of other previously reported sources of fibre (Fig. 8 ) . It should benoted that the general equation excluded those diets containing high levels ofadded straw. However, the reason for this was not the higher levels of ligninthat would be expected to be present in these diets. Other fibre sources (e.g.grape mart) have high levels of lignin and yet did not deviate from the re-sponse predicted by the general equation. It would seem that other issues con-tribute to the anomalous responses recorded with diets high in straw and it isprobable that factors including particle size are important with this particularraw material.


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Mixed Diets

0.7-

0.6-n

g OS-

0.4-

0.3 150

n 25

100 150 200 250 300 350 400ADF (g/kg DM)

Fig. 8. Comparison between the response of the general equation from the current study (eqn.( I), Table 3(C) ) and previously published work considering different sources of fibre.

Regressi on anal y ses using more han one ndependent var i abl e

Step-wise linear regression an sis allows the effects of additions of inde-to the model tare presente

ber of independent variables improved thepet owever, improvpar h that achievedvariable.

For those reasons discussed above, diet ed on added fat, straw anwere subsequently considered separately individually.

It should be noted that regression equations are mathematical fubut that their applicability should not be viewed solely in te s of accuracyas indicated by high R values. Thus, in multiple linear regressions it is as-sumed that all the independent variables are independent of one another.However, it could be argued that the presence of more than one icrm for fibre(e.g. CF and NDF) is not valid as the terms are not strictly ineach other as they are estimating a similar fract . It may therefore be in-appropriate to use equations where this occurs. wever, the original corre-lation matrix had revealed that, in fact, the c ation between AADL was poor, and that th was occasionally a significant improvthe proportion of the ove ariation accounted for independent variableswhen these two te s were included in the sa n this basis, theuse of both of them would seem justifie


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56 C. DE BLAB ET AL.

Regressi on nal y si s mpl oy i ng hemi cal anal y ses quared nd at es fi nclusion f eedst ufl as ndependent ar i abl es

The insensitivity of those chemical measurements used to predict changesin the nutritive value of mixed diets based on added fat, pulps and straw hasbeen discussed above. s a means of overcoming this difficulty, further ap-proaches were considered. It is possible that increases in fibre content maynot be linearly related to changes in the nutritive value of mixed diets. AC-cordingly squared terms were employed for ADF in Analyses B, D and H.However, in no case did the addition of this term improve the accuracy ofprediction significantly. Although such an improvement was reported in dietsfor pigs by Wiseman and Cole ( 1983) using CF and modified ADF, the finalR* ata reported were similar to those in the current study without squaredterms.

further analysis considered the use of rates of inclusion of pulps and strawas independent variables. Such an approach did not significantly improve theaccuracy of prediction for Analyses B and D, but did have a large effect onthose analyses which considered pulps and straw separately (F and G, respec-tively, for beet and citrus pulps, and H for straw ). These analyses confirmedthe insensitivity of those chemical measurements employed in the predictionof the nutritive value of diets.

The magnitude of the difference between the GEn values predicted fromthe general equation and those actually determined was viewed in terms ofthe regression of this difference against chemical measurements and the ratesof inclusion of feedstuffs. Data are presented in Table 4.term for the rate of inclusion of beet and citrus pulps retively high R2, ndicating that the difference between the predicted and actualGEn could be explained largely by the addition of these two feedstuffs (incontrast to the lack of ability of chemical measurements to achieve this). Incontrast, the presence of rates of inclusion of added fat (in the form of etherextract content of the diet ) and straw did not improve the accuracy of predic-tion to such an extent. The possibility exists, therefore, that these two com-modities were interacting in a variable way with other components of the dietsin which they were contained.

The general conclusions to be drawn from this study are that prediction ofthe nutritive value of mixed diets fed to rabbits, as expressed in terms of GEbwas in general more accurate when ADF was used in comparison to CF as theindependent variable. The addition of further chemical measurements as in-dependent variables improved the accuracy of prediction, but often only mar-ginally. Diets containing added fat, beet pulp, citrus pulp and straw reducedthe accuracy of prediction, and individual functions were derived for thosediets containing these four commodities. Prediction equations can be appliedto diets as long as the range in measurements for chemical componebeneath or beyond those studied. In addition, diets containing added fat, pulps


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TABLE 4

Regression of the difference between the actual znd predicted coefficient of digestibility of gross en-ergy (GE,,A-GEnP, dependent variable) on chemical measurements and rates of inclusion of rawmaterials (independent variables). Cl&P is from eqn. (b), Table 3(C)

(A) Diets containing added fat (n= 25). Original prediction equations are presented in Table 3 (E)

Intercept al R2 rsd(W

: Y -0.020+0.0161

PzO.225

0.00077* 0.00023

P= 0.003

0.33 1 0.016

rsd, residual standard deviation.

B ) Diets containing added beet pulp (n = 10). Original prediction equations are presented in Table

3(F?Intercept al a2

W-J) (ADF)R2 rsd

1 Y=

2 Y=

0.015 0.00016f0.0140 + 0.00005

PcO.317 P=O.Oll

0.059 0.00018+ 0.0242 + 0.00004

P= 0.046 P= 0.003

0.579 0.018

-0.00024 8.738 0.015f 0.00011

PzO.078

(C) Diets containing added citrus pulp (n = 7). Original prediction equations are presented inTable 3 (G)

Intercept C: a2W-J) (ADF)

R2 rsd

Y=

Y=

- 0.032 +0.00025+ 0.0296 0.00006

P=O.326 P=0.0010

0.174 + 0.00034+ 0.0827 + 0.00006

PzO.104 P= 0.004

-0.0012+ 0.00047

PcO.062

0.764 0.03 1

0.910 0.02 1

D ) Diets containing straw at rates greater than 200 g kg- n 25 ) Original prediction equationsare presented in Table 3(H)

Intercept al a2(ST) (ADF)

R rsd

1 Y= 0.057 -0.00027 0.378 0.034f 0.0262 + 0.00007

PzO.041 P=O.OOl

2 Y= 0.113 -0.00027 -O.OOO~F 0.475 0.032

kO.0372 +0.00007 +0.00015PzO.006 P=O.OOS PcO.056

-


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58 C DE BLAS ET AL

and straw should be treated separately for the purposes of predicting nutritivevalue. Finally, it is crucial to note that the equations are only applicable tothose diets with chemical compositions within the range of values employedin the current study. Extrapolation beyond or beneath this range is notjustified.

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analyses of fibre contents. Anim. Prod., 21: 275-284.Maertens, L., Moermans, R. and De Groote, G,, 1988. Prediction of the apparent digestible

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prediction of digestible energy for animal feed

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