Methodology for foodsubsector modernization :the case of meat productionin Belgium

Lebailly Ph. .. Bumy Ph.

Faculté des Sciences AgronomiquesUnité d'Economie ruralePassage des Déportés, 2B - 5800 Gembloux

UDC-nr .631513.2 (493)

Keywords Slaughterhouses, location, restructuring

1177 R~Yu(, dl' l'Agriculture . Landbouwnjdschrift. 1989. Vol. ·n. n'- 5

Surnrnaryln this paper the authors present a rnethodology likely ta explainand quantify the factors contributing ta the dynarnics of a particu-larIy irnpenetrable subsector in Belgiurn : the butcher's rneat.The results obtained can help the decision-makers ta take rneasuresin order ta realize the restructuring of the Belgian slaughterhousesnetwork. On a long-terrn basis it is possible ta define a policy in

larder ta stirnulate sorne agro-alirnentary activities by acting, not onlyon the slaughterhouses, but also, sirnultaneously, on an the deficientup- and downstrearn elernents.

using statistical methods. The methodologypresented here has been applied to the meatsubsector in Belgium, in order to find a stra-tegy for the restructuring of slaughterhouses.

1. Introductionln most countries the rationalization of themain parts of agro-industrial subsectors is,nowadays, more and more unavoidable.Nevertheless the problem is rather complex.Many elements of information are needed(economical, sanitary, political, juridical,sociological, ...) and many professional skillsare required.Besides, the problem is not ta study the sett-lement of new plants, but rather ta considerthe modemization of existing tools, inheri-tance of the past.The rational approach of the problem, basedon the optimization of the system and lea-ding ta the construction of processing units,having eliminated the present structures,must be rejected, because it is not opera-tional.lndeed, taking into account only purelytheoretical considerations induces a completespatial modification of the agro-industrialactivities. This changes the relation withinan agro-alimentary subsector thoroughly andis difficult to be accepted by the professio-nals and the political factors, from a humanpoint of view (social cast, organizationproblems).Nevertheless, the determination of the priori-ties needs an integration of the strengths andweaknesses of the different constituting partsof the agro-alimentary subsectors. It is diffi-cult ta realize this integration without usingthe methods of the quantitative geographicalanalysis. ln fact, the purpose of this paper ista show that the researcher is able ta dealwith the problems linked with the moderni-zation of an agro-alimentary subsector, by

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2. MethodAn adapted methodology was immediatelychosen, allowing a smooth restructuring ofthe subsector which is studied here, ln fact,methods allowing a rationalization of thesystem by optimization have been abando-ned for two main reasons. The first one isrelated to the social aspects mentionedabove. The second one is the difficulty tadefine the function which must be optimi-zed. The minimization of the transportationcosts alone must be rejected in countrieshaving goad means of communication.The method proposed can be divided inthree parts. The first one consists in definingthe limits of the study, the second one dealswith the integration of space in the econo-mical analysis and the third one tries to findthe equation measuring the level of activityof the processing units, according to ele-ments situated upstream and downstream.

2.1. Limits of the studyln this paragraph we discuss the precautionswhich must lead the researcher for thechoice of the population, the period and thevariables. ldeally, studying the location ofthe activities of an agro-alimentary subsectorneeds the collection and treatment of datawhich are very precisely established in thespace.Nevertheless, it is true that choosing thesmallest base unit leads to several technical

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and statistical problems. The informationrelative to the subsector studied here, whichis necessary to build a model, is not alwaysavailable at the lowest level.Besides, the collection, the encoding and thetreatment of numerous observed variablescan uselessly lengthen the preliminary stepof the study. ln fact, what seems to beimportant in the choice of the population isthe area of action of the processing unit, sothat most of the chosen base units have anactivity for the link of the subsector which isconsidered.A priori, the restructuring of a link of anagro-industrial subsector appears to beessentially a spatial problem and, in thelimits of this study, the factor 'space" will beprefered to the factor "time". The data rela-tive to the meat subsector studied here comefrom the most recent cens uses available.Nevertheless, it is necessary to see if sornespecial economical and political factors donot have effects on the values of sorne varia-bles concerning the year of reference.The dependent variable which will beexplained is the quantity of meat producedin the geographical areas during a year. Theindependent variables are the upstream anddownstream variables. They are distinctlytreated according to the different links of thesubsector they characterize.

2.2. Insertion of space in the economicalanalysis .The establishment of the equation measuringthe slaughter activity must integrate the,space in the economical analysis. The inte-gration of space is not only the introductionof a descriptive element, but an operation alconcept. Space is not economically neutral.It appears mainly as the environment wherean action takes place, a structured environ-ment, but which may see its structures tho-roughly modified by a voluntarist policy oforganization of the soil disposition, ofimprovement of the communication means,and of valorization of the natural resources(Lajugie et al., 1979 ).The spatial theory and its economical conse-quences were first described by the works oftwo German economists : von Thünen, thepioneer, who tried to de termine the bestlocation for agricultural production, accor-ding to the different markets, and whopublished in 1826 his famous work : "Derisolierte Staat in Beziehung auf Landwirts-

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chaft und national Ôkonornie"; and Weber,who studied the rational location of rawmaterial processing facto ries, in order toreduce useless transportation to consumptionmarkets, and whose general theory is develo-ped in his book : "Uber den Standard derIndustrien" published in 1879.Several authors showed the weaknesses ofWeber's theory (Claval, 1969; Ciceri et al.,1977, Lajugie et al., 1979; ...) which, never-theless, appeared to be fruitful by beingoften adapted and reviewed. .The gravity models have been established byanalogy with Newton's gravitation law, byapplying the newtonian logic to geography(Lange, 1982). The variables studied are con-sidered as masses with forces, moving in afield of interrelations.Many geographical works were based on thegravitation law and notably, at the beginning,by Reilly (1931), who, according to empiricalresearches about the commercial attractionareas of towns in the United States, formula-ted the gravitation law for the retail distribu-tion, usually called "Reilly's law".ln fact, all gravitational models are based ontwo postulates : the first one implies that thestrength of the interaction between two spe-cial units, in economical terms, is inverselylinked with 'the distance separating them; thesecond one implies that the probability ofsettlement of a plant is changed (increasedor decreased) wh en another plant is alreadyin the neighbourhood (Ciceri et al., 1977).ln order to insert space i~ the economicalanalysis, a peripherality index can be defi-ned. It is also called the potential, accessibi-lity or proximity index. Ir is appropriate tomeasure the reciprocal influence of eachpoint of a given geographical area. The indexis one of the results of thedevelopments ofthe theory of mutual attraction in a spatialfield. Different general formulations havebeen proposed (Harris, 1954; Nadasdi, 1971;Ciceri et al., 1977; Keeble et al., 1986, andothers).ln the limits of this study, the formulation ofNadasdi is retained. It postulates a uniformdistribution in the space, considering thebase unit as a point :

n Xj

Sj

dkIJp.

1+

X·1

S'1j=lj *i

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where:Pi is the peripherality index of the base unit;k is a constant;Si is the area of the base unit i;Sj is the area of the base unit j;dij is the distance between the base units iand j;Xi is the measure of the weight of the varia-ble in the base unit i;Xj is the measure of the weight of the varia-ble in the base unit j.The distance between each couple of points(i, j) is measured by the euclidic distancebased on the "longitude - latitude" coordina-tes of the central points of the base units. lncountries with well developed transportationinfrastructures, the bird flight distance is anacceptable measure (Yates, 1963).The problem is still ta define a methodologyin order ta estimate the value of the cons-tant k, which varies from 1 ta 2 according tathe specialists (Ciceri et al., 1977).When the potential of a base unit is calcula-ted, a high value of k favours the contribu-tion of the base units which are just around.lnversely, a value near 1 is more neutral.The variables may be transformed with theformula above for different values of k (forexample : I, 1.5 and 2). After that, the rela-tion between the dependent variables retai-ned and the transformed variables (indepen-dent variables), ta which the variables beforetransformation are added (the variablesbefore transformation correspond ta k = 00),

can be studied.The choice of the independent variables canbe done bya forward selection, which requi-res a minimum of mathematical operations(Dagnelie, 1982). The significance levels ofthe t and F tests are fixed in order ta reducethe number of variables appearing in theregression equation, and ta facilita te thecomparisons berween the equations.By comparing the different coefficients ofdertermination (R2) which are calculated, itis possible ta make a choice within thetransformed variables retained by favouringthe equations for which R2 is high.

2.3. Multivariate analysisThe multiple regression is used ta define theequation which will later describe the spatialdistribution of the processing units activities,according ta the variables related ta the stu-died subsector.Globally, regression problems are double:

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they concern the choice of the independentvariables and the estimation of the parame-ters which come out of the model.The choice of the independent variables isdone with the help of the stepwise regres-sion method, which proceeds by successiveintroduction of the different variables, but sathat, before the introduction of a new varia-ble, the significance of the already presentvariables in the equation is tested.Practically, the results of the stepwise regres-sion method are similar ta those obtained byforward selection. Nevertheless, the stepwisemethod has the advantage· ta put out thevariables which become useless or evenharmfull (Dagnelie, 1982). ln order ta beclear, the significance level of the t and Ftests is defined ta be equal ta 0.05. Besides,the maximum of five variables have beenselected.The estimation of the parameters needs rela-tively complex operations, which are explai-ned by different authors (Ciceri et al., 1977;Beguin, 1979; Dagnelie, 1982; ...).ln this study, only the estimation of thestandard-errors of the partial regression coef-ficients and the determination coefficients R2are realized. The multiple regression, howe-ver, presents a major disadvantage : it givesequations established according ta the obser-ved values characterizing elements of whichthe level of activity has ta be foreseen. Byproceeding like this, the pre-existing situa-tion is strengthened. ln order ta avoid this ~problem, the equations have been establis-hed, in a second time, by the "jackknife"method, which is discussed by severalauthors (Miller, 1974; Bissel and Fergusson,1975; De Bast and Palm, 1987; ...).The model is calculated n times (n = thenumber of base units), by suppressing eachtime the value of the dependent variablerelative ta one base unit. For a base unit .considered, the estimated activity is given bythe model established on the n-I otherunits. By this means, the estimation relativeto a base unit is, each time.tindependent ofthe value which -is observed for this baseunit.The sum of the squares of the differencesbetween the observed activities and the acti-vities sa estimated is then calculated anddivided by n-I: This division gives the meansquare, MS, which is then compared withthe estimated variance of the activity inorder ta evaluate the part of the variance

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which is explained by the "jackknife" model.r2j = ((T2y - M5) / (T2y

The parameter r2j is so called because of itsanalogy with the coefficient of determination.

3. Application to the meat subsector inBelgium

3.1. General remarksThe method described before has beenapplied ta the meat subsector in Belgium inorder to propose a reorganization of theslaughterhouses network. The base unitsconsidered are the 43 administrative geogra-phical areas (1) (subdivisions of the 9 pro-vinces) which constitute the country. Theyear of reference is 1985. -Al the variables which have been used forthe different types of analysis belong ta themeat subsectar.The dependent variables have been definedfrom a practical point of view. Three typesof slaughtering chains have been con-sidered :- the first one used for large anirnals (bovi-nes and horses);- the second one for veals;- the third one for small cattle, essentiallypigs.The independent variables which have beentaken inta account in order ta quantify andqualify the different parts of the meat sub-sector and which are situated before theslaughterhouse are numerous and come fromthe yearly agricultural and horticultural cen-sus of May, 15th.Conceming the downstream parts of thesubsector, less elements of information areavailable : the number of slaughterhouseswhich are authorized to export, and 'thevariables relative to consumption.

3.2. Results

3.2.1. Integration of spaceA priori, it seemed important to take spaceinto account in a model measuring theslaughtering activity. Several transformationsof the variables, which are able to insert thefactor "space" in the economical analysis,were tried.For different sets of independent variables,the best adequation between the variabilityof the dependent and the independent varia-ble was sought by calculating the coefficientsof determination of the regression equations.

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As a whole, the results can lead to the con-clusion that the methodic integration ofspace in the economical analysis does not,or weakly in sorne cases, improve theregression.From a theoretical and rational point ofview, the problem should essentially be aspatial one. However, according to the varia-bles which play the most important role, itseems that the slaughtered quantities arerelatively independent of space. The slaugh-tering activity, for the different types of. ani-mals, mainly depends on the specifie charac-teristics of the constituting parts of the meatsubsector measured within the districts.However, the geographical areas are notcompleteley closed areas. There are exchan-ges of meat and living cattle among then,but the quantities which go in seem tobalance those which go out. ln fact, theapproach adopted allows a first conclusion :the slaughtering activity seems to be essen-tially linked with the strong and weakaspects of the elements of the meat subsec-tor within each geographical unit.50, it already appears that it is not relevantto justifiy the establishment of a slaughter-house in a deterrnined place by consideringupstream and downstream elements charac-terizing the neighbouring districts.

3.2.2. Multivariate analysis

3.2.2.1. Largè animais s~aughtering (y 1)The general equation obtained with the step-wise regression method, for the quantities ofmeat from large animals slaughtered, is thefollowing:y I=B45.864+238.558x33 - 459.870x47

(83.698) (131.351)- 2034.926x55+0.005x81 + 1753.607x85

(966.611) (0.001) (303.907)withr2 = 0.73;x33 = number ofveals sold at birth on themarket;x47 = number of lean large cattle, sold tci afarmer for fattening;X55 = number of lean large cattle, sold toimportant dealers, for fattening;X81 = veal meat consumption;X85 =. number of slaughterhousès allowed toexport.The standard-errors of the regression coeffi-cients are indicated between brackets.The part of the total variance explained by

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each independent variable (partial r2) is par-ticularly important for the downstrearn varia-bles (respectively 0.41 for X85 and 0.19 forx81). The variables relative ta the purchasesof the lean animais weakly contribute to theexplanation of the variability (partial r2 equalto 0.07 for x47, 0.04 for X33, and 0.03 forx55).'Consequenùy, it appears that it isimportant to see the signification of theappearance of the variable x85, which cons-titutes the key element of the equation ofregression.First, because there is a lack of statisticalinformation, this variable was chosen inorder to take into account experts, whichmust be done by slaughterhouses that areallowed to do so.The high explanatary power of the variablex85 canseem obvious : large cattle slaughte-ring is explained by the number of effica-cious slaughterhouses. ln fact, among theslaughterhouses which are allowed to export,important differences of activity can beobserved. 50, for example, in 1985, the dis-trict of Liège slaughtered 48 times more thanDinant, and Westrozebeke 61 times morethan Maasmechelen. Consequently, theimportant role played by x85 in the equa-tion of regression is less predictable than itseemed. Besides, x85 does not only reflectthe number of exporting slaughterhouses.;Indeed, the slaughterhouses today in Belgiummust often work for the exclusive needs ofan important meat dealer, or are in thehands of a main user. ln this context thevariable X85 is usefull to identify the impor-tant meat dealer, main operator of the meatsubsector, Finally, when the regression ismade without the variable x85, it is substitu-ted by the number of eut rooms which areallowed to ex port, which also reflects theimportance of the knackers' activity.The appearance of the variable X81 in theequation can seem curious, but this variableis linked with the variables relative to con-sumption in general and large cattle is prin-cipally slaughtered near the consumptioncentres. The explanation of the phenorneonlies in tradition and in the less integratedstructure of the meat sector, where the stan-dardization of the products is slower andmore difficult th an in other sectors.ln the past, only towns which were richenough were able to afford the building of aslaughterhouse on their terri to ry. The slaugh-tering of large cattle on farm being forbidden

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and frigorifie. transportation non-existing, themain urban centres became the places wherequantities offered concentrated and whereslaughtering was done.Nowadays, the inheritance of the past is stillobservable, because of the non-standardiza-:tion of the product.The dealers, whos are mainly located in theconsumption centres, wish to see the carcas-ses in o~der ta appreciate .their quality. 50,the sales become easier when the slaughter-house is located near the potential buyers.The variables X47 and X55 have a negativecontribution. They are relative to lean cattletrading, the slaughtering of large animaisbeing, logically, negatively influenced by thesales of lean cattle fattened elsewhere. Thepresence of X33, which has a weak contribu-tion, is more difficult to justify from an eco-nomical point of view.

3.2.2.2. Number of veals slaughtered (y2)The specificity and the concentration of vealslaughtering and, more widely, of the vealsubsector, are remarkable in Belgium.The result obtained from the independentvariables,y2 = -582.959 + 2.954 x32

(0.113)withr2 = 0.94;x32 = number of areas in cattle-sheds forveals early slaughtered,shows the importance of the upstream varia-bles in relation with the intensive fatteningof veals, in order to explain the slaughteringactivity. The narrow relation between vealslaughtering and the number of areas incattle-sheds for veals early slaughtered raisesanother question. What are the main factorsable ta influence the whole system ?ln fact, it seems difficult, according to theavailable information, to go further in theanalysis and ta identify the main actors ofthis subsectar (the farmer, the slaughter-house manager, the feed producer, ...).

3.2.2.3. Small ~nimals slaughtering (y3)The slaughtering of small cattle (mainly pigs,secondarily sheep) is the variable whichmost significantly influences the total quanti-ties produced in the districts.The multiple regression equation, comingfrom the 85 independent variables, is thefollowing :

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y 3=4690.398 - 12.271 x6 + 0.138 X24 +(2.806) (0.034)

9569.454x85(1107.390)

withr2 = 0.80;X6 = number of bovidae owners;X24 = number of fattening pigs;X85 = number of slaughterhouses allowed toexport.The standard-errors of the coefficients ofregression are between brackets. The equa-tion is built on upstream independent varia-bles relative to bovines and pigs and on thevariable giving the number of slaughterhou-ses allowed to export.Quantitatively, X85 plays the most importantrole : it contributes for 0.63 in the totalvariance.The comments already made (large cattleslaughtering) conceming this variable arealso appropriate here.However, it is notable to see that the regres-sion coefficient is higher in this case, theslaughtering of small cattle being quantitati-vely more important than the slaughtering oflarge cattle.Besides, there is a link between the smallcattle slaughtering and the specializationtowards pork production. Consequently, it isnot surprising to see the appearance, in theequation, of the variable X24, which indica-tes the total of fattening pigs (partial r2 =0.09).The appearance of the variable X6 (partial ~2

= 0.08) is justified by statistical reasons.

3.3. Comments on the method and con-clusionsThe use of the multiple regression allows usto get general equations which are able toexplain the slaughtering activity for the prin-cipal types of anirnals in 43 administrativecircumscriptions in Belgium. The equationsobtained have high coefficients of deterrnina-tion and, consequently, show the largeexplaining power of the independent varia-bles kept. Among these ones, the role of thevariable relative to the number of slaughter-houses allowed to export is remarkable.Except for the equation conceming theslaughtering of veals, it is the principal varia-ble (the highest partial r-).ln fact, the methodology used has two majordisadvantages. The first one consists in thefact that, among the independent variables,

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sorne elements, situated between productionand consumption may disappear in the longterm, according to the new trends in themeat subsector. These elements are the car-cass disposal plants and the slaughterhouses.Using the multiple regression, the situationis fixed by a structural rigidity of the cons ti-tuting parts of the meat subsector. Thesecond one is that the equations establishedtake the observed values into account in thedistricts of which the level of activity has tobe foreseen. Proceeding like this means thatthe existing situation is accepted and streng-thened.Nevertheless, the equations obtained makepossible the proposition of priorities to thedecision makers, in order to modemize theslaughterhouses network in Belgium.The application of the "jackknife" methodgives estimations of the slaughtering activitywhich avoids the problems described above.This method allows a more fundamentalquestioning of the slaughterhouses networkin Belgium. That will lead to importantchanges in the habits. That is no longer amodemization but a restructuring.By considering onlythe extreme parts of themeat subsector and ignoring the inheritanceof the past, the situation is analysed from amore rational point of view.The results must not be considered sensustricto. They permit to appreciate the appro-priateness between the slaughtering activityand the srrong and weak aspects of the meatsubsector within the districts. 50, a longterrn policy can be defined in order to sti-mulate agro-alimentary activities by dealingnot only with slaughterhouses, but, in thesame time, with all the weak up- and 'downstream elements. Globally, large diffe-rences are found in sorne districts, betweenthe estimation and the slaughtering activityobserved in 1985. Consequently, the stressmust be laid,' once again, on the importanceof the subsector elements observed withinthe districts in order to estimate the quanti-ties slaughtered.The interest of this approach lies in the'comparison between the three activity levels(observed, estimated without "jackknife" andestimated with "jackknife"). According to theresults, the districts can be gathered in fourgroups.The first group is cornposed of districtswhere the slaughtered quantities estimatedare much less important than the observed

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ones. ln the beginning, the slaughtered quan-titi es are not negligible. The estimation reali-zed by taking exclusively the elements of themeat subsector into account weakens therole played by these districts. After that, byoperating only on production and consump-tion characteristics and by ignoring thesituation observed within the district ofwhich the level of activity has to be fore-seen, the quantities estimated are conside-rably lowered compared to the quantitiesobserved. ln fact, in those districts, other fac-tors than those taken into account in theequation energize the slaughtering activity. Itseems that the main factor is not in relationwith the production and/or consumptioncharacteristics.For this kind of districts, the decisionmakers must be caref~l when they grant sub-sidies. Indeed, the slaughtering activityobserved essentially lies on relatively subjec-tive elements (dynamism of knackers, exis-tence of sorne facilities, ...) which may not.last.The second group is the opposite : theslaughtering activity observed is weaker thanthat estimated with the multiple regressionwithout 'jackknife", the last one being wea-ker than the quantities estimated with the"jackknife" method. ln this case, it seemsthat the strorig points of the meat subsectorare under-used compared with their poten-tial. Consequently, the existing slaughterhou-ses in those districts have to be modernized.The third group consists of the districtswhere the quantities observed and estimatedare similar. ln this case, when the quantitiesare sufficient, these slaughterhouses may beequipped at first.Finally, the fourth group gathers the districtsfor which the global level of activity is taolow ta face the developrnent of the slaughte-ring activities. For these ones the problem ofpublic service subsists. The service will notcontinue but we must admit that these unitsare not econornically viable.

Notel. On January l st, 1986, there were, on theBelgian terri tory, 43 administrative geogra-phical districts, which area went from16, 138 ha (Brussels) to 201, 621 ha (Ver-viers). AU these districts had a slaughteringactivity. The number of slaughterhouseslocated in the same district went from one(Arlon, Brussels, Namur, Mouscron, Tournai)

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to eleven (Alost and Turnhout). Importantdifferences were observed between the quan-tities slaughtered in the 43 districts (thequantities slaughtered in the district of Cour-trai were 163 times more important than thequantities slaughtered in the district ofVirton).

ReferencesAYDALOT, Ph., GUIGOU,].L. et HURIOT, ].M.1974. Théorie économique et utilisation del'espace 211 p., Paris, Cujas, Coll. T.E.M. Espace.

BALDWIN, E.D., BABIKER,B.l. and LARSON, D.W.1987. Spatial organization of marketing facilities indeveloping countries : a case study of oilseeds inSudan. Agricultural Economies, 1, 209-227.

BEGUIN, H. 1979. Méthodes d'analyse géographi-que quantitative 252 p., Paris, Librairie technique,série droit.

BISSEL,A.F. and FERGUSON, R.A. 1975. Thejackknife : tay, tool or twoedged weapon. Statisti-cian, 24, 79 - 100.

BROUSSOLLE,e., FOUET, ].P. et TROCHET, T.1986. Localisation des activités de production etde transformation du lait. Economie rurale, 172,41-47.

CHANDON,].L. et PINSON, S. 1981. Analyse typo-logique - Théories et applications 254 p., Paris,Masson.

CHEVAILLER,j.c. 1978. Eléments d'économétriespatiale 148 p., Paris, Sirey.

CICERI, M.F., MARCHAND, B. et RIMBERTS,S.1977. Introduction à l'analyse de l'espace 175 p.,Paris, Masson.

CLAVAL, P. 1969. La localisation des activitésindustrielles. Revue géographique de l'Est, 1-2, 187- 214.

COQUART, D. 1983. L'industrialisation de lafilière viande bovine. Economie rurale, 153, 50 -52.

CORTIAESEN, G.P. et VAN LOGTESTijN, ].G.1979. Le transport des porcs d'abattage aux Pays-Bas. Revue des technologies vétérinaires et alimen-taires, 149, 19 - 23.

CRAPLET, e. 1965. La viande de bovins. De l'éta-ble de l'éleveur à l'assiette du consommateur 1108p., Paris, Vigot frères.

CROSSLEY,Je. 1976. The location of beef proces-sing. Annals of the Association of American Geo-

Revue de l'Agriculture - Landbouwtijdschrift. 1989. \'01 42. n

graphers, 66 (1), 60 - 75.

DAGNELIE, P. 1982. Analyse statistique à plu-sieurs variables 362 p., Gembloux, Presses agrono-miques.

DAGNELIE, P. 1983. Théorie et méthodes statisti-ques. Applications agronomiques (2 vol.) 378 +464 p., Gembloux, Presses agronomiques.

DE BAST,A. et PALM, R. 1987. Prévision de pro-ductions agricoles dans les dix pays de la Com-munauté Européenne: rapport n? 6 21 p., Gem-bloux, Faculté des Sciences Agronomiques.

DEMOULIN,]., VAN HEGHE, G., VERTESSEN,]. etBODDEZ, G. 1966. La rationalisation du réseaud'abattoirs. Analyse préparatoire de la production,de la consommation et du réseau existant 85 p.,Bruxelles, Institut Economique Agricole".-. .

FENN, M.G. 1979. La commercialisation du bétailet de la viande 219 p., Rome, Organisation desNations Unies pour l'Alimentation et l'Agriculture.

FOURICHON, ].P. 1986. La filière bovine. Agrafilières 215 p., Paris, Agra.'

GUIGOU,].L 1972. Théorie économique et trans-formation de l'espace agricole 321 + 304 p., Paris,Gauthier-Villars.

HAGETT, P. 1973. L'analyse spatiale en géographiehumaine 390 p., Paris, Colin.

HARRIS, C 1954. The market as a factor in thelocalization of industry in the United States.Annals of the Association of American Geogra-phers, 44 (4), 315-348.

KEEBLE,D., OFFORD,]. and WALKERS, S. 1986.Peripheral Regions in a Community of Twelve Sta-tes 154 p., Cambridge, Department of Geography,University of Cambridge.

LAJUGlE,]., DELFAUD, P. et LACOUR, C 1979.Espace régional et aménagement du territoire 884p., Paris, Dalloz.

LANGE, B.1982. Contribution à l'étude de la loca-lisation des activités agricoles en Belgique. Thèsede doctorat 316 p., Gembloux, Faculté des Scien-ces Agronomiques de l'Etat.

LATHAM, W.R. 1978. The measurement of spatialassociation. The Review of Regional Studies, 7 (1),20 - 30.

LEBAILLY,Ph. 1988. Etude économique des abat-toirs en Belgique. Thèse de doctorat 226 p., Gem-bloux, Faculté des Sciences Agronomiques del'Etat ..

LLOYD, P.E and DICKEN, P. 1972. Location inspace. Theoretical approach to economie geo-graphy 474 p., London, Harper & Row.

Mc CARTY, H.]., HOOK, ].C and KNOS, D.C1956. The Measurement of Association in Indus-trial Geography, Iowa, State University of Iowa.

MAC LEAN, A. and PICKARD, D. 1975. Livestockmarketing systems in EEC-Countries 102 p., Ash-ford, Center for European Agricultural Studies,Wye College.

MALINVAUD,E. 1964. Méthodes statistiques del'économétrie 634 p., Paris, Dunod.

MILLER,R..G. 1974. The jackknife : a review. Bio-metrika, 61, 1 - 15.

NADASDI, 1. 1971. Carte de potentiel de popula-tion de la Belgique. Bulletin de la Société d'Etudesgéographiques, 40 (1), 237 - 246.

RICHTER, R. 1969. The impact of industrial linka-ges on geographie association. Journal of RegionalSciences, 9, 19 - 28.

SMALEY,R. 1978. Guidelines for establishing beef-packing plants in rural areas 85 'p., Washington,Agriculture handbook, n? 513. "

SOUFFLET, ].F. 1980. Forces et faiblesses de lafilière bovine 144 p., Dijon, Laboratoire de recher-ches de la chaire des sciences économiques del'ENSSAA. .

VAN TRAPPEN,W. et ZWANEPOEL, 0.1975.Intégration verticale et contrats en agriculture -Belgique Commission des communautés européen-nes. Informations internes sur l'agriculture, 144,61 - 112.

YATES, M. 1963. Hinterland delimitation : a dis-tance minimizing approach. Professional Geogra-phers, 15 (6), 7 - 10.

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RésuméMéthodologie en vue de la modernisation d'une filière agro-alimentaire: le cas de la pro-duction de viande en Belgique .

Dans cet article, les auteurs présentent uneméthodologie susceptible d'expliquer et dequantifier les facteurs contribuant à dynami-ser une filière particulièrement opaque enBelgique : la viande de boucherie.Les résultats obtenus peuvent aider les déci-deurs à prendre des mesures en vue de réali-

ser la restructuration du réseau d'abattoirs enBelgique. A long terme, il est possible dedéfinir une politique ayant pour but de sti-muler les activités agro-alimentaires en agis-sant non seulement sur les abattoirs maisaussi, simultanément, sur tous les élémentsdéficients de l'amont et de l'aval.

ZusammenfassungMethodologie for die Modernisierung eines Futterminelnetzes : die Fleischproduktion inBelgien

ln diesem Artikel sprechen die Autoren übereine Methodologie, die die Faktoren erklârenund quantifizieren kônnen, die zum Dynami-sieren eines besonders unklaren Netzes inBelgien beitragen : des Schlachtfleisches.Die erzielten Ergebnisse kônnen den Politi-kern helfen, Mafsnahrnen zu ergreifen, um

die Umstrukturierung des Schlachthausnetzesin Belgien zu verwirklichen. Langfristig ist esmôglich, eine Politik zu bestimmen, die dasFuttermittelleben antreiben würde. Siekônnte nicht nur die Schlachthaüser beein-flussen, sondern auch alle schwachen Ele-mente des Netzes.

SamenvattingMethodologie inzake de modernisering van de agrovoedingsbedrijfskolom : de vleesproduh-tie in België

..

ln dit artikel wordt een methode voorgesteldom de factoren die bijdragen tot eenverhoogde dynamiek van de bijzonderondoorzichtige bedrijfskolom slachtvee inBelgië te verklaren en te kwantificeren.De bekomen resultaten kunnen de beleids-mensen helpen bij het treffen van maatrege-len met het oog op de herstructurering van

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het slachthuisbestel in België. Er kan oplange termijn een beleid worden uitgestip-peld met als doel de bedrijvigheid in deagro-voedingssector aan te wakkeren, waarbijniet alleen de slachthuizen worden betrok-ken, maar tegelijkertijd ook alle gebrekkigfunctionerende elementen in de toeleverings-en afnamesectoren worden aangepakt.

Revue dl' l'Agriculture - Landbouwtijdschrifr. 1989, Vol. 42. n 5