Marion Desquilbet
email: desquilb@roazhon'inra.fr.

David S Bullock
email: dsbulloc@liuc.edu.

David S Bulloclç

Who pays the costs of non-GMO segregation and identity preservation?

This paper proposes an analytical framework to examine the market and welfare impacts of GMOs, when some consumers refuse genetically modified organisms (GMOs) and when two supply channels are segregated (one for goods that containing GMOs and one for non-genetically-modified identity-preserved goods). Our analytical framework begins at the level of individual farmers, handlers and consumers, to build up market supply and demand functions. This allows us to circumvent the difficulties of conducting supply and demand analysis in the different horizontally and vertically related maxkets concerned by GMOs and market segregation. we represent explicitly the costs of non-GMO segregation and identity preservation (F) for both producers of non-GM IP goods and producers of non-IP goods, and how thess costs vary depending on the relative sizes of the two production charurels. We then illustrate our model by a simulation of potential adoption of GM rapeseed with non-GMO market segregation in the European Union (EU)' We analyze how the costs of IP are distributed among heterogenous producers, handlers and consumers in this simulation' Who pays the costs of non-GMO segregation and identity preservation?l l.Introduction Some major world agricultural markets are currently facing a dual situation with regard to genetically modified organisms (GMOs), with many farmers growing GMOs and many consumers reluctant to eat them, or mzmy national regulations slow to accept their use. As a result, some seed producers, farmers, grain handlers and food processors are striving to preserve the identity of non genetically modified goods by keeping them segregated from genetically modified (GM) goods, to meet demand for non-GM goods. A dual stream of supply and marketing is developing, one for goods without GMOs above given tolerance levels, and one for goods that contain GMOs above these tolerance levels. This paper proposes an analytical supply and demand framework to examine the economic effects of this non-GMO segregation and identity preservation (IP). The original features of our model are that we begin at the level of individual agents to build up market supply and demand functions, and that we represent explicitly the costs of non-GMO segregation and IP for both producers of non-GM IP goods and producers of non-IP goods. We then conduct a simulation of potential adoption of GM rapeseed with non-GMO IP in the European Union (EU)' and we analyzehow the costs of IP are distributed among different agents.

2. The literature So far, studies on the economic effects of GMO labeling and non-GMO segregation and IP have mainly examined how to account for differentiated t We thank Hervé Guyomard and Vincent Réquillart, INRA ESR, France, for helpful comments on previous versions of this paper.

consgmers' attitudes towards GMOs in an analyical framework, and what are the effects of market segregation on consumers. However, less attention has been given to the way to model costs of segregation and IP and how these costs affect producers.

Mayer and [START_REF] Mayer | Economics of transgenic herbicide-tolerant canola: The case of 'Western Canada[END_REF] analyze graphically the effects of market, segregation between GM and non-GM products on the Canadian rapeseed (canola) market. They consider two different demand curves for GM canola and for non-GM canola, and two interdependent supply curves, one for GM canola and one for non-GM canola, with the two goods being substitutes in supply, so that two distinct prices arise in equilibrium for GM and non-GM canola. They do not model explicitly how the costs of segregation of GM and non-GM products are shared between producers, but they suggest three possibilities: the costs are borne by all canola production, by only hansgenic production, and by only non-GM production.

Nielsen, Thierfelder and [START_REF] Nielsen | Consumer Attitudes Towards Genetically Modified Foods: The Modelling of Preference Changes[END_REF] introduce different GM and non-GM production technologies and different preferences towards GM and non-GM products in a CGE framework and illustrate the effects of different representations of preference changes. Their paper is mainly aimed at showing how to model differentiated preferences towards GM and non-GM products in the CGE framework' They do not introduce costs of segregation, and their only conshaint for non-GM processed products is that they are produced using a non-GM bulk ingredient' without adopting a formal modeling framework, Golan and Kuchler (2001) consider the change from an initial equilibrium with only non-biotech food to a final equilibrium with both biotech food and identity-preserved non-biotech food' They argue that when GMOs are introduced, externality costs are imposed on producers of the non-GM good to ensure that its non-GM identity is preserved until its final consumption. According to their analysis, these externality costs are only borne by non-GM producers. Then, compared to the initial equilibrium, in their final equilibrium, consumers who are indifferent between GMOs and non-GMOs pay a lower price, due to GMO costs savings, while consumers who prefer non-GMOs pay a higher price, due to IP costs.

Giannakas and Fulton (2001) examine the effects of GMOs and GMO labeling on consumer demand and consumerwelfare, using a model of differentiated consumer preferences on GM and non-GM products. They analyze how the effects of introduction of GMOs and of GMO labeling depend on the degree to which GM cost savings are passed to consumers, on the level of consumer aversion to GMOs, on the segregation costs associated with mandatory labeling, and on the extent of mislabeling. They argue that while both non-GM and GM producers may face some segregation costs, these costs will always be higher for producers of the IP good than for producers of the GM good, due to the effort required in preserving the identity of the non-GM good by keeping it separate from the GM good.

3. Sources ofthe costs of segregation and identity preservation The studies reviewed above account for differential characteristics among consumers, and notably point out that GMOs and non-GMO segregation can cause some consgmers to win while others lose. However, they give less attention to the ïriay segregation and IP costs are borne. Moreover, they make different, and sometimes even contradictory a priori assumptions about these IP costs. In this context, our aim is to analyze more closely how these IP costs arise, which producers bear these costs, how farmers and handlers are differentiated with respect to these costs, and if the effects of GMOs and non-GMO segregation and IP on their welfare are also variable.

It is possible to distinguish two main categories of costs of non-GMO segregation and IP [START_REF] Bullock | The economics of non-GMO segregation and identity preservation[END_REF]. The first category is costs to prevent commingling of GMOs and non-GMOs, i.e. to keep non-GMOs intended to IP physically separated from GMOs along the supply chain. In particulato costs are incurred to prevent cross-pollination, to clean farm, handling, hansportation and processing equipment, and to dedicate one part of this equipment to GMOs while dedicating the other to non-GMOs. The second category is of costs to correct the information asymmetry about the GMO or non-GMO nature of the goods, i.e. to ensure the buyer that grain that is claimed as non-GM by the seller is actually non- GM. These costs are from chemical testing and drawing up contracts between buyers and sellers and monitoring their abidance. These additional costs have three important characteristics. First, they are likely to arise for both producers of non-GM IP goods (which we will call simply "IP goods") and producers of goods for which no steps are taken to prevent GMO commingling (which we will call "regular goods"). Second, these additional costs are likely to be different from one agent to the other. Third' these costs are likely to depend on the types of goods, i.e., IP or regular goods, produced by other agents in the economy. These three characteristics of IP costs are illustrated below, using partly the example of the current IP channel in the United States (US) and in the EU.

Farm costs of segregation and IP In the case of farmers, it is possible to cite three IP costs that vary among farmers, that depend on the size of the IP channel or that arise for both producers of the regular good or producers of the IP good. The first is the cost of transporting IP grain to an elevator willing to accept it. Cunently in the US, IP crops make up a small share of total supply, and only a fraction of all elevators are participating in the IP charurel. Moreover, some of these elevators are only receiving IP crops from farmers during specified periods, and not near harvest time. In this situation, the additional cost of delivering IP grain to an accepting elevator varies among farmers. The additional cost is small for a farmer located near an elevator accepting IP grain at harvest time, or for a farmer possessing adequate on-farm storage and located near an elevator accepting IP grain only out of harvest time. On the contrary, the additional cost is likely to be dissuasive for a farmer located far away from an elevator accepting IP grain, or for a farmer possessing inadequate on-farm storage capacity, even though he may be located near an elevator accepting IP grain only out of harvest time. In addition, for a given farmer, the cost of delivering IP grain to an elevator accepting it depends on the size of the IP charurel. If the share of IP crops in total supply increases, some new elevators will start to accept IP crops, or will accept them during wider periods of time. Then, the cost of participating in an IP channel will decrease for some farmers located near these elevators. Yet simultaneously, as the size of the IP charurel grows, a similar cost of participating in the regular channel will arise for producers of the regular good. Lr the extreme situation where the size of the regular channel is very small, the cost of transporting regular grain to an elevator willing to accept it could become dissuasive for many farmers.

The second type of farm IP cost that varies among farmers is the cost of preventing cross-pollination by GM plants. For cross-pollinated species (including com and rapeseed, but excluding soybeans which are almost exclusively self- polinated), pollen from neighboring GM fields can fertilize plants in a non-GM field and lead to the commingling of GM and non-GM seed or grain' To prevent cross pollination, it is necessary to adopt costly measures such as increasing distance between one's non-GM fields and GM fields, or harvesting border rows separately.

Here again, this cost varies among farms (for example, depending on the presence of natural barriers or depending on wind direction). It also varies for a given farmer depending on the share of GM crops in total supply (this cost increases for some farmers as the share of GM crops in total supply increases, when some of their neighbors begin to grow GM crops). (Here, no similar cost exists for producers of the regular good, because regular producers do not have to prevent cross-pollination of their crops by non-GM pollen).

The third type of farm IP cost that varies among farmers is the opportunity cost bome from not using GM technology in production. Several studies underline that economic benefits from adopting GMOs vary widely between farmers (Bullock and Nitsi, 2001; McBride and Books, 2000; Desquilbet, Lemarié and Levert, 2001). One main reason is that different farmers face different weed situations, or different insect pressures, so that pesticide cost reductions or yield changes following from GMO adoption vary among them. Then, the potential indirect cost of not using a GM seed in order to grow an IP crop varies among farmers.

Handling and processing costs of segregation and IP At the handling stage, too, some examples show that IP costs vary arnong handlers and vary depending on the size of the IP channel in total supply. In the current situation where the IP channel is small in the USA, because of the physical design of their facilities, some elevators have smaller costs than others of participating in the Ip channel. For example, shict tolerance levels can be attained more easily in storage locations that have multiple paths (as opposed to a single path) of dump pits, legs, conveyors belts, etc, along which grain is moved before being stored' It is also easier to segregate IP crops in a facility with multiple small storage bins rather than few large bins. Moreover, having different elevators in close proximity is an advantage for some handlers that may dedicate some elevator locations to GMOs and others to non-GMOs. This situation will change if the share of IP crops in the handling system increases and new elevators enter the IP channel. In the EU, where only IP crops are supplied, all facilities are used exclusively for IP crops. In this case, this physical design and location of elevators does not create IP cost differences among handlers. Similarly, regular crops may also bear a cost of segregation. For while regular crops need not be kept clean of non-GM crops, segregation still can lead to costs of capacity underuse, cleaning costs, and management costs to organize more complicated grain flows. Similar cost differences and variations of cost with the size ofthe IP channel apply to food processors.

Analytical framework

In order to analyze the aspects of non-GMO segregation and IP described above, we develop a model allowing welfare analysis of GMOs and IP using supply and demand analysis. To conduct an applied analysis, a typical procedure would be to assume functional forms for the supply and demand functions of the pertinent markets, then to calibrate the model's supply and demand parameters. This calibration step would require either to estimate these parameters using historical price and quantity data or to define their value a priori. Our description of IP costs suggests that the aggregate quantities of each type of good supplied by farmers, handlers and processors depend on the prices ofregular and IP goods at the different stages and on the relative sizes of the regular and IP channels. Aggregate final demands for both types of goods are expected to depend on the prices of regular and IP goods at the consumer stage. It would be a challenge to calibrate market supply and demand function parameters in a multi-market, vertically-connected sector, since very little data exists to suggest what the pertinent supply and demand parameters might be.

The alternative formulation that we suggest here is to start from supply and demand at the individual level, and to define aggtegate supply and demand functions by summing up individual supplies and demands. The advantage of this formulation is to avoid a priori assumptions on functional forms at the aggregate level. Rather than defining elasticities of supply with respect to prices of each good and with respect to the relative sizes of the two channels, quantity changes as a reaction to price and relative size changes are then derived implicitly from assumptions on parameters at the individual ]evel, which are easier to calibrate a priori. Another interesting feature results from this formulation at the individual level. Since the model consists in modeling heterogeneous individual agents, it allows to distingUish how GMOs and non-GMO segregation affect the heterogenous members of the same interest groups.

Our model is of two vertically related markets, a market of agricultural products at the farm stage and a market of agricultural products at the handling stage, and two countries, a domestic country (the EU), and the rest of the world (ROW)' 'We consider four different goods that may be produced by farmers. The first good (indexed by z) is rapeseed gfown from a non-GM seed, but for which no steps are taken to prevent possible commingling with GM rapeseed, or which is not delivered to handlers accepting IP crops. The second good (indexed by g) is rapeseed from a GM seed' The third good (indexed by i) is non-GM IP rapeseed (later referred to simply as "IP"), grown from a non-GM seed, for which special efforts are made to avoid any commingling with GM rapeseed. The fourth good (indexed by a) is an alternative crop. Handlers buy rapeseed from farmers, to produce either regular handled rapeseed (indexed by r), or IP handled rapeseed (indexed by t). 'we assume that IP handled rapeseed can only be produced using IP farm rapeseed. Regular handled rapeseed is rapeseed that cannot be sold as IP and is produced using GM rapeseed or non-GM non-IP rapeseed (handlers view these as the same product). For simplicity, we make the partial equilibrium assumption that farm $op a has a constant price and we do not consider any cross-effects between handling of rapeseed and handling of the altemative good. Consumers buy regular rapeseed and IP rapeseed from handlers, they buy a close substitute to rapeseed in consumption, and they buy a numeraire good.

This model allows us to take into account three simultaneous shifts in supply andlor demand curyes in three different markets. First, the GMO technology lowers costs of production for some subset of all farmers. Second, for many consumers, worry about possible health and environmental effects of GMOs causes their demand for GM versus non-GM products to shift in favor of non-GM products. This shift in preferences begets a third shift, this time in the demand for segregation and identity preservation of non-GMOs.

Domesticfarmers

We consider a set of F farmers, each of whom may produce the four different farm crops fl, g, i or a. Each of the four crops is produced using land, owned by each farmer in equal area L, and a set of variable inputs. Each of the four goods is produced under competitive conditions using a Leontief technology, with the variable inputs perfectly elastic in supply and land perfectly inelastic in supply. Handlers do not distinguish between the two types of regular rapeseed, i.e., non-GM non-IP rapeseed and GM rapeseed. Therefore, farmers are paid the same price for these two products.

There is a production externality for regular and IP rapeseed producers. As a result, the costs of production of regular rapeseed depend on the share of IP rapeseed in total handled rapeseed. In the same way, the costs of production of IP rapeseed depend on the share of regular rapeseed in total handled rapeseed.2 Farmers get the same constant yield y for each of the three types of rapeseed. Let w, denote the farm price of regular rapeseed (i.e., rapeseed n or rapeseed g), let w, denote the farm price of IP rapeseed (i.e., rapeseed i), let v denote the vector of variable input prices, and let s denote a govemment per-hectare subsidy on rapeseed. 'We denote the per hectare crop-specific restricted profit on crop fr by farmer f as nv (.), k:n, g, i. The crop- specific per-hectare profit function for crop n is given by:3 (l) n'! (w,,v,t,,s)

= M*(w, y+ s -cn, (v) -, o Q)r,; 0)
where c,r(v) is farmer/s per-hectare variable cost of production for $op n when regular rapeseed is the only good supplied; eo(v) is a production extemality parameter for regular rapeseed; r, is the share of IP rapeseed to total rapeseed (i.e. to IP ptus regular rapeseed) in the handling system.

t Some causes of these externality costs were presented in section 2: costs to bring regular or IP rapeseed to an elevator accepting it; cost to prevent GM cross-pollination of IP rapeseed. By nature, thlse costs depend on whereïthJr tégohr u"â tp farmers and elévators are located. We take them into account in a simple way in our non-siatial framework, by assuming that they depend only on the shate ofthe other good (IP orregular) intotal aggregate handled quantities. 3 It can be shown that the profit function given in (1) is derived from a Leontief production function of ( ^ -l tlretype tYnr=uinl\xsn,Êr7x1n,Fz\xzo,"',f^yx^n'TZrf'where'wehave ot'Fit " lT, vx'"r "' '-r ) (for j a[,..,,*l), errrd f are paxameters describi"g the technology when farmer/grows crop n, rsn,.,.,xan are quantities of variable inputs 0,..., m the farmer devotes to crop n, and L,,is the amount of land the farmer devotes to crop z. Input 0 is ueeded only when r, is strictly positive, and the larger is r,, the higher is the quantity of input 0 necessary to produce the amount Y,n.Let v=(vo,"',v") denote the vector of prices of inputs 0,... , ttN. Then it can be shown that yield y is equal to y' ",rrr=EH and eo(v)ri =T', '

The crop'specific profit functions for crops g and i are defined in an analogous manner as:

(2) n { (w,,v, t,, s) = M*(w, y + s -c * (v) -e o Q)r, ; 0)

(3) r r (w,,v,r,,s) = Max(w,y+ s -cn (v)e y (v)r,, 0)

where en@) is a production externality parameter for IP rapeseed; r, is the share of regular rapeseed to total rapeseed in the handling system, equal to l-r,.

The profit level obtained from the alternative crop is constant and equal to tto for each farmer. The technology specified (see foohrote 1) implies that for each crop the farmer has constant retums to scale. Therefore the farmer will never devide his land among different crops, but will always find it optimal to grow only one crop, the one yielding the mae<imum profit level (provided that this profit level is positive).

Farmer/s ma,ximum per-hectare profit is given by (suppressing arguments v and s):

( 4) x""* I 1w,, w i, r i) = Max(îr nr (w t, T t); n d (w,, t,); x r (w,,L -t,); x " ) Depending on prices, some farmers may then find it optimal in equilibrium to grow GM rapeseedo while others {ind non-GM seed more profitable, some with and some without identity-preserving their crop. Farmer/s supply function for ctop k=n, g i is then defined by:a $) q'{ (w,,wi,t;)=

The aggregate farm supply function of crop k:n, g, i is denoted q'l' ('), and is defined as the sum of individual supplies for that crop:

y L if x**r (.)= nq (.) 0 otherwise a Equation ( 4) assumes that the profit-maximizing crop is unique. In the case where more than one ..pi-rn4t6; profit levels is equâl to the maximum prôfit level, we,arbitarily decide that the farmer groïs'only one of the profit-rùximizing crops, *ith ctop n being the most preferred, then g, then i, then a.

(6) q'fF (w,,w,,r F ) =Zq{ ('il,,wi,ri) . f=1 Domestic handlers 'We consider a set of I/ handlers indexed by h : 1, ..., .I/ who may produce the two different tlpes of handled rapeseed, r or i. Handled rapeseed is produced with a Leontief technology, combining farm rapeseed, storage capacity and variable inputs, with one unit of farm rapeseed necessary to produce one unit of handled rapeseed.

RegUlar handled rapeseed is produced using rapeseed n or g, and the amount of regular rapeseed bought from farmers by handler ft is by definition Qrh --Qnh + qsh. W handled rapeseed is produced using farm I rapeseed i. Storage capacity is owned by each handler in a given quantity Q and is perfectly inelastic in supply, and variable inputs are perfectly elastic in supply. There is a production externality for regular and IP rapeseed producers. As a result, the costs of production of regular rapeseed depend on the share of IP rapeseed in total handled rapeseed. Lr the same way, the costs of production ofIP rapeseed depend on the share ofregular rapeseed in total rapeseed.5

We denote the per heotare crop-specific restricted profit of handler h on crop k as fi* (.), k:r, i. The crop-specific per-unit-oÊcapacity profit function for crop r is givenby:

(7) n'^ (p,w,,we,T i) = Max(P, -w, -c,o(w ")

e,n(w ")r,; 0)' (8) o'' (p, -w i, w e,T r) = Max(p,w, -c i1,(w e) -e,r (w ")r, ; 0)' 5 This externality cost arises because of the cost of dedicating some equipment to regular rapeseed and some to IP rapeseed (see section 2).

r4

Because of the linearity of the profit functions, it is always optimal for a handler to handle only one crop, the one that yields the maximum profit level. Therefore we have (omitting argument w"):

(9) n*' 1p, -w i, p, -w,,t i) = M*(r'^ (p, -w,,r ;); n'n (p, -w,,l -r )) .

We assume that costs c*(w") and c,u(w") vary among handlers (some handlers have technological advantages in handling regular or IP rapeseed because different grain elevators are configured differently). In equilibrium, some handlers may find it most profitable to handle regular rapeseed, while others may find it most profitable to handle IP rapeseed. Handler ft's supply function for handled crop (for k e {i, r}) is then identical to handler h's demand function for farm crop Ë and defined by:

(10) qî,ou1p,-w,,p,-r,,r,7={! 'f .

ono.*n(')=n*(')

.

LU omerw$e

The aggregate handler supply function of crop fr, which is identical to the aggregate handler demand function of crop k, k e {i, r), is denoted q'f'(.), and is defined as the sum of individual handler supply functions:

(tI) q'/nu (p, -v, r, p r -w,,r,) =Lq'fo (p, -w,, p i -w,,r,) .

h=l D ornestic Jinal consumers

We consider a set of C consumers, and we assume that the utility of consumer c € {1, . . . , Cl takes the form:

(12) U"(q,,et,Qt,Q")= a K+q" if onQ,lq,*ou"qu2K a(o,,Q, + q, + ou"qu) + q, otherwise where r is regular rapeseed, i is IP rapeseed, ô is a close substitute with rapeseed in consumption, e is a numeraire good, 4y denotes the quantity of goodi, arLd o,",6 b" , a aîd K are constant positive parameters.

'We assume that for c=1,...,C, cbr70 and 03o,, <l: each consumer may reach subutility u from consuming the substitute good, but some consumers have no utility from consuming regular rapeseed; and to get the subutility z from consuming rapeseed only, it is always necgssary to consume at least as much regular rapeseed as IP rapeseed (but for some consumers these quantities may be equal). Consumer c's Marshallian demand functions for goods r, i, and b and indirect utility function are given by:6 e3) q!,(p,,pi,po)={:

ir P'1Pi6" and P'6t'3Pu6'" L0 otherwise $0 qi"(p,,p,,p)={T f .,0'::lo' and Pi6usPt

L0 otherwise 0ù qf"( P,, Pi,Pt)=a K if pto," 1p,ao" and Pr 1Pgt" 6b, 0 otherwise (16) V"(p,,pi,pt,M)=M +a K-K M"(*,e,,H'

The money metric indirect utility function of consumer c is given by: (17) p" (pt ,Fo ,M) =V" (P' ,M) -V'(Fo ,M) , where F' = (pi, pi , p') is a vector of prices in situation s'

Each consumer consumes only one of goods b, r, ot i. We consider a set of C consumers with different values for parameters a," and 06", &Ttd, so that in equilibrium some may consume regular handled rapeseed, some may consume non- GM, IP handled rapeseed and some may consume the substitute good. Consumers' aggregate demand function for good k e {r,r, ô} is denoted qf'(.) and is defined as the sum of individual demands for that good: C 0g) qf' (p,, p) =Zqfi (p,, p,), tc e {4,n\.

c=l Excess demandfrom the rest of theworld and equilibrium conditions 'We assume that consumers in the rest of the world are indifferent between regular and IP rapeseed. Because the consumer price of IP rapeseed, pi, is necessarily higher than the consumer price of regular rapeseed, pr, iî ourmodel, consumers in the rest of the world consume only regular rapeseed. The excess demand of regular rapeseed in the rest of the world is denoted by q!"(p,). Equilibrium conditions in our model are as follows:

Q9) q'fF (w,,w,,r,) + q'f' (w,,w,,t i) = qioo' (p, -w,, p rw i,t 1) (20) qilF (w,,w1,t7) = q;*' 1p, -wr,pi -wi,tt)

(21) q',on' (p, -w r r p t -w ;,r i) = qlAc (p,, p ) + q! (p,) ( 22) qi*' 10, -w,, p i -w,,r i) = qlAc (p,, p ) (23) r, = ql*'(p,-w, ,P, -'A),rtt) qi*' (p, -w r, p t -w t,T i) + q',*' 1p, -w * p, -w,,t,)

Equation ( 19) states that in equilibrium, the quantity of GM rapeseed plus the quantity of non-GM, non-IP rapeseed supplied by domestic farmers is equal to the L7 quantity of regular rapeseed demanded and supplied by domestic handlers. Equation (20) states that in equilibrium, the quantity of IP rapeseed supplied by domestic farmers is equal to the quantity demanded and supplied by domestic handlers.

Equation ( 21) states that in equilibrium, the quantity of regular rapeseed supplied by domestic handlers is equal to the quantity demanded by domestic consumers plus the excess quantity demanded by the rest of the world. Equation ( 22) states that in equilibrium, the quantity of IP rapeseed supplied by domestic handlers is equal to the quantity demanded by domestic consumers. Equation ( 23) states that in equilibrium, the value of r, is equal to the quantity of IP rapeseed supplied by domestic handlers divided by the total quantity of rapeseed handled by domestic handlers. The five equations ( 19) -( 23) may be solved for four equilibrium prices, (w,, wi, P" Pù and the equilibrium share of IP crops in the handling system, r,.

Calibration of the model

We develop a simulation model to illustrate empirically our modeling framework in the case of rapeseed in the EU and the rest of the world. This model is developed using realistic assumptions on parameter values, presented in this section.

Simulation results are presented in section 6. The simulation model represents the rapeseed market in the EU and the rest of the world n 199912000. h the simulation modol, quantities are in metric tons (denoted by t)n areas are in hectares (denoted by ha), and prices are in euros. In the baseline situation, only non-GM non-IP rapeseed, i.e., rapeseed n, is produced by EU farmers, and due to regulations GM rapeseed cannot be produced or imported in the EU. h the baseline situation this farm rapeseed n is processed by handlers into regular handled rapeseed, i.e., rapeseed r, which is supplier sets the GM seed price at its profit-maximizing level, with other input prices kept constant, they estimate a mean farm herbicide cost reduction of 64.6 euros/ha.

They estimate an empirical standard deviation of 30.2,with7l% of farmers adopting

GMOs. In our model, we implicitly define per-hectare GMO cost reductions 71,...t 1'2sss by the equation O(rr) = f 12000 , for f = 1, ..., 2000 , where o(r) is the normal cumulative distribution function of a random variable r of mean 64.6 and standard deviation 30.2, To avoid correlation between GMO cost reductions and production costs for rapeseed n,we take a random permutation {rr', ..,ot ooo' } of the list {4, ...,lzooa}.We then define cri 6 cn, minus the GMO cost reductioî, rr', plus the license cost of the GM seed, denoted lc . That is, cd = cnf -rr'+lc , f =L...,2000. In the baseline situation, we set the license cost to 1000 euros/ha, which is high enough to cause no farmer to grow GMOs. Irr the simulation, in which GMOs are adopted in the EU, we set the license cost to 48 eurosltra. (IVe checked that this value results tn a 7lo/o adoption rate of GMOs by farmers growing rapeseed, in a situation where no consumer refuses GMOs). Vfith these assumptions, GM technology is cost-saving for some farmers, but not all farmers (we may have cn. ) cd or cnf < c" depending on the farm).

For a given farmerf, the value cir-cnf represents the difference between the per-hectare production costs of IP rapeseed and non-GM non-IP rapeseed, excluding the externality cost. V/e expect that farmers have positive costs of identity preservation, but that these costs are very small in a situation when only IP crops are It This assumption is consistent with [START_REF] Agreste | Chiffres et Données, Agriculture[END_REF] 2l grown, i.e. we expect the above cost difference to be very small.l2 We arbiharily set: cir = cnî +0.1 eurosftra, f =1,..., 2000.

'We can expect the average farm externality parameter for IP rapeseed to be higher than the average farm externality parameter for non-GM non-IP or GM rapeseed. For the externality parameters for IP rapeseed include the potential higher cost of buying a non-GM IP seed (which costs more because seed production companies must also prevent cross-pollination) and of preventing cross-pollination, while no equivalent costs exist for regular rapeseed. Moreover, we can expect the potential additional cost to bring IP rapeseed to an elevator accepting it to be higher than the equivalent cost for regular rapeseed. (Because handling IP rapeseed is more difficult than handling regular rapeseed, it can be expected that in average farmers would face higher restrictions about where to deliver IP than they would face for regular rapeseed). Moreovor, these externality costs are expected to vary between farmers. Given these expectations, we arbitrarily define {e,1,...,e,2ss0 } by a random permutation of the list composed of 2000 ordered equidistant points between 0 and 20 euros/ha, and we arbitrarily define {êi1,...,êi2soo } by a random permutation of the list composed of 2000 ordered equidistant points between 0 and 40 euros/tra'

Handling productions costs

Parameters c,^ (the cost of handling regUlar rapeseed, excluding the cost of farm rapeseed and the handler's extemality cost) are calibrated in an analogous manner to paramet ers c nr: in the case where no IP rapeseed is supplied ( r, = 0 ), we assume that the relation q',0* = F @, -w,)ot is verified when the handler with the highest cost c., among handlers handling regular rapeseed is indifferent between handling regular rapeseed or handling nothing.

'We then ftnd: c,n =3!x10-6 xh2 .

We expect the cost difference cihc,n to be positive for each handler. That is, we expect that for each handler, the cost of handling IP rapeseed in a situation in which IP rapeseed strongly predominates in the handling system would be higher than cost of handling regular rapeseed in a situation in which regular rapeseed predominates. The additional cost would be derived from writing and enforcing contracts along the supply chain, and from using chemical tests to prove the absence of GMOs.

'We also expect these costs to vary depending on handlers (depending on the complexity of handling operations).t' Wt generate a set of parameters cp bY taking 1500 equidistant points between 2 and 4 euros/t. We take a random permutation {c,on',,..,ciphrsoo } of the list {",rn,.,.,cbnr*I. We then define: cih = crh r c;rtr', h = 1r..., 1500.

We can expect the average handling externality parameter for IP rapeseed to be higher than the average handling extemality parameter for regular rapeseed. As a matter of fact, when both regular and IP rapeseed are present in the handling system, the costs of cleaning equipment are expected to be higher in average for IP rapeseed (for which any GMO commingling should be avoided) and the costs of dedicating equipment to IP rapeseed are expected to be higher in average than the costs of dedicating equipment to regular rapeseed (for example, having a common dump pit in 13 Testing costs depend on the number of different GMOs that must be identified and on the available tesis. foi example, [START_REF] Bullock | The economics of non-GMO segregation and identity preservation[END_REF] estimate tttut T.9" cuÏent situation in the US a typi"af testing "àrt i, 0.87 $/t for soybeans and 3.i1 $/t for corn. V/ith an exchange rate of 1'12 euros ioi t $, these costs correspond to 0'97 and3.7 ewos/t, respectively' an elevator makes it more difficult to dedicate equipment to IP rather than regular rapeseed). Moreover, these externality costs are expected to vary between handlers.

Given these expectations, we arbitrarily define handling externality parameters {€,1,.,.,ê,Éso} by a random permutation of the list composed of 1500 ordered equidistant points between 0 and 20 eurosAEra, and we arbitrarily define {e,t,...,e,rroo} by a random permutation of the list composed of 1500 ordered equidistant points between 0 and 40 euros/ha. D omes tic deman d p ar ameter s In the baseline simulation, only regular rapeseed and the substitute good are consumed and all consumers are indifferent between regular and IP rapeseed, i.e. all parameters orc ate equal to l. Consumers are indexed such that o^ 1...< orro*. To calibrate values 661t,,,tobzooo, Wo assume that the constant elasticity form qln" =T p,-o't is verified when the consumer with the lowest preference for good r among those consuming good r is exactly indifferent between consumption of good r or good ô. we then obtain i ob" = 10-6 x ct , for c = 1,..., 2000. In the simulation, we assume that one consumer out of two refuses non IP rapeseed. In this case, o", is set equal to zero for even values of c, and to 1 for odd values of c.

Rest of theworld we take constant elasticity supply and demand curves for handled regular rapeseed in the rest of the world. The supply elasticity is 0.5 and the demand elasticity is -0.5 in the baseline situation. we take production and consumption levels in the rest of the world respectively equal to 3l million t and 32 million t in the baseline eqnal to 1.24 million tons. In the IP rapeseed market, EU farm and handlers' production and EU domestic consumption are equal to 5.10 million tons. l[/'ho wins and who loses Here we compare profits and utilities in the baseline situation to the non- baseline situation. Total profits of farmers decrease by 30 million euros, total profits of handlers decrease by 12 million euos and total utility of domestic consumers decreases by 58 million euros (Table 1). In total, domestic welfare decreases by 100 million euros.

Tables 2 to 4 show the change in profits and utilities for different groups of domestic farmers, handlers and final consumers. Consumers who refuse to consume GMOs lose from the introduction of GM technology, even though IP products are available, because the price they have to pay for IP rapeseed is higher than the price they paid for regular rapeseed (that was all non-GMO) in the baseline situation' These consumers, holilever, do not bear alone all the costs of IP. Profits of farmers who keep producing non-GM non-IP rapeseed decrease. PrOfitS Of farmers who turn from non- GM non-IP rapeseed to IP rapeseed or to the altemative crop decrease as well' Profits of handlers who keep handling regular rapeseed or who stop handling rapeseed decrease too.

Even though identity preservation creates externality costs for regular rapeseed' farmers with a high cost advantage in GM rapeseed relative to non-GM rapeseed win from the introduction of GM technology. And consumers who sense no difference between GMOs and non-GMOs win, because they face a lower price than in the baseline situation (although this price may have been even lower in the absence of extemality costs due to the existence of the IP rapeseed supply channel).ra Finally, handlers efficient at handling IP rapeseed win.

Conclusions

The situation of GMO and non-GMO market segregation raises a range of positive as well as normative questions: who pays the costs in the absence of intervention; who should pay the costs; what kind of government intervention can increase global welfare. In this paper, we propose a framework specifying supply and demand fi.rnctions at the individual level to study the first question. This framework allows us to circumvent the difficulties of conducting supply and demand analysis in the different horizontally and vertically related markets concerned by GMOs and market segregation. Lr addition, because our framework is built on individual heterogenous agents, it allows us to quantiff welfare effects for farmers, handlers or consumers in general, but also welfare effects depending on various characteristics that describe the various agents.

Our simulation results are still preliminary given the lack of data on costs of GMO segregation and identity preservation. But they point in several interesting directions and suggest further research. First, it would be important to conduct sensitivity analysis on some key parameters of the model, notably parameters describing the costs of IP, and parameters describing the preferences of consumers towards GMO and non-GMO products. Second, this framework could be extended to analyze the welfare effects of public policy instruments, such as a taecation of GMO producers or a subsidy to non-GMO producers.

ra Our analysis stictly considers economic effects. We do not consider the possibility that GMOs might actually have harmful health effects on consumers or harmfirl environmental effects. Note: This Table assumes that the yield on the alternative crop is equal to the yield on rapeseed. Column 1 defines the farmers belonging to the group. For example, "rapeseed n ) rupeseed g" is the farmers growing crop n in the baseline situation and crop g in the simulation. Column 2 indicates the number of farmers to whom the change described in column I applies. Column 3 indicates the average change in profit for this group from the baseline situation to the simulation. The average change in money metric utility in equivalent per ton of rapeseed i is obtained by dividing the average change in money metric utility per consumer by the parameter K of the utility function (this parameter gives the quantity of rapeseed i that each consumer would consume if he chose to consume rapeseed i).

  Change in money metric utility of domestic final consumers'groups (baseline situation to simulation) Note:

Table 1 .

 1 Prices, quantities, profits and utilities in the baseline situation and in

			the simulation
			baseline situation	simulation 1
	prices (euros/t)
	wr	t52	r47.5
	P,	183	181.6
	wi	n.a.	t53.4
	Pt	n.a.	195.6
	quantities (million t)
	q':'	11.55	0.05
	sAF q I	0	6.47
	q:*'	11.55	6.52
	qy	10.55	5.28
	q	dM	1.0	t.24
	q',n'=qion'=qfn'	0	5.10
	profits and utilities (million
	euros)
	domostic farmers' profit	2618	2s88
	domestic handlers' Profit	262	250
	domestic consumers' utility	2000(M+10550) -2 895	2000(M+10ss0) -2953
	domestic total welfare	2000(M+10s50)-15	2000(M+1 0ss0)-1 15

Table 2 ,

 2 Change in profits of farmers' groups (baseline situation to simulation)

,ffe arbitarily decide that each consumer consumes only one of the goods tl ',111-b Ïlen he is inrlifferentbetweentwo;;tbtr; of these goods, withgood d-being the mostprefened, then r,Ihenb'

For example, in the current situation in France, only non-GM corn is-groqn, although some varieties of GM .o* *à authorized. To deliver IP corn, the only costly action for a farmer is to sign a contact with the elevator certifying that no GM seed was used'

either consumed domestically or exported to the rest of the world. Consumers in the EU are indifferent between regular and IP rapeseed.T Baseline EU farm and handled rapeseed production (q'iâ' = q:f') is equal to 11.55 million tons, out of which 10.55 million tons are consumed domestically and 1 million ton is exported to the rest of the world. The rest of the world produces 3l million tons of rapeseed and consumes 32 million tons. The EU farm rapeseed price is 152 euros/t, and the EU handled rapeseed price is 183 euros/t. The EU subsidy on rapeseed is 564 euros/ha.8 The price of the close substitute to rapeseed in consumption is p, :1 83 euros/t. In our model we assume the numbers of tlpes of domestic farmers, handlers and consumers to be F:2000; i/:1500; C-1000.e In the baseline situation, 1000 farmers grorri/ rapeseed while 1000 farmers grov/ the alternative crop a; 1000 handlers handle rapeseed and 500 handlers do not handle rapeseed; 1000 consumers consume rapeseed while 1000 consumers consume its close substitute b.Land on each farm is Z:3,500 ha; storage capacity per handler is ft11,550 t; parameter of the utility function is K:10,550 t. EU rapeseed yield is 3.3 Uha. Ir the baseline situation, 7 In our simulation model, the EU is a net exporter of rapeseed. As a result, in the initial situation, even consumers who refuse to consume GMOs are not worse off from consuming non IP rapeseed, given that all rapeseed they consume is produced in the EU, and is non-GM in the initial situation. In reality, we can think of two reasons why some consumers would want to consume only IP rapeseed even if no GMOs were produced in the EU: l) Consumers could be rrninformed about the origin of the rapeseed they consume, or worried about consuming rapeseed that could have been cross-pollinated by GM rapeseed from a trial field. 2) The EU is a net exporter ofrapeseed and rape oil, but is a net importer of rape meal. Imported rape meal is mainly used for livestock feed. Therefore, some consumers rnay prefer IP to assurs themselves about not having eaten meat from an animal fed with GMOs.

8 EU rapeseed area, yield, production and exports from Oil \Morld Statistics Update, year 199912000, March j0, ZOOI . A rapeseed net export equivalent was calculated using a price-weighted average of EU net exports ofrapeseèd, net exports ofrape oil and a net imports ofrape meal. Per hectare rapeseed subsidy from les cahiers de I'OMC, modèle MOMC: marchés céréaliers: perspectives européennes à I'horizon 2005, October 2000, ONIC, France. Farm production price from Agreste, Agreste conjoncture, le bulletin, no5, May 2001, Ministère de I'Agriculture et de la Pêche, France. Handled export price from CETIOM, Colza d'hiver: les techniques culturales, le contexte économique, May 2000, France.

e We refer to them later simply as "farmerst', "handlers" and "consumers", but they are rather types of farmers, handlers and consumers. T9 price elasticities of farm rapeseed supply in the EU and handled rapeseed supply in the RO\tr are equal to 0.5. Price elasticities of handled rapeseed demand in the EU and in the ROTV are equal to -0.5.

Parameters forfarmers

We index farms in the order of their per-hectare variable cost of production for $op n'ri.e., cn, (...( cnzo'o. To calibrate values cy1t,,.tc,rzooo ffid lTa,we consider the case where only non-GM non-IP rapeseed and the altemative crop are grown (rr = 0).

V[e use/'to denote the index number of the farm with the highest per-hectare variable cost of production for crop n (c,r) arnong all farmers growing crop n in equilibrium.

We then rely on two assumptions. First, we assume that the aggregate farm supply function for crop n takes on the constant elasticity form q'fF =dw,0'5, when the marginal fam f is indifferent between producing $op n or crop 4.10 Second, we assume that the average revenue of the 1000 farmers who grortv rapeseed n in the baseline situation, including the subsidy, is equal to twice their average cost "nr.1r

W" use these two assumptions in the appendix to show that in our model no and cnf are defined by: xo=198.7 aîd cn = 501'6x10-6 *{'*564-no ,forf= l' "'' 2000'

Values cgt,...,crzaeo aee calibrated using results of Desquilbet, Lemarié and Levert (2001), who estimate potential adoption of GM rapeseed using data on non- GMO herbicide costs from French rapeseed farmers. In a simulation where the GMO l0

The assumption of constant elasticity supply and demand curves is coulmon in models with research- induced supply shifts. The most common-alternative is linear supply and demand curves' In our case' so*e farmeis have negative production costs with a linear supply curve, because the elasticity of ,rppfy is less than t ii tle baseline situation. We prefer the ôônstant elasticity assumption, which inid1iËr that production costs are necessarily positive (iee Alstoq Norton and Pardey, p' 61)' situation (Oil World Statistics Update, year 199912000, March 30,200I, and Table 1).

We obtain ROIV domestic supply and demand curves defined by qrRow -183-05 x3lxl06 p,o't *d qdRovl =1830's x32xl06 p.-o't. The import demand curve is then defined by qf, = ndnowq'*on .

Simulation

We present the results of a simulation analyzrng the consequences of the simultaneous introduction of GM rapeseed technology, a shift in half of the consumers' preferences in favor of non-GM IP rapeseed (good i), and the introduction of segregation and identity preservation in the EU.

Equilibrtun prices and quantities

Compared with the baseline situation, the equilibrium price of farm regular rapeseed decreases by about 30Â to 147.5 euros/t from 152 euros/t. The equilibrium price of handled regular rapeseed decreases by about 0.8% to 181.6 euros/t from 183 euros/t. The equilibrium price of farm IP rapeseed is higher than the price of farm rapeseed at the baseline, at 153.4 euros/t. The equilibrium price of handled IP rapeseed is equal to 195.6 euros/t. There is a 42.2 euros/t wedge between the price of farm IP rapeseed and handled IP rapeseed, and 34.1 euros/t wedge between the price of farm regular rapeseed and handled regular rapeseed. These wedges are higher than the 31 euros/t wedge between farm-gate and handler prices in the baseline scenario, and reflect higher costs due to identity preservation.

In the regular rapeseed market, EU farm and handled supply is equal to 6.52 million tons. More than99% of farm regular rapeseed is GM rapeseed. EU domestic consumption is equal to 5.28 million tons, and exports to the rest of the world are Appendix: calibration of the model -Farrn production costs of rapeseed n and profit on the alternative crop: We calibrate our model considering the case in which no non-GM IP rapeseed is grown (r, = 0), and therefore where there is no extemality cost for farmers growing regular rapeseed.

When farm. .f =L,...,2000 grows rapeseed n, its per-hectare profit is TtnI =wry+s-c,, anditstotalproduction is qi=yL.Fromthebaselineproduction and price values, o=(tSZ)-o'sx11.55x106. In an equilibrium where farms l to f produce good z, profits the marginal farmf' of growing $op n will equal its profits from gfOwing the alternative crop, so ftnl' =Wr!*t-cn[,=/ro. Therefore in equilibrium, w,=no-s+co''. since aggregateproduction is q'l':f yL then v assuming the aggregate supply frrnction takes on the constant elasticity functional Fï9 0.5 form implies q',lF = a(w,)o't; that is, ft y L = a , or, equivalently, Eo +cn,= +s. Using values of parameters d, L, s and y, this defines: ltn +cn, = 501.6x10-6 x f'2+564. The constant elasticity functional form of aggregate supply function imPlies:

(A1) c", = 501.6x10-6 x f2 +564-no fotf=1, ,.. ,2000'

Per hectare revenue on rapeseed n in baseline situation is: wry + s =152x3.3+564=1065.6 euros/ha. Then, under the assumption that per hectare revenue is twice the mean of the per hectare costs of the 1000 farmers who grow crop in in the baseline equilibrium, we have:

,( f'y L I d, 30

From the equations (A1) and (A2), we can solve for ro and c,f ,,f: 1, ..., 2000.

-Handling costs of rapeseed r: From baseline production and price values, we have:

p=31-o'sxll.55x106.Theconstantelasticityformcanalsobewritten hQ=F",^o't, r 1000 (A2\ L Ic-. =1065.612=532.8euros/ha.

' ' 10007 '

or equivalent ly: c,o =( ry\' = 3 1 x 10-6 x hz .

\B )

It can then be written: c K =r(*)", or, equivalently, or, = or(kJ-'. trrn, -Domestic demand parameters: From production and price baseline values, 7:

(tSg)ntx10.55x106. When consumers I to c consume good r, the aggregate production it qf;" = c K, and the constant elasticity form is verified for oo, = Pt I P, values of paramet erc % p b and K, this defines i 6 b" = 1 0-6 x c', lot c = I,..., 2000.


Isabelle Piot-Lepetit
email: isabelle.piot@roazhoninra.fr

Monique Le Moing

Maud Ulvé

The French pig production structure : between competitiveness and manure management

Keywords: Directional distance function, Structural efficiency, Data Envelopment Analysis, Pig farming system, Manure management

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

1. Introduction significant productivity gains, particularly for large-scale producers. Pig farming is becoming more regionally concentrated. A major factor encouraging the development and uptake of productivity enhancing technologies has been the intense competition in the meat market and the long run decline in real prices received by farmers, which in turn is driven by productivity improvements.

Disposal of pig manure is usually driven by lowering disposal cost rather than optimising the nutrient needs of crops and pasture, leading to detrimental environmental costs. Because pig manure is a low density nutrient fertiliser source and costly to transport over long distance compared to inorganic fertilisers, areas with high intensity in pig production usually have a surplus of nutrient manure. This has lead to an increase in residual pig manure in the environment in these areas where they can degrade water and air quality and impose human health and environmental pollution costs on society. Policy measures to deal with that problems are predominately regulatory and are increasing in severity and complexity. Supports have been provided to offset the increased costs imposed by regulations. In particular, supports have often been given to reduce the level of capital expenditure required to bring production facilities into conformity with regulations. The impact of regulations depends upon the size of farms. It can influence producer behaviour and can have a direct effect on the resulting environmental benefit and on competitiveness of producers (OECD, 2003).

The aim of the paper is to provide an evaluation of the competitiveness of the French pig sector. A nonparametric frontier analysis which allows efficiency benchmarking, called Data Envelopment Analysis (DEA), is used. The efficiency evaluation is implemented in both economic and environmental terms. This paper used an approach proposed by Maudos et al. (2000) for obtaining productive efficiency measurements which enable two components to be differentiated : one associated with the degree of efficiency within each French pig farming system called intra-system efficiency and another associated with the structure of production in the pig sector called structural or composition efficiency. The former component captures gains that can be achieved both from a competitive and environmental point of view if all farms in each pig farming system improve their productive efficiency. This latter component shows gains that result from structural changes in the productive specialisation of the French pig farming system. As the livestock sector is highly constraint by environmental regulations in the European Union, DEA models used in this paper take into account the fact that animals produce manure. An undesirable output is introduced in the specification of the best-practice frontier of each pig farming system and performance benchmarks are deflrned in a direction that allows for a production increase jointly with a pollution decrease. The use of the directional efficiency measurement defined by Chung et al. (1997) allows us to evaluate improvements of the competitiveness of farms that can be achieved without increase of pollution and changes in the observed productive structure of French farms with a pig farming activity.

The paper is organised as follows. Section 2 provides a description of the impact of nitrogen pollution from intensive livestock units on the environment and of the main European directives to control and reduce nitrogen pollution. Section 3 presents formally the DEA models implemented in this study. Section 4 characterises the data set and section 5 presents the main results. Finally, the section 6 concludes on the major findings of this paper.

2. Nitrogen emissions and European environmental regulation in the pig sector.

In Europe, the quality of available drinking water resources is one of the most important issues of environmental concern in areas with intensive livestock population. The European drinking water standard is a nitrate level less than 50 mgll.It is expected that about 25 Yo of the agricultural soils in the European Union (EU) is above this standard (Brouwer and Van Berdum, 1996). This is due to either the high surplus of nitrogen from agriculture or due to vulnerability of the soil to leaching. Livestock production systems contribute to a significant proportion of the nitrogen pollution burden in large areas of Europe. High supply levels of animal manure from intensive livestock farming create problems of water and soil pollution.

2.L Nitrogen emissions

Nitrogen is imported to the farm in feed and fertiliser and exported as part of pig and crop output. Nitrogen is lost through the production process but the substantial part of nitrogen emission from pig farms originates from the production of pig manure. Part of the nitrogen content of manure is lost in stalls and during storage through ammonia evaporation. When the remaining manure is utilised in plant production, the utilisation level is substantially lower than the utilisation level for chemical fertiliser. The part of the nitrogen content that is not ultimately incorporated into crops is washed out as nitrate or denitrified along with surplus nitrogen from chemical fertilisers. Although nitrogen losses are not a good indicator of the total environmental effect of emissions which are known to vary from locality to locality and across farms, it is usually used as an indicator of the local and global environmental effects of nitrogen losses. Major impacts of nitrogen emissions to the environment arise from leaching of nitrates to surface waters and ground waters, emissions of ammonia to the atmosphere, as well as denitrification. High levels of phosphate and nitrate cause eutrophication of surface waters and affect biodiversity through the depletion of plants and animals and growth of algae. Emissions of ammonia contribute to acidification of soils and waters. The environmental impact of livestock production varies across regions and farming types.

Concentration of livestock production due to economies of scale makes the problem especially acute in some regions and in particular, in the western part of France.

There is a common understanding in Europe that major adjustments are required by livestock production in meeting targets formulated by govemments, either at regional, national and community level. This implies mainly to regions with a high concentration of livestock production with an emphasis on pig production. A wide variety of adjustment processes can be considered including measures at source of pollution, as nutritional and fertiliser management, adjustment of farm structure by reducing intensity of production and by taking end-of-pipe measures as, processing and treatment of livestock manure. Member states of the EU generally design their policies by applying the Nitrates Directivel.

2.2. The EU Nitrqtes Directive t 1n the context of European policy, a directive is a legal instrument which informs Member states of goals and of a time frame for their achievement. The implementation of a directive is left to Member states, thus allowing them to achieve the common goal in ways that recognise their national character.

In December 1991, the directive concerning the protection of waters against pollution caused by nitrates from agriculture sources (9Il676lEEC) was announced. One of the main elements of the directive was that the application of animal manure in the Nitrate vulnerable zones should not exceed 170 kg of nitrogen per hectare. This standard should be met at farm level by the year 2003. Two four-years periods up to the year 2003 was identified in the Nitrate directive, during which a gradual reduction in the level of application is allowed. By the end of the firstperiod, from 1995 until 1999,it was possible to apply a maximum of 210 kg of nitrogen from manure. The application of nitrogen from livestock manure was reduced during the second four years period, for achieving the level of 170 kg by the year 2003. To fulfil these objectives, the directive contains three main provisions.

From December 1993, EU member states had monitored all water bodies and had identified areas where water quality is threatened by nitrate pollution from agriculture. These areas are designated as Nitrate Vulnerable Zones (lt{Vzs). NVZs are identified as land areas where agricultural production contributes to drinking water quality problems when drinking waters contain more than 50 mg of nitrate per litre or to the eutrophication of aquifers when algae growth causes oxygen depletion and death or migration of higher level life farms.

By December 1993, member states had established voluntary codes of good agricultural practices in order to allow for farmers to fulfil the objectives of the Nitrates directive. Such codes cover, where relevant, issues such as the periods of the year when the application of fertiliser is inappropriate, the land application of fertiliser to steeply sloping ground and near water courses, the capacity and construction of storage vessels for livestock manure and the procedures for land application, including rate and uniformity of spreading, of both chemical fertiliser and animal manure, that will maintain nutrient losses to water at an acceptable level.

In addition, member states may include in their codes of good agricultural practices measures regarding the land use management, the use of catch crop, the establishment of fertiliser plans and keeping record on a farm-by-farm basis on fertiliser uses or the management of irrigation systems.

By December 1995, member states had developed action programmes for their NVZs, to be implemented no later than December 1999. These mandatory programmes consist of the standards contained in the voluntary codes and include rules relating to the periods when the land application of certain types of fertilisers is prohibited, the capacity of storage vessels for livestock manure and the limitation of the land application of fertilisers, consistent with good agricultural practices and taking into account the characteristics ofthe concerned vulnerable zones as soil, climate or cropping practices.

The other EU Directives to control nitrogen pollution

The Integrated Pollution Prevention and Control (IPPC) directive was adopted on November 1995 (96/C 87/02). This directive lays down measures designed to reduce emissions in the air, water and land from certain activities, including measures conceming wastes in order to achieve a high level of protection of the environment. Regarding agricultural activities, the directive is applicable to installations for intensive rearing of pigs with more than 2,000 places for production pigs (over 30 kg) or 750 places for sows.

The Environmental Impact Assessment directive (EIA) concerns the effects of certain public and private projects on the environment. Regarding agriculture, it includes facilities for the intensive rearing of pigs more than 3,000 places for pig production (over 30 kg) or 900 places J for sows. It includes projects for the restructuring of rural land and holdings, the use of uncultivated land or semi-natural areas for intensive agricultural purposes, water management for agriculture, including inigation and land drainage projects, initial aforestation and deforestation for the purposes of conversion to another type of land use and intensive livestock installation. Now, all these directives are included in the Framework Directive on Water Quality in the EU from October 2000 which is an attempt to built a general policy framework concerning the improvement of water quality.

2.4. The implementation of these directives in France

The French system of nitrogen pollution reduction is built on the Act on Classified Installations for Environmental protection from 1976 which is a general framework in which polluting installations must be integrated. Regarding agriculture, only rearing activities are concerned. This act recognizes three legal systems for holdings which imply different restrictions on the application and spreading of animal manure and nitrogen balance sheets:

' Farms with less than 50 livestock equivalents (LE)2 are managed by the Departmental Sanitary Regulation; ' Farms with a number of LE between 50 and 450 arc managed by a statement system;

' Farms with more than 450 LE must obtain a production permit from their Prefecture (administrative headquarter). This procedure involves a public inquiry and an impact study of the rearing activity.

The implementation of the Nitrate directive in the French legislation was realized between 1993 and 1996. The nitrate vulnerable zones (NVZs) were identified and delimitated. At this time, the France country is divided in three different zones: non vulnerable zones, vulnerable zones with a nitrate level which exceed 50mg/l or which is upper than 40 mg/l with an increasing trend and structural manure surplus zones (ZES) where the number of livestock units implies more than 170 kg of nitrogen per ha. At the same period, the Corpen organisation was in charge of the building of the good agricultural practice code. This code is mandatory in the NVZs and the ZES and voluntary in the other zone. The first four-years action program, called the Agricultural Pollution Control Program (PMPOA), was defîned in November 1993. It only concerns intensive rearing activities. This program provides a financial support to farmers in order to adapt their holdings and manure storage capacities to fulfil the code of good agricultural practices and the standard of 170 kg of nitrogen per hectare. Furthermore, a farmer who enters the program and does not respect its obligation could pay a fine. The second four-year action program was defined by a enforcement order from January 200L A more stringent action program was defined for the ZES in January 1998. It implies no increase in livestock numberso nitrogen emissions are restricted to 140 kg per hectare and the implementation of end-of pipe measures as processing and treatment of livestock manure are made compulsory. Approximately half of Brittany is included in the ZES.

Furthermore, a program had been set up in which all farms could pay a graduated pollution tax. This affects the larger farms since 2000 and the smaller farms since 2001 and 2002. The amount of the tax charged depends on the degree of pollution, equipment used, manure storage, manure use and scale of production. In 2002, it could be based partly on nitrogen levels.

3. Evaluation of the competitiveness and manure management of French pig farms: a DEA modelling.

The Data Envelopment Analysis approach is a nonparametric mathematical programming approach. This method explicitly includes the inefficient use of resources. It allows for defining a best-practice frontier on the most efficient firms and then, an individual inefficiency measurement can be defined which describes the distance of each observation in the data set from the best-practice frontier (Famell, 1957; Chames et al., 1978). In this paper, we extent previous studies by taking into account that agricultural production creates polluting emissions (Fàre et al. 2001; Chung et al., 1997) and by distinguishing productive efficiency measurements between an intra-system efficiency and a structural efficiency (Maudos et al., 2000). These measurements allow for studying the efficiency of the main French pig farming activities at an individual level and for studying the efficiency of the pig sector.

3.I. Modelling technology with polluting emissions

Generally, agricultural production analysis is concemed with describing the relationships that characterise the transformation of inputs as, land, labour or purchased materials, into marketable outputs as, wheat, milk or meat. Such outputs are designed as desirable in the sense that they are demanded by consumers and yield utility in consumption. However, agricultural products processes also create outputs which society deems undesirable because they yield disutility in consumption. These bad outputs as, ground and surface water contamination, runoff and leaching of nitrogenous fertilisers and pesticides or greenhouse gas emissions to the atmosphere, impose costs. These costs can be either in monetary terms when, for example, water supply companies are faced with the cost of removing contaminants from supplies or in indirect terms such as rural landscape damages.

To be formal, let us denote the good outputs by y e Ry , the undesirable or bad outputs by b e Àf and inputs by x e Àf . Then, the technology can be described in a very general way via the output set:

P(x)={O,t):xcanproduce (y,b)\ (1)
In words, for each input vector x, the output set P(x) consists of all the combinations of good and bad output vector (y,b) that can be produced by the input vector. Following Ffue et al.(I994), we model the idea that it is costly to reduce the bad outputs by imposing the assumption of weak disposability of undesirable outputs as:

(y,b).P(x)et 0<0<limply (9y,)b)eP(x)

(2)

This assumption states that a reduction in undesirable outputs is feasible only if goods are simultaneously reduced, given a fixed level of inputs. This means that abatement uses resources that otherwise could have been used to expand pig production. In addition, we assume that the good or desirable outputs are freely disposable which means that good outputs may be reduced without the reduction of the undesirable outputs.

(*,y).P(x)et y'3yimply(y',b) eP(x)

(3)

The basis problem with pollution is that the production of goods such pig meat is accompanied by the joint production of undesirable by-products such as manure. The notion ofjoint production of good and bad outputs is modelled by: if(y,b)eP(x)andb=0theny=9

(4) This means that no undesirable outputs are produced (b:0) only if none of the good outputs are produced (y:0).Altematively, if some good outputs are produced then some bad outputs must also be produced.

We assume that there are k:1,...,K observations of inputs and outputs (xo,y1,,bo). Following F'àre et al. (1994), we can construct an output set that satisfies above conditions:

In this DEA formulation, the inequalities for inputs and desirable outputs in (5) make them freely disposable. The undesirable outputs are modelled with equalities; this makes them weakly disposable. Furthermore, the intensity variables zo,k=I,...,K are taken to be nonnegative. This imply that the production technology (5) exhibits constant returns to scale (cRS).

3. 2. Indiv i dual pr o du ct iv e ffi c i ency m e a s ur e me nt A noticed above, desirable outputs are uedited positively by society since they increase utility in consumption while undesirable outputs are credited negatively since they involve either monetary costs or indirect disutility in consumption or in health. Thus, the productive efficiency measurement used herein is defined in a sense that allows for the possibility of crediting firms for the reduction of undesirable outputs and expansion of desirable outputs by using a directional output distance function. Following Chung et al. (1997), it is defined as:

D"(x,y,b) =.upp :(y,b)+ Fg eP(x))

(6) where g is the vector of direction in which outputs are scaled. In our case, the good and bad outputs are treated asymmetrically, thus g = (y,-b) . B is the maximum feasible expansion of the desirable outputs and contraction of the undesirable outputs in identical proportions for a given level of inputs.

For each observation, the directional distance function is computed as the solution to a linear programming problem. For example, for k', D"(xo,yr,bo,i!r,,-br) = max Ê K s.t. Ztp*>-(l+ Ê)y0,. ffi=1,...,M k=l K Ztob^ = (l -p)bk,, ,s = 1,...,S frontier and is inefficient relatively to benchmark peers.

Intra-farming system and structural fficiency measurements

There is various way of producing pig meat in France. Some farms are specialised in pig production while others are crop, beef or dairy producers. In the latter case, pig production is only one additional activity on farms. Furthermore, the scale of production of these farms is very large. It ranges from small size to very-large size as noticed by XXX et a1.,2003. It is also obvious that providing productive efficiency measurements for the pig sector is complicated. Most of the time, the sector analyses consider an aggregate product from aggregate inputs and study the aggregate inefficiency by defining an aggregate best-practice frontier. The multiproduct nature of the pig sector is ignored. To measure correctly the productive efficiency of the pig sector, the paper considers the existence of different systems of pig production, each one with different technologies and different degrees of inefficiency. By doing this, our analysis allows for defining two sources of inefficiency at the sector level: the intra-system inefficiency which is associated with deficient use of resources allocated to each pig farming system and the structural inefficiency which is due to incorrect allocation of resources among systems given their particular technologies of production. According to the latter component of productive efficiency, the pig sector can gain effîciency simply by decreasing the weight of pig farming systems in the sector with the less productive technology and vice versa.

In our DEA context, these measurements are defined by using the direction output distance function developed in Chung et al. (1997). Firstly, the productive inefficiencies in each pig farming system are obtained by implementing the linear programming defined in (7).

Secondly, we calculate, the aggregate level of production that could have been achieved by each pig farming system if they were efficient. This step enables us to measure the true productive efficiency of the pig sector and to divide this measurement into the part of inefficiency that is due to unsuitable productive specialization at the sector level and the other part that is due to inefficient use of the resources allocated to each pig farming system. This breakdown allows us to analyze the contribution of productive efficiency and of each of its components to the evolution of the pig production structure among the actual farming systems.

Let us assume that there is rR pig farming systems. The aggregate output of the pig sector (Y e R{ ) is obtained as a sum of the outputs of each pig farming system in the sector:

Y (8)
As noticed above the aggregate best-practice frontier should not be obtained from data of aggregate production. The efficient aggregate production at the sector level is obtained as the sum of the pig farming system outputs once the inefficiencies in each of them wherc p,, is the inefflrciency of each farm j from the pig farming system r previously calculated in (7). Being efficient in each production system does not guarantee being efhcient in aggregate production level since it could subsist an inefficiency associated with the sector composition. Being efficient at the sector level implies being efficient in each and every production system (intra-system efficiency) and having a efficient structure of production at the sector level (structural efficiency).

Thus, for each pig farming system, the total productive efflrciency QPD measurement is obtained by solving the following linear programming problem. For example, for r', sTE,, = 0"7X,,,1,, 8,,;Y,,,-8,,) = ^a* Fl," ,R s.t. Zl,i,, > 1t+ pf"1i',, m =1,...,M r=1 rR Zl,Ê,, = (t-B|rE)Ît,,, .s = 1,...,,S ï'

),,>0

) Xr,, n=lr...rn r =lr...rR

Finally, the intra-farming system efficiency measurement can be derived from

ISE =TPE STE I l,X,n (l l) (r2) r=l 4.Data
The data set is drawn from the Farm Accountancy Data Network (FADN) for France and covers the production year from 1996 to 2000. The FADN is an annual survey of farms that is stratified according to region, economic size and type of farming. A sub-sample of farms with a pig farming activity was extracted from this data set to form an unbalanced panel of around 600 individuals per year (3,000 observations).

4.L Mqin French pigfarming systems

In order to obtain a structural information on main pig farming systems in Franceo an Explanatory Data Analysis was implemented. The extraction of this structural information from the data set had provided different clusters of farms with a homogenous productive structure, built on three items : intensity of production (production per hectare), intensity of labour (production per worker) and diversity of production (level of specialization in pig production). A classification in 8 classes was obtained. The statistical stability of the classification was confirmed by an Evolutionary Data Analysis over the time period 1996- 2000. Then, a Discriminant Analysis was applied for allocating each farm in a cluster (XXX et al., 2003). This classiflrcation is presented in Table 1. In this analysis, we consider only one desirable output: the total production of each farm Qt) which is a catch-all variable defined as revenue derived from all farm enterprises. Livestock and crop products used on farm are deducted from this variable.

Four inputs are considered: agricultural land (xt) which is defined as total utilised agricultural area (in hectares) for each farm; livestock herd (x2) which represents the size of the livestock farming activity (in livestock units) of the farm; Labour which is defined as total annual worker units (x3); and variables costs (xa) which is made up of annual expenditure for crop and livestock production.

Only one undesirable output is represented within this modelling. It is defined as an estimate of excess nitrogen (ô). Individual nitrogen balances are constructed by using estimates of flows of nitrogen across the farm boundary. It is a tool used to provide insight into flows of nitrogen. Mineral balances are defined as the difference between input and output flows. Input flows compare: nitrogen from inorganic fertilisers; nitrogen from organic manure (exclusive of losses due to emissions of ammonia); and nitrogen by deposition from the atmosphere.

Output flows include the uptake of harvested crops and livestock sold (Meisinger and Randall, 1991).

Main results

The average values for the individual productive efficiency measurements which are based on the directional distance functions as specified in (7) are reported in Table 2. Due to a problem of sample size relatively to DEA model size, farms from class 3 have been grouped with farms from class 4 which are the most similar in terms of size, productive specialization and technological processes.

On average, the individual productive fficiency in our sample is 1.175. Thus, desirable outputs (total production) can be inueased from around 17 %jointly with a decrease in the production of undesirable outputs (nitrogen emissions) from the same amount. This results is variable along the time period. On average, the most efficient years were 1996 and 2000 with an inefficiency rate of 15 oÂ. The most inefficient year was 1998 with a rate of 20 oÂ. These last results reflect the economical context of production. The time perio d 1996-2000 comesponds to the higher part of the pig production cycle. From 1996 to 1998, producers were affected by a very important decrease in production prices. Prices are so low that production costs were not covered. Furthermore, a more restrictive environmental regulation was implemented in France in 1998. It affects the most polluted areas from France (ZES), with the most intensive and concentrated pig production systems, mainly the western part of France.

Among the productive systems, the most efficient one is, on average, the type 5 (pig producers with medium-sale farms) with an inefficient rate of 8 %. A good performance is obtained by farms from class I (crop producers with large-scale farms) and from class 3 and 4 (pig producers with very large and large-scale farms) with an inefficient rate of 11 o/o. Thus, the most efficient farms are those specialised in pig or crop production with a relatively largescale of production. At the opposite, the less efficient farms are those from class 7 with a mixed activity of beef and dairy production, from class 8 with a beef production activity and from class 2 with a crop production activity. These three types of farms have in common a small-sale of production. Thus, large pig farms seem enjoying competitive advantages of scale while small-scale pig farms seem to be more affected by costs imposed on producers by manure management regulations.

Intra-pig forming system efficiency (ISE) measurements indicate the percentage increase of production that each farming system could achieve if it were efficient within each farm. They are presented in Table 3. The pig farming system with the lowest intra efficiency score is the number 8, small-scale farms specialized in beef production. Thus, a reduction in efficiency in this productive class, which is among the most inefficient one at the individual level (see,

Table 2), will induce at the sector level an increase of around II o of the total production simultaneously with a decrease of manure emissions from the same amount. At the opposite, farms from class 1 (large-scale farms specialized in crop production) which are among the most efficient at the individual level, could, on average, increase desirable outputs jointly with a decrease in undesirable outputs of 48 % by reduction of their inefficiency. Thus, the potential impact of these farms on the improvement of competitiveness of the French pig sector under manure management regulation is more important.

Furthermore, we can notice that the year 1998 had an impact on intra-system efficiency. The change in the economic context of production and in the environmental regulation has induced improvement of farms' efficiency. The additional cost involved by production price decrease and by manure management regulation was best integrated by the largest-scale farms of the sample than by the smallest-scale ones.

Sfructural fficiency (STE) measurements indicate the percentage increase of production that can be obtained by modifying in the best possible way the productive specialisation of each pig farming system. Over the entire time period, mixed farms with crop and pig production are the most efficient. Due to the joint management of confined animal manure and plant nutrient needs, animal manure can provide a valuable nutrient sourcq for crop growth. The more intensive pig rearing activities are the less efflrcient. Without any technical or manure management change, it would be difficult for these farms to increase their production level with a joint decrease of their environmental impact.

After the year 1998, farms from class 8, small-size farms specialised in beef production are more efficient than farms specialised on pig production whatever their size. In modifying their productive specialisation, they can contribute to an average increase of 16 %o to the pig sector production while farms from classes 3 and 4,large and very large-scale farms specialised in pig production, can contribute for 80 %. This last result is very high but it highlights a very important point which is the high environmental restriction on these pig farming systems. As they produce a high level of production on a very small land area, they can contribute to the competitiveness of the pig sector. However, as there exists constraints on manure spreading and in general, on manure management, a change of there productive specialisation would be areal advantage to expand production without pollution increase.

Total productive fficiency (TPE) measurements, which is the product of intra-system efficiency and structural efficiency, represents the potential increase in output that the pig sector could obtain by elimination both the inefficiency with which it operates in each pig farming system (intra-pig farming system efficiency, /SE") and that resulting from the choice of an inefficient specialisation (structural efficiency, STE).The levels of total productive efficiency whose values appear in Table 4 show that at the beginning of the time period (1996), only farms from class 1, large-sale farms specialized in crop production, appear to be efficient. With the change in the economic context, mainly the fall in production prices, this advantage disappear. After the year 1998 and the implementation of a more stringent environmental regulation on rearing activities in manure surplus zones (ZES), farms specialised in crop production and in beef production become efficient. These farms have the higher land area of the different farming systems, thus the change in the regulation does not involve an incitation for production improvement mainly in manure management as higher as for the other productive systems.

I2 The aim of the paper is to provide an evaluation of the competitiveness of the French pig sector together with an improvement in manure management. A nonparametric frontier analysis which allows efficiency benchmarking, called Data Envelopment Analysis (DEA), is used. The efficiency evaluation is implemented in both economic and environmental terms.

As the livestock sector is highly constraint by environmental regulation in the European Union, the DEA models used in this paper take into account the fact that animals produce manure. An undesirable output is introduced in the specification of the best-practice frontier and performance benchmarks are defined in a direction that allows for a production increase jointly with a pollution decrease. The use of the directional efficiency measurement allows us to evaluate improvements of the competitiveness of farms that can be achieved without increase of pollution.

Furthermore, a productive efficiency measurement is defined which enables two components to be differentiated: one associated with the degree of efficiency within each French pig farming system called intra-system efficiency and another associated with the structure of production in the pig sector called structural or composition efficiency. The former component captures gains that can be achieved both from a competitive and environmental point of view if all farms in each pig farming system improve their productive efficiency. This latter component shows gains that result from structural changes in the productive specialisation of the French pig sector.

These results provide an illustration of the potential evolution of the French pig sector. At the individual level, large pig farms seem enjoying competitive advantages of scale while small- scale pig farms seem to be more affected by costs imposed on producers by manure management regulations. They have less production across which to spread the cost of standard regulations. Howevero at the sector level, farms with the highest land arca are the more efficient. This point highlights the impact of environmental regulations, mainly by the targeted limit on the application of animal manure specified in the Nitrate Directive, which set a maximum rate of 170 kg of nitrate per hectare and of all manure management restrictions.

Concerning the structural efficiency of farms, mixed farms with crop and pig production are more efficient. Due to the joint management of confined animal manure and plant nutrient needs, animal manure can provide a valuable nutrient source for crop growth. The most intensive pig rearing activities are less efficient. Without any technical or manure management change, it would be difficult for these farms to increase their production level with a joint decrease of their environmental impact. t6

  TP E,, = Do(X,,,Y,,, 8,,;Y,,,-8,,)max 8,, R s./. 21,Y,^2(I+ B,)Y,,. m=1,...r =I,...,R and the structural efficiency measurement by solving the following linear programming problem:

  farming systems: 1: Large-scale farms specialized in crop production 2: Medium-scale farms specialized in crop production 3 and 4: Large and very large-scale farms specialized in pig production 5: Medium-scale farms specialized in pig production 6: Large-scale farms specializedin dairy production 7: Small-scale farms specializedin beef and dairy production 8: Small-scale farms specialized in beef production Mean

  farming systems: 1: Large-scale farms specializedin crop production 2: Medium-scale farms specialized in crop production 3 and 4: Large and very large-scale farms specialized in pig production 5: Medium-scale farms specialized in pig production 6: Large-scale farms specializedin dairy production 7: Small-scale farms specialized in beef and dairy production 8: Small-scale farms specialized in beef production Mean

  -scale farms specialized in crop production 2: Medium-scale farms specialized in crop production 3 and 4: Large and very large-scale farms specialized in pig production 5: Medium-scale farms specialized in pig production 6: Large-scale farms specializedin dairy production 7: Small-scale farms specializedinbeef and dairy production 6. Conclusions

  Thus, the individual productive efficiency measure (IPE=\+ Ê) under constant retums to scale (CRS), also called the overall technical efficiency measure, is obtained by solving the above linear programming model. The vector of weights z is attached to each of the efficient observations and allows for characterising benchmark peers. A separate linear programming problem is solved to obtain the IPE score for each observation in the sample. If IPE =1, the observation is on the best-practice frontier and is efficient under CRS. The observation is one of the benchmark peers of the sample. If IPE < 1, the observation lies below the best-practice

	k=l
	lzox^2 xr, n =1,...,n k=1
	zo> 0 k =1,...,K

Table 1 .

 1 Classification of French farms with apig farming activity in 8 classesClass I Large-scale farms specialized in crop production Class 2 Medium-scale farms specialized in crop production Class 3 Very large-scale farms specialized in pig production Class 4 Large-scale farms specialized in pie production Class 5 Medium-scale farms specialized in pig production Class 6 Large-scale farms specialized in dairy production Class 7 Small-scale farms specialized in beef and dairy production Class 8 Small-scale farms specialized in beef production French pig production is provided by different types of farming systems. Production specialisation of farms are pig, crop, beef or dairy production. The scale of the production activity ranges from small to very large farms. Thus, this classification highlights the diversity of pig farming systems in France. Furthermore, this classification of the data set in homogeneous clusters of farms allows us to implement a Data Envelopment Approach on each cluster since they describe homogeneous productive structures.

	4.2. Description of DEA variables

Table 2 .

 2 Individual productive efficiency (IPE) measurements

	1996	1997	I 998	1999	2000	Mean
	1.109	1.163	1.208	1.2s6	1.082	1.1 16
	1.200 r.081	1.177 t.n0	1.2t6 1.133	1.288 1.072	1.184 1.111	1.213 1.1 10
	1.065	l.t 13	l.053	1.080	1.090	1.080
	1.100	1.181	1.192	1.16s	1 .153	1.158
	1.273	1.298	1.393	1.256	1.275	1.299
	1.263	1.182	1.252	1.20s	1.t97	1.219
	1.156	I.I75	1.207	1.189	1.156	1.175

Table 3 .

 3 Infa-pig farming system efficiency (1SE) and structural efÏiciency 6fD measurements

			ISE					STE
	1996	1997	1998	1999	2000	Mean	1996	1997	1998	1999	2000	Mean
	1.000	1.748	L883	1.770	1.000	1.480	1.000	1.000	1.000	1.190	1.000	r.038
	1.121	1.015	1.877	1.000	1.000	1.202	t.371	1.382	1.877	1.000	1.000	1.326
	1.000	1.163	2.650	1.000	1.000	1.362	1.310	1.664	2.491	2.010	1.725	1.840
	1.000	.000	2.246	1.000	r.000	1.249	1.610	1.705	2.246	2.308	1.133	r.800
	1.057	.000	1.456	1.086	1.165	1.153	1.342	1.993	1.456	1.670	1.373	1.567
	1.192	.265	1.440	1.441	1.200	L307	1.000	l.0l I	1.000	1.248	1.407	1.133
	1.038	t.038	1.463	1.000	1.000	1.108	1.366	r.000	1.463	1.000	1.000	1.166
	1.107	1.176	1.8s9	r.r85	1.0s2	1.276	1.286	1.394	1.648	1.489	1.234	1.390

Table 4 ,

 4 T otalproductive efficiency QPE) measurements

	1996	1997	1998	1999	2000	Mean
	r.000	1.748	1.883	2.106	1.000	1.547
	1.537	1.404	1.877	1.000	1.000	1.363
	1.310	1.935	2.650	2.010	1.725	1.926
		1.70s	2.246	2.308	r.133	r.800
		1.993	1.4s6	1.8 l4	1.600	1.656
		1.279	1.440	1.798	1.688	1.479
		1.038	1.463	1.000	1.000	1.276
		1.586	1.859	1 .719	1.306	1.578

One livestock equivalent (LE) corresponds to one fattening pig. A sow or a boar corresponds to

LE and a piglet to 0.2L8.

: Small-scale farms specialized in beef production Mean

The research on which this paper is based was supported by the GIS "Porcherie Verte".