Accession of the East-European countries to the European Union and the impacts on arable world and European markets: a WEMAC analysis

Keywords: partial ana]ysis, world market, accession N

This contribution proposes an evaluation of the impacts of the accession of the East-European countries to the European Union by a quantitative modelling analysis. The study analyzes the effects of the latest CAP reform and the European enlargement both on the world and European arable markets. By using a world arable multi-market mode!, the paper provides a market outlook through the year 2010 which includes the recent reform of the Common Agricultural Policy. It compares three scenarios. the first one the situation of 2002 is supposed to remain in place indefinitely and the European Union is composed of 15 countries, the second one includes the effects of the 2003 CAP reform and in the third scenario incorporates the 2003 CAP reform and the European enlargement. For most of the cases, impacts of the reform and of the enlargement are the same but with a different magnitude, the enlargement scenario affects world cereal markets more than CAP reform. The en largement EU-25 scenario has, in general, higher impacts than that of CAP reform EU-15.

Introduction

The paper focuses on the consequences of enlargement of the European union to the east-European countries both on the world and European arable markets. By using a world arable multi-market mode!, the paper provides a market outlook through the year 2010 which includes the recent reform of the Common Agricultural Policy. Furthermore, two scenarios based on different sets of assumptions regarding the implementation of the Common Agricultural Policy in the new member States are considered. The consequences for grains and oilseeds markets at both the European and the world level are investigated.

The world arable multi-market mode! used to study the impact of the accession of the East-European countries is a partial and dynamic econometric mode! called WEMAC (acronym WEMAC stands for World Econometric Modelling of Arable Crops), which focuses on the world arable crops markets. The mode! covers world markets for the main cereals and oilseeds. As the assessment of the impact of agricultural policies depends crucially on the level of world prices, one major aim of the mode] is to analyze the price formation mechanism in world cereals and oilseeds markets.

The major trading countries are individually modelled with the rest of the world aggregated into one region. An important feature of the modelling is that we do not consider the European Union as a bloc: the mode! provides country level estimates for each of the major crops producers of the European Union (France, Germany, ltaly, Spain and the United Kingdom).For the East-European countries, the mode] provides country level estimates at least for the supply equation for the most important countriess. For ail the other countries modelled, the mode] generates the main domestic market variables: supply, demand, domestic prices and trade estimates. Each of the country sub-models contains the behavioural response of economic agents to changes in prices and exogenous variables.

The paper is organized as follows. In the next section, the mode! structure is presented along with the general fcatures and national details un the specifications applied to study major arable crops exporters and importers behaviors. Potential effects of the enlargement of the European union to the east-European countries on European Union and world arable crops markets are discussed in section 3. The fourth section concludes.

The structure of the World Econometric Modeling of Arable Crops model

This section outlines the general structure and modeling approach used in the World Econometric Modeling of Arable Crop (WEMAC). Before presenting the behavioral equations of each regional sub-models (in the modeling, a country represents a state and a region refers to an aggregate of different countries), we first describe the general features of the mode!, the current country and commodity coverage.

Mode[ overview

WEMAC is an econometric, dynamic, multi-product, partial equilibrium mode! that focuses on arable crops. The mode] covers several countries/regions and includes a rest of the world aggregate. Major producing, exporting and importing countries are included in the model. The mode! generates medium-term annual projections for the main cereals and oilseeds markets, under alternative external conditions such as exchange rates or rnacro-economic shocks, and it is used for policy analysis.

The aims of WEMAC are to provide quantitative evaluations of national and international agricultural policies reforms and to carry out annual market projections over a medium-term perspective and for the main arable crops. This mode! consists of sets of equations which (except for accounting identities) will be estimated using standard econometric techniques (estimations realized with the software Eviews). Most of the equations in the mode! are estirnated using annual data from the period 1970-2001 (or shorter intervals if data are unavailable).

The mode! provides information on production, consumption, domestic prices and trade for the main cereals and oilseeds. Agricultural and trade policies are included in each country cons idered in the mode) to the extent that they affect the supply and demand decisions of the economic agents. The mode! consists of a set of country or regional sub-models with linkages established across countries and commodities.

Projections are the results of WEMAC over a ten year period if current situation and trade policies are assumed to remain in place indefinitely. Simulations will be results of exogenous shock or changes in policy variables.

The country coverage includes ail the main producing and consuming countries of arable crops and allows analyzing simultaneously equilibria on the world markets for the main cereals and oilseeds. The countries modeled are: Argentina, Brazil, Canada, China, European Union, India, United States, Central and Eastern European Countries (CEEC), Russia, Ukraine and a bloc North Africa and Middle East. An important feature of the modeling is that we do not consider the EU as a bloc. Indeed the mode! provides country levels estimates for France, Germany, ltaly, Spain, and United-Kingdom while treating the other countries of the EU as a group. In the definition of the "Rest of the Union", we take into account EU country en largement over the estimation period. The bloc CEEC is composed of 6 countries: Bulgaria, Hungary, Poland, Romania, Slovakia and Czech Republic. The mode! generates supply, demand, domestic prices, and trade estimates for most of the regions/countries. Additional countries (Russia, Ukraine, Africa and Middle East), which are important players on cereals world trades are included. For these countries, only the net trade function is endogenized. And the rest of the world aggregate is exogenous.

Each regional sub-model consists of the following sets of behavioural equations: production(harvested area, yields), consumption (feed use, food use, crush for oilseeds, and stocks), price linkages (prices transmission mechanism between domestic and world prices), trade flows (import and export equations) and the market clearing. The market clearing which must be satisfied for cach country, corresponds to the following identity : Beginning stock + production + imports = Ending stock + consumption + exports.

For CEEC region, the mode! provides country level estimates at least for the supply equations. For these 6 countries, we estimate harvested areas, yields and domestic prices. The other behavioural equations are estimated for the aggregate region of 6 countries.

The mode! is based on econometric estimates of behavioral equations. In ail countries, the equations are estimated in levels, as this offers the advantage of ranking the effects of ail the explanatory factors (prices, technical change, policies, ... ). Most of the equations in the mode! are estimated using annual data from the period 1970-2001 (or shorter intervals if data are unavailable). The main source for production, consumption, stocks, imports and exports in the WEMAC model is the USDA' s Production Supply and Distribution (PS&D) database. For the prices we used the database from USDA/ERS and from national agriculture departments. For the European Union ail the data were obtained from NewCronos data bank of EUROST AT1

• Tariff data and other data on border measures were taken from the OECD database. Macroeconomic data such as income, exchange rates and inflation are obtained from the International Monetary Fund (IMF) (to have further information on the database, the tools developed to select data and to treat results of the mode!, see [START_REF] Herrard | the WEMAC model:database and results management[END_REF].

Another key issue wi th the dataset is to choose rule adjustments when net global trade for a commodity is no equal to zero. In general we adjusted the Rest of the W orld data so net global net trade would be zero.

Behavioral equations and model closure

The WEMAC mode! includes behavioral equations describing domestic supply, demand, stocks, prices linkages and trade flows, which defines the general structure of the country sub-models.

We first describe the way used for modeling Domestic s upply Individual models used for each country/region have been estimated by introducing cross-linkages between other arable crops. Production in country/region is determined as the product of estimated harvested area and yield equations. In each sub-model, we assume a specific separability structure in crop production. According to that assumption land allocation decisions are taken in three stages. In a first stage, producers split the total available area between fodder crops and arable crops. In the second stage, they allocate the area under arable crops among industrial crops and cereals and oilseeds. In the third stage, the area of cereals and oilseeds is di vided among the arable crops cultivated in the considered country. The land a llocation scheme is specific to each country studied. We restrict to the third stage where the total area under grains and oilseeds is assumed to be fixed but allocable across the various grains and oilseeds (Coyle 1993, Guyomard and[START_REF] Meilke | Challenges in quantitative Economie analysis in Support of multilateral Tracte negotiations[END_REF]. Hence, we mode! a system of arable crops acreage demand as conditional on total arable crops areas. The crop acreage equations can be written as

suri,t = sur( PJ,r / v 1 _ 1 , surgcu 1 , Z 1 ) i, j = I, ... ,n (1)
where suri,r is the crop acreage in year t for the commodity i, pj_, is the expected crop output pricej (j=l, ... n), v,_ 1 the input price in t-1, surgcu 1 is the total crop acreage a llocated to arable crops and Zi defines a vector of exogenous which could have an impact (these variables depend on the country and include for instance domestic policy variables). We assume that producers do naïve expectations on prices, i.e. p'j_, = Pj.,-i . For the estimations, symmetry conditions and adding-up restrictions are imposed.

By and large, the traditional factors behind yield changes are weather conditions (rainfall, temperature), technological innovation (generally introduced as a linear trend with a positive effect), commodity price.

In the WEMAC mode!, the general s pecification of the yield equation is as follows

(2)
where rdti,r defines the yield of crop i in year t, p;, the expected price in year t for crop i, t a linear trend and T is a linear trend. Added to this basic specification are other variables such as dummy variables. Dummy variables measure gain or Joss of yield for a specific crop in specific years, owing in particular to exceptional weather conditions. T hey are used for years when there are peaks and troughs in yield indicating climate events (such as the drought in 1976 in the EU). They are not necessarily the same for ail of the commodities or countries under analysis. This is because the yield patterns for each crop in the same season are highly contrasted. Also, countries do not necessarily experience the same climate events at the same time.

When producers make these production decisions, they do not usually know the price of the commodity, except in the case of regulated markets, and must therefore rely on expected prices.

Yarious expectation patterns were tested, based on prices lagged by one, two or three periods, the more convincing scherna was the price lagged by one period.

The area and yield equations are jointly estirnated using the iterative Seemingly Unrelated Regression Method.

It is worth noting that in contrast to other partial world models, we do not include income per hectare in the specification, this choice allows to distinguis h the effect of prices and other policy instruments As noted above, previous general specifications rnay vary for some individual countries particularly for the European Union and the United States in order to take into account the respective domestic policies.

For the supply modelling, in the European Union we include direct payments introduced by the European Union in 1992 in area equations. The CAP reform in May 1992 consisted largely in reducing support prices and offsetting the ensuing Joss of income with direct payments based on factors of production, i.e. acreage in the case of COP (cereal, oilseed and protein) crops. In the literature on decoupling, these area payments are usually defined as partially decoupled instruments (OECD, 2002). Indeed, they affect the level of supply via land allocation mechanisms, in that they promote decisions to put land down to crops that ensure the highest area payments. These payments have a direct effect on land dernands for each crop. The land set-aside policy is taken account by introducing it in the second stage in the land allocation decisions.

For the United States, we incorporate the influence of commodity programs in the area and yield equations by including an additional explanatory variable, which corresponds to the average (per hectare) of the production flexibility contracts, the deficiency, diversion and disaster payments. Furthermore, the marketing loan is included in the definition of the producer price (both for supply equations and prices linkages equations).

Domestic demand

In each country, total cereals consumption is the sum of food/consumer demand, feed demand, crushing demand and ail other uses of cereals (which includes seed, waste and industrial use). This last use is rather s mall and is treated as exogenous in the mode!.

Food/consumer demand is estimated per capita and is specified by the following equation

(3)
where nfoi,t is the per capita non-feed demand for the crop i, pc;, 1 the real consumption price, pib 1 I pop, is per capita real income. The lagged variable nfhi,,-l is inc luded to represent the partial adjustment toward desired dernand. Total food use is determined as the product of per capita food use and population.

Beca use feed is an input into the livestock production equation the theoretical specification of feed demand follows the derived demand approach. The demand for feed use of cereals is essentially determined by changes in livestock production and changes in the prices of individual feedstuffs.

The determination of cereals feed demand rest upon a two stage framework In the first step, the demand functions for cereals and for oilseeds meals are determined. Feed demand is expressed as the following system cfocer, = cfocer (pccer,, , pcva ,,, pc psc,,, PCamp ,, , PBk,) cfotxt, = cfotxt (pCcer,t, pcrx, ,t, pc psc,,, PCamp,t

, PB kt ) (4)
where cfocerr is the aggregated feed demand for cereals, cfotxtr is the aggregated feed demand for oilseeds meals. Both variables depend on the real price of the cereals PCcer,t, the real price of the meals pcw,t , the price of the cereal substitute product pc psc,r and the price of other protein feeds (protein crops, corn gluten feed, fish and meat meals) pcamp,r, PBk, the livestock productions (we distinguish three types of livestock: production poultry, beef and pork production). Both equations are estimated together with constraint of symmetry on prices.

In the second step demand functions for the different cereals and oilseeds meals are expressed in terms of shares with respect to the total amount of the considered feed demand. For the cereals, they depend on the real prices of competing cereals and on the livestock productions (poultry, beef and pork production).

Feed demand in the second step is determined as a system of share, each share is expressed as the following equation:

cfoi,t ---= cfo(pci r ,PC/ r,PBk 1 ) cfocer 1 ' ' ' (5)
cfoi t where ' is the share of the cereal i in the total cereal in year t, pc;,, the real price of the cereal i, cfocer 1 pc1., the real prices of competing cereals and PBk, is the livestock productions (with k=poultry, beef and pork production). Ail equations are estimated together with constraint of symmetry on prices and adding-up restrictions.

In the same way, each share of meals is a function of the real price of the meal, the real prices of the competing meals and the livestock productions.

Stocks

The stock level depends, generally, on the market price and on the beginning stock (lagged value of the stock variable). This structure is modified to accommodate policy intervention, in particular, in EU, United States and China. The intervention system in EU is modelled in stocks equations with the intervention price of the considered commodity. In United States, adjustments are included for cereals to account for government Joan program and stockholding policies: Commodity Credit Corporation inventory and Farmers-Owned Reserve program In China, different programs of cereals selfsufficiency are implemented during the two last decade. Hence, we take into account these programs in the evolution of Chinese stocks.

Price transmission

A single world price is assumed to exist for each of the commodity. World prices are expressed in US dollars. Domestic prices are expressed in local currency. Exchange rates are treated as exogenous.Since producer and market domestic prices are different we have two types of transmission price equations to estimate. Except where there are set by government, domestic prices are linked to world prices via linkage equations including exchanges rates. The system to estimate can be written as P;,, =p(pm;.,,h;_ 1 ) pc;,, = pc(p;_,) (6) (7) where Pu is the producer price of the crop i pm;, 1 the world price converted in local currency and h;, 1 other exogenous variables that affect prices levels (the specification allows to take into account domestic policies for instance, in the case of the European Union we introduce the support price as an additional explanatory variable)" These price equations are estimated using Three-Least Squares method. ln the European Union, at the level of the individual country mode!, most prices are linked to the French market prices which are generally defined as the leading prices.

Import and export equations

The WEMAC mode! distinguishes between imports and exports. Many barriers to tracte remain in place and there has been an expansion of the relative importance of non-tariff barriers to tracte e.g. tariff-rate quotas and preferential access agreements. The nature and operation of such measures varies between countries and in-itself complicates the modelling of tracte flows [START_REF] Meilke | Challenges in quantitative Economie analysis in Support of multilateral Tracte negotiations[END_REF].

The general specification of the imports equation is as follows:

ipc • imt i 1 = imt(pib 1 , -. -1 '- 1 , T , ddd i 1 ) ' zpmi,t ' (6)
ipci,I where imti,t defines the imports of crop i in year t, pib 1 the real income of the country, the ipmi,I ratio between domestic and world price of crop I m year t, (which measures an indicator of competitiveness), T the trend which allows to the growing openness of the economy in the world market. and ddd;, 1 the tariffs (when they exist).

We distinguish imports on the import regime under which they enter. Indeed, for countries applying of the tariff quotas, we do not estimate the total of the imports but the level of imports which the tariff quota used are withdrawn . Hence the imports that occur under the Tariff Rate Quotas (TRQ) system are exogenous and equal to the scheduled TRQ. We defined an import equation for the out of quota imports which is calculated as the following way.

iml;, 1 =imt;, 1 -trqi,r where trq;, 1 defines the tariff rate quota of crop i in year t (considered as exogenous).

Then we estima te the import demand without the tariff rate

ipc • iml; 1 =iml(pib 1 ,-. - 1 ' - 1 ,T ,ddd; 1 ) , zpmi,1 '
where ddd';.i defines the general tariff (except tariff quota) of crop i in year t.

The general specification of the exports equation is as follows :

ipmi,r P exti 1 -ext (-.--,qpr;

1 ,ctt; 1 , subi 1 ) , lPCi ,t ' ' ' (7) (8) (9)
. . . ipmi,r where ext;,, defmes experts of crop 1 m year t, -.--the ratio between domestic and world price of 1pc;,, crop i in year t, (which measures an indicator of competitiveness), qpri,t the domestic supply (production plus beginning stocks) of the commodity considered, cttf,, the domestic demand of trade partners and sub;, 1 the unit amount of the export subsidy i (when it holds). A time trend is sometimes included in export equations to represent the entry of new competing countries.

For the cereals exported as food aid, instead of considering experts total, we estimate total experts of the volume exported minus the level of food aid. Hence we calculate an another variable called « free exportations » as follows.

exli,t = ext;, 1 -aidi,t (10) where exl are the free exports, aid;,1 define crops exported under food aid programs (which is considered as exogenous).

Then we estimate that variable using the explanatory factors described above.

ipmi,r

P exli 1 -exl(-.--,qpr; 1 ,ctti 1 ,sub; 1 ) ' zpc;,, ' ' ' (11)
Closure of the mode!

For each country mode!, market clearing is obtained by selecting one behavioral variable as a residual variable. It means that a variable is not cstimated but calculated as the residual to check the equilibrium on each regional market. The choice of the residual variable depends on policy and market characteristics relevant for the specific commodity. The flowchart in figure l provides an overview for the specification of each country mode!. ln the example illustrated in figure 1, the variable ending stock defines the residual variable. Area harvested Supply r ---- Market clearing condition that determines the world prices is implemented by forcing the sum of net trade across ail countries in the mode) to zero. On the world market clearing is obtained by endogenous world price. Hence, after modeling each country, the net trade positions are summed across all countries. The clearing identity imposed is that the sum of ail countries net trade is equal to zero. This identity determines the world price of the corresponding product. Simultaneous solutions of the models are obtained with market clearing equilibriums of the different cereals. We use the Newton's method to solve the world price.

1 1 1 1 1 Yields 1 1 1 1 1 1 1 N 1 1 1 1 1 1 Focxi derrnnd 1 1 1 1 1 D:!mand 1 1 1 1 1 1 1 Feed demand 1 1 1 1 1 1 D:

Scenarios and simulations results

This study analyzes the effects of the latest CAP reforms and the European enlargement. It compares three scenarios. In the reference scenario, the situation of 2002 is supposed to remain in place: the European Union is composed of 15 countries, the 2003 CAP reform is not considered and the ten new members maintain their independent economic. Hence Hungary, Poland, Slovakia and Czech Republic are included in the CEEC region, and the other new members (Slovenia, Estonia, Latvia, Lithuania, Cyprus and Malta) are stored in the aggregate Rest of the world data. The second scenario, "CAP reform EU-15", considers the 2003 CAP reform in the EU-15 but it does not change the country coverage. The third scenario, "Enlargement EU-25", incorporates the 2003 CAP reform and the European enlargement. To include the new members in the EU, we remove Hungary, Poland, Slovakia and Czech Republic from the CEEC region from 2004 (Bulgaria and Romania are included in the rest of the world data from 2004) and we add these countries in a New Members bloc. The other countries Slovenia, Estonia, Latvia, Lithuania, Cyprus and Malta are removed from the rest of the world data and are added in the New Members bloc. So we obtain a region of ten new members which is introduced in the region EU from 2004. The countries considered in the EU region from 2004 are: France, Germany, Spain, Italy, United Kingdom, a bloc Rest of EU-15 and a bloc New Members. Supply, demand, and trade are aggregated for the ten new members.

CAP reform EU-15

The reform is being implemented starting in 2004.

The current intervention price for cereals is maintained with the exception of rice for which the price is eut nearly by half. Rye is excluded from the intervention system. Changes are in the monthly increments which are eut by half.

Regarding decoupling, members states are expected to implement CAP reform in different ways, therefore resulting in different degrees of decoupling. Since, we consider five individual countries, we can assume different degrees of decoupling for these countries. We assume partial decoupling for France from 2006, more precisely we assume that 25% of the area payments is still coupled to the production. We assume total decoupling for Germany, Italy, United-Kingdom and the Rest of the EU from 2005, and for Spain from 2006.

Modulation (reduction in direct payments for large farms) rates are not considered in the scenarios.

The "CAP reform EU-15" scenario is implemented from 2004 in the EU region composed of 15 countries. The enlargement is not considered in this scenario. Hence we suppose the ten new members maintain their independent economic: Hungary, Poland, Slovakia and Czech Republic are included in the CEEC region, and the other new members (Slovenia, Estonia, Latvia, Lithuania, Cyprus and Malta) are stored in the aggregate Rest of the world data.

Accession (Enlargement EU-25)

Accession took place in May 2004 with policy changes being phased in over time as well. In general CAP policies are adopted by the new members but with some variations to accomrnodate accession.

Hypothesis on the evolution of new members markets are obtained from 2004 FAPRI baseline.

The "Enlargement EU-25" scenario is implemented starting in 2004. In this scenario, the 2003 CAP reform is considered. Hence ail features of the "CAP reform EU-15" scenario are included in the EU-15 and the country coverage is modified. To include the new members in the EU, we remove Hungary, Poland, Slovakia and Czech Republic from the CEEC region from 2004 and we add these countries in a New Members bloc. The other countries Slovenia, Estonia, Latvia, Lithuania, Cyprus and Malta are removed from the rest of the world data and are added in the New Members bloc. So we obtain a region of ten new members which is introduced in the region EU from 2004. The countries considered in the EU region from 2004 are: France, German y, Spain, Italy, United Kingdom, a bloc "Rest of EU-15" and a bloc "New Members". Supply, demand, and trade are aggregated for the ten new members.

In both scenario, an attempt is made to incorporate the WTO proposais. The choice of implementation in the mode) is complicate by the fact that the modalities proposai only suggests average rates of reduction for export subsidies and tariffs. In the scenario these are applied uniformly across commodities. Expenditure estimates are based on the assumption that WTO notifications will be made in a sirnilar way in the future as they have in the past.

Results of scenarios

The impacts of the CAP reform and enJargement scenarios on world cereal markets are shown in tables 1-4. For each scenario, the tables report the averages of the 5 annual levels, and percent-change in levels.

For most of the cases, impacts of the reform and of the en largement are the same but with a different magnitude, the enlargement scenario affects world cereal markets more than CAP reform. We detail the results by crop.

On the maize market, the CAP reform EU-15 has higher impacts than that of wheat markets. The world maize price increases by an average of 0.46%. This change is slight relatively to the impact on European maize net trade. The European maize exports decline by an average of 6.43%. The CAP reform has positive impact for ail exporters except the EU-15 and the world maize market is largely dominate by the United States. American maize exports increase by an average of 0.83%, this is the reason why the impact on world maize price is so slight. For importers, the CAP reform decreases imports of Canada and of India, and Canada which is the smallest importer, is the most affected by the reform (diminution of 1.1 % ).

The enlargement EU-25 scenario has, in general, higher impacts than that of CAP reform EU-15. The world maize price increases by an average of 0.49%. We can notice that the impact on maize is smaller than that on soft wheat whereas it was exactly the contrary in the CAP reform scenario. The accession of the ten new members increases European maize exports by an average of 26.5%. The new members are net exporters of maize and the accession of these ten countries removes the negative impact of the CAP reform on European maize exports. Bence the changes in world maize price is caused by the slighter diminution of American maize exports chan in the CAP reform. The changes are higher for importers, Canada which is the smallest importer is the most affected by the enlargement of the EU (diminution of3.31 %). The CAP reform and enlargement scenarios have different impacts on the world barley market relatively to the impacts on wheat and maize markets.

Net exporters

Argentina

The world barley price declines by an average of 0.43% under the CAP reform EU-15. This diminution is caused by the impacts on barley trade in particular in EU. The barley exports of ail exporters decrease slightly except for EU: European exports increase by 0.27%. The barley imports increase for ail importers due to the reduction of world price. The change in United States seems to be considerable because of the low imported quantity. The impacts of the CAP reform on the world barley markets are relatively slight.

The barley market is more sensitive with the barley world price decreasing by 1.92% on average under the enlargement scenario. The barley exports increase for EU and Canada, but decline for Argentina, Russia and Ukraine. The changes are higher than that of the CAP reform. The accession of the ten new members increases European barley exports by an average of 11.85%. The EU confirrns its leading position in the world barley market. The imports increase for China and United States, the highest change is in the United States, but the imported quantity is still low.

Conclusion

For most of the cases, impacts of the reform and of the en largement are the same but with a different magnitude, the enlargement scenario affects world cereal markets more than CAP reform. The en largement EU-25 scenario has, in general, higher impacts than that of CAP reform EU-15.

On the wheat market, the CAP reform EU-15 increases slightly the world price (0.17% ). The enlargement EU-25 scenario has higher impacts than that of CAP reform EU-15 (augmentation of 0.68%). On the maize market, the CAP reform EU-15 has higher impacts than that of wheat markets. Impacts of enlargement scenario on world maize price are not really higher than that of the CAP reform scenario. The United States are largely major exporter of maize, the slighter diminution of American maize exports allows to explain the changes in world maize price. The CAP reform and enlargement scenarios have different impacts on the world barley market relatively to the impacts on wheat and maize markets. The barley market is more sensitive with the barley world price decreasing by 1.92% on average under enlargement scenario. This change is caused by the impact of the accession on European barley trade. The enlargement allows EU to confirm its leading position in the world barley market.

Figure l .

 l Figure l. Country modelling structure

Canada European Russia Ukraine Union

	Baseline		395	2869	9972	960	1099
	(thousand metric tons)
	CAP	reform EU-15	395	2859	9999	959	1098
	(thousand metric tons)
	% change		-0.06	-0.37	0.27	-0.03	-0.15
	En largement	EU-25	395	2831	11154	958	1090
	(thousand metric tons)
	% change		-0.14	1.32	11.85	-0.14	-0.87
						Net importers
				Brazil	China	CEEC	United States
	Baseline		180	3763	401	528
	(thousand metric tons)
	CAP	reform EU-15	180	3768	402	536
	(thousand metric tons)
	% change		0	0.15	0.24	37.08
	Enlargement	EU-25	180	3771	0	557
	(thousand metric tons)
	% change		0	0.24	0	89.64

Table 4 .

 4 Barley net trade (2004Barley net trade ( -2009 average) average)

The reason for using Eurostat is that in the model intra-EU trade must be netted out. lndeed PSD database include intra-EU trade. That required data are available from Eurostat.

Soft wheat

Maize Barley

Baseline On the wheat market, the CAP reform EU-15 has small impacts. The world soft wheat price increases slightly because changes in European soft wheat net trade are nearly negligible (-0.03%). In net exporters, the CAP reform has positive impact on net trade except for Argentina and EU-15. countries which are the most affected by the CAP reform are the smallest exporters ie: CEEC region. In net importers, the CAP reform has positive impact on net trade for Brazil and North Africa, and has negative impact for China and lndia. The changes are slight but are higher than that for net exporters. India is the most affected by 0.35%.

The enlargement EU-25 scenario has higher impacts than that of CAP reform EU-15. The world soft wheat price increases by an average of 0.68%. The accession of the ten new members augments European soft wheat net trade by an average of 9%. Ex ports in Ukraine and United States increase by 0.21 % and 0.26% whereas the changes was 0.05% and 0.01 % respectively. This is caused by the increase in world price. The changes are still negligible for importers, India is the only one country which is affected by the increase in world price, Indian imports decline by an average of 2.13%.

Net exporters

Argentina


Francois Bonnieux

Pierre Dupraz

Karine Latouche

François Bonnieux

Agri-environmental programmes : convergence and difference between the EU and other countries

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

Neither reduction of negative externalities nor simulation of positive ones cao be achieved al a satisfactory /eve/ on the basis of market activities, there ;5 therefore a need for agri. environmental programmes. The primary difference between agri•environmentaJ programmes is they do not address the same problems. But, are there differences when environmental concerns are close?

Convergence between programmes reducing negative externalities

Soil erosion problems • associated with grain production and sheep breeding,

• experienced by major exporting countries. Conservation programmes rely on voluntary approaches. They combine:

• persuasion and education,

• with financiaJ compensation ta farmers applying approved sail conservation practices. Menus vary with society's willingness to pay for conservation • Modest compensations (Argentina),

• Ambitious initiatives (USA).

Water pollution from intensive livestock farming is mainly a European problem but concerns some places in the other countries, especially Canada and the USA.

Programmes against water pollution rely on a command and control approach. They combine:

• Land use control & easements,

• Permits & standards,

• Cross-compliance, with cast sharing for complying with regulation. Programmes are complex and poorly enforced. The use of environmental taxes remain marginal.

Difference between programmes promoting positive externalities

Programmes result from two contrasted views of nature:

•A man-made countryside (Europe & Japan),

•Pristine nature & wildlife (Americas & Australia).

They combine regulation, cross-compliance and voluntary-payment schemes.

Management agreements with financial compensation originated in the EU and are going ta be applied in Japan. Recent initiatives in the USA must be mentioned. These programmes assume that either separate provision of commodity and noncommodity outputs is not physically possible or economies of scope exist. Their objective is to achieve the levels of benefits that society desires.

Questions and issues

Agricultura1 production affects the environment in many ways, generating negative and positive effects. Neither reduction ofnegative externalities nor simulation ofpositive ones can be achieved at a satisfactory level on the basis of market activities, there is therefore a need for agri-environmental programmes. They are often considered as a means to both support farm income and comp1y wilh WTO provisions. Indeed, there is a shift from price support towards agri-environmental programmes.

A number of countries have experienced agri-environmental programmes targeting the reduction of negative externalities. In contrast there is a few programmes whose goals are related to the provision of benefits. Most of them are applied in Europe under the umbrella of mu1tifunctionality. This concept stems from the fact that agriculture generates many beneficial effects which are not easily measurable and are not va1ued in the market place.

Since they have a social value, il is justified on economic grounds to compensate farmers for providing these non-commodily outputs. Multifunctionality therefore allows the integration of agricultural, environmenta1 and rural policies.

The primary difference between agri-environmenta1 programmes is they do not address the same problems. Key explanatory factors are related to the resource base and to society preferences for the environment as weil as polilical considerations as il is emphasised in a l "This document preseors results obrained within the EU project SSPE-CT-2003-S020?O on Integrated tools ta design and implemenl Agro Environmenral Schemes (http//:merlin.Jusignan.inra.frIITAES/website). Il does not necessary reflee! the view of the European Union and in no way anricipates the commission's future poliey in this area."

comparison between the EU and the US [START_REF] Baylis | Agri-environrnental programs in the United States and European Union[END_REF]. But, are programmes similar when environmental concems are close?

A comparison between the EU and a sample of countries allow to clarify differences and similarities between agri-environmental programmes. This sample includes among others the US, countries such as Argentina whose agriculture and food sector have an important contribution to the overall economy, and countries such as Switzerland and Japan where agriculture is marginal from an economic perspective. The support to farming, assessed by the producer subsidy equivalent, ranges from about zero up to 75%.

Overview of possible policy instruments

Agri-environmental programmes provide either a response to the pressures of farming on the environment or a means to encourage opportunities that agriculture creates for the environrnent. The wide variety of specific policy instruments ranges from voluntary to mandatory approaches (Claassen et al., 2001;[START_REF] Gatto | The economic nature ofstewardship: complementarity and trade-offs with food and fibre production[END_REF]OECD, 2003a).

Voluntary approaches include persuasion (information, advise, extension, education) and market creation, pure marketing needing sorne institutional change. Labelling standards for private goods helps create efficient markets for goods produced with environrnental sound practices. Certification standards will generally be effective only where private gains from participation can be captured in a market setting. Organic farming as weil as a number of AOCs provide examples where farmers are remunerated by selling commodities with the price being positively influenced by the technology which is applied.

In contrast, a command-and-control approach to public policy is one where political aulhorities simply mandate the behaviour in law, then use whatever enforcement machinery (courts, police, fines) is necessary to get people to obey the law. In the case of farming, this approach consists of relying on a number of standards of various types to bring about improvements in environrnental quality. Zoning, restrictions and bans on certain substances as weil as weil as prescribed practices are very popular worldwide.

As a matter of fact, politicians and the general public prefer regulation while economist are more inclined to recommend economic incentive-based policies. They can provide payments to encourage environmentally beneficial activities, or levy charges designed to discourage environrnentally harmful activities. Liability is a third type of economic incentive. The basic idea is that if a farmer harms someone, it must compensate this person for damage. The important issue is that the farmer take ail potential damage from his activity into account when deciding how carefully to operate. This creates an incentive and allows to intemalise extemal effects as subsidies and taxes do.

Cross-compliance mechanism refers to the linking of environmental conditions to agricultural support payments [START_REF] Baldock | Cross-compliance within the Common Agricultural Policy: a review of options for landscape and nature conservation[END_REF]. Compliance with a series of restrictions related to the environrnent, food safety or animal welfare is required to be eligible for other programmes. Thus ex ante, each farmer has to compare additional costs involved by the compliance mechanism to the additional benefit from the support scheme. This instrument indirectly results into the provision of positive extemalities. evertheless the voluntary nature of cross-compliance mechanism may be questioned. Indeed, where support payment are high, it is very close to a mandatory tool (OECD, 2003b).

Finally, from a positive perspective, the real nature of AESs has to be acknowledged. Most are voluntary in nature and involve farmers entering management agreements in retum for financial compensation [START_REF] Ozanne | Moral hazard, risk aversion and compliance in agri-environmental policy[END_REF]. Compensation takes into account profit foregone and the need to provide an economic incentive to change farrning practices. There is sorne evidence that participation rate is increasing with the level of compensation. They have been developed by policy-makers and lobbyists to provide pragmatic responses to new policy problems.

Overview of agri-environmental programmes

The main environmental impacts of agriculture on the environment are related to soil erosion, water quality degradation and wildlife habitats. Soil erosion is mostly associated with grain production and sheep breeding. It is traditionally linked with the use of marginal land including highly erodible soils and drained wetlands, and generates adverse effects both onsite and off-site. Major exponing countries have experienced soil erosion problems which in sorne cases threaten long-terrn soil productivity. Encouraging farming practices which prevent land degradation due to erosion, salinity, continuous cropping or deforestation is then a key issue to achieve a sustainable agriculture. Conservation programmes have been designed and implemented in a number of countries such as Australia, Argentina, Canada and the US, where a large share of cropland is impaired by erosion. These voluntary programmes combine persuasion with financial compensations to farrners applying approved soil conservation practices. Those programme menus vary with society's willingness-to-pay for the conservation of environmental resources. It is limited to extension effort and modest compensations in Argentina, while more ambitious initiatives exist in the US. Australia favours training and extension through local committees thanks to the landcare program.

Water quality protection has been a major component of environmental policies since the sixties. Most of the focus of clean water legislation has been on point sources, primarily the discharge from factories and municipal waste treatrnent plants. Attention has tumed quite recently to non-point sources, primarily nitrogen and phosphorus runoff from agricultural operations. This is an issue, at least locally, in ail countries which have been reviewed. It is more serious in areas with a high population density where the ratio of land to other inputs is low. There is a correlation between the most serious water problems and large-scale livestock enterprises. Indeed harmful effects resulting from manure spreading add to those due to an overuse ofmineral fertilisers and chemicals. This is illustrated by many high polluted spots in the EU but also in sorne places in Canada and the US. Otherwise, intensive cropping on irrigated land also leads to nitrate and pesticides problems as shown by cotton production in Australia. The recent growth of irrigated corn in the Argentinean Pampas will likely lead to water quality degradation.

Policy responses to environmental damage resulting from intensive Iivestock farming mainly rely on command-and-control. Land-use controls including zoning, public purchase of land and easements targeted at critical areas for protecting water resources are widely implemented. In addition, perrnits for the largest installations and standards related to manure storage on the site and disposaI of manure are commonly used. In a number of cases operators are committed to produce a farm level nutrient management plan that demonstrates that waste is being safely collected and disposed of. Economic incentives primarily take the forrn of cost-sharing with grants and low-rate loans for complying with regulations related to housing and manure storage installations. The Netherlands have experienced a more ambitious scheme with a tax put on nitrogen and phosphorus surpluses. The use of environmental taxes as an 3 agri-environmental mechanism remains very limited, though a few tax programmes do exist.

For instance, a number of States in the US have input taxes, mainly on fertilisers. But there are applied for revenue generation rather than environmental protection. Since their rate is low they lead to a modest diminution of fertiliser demand.

In contrast to pollution from point sources, programmes for reducing pollution from agriculture are mostly based on voluntary approaches providing education, techrtical and costsharing assistance. They therefore involve a transfer payment form tax payers to farmers.

Otherwise they are very complex to implement, control and enforce, and finally water problems persist. In spite of a growing public concem about runoff from agriculture, public policy still remain inefficient. A series of lawsuits illustrate the disagreement of citizens who suffer disamenities from livestock farming or defensive expenditures involved by nitrate contamination of water. Paradoxically in our developed societies, agriculture maintains a profound political influence, the practical consequence of which is that agriculture has been somewhat indulged and favoured in comparison with other activities, especially conceming polluting emissions. lmplicitly, agriculture benefits presumptive property rights that have distorting effects [START_REF] Bromley | Environmental benefits from agriculture: issues and policies[END_REF].

Distorting effects are also generate by water-pricing policies. They do not provide adequate incentives for farmers to use water resources efficiently and therefore do not ensure a balance between abstraction and recharge of groundwater. This market failure results into a decrease of production costs and contributes to exhaust the resource. Several examples illustrate this case in Argentina, Australia and the US. In the long run the implementation of the water framework directive of 23 October 2000, is likely to result into more efficient water programmes which should be consistent with the polluter-pays principle in the EU. Indeed, according to the principle ofrecovery of the costs of water services, including environmental and resource costs, farming should be charge for both water consumption and negative extemalities.Asimilar decision has been made in Australia.

Europeans as well as Japanese view the countryside and to sorne extent the natural environment as man-made while a number of people, including Americans and Australians, see nature as being undisturbed by any human activity [START_REF] Hodge | Agri-environmental relationships and the choice of policy mechanism[END_REF]. These contrasted sensibilities are rooted in a long history and are exacerbated by the pressure of a high population density on natural resources. They lead to very different preferences related to the balance between commodity and non-commodity outputs. In the former case, farmers are subsidised to pursue traditional practices and maintain pastures on lands that would otherwise abandoned, while in the latter case farmers are paid to retum farmland to wildemess.

EU agri-environmental schemes focus on the positive environmental extemalities generated by agricultural production. They assume that either separate provision of commodity and noncommodity outputs is not physically possible or economies of scope exist. Since prices do not exist for non-commodity outputs, there is no market mechanism that ensures that social optimum is achieved. Farmers do not have incentives to intemalise the environmental benefits into their activities when decisions are made. If commodity and non commodity outputs are substitutable then the agri-environmental performance is likely to be sub-optimal (Bonnieux and Weaver, 1996). If there is no adequate scheme or regulatory framework, there is an under-supply of positive environmental extemalities relative to the levels that society desires.

Agri-environmental programmes are justified on conventional economic grounds once the positive extemalities generated by agriculture are considered. Compensation takes into account profit foregone and the need to provide an incentive to change farrning practices. Do they significantly impact commodity supply leading to trade distortions or not still remains an open question. There are however the non-commodity output supports a market activity such as agri-tourism. In particular a beautifullandscape maintained by traditional farming practices is an input to tourist trade. The policy agenda include similar initiatives in Japan and Korea while Switzerland relies on compliance mechanisms which tie the receipt of benefits from unrelated programmes to sorne level of environrnental performance [START_REF] Bonnieux | Agriculture et environnement: bilan et perspectives[END_REF]. The high level of farrn support is mainly justified by multifunctionality arguments in Switzerland.

Concluding comments

According to the existing national or local situations, agri-environrnental programmes address the reduction of negative externalities or focus on the positive externalities generated by agricultural production. Alternative explanations for the observed differences between the EU and other countries can be related primarily to differences in factor endowments and national contexts including stakeholders' preferences, cultural and institutional aspects, and secondarily to the pressure to liberalise international trade.

There is a convergence between programmes targeting the reduction of negative externalities. They are far from the implementation of the polluter-pays principle as far as they involve subsidisation of the farrn sector by tax payers and are likely to result into trade distortions. The enforcement of programmes targeting pollution from livestock farming is very costly, thanks to their complexity. In addition penalties for cheating are generally low and do not induce operators to behave appropriately. Finally public authorities may be benevolent with trespassers.

Main differences result from the acknowledgment of multi functionalty as a concept to rationalise a public policy. The bias of European countries and Japan towards agrienvironrnental programmes favouring beneficial effects reflects society's needs. The appeal of rural areas for recreation and tourism is directly linked to the increase in income and urbanisation process. Higher incomes encourage the demand for environrnental quality and countryside amenities, since they are accompanied by higher education increasing the awareness of pollution and its harrnful effects. In a longer run, this may result into a shortage of supply of nature and boost the countryside resources.


Catherine C Benjamin

Isabelle Piot-Lepetit

Modelling dynamic beef supply response using a microeconomic approach : an application to the French cattle sector

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

an increase in direct payments. This contribution foc uses on the dynamics of beef supply response in the French beef sector. The objective of this paper is to provide a mode! of beef suppl y response within a dynamic microeconomic framework. The aim of this mode! is the understanding of cattleman decisions and to assess how the behaviour of beef producers is sensitive to changes in production prices and to changes in premiums. ln particular, the paper studies how the beef supply response is modified when various exogenous variables like prices or policy instruments are changcd.

1 N R A E s R R E N C
where FC, = ÀFK, and MC, = ÀM K, represent females and males calves, respectively.

Assume that there is no calf mortality. The simplification does not change the final results, since the mortality rate is very small and can be neglected in the maximisation of the profit function.

The following constraints sumrnarise the stock-flow relations implied by above assumptions on cattle inventory management. The first constraint describes the evolution of the capital stock. Cows may be kept in the reproductive herd or sold fo r slaughter. The variation of the actual stock of suckling cows derives from the addition of newly bred heifers 0 1 (gross investment) and from the sale of cows for slaughter VK, (d isinvestment).

(2)

The second equation depicts the evolution of the stock of heifers H ,. In each period, the initial stock of heifers and the age d istri bution of this stock are given from the producer's point of view. Hence, the optimal decision about hcifers concerns sale of heifers for s laughter, or selection of heifers for the stock of capital (reproductive herd) and breeding heifers. Breeding increases the stock of cows, since once bred, heifers are cows by definition.

Thus, the variation of the actual stock of heifers derives from the addition of newly fe mal e calves FC,, which have reached the age of twelve months, from the selection of heifers to reproductive purpose 0 1 , and from the sa le of heifers fo r s laughter VH,.

(3) Similar decisions must be taken with respect to the stock of male animais. However, the decision concerning ma le cattle clearly does not affect future production of calves.

For male cattle, the cattleman has only two possibilities: sale for slaughter or placement on feed to be sold in the future. T hus, the variation of the actual stock of old male cattle derives from the addition in the stock of male calves 17 1 , which have reached the age oftwelve months, and from the sale ofsome ofthem for slaughter VB,.

B1+ l -81 = 111 -VBr (4)
The cattleman can sale male calf now for slaughter or it can enter the male cattle herd. Thus, the allocation of the production o f male calves (MC,) deri vcs from the selection of young m ale cattle for the replacement of the male cattlc hcrd 17 1 , and from the sale of some of them for slaughter VMC,.

MC1 = llt + VMC1
(5)

Mndelling representative cattle111a11 heluiviour

The cattleman is assumcd to maximise profits, not only for this period, but over his entire production horizon.

Thus, the procluccr tri es to maximise the expectecl net present value ofhis enterprise.

First, a profit fonction is constructed using years as the unit observation periocl. This profit function is quadratic in its arguments, a fact that guarantees a unique maximum and that satisfies the condition for the existence of certainty equivalents. The fonction is maximised by dynamie programming methods to obtain the relevant behavioural relations, which determine the econometric specification.

The cattleman's revenue cornes from selling animais for slaughter, at any point in time, the premiums granted accord ing to the number of animals1 , whi le his costs consists of various production and investment costs (maintenance and ageing costs of the cattle stock kept on farm, cost of breeding/producing calves, feeding costs).

Assume that the cattleman maximises his profits not only for this period, but for the whole period for which he is in business. Assume further that we know that the cattleman will be in business Tperiods and then retire. In each period, he must make decisions based on actual facts and on facts that he does not yet know for the future periods.

The featurcs of cattle herd management described above translate into the different choice options for each decision variable . Male are animais that, from the decision-making point of view, can either be sold now or in the future. Heifcrs can also be sold now, or can be bred and thereby be transformed into eows (added to the capital stock), and in turn used to produce new calves. The reproductive capacity of heifers makes them different, and this difference is expressed in the profit fonction.

Keeping an ani mal one more period entails a cost and yields the benefit ofbeing able to sell it in the future. If the cattleman acts rationally, he will decide how many cattle to keep in such a way as to equate marginal expected cost and marginal expected benefit, both discounted to the present period. Given constant unit costs, the fonction to be maximised by the cattleman may be approximated by the following quadratic expression: -VBr) +rEr {n(VKr+I ,VHr+I , 81+1 , VMCt+I, VBt+t, 11t+l / Kt+I, Ht+I , Bt+l ,t + J)})

n( VK1 ,VH 1 , 81 .VMC 1 YBt, 11t I Kt, H1 , Bt ,t) = Max(PKrVK1 + PH1VH1 + PM1VMCr + PBrVBt +sK,Kr +sMt llt +. 1 •st 1 lt-l 1 ,, 2 1 2 -2 b1(!<.r -VK1 +8r) -2 b2(K 1 -VKr) 1 2 -2b3(C,) 1 2 1 2 -2 b4(Hr -VH, -8, +FC1) -2 b5(H 1 -VH,-8t) 1 2 1 2 -2 b6(Br -VB1 +111) -2 b7(Bt
subject to the constraints ( 1) to ( 5).

(

) 6
Where the subscript t refers to the current period, and where n(VK,, VH,, 8 1 ,VMC,, VB,, 11, / K,, H,, 8 1 , t) is the expected present value of profits. The slaughter prices are PK, for suckling cows, p 11 , for heifers, PM, for male calves, JJn, for male cattle and the headage premiums are respectively for the suckler cows .1•x,, and for the male cattle s. 11 1, and .1• 8 , .

The terms of ;,r may be interpreted as fo llows:

• PK 1 VK1 + JJH1VH1 + /JMtVMC1 + PB1VBt is the total revenue rrom selling various cattle categories: suckler cows, hcifers, male calves, male cattlc.

• .1• K,K1 ++.1•M1Tlt +s3 1 111-I is the revenue from premiums. The former premium is granted per cows each year whi le the latter is granted only twice in the male cattle life. ln our mode 1, we assume that a male calf which enter in the cattlc herd is grown until the age of two years and that the special beef premium is obtained when a male cnters in the cattle herd at the age oftwelve month and when he reaches the age oftwo years, i.e., after one ycar in the cattlc herd 2 .

• _ .}__b 1 ( K, -VK, + 0 1 )2 is the maintenance cost of the capital stock.

2

• _.}__h 4 (H, -VH, -0 1 + FC,) 2 and _ .}__b 6 ( B, -VB, +17 1 )2 is the feeding cost ofrespectively, heifers and male 2 2 cattle. Feeding costs for heifers are assumed to differ from those for male cattle for the following reasons: first, if heifers are kcpt in part for possible future breeding, there is no need to feed them as muchas male cattle; second, the capacity ofheifers for transforming feed into weight is less than the capacity of male cattle.

• -.}__ b 3 ( C, ) 2 is the production and maintenance cost of calves. 2

• -½hz(K 1 -VK 1 )
2 , -½b 5 (H, -VH, -0 1 )2 and -½b 7 (B,-VB,)2 is the cost of holding respectively the capital stock . th e heifers and the male cattle due to ageing. The ageing costs are a lso assumed to be different for cows, heifers, and male cattle. lt is the additional cost involved in keeping the animal one more period, resulting from the need for more feed and the higher probability of death, etc., as the animal becomes older. The principal clement of the aging cost, however, is the loss in value at sale. Animais sold for slaughter are classified by weight, sex, and age. Olcler animais are gcnerally worth less, ceteris paribus. The procluctivity and the expected benefits and costs differ according to age.

• ,-is the one period discount rate,

• E is the cxpectation operator,

The main notations used in the framework are summarised in the table 1.

(lnsert Table 1)

Empirical lmplementation: the maximising solution

The mcthod used is dynamic programming, which is a recursive maximisation procedure starting from the last period T, i.e., the period after which the cattleman will retire. ln the usual fashion, having obtained the solution for this period. wc thcn sol ve for the next to last period; and so on, as many times as necessary for determination o f the general solution until period t, for any t (Howard, 1966 , 317-320).

The mod e! represented by equations ( 2) and ( 3) is equivalent to maximising

rc(//Ki,VH ,, 8,,VM C,YB 1 , rJ 1 / K 1 , H 1 , 8 1 , t) = L ,. r -, [n, -Pu( K, -K,_ 1 + VK 1 _ 1 -8,_ 1 ) -P2., (H, -H,-1 +VH ,_1 + 8,_1-FC,_1) -h,( B, -B,-1 +VB,_1 -ri,-1)] (7)
whcre the Lagrange multipliers Pi,, , P 2 ,, and P 3 ,, are the shadow prices of an animal in stock for cows, heifers and male cattle . Expression ( 7) is maximised with respect to VK,, VH,. 8,, VMC,, VB, and 17 1 • The first-order conditions with respect to VK,, VH,, 8 1 , VMC,, VB, and 17 1 are as follows:

;

; = -b 1 (K, -VK, + 8, ) + b 4 (H, -VH, -ô, + FC, ) + b 5 (H, -VH, -ô,) +rP 1 ,,+I -rP 2 _ 1 + 1 = 0 1
Solving for VK,, VH,, VMC,, VB,, 8, and 17 1 we find 3:

1 1

VK, = K, + -pK, --pH, b2 b2 1 r b VH , = H , -8, + --pf-1, ----P21+1 +--4 -FC, b 4 + b 5 b 4 + b 5 • b 4 + b 5 1 1 1 r 1 1 1 1 11, = -JJn, -(-+ -) PM, + -p3 1+1 + ( -+ -)sM, +(-+ -)snr +I ~ ~ ~ ~ , ~ ~ ~ ~
3 De1ails or calculations are availablc from the aut hors.

(

) (9) ( 10) ( 11) ( 12) 8
Substituting these equations into the three constraints controlling the evolution of the stock variables, K,, H,, and B, are given by:

1 ,. KI =--P,-!r-1 +-Pit b1 b1 . ( 14
) 1 r b 5 H = ----11,, + --A 2 +--FC 1 I I I t -1 h b 1-' ,/ b b 1- )4 + 0 s 4 + 5 4 + 5 ( 15) ( 16)
The first-order conditions for the stock variables K,, H, and B, are:

since C, = ÀK,, MC 1 = À1v1K 1 = VMC 1 -11 1 and FC 1 = "-FK 1
Thus, the shadow prices are given by:

P 2,1 = P111 PJ., = 1 1 B1 r 1 Y= 1 + -Yo , Yi = -, b1 Y Yo Y2 =--, ybl and À, Y _ _Af_ 5 - y
At the optimum, the Lagrange multipliers associated with the stock-flow constraints for heifers and male cattle are equal to the corresponding slaughter prices. The expression of the shadow price for cows is more compl icated, owing to the reproductive capacity of these animais: the shadow price of cows is a linear combination of thei r slaughter value PK, and their capital value in production, which in turn depends on expected future prices of heifers.

The first conditions derivcs above ean be solved for each endogenous variable in terms of past, current and fut ure price lcvels, and lagged va lues of the capital stock. Following Nerlove and Fornari (1998), we obtain the E.-q1ecrationa/ly Conditional Reduced Form (EC RF) of the dynamic maximisation mode!, that expresses cndogcnous va riables in terms of cxogcnous and predetermined ones, conditional on expectations of future levels of cxogenous variables.

Omitting the cxpectations operator for simplicity, the ECRF for the optimal decision variables (VK,, VH,, ô 1 , VMC,, /IB" 17 1 ) and for the stock variables (Kr, H,, B,) are the equations:

( 17) ( 18) ( 19) (20) (21) 1 r 1 BI= --JJA/1 -1 +-PB1 +-(SM1-I + rsBi) h 6 b 6 b 6 (22) (23) (24)
(25)

The expected cffects of the exogcnous variables (observed and anticipated prices, levels of current and anticipated premiums) on the endogeno us vari ables (numbers of animais and levels of slaughtering) derived fro m the mode l are summarised in Tab les 2.

(lnscrt Table 2a)

(lnsert Table 2b) (l nsert Table 2c)
The only unambiguo us effect concerns the impact of headage premiums. The sukler cow premiums should have always a positi ve effect on the sales and the number of animais. The effect of anticipated priees cannot be predictcd in gcneral, while the impact of observed prices is in general indeterminate, depending on the value of the estimated shadow costs.

Econometric analysis

ln the fo llowing sectio n we describe the econometric estimation techniques, the data used in the econometric analysis, and lastl y d iscuss the results from the estimation.

Price and pre111i11111 expectation for11111/atio11

For cach anticipated price, we use the fo llowing fornrnlation * P t+ I = <XJJJt-2 +a2P1-I + a3p1

*

where JJ 1+ J is the anticipated price that each producer makes in t for the fo llowing time period t+ / , Pt-i is the current pricc in t-i ,and a 1 , a 2 , a 3 are the coefficients to be estimated.

For cach antic ipated premium, we use naive expectations since each producer knows the value of premiums for cach ycar:

* .1• l + I = St

The data

The data for France are drawn fro m the national Farm Business Survey for the 1995-1 997 years. These is annual nati onal survey collected by agenc ies of the government. The sample of fanns are chosen so as to be representative of natio na l agriculture in the country. ln general, each survey fam1 remains in the survey for 5 or 6 ycars. Hence, a balanced panel of34 3 farms can be constructed for the period.

The re. rnltsfor . rnckler cmvs

Equations ( 17) to ( 25) have been estimated by OLS. Although the EC RF expressions determine the optimal supply fo nctions and gross in vestment behaviour of the producer, in terms of his anticipations of future price leve ls and predetermined variables, their evident complexity and the presence of many cross-and within cquations restrictions prcsent formidable obstacles to empirical investigation. The large number of leads and lags in the pricc and stock variables, the appearance of the same parameters in different equations, the large number and non-lincar nature of cross-equation restrictions on parameters, create difficulties in direct estimation. Hence, in a first stcp wc cstimatc the unrestricted model.

(insert Table 3)

For the number of the cow, ail the coefficients in the regression which are statically significant have the predictcd sign. We can note the negative effect of the price ofheifers showing the substitution between cows and heifers. The suckler cow premium has an important positive effect. For the equation explaining the sales of cows as expected from the mode], the price of the cows and the suckler cow premium have a positive and significant effect.

The last table pro vides a comparison between the effect of a decrease in the current price level and an increase in the current premium for the suekler cows.

Tableau 4. Elasticitics ofsuckler cow variables relative to slaughter price and premium.

Variables

Slaughter price for cows Suckler cow premium Number of cows -0,00026 0,959 Sales of cows 0,313 1,007

Hcifcrs added to the herd 0,029 0,925 ..

Elast1c1t1es are evaluated for the average point of the sample For cach variable, there exists a positive effeet of the premium which is larger than the effect of slaughter price.

Concluding remarks

This paper has provided a dynamic optimisation mode! to describe the behaviour of a representative cattleman maximising his expected profits over an infinite time horizon. lt allows us to derive a reduced form which dcfin cs cach cndogenous variables (sales of different animal categories and investment in the herd) by current, past and future exogenous variables as slaughter prices or headage premiums. The mode! is estimated on a balanccd panel data set of 353 farms for the French cattle sector for the period 1995-1997.

Results from the estimation show a positive effect of the premium which is larger than the effect of slaughter pricc concerning the choices of the number of cows maintained in the herd and the sales of cows during the current period.

The disaggregation by animal categories is helpful in assessing supply response to changes in market conditions or in agricultural policy. This work can be extended in two directions. Firstly, the various equations of the reduccd form can be estimated simultaneously. Secondly, it is possible to simulate different reforms of the Common Agricultural Policy.

T

Table 2a .

 2a abl e 1. Notations and definitions Expected effects of observed past and current priees

	Number of animais (stock variables) Slaughter prices Calves	Heifers		Cows		Male eattle
	K,	Reproductive herd at the beginning of the period t
	H,	Stock o fheifers at the beginning of the period t PMr-1 PM, PHr-1 PH,	PKr-1	PK,	Pa,
	B, C, Reproductive herd K,	Male cattle at the beginning of the period Total calves bom at period t m -? ?	ni	+	m
	FC, Stockofheifers H,	Females calves born at period t ni -?	+	m	+	m
	MC,	Males calves born at period t
	Slaughter variables (disinvestment) VK, Sales of cows for slaughter Male cattl e 8 1 -m	m	m	ni	+	+
	VH , Cows sales VK,	a les Heifers for slaughter Ill -	?	?	m	+	ni
	VB,	Sales of male cattle for slaughter
	VMC, Sales of heifers VH,	Males calves sales for slaughter + + ?	?	+	?	ni
	lnvestment 11, Sales of male cattle VB,	Selection ofyoung male cattle for replacement of the male --ni ni ni	ni	+
	o, Sales of males calves	Heifers added to the reproductive herd m + ?	+	ni	+	-
	Slaughtering prices VMC,
	PK, Seleetion of young male	Slaughter cows price Ill -	m	m	m	m	+
	P11, cattle 11,	Slaughter heifers price
	flM, Ps, Heifers added to the reproductive herd o,	Slaughter males calves price Slaughter males calves price ni m m	?	m	+	ni
	Premiums
	SK,	Sucklcr cow premium
	s M,	First special beefpremium
	ss,	Second special beefpremium

ni means that the variable is not included in the specification. The lagged b eef pnce does not appear in any equation.

Table 26 .

 26 Expected effects of the antieipated variablesFor the stock of heifers ( H,) and the number of male cattle ( 8 1 ), there are no anticipated variables in the equations.

	Slaughter prices	Calves		Heifers	Cows	Male cattle	Premiums
		PMr+I	PHr+I	PHr+2	PKr+I	PBr+I	SKr+I	sn,+1
	Reproducti ve herd K,	ni	+	ni	ni	m	m	ni
	Sales ofcows VK,	Ill	+	ni	m	ni
	Sales ofheifers VH,	-	?	-	-	ni	-	ni
	Sales of male cattle VB,	Ill	Ill	m	ni	ni	m	+
	Sales of males ealves	m	+	m	m	-	m	-
	VMC,
	Select ion of young male	Ill	ni	m	m	+	Ill	+
	cattle 11 1
	H ei fers added to the reproductive hercl o,	+	+	+	+	ni	+	m

Table 2c .

 2c Expected effects of the premiumsFor the heifcrs added to the reproductive herd ( o, ), there are only an effects of the expected Suckler cow premium.

	Premiums	Suckler cows	Male cattle (first)	Male cattle (second)
		.\•K,-1	SK,	sM,-1	SMt	so,
	Reproductive herd K,	ni	+	ni	ni	ni
	Stock of heifers H,	+	ni	ni	ni	ni
	Male cattle B,	ni	ni	+	m	+
	Sales of cows VK,	ni	+	n,	Ill	111
	Sales ofheifcrs VH,	+	+	ni	ni	ni
	Sales of male cattle VB,	ni	ni	+	+	+
	Sales of males calves	+	Ill	ni	-	ni
	VMC,
	Selectio n ofyoung mal e	ni	ni	ni	+	n,
	cattle 11 1

Table 3 .

 3 Estimatcs for the numbcr of cows, the sales of cow sand the number of heifers added to the herd The t ratios are repo11cd 111 brackcts under the coeffi cients.

			Dependent variables
		Number of cows K,	Cows sales VK,	Heifers added to the herd
	lntcrcept	5. 141	-3.51 8	-4.037
		(2.54)	( 1.63)	(-2.11 )
	fJKr	0.238* 10-•'	o.550* 1 o•J	0.543*10••
		(-0. 10)	(2.55)	(0. 19)
	PK,-1	-	-	0.594* 10•-' (2.06)
	PK,-2	-	-	0.221 * 1 o•J (0.8 1)
	P11,	-0.346* 1 o••	-0.137*10" 4	-0.103* 10"j
		(-1.86)	(-0.09)	(0.66)
	P111-I	-o. 1 76* 1 o••	0.7 15* 10" 4	0.1 12*10-j
		(-0.09)	(0.47)	(0.71)
	P11,-2	0.224*10••	0.307* 10-j	0.186*10-'
		(-1.86)	( 1. 99)	( 1. 18)
	PM,	-0.647* 1 o-•'	-0.442*10-j	0.181 * 1 o••
		(-2.06)	(-1.75)	(0.05)
	PM,-1	-	-	0.1 86* 10-' ( 1. 18)
	JJ Mt-2	-	-	-0.750* 10" 4
				(0.25)
	sK,	0.760* 10"J	0.152*10-j	0.145*10-j
		(44.87)	( 11.16)	( J 0.23)
	R2	0.87	0.36	0.34
	DW	1.83	1.89	1.96

s..,.., the anncx I for more details on the prcmiums granded undcr the Common Agricultural Policy.

ln l:1c1. du ring the s1udying period. the special premium was obtained bctween 10 and 22 months and after 23 months.

Annex I Definition and Construction of Variables

The mode! used requires a desegregation of the livestock into four categories of animais: calves, heifers, cows and the number of male cattle. ln the data set, we have available information for each category of animais on the stock of an imais on the farm at the beginning of the period.

Furthcrmore wc have information on levels slaughtering for each category of animais measured in value and in number of animais. Hence we can calculate the slaughter price.

For the premiums, wc have avai lable information on the total of amount of premiums for each category of animais. Under the Common Agricultural Policy, the premiums are granted accordingly to the number of animais (each year for suckler cows, once or twice in the life for male bovine animais), but they are submitted to a maximum of hcads on each holding (for male premium) and a maximum stocking density, that is, live units per hectare (for both male and suckler premiums). From a technical point ofview, a special beefpremium is granted twicc in the cattle life within ceilings set at regional level on up to 90 mal e animais per age bracket, per calendar year and pcr holding. The first special premium is obtained when male cattle age is between 10 and 22 months and the second one after 23 months. For holding suckler cows, a premium is granted each year. This entitlement is restricted by an individual ceiling set by reference to a base year ( 1992 in France). Eligibility of animais for the spccial premium or for the suckler cow premium is limited by the application of a density factor of (for 1997) 2.0 livcstock units (LU) per hectare of forage for the animais which a premium application has been made. An additional extensification premium is payable per head of eligible suckler cows and male cattle if the stocking density is Jess than 1 .4 or 1.0 LU/ha. By using the total amount of premiums in the estimations, we take into account the ceilings