CAPRI. Modelling alternative technologies based on the RAUMIS-NRW approach. preliminary version. Working Paper Wolfgang Löhe and Wolfgang Britz

Transcription

1 COMMON AGRICULTURAL POLICY REGIONAL IMPACT ANALYSIS CAPRI preliminary version Working Paper Modelling alternative technologies based on the RAUMIS-NRW approach Wolfgang Löhe and Wolfgang Britz University of Bonn Universität Bonn

2 Wolfgang Löhe is research assistant at the Institute for Agricultural Policy, Market Research and Economic Sociology. His research is focused on modelling the agricultural sector of Germany. Wolfgang Britz, has a post-doc position as research assistant and lecturer at the Institute for Agricultural Policy, University of Bonn, and is specializing in quantitative economic modelling. In the CAPRI group Bonn, he is responsible for the methodological concept and the EDP realization. Address: Institut für Agrarpolitk, Universität Bonn, Nußallee 21 D Bonn Phone: Fax: URL: (Löhe) or (Britz) name@agp.uni-bonn.de The series "CAPRI, Working papers" contains preliminary manuscripts which are not (yet) published in professional journals and are prepared in the context of the project "Common Agricultural Policy Impact Analysis", funded by the EU-Commission under the FAIR program. Comments and criticisms are welcome and should be sent to the author(s) directly. All citations need to be cleared with the author(s).

3 Modelling alternative technologies based on the RAUMIS-NRW approach 1 Introduction Module for technological alternatives in CAPRI Theoretical base and specification of alternatives Methodological and technical realization First experiences in simulation behaviour Conclusions Annex... 13

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5 1 Introduction Modelling alternative technologies based on the RAUMIS-NRW approach Based on the experiences made with the RAUMIS-NRW approach for North Rhine-Westfalia (LÖHE, 1998), alternative technologies are implemented in CAPRI. Since the approach is tested exploratively in CAPRI, up to now a set of alternative mechanical resp. technical activities is defined only for plant production in one German region (D_100). If the CAPRI-partners reckon this approach as a suitable depiction of the "real world" in the model, we easily can include the branches of fodder and animal production in a next working step. The policy context for modelling less intensive production alternatives has been described already in working paper (LÖHE, W., BRITZ, W., 1997). Based on this background main objective of this working paper is to describe the theoretical base and the methodological realization of implementing alternative technologies in CAPRI. The presentation of first experiences made with this approach in simulation runs, i.e. allocational behaviour due to price or premium changes, is an additional target. 2 Module for technological alternatives in CAPRI 2.1 Theoretical base and specification of alternatives 1 The mathematical programming approach in CAPRI maximizes regional agricultural income according to the relative competitiveness of the different production alternatives. The module for technological alternatives expands the matrix of activities for the production of each crop in the model. Insofar the programming approach will "choose" the most competitive linear combination of the technological alternatives for a given crop and the optimal rotation for all crops. The principle procedure for adaptation of endogeneous intensity is shown in chart 1. The alternative technologies are specified in relation to the traditional production activity (exemplary in the chart "WWEI 4 "). They differ not only in nitrogen use and yield - as shown for didactical purposes in the chart - but simultaneously in different input coefficients of variable machinary costs, plant protection and seed costs, requirements for labour and depreciation costs, etc. As described in the working paper mentioned above, the alternatives are specified to the extent possible on the base of standard calculation data, published by KTBL, 2 the responsible body for Standard Gross Margin calculations in Germany. 1 More detailled in LÖHE, W., BRITZ, W. (1997): EU's Regulation 2078/92 in Germany and experiences of modelling less intensive production alternatives. CAPRI-Working paper 97-05, Bonn and LÖHE, W. (1998): Extensivierungspotentiale in der Landwirtschaft. Regional differenzierte Simulationsanalysen unter alternativen agrar- und umweltpolitischen Rahmenbedingungen für die Landwirtschaft in Nordrhein-Westfalen. In: HENRICHSMEYER, W. (Hrsg.): Studien zur Agrar- und Umweltpolitik, Aachen. 2 Kuratorium für Technik und Bauwesen in der Landwirtschaft.

6 2 Chart 1: Linear approximation of a quadratic relation by alternative production activities Yield Y 4 Y * WWEI 4 Y 3 WWEI 3 Y 2 WWEI 2 Y 1 WWEI 1 N 1 N 2 N 3 N * N 4 Nitrogen Input Source: HAZELL, P. B. R., NORTON, R. D. (1986): a.a.o., S. 38. Still, the KTBL cannot provide all the necessary information. Therefore, additional interviews of experts have been carried out. The interviews focused on expected relative changes of input- and output-coefficients under different alternative technologies compared to a traditional technology. Main results of these interviews has been discussed in CAPRI-working paper and are not repeated here. 2.2 Methodological and technical realization Up to now, explorativly 11 alternative technologies are implemented for the main cash-cropactivities. The definition shall both ensure a flexible model response to changes in market and/or price policy (endogenous adaption of intensity) and enable the modelling of political measures, such as EU's regulation 2078/92. Table 1 shows the alternative technologies defined in CAPRI. First simulation tests with this set of alternatives may reveal that only a small number of alternatives are relevant for our modelling purposes. If so, we easily can reduce the number to be implemented in the standard-core-model. The specification of these alternative technologies follow the experience gained in the RAUMIS-NRW project. Please notice, that this specification procedure is just a first explorative step. A transfer of the specification chosen here to other NUTS-II-regions and countries has to be checked carefully by the partners in the different regional clusters!

7 Table 1: Traditional production system Definition and coding of alternative technologies in CAPRI Definition of production alternatives Traditional tillage (plough), ban of herbicides Traditional tillage, ban of pesticides Traditional tillage, ban of min. fertilizer Traditional tillage, total ban of min. fertilizer and pesticides Conserving tillage Conserving tillage, ban of herbicides Conserving tillage, ban of pesticides Conserving tillage, ban of min. fertilizer Conserving tillage, total ban of min. fertilizer and pesticides No-tillage Source: Institute for Agricultural Policy (IAP), Bonn University, CAPRI-Code T TRNH TRNP TRNF TRNN CONS CONH CONP CONF CONN NOTI Generally, the specification of the technological alternatives is done in relation to the traditional (i.e. "intensive") technology. Table 2 exemplary shows the relative "technology factors" of input and output coefficients for the production group "winter cereals", which comprises soft wheat, durum wheat, rye and barley. Other groups are summer cereals (oats, maize and other cereals), oilseeds (rape, sunflower, soybeans and other oils), pulses, silage, sugar beet and potatoes. The relative factors refer to yield, input of seep and plant protection, repairs and energy costs. Additionally, fix costs for labour use and average depreciation have been implemented in this approach. The experience with the RAUMIS-NRW approach revealed that especially differences in fix costs between the alternative technologies have a huge impact on their relative competitiveness. Since the SPEL/EU-BM data base provides data on labour and depreciation costs (differentiated for mashinery and buildings) on the sectoral level, we used data from he regionalized RAUMIS-data base to "distribute" this sectoral data among the CAPRIproduction-activities. The regionalized data in RAUMIS are generated by the so-called "Techno-Module" described below. Generally, we assume a linear relationship between variable costs and yield unless other information is available. This means for example that we expect a 30%-reduction of nitrate fertilizer (NITF) for the technology TRNF in the group of winter cereals because the yield relation is also -30%. The matrix of technology factors comprises information only if this linear yield dependency does not hold. Due to the incorporation of alternatives for one production activity, new aspects are introduced concerning the calibration of the model to base year data and its simulation behavior. If the model is free to choose any linear combination of the alternatives, one could either calibrate to areas or quantities. If only the area of e.g. wheat is fixed, the model would choose the cost mininal technology (or combination of these) to produce wheat. Nothing would guarantee that production quantities or average I/O-coefficients of the base would be met. The problem can be circumvented only if shares for the alternatives for the base year are set, either derived from statistics or assumed ones. These shares can be found in the line "LVL" in table 2 and determine the calibration bounds in the first step of the PMP approach. 3

8 4 Table 2: Technology factors of input and output coefficients in relation to the traditional technology in the group of winter cereals + WCER.TRNH WCER.TRNP WCER.TRNF WCER.TRNN YIEL SEEP PLAP 0.65 eps eps REPV ENEV DEPM LABO LVL * + WCER.CONS WCER.CONH WCER.CONP WCER.CONF WCER.CONN WCER.NOTI YIEL SEEP PLAP eps eps 1.22 REPV ENEV DEPM LABO LVL "eps" = number close to zero Source: Institute for Agricultural Policy (IAP), Bonn University, The average I/O coefficients found in the data base for the calibration year can be interpreted as a weighted average of the different technologies (including the traditional ones whose relative factors are always 1) with their level shares as weights. Based on the known average and the relative factors and level shares from table 2, I/O cofficients for the different acitivites can be calculated: (1) Y = Y a where: AV a1 1 LEVL Y relation a1 a1 Y a = Yield of technology a Y AV = Yield of average technology taken from statistics a, a1 = Index of alternatives (incl. traditional alternative) Y relation = Yield relation between alternative a and traditional technology T Table 2 shows exemplary the calculation for three alternative technolgies of soft wheat production. The yield of average activity as derived from statistics is assumed to be 6 t/ha. The shares of the alternative technologies SWHE(2) and SWHE(3) are set to 10% resp. 5%. From

9 our experts interviews (s. LÖHE, 1998) we know the relative factors between these alternatives and the traditional production system (0,86 and 0,7). 5 Table 2: Exemplary calculation of yield coefficients SWHE (Average) SWHE (1) Traditional SWHE (2) Alternative SWHE (3) Alternative SWHE (t) 6,0 6,2 5,3 4,3 Yield relation 1,0 1,03 0,86 0,70 LEVL (ha) 1,0 0,85 0,1 0,05 Source: Institute for Agricultural Policy (IAP), Bonn University, Input relations are the same for coefficients assumed to be linear dependend on yield. The same procedure applies to input coefficients which are explicitly edited. As described above, one asset in modelling alternative technologies is the consideration of labour and depreciation costs. 3 In the modelling system RAUMIS we use a so called "technomodule" to calculate these cost components. The aim of this module is to depict scale effects in certain costs based on average farm size in the regional unit. In a first step, costs for main work steps, e.g. ploughing, drilling or harvesting of corn or potatoes. and four plot sizes (i.e. 1, 2, 5, 20 hectares, see Chart 3) have been defined for plant production, The specification comprises - apart from investment costs and labour requirements - also variable mashinery costs (repairs and energy) and is based on calculation data for maschinery suitable for each plot size class. The four points A to D in chart 3 show a linear approximation of an isoquant with alternative requirements of labour and capital input, for example for ploughing one hectar. Next, the relation between the crop acitivities in the model and these works step is defined. Soft wheat, for example, requires 1 times ploughing, 4 times fertilizing, 1 time harvesting, etc. allowing to calculate the cost components for an crop activity and a specific plot size. Finally, average costs for the activities in a region are depicted based on assumptions according to the relation of average plot size and average farm size in ha. The definition of technological scale classes and the adequate mashinery could be a further step in the adaptation of the RAUMIS "techno-module" to CAPRI-requirements. Similary to crop production, RAUMIS specifies scale effects in animal production, too, considering differences in costs costs for buildings (stable), milk-equipment, fodder-equipments and manure-equipment according to four herd size classes. The calculative data from RAUMIS are adjusted to ensures consistency to the sectoral data based on SPEL/EU-BM. An improvement of this approach would be the implementation of farm-sample data such as FADN-Data.So far the definition and specification of the alternative technologies have been described, including the calibration to the base year data. As we know, calibration is not longer a concern if PMP's are used. After the PMP calibration step, duals on the calibration bounds for all the alternative are available besides linear costs terms. The question is now how to derive a realistic simulation behavior of the model based on these data. 3 see: LÖHE, W.: Specification of variable inputs in RAUMIS, CAPRI-Working paper 97-07

10 6 Chart 3: Substitutional relationship between labour requirements and depreciation costs Labour input per production unit L(a) Class A L(b) L(c) L(d) Class B Class C Class D C(a) C(b) C(c) C(d) Capital input per production unit Source: Adapted from HAZELL, P. B. R., NORTON, R. D. (1986): Mathematical programming for economic analysis in agriculture, New York, p. 36. The question covers to two aspects: 1. how to model rotational responses of the model, i.e. the changes in total area of the different acticities to changes in prices, premiums, quoatas etc. 2. and how to depict adjustment in technological alternatives, e.g. a switch from traditional ploughing to no tillage, due to economic incentives. Certainly, the two aspects are closely related. The reasoning underlying the following simulation experiments is the following. It is assumed that one farm will normally only realize one alternative for all crop production activities because investment costs will prevent farmers from buying machinery for a certain working step twice, e.g. a direct drilling equipment and a plough. Rotational effects are hence only assummed "inside" of an alternative and mapped to "cross-effects" between activities inside an alternative. These cross effects define off-diagonal elements of the quadratic cost function. First experiments showed that rotational effects could be depicted quite nicely but that the model produced very stable results concerning the shares of the different alternatives. Based on experience with RAUMIS and expert opinion, reactions concerning the share of the alternatives with drastic changes in prices or premiums are jugded very plausible and the reaction of the model hence unplausible. In order to introduce more reagibility concerning the alternatives, cost-effects of expansion or reduction of alternative technologies cross-effects have been introduced into the model. Costeffects between technological packages arise especially if certain alternatives are - in terms of production technology - quite close to each other. If a farmer for example has already invested in mashinery for one of such alternatives, his costs to switch over to a another, similar alterna-

11 tive technology are definitively lower, e.g. from conservative practise to conservative without herbicides. The modeling of these cross effects consists of three elements: Definition of constraints in the simulation model which add up the realized level of each alternative technology "a": (2) LEVLa = LEVL, where: i i a LEVL a = Level constraint of realized alternative technologies "a" LEVL i,a = Level of the alternative technology "a" and activity "i" A term in the objective function takes care of additional costs or benefits for each alternative technology "a" related to the expansion of alternative technologies "b": (3) LEVLa CONSTa GRADAa, blevlab where: a b CONST a = Constant term for alternative "a" of PMP calibration GRADA a,b = Non-linear PMP-term between alternative "a" and "b" LEVLA b = Level expansion of alternative "b" For the calculation of the PMP-terms it is assumed that the total effect on the objective function is set to zero in the base year. This is in contrast to the effects between individual production activities because the duals from the calibration step are already covered by the PMPs of the individual acitivities. Insofar for each alternative "a" equation (3) must yield zero if base year levels are used. The average marginal costs of the alternative technologies are derived by the following equation: (4) where: ia LEVL ia LEVL * MC a ia MC ia = Marginal costs for alternative "a" and product "i" The average marginal costs and the matrix of cross-effects between alternative technologies are used to define support points for the ME-PMP-approach (see BRITZ, W., HECKELEI, T. (1998): PMP-calibration and "Maximum Entropy Approach", paper in preparation). The specification of cross-effects has to follow information either published in the literature or by expert knowledge. First plausibility checks on allocational behaviour of different technologies can be derived in simulation runs (e. g. simulations for price changes). 3 First experiences in simulation behaviour It has to be mentioned that all of the following results refer to one selected region in Germany (i.e. D_100: Baden-Württemberg) which is close to the German sectoral average in terms of yield potentials. The results just have an explorative value and should not be overinterpreted (means by real numbers). In a first step we are going to analyse the impacts of specification of fix costs (labour and depreciation). In a comparative-static version of the modelling system CAPRI, fix cost should be considered explicitly in the objective function anyway because they have a big influence on production decision in the mid-term perspective. 7

12 8 Chart 4 shows consequences of a 30% price reduction for cereals on main production activities. The "reference run" shows the production structure in the "CAPRI-base year" The next two colums show results of a price reduction for cerals by 30% in relation to "reference run". Chart 4: 250 Production structure in the reference run and in simulation runs with a 30%-cereal-price-reduction with and without specification of fix costs Reference run with fix costs;-30% without fix costs;-30% Softwheat Rye Barley Oats Maize Rape Sunflower Silage in ha Source: Institute for Agricultural Policy (IAP), Bonn University, As shown in the chart, we can observe a "smoother" allocation behaviour of the model when considering fix costs in the objective function. The adjustments of production structure in the main cash crops are not as drastic as without fix costs (for example soft wheat, barley). Due to fix costs, the marginal value of agricultural area decreases from 331 ECU (without fix costs) to 41 ECU. This and the change of dual values on the bounds in the base year 1993 lead to the different allocation behaviour described above. Simulation results of a steady decrease in cereal prices are shown in chart 5. Cereal prices are reduced stepwise by 5%. Fix cost are taken into account. The simulation results reveal a continuous decrease in cultivation of main cash crops which are soft wheat and barley. We can observe a slight change in relative competitiveness between these cash crops and rye and oats which can increase their share of cultivated area. Additionally, also rape gathers a higher relative competitiveness; cultivation of oilseeds area increases slightly, too. Since the marginal costs of arable land decrease due to price reduction in cereals, the competitiveness of fodder production on arable land, especially silage production, increases. The 30% price reduction in cereals causes an increase of silage production of more than 10% in relation to the reference run. Area without a positive income resp. with an income effect lower than received in the set aside programme, are left fallow so that the share of "Fall" increases steadily in the simulation runs.

13 9 Chart 5: Production structure in the reference run and in simulation runs with stepwise price reduction by 5% with specification of fix costs 250 ref 5% with fix 10% with fix 15% with fix 20% with fix 25% with fix 30% with fix in ha Softwheat Rye Barley Oats Maize Rape Silage Fall Source: Institute for Agricultural Policy (IAP), Bonn University, Now we shall analyse if the alternative technologies affect the allocation behaviour of the model and if those production activities gain relative competitiveness in the above mentioned price scenarios. Chart 6 shows the share of the aggregated alternative technologies (exclusive traditional technology) for main production groups. The results reveal that with a continuous price reduction alternative technologies can increase their share of the cultivated area in different production groups. In the reference run the share of the alternative technologies (excl. trad. technology) in every product group has been assumed to reach 7% (see chapter 2.2). Generally, a price reduction in cereals leads to a higher share of alternative production systems. This is especially for soft wheat, barley and other cereals but also for rye and oats. Rape and silage are influenced indirectly by a decline of relative competitiveness of cereals. Not only the cultivation of oilseeds and silage increase (see chart 5) but also the share of alternative technologies within those production groups. Last topic of analysis shall be the influence of the cross effects between alternative technologies on the simulation behaviour. Chart 7 shows the share of alternative technologies (excl. trad. technology) in every product group within a price reduction of 30% for cereals. The bars represent scenarios with and without consideration of technical cross effects. Fix costs are taken into account. As expected (and intended) the share of alternative technologies is higher in the scenario with consideration of technical cross effects between alternative technologies. We have a high correlation between the general development of production structure and share of technologies.

14 10 This means the higher the decrease of area for example of soft wheat the higher the increase of technical alternatives in soft wheat and vice versa (see Maize). Chart 6: Share of alternative technologies (excl. trad. technology) in the main product groups with stepwise price reduction by 5% 12,00 in % 5% 10% 15% 20% 25% 30% 10,00 8,00 Reference 7% 6,00 4,00 2,00 0,00 Softwheat Rye Barley Oats Maize Other Cereals Rape Silage Source: Institute for Agricultural Policy (IAP), Bonn University, Simulation runs without fix costs reveal the same effects in tendency but even more drastically.

15 11 Chart 7: Share of alternative technologies (excl. trad. technology) in the main product groups with alternative handling of technical cross effects 12 in % 10 8 Referenc e 6 7% 30% with cross effects 30% without cross effects Softwheat Durumwheat Rye Barley Oats Maize Other Cereals Pulses Rape Sunflower Silage Source: Institute for Agricultural Policy (IAP), Bonn University, Conclusions Conclusions of the explorative work in modelling alternative technologies can be summarized as follows: A set of alternative technologies has been defined for crop production. Definition of these alternatives shall both ensure a flexible model-response to changes in market and price policy and enable the modelling of political measures The specification of these alternative technologies is done by factors in relation to the traditional production alternative derived from the modelling experience in the RAUMIS- NRW project. Additionally, we implemented cross effects between certain alternative technologies in order to to depict the cost-effects of expansion or reduction of alternative technologies. If the CAPRI-partners reckon the methodological approach as a suitable procedure the specification of relative factors for all of the EU-regions, at least on Member State level, will be a huge task in future working steps. Additionally, the specification of labour requirements and average depreciation costs per production alternative are essential both for the comparative static approach of CAPRI and for the differentiation of alternative technologies. The specification approach in RAUMIS of these coefficients has been described ("techno-module"). We should discuss if this approach may be used in CAPRI. First experiences in simulation behaviour reveal a high influence of fix cost on the optimization procedure. In simulation runs focussing on alternative technologies we could observe an increasing share of these alternatives on the cultivated area in price reduction sce-

16 12 narios. The implementation of cross-relations between alternative technologies even increase this effect.

17 13 5 Annex Calculative depreciation and labour costs in CAPRI-Region D_100. Labour costs per unit and hour (LU) assumed to be 10 ECU/LU. Not yet adjusted to sectoral data! D_100.SWHE.DEPM 99.1 D_100.TWIN.LABO D_100.SWHE.LABO D_100.OWIN.DEPM D_100.DWHE.DEPM 99.1 D_100.OWIN.LABO D_100.DWHE.LABO D_100.NURS.DEPM D_100.RYE.DEPM 99.1 D_100.NURS.LABO D_100.RYE.LABO D_100.FLOW.DEPM D_100.BARL.DEPM 99.1 D_100.FLOW.LABO D_100.BARL.LABO D_100.OCRO.DEPM D_100.OATS.DEPM 88.6 D_100.OCRO.LABO D_100.OATS.LABO D_100.MILK.DEPB D_100.MAIZ.DEPM 88.6 D_100.MILK.LABO D_100.MAIZ.LABO D_100.BEEF.DEPB D_100.OCER.DEPM 88.6 D_100.BEEF.LABO D_100.OCER.LABO D_100.CALF.DEPB 80.0 D_100.PULS.DEPM 91.5 D_100.CALF.LABO 90.0 D_100.PULS.LABO D_100.PORK.DEPB 30.4 D_100.POTA.DEPM D_100.PORK.LABO 26.0 D_100.POTA.LABO D_100.MUTM.DEPB 26.1 D_100.SUGB.DEPM D_100.MUTM.LABO 20.0 D_100.SUGB.LABO D_100.MUTT.DEPB 26.1 D_100.RAPE.DEPM 97.9 D_100.MUTT.LABO 20.0 D_100.RAPE.LABO D_100.EGGS.DEPB D_100.SUNF.DEPM 97.9 D_100.EGGS.LABO D_100.SUNF.LABO D_100.POUL.DEPB D_100.OOIL.DEPM 97.9 D_100.POUL.LABO D_100.OOIL.LABO D_100.OANI.DEPB 29.2 D_100.OIND.DEPM D_100.OANI.LABO D_100.OIND.LABO D_100.OROO.DEPM D_100.CAUL.DEPM D_100.OROO.LABO D_100.CAUL.LABO D_100.GRAS.DEPM 63.2 D_100.TOMA.DEPM D_100.GRAS.LABO D_100.TOMA.LABO D_100.SILA.DEPM 90.6 D_100.OVEG.DEPM D_100.SILA.LABO D_100.OVEG.LABO D_100.CALV.DEPB 80.0 D_100.APPL.DEPM D_100.CALV.LABO 90.0 D_100.APPL.LABO D_100.RCAL.DEPB 80.0 D_100.OFRU.DEPM D_100.RCAL.LABO 90.0 D_100.OFRU.LABO D_100.HEIF.DEPB 98.1 D_100.TAGR.DEPM D_100.HEIF.LABO D_100.TAGR.LABO D_100.PIGL.DEPB D_100.TABO.DEPM D_100.PIGL.LABO D_100.TABO.LABO D_100.FALL.DEPM 37.3 D_100.TWIN.DEPM D_100.FALL.LABO 28.0

18 List of CAPRI Working Papers: 97-01: Britz, Wolfgang; Heckelei, Thomas: Pre-study for a medium-term simulation and forecast model of the agricultural sector for the EU 97-02: Britz, Wolfgang: Regionalization of EU-data in the CAPRI project 97-03: Heckelei, Thomas: Positive Mathematical Programming: Review of the Standard Approach 97-04: Meudt, Markus; Britz, Wolfgang: The CAPRI nitrogen balance 97-05: Löhe, Wolfgang; Britz, Wolfgang: EU's Regulation 2078/92 in Germany and experiences of modelling less intensive production alternatives 97-06: Möllmann, Claus: FADN/RICA Farm Accountancy Data Network Short Introduction 97-07: Löhe, Wolfgang; Specification of variable inputs in RAUMIS 97-08: María Sancho and J.M. García Alvarez-Coque; Changing agricultural systems in the context of compatible agriculture. The Spanish experience Helmi Ahmed El Kamel and J.M.García Alvarez-Coque; Modelling the supply response of perennial crops is there a way out when data are scarce? 97-10: Patrick Gaffney; A Projection of Irish Agricultural Structure Using Markov Chain Analysis 97-11: P.Nasuelli, G.Palladino, M.Setti, C.Zanasi, G.Zucchi; A bottom-up approach for the CAPRI project 97-12: P.Nasuelli, G.Palladino, M.Setti, C.Zanasi, G.Zucchi; FEED MODULE: Requirements functions and Restriction factors 98-01: Heckelei, Thomas; Britz, Wolfgang: EV-Risk analysis for Germany 98-02: Heckelei, Thomas; Britz, Wolfgang; Löhe, Wolfgang: Recursive dynamic or comparative static solution for CAPRI 98-03: Löhe, Wolfgang; Britz, Wolfgang: Modelling alternative technologies based on the RAUMIS-NRW approach 98-04: Sander, Reinhard: General status of the project