CAPRI EU's Regulation 2078/92 in Germany and experiences of model ing less intensive production alternatives

Transcription

1 COMMON AGRICULTURAL POLICY REGIONAL IMPACT ANALYSIS CAPRI preliminary version Working Paper EU's Regulation 2078/92 in Germany and experiences of modelling less intensive production alternatives 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 Agrarpolitik, Universität Bonn, Nußallee 21 D Bonn Phone: Fax: URL: s: (loehe) or (britz) loehe@agp.uni-bonn.de or britz@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 programme. 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 EU's Regulation 2078/92 in Germany and experiences of modelling less intensive production alternatives 1 Introduction The EU-Regulation 2078/ General frame Implementation in Germany Acceptance of regional programmes in Germany Explorative approach for modelling less intensive production alternatives Intensity module Module for less intensive production alternatives Modelling less intensive production alternatives in CAPRI - Conclusions Intensity module Alternative technologies embedded in the optimisation step References APPENDIX... 13

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5 1 Introduction The 1992 CAP-Reform did not only change the frame for market and price policy in the EU. Regarding agriculture as an important "steward of the environment" (SCHEELE, 1996) the EU established with the Council Regulation 2078/92 a policy frame for financial support of environmental programmes in the Member States. The regulation 2078/92 is one of the accompanying measures (the others are "forestry measures" and "early retirement measures") which aim to support the market and price policy of the CAP. This working paper gives a survey on the establishment of extensification programmes under Regulation 2078/92 in Germany. Following a general description of targets of this Regulation the realization in Germany is analysed. Data on area with extensified agricultural production will give an impression of the acceptance of those programmes in Germany. In order to give a base for discussion on how to consider less intensive production alternatives in the modelling system CAPRI, a methodological approach from an explorative project for North Rhine-Westfalia (LÖHE, 1997) is described. 2 The EU-Regulation 2078/ General frame The EU-Regulation 2078/92 gives a general frame for certain environmental policy measures in agriculture. Premiums are paid to farmers in relation to the obligations taken on. Farmers commit themselves voluntarily for a minimum period (5 years) to the measures under the programmes. Main targets of the Regulation are to (REGULATION 2078/92) "accompany the changes to be introduced under the market organization rules contribute to the achievement of the Community's policy objectives regarding agriculture and environment contribute to providing an appropriate income for farmers". The following list of measures can be financed within the Regulation 2078/92 (REGULATION 2078/92): to reduce substantially the use of fertilizers and/or plant protection products, or to keep the reduction already made, or to introduce or continue organic farming methods to switch, by means other than those above to more extensive forms of cropping to reduce the proportion of sheep and cattle per forage area

6 2 to use other farming practices compatible with protecting environment and natural resources to ensure the upkeep of abandoned farm- or woodlands to set aside farmland for at least 20 years to improve the training of farmers with regard to practices compatible with environmental requirements. The EU-Regulation just gives a common frame. The implementation in the Member States is in their own responsibility. Empirically, the implementation has been very diverse, some countries have concentrated programmes on single activities whereas others have provided for regional programmes covering the whole range of activities (PRIEBE, 1996). None of the measures is obligatory for the individual farmer whereas the implementation of the adequate programmes on Member States level is required. According to the EU-principle of subsidiarity most Member States have chosen a regionally decentralized implementation to the regions. The calculation of regionalized premiums paid should be based on farmers opportunity costs of programme participation. An incentive is added to persuade a sufficient number of farmers to join the programmes (PRIEBE, 1996). All regional programmes which meet the criteria of Regulation 2078/92 are cofinanced by the EU. Upper limits for premiums cofinanced from Community funds are laid out in the Regulation. Community's finance is provided at the rate of 50% respectively 75% in less prosperous regions (Objective I regions). The rest is paid by the Member State. The budget in 1996 is ECU 1.4 billion, which is 3.5% of Community's agricultural spending (PRIEBE, 1996). 2.2 Implementation in Germany Actually, in Germany the EU-Regulation 2078/92 is primarily implemented through the "Gemeinschaftsaufgabe Verbesserung der Agrarstruktur und des Küstenschutzes" 1 (GAK). The GAK is a bundle of agricultural policy measures managed by both the "Länder" and the Federal Government. The Federal contribution to GAK budgets is 60%. 2 Measures supporting environmentally-sensitive farming within the GAK are rather restrictive. GAK contributions are limited to those measures that support farming adapted to market and regional conditions (HÖLL AND MEYER, 1996). So, not all measures foreseen under EU-Regulation are implemented in the GAK. Some of them remain exclusive "Länder"-tasks. Consequently, we can observe that most "Länder" with a large agricultural budget provide programmes under Regulation 2078/92 externally of GAK but co-financed by the EU (MEHL AND PLANKL, 1995) Generally, the measures under GAK are divided into the following groups: extensive crop production, including perennials extensive grass land management organic farming The German approach is characterized by the fact that the entire territory is covered, although not all measures of EU-Regulation 2078/92 are applied to all places. The implementation of 1 Literal translation: Common Task of Improving Agricultural Structures and Coastal Protection. (HÖLL and MEYER, 1996) 2 The EU covers 50% (75% in less prosperous regions) of premiums. The rest (50% resp. 25%) is splitted into 60% Federal's contribution and 40% contribution of the "Land".

7 programmes is not restricted to specific environmentally-sensitive areas (HÖLL AND MEYER, 1996). The following table shows the frame for measures under GAK, i.e. programme formulation and related premiums. The "Länder" have the flexibility to define the Sub-measures and modify premiums between -40% and +20%. A further increase of the premiums is possible, but not longer co-financed by the Federal Government. 3 Table 1: Description of supporting measures for a environmentally-sensitive agriculture under the GAK Measures Sub- Measures Premiums (DM) I. Introduce or continue extensive arable and permanent crop production 1. Ban of mineral fertilizer and chemical plant protection products Arable land Introduce 250,- Continue. 200,- Perennials 1.200, ,- 2. Ban of mineral fertilizer Arable land Introduce 150,- Continue. 120,- Perennials 250,- 200,- 3. Ban of herbicides Arable land Introduce 150,- Continue. 120,- Perennials 350,- 300,- II. Introduce or continue extensive grass land management 1. Reduce the proportion of sheep and cattle per forage area; min 0,3 to max. 1,4 livestock units/ha per reduced unit of sheep and cattle per ha grass land 450,- DM; minimum 250,- DM/ha per increased number of grass land area 250,- DM/ha 2. Change from arable land into extensive grass land with min 0,3 and max. 1,4 livestock units of sheep and cattle per ha 600,- DM per ha III. Introduce or continue organic farming 1. Introduce or continue organic farming Arable / Grass land Introduce 250,- Continue. 200,- Permanent 1.200,- 1000,- Source: Own, based on BUNDESMINISTERIUM FÜR ERNÄHRUNG, LANDWIRTSCHAFT UND FORSTEN (1996): Rahmenplan der Gemeinschaftsaufgabe "Verbesserung der Agrarstruktur und des Küstenschutzes" für den Zeitraum 1996 bis Drucksache des Deutschen Bundestages Nr. 13/4349 vom , S The GAK implementation of Regulation 2078/92 has been notified to the EU in 1993 but support schemes are available to farmers only on the basis of specific programmes on Länder level (HÖLL AND MEYER, 1996). 2.3 Acceptance of regional programmes in Germany In general, we can observe in Germany that programmes for environmentally-sensitive farming are particularly taken up in less favourable areas (HÖLL AND MEYER, 1996; LÖHE 1997). This is mostly due to an insufficient adaption of the programmes and premiums to the local conditions. Chart 1 shows supported areas by type of measures.

8 4 Chart 1: Supported area by type of measures in Germany / / in ha Grass land Arable land Permanents Organic farming 0,2 0,5 Set aside (20 years) 1,4 2,5 Upkeep of abandoned areas Upkeep of trad. landuse Envir. rel. basic support Source: BMELF (1996, 1997): Agrarbericht. In Germany, an average of 4.7 Mill ha agricultural area (AA) has been supported under the EU-Regulation in 93/95 corresponding to a share of approximately 26 %. The measure "environmentally related basic support" (only in Bavaria and Saxony) covers already 60 % of total supported area, followed by extensification of grass (some 25 %) and arable land (some 12 %). 3 Explorative approach for modelling less intensive production alternatives In an explorative project for North Rhine-Westfalia (LÖHE, 1997) the Institute for Agricultural Policy at University of Bonn could gain some experience in modelling less intensive production alternatives within the frame of an activity based programming approach. The "Regionalized Agricultural and Environmental Information System for North Rhine - Westfalia" (RAUMIS-NRW), based on the modelling system RAUMIS, has been developed in order to depict less intensive production alternatives which are of major importance for the analysed region. The RAUMIS-NRW is used to analyse future extensification potentials and impacts of political measures at the "Länder"-level. The modelling system is an activity-based approach which represents the agricultural sector of North Rhine - Westfalia according to the Economic Account for Agriculture (EAA) by 31 regional models, representing the interdependencies between agricultural production and the environment by environmental indicators. Chart 2 gives an overview on the structure of RAUMIS-NRW.

9 5 Chart 2: Scheme of the modelling system RAUMIS-NRW Agricultural Policy Scenarios Base Model: Ex-post data preparation Environmental Policy Scenarios Intensity Module Feedback Module for less intensive production alternatives Linear Programming Approach Traditional Production Alternatives Less intensive Production Alternatives kcabdee F Envirionmental Indicators Agricultural Variables - Production Structure - Input - Agricultural Income Environmental Variables - Nitrogen Balance - Indicator of Biodiversity To capture different possibilities for technological adjustments the following parts are included in the model: 1. Based on the concept of neoclassical yield functions, the optimal special intensity for yieldenhancing inputs is determined on the basis of the relative product/input price ratios: intensity module. 3 3 Cf. Weingarten, P. (1990): Development of an approach for estimating the effects of changing nitrogen and product prices on nitrogen application and yield in the regions of the Federal Republic of Germany, taking the example of winter wheat. Diploma thesis, Bonn, and Weingarten, P. (1996): Grundwasserschutz und Landwirtschaft. Eine quantitative Analyse von Vorsorgestrategien zum Schutz des Grundwassers vor Nitrateinträgen, Kiel. The description of the RAUMIS intensity module is taken from Meudt, M. (1997): Environmental indicators in German policy informations systems. In: Crabtree, B. and Brower, F. (Eds.): Environmental indicators and agricultural policy, London.

10 6 2. A set of alternative mechanical/technical processes is defined for plant production (traditional tillage by plough, reduced tillage (cultivator), no-tillage and extensive grassland production) that differ according to the use of machinery and thus decision-related depreciation costs 4, labour requirement, yield, yield-enhancing inputs and other variable costs: module for less intensive production alternatives. 3.1 Intensity module The intensity module, identical to the RAUMIS, is based on quadratic yield functions for 10 different crops which were estimated via OLS out of field research data on N-yield relationships. In order to abstract from different locations of the individual experiments, the maximum yield on each location and the related N input were normalised to unity. To do so, each series was standardised by dividing each N-input by the N-level corresponding to the maximum yield and each yield by the maximum yield of this series. In order to use the resulting normalised quadratic functions from the field research in the context of a simulation model, three assumptions were made: 1. that the nitrogen input needed to achieve maximum yields are identical in the short and long term, but 2. that yield with zero-n input is in a medium- to long-term perspective 20-30% lower than in short-term perspective, because field experiments with yearly changing test plots tend to overestimate yields achieved with low N fertilising due to high N stocks in soils which are mobilised under nutrient stress. 3. that the relation between maximum yield and yield at zero N input is an universal constant for a given crop which allows to map the normalised function back to specific locations and points in time. Consequently, absolute region-specific yield functions can be derived out of estimated normalised ones if a point and the derivative at it are known. All three terms of the quadratic yield function can then derived together with assumption 3. Assuming profit maximising behaviour, planned yields and observed N fertilising should lay on a point of the absolute yield function (see chart 3: N1,Y1) were marginal costs for N- fertilising are equal to marginal revenue. The later one is not identical to the price multiplied with marginal yield because additional variable cost elements - part of chemical plant protection, fertilising of P, K and Ca, machine, energy and labour input - are thought yield dependent, too. Assuming a linear relationship between these cost elements and yield, the per unit yield costs for these elements must be subtracted from the output price to derive marginal revenue. 4 For the purpose of depicting technology-related depreciation costs, the medium-term simulation analyses assume continuous reinvestment.

11 7 Chart 3: Intensity module of RAUMIS-NRW Yield Y 2 Y 1 Y 2 * α 3 trend shift α 2 tan α = Yield function in t + n α 1 Yield function in t P N P Prod - P var. Inp. N* 2 N 1 N 2 Nitrogen In order to abstract from yield fluctuation to arrive at planned instead of observed yield, three year averages were used when recovering the absolute functions. However, without knowing the absolute yield function or observed average regional N input in individual crops, other information must be used to construct the N input at the observed yield. Linear calculation functions estimated by KTBL out of farm data network data, adjusted to regional conditions and corrected to match the sectoral total N fertilising were therefore used to determine the absolute N input for a crop in given region and year. 5 In order to shift the absolute functions over time due to technical progress, N fertilising, related yield and the prices were trend projected. The point obtained on the shifted absolute yield function (Y2,N2) would then be optimal for the projected price relation α2 if the projection algorithm would work perfectly. Consequently, in a simulation run with simulated price relation α3 differing from trend projected one α2, the optimal intensity would change. Chart 3 shows the effect of product price reduction compared to the trend projected price relation yielding a new optimum with lower input intensity at N 2 *, Y 2 *. The SPEL-MFSS applies a similar approach, although the normalized functions were not derived out of field experiment data. The intensity module is based on two basic underlying assumption: 1. The optimal intensity for each crop can be determined independently from the rotation. Whereas MFSS does only change purchased variable inputs and assumes implictly that fixed factors as labour and capital are not restricting the choice of the intensity, RAUMIS takes explicitly labour and machinery input into account in the intensity module. The assumption makes sense because RAUMIS uses continous re-investment coupled directly to the individual activities and buys labour at a fixed price during the optimsation. 5 see Meudt M. & W. Britz,..., CAPRI Working paper 97-04

12 8 2. Technical progress can be estimated by independent trend projections for (1) realized yields, (2) average constructed N input per crop, year and region and (3) the prices for outputs which yield together a new economic optimal point on an absolute yield function. Although both assumption can be surely discussed, MFSS, RAUMIS and RAUMIS-NRW have yielded a plausible behavior based on them. However, the use of the two-step approach is restricted to simulations were (expected) prices are known in advance and not endogenously determined in the optimisation step. That is not the case if output prices are determined endogenously e.g. in a comparative static model, or in any model were opportunity costs for fix factors depend on the production program and determine the optimal intensity. Not at least, the fact keeps disturbing that during simultions a quadratic, time shifted yield function is used to determine the optimal intensity whereas the construction of the ex-post data is based on linear, time independent functions. 3.2 Module for less intensive production alternatives In addition to the intensity module, the module for less intensive production alternatives ensures a further endogenous adaptation of production intensity in response to different agricultural and/or environmental policies, e.g. extensification programmes such as the Regulation 2078/92 or drastic changes in the general market and price policies such as extreme low-price scenarios. Each of the 21 cash crops such as soft wheat, rye, rape seed may be produced by up to 24 alternative production activities characterised by different levels of production intensity, differentiated by the manner of tillage, management of fertilizer use and plant protection systems. Table 2 shows, exemplary for soft wheat, the differentiation of these activities. Additionally, arable land may be converted to extensive grass land. Grass land farming is split up into meadow and pasture land, each differentiated by three intensity levels: traditional, intermediate and extensive. The extensive alternatives are characterised by a ban of mineral fertilizer and plant protection products according to measures under regulation 2078/92. The nutrient loss is partially compensated by increased manure spreading. The intermediate technology depicts reduced use of mineral fertilizer in grass land production and less mowing cuts in the case of meadows.

13 9 Table 2: Production alternatives for soft wheat in the "low intensity module" of RAUMIS-NRW Three alternatives of tillage: "Traditionell" tillage (plough) Reduced tillage (cultivator) No-Tillage... each in combination with: Ban of mineral fertilizer; substitution by manure in three steps of intensity: 33 % nitrogen per ha 66 % nitrogen per ha 100 % nitrogen per ha Ban of chemical plant protection products: only herbizides all chemical plant protection products Ban of mineral fertilizer and of chemical plant protection products; substitution by manure in two steps of intensity (s.a.): 33 % nitrogen per ha 66 % nitrogen per ha In animal production, the model differentiates between traditional feeding and the use of forage from extensified grass land in the case of ewes, suckling cows and fattening of bulls, respectivly use of free amino acids and phytase for fattening of pigs which leads to reduced nitrogen and potassium content of manure. The production alternatives are specified to the extent possible on the basis of standard calculation data, published by KTBL. 6 Still, the KTBL cannot provide all the necessary information. Therefore, an additional literature survey and interviews of experts on agricultural production technology have been carried out (LÖHE, 1997). The interviews focused on expected relative changes of input/output-coefficients levels under different extensification measures compared to traditional intensity. 7 The answers had to be edited for the main production groups (winter-/summer cereals, oilseeds, pulses, etc.) Tables in the annex give an overview on the results. Based on calculation data and expert information, the less intensive production alternatives were specified related to the regional average input/output coefficients in the base year Fortunately, the situation in 1991 was still characterised by comparably high administrative prices and absence of extensification programmes, allowing to define the "traditional" intensity simply by using average input/output coefficients. In other words, the proceeding assumed that no extensificated technologies were realized in the base year. The mathematical programming approach maximizes regional agricultural income according to the relative competitiveness of the different production alternatives, simultaneously 6 Kuratorium für Technik und Bauwesen in der Landwirtschaft. 7 The questionnaire included among others: "Which change of output coefficient do you expect if you choose "reduced tillage" or "no-tillage" instead of "traditional tillage by plough"?"

14 10 determining the most competitive linear combination of one, some or all technology alternatives for a given crop and choosing the optimal rotation for all crops. This principle for the intensity adaptation is shown in chart 3. Chart 3: Linear approximation in 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. As expected, in the calibration run for the base year 1991 with high agricultural producer prices and no premiums paid for extensification, less intensive production alternatives were not realized in the regions of North Rhine-Westfalia. The connection between the intensity module and the extensificated alternatives works based on relative differences. The intensity module, based on the underlying field experiments, assumes traditional farming practises and thus adjusts the absolute variable inputs and yields for the traditional intensity level. The alternatives are then defined in absolute terms by using fixed relative relations to the traditional one (see tables in the annex). During simulations, the model is technically free to depict any combination of the different production alternatives. However, in order to model the effect of the existing capital stock suited for traditional tillage practices, the maximum share of less intensive tillage alternatives is set to 30% for the region as a whole. Under the GAK programme, premiums are not paid for extensification of individual crops or fields, but of the whole farm branch, i.e. all crops on arable land, perennials or grass land. Regional models cannot easily cope with that kind of farm specific restrictions, but must map them to average regional ones. In order to model the farm branch concept for "crops on arable land", premiums for one ha of less extensive cereal production are only paid if a specific area of maize, potatoes and sugar beet is extensified, too. Otherwise, the model would only extensify cereal production thus violating the GAK-programme restriction.

15 4 Modelling less intensive production alternatives in CAPRI - Conclusions As explained above, the RAUMIS-NRW applies a two step approach: outside of the optimisation step, the intensity module defines yield and variable inputs for a traditional technology and derives from them a set of less intensive production alternatives. During optimisation, both the crop rotation, herd sizes and the mixture of the different alternatives for producing one specific output are determined Intensity module This process yields some disturbing properties according to the underlying optimisation problem which were mentioned above. The most obvious one is the fact that the data generation process for distributing the given sectoral amount of variable inputs to the different activities ex-post is done without assuming profit maximising behaviour but that during simulation runs the resulting ex-post data are projected under the assumption that resulting data are economically optimal. Therefore, the discussion of a more consistent approach seems appropriate. The aim of such an approach is to construct ex-post input coefficient for each activity and all regions which are in line with the assumptions applied later on in the simulations. 4.2 Alternative technologies embedded in the optimisation step The RAUMIS-NRW approach shows about the upper bound for depicting different technological alternatives in a regional modelling context. At least a man year was invested to define the political measures in the field of extensification for the use in an agricultural sector model and to collect data and information on extensified production in a specific region. Especially, information on extensification either from statistics or literature is still scarce, forcing the model team to conduct time consuming expert interviews. In the context of CAPRI, a similar approach is simply impossible under the project s financial and time restrictions. However, the RAUMIS-NRW simulations show that only a limited number of alternatives are really of importance under the frame of EU regulation 2078/92 or further CAP liberalisation steps. At least for Germany, RAUMIS-NRW offers the information to pick up the most important two or three technology alternatives for the use in CAPRI. Experts may be the source to define a similar small set for other Member States. In opposite to RAUMIS-NRW, CAPRI faces a serious problem in the base year calibration already. Due to the fact that a major change in the general frame of the CAP has taken place, accompanied by extensification programmes, average sectoral or regional input/output coefficients reflect already a mixture of "traditional" and extensified production technologies. Therefore, the share of less intensive alternatives must be known or set for each crop for the base year in order to split up the average coefficients to the different alternatives. Fortunately, at least for Germany, the "farm branch restriction" allows to map the information on total ha in specific programmes by sensible assumption to ha for individual crops.

16 12 References Bundesministerium für Ernährung, Landwirtschaft und Forsten (different years): Agrarbericht. Bundesministerium für Ernährung, Landwirtschaft und Forsten: Rahmenplan der Gemeinschaftsaufgabe "Verbesserung der Agrarstruktur und des Küstenschutzes" für den Zeitraum 1996 bis Drucksache des Deutschen Bundestages Nr. 13/4349 (Bonn). Höll, A., Meyer, H. v. (1996): Germany. In: Whitby, M. (ed.): The European Environment and CAP Reform - Policies and Prospects for Conservation. CAB International (Oxford). Kuratorium für Technik und Bauwesen in der Landwirtschaft (1993): Datensammlung zur Betriebsplanung (Darmstadt). Löhe, W. (1997): Extensivierungspotentiale in der Landwirtschaft. Regional differenzierte Simulationsanlysen unter alternativen agrar- und umweltpolitischen Rahmenbedingungen für die Landwirtschaft in Nordrhein-Westfalen, Diss. forthcoming, (Bonn). Meudt, M. (1997): Environmental indicators in German policy informations systems. In: Crabtree, B. and Brower, F. (Eds.): Environmental indicators and agricultural policy (London). Priebe, R. (1996): European Union: The EU's Agri-Environment Regulation 2078/92 Framework. Scheele, M. (1996): The Agri-environmental Measures in the Context of the CAP Reform. In: Whitby, M. (ed.): The European Environment and CAP Reform - Policies and Prospects for Conservation. CAB International (Oxford). Weingarten, P. (1990): Development of an approach for estimating the effects of changing nitrogen and product prices on nitrogen application and yield in the regions of the Federal Republic of Germany, taking the example of winter wheat. Diploma thesis (Bonn). Weingarten, P. (1996): Grundwasserschutz und Landwirtschaft. Eine quantitative Analyse von Vorsorgestrategien zum Schutz des Grundwassers vor Nitrateinträgen (Kiel).

17 APPENDIX Table A.1: Relative yield losses by alternative tillage practices against ploughing Reduced tillage (Cultivator) No-tillage Average Deviat. Average Deviat. Winter cereals 0,92 0,05 0,86 0,05 Sommer cereals 0,90 0,02 0,79 0,06 Pulses 0,89 0,02 0,76 0,02 Oilseeds 0,88 0,04 0,79 0,06 Corn for silage 0,89 0,02 0,77 0,05 Corn 0,89 0,02 0,79 0,04 Sugar beets 0,89 0,02 0,77 0,07 Potatoes 0,88 0,03 # # Source: Interview. Table A.2: Relative change of seed by alternative tillage practices against ploughing Reduced tillage (Cultivator) No-tillage Average Deviat. Average Deviat. Winter cereals 1,03 0,05 1,09 0,05 Sommer cereals 1,04 0,08 1,09 0,06 Pulses 1,03 0,05 1,03 0,05 Oilseeds 1,05 0,09 1,09 0,11 Corn for silage 1,01 0,02 1,09 0,02 Corn 1,01 0,02 1,04 0,05 Sugar beets 1,02 0,04 1,04 0,05 Potatoes 1,00 0,00 # # Source: Interview.

18 14 Table A.3: Relative change of chemical plant protection by alternative tillage practices against ploughing; Relative yield losses by alternative management of plant protection against "traditionell" production alternative Reduced tillage (Cultivator) No-tillage Ban of herbicides Ban of total chem. plant protection Average Deviat. Average Deviat. Average Deviat. Average Deviat. Winter cereals 1,11 0,07 1,22 0,04 0,86 0,08 0,70 0,13 Sommer cereals 1,09 0,05 1,20 0,00 0,90 0,06 0,75 0,13 Pulses 1,07 0,07 1,20 0,00 0,86 0,03 0,69 0,09 Oilseeds 1,11 0,07 1,22 0,04 0,86 0,05 0,71 0,10 Corn for silage 1,11 0,07 1,24 0,09 0,73 0,08 0,68 0,10 Corn 1,11 0,07 1,24 0,09 0,73 0,08 0,68 0,10 Sugar beets 1,13 0,08 1,27 0,04 0,76 0,16 0,66 0,16 Potatoes 1,08 0,08 # # 0,85 0,09 0,64 0,13 Source: Interview. Table A.4: Relative change of labour use by alternative management of plant protection against "traditionell" production alternative; Share of costs for herbicides on costs for total chemical plant protection input Ban of herbicides Ban of total chem. plant protection Share of herbicides on total plant protection Average Deviat. Average Deviat. Average Deviat. Winter cereals 1,20 0,28 1,20 0,28 34,6 10,9 Sommer cereals 1,20 0,28 1,20 0,28 44,3 17,4 Pulses 1,35 0,21 1,35 0,21 56,5 24,3 Oilseeds 1,45 0,07 1,45 0,07 61,0 18,5 Corn for silage 1,50 0,50 1,75 0,35 84,8 17,0 Corn 1,50 0,50 1,75 0,35 83,8 19,3 Sugar beets 2,57 2,11 3,25 2,47 79,0 16,7 Potatoes 1,83 0,29 2,00 0,71 28,6 9,5 Source: Interview.

19 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 out when data are scarce?