A Comparison of CAPRI and SEAMLESS-IF as Integrated Modelling Systems

  • Wolfgang BritzEmail author
  • Ignacio Pérez Domínguez
  • Thomas Heckelei


SEAMLESS-IF and CAPRI are both integrated agricultural modelling systems for policy impact assessment at EU level, linking model components across scales and between the economic and bio-physical domains. However, the overall design, focus and representation of agricultural sub-systems vary between them. This chapter describes and compares the main characteristics of SEAMLESS-IF and CAPRI, looking at objectives, concepts for database and model linking, modelling approaches at farm level and technology representation, agri-environmental indicators and baseline construction for forward looking impact assessment. Observed differences in these areas follow from SEAMLESS-IF focusing on field and farm level components stressing bio-economic interrelations and technological innovation, whereas CAPRI adopts a more market oriented perspective with full coverage of EU policies. Software design in SEAMLESS-IF is shaped by flexible component integration and a strong client oriented graphical user interface. CAPRI instead stresses simulation performance and exploitation of results by modellers.


European Union Common Agricultural Policy Farm Type Supply Module Integrate Modelling System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Adenaeuer, M. (2005). Modelling the European sugar sector - Incentives to supply sugar beets and analysis of reform. Options. Dissertation, Bonn University, Germany.Google Scholar
  2. Armington, P. S. (1969). A theory of demand for products distinguished by place of production. IMF Staff Papers, 16, 159-178.Google Scholar
  3. Bauer, S. (1978). Quantitative Sektoranalyse als Entscheidungshilfe für die Agrarpolitik - Ein dynamisches Analyse- und Prognosesystem für die Landwirtschaft in der Bundesrepublik Deutschland. Dissertation, University of Bonn, Germany.Google Scholar
  4. Bezlepkina, I., Pérez Domínguez, I., Heckelei, T., Romstad, E., & Oude Lansink, A. (2007). EXPAMOD: Component to statistically extrapolate from FSSIM models to other farm types and regions including aggregation to NUTS2: Motivation, description and prototype. PD3.6.11.2 (SEAMLESS Integrated Project, EU 6th Framework Programme, contract no. 010036-2).Google Scholar
  5. Britz, W., & Heckelei, T. (2000a). Positive mathematical programming with multiple data points: A cross-sectional estimation procedure. Cahiers d’Economie et Sociologie Rurales, 57(4), 28-50.Google Scholar
  6. Britz, W., & Heckelei, T. (2000b, October). Effekte von Agenda 2000 auf den deutschen und französischen Agrarsektor - Anwendung des regionalisierten Modellsystems CAPRI. Paper presented at Gemeinsame Tagung von SFER und Gewisola, Strasbourg.Google Scholar
  7. Britz, W., Heckelei, T., & Kempen, M. (Eds.) (2007). Description of the CAPRI modelling system. Final report of the CAPRI-Dynaspat Project. Bonn, Germany: Institute for Food and Resource Economics, University of Bonn.Google Scholar
  8. Britz, W., Heckelei, T., & Pérez Domínguez, I. (2006). Land use effects of decoupling: An EU wide, regionally differentiated analysis. Agrarwirtschaft, 55, 215-226.Google Scholar
  9. Britz, W., Helming, J., & Pérez Domínguez, I. (2003). Development of models and tools for assessing the environmental impact of agricultural policies. Final report for DG-ENV.Google Scholar
  10. Britz, W., & Jacquet, F. (2006). EU-MED Agpol: Impacts of Agricultural Trade Liberalisation between the EU and Mediterranean countries. Proceedings of the Workshop on Euro-Med Association Agreements Agricultural Trade - Regional Impacts in the EU, Jointly organised by DG RTD and DG JRC with the collaboration of DG AGRI, Brussels on 14 February 2006.Google Scholar
  11. Britz, W., & Leip A. (2008, February). A statistical meta model of DNDC to estimate nitrogen fate and irrigation water needs at 1 x 1 km grid at Pan-European scale. Paper presented NEU-annual meeting PSG2, Ghent.Google Scholar
  12. European Commission. (2004). Prospects for agricultural markets 2003-2010. Retrieved January 9, 2004 from
  13. European Commission. (2007a). Prospects for agricultural markets and income in the European Union 2006-2013. Directorate-General Agriculture and Rural Development, Brussels. Retrieved February, 2007 from
  14. European Commission. (2007b). Rural Development in the European Union - Statistical and Economic Information - Report 2007. Retrieved February 2007, from
  15. FAO. (2003). World agriculture: Towards 2015/2030. Summary report. Rome: Food and Agriculture Organization of the United Nations.Google Scholar
  16. Frohberg, K., & Britz, W. (1994). The World Food Model and an assessment of the impact of the GATT agreement on agriculture. Research Report. Bonn, Germany: Institute for Agricultural Policy.Google Scholar
  17. Funtowicz, S. O., & Ravetz, J. R. (1994). The worth of a songbird: Ecological economics as a post-normal science. Ecological Economics, 10, 197-207.CrossRefGoogle Scholar
  18. Heckelei, T., & Britz, W. (2005, February). Models based on positive mathematical programming: State of the art and further extensions. Invited paper presented at the 89th EAAE Symposium, Parma, Italy.Google Scholar
  19. Heckelei, T., & Wolff, H. (2003). Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy. European Review of Agricultural Economics, 30, 27-50.CrossRefGoogle Scholar
  20. Henrichsmeyer, W., Cypris, C., Löhe, W., Meudt, M., Sander, R., von Sothen, F., Isermeyer, F., Schefski, A., Schleef, K.-H., Neander, E., Fasterding, F., Helmcke, B., Neumann, M., Nieberg, H., Manegold, D., & Meier, T. (1996). Entwicklung eines gesamtdeutschen Agrarsektormodells RAUMIS96. Endbericht zum Kooperationsprojekt. Forschungsbericht für das BML (94 HS 021). Bonn/Braunschweig, Germany.Google Scholar
  21. Hertel, T. W. (ed). (1997). Global trade analysis: Modeling and applications. New York: Cambridge University Press.Google Scholar
  22. Jansson, T. (2007). Econometric specification of constrained optimization models. Dissertation, University of Bonn, Germany.Google Scholar
  23. Junker, F., Britz, W., Heckelei, T., Pérez Domínguez, I., & Wieck, C. (2005, June). How sustainable is the latest CAP Reform under the possible trade liberalisation outcomes of the Doha Round? Paper presented at the IATRC Summer Meeting 2005, Sevilla, June 16/17: ‘Pressures for Agricultural Reform: WTO Panels and the Doha Round Negotiations’.Google Scholar
  24. Kanellopoulos, A., Berentsen, P., van Ittersum, M.K., & Oude Lansink, A. (2007). Assessing the forecasting capacity of a bio-economic farm model calibrated with different PMP variants. Farming Systems Design 2007: An International Symposium on Methodologies for Integrated Analysis of Farm Production Systems Farm-Regional Scale Design and Improvement (pp. 55-56). Gorgonzola (MI), Italy: Global Print.Google Scholar
  25. Kempen, M., & Kränzlein, T. (2008, January). Energy use in agriculture: A modelling approach to evaluate energy reduction policies. Paper prepared for presentation at the 107th EAAE Seminar, Sevilla. Retrieved from
  26. Leip, A., Marchi, G., Koeble, R., Kempen, M., Li, C., & Britz, W. (2008). Linking an economic model for European agriculture with a mechanistic model to estimate nitrogen losses from cropland soil in Europe. Biogeosciences, 5, 73-94.CrossRefGoogle Scholar
  27. Li, C., Frolking, S., & Harriss, R. (1994). Modelling carbon biogeochemistry in agricultural soils. Global Biogeochemical Cycles, 8(3), 237-254.CrossRefGoogle Scholar
  28. Oenema, O., Oudendag, D., Witzke, P., Monteny, G.J., Velthof, G.L., Pietrzak, S., Pinto, M., Britz, W., Schwaiger, E., Erisman, J.W., de Vries, W., van Grinsven, J.J.M., & Sutton, M. (2007). Service contract: Integrated measures in agriculture to reduce ammonia emissions. Final report. (070501/2005/422822/MAR/C1). Wageningen, The Netherlands: Alterra.Google Scholar
  29. Pérez Domínguez, I. (2006). Greenhouse gases: Inventories, abatement costs and markets for emission permits in European agriculture - A modelling approach. Dissertation, University of Bonn, Germany.Google Scholar
  30. Pérez Domínguez, I., Bezlepkina, I., Heckelei, T., Oude Lansink, A., Romstad, E., & Kannellopoulos, A. (2009). Capturing market impacts of farm level policies: a statistical extrapolation approach using biophysical characteristics and farm resources. Environmental Science and Policy, 12(5) 2009, 588-600.Google Scholar
  31. Pérez Domínguez, I., Gay, S. H., & M’Barek, R. (2008). An integrated model platform for the economic assessment of agricultural policies in the European Union. Agrarwirtschaft, 57(8), 379-385.Google Scholar
  32. Van Ittersum, M. K., Ewert, F., Heckelei, T., Wery, J., Alkan Olsson, J., Andersen, E., et al. (2008). Integrated assessment of agricultural systems - A component-based framework for the European Union (SEAMLESS). Agricultural Systems, 96, 150-165.CrossRefGoogle Scholar
  33. Van Tongeren, F., van Meijl, H., & Surry, Y. (2001). Global models applied to agricultural and trade policies: a review and assessment. Agricultural Economics, 26(2), 149-172.CrossRefGoogle Scholar
  34. Witzke, H.P., Britz, W., & Kuhn, A. (2004). Outlook on selected agricultural variables for the 2005 State of the Environment and the Outlook Report of the European Environmental Agency. Eurocare Final Report to EEA.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Wolfgang Britz
    • 1
    Email author
  • Ignacio Pérez Domínguez
    • 2
  • Thomas Heckelei
    • 1
  1. 1.Institute for Food and Resource Economics, Chair for Economic and Agricultural Policy, University of BonnBonnGermany
  2. 2.Public Issues DivisionAgricultural Economics Research Institute (LEI)The HagueThe Netherlands

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