Sustainable Farming Behaviours: An Agent Based Modelling and LCA Perspective

Part of the Understanding Complex Systems book series (UCS)


The paper is focused on the application of ABM (Agent Based Models) to simulate the evolution of the agricultural system of the Grand Duchy of Luxembourg, which aims at the evaluation of the potential environmental impacts arising from policy implementation, following the methodology known as Consequential Life Cycle Assessment (CLCA). The novelty of our approach is on the multi-modeling consideration of the problem of how to evaluate potential environmental impact of farmer’s behaviours. We consider the coupling of a computational model (ABM) and a matrix-based LCA model. The paper only presents preliminary results, exploring the influence of farmers’ environmental awareness on the environmental impacts linked to farming activities. This is possible thanks to the attribution to the agents’ profiles of one specific feature which simulates their “green consciousness level”.


Life Cycle Assessment Agent Base Model Life Cycle Impact Assessment Green Consciousness Life Cycle Assessment Result 
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.



Luxembourg’s National Research Fund (FNR) is acknowledged for the financial support of project MUSA with id: C12/SR/4011535.


  1. 1.
    Berger, T.: Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agric. Econ. 25, 245–260 (2001)CrossRefGoogle Scholar
  2. 2.
    Bert, F.E., Rovere, S.L., Macal, C.M., North, M.J., Podestá, G.P.: Lessons from a comprehensive validation of an agent based-model: the experience of the pampas model of argentinean agricultural systems. Ecol. Model. 273, 284–298 (2014)CrossRefGoogle Scholar
  3. 3.
    Bert, F., North, M., Rovere, S., Tatara, E., Macal, C., Podestá, G.: Simulating agricultural land rental markets by combining agent-based models with traditional economics concepts: the case of the argentine pampas. Environ. Model. Softw. 71, 97–110 (2015).
  4. 4.
    Berta, F.E., Podestá, G.P., Rovere, S.L., Menéndez, A.N., North, M., Tatarad, E., Laciana, C.E., Weber, E., Toranzo, F.R.: An agent based model to simulate structural and land use changes in agricultural systems of the argentine pampas. Ecol. Model. 222, 3486–3499 (2011)CrossRefGoogle Scholar
  5. 5.
    Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. 99 (Suppl. 3), 7280–7287 (2002)CrossRefGoogle Scholar
  6. 6.
    Cooper, J.S., Fava, J.A.: Life-cycle assessment practitioner survey: summary of results. J. Ind. Ecol. 10 (4), 12–14 (2006)CrossRefGoogle Scholar
  7. 7.
    European Commission: Commision Regulation (EC) No 1242/2008 of 8 December 2008 establishing a Community typology for agricultural holdings. Official Journal of the European Union (2008)Google Scholar
  8. 8.
    Filatova, T., Parker, D., Van der Veen, A.: Agent-based urban land markets: agent’s pricing behavior, land prices and urban land use change. J. Artif. Soc. Soc. Simul. 12 (1), 3 (2009)Google Scholar
  9. 9.
    Goedkoop, M., Heijungs, R., Huijbregts, M., De Schryver, A., Struijs, J., van Zelm, R.: Recipe 2008. A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level. Report I: Characterisation, 1 (2009)Google Scholar
  10. 10.
    Happe, K., Kellermann, K., Balmann, A.: Agent-based analysis of agricultural policies: an illustration of the agricultural policy simulator AgriPoliS, its adaptation, and behavior. Ecol. Soc. 11 (1), 329–342 (2006). CrossRefGoogle Scholar
  11. 11.
    Howitt, R.: Positive mathematical programming. Am. J Agr. Econ. 77, 329–342 (1995)CrossRefGoogle Scholar
  12. 12.
    ISO: Environmental management—Life cycle assessment—Principles and framework. ISO 14040:2006, International Organization for Standardization, Geneva (2010)Google Scholar
  13. 13.
    KTBL: Faustzahlen für die Landwirtschaft (in German). ISO, Kuratorium für Technik und Bauwesen in der Landwirtschaft, Darmstadt (2006)Google Scholar
  14. 14.
    Le, Q.B., Park, S.J., Vlek, P.L., Cremers, A.B.: Land-use dynamic simulator (LUDAS): a multi-agent system model for simulating spatio-temporal dynamics of coupled human–landscape system. I. Structure and theoretical specification. Ecol. Inform. 3 (2), 135–153 (2008)Google Scholar
  15. 15.
    Marvuglia, A., Benetto, E., Rege, S., Jury, C.: Modelling approaches for consequential life-cycle assessment (c-lca) of bioenergy: critical review and proposed framework for biogas production. Renew. Sust. Energ. Rev. 25, 768–781 (2013)CrossRefGoogle Scholar
  16. 16.
    Murray-Rust, D., Robinson, D.T., Guillem, E., Karali, E., Rounsevell, M.: An open framework for agent based modelling of agricultural land use change. Environ. Model. Softw. 61, 19–38 (2014).
  17. 17.
    OMG: OMG system modeling language. (2012)
  18. 18.
    Parker, D.C., Hessl, A., Davis, S.C.: Complexity, land-use modeling, and the human dimension: Fundamental challenges for mapping unknown outcome spaces. Geoforum 39 (2), 789–804 (2008)CrossRefGoogle Scholar
  19. 19.
    Rege, S., Arenz, M., Marvuglia, A., Vázquez-Rowe, I., Benetto, E., Igos, E., Koster, D.: Quantification of agricultural land use changes in consequential Life Cycle Assessment using mathematical programming models following a partial equilibrium approach. J. Environ. Inform. 26 (2), 12–139 (2015)Google Scholar
  20. 20.
    Schreinemachers, P., Berger, T.: An agent-based simulation model of human-environment interactions in agricultural systems. Environ. Model. Softw. 26 (7), 845–859 (2011).
  21. 21.
  22. 22.
  23. 23.
    Vázquez-Rowe, I., Rege, S., Marvuglia, A., Thénie, J., Haurie, A., Benetto, E.: Application of three independent consequential LCA approaches to the agricultural sector in luxembourg. Int. J. Life Cycle Assess. 18 (8), 1593–1604 (2013)CrossRefGoogle Scholar
  24. 24.
    Vázquez-Rowe, I., Marvuglia, A., Rege, S., Benetto, E.: Applying consequential LCA to support energy policy: land use change effects of bioenergy production. Sci. Total Environ. 472, 78–89 (2014)CrossRefGoogle Scholar
  25. 25.
    Weidema, B., Bauer, C., Hischier, R., Mutel, C., Nemecek, T., Reinhard, J., Vadenbo, C., Wernet, G.: The ecoinvent database: overview and methodology, data quality guideline for the ecoinvent database version 3 (2013). Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Luxembourg Institute of Science and Technology (LIST)BelvauxLuxembourg

Personalised recommendations