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An Agent-Based Model of Multifunctional Agricultural Landscape Using Genetic Algorithms


  • Soman, Sethuram
  • Misgna, Girmay
  • Kraft, Steven E.
  • Lant, Chris
  • Beaulieu, Jeffrey R.


Landowner characteristics influence his/her willingness to change landuse practices to provide more or less environmental benefits. However, most studies of agricultural/environmental polices identify landowners as homogenous. And, the primary cause of failure of many environmental and other polices is the lack of knowledge on how humans may respond to polices based on changes in their behavior (Stern, 1993). From socioeconomic theory and empirical research, landowners can be identified as individuals who make agricultural landuse decisions independently based on their objectives. Identifying possible classes of landowners, assessing how each would potentially respond to policy alternatives, and the resulting pattern of land uses in a watershed or a riparian corridor would be very useful to policy makers as they evaluated alternatives. Agricultural landscapes are important producers of ecosystem services. The mix of ecosystem services and commodity outputs of an agricultural landscape depends on the spatial pattern of land uses emerging from individual land use decisions. However, many empirical studies show that the production of ecosystem services from agricultural landscapes is declining. This is consistent with research conducted over the last few decades showing there is a narrow range of social circumstances under which landowners are willing to make investments in the present to achieve public benefits in the future through investing in natural capital resulting in public goods which are frequently produced as ecosystem services. In this study an agent-based model within a watershed planning context is used to analyze the tradeoffs involved in producing a number of ecosystem services and agricultural commodities given price and policy scenarios while assuming three different types of agents in terms of their goals. The agents represent landowners who have been divided into a number of different groups based on their goals and the size of their farm operations. The multi-agent-based model is developed using a heuristic search and optimization technique called genetic algorithm (GA) (Holland), which belongs to a broader class of evolutionary algorithms. GAs exhibit three properties (1) they start with a population of solution, (2) they explore the solution space through recombination and mutation and (3) they evaluate individual solutions based on their appropriate fitness value(s), for example given profit maximizing agents this would be gross margin. A GA is a heuristic stochastic search and optimization method, which works by mimicking the evolutionary principles and chromosomal processing in natural genetics. The three economic agents that are modeled are based on variations in their objective functions and constraints. This study will help in identifying the tradeoffs associated with various agents in the provision of ecosystem services and agricultural commodities. The agent model developed here will help policy and decision maker identify the various agents within the watershed and assess various policy options based on that information. The study will also help to understand the interaction and feedback between the agents and their environment associated with various policy initiatives. The results of the study indicate that the agent model correctly predicts the actual landuse landcover map by 75 percent.

Suggested Citation

  • Soman, Sethuram & Misgna, Girmay & Kraft, Steven E. & Lant, Chris & Beaulieu, Jeffrey R., 2008. "An Agent-Based Model of Multifunctional Agricultural Landscape Using Genetic Algorithms," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6142, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea08:6142

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    References listed on IDEAS

    1. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
    2. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    3. Beaulieu, Jeffrey R. & Bennett, David & Kraft, Steven E. & Sengupta, Raja, 1998. "Ecological-Economic Modeling On A Watershed Basis: A Case Study Of The Cache River Of Southern Illinois," 1998 Annual meeting, August 2-5, Salt Lake City, UT 21019, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Matthew Rabin, 1998. "Psychology and Economics," Journal of Economic Literature, American Economic Association, vol. 36(1), pages 11-46, March.
    5. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    6. Maybery, Darryl & Crase, Lin & Gullifer, Chris, 2005. "Categorising farming values as economic, conservation and lifestyle," Journal of Economic Psychology, Elsevier, vol. 26(1), pages 59-72, February.
    7. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 25(2-3), September.
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    More about this item


    Multifunctional agriculture; Agent based modeling; Genetic Algorithm; Environmental Economics and Policy; Land Economics/Use;

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