Advertisement

An Agent-Based Model for Agricultural Supply Chains: The Case of Uganda

  • F. CaravelliEmail author
  • F. MeddaEmail author
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Uganda is a landlocked country in East Africa with a population estimated at 35 million. 85 % of the population still lives in rural areas and survives mainly on subsistence farming by growing crops such as matooke, beans, sweet potatoes, coffee (for export), cassava, maize, millet, groundnuts, sorghum, and sesame. There are many obstacles to moving towards sustainable, market oriented crop production. In this research study, we focus on the effect of logistics costs on crop prices from the farm gate through to markets.

Keywords

Social Network Supply Chain Sweet Potato Large Farmer Road Infrastructure 
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.

Notes

Acknowledgments

We thank the World Bank for the support in the course of this study.

References

  1. 1.
    Alfi, V., Cristelli, M., Pietronero, L., Zaccaria, A.: Mechanisms of self-organization and finite-size effects in a minimal agent based model. J. Stat. Mech. Theory Exp. (2009)Google Scholar
  2. 2.
    Axelrod, R.M.: The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press (1997)Google Scholar
  3. 3.
    Epstein, J.M., Axtell, R.L.: Growing Artificial Societies: Social Science From the Bottom Up. Brookings Institution Press (1996)Google Scholar
  4. 4.
    Helbing, D.: Social Self-Organisation: Agent Based Simulations and Experiments to Study Emergent Social Behaviour. Springer (2012)Google Scholar
  5. 5.
    Schelling, T.C.: Micromotives and Macrobehaviour. WW Norton and Company (2006)Google Scholar
  6. 6.
    Africa Infrastructure Country Diagnostic (n.d.): Uganda: Interactive Infrastructure Atlas. Retrieved from Africa Infrastructure Knowledge ProgramGoogle Scholar
  7. 7.
    Blyde, J., Iberti, G.: A better pathway to export: How the quality of road infrastructure affects export performance. Int. Trade J. 28(1), 3–22 (2014)CrossRefGoogle Scholar
  8. 8.
    Hodges, R., Buzby, J.C., Bennet, B.: Postharvest losses and waste in developed and less developed countries:opportunities to improve resource use. J. Agric. Sci. 149(S1), 37–45 (2011)CrossRefGoogle Scholar
  9. 9.
    Albert, R., Barabasi, A.L.: Rev. Mod. Phys. 74 (2002)Google Scholar
  10. 10.
    Daley, D.J., Kendal, D.G.: Stochastic rumours. J. Inst. Math. Appl. 1 (1965)Google Scholar
  11. 11.
    Caravelli, F., Medda, F.: An Agent-Based Model for Agricultural Supply Chains in Uganda, to appear as a World Bank reportGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Invenia Technical ComputingWinnipegCanada
  2. 2.Department of Computer ScienceUCLLondonUK
  3. 3.London Institute of Mathematical SciencesLondonUK
  4. 4.QASER LabUCLLondonUK

Personalised recommendations