Modeling Complex Crop Management-Plant Interactions in Potato Production under Climate Change

  • Niklaus Lehmann
  • Robert Finger
  • Tommy Klein
Conference paper
Part of the Operations Research Proceedings book series (ORP)


High water withdrawals for agricultural purposes during summer months led during the last decades repeatedly to intolerable ecological conditions in surface water bodies in the Broye region located in Western Switzerland. In this study, we assess different irrigation water withdrawal policies with respect to a farmer’s income and to the optimal irrigation and nitrogen fertilization strategies in potato production under current and future expected climate conditions in the Broye region. To this end, we optimize nitrogen and irrigation management decisions in potato production by the use of a bio-economic model and genetic algorithms. Our results show that limiting the water withdrawal amount leads to a substantial decrease in irrigation use, whereas the reduction in a farmer’s income in potato production is supportable.


Irrigation Water Potato Production Water Withdrawal Policy Scenario Certainty Equivalent 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gen, M., Cheng, R.: Genetic Algorithms and Engineering Optimization. Wiley-Interscience, (2000)Google Scholar
  2. 2.
    Bundesgesetz vom 24. Januar 1991 ber den Schutz der Gewsser (Gewsserschutzgesetz, GSchG)Google Scholar
  3. 3.
    Finger, R., Schmid, S.:Modeling Agricultural Production Risk and the Adaptation to Climate Change. Agricultural Finance Review. 68, 25–41. 2008Google Scholar
  4. 4.
    Muehlberger de Preux, C.: Broye: Fish or Chips?. UMWELT. 02/08, 26–27 (2008)Google Scholar
  5. 5.
    Musshoff, O., Hirschauer, N.: Optimizing Production Decisions Using a Hybrid Simulation–Genetic Algorithm Approach. Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie. 57, 35–54 (2009)CrossRefGoogle Scholar
  6. 6.
    Semenov, M.A. and Barrow, E.M.: Use of a StochasticWeather Generator in the Development of Climate Change Scenarios. Climatic Change. 35, 397–414 (1997)CrossRefGoogle Scholar
  7. 7.
    Stoeckle, C.O. and Donatelli, M. and Nelson, R.: CropSyst, a Cropping Systems Simulation Model. European Journal of Agronomy. 18, 289–307 (2003)CrossRefGoogle Scholar
  8. 8.
    Torriani, D.S. and Calanca, P. and Schmid, S. and Beniston, M. and Fuhrer, J.: Potential effects of changes in mean climate and climate variability on the yield of winter and spring crops in Switzerland. Climate Research. 34, 59–369 (2007)CrossRefGoogle Scholar
  9. 9.
    Wall, M.: GAlib: A C++ Library of Genetic Algorithm Components. Technical report, Massachusetts Institute of Technology, (1996)Google Scholar
  10. 10.
    Weber, M., Schild, A.: Stand der Bewsserung in der Schweiz - Bericht zur Umfrage 2006, (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute for Environmental DecisionsETH ZurichZurichSwitzerland
  2. 2.Agroscope Reckenholz-Taenikon ART Research StationZurichSwitzerland

Personalised recommendations