Optimizing Wheat Management towards Climate Change: A Genetic Algorithms Approach

Niklaus Lehmann, Robert Finger, and Tommy Klein


Genetic Algorithms, Agricultural Modelling, Climate Change Adaptation, Natural Resource Management


Climate change will alter crop growth conditions in Europe. Therefore, farmers are expected to change their management decisions in order to minimize negative climate change impacts on their income and their production risks. In this paper, we optimize management options in winterwheat production in western Switzerland. We maximize a farmer's utility through the optimization of nitrogen fertilization and irrigation strategies under different climate scenarios. To this end, the crop growth model CropSyst is linked with a developed economic decision that takes profit margin and production risk into account and agricultural management decisions are optimized using genetic algorithms (GAs). Our results show that under future expected climate conditions less nitrogen fertilizer is used and fertilization events take earlier place. Irrigation, however, is even under a climate change scenario for the period 2080-2099 not a profitable adaptation measure. Although winterwheat yields decrease under future climate conditions and the chosen optimal management decisions up to 20%, the farmers' financial losses remain in all climate change scenarios under 15%. This affirms that negative climate change impacts on wheat production in Switzerland are rather small if possible adaptation measures in the agricultural management are taken into account.

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