Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Environmental Modelling & Software
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Environmental Modelling & Software
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Environmental Modelling & Software
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We present a bioeconomic modeling approach that links the biophysical crop growth model CropSyst to an economic decision model at field scale. The developed model is used in conjunction with a genetic algorithm to optimize management decisions in potato production in the Broye catchment (Switzerland) in the context of different irrigation policy scenarios. More specifically, we consider the effects of water bans, water quotas, and water prices on water consumption, profitability, and the financial risks of potato production. The use of a genetic algorithm enables the direct integration of the considered decision variables as management input factors in CropSyst. We employ the farmer's certainty equivalent, measured as the expected profit margin minus a risk premium, as the objective function. Using this methodological framework allows us to consider the potential impacts of policy measures on farmers' crop management decisions due to their effects on both expected income levels and income variability. Our results show that the region's current water policy, which frequently prevents irrigation during hot and dry periods by banning water withdrawal, causes high levels of income risk for the farmer and increases the average water demand in potato production. In contrast, the implementation of an appropriate water quota could significantly decrease water consumption in potato production while allowing the farmer's certainty equivalent to remain at the same level as it is under the current irrigation water policy.