Dealing with Uncertainty in Agent-Based Simulation: Farm-Level Modeling of Adaptation to Climate Change in Southwest Germany

Climate change will most likely confront agricultural producers with natural, economic, and political conditions that have not previously been observed and are largely uncertain. As a consequence, extrapolation from past data reaches its limits, and a process-based analysis of farmer adaptation is required. Simulation of changes in crop yields using crop growth models is a first step in that direction. However, changes in crop yields are only one pathway through which climate change affects agricultural production. A meaningful process-based analysis of farmer adaptation requires a whole-farm analysis at the farm level. We use a highly disaggregated mathematical programming model to analyze farm-level climate change adaptation for a mountainous area in southwest Germany. Regional-level results are obtained by simulating each full-time farm holding in the study area. We address parameter uncertainty and model underdetermination using a cautious calibration approach and a comprehensive uncertainty analysis. We deal with the resulting computational burden using efficient experimental designs and high-performance computing. We show that in our study area, shifted crop management time slots can have potentially significant effects on agricultural supply, incomes, and various policy objectives promoted under German and European environmental policy schemes. The simulated effects are robust against model uncertainty and underline the importance of a comprehensive assessment of climate change impacts beyond merely looking at crop yield changes. Our simulations demonstrate how farm-level models can contribute to a process-based analysis of climate change adaptation if they are embedded into a systematic framework for treating inherent model uncertainty.