Computer simulation models can provide valuable insights for climate-related analysis and help streamline policy interventions for improved adaptation and mitigation in agriculture. Computable general equilibrium (CGE) and partial equilibrium (PE) models are currently being expanded to include land-use change and energy markets so that the effects of various policy measures on agricultural production can be assessed. Agent-based modelling (ABM) or multi-agent systems (MAS) have been suggested as a complementary tool for assessing farmer responses to climate change in agriculture and how these are affected by policies. MAS applied to agricultural systems draw on techniques used for Recursive Farm Programming, but include models of all individual farms, their spatial interactions and the natural environment. In this article, we discuss the specific insights MAS provide for developing robust policies and land-use strategies in response to climate change. We show that MAS are well-suited for uncertainty analysis and can thereby complement existing simulation approaches to advance the understanding and implementation of effective climate-related policies in agriculture.

 Agent-based modelling is a suitable tool for improving the understanding of farmers' behaviour.  Review 20 agricultural ABM addressing heterogeneous decision-making processes in the context of European agriculture.  Considerable scope to improve diversity in representation of decision-making by combining existing modelling approaches.  More coordinated and purposeful combinations of ABM and hybrid modelling approaches are needed.  Results provide an entry point for collaboration of agent-based modellers, agricultural systems modellers and social scientist.

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.