Agriculture is one of the sectors most likely to be impacted by climate change. Agricultural producers have always operated under high levels of production and price risk, but there are concerns that climate change will further exacerbate these risks while making recent historical experience less predictive of future conditions. The impacts are generally expected to increase over time as temperatures become more likely to exceed thresholds that negatively impact crop growth and the distribution of precipitation is increasingly altered. However, there is considerable variation in future climate projections both temporally and spatially as well as differences in responsiveness to climate change across different crops and production practices. Agriculture is a very heterogeneous sector, making it important to incorporate disaggregated biophysical data within economic models used to assess the potential impacts of alternative climate and policy scenarios. To assess potential long-term implications of climate change on landowner decisions regarding land use, crop mix, and production practices, we combine the outputs of global circulation models (GCMs) with the Environmental Policy Integrated Climate (EPIC) crop process model and the Forest and Agricultural Sector Optimization Model (FASOM) economic model. GCMs use assumptions regarding future emissions and atmospheric concentrations of GHGs as model inputs to simulate impacts on the future spatial distribution of temperature and precipitation across the globe. The outputs of the GCMs were then incorporated into EPIC to simulate the impacts of alternative climate scenarios on crop yields over time. Crop growth is simulated by calculating the potential daily photosynthetic production of biomass. Daily potential growth is decreased by stresses caused by shortages of solar radiation, water, and nutrients, by temperature extremes and by inadequate soil aeration. Thus, EPIC can account for the effects of climate-induced changes in temperature, precipitation, and other variables, including episodic events affecting agriculture, on potential yields. The model also includes a nonlinear equation accounting for plant response to CO2 concentration and has been applied in several previous studies of climate change impacts. In this application, we simulated yields for barley, corn, cotton, hay, potatoes, rice, sorghum, soybeans, and wheat under each climate scenario considered. These crop yields were then used as inputs into a stochastic version of FASOM to assess market outcomes given climate-induced shifts in yields that vary by crop and region. The stochastic version of the model is used to model crop allocation decisions by crop and management categories based on the relative returns and risk associated with alternative cropping patterns under each of the modeled scenarios. This enables exploration of potential shifts in cropping patterns within and across regions in response to changing yield distributions as well as the associated price effects. In addition to implications for landowner decisions regarding land use, crop mix, and production practices, changing agricultural risks could potentially affect the performance of risk management strategies such as crop insurance programs. Thus, we also explore the potential implications of changes in yield and price distributions for these insurance markets.