Background/Question/Methods The recent U.S. Renewable Fuel Standard calls for 36 billion gallons of ethanol production by 2022 with over half produced from plant biomass. The goals of our research are to evaluate different empirical modeling approaches of feedstock crop yields and assess the sensitivity of biomass feedstock production to climate change. Switchgrass (Panicum virgatum L.), a warm season perennial grass native to North America was chosen as model bioenergy species. Yield data from 1,345 observation points associated with 37 field trial locations across the United States were gathered from 21 reference papers.  We compared different modeling approaches, including generalized linear models, generalized additive models, and recursive partitioning to explore the relationships between switchgrass yield and environmental variables. Explanatory variables include climate variables, soil type, and origin of the switchgrass cultivar. We used PRISM climate data on minimum and maximum temperature (tmin and tmax) and precipitation (ppt) to model variation in switchgrass biomass yields as a function of climate. Climate data were summarized by month, growing season (April-September), and year before harvesting. To determine the most relevant explanatory variables we performed independent second order polynomial regressions between switchgrass biomass yield explanatory variables and determined their predictive power based on the coefficient of determination (R²). 
Results/Conclusions Preliminary results show that average March tmin and tmax, followed by average February tmin and tmax had the strongest association with switchgrass biomass yields. These results suggest that winter/early spring temperature may influence winter survival and the length of the growing season, which might be especially important for the northern distribution of the species. Annual precipitation followed by April precipitation and average growing season precipitation had the strongest associations with switchgrass yield. Future modeling efforts will use a climate-envelope approach to model switchgrass yields as a function of climate and soils and will incorporate downscaled GCM data for future climate change scenarios from the Community Climate System Model (CCSM) to predict potential changes in switchgrass productivity.