The perceived lack of dependable feedstock supplies has been cited as a major investment risk in the bioenergy industry. Information on the spatial and temporal variability of feedstock yields is needed to ensure that enough biomass is produced to meet production demands. Regional and site-specific differences in climate, soil type, topography, and management practices can result in significant variations in feedstock yields and quality. Canola, Brassica napus, is a highly versatile oilseed crop and a potential source of biodiesel in the US Great Plains and Pacific Northwest. The spatial and temporal characterization of canola yields in the Great Plains and Pacific Northwest will provide information, not only on the viability of the emerging biodiesel industry, but sustainability of management practices, and the associated environmental impacts. In this preliminary study, we will apply the ALMANAC model to estimate spring and winter canola yields across the US central Great Plains, and their inter-annual variation. Prior to running the model simulations, we will parameterize the ALMANAC model using data from preliminary field experiments conducted at Temple, TX from 2011-2012, data reported in the literature, and expert judgment. The model will be validated using previous experimental data from Akron, CO from 1993-1994 and 2005-2006 and the National Winter Canola Variety Trails at 26 locations in 18 states from 2003-2011. Some of these parameters will later be revised as more detailed data becomes available from field trials conducted at 10 new locations across the study region beginning in 2013. Simulation models are the most efficient and cost-effective way to obtain bioenergy feedstock production estimates, along with realistic assessments of yield variability. Overall, model simulation outcomes will aid bioenergy companies, policy makers, investors, land managers, environmentalists, and the general public to make appropriate land use planning and management decisions regarding this evolving sector of the economy.