Emerging cellulosic bioenergy markets can provide land managers with additional options for crop production decisions. Integrating dedicated bioenergy crops such as perennial grasses and short rotation woody species within the agricultural landscape can have positive impacts on several environmental processes including increased soil organic matter in degraded soils, reduced sediment loading in watersheds, lower green house gas (GHG) fluxes, and reduced nutrient loading in watersheds. Implementing this type of diverse bioenergy production system in a way that maximizes potential environmental benefits requires a dynamic integrated modeling and data management strategy. This paper presents a strategy for designing diverse bioenergy cropping systems within the existing row crop production landscape in the midwestern United States. The integrated model developed quantifies a wide range environmental processes including soil erosion from wind and water, soil organic matter changes, and soil GHG fluxes within a geospatial data management framework. This framework assembles and formats information from multiple spatial and temporal scales. The data assembled includes yield and productivity data from harvesting equipment at the 1m scale, surface topography data from LiDAR mapping at the less than 1m scale, soil data from US soil survey databases at the 10m to 100m scale, and climate data at the county scale. These models and data tools are assembled into an integrated computational environment that is used to determine sustainable removal rates for agricultural residues for bioenergy production at the sub-field scale under a wide range of land management practices. Using this integrated model, innovative management practices including cover cropping are then introduced and evaluated for their impact on bioenergy production and important environmental processes. The impacts of introducing dedicated energy crops onto high-risk landscape positions currently being manage in row crop production are also investigated.