Agricultural residues are the largest potential near term source of biomass for bioenergy production. Sustainable use of agricultural residues for bioenergy production requires consideration of the important role that residues play in maintaining soil health and productivity. Innovation equipment designs for residue harvesting systems can help economically collect agricultural residues while mitigating sustainability concerns. A key challenge in developing these equipment designs is establishing sustainable reside removal rates at the sub-field scale. Several previous analysis studies have developed methodologies and tools to estimate sustainable agricultural residue removal by considering environmental constraints including soil loss from wind and water erosion and soil organic carbon at field scale or larger but have not considered variation at the sub-field scale. This paper introduces a computational strategy to integrate data and models from multiple spatial scales to investigate how variability of soil, grade, and yield within an individual cornfield can impact sustainable residue removal for bioenergy production. This strategy includes the current modeling tools (i.e., RUSLE2, WEPS, and SCI), the existing data sources (i.e., SSURGO soils, CLIGEN, WINDGEN, and NRCS managements), and the available high fidelity spatial information (i.e., LiDAR slope and crop yield monitor output). Rather than using average or representative values for crop yields, soil characteristics, and slope for a field, county, or larger area, the modeling inputs are based on the same spatial scale as the precision farming data available. There are three challenges for developing an integrated model for sub-field variability of sustainable agricultural residue removal—the computational challenge of iteratively computing with 400 or more spatial points per hectare, the inclusion of geoprocessing tools, and the integration of data from different spatial scales. Using a representative field in Iowa, this paper demonstrates the computational algorithms used and establishes key design parameters for an innovative residue removal equipment design concept.