This study developed a computational strategy that utilizes data inputs from multiple spatial scales to investigate how variability within individual fields can impact sustainable residue removal for bioenergy production. Sustainable use of agricultural residues for bioenergy production requires consideration of the important role that residues play in limiting soil erosion and maintaining soil C, health, and productivity. Increased availability of subfield-scale data sets such as grain yield data, high-fidelity digital elevation models, and soil characteristic data provides an opportunity to investigate the impacts of subfield-scale variability on sustainable agricultural residue removal. Using three representative fields in Iowa, this study contrasted the results of current NRCS conservation management planning analysis with subfield-scale analysis for rake-andbale removal of agricultural residue. The results of the comparison show that the field-average assumptions used in NRCS conservation management planning may lead to unsustainable residue removal decisions for significant portions of some fields. This highlights the need for additional research on subfield-scale sustainable agricultural residue removal including the development of real-time variable removal technologies for agricultural residue.

Environmentally benign, economically viable, and socially acceptable agronomic strategies are needed to launch a sustainable lignocellulosic biofuel industry. Our objective was to demonstrate a landscape planning process that can ensure adequate supplies of corn (Zea mays L.) stover feedstock while protecting and improving soil quality. The Landscape Environmental Assessment Framework (LEAF) was used to develop land use strategies that were then scaled up for five U.S. Corn Belt states (Nebraska, Iowa, Illinois, Indiana, and Minnesota) to illustrate the impact that could be achieved. Our results show an annual sustainable stover supply of 194 million Mg without exceeding soil erosion T values or depleting soil organic carbon [i.e., soil conditioning index (SCI)>0] when no-till, winter cover crop, and vegetative barriers were incorporated into the landscape. A second, more rigorous conservation target was set to enhance soil quality while sustainably harvesting stover. By requiring erosion to be <1/2 T and the SCI-organic matter (OM) subfactor to be >0, the annual sustainable quantity of harvestable stover dropped to148 million Mg. Examining removal rates by state and soil resource showed that soil capability class and slope generally determined the effectiveness of the three conservation practices and the resulting sustainable harvest rate. This emphasizes that sustainable biomass harvest must be based on subfield management decisions to ensure soil resources are conserved or enhanced, while providing sufficient biomass feedstock to support the economic growth of bioenergy enterprises.

Biomass provides a renewable pathway to support current and future energy needs for liquid transportation fuels, and is also being investigated as a low net carbon feedstock for electricity generation. To leverage this renewable source of energy requires the development and utilization of biomass resources beyond the current production levels. One source of renewable biomass energy feedstock is agricultural residues. However, a recent study[1] identified six factors that limit sustainable agricultural residue removal, and it stated that a comprehensive assessment of sustainable residue removal limits must consider each of the six factors. These factors are: (1) soil organic carbon, (2) wind and water erosion, (3) plant nutrient balances, (4) soil water and temperature dynamics, (5) soil compaction, and (6) off-site environmental impacts. Each of these factors is described by a set of disparate and heterogeneous models that are not currently integrated together. In addition each of the models has been validated, developed, and is currently maintained by a subject area expert separate from the other models. Recoding the complete set of models into a single monolithic software structure is impractical due to the time needed to develop and validate the completed set of models. Instead an extensible software framework is needed that can integrate the model set together enabling analysis and optimization of agricultural residue harvest for energy usage. This paper presents an integrated modeling strategy that incorporates these model sets together with the needed GIS information within a single integrated computational engineering framework. This integrated computational engineering framework has been implemented to facilitate high fidelity spatial assessments of biomass resource management. A case study demonstrating initial implementations of the resulting interactive analysis and optimization framework is presented. The case study demonstrates how multiple constraints can be simultaneously considered as a part of assessing sustainable agricultural residue removal potential.

Abstract. Nitrogen application is a standard practice for maximizing productivity of an agronomic system. The challenge is that many commercial scale agricultural systems are inefficient in utilizing the nitrogen that is applied. Therefore, understanding the impact of land management practices on nitrogen use inefficiencies within the agroecosystem is critical. This paper presents an integrated model that quantifies the impact of various land management practices on specific agroecosystem units. This integrated model is composed of the Wind Erosion Prediction System (WEPS), the Revised Universal Soil Loss Equation, Version 2 (RUSLE2), the Soil Condition Index (SCI), and the daily CENTURY model, DAYCENT. The integrated model was used to determine the impact of land management strategies on greenhouse gas emissions and nitrate leaching in a 60.5 ha field in Webster County, Iowa, USA. It was found that nitrogen use efficiency can vary significantly across a field and that integrated land management strategies can reduce overall nitrogen losses.

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. 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 intr...