This paper presents an agricultural residue removal decision framework that couples the environmental process models WEPS, RUSLE2, SCI, and DAYCENT. One of the goals of this integrated model is to quantify the impacts of land management strategies on soil organic carbon and CO2 emissions. Soil, climate, and land management practices are considered in determining sustainable residue removal rates using wind- and water-induced soil erosion and qualitative soil organic carbon constraints and to quantify the long-term impacts of sustainable residue removal on soil organic carbon and greenhouse gas emissions. Using this integrated model sustainable residue removal for four crop rotations, three tillage regimes, and four soil types representing nearly 70% of the arable acres in Boone County, Iowa are examined. Each scenario was performed for a twenty-year period. Soil organic carbon and CO2 emission results are aggregated by soil type using crop rotation and tillage statistics. The soil type results are aggregated using a normalized percentage area to provide a county level estimate of soil organic carbon changes and CO2 emissions. Results show that for the largest sustainable residue removal rate that soil organic carbon increased 3.53 - 6.63 Mg/ha over the 20 year simulation and that CO2 emissions ranged from 3.50 - 4.23 Mg/ha across the four soil types resulting in an average increase of soil organic carbon of 4.85 Mg/ha and CO2 emission of 3.77 Mg/ha at the county level.

The notion of renewable energy provides an important mechanism for diversifying an energy portfolio, which ultimately would have numerous benefits including increased energy resilience, reduced reliance on foreign energy supplies, reduced GHG emissions, development of a green energy sector that contributes to economic growth, and providing a sustainable energy supply. The conversion of autotrophic algae to liquid transportation fuels is the basis of several decades of research to competitively bring energy-scale production into reality; however, many challenges still remain for making algal biofuels economically viable. Addressing current challenges associated with algal production systems, in part, requires the ability to assess spatial and temporal variability, rapidly evaluate alternative algal production system designs, and perform large-scale assessments considering multiple scenarios for thousands of potential sites. We introduce the development and application of the Algae Logistics Model (ALM) which is tailored to help address these challenges. The flexible nature of the ALM architecture allows the model to: 1) interface with external biomass production and resource assessment models, as well as other relevant datasets including those with spatiotemporal granularity; 2) interchange design processes to enable operational and economic assessments of multiple design configurations, including the integration of current and new innovative technologies; and 3) conduct trade-off analysis to help understand the site-specific techno-economic trade-offs and inform technology decisions. This study uses the ALM to investigate a baseline open-pond production system determined by model harmonization efforts conducted by the U.S. Department of Energy. Six sites in the U.S. southern-tier were sub-selected and assessed using daily site-specific algae biomass productivity data to determine the economic viability of large-scale open-pond systems. Results show that costs can vary significantly depending on location and biomass productivity and that integration of novel dewatering equipment, order of operations, and equipment scaling can also have significant impacts on economics.

Successful development of a large-scale microalgae-based biofuels industry requires comprehensive analysis and understanding of the feedstock supply chain—from facility siting and design through processing and upgrading of the feedstock to a fuel product. The evolution from pilot-scale production facilities to energy-scale operations presents many multi-disciplinary challenges, including a sustainable supply of water and nutrients, operational and infrastructure logistics, and economic competitiveness with petroleum-based fuels. These challenges are partially addressed by applying the Integrated Assessment Framework (IAF) – an integrated multi-scale modeling, analysis, and data management suite – to address key issues in developing and operating an open-pond microalgae production facility. This is done by analyzing how variability and uncertainty over space and through time affect feedstock production rates, and determining the site-specific “optimum” facility scale to minimize capital and operational expenses. This approach explicitly and systematically assesses the interdependence of biofuel production potential, associated resource requirements, and production system design trade-offs. To provide a baseline analysis, the IAF was applied to a set of sites in the southeastern U.S. with the potential to cumulatively produce 5 billion gallons per year. The results indicate costs can be reduced by scaling downstream processing capabilities to fit site-specific growing conditions, available and economically viable resources, and specific microalgal strains.

The objective of this milestone is to develop an integrated decision support platform for determining the impacts of agricultural and forestry management practices on the environmental and economic performance of a landscape. Vast datasets and many dynamic simulation models are available to evaluate environmental performance parameters, including soil erosion from wind and water, soil carbon dynamics, nitrogen cycles, greenhouse gas fluxes, nutrient runoff, and sediment runoff. The challenges are that these tools (1) are typically designed to consider a limited subset of critical environmental parameters, (2) often operate at different spatial and temporal scales, (3) are built using inconsistent computational approaches, and (4) are often not applicable across all regions and agronomic management systems. The Landscape Environmental Assessment Framework (LEAF) overcomes these challenges and integrates these varied tools into an effective, single computational platform. For the first time, because of LEAF, disparate models and datasets can seamlessly and comprehensively work together to provide decision makers with reliable, site-specific environmental performance assessments at the subfield scale.

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.

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. INTRODUCTION Global initiatives to develop renewable, low carbon energy sources have identified biomass feedstocks as a resource with significant potential [2]. Biomass feedstocks provide a renewable pathway to support liquid transportation fuels [3], and are also being investigated as a low net carbon feedstock for electricity generation. As in many countries, the United States has set national targets for bioenergy production through biofuel and biopower generation [3,4]. Meeting these goals requires development and utilization of biom—ass resources well beyond current production levels. Previous studies have

Agricultural residues have significant potential as a feedstock for bioenergy production, but removing these residues from the land can have negative impacts on soil health. Because of this computational tools are needed that can help guide decisions on the amount of agricultural residue that can be sustainably removed. Models and datasets that can support decisions about sustainable agricultural residue removal are available; however, no tools currently exist that are capable of simultaneously addressing all of the environmental factors that can limit the availability of residue for bioenergy production. This paper presents an integrated framework of models and data that provide a coupled a set of environmental process models and databases that can support agricultural residue removal decisions. Specifically the RUSLE2, WEPS, and Soil Conditioning Index models have been integrated together with the disparate set of databases providing the soils, climate, and management practice data required. The integrated system has been demonstrated for two example cases. In the first case the potential impact of agricultural residue removal is explored. In the second case an aggregate assessment of the agricultural residues available bioenergy production in the state of Iowa is performed.

Agricultural residues have significant potential as a near term source of cellulosic biomass for bioenergy production, but sustainable removal of agricultural residues requires consideration of the critical roles that residues play in the agronomic system. Previous work has developed an integrated model to evaluate sustainable agricultural residue removal potential considering soil erosion, soil organic carbon, greenhouse gas emission, and long-term yield impacts of residue removal practices. The integrated model couples the environmental process models WEPS, RUSLE2, SCI, and DAYCENT. This study uses the integrated model to investigate the impact of interval removal practices in Boone County, Iowa, US. Residue removal of 4.5 Mg/ha was performed annually, bi-annually, and tri-annually and were compared to no residue removal. The study is performed at the soil type scale using a national soil survey database assuming a continuous corn rotation with reduced tillage. Results are aggregated across soil types to provide county level estimates of soil organic carbon changes and individual soil type soil organic matter content if interval residue removal were implemented. Results show interval residue removal is possible while improving soil organic matter. Implementation of interval removal practices provide greater increases in soil organic matter while still providing substantial residue for bioenergymore » production.« less

Research barriers continue to exist in all phases of the emerging cellulosic ethanol biorefining industry. These barriers include the identification and development of a sustainable and abundant biomass feedstock, the assembly of viable assembly systems formatting the feedstock and moving it from the field (e.g., the forest) to the biorefinery, and improving conversion technologies. Each of these phases of cellulosic ethanol production are fundamentally connected, but computational tools used to support and inform analysis within each phase remain largely disparate. This paper discusses the integration of a feedstock assembly system modeling toolkit and an Aspen Plus® conversion process model. Many important biomass feedstock characteristics, such as composition, moisture, particle size and distribution, ash content, etc. are impacted and most effectively managed within the assembly system, but generally come at an economic cost. This integration of the assembly system and the conversion process modeling tools will facilitate a seamless investigation of the assembly system conversion process interface. Through the integrated framework, the user can design the assembly system for a particular biorefinery by specifying location, feedstock, equipment, and unit operation specifications. The assembly system modeling toolkit then provides economic valuation, and detailed biomass feedstock composition and formatting information. This data ismore » seamlessly and dynamically used to run the Aspen Plus® conversion process model. The model can then be used to investigate the design of systems for cellulosic ethanol production from field to final product.« less