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Conventional feedstock supply systems exist and have been developed for traditional agriculture and forestry systems. These conventional feedstock supply systems can be effective in high biomass-yielding areas (such as for corn stover in Iowa and plantation-grown pine trees in the southern United States), but they have their limits, particularly with respect to addressing feedstock quality and reducing feedstock supply risk to biorefineries. They also are limited in their ability to efficiently deliver energy crops.

Large-scale modeling systems have long been viewed as potentially valuable tools for evaluating farm policy. They have received increased attention in recent years, in part because of the added complexity of U.S. farm programs and the fuller integration of the U.S. farm sector with nonfarm sectors and world agricultural commodity markets. Instability in the world economy, changed macroeconomic policies, credit and debt positions, and agricultural trade regulations have significant impacts on U.S. agriculture in the short run and more pronounced long-run implications.

This paper examines the impact of biofuel expansion on grain utilization and distribution at the state and cropping district level as most of grain producers and handlers are directly influenced by the local changes. We conducted a survey to understand the utilization and flows of corn, ethanol and its co-products, such as dried distillers grains (DDG) in Iowa. Results suggest that the rapidly expanding ethanol industry has a significant impact on corn utilization in Iowa.

IBSAL is a dynamic simulation model of the connections existing between feedstock producers, biorefinery locations and the requisite storage and distribution systems. The model is primarily focused on the front end of the biofuels supply chain at the local level. The local data sources that are inputs include field area, dry matter, production equipment, soil and biomass moisture, weather conditions, transportation networks and associated costs. The model was developed at Oak Ridge National Laboratory.
This model can be downloaded from