SummaryCorn stover is the residue that is left behind after corn grain harvest. We have constructed a life-cycle model that describes collecting corn stover in the state of Iowa, in the Midwest of the United States, for the production and use of a fuel mixture consisting of 85% ethanol/15% gasoline (known as "E85") in a flexible-fuel light-duty vehicle. The model incorporates results from individual models for soil carbon dynamics, soil erosion, agronomics of stover collection and transport, and bioconversion of stover to ethanol.Limitations in available data forced us to focus on a scenario that assumes all farmers in the state of Iowa switch from their current cropping and tilling practices to continuous production of corn and "no-till" practices. Under these conditions, which maximize the amount of collectible stover, Iowa alone could produce almost 8 billion liters per year of pure stover-derived ethanol (E100) at prices competitive with today's corn-starchderived fuel ethanol. Soil organic matter, an important indicator of soil health, drops slightly in the early years of stover collection but remains stable over the 90-year time frame studied. Soil erosion is controlled at levels within tolerable soil-loss limits established for each county in Iowa by the U.S. Department of Agriculture.We find that, for each kilometer fueled by the ethanol portion of E85, the vehicle uses 95% less petroleum compared to a kilometer driven in the same vehicle on gasoline. Total fossil energy use (coal, oil, and natural gas) and greenhouse gas emissions (fossil CO 2 , N 2 O, and CH 4 ) on a life-cycle basis are 102% and 113% lower, respectively. Air quality impacts are mixed, with emissions of CO, NOx, and SOx increasing, whereas hydrocarbon ozone precursors are reduced.This model can serve as a platform for future discussion and analysis of possible scenarios for the sustainable production of transportation fuels from corn stover and other agricultural residues.

Process-based model analyses are often used to estimate changes in soil organic carbon (SOC), particularly at regional to continental scales. However, uncertainties are rarely evaluated, and so it is difficult to determine how much confidence can be placed in the results. Our objective was to quantify uncertainties across multiple scales in a processbased model analysis, and provide 95% confidence intervals for the estimates. Specifically, we used the Century ecosystem model to estimate changes in SOC stocks for US croplands during the 1990s, addressing uncertainties in model inputs, structure and scaling of results from point locations to regions and the entire country. Overall, SOC stocks increased in US croplands by 14.6 Tg C yr À1 from 1990 to 1995 and 17.5 Tg C yr À1 during 1995 to 2000, and uncertainties were AE 22% and AE 16% for the two time periods, respectively. Uncertainties were inversely related to spatial scale, with median uncertainties at the regional scale estimated at AE 118% and AE 114% during the early and latter part of 1990s, and even higher at the site scale with estimates at AE 739% and AE 674% for the time periods, respectively. This relationship appeared to be driven by the amount of the SOC stock change; changes in stocks that exceeded 200 Gg C yr À1 represented a threshold where uncertainties were always lower than AE 100%. Consequently, the amount of uncertainty in estimates derived from process-based models will partly depend on the level of SOC accumulation or loss. In general, the majority of uncertainty was associated with model structure in this application, and so attaining higher levels of precision in the estimates will largely depend on improving the model algorithms and parameterization, as well as increasing the number of measurement sites used to evaluate the structural uncertainty.

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