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While the US Midwest is expected to serve as a primary feedstock source for cellulosic biofuel production, the impacts of residue harvesting on soil organic carbon (SOC) may greatly limit sustainable production capacity. However, viable feedstock production could be realized through adoption of management practices and cropping systems that offset residue-harvest- induced SOC losses. Sequestration of SOC can be enhanced by increasing the duration of crop soil cover through cover or double cropping or cultivation of dedicated perennials. However, assessing the efficacy of such options across sites and over long periods is experimentally challenging. Hence, we use the Environmental Productivity Integrated Climate (EPIC) model to provide such an assessment. Model-data integration was used to calibrate and evaluate model suitability, which exhibited reasonable effectiveness through R² of 0.97 and 0.63 for SOC stock and yield, respectively. Long-term simulations indicate considerable capacity for offsetting SOC loss. Incorporating rye (Secale cereal L.) into continuous corn (Zea mays L.) and corn--soybean [Glycine max (L.) Merr.] systems offset the SOC losses induced by harvesting 21.2 and 38.3% of available stover, respectively. Similarly, converting 20.4% of corn--soybean land to miscanthus (Miscanthus x giganteus J.M. Greef & Deuter ex Hodkinson & Renvoize) or 27.5% of land to switchgrass (Panicum virgatum L.) offset the SOC impacts of harvesting 60% of stover from the remaining corn--soybean lands. These responses indicate that adoption of such measures would sizably affect the life cycle consequences of residue-derived biofuels and expand estimates of sustainable cellulosic feedstock production capacity from the US Midwest. [ABSTRACT FROM AUTHOR]

Abstract: Process‐based models are increasingly used to study agroecosystem interactions and N2O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. This work provides an evaluation of simulated N2O flux from three process‐based models: DayCent, DNDC, and EPIC. The models were calibrated and validated using data collected from two research sites over five years that represent cropping systems and nitrogen fertilizer management strategies common to dairy cropping systems. We also evaluated the use of a multi‐model ensemble strategy, which inconsistently outperformed individual model estimations. Regression analysis indicated a cross‐model bias to underestimate high magnitude daily and cumulative N2O flux. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, and we found significant variation in model estimates of heterotrophic respiration, denitrification, soil NH4+, and soil NO3−, which may indicate that additional types of observed data are required to evaluate model performance and possible biases. Our results suggest a bias in the model estimation of N2O flux from agroecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development. [ABSTRACT FROM AUTHOR]