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Biofuel crops may help achieve the goals of energy-efficient renewable ethanol production and greenhouse gas ( GHG) mitigation through carbon (C) storage. The objective of this study was to compare the aboveground biomass yields and soil organic C ( SOC) stocks under four crops (no-till corn, switchgrass, indiangrass, and willow) 7 years since establishment at three sites in Ohio to determine if high-yielding biofuel crops are also capable of high levels of C storage. Corn grain had the highest potential ethanol yields, with an average of more than 4100 L ha−1, and ethanol yields increased if both corn grain and stover were converted to biofuel, while willow had the lowest yields. The SOC concentration in soils under biofuels was generally unaffected by crop type; at one site, soil in the top 10 cm under willow contained nearly 13 Mg C ha−1 more SOC (or 29% more) than did soils under switchgrass or corn. Crop type affected SOC content of macroaggregates in the top 10 cm of soil, where macroaggregates in soil under corn had lower C, N and C : N ratios than those under perennial grasses or trees. Overall, the results suggest that no-till corn is capable of high ethanol yields and equivalent SOC stocks to 40 cm depth. Long-term monitoring and measurement of SOC stocks at depth are required to determine whether this trend remains. In addition, ecological, energy, and GHG assessments should be made to estimate the C footprint of each feedstock. [ABSTRACT FROM AUTHOR]

Switchgrass (Panicum virgatum L.) is a C4, warm season, perennial native grass that has been strongly recommended as an ideal biofuel feedstock. Accurate forecasting of switchgrass yield across a geographically diverse region and under future climate conditions is essential for determining realistic future ethanol production from switchgrass. This study compiled a switchgrass database through reviewing the existing literature from field trials across the U.S. Using observed switchgrass data, a process-based model (ALMANAC) was developed. The ALMANAC simulation results showed that crop management had more effect on yield than location. The ALMANAC model consists of functional relationships that provide a better understanding of interactions among plant physiological processes and environmental factors (water, soil, climate, and nutrients) giving realistic predictions in different climate conditions. This model was used to quantify the impacts of climate change on switchgrass yields. Simulated lowland switchgrass would have more yield increases between Illinois and Ohio in future (2021–2050) under both Representative Concentration Pathway (RCP) 4.5 and 8.5 pathways with low N fertilizer inputs than high N fertilizer inputs. There was no significant effect of climate variability on upland simulated yields, which means that N fertilization is a key factor in controlling upland switchgrass yields under future climate conditions. [ABSTRACT FROM AUTHOR]