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Title:   Author  item
                                            FIELD, JOHN L - Colorado State Universityitem
                                            MARX, ERNIE - Colorado State Universityitem
                                            EASTER, MARK - Colorado State Universityitem
                                            Adler, Paulitem
                                            PAUSTIAN, KEITH - Colorado State University
  Submitted to: Global Change Biology Bioenergy
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 10/7/2015
                                    Publication  Date: 2/29/2016
                                    Citation: Field, J., Marx, E., Easter, M., Adler, P.R., Paustian, K. 2016. Ecosystem model parameterization and adaptation for sustainable cellulosic biofuel landscape design. Global Change Biology Bioenergy. doi: 10.1111/gcbb.12316.Interpretive Summary: Multiple factors can affect the carbon footprint of biofuels, such as soil type, land use, and management intensity, however there are few examples of available data to calibrate across this range of factors. We compiled a dataset of switchgrass field trials in the United States with yield, changes in soil carbon, and soil nitrous oxide emissions, spanning a range of climates, soil types, and management conditions to calibrate and validate our model. We found that the switchgrass yields and greenhouse gas emissions varied greatly across a landscape large enough to supply the biorefinery in response to variations in soil type, land use history, and management intensity, providing the bioorefinery significant opportunities to minimize the carbon footprint of their feedstock. These results demonstrate the value of this modeling approach to identify strategies to mitigate greenhouse gas emissions and minimize the carbon footprint of the bioenergy feedstock production.
                                Technical Abstract: Renewable fuel standards in the US and elsewhere mandate the production of large quantities of cellulosic biofuels with low greenhouse gas (GHG) footprints, a requirement which will likely entail extensive cultivation of dedicated bioenergy feedstock crops on marginal agricultural lands.  Performance data for such systems is sparse, and non-linear interactions between the feedstock species, agronomic management intensity, and underlying soil and land characteristics complicate the development of sustainable landscape design strategies for low-impact commercial-scale feedstock production.  Process-based ecosystem models are valuable for extrapolating field trial results and making predictions of productivity and associated environmental impacts that integrate the effects of spatially variable environmental factors across diverse production landscapes.  However, there are few examples of parameterizing such models against field trials on both prime and marginal lands or conducting landscape-scale analyses at sufficient resolution to capture interactions between soil type, land use, and management intensity.  In this work we used a data-rich, multi-criteria approach to parameterize and validate the DayCent ecosystem biogeochemistry model for upland and lowland switchgrass using data on yields, soil carbon changes, and soil nitrous oxide emissions from US field trials spanning a range of climates, soil types, and management conditions. We then conducted a high-resolution case study analysis of a real-world cellulosic bioenergy landscape in Kansas in order to estimate feedstock production potential and associated direct biogenic GHG emissions footprint.  Our results suggest that switchgrass yields and emissions balance can vary greatly across a landscape large enough to supply a biorefinery in response to variations in soil type and land use history, but that within a given land base both of these performance factors can be widely modulated by changing management intensity.  This in turn implies a large bioenergy landscape design space within which a system can be optimized to meet economic or environmental objectives.


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