OOS 46-1 - SEIMF: A spatially-explicit integrative modeling framework to evaluate the productivity and sustainability of biofuel crop production systems

Friday, August 12, 2011: 8:00 AM
16B, Austin Convention Center
Xuesong Zhang1, R. Cesar Izaurralde1, David Manowitz2, Tristram O. West1, Wilfred M. Post3, Allison M. Thomson1, Prasad Bandaru1, Jeff Nichols3 and Jimmy Williams4, (1)Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, (2)Pacific Northwest National Laboratory, (3)Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, (4)Blackland Research & Extension Center, AgriLIFE Research, Temple, TX
Background/Question/Methods

The potential expansion of biofuel production raises food, energy, and environmental challenges that require careful assessment of the impact of biofuel production on greenhouse gas (GHG) emissions, soil erosion, nutrient loading, and water quality.  In this study, we describe a spatially-explicit integrative modeling framework (SEIMF) to understand and quantify the environmental impacts of different biomass cropping systems. This SEIMF consists of three major components: 1) a geographic information system (GIS)-based data analysis system to define spatial modeling units with resolution of 56 m to address spatial variability, 2) the biophysical and biogeochemical model EPIC (Environmental Policy Integrated Climate) applied in a spatially-explicit way to predict biomass yield, GHG emissions, and other environmental impacts of different biofuel crops production systems, and 3) an evolutionary multi-objective optimization algorithm for exploring the trade-offs between biofuel energy production and unintended ecosystem-service responses.

Results/Conclusions

The SEIMF developed in this study is expected to provide a useful tool for scientists and decision makers to understand sustainability issues associated with the production of biofuels at local, regional, and national scales.

The major functions of the SEIMF are illustrated when applied to a 9-county Regional Intensive Modeling Area (RIMA) in SW Michigan to 1) simulate biofuel crop production, 2) compare impacts of management practices and local ecosystem settings, and 3) optimize the spatial configuration of different biofuel production systems by balancing energy production and other ecosystem-service variables. Potential applications of the SEIMF to support life cycle analysis and provide information on biodiversity evaluation and marginal-land identification are also discussed.

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