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Global simulation of bioenergy crop productivity: analytical framework and case study for switchgrass

A global energy crop productivity model that provides geospatially explicit quantitative details on biomass
potential and factors affecting sustainability would be useful, but does not exist now. This study describes a
modeling platform capable of meeting many challenges associated with global-scale agro-ecosystem modeling.
We designed an analytical framework for bioenergy crops consisting of six major components: (i) standardized
natural resources datasets, (ii) global field-trial data and crop management practices, (iii) simulation units and
management scenarios, (iv) model calibration and validation, (v) high-performance computing (HPC) simulation,
and (vi) simulation output processing and analysis. The HPC-Environmental Policy Integrated Climate
(HPC-EPIC) model simulated a perennial bioenergy crop, switchgrass (Panicum virgatum L.), estimating feedstock
production potentials and effects across the globe. This modeling platform can assess soil C sequestration,
net greenhouse gas (GHG) emissions, nonpoint source pollution (e.g., nutrient and pesticide loss), and energy
exchange with the atmosphere. It can be expanded to include additional bioenergy crops (e.g., miscanthus,
energy cane, and agave) and food crops under different management scenarios. The platform and switchgrass
field-trial dataset are available to support global analysis of biomass feedstock production potential and corresponding
metrics of sustainability.

Contact Person
Keith L. Kline
Contact Organization
Oak Ridge National Laboratory
Contact Phone
Contact Email
Bioenergy Category
Publication Date
DOI is live on OSTI.
Data Source
GCB Bioenergy
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.