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

Shujiang Kang, Sujithkumar Surendran Nair, Keith L. Kline, Jeffrey A. Nichols, Dali Wang, Wilfred M. Post, Craig C. Brandt, Stan D. Wullschleger, Nagendra Singh, Yaxing Wei

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)14-25
Number of pages12
JournalGCB Bioenergy
Issue number1
StatePublished - Jan 2014
Externally publishedYes


  • Biofuel
  • Biomass
  • EPIC
  • Model
  • Soil organic carbon
  • Sustainability


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