Environmental limitation mapping of potential biomass resources across the conterminous U nited S tates
[1];
- PRISM Climate Group Northwest Alliance for Computational Science and Engineering 2000 Kelley Engineering Center Oregon State University Corvallis OR USA
- Department of Crop and Soil Science Oregon State University 125 Crop Science Building Corvallis OR USA
- Bioenergy Resource and Engineering Systems Group Environmental Sciences Division Oak Ridge National Laboratory PO BOX 2008 MS6036 Oak Ridge TN 37831‐6036 USA
Abstract Several crops have recently been identified as potential dedicated bioenergy feedstocks for the production of power, fuels, and bioproducts. Despite being identified as early as the 1980s, no systematic work has been undertaken to characterize the spatial distribution of their long‐term production potentials in the United states. Such information is a starting point for planners and economic modelers, and there is a need for this spatial information to be developed in a consistent manner for a variety of crops, so that their production potentials can be intercompared to support crop selection decisions. As part of the Sun Grant Regional Feedstock Partnership (RFP), an approach to mapping these potential biomass resources was developed to take advantage of the informational synergy realized when bringing together coordinated field trials, close interaction with expert agronomists, and spatial modeling into a single, collaborative effort. A modeling and mapping system called PRISM‐ELM was designed to answer a basic question: How do climate and soil characteristics affect the spatial distribution and long‐term production patterns of a given crop? This empirical/mechanistic/biogeographical hybrid model employs a limiting factor approach, where productivity is determined by the most limiting of the factors addressed in submodels that simulate water balance, winter low‐temperature response, summer high‐temperature response, and soil pH , salinity, and drainage. Yield maps are developed through linear regressions relating soil and climate attributes to reported yield data. The model was parameterized and validated using grain yield data for winter wheat and maize, which served as benchmarks for parameterizing the model for upland and lowland switchgrass, CRP grasses, Miscanthus, biomass sorghum, energycane, willow, and poplar. The resulting maps served as potential production inputs to analyses comparing the viability of biomass crops under various economic scenarios. The modeling and parameterization framework can be expanded to include other biomass crops.
- Research Organization:
- Oregon State Univ., Corvallis, OR (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office (BETO); United States Dept. of Agriculture (USDA); Oregon State Univ. (United States)
- Grant/Contract Number:
- AC05-00OR22725; FC36-05GO85041; PO 4500073924
- OSTI ID:
- 1417003
- Alternate ID(s):
- OSTI ID: 1423054; OSTI ID: 1460580
- Journal Information:
- Global Change Biology. Bioenergy, Journal Name: Global Change Biology. Bioenergy Vol. 10 Journal Issue: 10; ISSN 1757-1693
- Publisher:
- Wiley-BlackwellCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
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