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The biobased economy is playing an increasingly important role in the American economy.

Through innovations in renewable energies and the emergence of a new generation of biobased products, the sectors that drive the biobased economy are providing job creation and economic growth. To further understand and analyze trends in the biobased economy, this report compares 2011 and 2016 report data.

Publication Year
Organization
Contact Email
GoldenJ17@ecu.edu
Attachment
Contact Person
Dr. Jay S. Golden
Contact Organization
East Carolina University
Bioenergy Category
Author(s)
Jay S. Golden , Robert Handfield , Janire Pascual-Gonzalez , Ben Agsten , Taylor Brennan , Lina Khan , Emily True

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.

Publication Year
Organization
Contact Email
Chris.Daly@oregonstate.edu
DOI
10.1111/gcbb.12496
Contact Person
Christopher Daly
Contact Organization
Oregon State University
Bioenergy Category
Author(s)
Christopher Daly , Michael D. Halbleib , David B. Hannaway , Laurence M. Eaton
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.
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