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This dataset contains data on agricultural crop and residue production by county in 2041. The agricultural crops in this dataset include barley, corn, cotton, grain sorghum, hay, oats, rice, soybeans, and wheat. The agricultural residues include barley straw, corn stover, oats straw, sorghum stubble, and wheat straw. The dataset was obtained from the database of the BT23 (Davis et al.,2024) for the near-term scenario with biomass market prices of up to $70 per dry ton.

For access to this dataset, please use the contact form and indicate this dataset by name.

Organization:
DOE
Author(s):
Jin Wook Ro , Maggie R. Davis , Chad Hellwinckel

This dataset contains data on forest production. The forestry products in this dataset includes hardwood, softwood, and mixed, and the dataset was obtained from the database of the 2023 Billion-Ton Report (Davis et al., 2024). The intended use is for the Feedstock Production Emissions to Air Model (FPEAM).

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Organization:
DOE
Author(s):
Jin Wook Ro , Maggie Davis , Hope Cook

This dataset contains data on agricultural crop and residue production by county from 2022 to 2041. The agricultural crop in this dataset includes barley, biomass sorghum, corn, cotton, energy cane, eucalyptus, grain sorghum, hay, miscanthus, oats, pine, poplar, rice, soybean, switchgrass, wheat, and willow, and the agricultural residue includes barley straw, corn stover, oats straw, sorghum stubble, and wheat straw. The dataset was obtained from the database of the BT23 (Davis et al., 2024) for the mature-market medium scenario with biomass market prices of up to $70 per dry ton.

Organization:
DOE
Author(s):
Jin Wook Ro , Maggie R. Davis , Chad Hellwinckel

This dataset contains harvesting, chipping, and production cost data for forestland production by region and forest harvest system. The dataset supports Biomass from the forested land base analysis in the BT23 (Davis et al., 2024) and subsequent modeling using the Forest Sustainable and Economic Analysis Model (ForSEAM). The cost data was updated by Burton English and is in 2014 dollars and 2021 dollars.

Author(s):
Burton English , Jin Wook Ro , Lixia Lambert , Maggie Davis , Matthew H Langholtz

Hellwinckel, C., D. de la Torre Ugarte, J. L. Field, and M. Langholtz. 2024. “Appendix C. Appendix to Chapter 5: Biomass from Agriculture.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316182.

Organization:
DOE
Author(s):
Chad Hellwinckel , Daniel DeLaTorre Ugarte , John L Field , Matthew H Langholtz

Davis, M., L. Lambert, R. Jacobson, D. Rossi, C. Brandeis, J. Fried, B. English, et al. 2024. “Appendix B. Appendix to Chapter 4: Biomass from the Forested Land Base.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316181.

Organization:
DOE
Author(s):
Maggie Davis , Lixia Lambert , Ryan Jacobson , David Rossi , Consuelo Brandeis , Burton English , Jeremy Fried

U.S. Department of Energy. 2024. “Chapter 8: Looking Forward and Next Steps.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316179.

Organization:
DOE
Author(s):
Matthew H Langholtz

Chapter 7.2 — Coleman, A., K. Davis, J. DeAngelo, T. Saltiel, B. Saenz, L. Miller, K. Champion, E. Harrison, and A. Otwell. 2024. “Chapter 7.2: Macroalgae.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316176.

Organization:
DOE
Author(s):
Andre Coleman , Kristen Davis , Julianne DeAngelo , Troy Saltiel , Benjamin Saenz , Lee Miller , Kathleen Champion , Eliza Harrison , Anne Otwell

Davis, M., L. Lambert, R. Jacobson, D. Rossi, C. Brandeis, J. Fried, B. English, et al. 2024. “Chapter 4: Biomass from the Forested Land Base.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316170.

Organization:
DOE
Author(s):
Maggie Davis , Lixia Lambert , Ryan Jacobson , David Rossi , Consuelo Brandeis , Jeremy Fried , Burton English , Robert Abt , Karen Abt , Prakash Nepal , Claire O’Dea , Jeffrey Prestemon , Matthew Langholtz

Jacobson, R., and S. Curran. 2024. “Chapter 2: Biomass Currently Used for Energy and Coproducts.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316167.

Organization:
DOE
Author(s):
Ryan Jacobson

Langholtz, M. H. 2024. “Chapter 1: Background and Introduction.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316166.

Organization:
DOE
Author(s):
Matthew H Langholtz

Videos

Organization:
DOE
Author(s):
Matthew H Langholtz , Maggie Davis , Chad Hellwinckel , Daniel DeLaTorre Ugarte , Rebecca Efroymson , Ryan Jacobson , Anelia Milbrandt , Andre Coleman , Ryan Davis , Keith L. Kline , et al.

Biofuels are promoted in the United States through aggressive legislation, as one part of an overall strategy to lessen dependence on imported energy as well as to reduce the emissions of greenhouse gases (Office of the Biomass Program and Energy Efficiency and Renewable Energy, 2008). For example, the Energy Independence and Security Act of 2007 (EISA) mandates 36 billion gallons of renewable liquid transportation fuel in the U.S. marketplace by the year 2022 (U.S. Government, 2007).

Author(s):
Emily Newes, Daniel Inman, Brian Bush

The U.S. Department of Energy (DOE) is promoting the development of ethanol from lignocellulosic feedstocks as an alternative to conventional petroleum-based transportation fuels. DOE funds both fundamental and applied research in this area and needs a method for predicting cost benefits of many research proposals. To that end, the National Renewable Energy Laboratory (NREL) has modeled many potential process designs and estimated the economics of each process during the last 20 years. This report is an update of the ongoing process design and economic analyses at NREL.

Author(s):
Aden, A.

A new addition to the growing biofuels resources list at AgMRC is a cellulosic ethanol feasibility template developed by agricultural economists at Oklahoma State University (OSU). The purpose of the spreadsheet-based template is to give users the opportunity to assess the economics of a commercial-scale plant using enzymatic hydrolysis methods to process cellulosic materials into ethanol. The OSU Cellulosic Ethanol Feasibility Template can be downloaded and modified by the user to mimic the basic operating parameters of a proposed ethanol plant under a variety of production conditions.

Author(s):
Rodney Holcomb

This paper examines the possibilities of breaking into the cellulosic ethanol market in south Louisiana via strategic feedstock choices and the leveraging of the area’s competitive advantages. A small plant strategy is devised whereby the first-mover problem might be solved, and several scenarios are tested using Net Present Value analysis.

Author(s):
Darby, Paul

This paper introduces a spatial bioeconomic model for study of potential cellulosic biomass supply at regional scale. By modeling the profitability of alternative crop production practices, it captures the opportunity cost of replacing current crops by cellulosic biomass crops. The model draws upon biophysical crop input-output coefficients, price and cost data, and spatial transportation costs in the context of profit maximization theory. Yields are simulated using temperature, precipitation and soil quality data with various commercial crops and potential new cellulosic biomass crops.

Author(s):
Egbendewe-Mondzozo, Aklesso

When the lignocellulosic biofuels industry reaches maturity and many types of biomass sources become economically viable, management of multiple feedstock supplies – that vary in their yields, density (tons per unit area), harvest window, storage and seasonal costs, storage losses, transport distance to the production plant – will become increasingly important for the success of individual enterprises. The manager’s feedstock procurement problem is modeled as a multi-period sequence problem to account for dynamic management over time.

Author(s):
Kumarappan, Subbu