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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.

For analyzing sustainability of algal biofuels, we identify 16 environmental indicators that fall into six categories: soil quality, water quality and quantity, air quality, greenhouse gas emissions, biodiversity, and productivity. Indicators are selected to be practical, widely applicable, predictable in response, anticipatory of future changes, independent of scale, and responsive to management.

Author(s):
R. A. Efroymson
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

Adding bioenergy to the U.S. energy portfolio requires long‐term profitability for bioenergy producers and
long‐term protection of affected ecosystems. In this study, we present steps along the path toward evaluating both sides of
the sustainability equation (production and environmental) for switchgrass (Panicum virgatum) using the Soil and Water
Assessment Tool (SWAT). We modeled production of switchgrass and river flow using SWAT for current landscapes at a

We quantify the emergence of biofuel markets and its impact on U.S. and world agriculture for the coming decade using the multi-market, multi-commodity international FAPRI (Food and Agricultural Policy Research Institute) model. The model incorporates the trade-offs between biofuel, feed, and food production and consumption and international feedback effects of the emergence through world commodity prices and trade.

Author(s):
Fabiosa,Jacinto F.

Land-use changes are frequently indicated to be one of the main human-induced factors influencing the groundwater system. For land-use change, groundwater research has mainly focused on the change in water quality thereby neglecting changes in quantity. The objective of this paper is to assess the impact of land-use changes, from 2000 until 2020, on the hydrological balance and in particular on groundwater quantity, as results from a case study in the Kleine Nete basin, Belgium.

Author(s):
Dams, J.

This study presents the results of comparing land use estimates between three different data sets for the Upper Mississippi River Basin (UMRB). The comparisons were performed between the U.S. Department of Agriculture (USDA) Natural Resource Conservation Service (NRCS) National Resource Inventory (NRI), the U.S. Geological Survey (USGS) National Land Cover Data (NLCD) database, and a combined USDA National Agricultural Statistics Service (NASS) Agricultural Census – NLCD dataset created to support applications of the Hydrologic Unit Model for the U.S. (HUMUS).

Author(s):
Santhi, Chinnisamy

National interests in greater energy independence, concurrent with favorable market forces, have driven increased production of corn-based ethanol in the United States and research into the next generation of biofuels. The trend is changing the national agricultural landscape and has raised concerns about potential impacts on the nation?s water resources. This report examines some of the key issues and identifies opportunities for shaping policies that help to protect water resources.

Author(s):
Schnoor, Jerald

Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S. The model is specifically developed for the analysis of land use in small regions (e.g., a watershed or province) at a fine spatial resolution.

Author(s):
Verburg,P.H.