This page provides supporting information (SI) prepared for the manuscript "Winter rye biomass can be an abundant and affordable US energy resource." It summarizes research developed through collaboration between USDA and Penn State winter rye subject matter experts and economists from ORNL's Bioresource Science and Engineering Group.
The residue retention requirement represents the minimum quantity of crop residue (dry tons per acre per year) that must remain on the field to satisfy soil sustainability constraints, including maintenance of soil organic carbon and limits on wind and water erosion. See Metadata description for residue retention coefficients.pdf for more detailed information.
The Ag Budget Operations Table presents a detailed compilation of operations along with their corresponding parameters and material inputs for both conventional and energy crops used in the Billion Ton 2023 study. This dataset encompasses a range of activities, including land preparation, planting, fertilization, pest management, land maintenance, and harvesting.
The Ag Budget Operations Table presents a detailed compilation of operations along with their corresponding parameters and material inputs for both conventional and energy crops used in the Billion Ton 2023 study. This dataset encompasses a range of activities, including land preparation, planting, fertilization, pest management, land maintenance, and harvesting.
Yield (i.e., tons of biomass per acre per year) is a key driver of production potential for many biomass resources. For agricultural resources, crop- and county-specific yields are an input to the economic modeling used to assess biomass production capacity in the BT23. Yields for agricultural biomass resources were derived from field trials from the Sun Grant Initiative Regional Feedstock Partnership, which served the basis for calibration of county yields (see 2016 Billion-Ton Report section 4.2.4)
This International Feedstocks data portal supports the Global Biomass Resource Assessment, a multi-country government-led initiative dedicated to advancing the global transition to a bioeconomy. This product shares data assembled from citable sources around the globe, as reported for current biomass production as well as potential additional future production in some cases. Data were compiled into consistent classes based on the most recent reports received (ranging from 2018 to 2024).
This write up summarizes the potential for biobased adhesives to be sourced from various material, specifically focusing on the following relevant factors:
1. Current biomass availability,
2. Market costs
3. Locations of industry/supply
4. Projections on how these materials will increase in availability according to their expected increased uses.
This project contributes to understanding and enhancing socioeconomic and environmental benefits of biofuels through modeling the effect of prices and policy incentives on fuel markets for “hard-to-decarbonize” transportation sectors. The main analytical tool used in this project is the BioTrans model, originally developed to assess and quantify the economic and energy security benefits of biofuels for light-duty vehicles and bioproducts.
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).
If you would also like access to this dataset, please use the "contact" button for a request to our research staff.
This dataset contains data on agricultural crop production. The agricultural crop in this dataset includes barley, corn, cotton, grain sorghum, hay, oats, rice, soybeans, and wheat, and the dataset was obtained from the database of the 2023 Billion-Ton Report (Davis et al., 2024) for the Feedstock Production Emissions to Air Model (FPEAM).
For access to this dataset, please use the contact form and indicate the dataset by name.
This dataset provides additional variables for modelers and other interested stakeholders for yield assumptions for modeled energy crops on agricultural land in the CONUS, as modeled by the POLYSYS model.
The yield unit was changed from lb/ac to dt/ac post-processing.
V0.1 changes include: tillage for subclass like 'energy crop' is now '[null]' and for subclass = 'Intermediate oilseeds' 'till' is now 'CT' (Conventional Tillage), format now uses pipe (|) delimiter.
This dataset includes waster resources prepared for BT23 Chapter 3. Please access the data through the BT23 Data Portal or directly at https://bioenergykdf.ornl.gov/bt23-wastes-download
Please cite as:
Milbrandt, A., and A. Badgett. 2024, Data from Biomass from waste streams, of Chapter 3 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (bioenergyKDF)Data Center, https://doi.org/10.23720/BT2023/2282886
This dataset includes ForSEAM and BioSUM model output prepared for BT23 Chapter 4, as well as USDA-FS Forest Inventory Analysis datasets used to calculate waste biomass from the forested land base. Please access the data through the BT23 Data Portal or directly at https://bioenergykdf.ornl.gov/bt23-forestry-download
A-customized-dataset-for-national-timberland-resources-modeled-with-ForSEAM
This dataset includes longitudinal measurements of water quality in four streams and rivers across the United States that were collected using the AquaBOT, an unmanned surface vehicle equipped with water quality sensors developed as part of a BETO-funded project ('Spatially resolved measurements of water quality indicators within a bioenergy landscape'). Measured water quality indicators include: nitrate concentration, temperature, specific conductivity, dissolved oxygen, turbidity, chlorophyll, and pH.
Short Rotation Woody Crop Production Scenarios Simulated for Idaho National Laboratory-ORNL Collaborations, June 2021.
Simulations under this dataset were targeted to a specific fuelshed in Iowa.
Integrated land management (ILM) applications were targeted under this research, although the results of these simulations are at the county level; downscaling post-processing will be applied.