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)
KDF Search Results
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.
The goal of this repository is to promote transparency and ease-of-access to the U.S. Department of Energy Bioenergy Technologies Office (BETO) supported public studies involving techno-economic analysis (TEA). As such, this database summarizes the economic and technical parameters associated with the modeled biorefinery processes for the production of biofuels and bioproducts, as presented in a range of published reports and papers.
on environment friendly and socio-economically sustainable renewable energy sources. However, commercial production of bioenergy is constrained by biomass supply uncertainty and associated costs. This study presents an integrated approach to determining the optimal biofuel supply chain considering biomass yield uncertainty. A two-stage stochastic mixed integer linear programming is utilized to minimize the expected system cost while incorporating yield uncertainty in the strategic level decisions related to biomass production and biorefinery investment.
Perennial grasses are touted as sustainable feedstocks for energy production. Such benefits, however, may be offset if excessive nitrogen (N) fertilization leads to economic and environmental issues. Furthermore, as yields respond to changes in climate, nutrient requirements will change, and thus guidance on minimal N inputs is necessary to ensure sustainable bioenergy production.
Practicing agriculture decreases downstream water quality when compared to non-agricultural lands. Agricultural watersheds that also grow perennial biofuel feedstocks can be designed to improve water quality compared to agricultural watersheds without perennials. The question then becomes which conservation practices should be employed and where in the landscape should they be situated to achieve water quality objectives when growing biofuel feedstocks.
The economic potential for Eucalyptus spp. production for jet fuel additives in the United States: A 20 year projection suite of scenarios ranging from $110 Mg-1 to $220 Mg-1 utilizing the POLYSYS model.
Link to the website with documentation and download instructions for the PNNL Global Change Assessment Model (GCAM), a community model or long-term, global energy, agriculture, land use, and emissions. BioEnergy production, transformation, and use is an integral part of GCAM modeling and scenarios.
Advanced biomass feedstocks tend to provide more non-fuel ecosystem goods and services (ES) than 1st-generation alternatives. We explore the idea that payment for non-fuel ES could facilitate market penetration of advanced biofuels by closing the profitability gap. As a specific example, we discuss the Mississippi-Atchafalaya River Basin (MARB), where 1st-generation bioenergy feedstocks (e.g., corn-grain) have been integrated into the agricultural landscape.