KDF Search Results

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Agricultural crop and residue production by county - near-term scenario

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

Conventional forest product data

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.

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

Agricultural crop and residue production by county - mature-market medium scenario

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

2021 Forestland Biomass Production Costs for the 2024 Billion Ton Analysis

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

Appendix C. Appendix to Chapter 5: Biomass from Agriculture

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

Appendix B. Appendix to Chapter 4: Biomass from the Forested Land Base

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

Chapter 8: Looking Forward and Next Steps

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: Macroalgae

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

Chapter 4: Biomass from the Forested Land Base

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

Chapter 2 Biomass Currently Used for Energy and Coproducts

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

Chapter 1: Background and Introduction

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

2023 Billion-Ton Report

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.

Stochastic Optimization of Cellulosic Biofuel Supply Chain under Feedstock

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.

Author(s):
Sharma, B. P. , T. E. Yu , B. C. English , C. Boyer , J. A. Larson

Influences of nitrogen fertilization and climate regime on the above-ground biomass yields of miscanthus and switchgrass: A meta-analysis☆

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.

Organization:
DOE
Author(s):
Huaihai Chen , Zhongmin Dai , Henriette I. Jager , Stan D. Wullschleger , Jianming Xu , Christopher W. Schadt
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

Avoiding Conflicts between Future Freshwater Algae Production and Water Scarcity in the United States at the Energy-Water Nexus

Sustainable production of algae will depend on understanding trade-offs at the energy-water nexus. Algal biofuels promise to improve the environmental sustainability profile of renewable energy along most dimensions. In this assessment of potential US freshwater production, we assumed sustainable production along the carbon dimension by simulating placement of open ponds away from high-carbon-stock lands (forest, grassland, and wetland) and near sources of waste CO 2 .

Organization:
DOE
Author(s):
Henriette I. Jager , Rebecca A. Efroymson , Latha M. Baskaran
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

Designing bioenergy landscapes to protect water quality

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.

Organization:
DOE
Author(s):
Jasmine A.F. Kreig , Herbert Ssegane , Indrajeet Chaubey , Maria C. Negri , Henriette I. Jager
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

Visualizing feedstock siting in biomass production: Tradeoffs between economic and water quality objectives

New domestic, renewable energy resources must be considered to increase energy security in the U.S. Ethanol production through second-generation (cellulosic) feedstocks will help the U.S. meet the legislative Renewable Fuel Standard, which mandates 36 billion gallons of renewable fuels by 2022. However, conversion of cropland to meet the cellulosic feedstock production goals may have unforeseen environmental consequences.

Author(s):
David E. Gorelick , Latha M. Baskaran , Henriëtte I. Jager

Effect of Land Use Change for Bioenergy Production on Feedstock Cost and Water Quality.

Producing renewable fuel from dedicated energy crops, such as switchgrass, has the potential to generate localized environmental benefits. This study uses high-resolution spatial data for west Tennessee to quantify the effects of producing switchgrass for cellulosic ethanol on the grey water footprint (GWF), or the amount of freshwater needed to dilute nitrate leachate to a safe level, relative to existing agricultural production.

Author(s):
Zhong, J. , T. E. Yu , C. D. Clark , B. C. English , J. A. Larson , C. L. Cheng

Genetic variation and expected per cycle biomass yield gain in lowland switchgrass.

Switchgrass (Panicum virgatum L.), a native of the North American prairies, has been selected for bioenergy research. With a focus on biomass yield improvement, this study aim (i) to estimate the genetic variation in biomass yield and important agronomic traits in ‘Alamo’, (ii) to determine correlations between biomass yield and agronomic traits, and (iii) to compare efficiency of phenotypic selection from a sward plot and advanced cycle half-sibs (ACHS) on the basis of space-plant performance.

Author(s):
Dalid, C. , A. M. Saxton , F. L. Allen , V. R. Pantalone , S. Nayak , H. Bhandari