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biomass feedstocks

This dataset contains data on agricultural residue production by county from 2022 to 2041. 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 from $50 to $130 per dry ton.

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

Publication Date
DOI
https://doi.org/10.23720/BT23/3017205
Data Source
2023 Billion-Ton Report: An Assessment of U.S. Renewable Carbon Resources
Bioenergy Category
Author(s)
Jin Wook Ro , Maggie R. Davis
isPartOf parent DOI
https://doi.org/10.23720/BT2023/2282885
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This dataset contains data on agricultural crop 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. The dataset was obtained from the database of the BT23 (Davis et al., 2024) for the mature-market medium scenario with biomass market prices from $30 to $130 per dry ton.

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

Publication Date
Organization
Lab
DOI
https://doi.org/10.23720/BT23/3017202
Data Source
2023 Billion-Ton Report: An Assessment of U.S. Renewable Carbon Resources
Bioenergy Category
Author(s)
Jin Wook Ro , Maggie R. Davis
isPartOf parent DOI
https://doi.org/10.23720/BT2023/2282885
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Description: BT23 update using the 2025 baseline and starting results in 2024 for Med $70 with updated budgets. Cost updates include:
• Increased the nitrogen application for the following crops: willow, camelina, carinata, pennycress, and willow.
• Removed roundup during establishment for the following crops: camelina.
• Harvest costs were updated because the combine width was adjusted. This affected the following crops: barley, camelina, carinata, corn, oat, pennycress, rice, sorghum, soybean, and wheat.
• Harvest cost was updated with biomass sorghum because an additional tractor was added to pull the high dump forage wagon, and the wagon width was adjusted to not constrain the harvest operation with the combine.
• Added poplar in the ag budget database for regions 1 and 13.

Because of the file size limit, the datasets are separated by feedstock type. The corresponding feedstock for each file are listed below.
med_crop_bdgt_conv_engy_070_20250914_com_crop_1.zip: Barley, Corn, Cotton, Grain sorghum, Hay
med_crop_bdgt_conv_engy_070_20250914_com_crop_2.zip: Oats, Rice, Soybeans, Wheat
med_crop_bdgt_conv_engy_070_20250914_en_crop.zip: Energy crops
med_crop_carb_070_20250914_com_crop_1.zip: Barley, Corn, Cotton, Grain sorghum, Hay
med_crop_carb_070_20250914_com_crop_2.zip: Oats, Rice, Soybeans, Wheat
med_crop_carb_070_20250914_en_crop.zip: Energy crops
med_crop_econ_070_20250914_com_crop_1.zip: Barley, Corn, Cotton
med_crop_econ_070_20250914_com_crop_2.zip: Grain sorghum, Hay, Oats
med_crop_econ_070_20250914_com_crop_3.zip: Rice, Soybeans
med_crop_econ_070_20250914_com_crop_4.zip: Wheat
med_crop_econ_070_20250914_herb_en_crop.zip: Herbaceous energy crops
med_crop_econ_070_20250914_woody_en_crop.zip: Woody energy crops
med_crop_prod_070_20250914_com_crop_1.zip: Barley, Corn, Cotton, Grain sorghum, Hay
med_crop_prod_070_20250914_com_crop_2.zip: Oats, Rice, Soybeans, Wheat
med_crop_prod_070_20250914_en_crop.zip: Energy crops
med_crop_qnty_070_20250914_com_crop_1.zip: Barley, Corn, Cotton, Grain sorghum, Hay
med_crop_qnty_070_20250914_com_crop_2.zip: Oats, Rice, Soybeans, Wheat
med_crop_qnty_070_20250914_en_crop.zip: Energy crops
med_resd_carb_070_20250914.zip: Agricultural residues
med_resd_econ_070_20250914.zip: Agricultural residues
med_resd_prod_070_20250914.zip: Agricultural residues

Usage Policy
N/A
Publication Date
Project Title
Biomass Resource Supply Analysis
Organization
Lab
Contact Email
langholtzmh@ornl.gov
DOI
10.23720/BT23/2998540
Data Source
POLYSYS
Contact Person
Matthew Langholtz
Bioenergy Category
Author(s)
Ryan Jacobson , Daniel delaTorre Ugarte , Matthew H. Langholtz , Robin Clark , Chad Hellwincke , Craig Brandt , Hope Cook , Maggie Davis , Jin Wook Ro
WBS Project Number
1.1.1.3
isPartOf parent DOI
10.23720/BT2023/2316171
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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.
Key fields within the dataset include equipment data, fertilizer and chemical application details, and seed information. Additionally, the dataset contains cost metrics such as purchase costs and labor costs, enabling users to effectively analyze the financial aspects of crop production.
To enhance understanding of the data, a supplementary spreadsheet is provided, containing field definitions that clarify the terminology and metrics used throughout the dataset.

Publication Date
Organization
Lab
Data Source
2023 Billion-Ton Report: An Assessment of U.S. Renewable Carbon Resources
Author(s)
Craig Brandt , Nicole Jennett , Jin Wook Ro , Maggie Davis
isPartOf parent DOI
10.23720/BT2023/2316171
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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.

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

Publication Date
Organization
Lab
Data Source
2023 Billion-Ton Report: An Assessment of U.S. Renewable Carbon Resources
Bioenergy Category
Author(s)
Jin Wook Ro , Maggie R. Davis , Chad Hellwinckel
isPartOf parent DOI
https://doi.org/10.23720/BT2023/2282885
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OSTI DOI Released
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OSTI DOI Infix

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.

Publication Date
Organization
Lab
Data Source
2023 Billion-Ton Report: An Assessment of the U.S. Renewable Carbon Sources
Bioenergy Category
Author(s)
Jin Wook Ro , Maggie R. Davis , Chad Hellwinckel
isPartOf parent DOI
10.23720/BT2023/2316171
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Abstract: Distributed storage and pre-processing of biomass feedstock at satellite storage locations (storage
depots) has been proposed in literature to reduce costs and improve efficiency of the supply system. The performance of such a system, however, has not yet been rigorously quantified and compared with conventional alternatives. This work presents such an analysis using the BioFeed optimization model. BioFeed is a system-level model that optimizes the important feedstock production activities and determines the optimal system configuration on a regional basis. The BioFeed model was first modified to enable modeling of mechanical pre-processing, such as pelletization and grinding, at the input or the output of storage facilities, which can be mandatory or optional. The model was used to study different Miscanthus production scenarios in southern Illinois. The results showed that making storage pre-processing mandatory increased the total cost by up to 16–53% as compared to the base case. However, it reduced the farmers’ share of the total cost by up to 13–39%. The exact values depended on the particular pre-processing technology and scenario modeled. The most cost-effective system consisted of a combination of pre-processing on the farms as well as at the storage facilities. The study recommended that biomass output from a hammer mill should be the biorefinery delivery specification; the hammer mills should be installed at the input of the storage facilities, but pre-processing at the storage facility should be optional. This led to the minimum total cost of 46.4 $ Mg−1.

Publication Date
Contact Email
kcting@Illinois.edu
Contact Person
K.C. Ting
Contact Organization
University of Illinois at Urbana-Champaign
Bioenergy Category
Author(s)
Shastri, Yogendra
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