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.
agriculture
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.
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.
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
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.
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.
This dataset contains data on agricultural crop and residue production by county in 2030. 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.
There is an inextricable link between energy production and food/feed/fiber cultivation with available water resources. Currently in the United States, agriculture represents the largest sector of consumptivewater usemaking up 80.7%of the total. Electricity generation in the U.S. is projected to increase by 24 % in the next two decades and globally, the production of liquid transportation fuels are forecasted to triple over the next 25-years, having significant impacts on the import/export market and global economies. The tension between local water supply and demand across water use sectors needs to be evaluated with regards to risk evaluation and planning. To this end, we present a systematic method to spatially and temporally disaggregate nationally available 5-year county-scalewater use data to amonthly 1/8° scale.Our study suggests that while 81.9 % of the U.S. exhibits unstressed local conditions at the annual scale, 13.7 % is considered water scarce; this value increases to 17.3 % in the summer months. The use of mean annualwater scarcity at a coarser basin scale (~373,000 ha)was found to mask information critical for water planning whereas finer spatiotemporal scales revealed local areas that are water stressed or water scarce. Nationally, ~1%of these Bunstressed^ basins actually contained water stressed or water scarce areas equivalent to at least 30 % and 17 %, respectively, of the basin area. These percentages increase to 34 % and 48 % in the summer months. Additionally, 15 % of basins classified as "unstressed" contained water scarce areas in excess of 10 % during the summer.
Increasing demand for crop-based biofuels, in addition to other human drivers of land use, induces direct and indirect land use changes (LUC). Our system dynamics tool is intended to complement existing LUC modeling approaches and to improve the understanding of global LUC drivers and dynamics by allowing examination of global LUC under diverse scenarios and varying model assumptions. We report on a small subset of such analyses. This model provides insights into the drivers and dynamic interactions of LUC (e.g., dietary choices and biofuel policy) and is not intended to assert improvement in numerical results relative to other works.
Demand for food commodities are mostly met in high food and high crop-based biofuel demand scenarios, but cropland must expand substantially. Meeting roughly 25% of global transportation fuel demand by 2050 with biofuels requires >2 times the land used to meet food demands under a presumed 40% increase in per capita food demand. In comparison, the high food demand scenario requires greater pastureland for meat production, leading to larger overall expansion into forest and grassland. Our results indicate that, in all scenarios, there is a potential for supply shortfalls, and associated upward pressure on prices, of food commodities requiring higher land use intensity (e.g., beef) which biofuels could exacerbate.
Biofuels are presented in rich countries as a solution to two crises: the climate crisis and the oil crisis. But they may not be a solution to either, and instead are contributing to a third: the current food crisis.
Meanwhile the danger is that they allow rich-country governments to avoid difficult but urgent decisions about how to reduce consumption of oil, while offering new avenues to continue expensive support to agriculture at the cost of taxpayers. In the meantime, the most serious costs of these policies – deepening poverty and hunger, environmental degradation, and accelerating climate change – are being ‘dumped’ on developing countries.