crop
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
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.
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.
Production, Supply and Distribution Data - USDA
This database contains current and historical official USDA data on production, supply and distribution of agricultural commodities for the United States and key producing and consuming countries.
FAOSTAT
FAOSTAT provides time-series and cross sectional data relating to food and agriculture for some 200 countries.
The national version of FAOSTAT, CountrySTAT, is being developed and implemented in a number of target countries, primarily in sub-saharan Africa. It will offer a two-way data exchange facility between countries and FAO as well as a facility to store data at the national and sub-national levels.
A Geographic Perspective on the Current Biomass Resource Availability in the United States
Biomass is receiving increasing attention as scientists, policy makers, and growers search for clean, renewable energy alternatives. Compared with other renewable resources, biomass is very flexible it can be used as fuel for direct combustion, gasified, used in combined heat and power technologies, or biochemical conversions. Due to the wide range of feedstocks, biomass has a broad geographic distribution, in some cases offering a least-cost and near-term alternative. The objective of this research is to estimate the biomass resources available in the United States and map the results. To accomplish this objective, biomass feedstock data are analyzed both statistically and graphically using geographic information systems (GIS). A GIS is a computer-based information system used to create, manipulate, and analyze geographic information, allowing us to visualize relationships, patterns, or trends that are not possible to see with traditional charts, graphs, and spreadsheets. While other biomass resource assessments concentrate on the economic or theoretical availability, this study estimates the technical biomass resources available in the United States (page 59). The estimates are based on numerous assumptions, methodologies adopted from other studies, and factors that relate population to the amount of post-consumer residue generation. The main contribution of this research is that it adds a geographic perspective to biomass research by answering questions such as where the resources are and how much is available.
Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000
Agricultural activities have dramatically altered our planet?s land surface. To understand the extent and spatial distribution of these changes, we have developed a new global data set of croplands and pastures circa 2000 by combining agricultural inventory data and satellite-derived land cover data. The agricultural inventory data, with much greater spatial detail than previously available, is used to train a land cover classification data set obtained by merging two different satellite-derived products (Boston University?s MODIS-derived land cover product and the GLC2000 data set). Our data are presented at 5 min ( 10 km) spatial resolution in longitude by longitude, have greater accuracy than previously available, and for the first time include statistical confidence intervals on the estimates. According to the data, there were 15.0 (90% confidence range of 12.2?17.1) million km2 of cropland (12% of the Earth?s ice-free land surface) and 28.0 (90% confidence range of 23.6?30.0) million km2 of pasture (22%) in the year 2000.
Deriving Comprehensive County-Level Crop Yield and Area Data for U.S. Cropland
Ground-based data on crop production in the USA is provided through surveys conducted by the National Agricultural Statistics Service (NASS) and the Census of Agriculture (AgCensus). Statistics from these surveys are widely used in economic analyses, policy design, and for other purposes. However, missing data in the surveys presents limitations for research that requires comprehensive data for spatial analyses.We created comprehensive county-level databases for nine major crops of the USA for a 16-yr period, by filling the gaps in existing data reported by NASS and AgCensus. We used a combination of regression analyses with data reported by NASS and the AgCensus and linear mixed-effect models incorporating county-level environmental, management, and economic variables pertaining to different agroecozones. Predicted yield and crop area were very close to the data reported by NASS, within 10% relative error. The linear mixed-effect model approach gave the best results in filling 84% of the total gaps in yields and 83% of the gaps in crop areas of all the crops. Regression analyses with AgCensus data filled 16% of the gaps in yields and crop areas of the major crops reported by NASS.