The United States is the leading consumer of corn worldwide. In 2024/2025, the U.S. consumed about 318.277 million metric tons of corn. China ranked second with a consumption volume of 316 million metric tons. That year, the EU consumed about 78 million metric tons of corn in that year. Corn production in the U.S. Corn for grain makes up about a 27.5 percent share of all U.S. crop area harvested in 2022, meaning that corn has the second largest crop area in the United States. By contrast, corn for silage only makes up a two percent share of the total U.S. crop area. In 2022, approximately 13.7 billion bushels of corn for grain were produced in the United States. The vast majority of corn grown in the United States is enhanced with biotechnology. Corn utilization in the U.S. Though it is a popular and widely available vegetable in the United States, most of the corn grown in the United States is processed into ethanol, used as animal feed, or used to manufacture high fructose corn syrup. Of the 20 billion metric bushels of corn utilized in the United States in 2020/2021, about five billion metric bushels went to the production of ethanol and other by-products. In 2021, the average American consumed 4.3 pounds of fresh sweet corn, a decrease from about 9.2 pounds of sweet corn in 2010.

What?

A dataset containing 313 total variables from 33 secondary sources. There are 261 unique variables, and 52 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021.

Why?

Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders.

How?

Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo.

What is 'new' or corrected in version 2?

Edited/amended: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster.

Added: 55 variables, mostly from the 2022 Ag Census.

Omitted: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column.

CRediT: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K

Acknowledgements: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance.

This data product provides three Excel file spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat).

Using Futures Prices to Forecast the Season-Average Price and Counter-Cyclical Payment Rate for Corn, Soybeans, and Wheat

Farmers and policymakers are interested in the level of counter-cyclical payments (CCPs) provided by the 2008 Farm Act to producers of selected commodities. CCPs are based on the season-average price received by farmers. (For more information on CCPs, see the ERS 2008 Farm Bill Side-By-Side, Title I: Commodity Programs.)

This data product provides three Excel spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat). Users can view the model forecasts or create their own forecast by inserting different values for futures prices, basis values, or marketing weights. Example computations and data are provided on the Documentation page.

Spreadsheet Models

For each of the three major U.S. field crops, the Excel spreadsheet model computes a forecast for:

1. the national-level season-average price received by farmers and
2. the implied counter-cyclical payment rate.

Note: the model forecasts are not official USDA forecasts. See USDA's World Agricultural Supply and Demand Estimates for official USDA season-average price forecasts. See USDA's Farm Service Agency information for official USDA CCP rates.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Webpage with links to Excel files For complete information, please visit https://data.gov.

In 2024/2025, it is expected that the United States will be the largest producer of corn worldwide with a production volume amounting to about ***** million metric tons. China and Brazil rounded off the top corn producing countries. Corn production Corn, also known as maize, is a grain plant cultivated for food. The origin of this grain remains unknown, however, many historians believe that corn was first domesticated in Mexico's Tehuacan Valley. Types of corn include sweet corn, popcorn, pod corn, flint corn, flour corn, waxy corn and dent corn. Corn is one of the most important crops in the United States. Over the last years, the country's corn farmers experienced constant increases in annual revenues. In 2022/23, the U.S. was responsible for almost one-third of the global corn production. Iowa and Illinois were the top U.S. states based on harvested area of corn for grain in 2023. That year, Iowa's corn for grain production value amounted to approximately ***** million acres. In 2022/23, the United States exported around **** million metric tons of corn, making the nation the world's second largest corn exporter. Mexico and China were the leading buyers of U.S. corn in 2022, purchasing approximately *** million bushels and *** million bushels respectively.

What?

A dataset containing 313 total variables from 33 secondary sources. There are 261 unique variables, and 52 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021.

Why?

Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders.

How?

Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo.

What is 'new' or corrected in version 2?

Edited/amended: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster.

Added: 55 variables, mostly from the 2022 Ag Census, and v2.1 added a .pdf file with descriptives of data sources and years, and a .sav file.

Omitted: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column.

CRediT: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K

Acknowledgements: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance.

The grain price margins between buyers and sellers (i.e., basis spread) is influenced by the infrastructure used to transport crops from collection points to ports, which can be disrupted by weather extremes like floods and severe storms. Such disruptions are expected to become more frequent, potentially increasing food insecurity and impacting farm incomes. On average, the U.S. accounts for one-third of global corn and soybean production from 2012/13 to 2020/21, so the infrastructure to move crops from the main growing region to the nation’s ports is critical to global crop and food markets. Despite the critical nature of these issues, there is limited research specifically examining the effects of weather extremes on the U.S. crop transportation network. This study investigates how weather extremes disrupt crop transportation networks, and, in turn, how those disruptions affect the basis spread of corn and soybeans. It uses basis spread data from nearly 5,000 U.S. midwestern corn and soybean elevators spanning from 2012 to 2020, along with natural disaster declarations to represent weather extremes affecting crop transportation. Using a three-step process, it calculates least cost transportation routes to a port, adjusts for weather disruptions, and integrates disaster, transportation cost, and control variables into a fixed effects, panel data model that explains variation in basis spread. Results show natural disasters, particularly flash floods and winter storms, negatively affect basis spread. The cost effects of natural disasters disrupting crop transportation routes further decrease basis spread. Strengthening crop transportation infrastructure to withstand flooding and winter storms could reduce disruptions in this network. These findings underscore the value of Federal and State policies that prioritize investments in resilient transportation infrastructure, particularly in regions prone to flash floods and winter storms. Strengthening this infrastructure could not only reduce the economic costs of weather disruptions but also affect farm income and food security.

NWISRL South Farm Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network in Kimberly, Idaho We report N2O emissions along with CO2 and CH4 from a silage corn (2013)–barley (2014)–alfalfa (2015) rotation under conventional tillage and sprinkler irrigation. The main study objectives were to evaluate the effectiveness of an enhanced-efficiency fertilizer (SuperU; stabilized granular urea with urease and nitrification inhibitors) to reduce N2O emissions when compared to granular urea, and determine GHG emissions from fall-applied dairy manure or composted dairy manure and spring-applied dairy manure. Nitrogen treatments were only applied during the first two years of the study. Compared to urea, SuperU plots emitted 53% less N2O during the monitoring period with corn, while no N2O emission reductions occurred in 2014 with barley. The N2O-N emission losses as a percentage of total N applied were 0.21% and 0.04% for urea and SuperU in 2013, respectively, with losses of 0.05% from both urea fertilizers in 2014. On average, N2O fluxes from fall and spring manure were statistically similar and greater than the other N treatments in 2014, and there was a lasting manure treatment effect on emissions when under alfalfa. Carbon dioxide fluxes, on average, were greatest from fall- and spring-applied manure during the first two years of study. Methane fluxes were negative on average, indicating microbial oxidation, and no differences occurred among the N treatments. Silage corn, barley grain, and alfalfa yields were statistically similar among all N treatments. This work demonstrates that SuperU can potentially reduce N2O emissions from irrigated cropping systems in the semiarid western United States while not affecting crop yields.