The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). It allows you to customize your query by commodity, location, or time period. You can then visualize the data on a map, manipulate and export the results as an output file compatible for updating databases and spreadsheets, or save a link for future use. Quick Stats contains official published aggregate estimates related to U.S. agricultural production. County level data are also available via Quick Stats. The data include the total crops and cropping practices for each county, and breakouts for irrigated and non-irrigated practices for many crops, for selected States. The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. The county data includes totals for the Agricultural Statistics Districts (county groupings) and the State. The download data files contain planted and harvested area, yield per acre and production. NASS develops these estimates from data collected through:

hundreds of sample surveys conducted each year covering virtually every aspect of U.S. agriculture

the Census of Agriculture conducted every five years providing state- and county-level aggregates Resources in this dataset:Resource Title: Quick Stats database. File Name: Web Page, url: https://quickstats.nass.usda.gov/ Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey data. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search.

This dataset contains estimates of proportional area of 18 major crops for each county in the United States at roughly decadal time steps between 1840 and 2017, and was used for analyses of historical changes in crop area, diversity, and distribution published in:Crossley, MS, KD Burke, SD Schoville, VC Radeloff. (2020). Recent collapse of crop belts and declining diversity of US agriculture since 1840. Global Change Biology (in press).The original data used to curate this dataset was derived by Haines et al. (ICPSR 35206) from USDA Agricultural Census archives (https://www.nass.usda.gov/AgCensus/). This dataset builds upon previous work in that crop values are georeferenced and rectified to match 2012 county boundaries, and several inconsistencies in the tabular-formatted data have been smoothed-over. In particular, smoothing included conversion of values of production (e.g. bushels, lbs, typical of 1840-1880 censuses) into values of area (using USDA NASS yield data), imputation of missing values for certain crop x county x year combinations, and correcting values for counties whose crop totals exceeded the possible land area.Please contact the PI, Mike Crossley, with any questions or requests: mcrossley3@gmail.com

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

The Cropland Data Layer (CDL), hosted on CropScape, provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. The data is created annually using moderate resolution satellite imagery and extensive agricultural ground truth. Users can select a geographic area of interest or import one, then access acreage statistics for a specific year or view the change from one year to another. The data can be exported or added to the CDL. The information is useful for issues related to agricultural sustainability, biodiversity, and land cover monitoring, especially due to extreme weather events. Resources in this dataset:Resource Title: CropScape and Cropland Data Layer - National Download. File Name: Web Page, url: https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php Downloads available as zipped files at https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php --

National CDL's -- by year, 2008-2020. Cropland Data Layer provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. National Cultivated Layer -- based on the most recent five years (2013-2020). National Frequency Layer -- the 2017 Crop Frequency Layer identifies crop specific planting frequency and are based on land cover information derived from the 2008 through 2020CDL's. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat. National Confidence Layer -- the Confidence Layer spatially represents the predicted confidence that is associated with that output pixel, based upon the rule(s) that were used to classify it. Western/Eastern/Central U.S.

Visit https://nassgeodata.gmu.edu/CropScape/ for the interactive map including tutorials and basic instructions. These options include a "Demo Video", "Help", "Developer Guide", and "FAQ".

This dataset includes sample data for the United States to run the weakly supervised framework as described in the paper titled A weakly supervised framework for high resolution crop yield forecasts, accessible at https://doi.org/10.48550/arXiv.2205.09016 The updated paper (including results from the US) is published in Environmental Research Letters: https://doi.org/10.1088/1748-9326/acf50e The software implementation of the machine learning baseline is available at: https://github.com/BigDataWUR/MLforCropYieldForecasting/tree/weaksup. Data 1. County data (county-data.zip) for county-level strongly supervised models: * CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022). * CSSF_COUNTY_US.csv: Crop productivity indicators including total above-ground production (kg ha-1), total weight of storage organs (kg ha-1), development stage (0-2). Source: de Wit et al. (2022). * METEO_COUNTY_US.csv: Meteo data including maximum, minimum, average daily air temperature (℃); sum of daily precipitation (PREC) (mm); sum of daily evapotranspiration of short vegetation (ET0) (Penman-Monteith, Allen et al., (1998)) (mm); climate water balance = (PREC - ET0) (mm). Source: Boogaard et al. (2022). * REMOTE_SENSING_COUNTY_US.csv: Fraction of Absorbed Photosynthetically Active Radiation (Smoothed) (FAPAR). Source: Copernicus GLS (2020). * SOIL_COUNTY_US.csv: Soil water holding capacity. Source: WISE Soil Property Database (Batjes, 2016). * YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022). 2. 10-km grid data (grid-data.zip) for grid-level strongly supervised models: * COUNTY_GRIDS_US.csv: Mapping between counties and grids. * CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level (similar to county data above). * METEO_GRIDs_US.csv: Meteo data at 10km grid level (similar to county data above). * REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level (similar to county data above). * SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level (similar to county data above). * YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha-1). Source: Deines et al. (2021), Lobell et al. (2020). 3. County labels and 10-km grid inputs (dscale-US.zip) for weak supervision: * COUNTY_GRIDS_US.csv: Mapping between counties and grids. * CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level. * METEO_GRIDs_US.csv: Meteo indicators at 10km grid level. * REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level. * SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level. * YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha-1). Source: Deines et al. (2021). * YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022). * CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).

I) SUMMARY

This database contains harmonized time series for the study of crop yields using remote sensing data and meteorological data. We collected information on soybean, corn, and wheat yields (t/ha) over the CONUS (continuous US) from USDA-NASS for years 2015–2018 at a county level, and collocated time series for the following variables:

• Enhanced Vegetation Index (EVI) from MODIS satellite (MOD13C1 v6 product)
• Soil Moisture (SM) from SMAP satellite through MT-DCA algorithm
• Vegetation Optical Depth (VOD) from SMAP satellite through MT-DCA algorithm
• Maximum temperature (TMAX) from Daymet v3
• Precipitation (PRCP) from Daymet v3

II) CONTACT

For questions, please email Laura Martínez-Ferrer at laura.martinez-ferrer@uv.es

III) DATABASE

For each crop type, we provided CSV files containing the time series of the variables and yield described above. Furthermore, additional information for spatial and temporal identification such as a county identifier and a year are included. Lastly, country-shapefiles (.shp) are added for geospatial representation. Further details in readme.txt file.

IV) CITE

We kindly encourage to cite the following works if this database is used

L. Martínez-Ferrer, M. Piles, G. Camps-Valls, Crop Yield Estimation and Interpretability With Gaussian Processes, IEEE Geoscience and Remote Sensing Letters, 2020, vol. 18, no 12, p. 2043-2047, DOI: 10.1109/LGRS.2020.3016140

A. Mateo-Sanchis, J. E. Adsuara, M. Piles, J. Muñoz-Marí, A. Pérez-Suay and G. Camps-Valls, "Interpretable Long-Short Term Memory Networks for Crop Yield Estimation," in IEEE Geoscience and Remote Sensing Letters, DOI: 10.1109/LGRS.2023.3244064

This data set contains annual county-level estimates of total atrazine use on 16 agricultural crops and four agricultural land uses between 1992 and 2007. For each year, atrazine use was estimated for all counties in the conterminous U.S. (except California) by combining (1) proprietary data from the DMRKynetec (DMRK) AgroTrak database on the mass of atrazine applied annually to agricultural crops, (2) county harvested crop acreage, from the 1992, 1997, 2002, and 2007 U.S. Department of Agriculture (USDA) Censuses of Agriculture, and (3) annual harvested crop acreage from the National Agriculture Statistics Service (NASS) for non-Census years between 1992 and 2007. Annual DMRK estimates of pesticide use on individual crops were derived from surveys of major field crops and selected specialty crops in multicounty areas referred to as Crop Reporting Districts (CRD). The CRD-level atrazine-use estimates were disaggregated to obtain county-level application rates by dividing the mass (pounds) of pesticides applied to a crop by the acreage of that crop in the CRD, to yield a rate per harvested acre. When atrazine-use estimates were not available for a CRD, crop, or year, an estimated CRD-level rate was developed following a hierarchy of decision rules that checked first for the availability of a crop application rate from surveyed atrazine application rate(s) for adjacent CRDs for a specific year, and second, the rates from surveyed CRDs within for USDA Farm Production Regions for a specific year or multiple years. The estimation method applied linear interpolation to estimate crop acreage for years when harvested acres for a crop and county were not reported in either the Census of Agriculture or the NASS database, but were reported by these data sources for other years for that crop and county. Data on atrazine use for the counties in California were obtained from farmers' reports of pesticide use collected and published by the California Department of Pesticide Regulation-Pesticide Use Reporting (DPR-PUR) because these data are more complete than DMRK survey data. National and state annual atrazine-use totals derived by this method were compared with other published pesticide-use estimates and were highly correlated. The method developed is designed to be applicable to other pesticides for which there are similar data; however, for some pesticides that are applied to specialty crops, fewer surveys are usually available to estimate application rates and there are a greater number of years with unreported crop acreage, potentially resulting in greater uncertainty in use estimates.

Annual crop data from 1972 to 1998 are now available on EOS-WEBSTER. These data are county-based acreage, production, and yield estimates published by the National Agricultural Statistics Service. We also provide county level livestock, geography, agricultural management, and soil properties derived from datasets from the early 1990s.

 The National Agricultural Statistics Service (NASS), the statistical
 arm of the U.S. Department of Agriculture, publishes U.S., state, and
 county level agricultural statistics for many commodities and data
 series. In response to our users requests, EOS-WEBSTER now provides 27
 years of crop statistics, which can be subset temporally and/or
 spatially. All data are at the county scale, and are only for the
 conterminous US (48 states + DC). There are 3111 counties in the
 database. The list includes 43 cities that are classified as
 counties: Baltimore City, MD; St. Louis City, MO; and 41 cities in
 Virginia.

 In addition, a collection of livestock, geography, agricultural
 practices, and soil properties variables for 1992 is available through
 EOS-WEBSTER. These datasets were assembled during the mid-1990's to
 provide driving variables for an assessment of greenhouse gas
 production from US agriculture using the DNDC agro-ecosystem model
 [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776;
 Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data
 (except nitrogen fertilizer use) were all derived from publicly
 available, national databases. Each dataset has a separate DIF.

 The US County data has been divided into seven datasets.

 US County Data Datasets:

 1) Agricultural Management
 2) Crop Data (NASS Crop data)
 3) Crop Summary (NASS Crop data)
 4) Geography and Population
 5) Land Use
 6) Livestock Populations
 7) Soil Properties

This dataset provides county-level data for Nitrogen fertilizer applied to county croplands [1000 kg N/yr]. This includes only those crops used in an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. Cropland area statistics are from the National Agricultural Statistical Service (NASS) for 1990 for most crops, though some are 1992 data from the Census of Agriculture. Data represent total of irrigated and non-irrigated areas. (see NASS Crops County Data).

This is based on 'typical' nitrogen fertilization rates for each of the crops. The fertilizer application rates (see Table below) were derived from USDA NASS state agricultural statistics bulletins.

Crop Typical' N Fert. Rate (kg N/ha) Alfalfa 0 Barley 75 Corn (grain & silage) 125 Cotton 100 Edible Bean 0 Idle Cropland 0 Non-Legume Hay 25 Oats 75 Pasture 0 Peanut 0 Potatoes 250 Rice 140 Sorghum 75 Soybean 0 Spring Wheat 50 Sugarbeets 150 Sugarcane 200 Sunflower 100 Tobacco 100 Vegetables 100 Winter Wheat 75

County crop areas were multiplied by the nitrogen fertilization rates given above to determine total N-fertilization of these croplands per year. The 1990 national total N fertilizer use calculated by this method (8.5 million tonnes N/yr) is 83% of the 1990 national N-fertilizer sales (10.3 million tonnes N/yr). The sales total is expected to be larger because it will include fertilizer sold for other uses (eg. lawns, golf courses, other non-crop uses) as well as farm-use fertilizer applied to crops not included in the crop database (eg. vineyards, orchards, sod). The source for N fertilizer sales is American Assoc. of Plant Food Control Officials, 103 Regulatory Services Building; University of Kentucky; Lexington, KY 40546-0275; Phone (606)257-2668 fax (606)257-7351.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

This dataset contains simulations for county-level maize production totals in the conterminous US for the year 2012 as well as hindcasts for maize production totals from 1979-2011 for use in validation and analysis. The zero lead time forecasts for 2012 are estimated from simulations of pDSSAT, a gridded high-resolution version of the detailed biophysical crop growth model CERES-Maize (as part of DSSAT). The simulations were conducted in December 2012 and published to figshare in January 2013 in anticipation of the release of the official statistics on county-level US maize yields from USDA NASS in February 2013.

This dataset contains simulations - performed as part of the project "Predicting agricultural impacts of large-scale drought:2012 and the case for better modeling" -- for county and state-level maize yields and production totals in the conterminous US for the year 2012 as well as hindcasts for maize yields and production totals from 1979-2011 for use in validation and analysis. The zero lead time forecasts for 2012 are estimated from simulations of pDSSAT, a gridded high-resolution version of the detailed biophysical crop growth model CERES-Maize (as part of DSSAT). The simulations were conducted in December 2012 and published to figshare in January 2013 in anticipation of the release of the official statistics on county-level US maize yields from USDA NASS in February 2013. Paper detailing this work is Joshua Elliott, Michael Glotter, Neil Best, Ken Boote, Jim Jones, Jerry Hatfield, Cynthia Rosenzweig, Leonard A. Smith, and Ian Foster, (2013). Predicting agricultural impacts of large-scale drought: 2012 and the case for better modeling. Mathematics and Computer Science Division Preprint ANL/MCS-P4034-0213 Argonne National Laboratory, 2013. RDCEP Working Paper No. 13-01. Available at http://www.rdcep.org/predicting-agricultural-impacts-large-scale-drought-2012-and-case-better-modeling and http://www.agmip.org/blog/2013/02/21/predicting-agricultural-impacts-of-large-scale-drought/

This EnviroAtlas data set depicts estimates for mean cash rent paid for land by farmers, sorted by county for irrigated cropland, non-irrigated cropland, and pasture by for most of the conterminous US. This data comes from national surveys which includes approximately 240,000 farms and applies to all crops. According to the USDA (U.S. Department of Agriculture) National Agricultural Statistics Service (NASS), these surveys do not include land rented for a share of the crop, on a fee per head, per pound of gain, by animal unit month (AUM), rented free of charge, or land that includes buildings such as barns. For each land use category with positive acres, respondents are given the option of reporting rent per acre or total dollars paid. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This data set is provided by EOS-EARTHDATA (formerly EOS-Webster). It provides acreage, production and yield statistics for U.S. field crops from the National Agricultural Statistical Service (NASS) for the years 1970 through 2003. Data can be subset by irrigated and non-irrigated areas. Sucrose content, where applicable, is also included. Data are at the county scale and include all counties in the conterminous USA. No spatial subsets are available. For more information, see the Data Guide. Data after 2003 may be obtained from NASS.

This dataset includes county-level annual data on maize (Zea mays L.) yield, soil physical and chemical characteristics, and mean weather data for 2000 through 2014 for IL, MI, MN and PA. The data were aggregated from public databases, including NASS Quick Stats (https://quickstats.nass.usda.gov/), NOAA Climate Data Online (https://www.ncdc.noaa.gov/cdo-web/) and the USDA-NRCS Web Soil Survey (http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm). U.S. counties were the experimental unit for this study, and all data are county-level averages. Covariances among county-level maize yield stability and environmental variability were analyzed using structural equation models (SEM) and linear mixed effects (LME) models. Resources in this dataset:Resource Title: Maize adaptability analysis. File Name: maize_aa_all_2000 to 2014.csvResource Title: SEM data, all states. File Name: sem_all.csvResource Title: Data Dictionary. File Name: DataDictionary.csv

Dataset Abstract County, state, and regional yields are not a product of KBS LTER research, but are scavenged from public sources such as http://www.nass.usda.gov/Data_and_Statistics/Quick_Stats/index.asp and made available here for the convenience of KBS researchers. Yields from KBS treatments may be interpreted in the context of local and regional yields. Local and regional yields are also considered when planning agronomic inputs. original data source http://lter.kbs.msu.edu/datasets/43

This dataset contains simulations for state-level maize production totals in the conterminous US for the year 2012 as well as hindcasts for maize production totals from 1979-2011 for use in validation and analysis. The zero lead time forecasts for 2012 are estimated from simulations of pDSSAT, a gridded high-resolution version of the detailed biophysical crop growth model CERES-Maize (as part of DSSAT). The simulations were conducted in December 2012 and published to figshare in January 2013 in anticipation of the release of the official statistics on county-level US maize yields from USDA NASS in February 2013.

Net Primary Productivity (NPP) for croplands can be estimated using a statistical method that includes factors for dry weight, harvest indices, and root:shoot ratios multiplied by yield data from the National Agricultural Statistics Service (NASS). This method has been documented and published by Prince et al. (2001), Hicke and Lobell (2004), and Hicke et al. (2004). We expanded this method by including factors for more crops and by using an estimated carbon content of 0.45 for agricultural crops to estimate (a) total net carbon uptake, (b) carbon in aboveground biomass, (c) carbon in belowground biomass, (d) carbon harvested and transported off site, and (e) the amount of carbon remaining on the surface following harvest. These five variables are included with their respective Federal Information Processing Standards (FIPS) codes for all counties in the contiguous U.S. from 1990-2005. A mean harvest efficiency of 0.95 was assumed across all crops. Total cropland NPP for the U.S. ranges from 378-527 Tg C yr -1 within years 1990-2005, and total carbon harvested and removed ranges from 161-228 Tg C yr -1 within years 1990-2005. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/carbonmanagement/cropcarbon/

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

This geospatial dataset represents climate change mitigation benefits from widespread cover crop adoption on U.S. cropland. We simulated changes in soil organic carbon stocks and nitrous oxide fluxes over a 20-year period for baseline cover crop adoption rates (derived from historical adoption rates) and a high cover crop adoption (80%) scenario in the continental U.S. Data were generated using the DayCent ecosystem model driven by cropping histories in the USDA National Resources Inventory (NRI) and associated agricultural management data. Here we present the mean and standard deviation of annual soil organic carbon stock changes and nitrous oxide fluxes for both baseline and high cover crop adoption scenarios on a county level. Methods We compared a high (80%) cover crop (CC) adoption scenario with the most current CC adoption rates in each region (NASS, 2017) and projected the 20-year soil organic carbon (SOC) stock change and N2O flux for each scenario. The DayCent biogeochemical model was used to simulate the effect of CC on 132,319 survey locations included in the National Resources Inventory (NRI), a program that monitors land use in the United States and cumulatively represent 94.1 Mha of cropland in the country. Either crimson clover (Trifolium incarnatum L.), cereal rye (Secale cereale L.), or radish (Raphanus sativus) CC were simulated depending on regional CC species preferences and compatibility with the crop rotation and management specific to each NRI location. A Monte Carlo approach adapted from Ogle et al. (2010, 2023) was used to quantify uncertainty associated with management input data and error in model parameters. We aggregated average annual SOC stock change and N2O flux for the baseline and high adoption scenarios at the county-level for each Monte Carlo iteration. We present the uncertainty as the standard deviation from 1000 iterations. We also present total cropland area and cropland with newly adopted cover crops at the start of the study for each county. Data are presented in a shapefile format with associated maps for visualization.