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.

Get statistical data on the estimated area, production and farm value of field crops in Ontario, including: * estimated seeded and harvested acres * yield * production * farm value by year

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

USA County Level Crop Yield Dataset

  Dataset Summary

This dataset contains county level crop yield across 763 counties from 1984 till 2018 in the US Corn Belt. The data was originally collected in Khaki et al. 2020, then further processed, augmented dedup-ed in Hasan et al. 2026. Here are the 9 unique states in the dataset:

Illinois Indiana Iowa Kansas Minnesota Missouri Nebraska North Dakota South Dakota

Each row of the CSV includes:

Weather: 6 weekly mean weather… See the full description on the dataset page: https://huggingface.co/datasets/notadib/usa-corn-belt-crop-yield.

This raster dataset represents the agricultural census data quality for harvested areas of rice crops. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003.

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).

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 raster dataset represents the agricultural census data quality for chestnut crop yields. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003. EarthStat.org serves geographic data sets with the purpose of solving the grand challenge of feeding a growing global population while reducing agriculture’s impact on the environment. The data sets on EarthStat allow users to map the distribution of crops globally, analyze the impact of climate change on crop yields, understand the impacts of fertilizer and manure use and much more.

This raster dataset represents the agricultural census data quality for maize crop yields. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003. EarthStat.org serves geographic data sets with the purpose of solving the grand challenge of feeding a growing global population while reducing agriculture’s impact on the environment. The data sets on EarthStat allow users to map the distribution of crops globally, analyze the impact of climate change on crop yields, understand the impacts of fertilizer and manure use and much more.

This raster dataset represents the agricultural census data quality for raspberry crop yields. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003.EarthStat.org serves geographic data sets with the purpose of solving the grand challenge of feeding a growing global population while reducing agriculture’s impact on the environment. The data sets on EarthStat allow users to map the distribution of crops globally, analyze the impact of climate change on crop yields, understand the impacts of fertilizer and manure use and much more.

Estimated areas, production, yield, average farm price and total farm value of principal field crops.

This raster dataset represents the agricultural census data quality for pineapple crop yields. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003.EarthStat.org serves geographic data sets with the purpose of solving the grand challenge of feeding a growing global population while reducing agriculture’s impact on the environment. The data sets on EarthStat allow users to map the distribution of crops globally, analyze the impact of climate change on crop yields, understand the impacts of fertilizer and manure use and much more.

State fact sheets provide information on population, income, education, employment, federal funds, organic agriculture, farm characteristics, farm financial indicators, top commodities, and exports, for each State in the United States. Links to county-level data are included when available.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: Query tool For complete information, please visit https://data.gov.

This raster dataset represents the agricultural census data quality for quinoa crop yields. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003. EarthStat.org serves geographic data sets with the purpose of solving the grand challenge of feeding a growing global population while reducing agriculture’s impact on the environment. The data sets on EarthStat allow users to map the distribution of crops globally, analyze the impact of climate change on crop yields, understand the impacts of fertilizer and manure use and much more.

Cover crops have critical significance for agroecosystem sustainability and have long been promoted in the U.S. Midwest. Knowledge of the variations of cover cropping and the impacts of government policies remains very limited. We developed an accurate and cost-effective approach utilizing multi-source satellite fusion data, environmental variables, and machine learning to quantify cover cropping in corn and soybean fields from 2000 to 2021 in the U.S. Midwest. We found that cover crop adoption in most counties has significantly increased in the recent 11 years from 2011 to 2021. The adoption percentage of 2021 is 3.3 times that of 2011, which was highly correlated to the increased funding for federal and state conservation programs. However, the percentage of cover crop adoption is still low (7.2%). The averaged county-level cover crop adoption rates in 2000-2010 and 2011-2021 are publicly available on Dryad. Methods We used the STAIR algorithm to fuse Landsat and MODIS to obtain daily 30-m NDVI time series from 2000 to 2021 for all the corn and soybean fields in the Midwest. Cover crop features were extracted by decomposing the STAIR NDVI time series into three components: potential cover crop growth features, soil baselines, and cash crop growth curves. The thresholds for cover crop features were modeled with the inputs of climatic variables, soil properties, and geographic locations. Finally, we compared the cover crop features and thresholds to determine cover crop fields.

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).

The Crops Dataset contains nineteen variables which represent different crops sown in China. For each crop (variable) the number of hectares of that crop sown are given. The following crops are represented: Cereal Grains, Corn, Cotton, Double Season Rice, Green Manure, Potatoes, Rapeseed, Rice and Rapeseed, Single Season Rice, Spring Wheat, Sorghum, Soybeans, Sugarbeets, Sugarcane, Tobacco, Vegetables, Winter Wheat, Winter Wheat and Corn, Winter Wheat and Rice.

See the references for the sources of these data.

China County Data collection contains seven datasets which were compiled in the early 1990s for use as inputs to the DNDC (Denitrification-Decomposition) model at UNH. DNDC is a computer simulation model for predicting carbon (C) and nitrogen (N) biogeochemistry in agricultural ecosystems. The datasets were compiled from multiple Chinese sources and all are at the county scale for 1990. The datasets which comprise this collection are listed below.

1) Agricultural Management 2) Crops 3) N-Deposition 4) Geography and Population 5) Land Use 6) Livestock 7) Soil Properties

Descriptions Excel Application Tool for Statewide Agricultural Water Use Data 2016 - 2020 Department of water resources, Water Use Efficiency Branch, Water Use Unit program, has developed an Excel application tool, which calculates annual estimates of irrigated crop area (ICA), crop evapotranspiration (ETc), effective precipitation (Ep), evapotranspiration of applied water (ETaw), consumed fraction (CF), and applied water (AW) for 20 crop categories by combinations of detailed analysis unit and county (DAUCo) over California. The 2016 – 2020 statewide agricultural water use data were developed by all 4 DWR’s Regional Offices (Northern Region Office, North Central Region Office, South Central Region Office, and Southern Region Office) using Cal_simetaw model for updating the information in the California Water Plan Updates-2023. Therefore, this current Excel application tool just covers agricultural water use data from the period of 2016 - 2020 water years. It should also be mentioned that there are 3 other similar Excel applications that cover 1998 - 2005 and 2006 – 2010, & 2011 - 2015 agricultural water use data for the California Water plan Updates 2005/2009, 2013, and 2018 respectively. Outputs data provided from this Excel application include ICA in acres, EP, both in unit values (Acre feet per acre) & volume (acre feet), ETc both in unit values (acre feet per acre), & volume (acre feet), ETaw, both in unit value (acre feet per acre), & volume (acre feet), AW, both in unit value (acre feet per acre) & volume (acre feet), CF (in percentage %) for WYs 2016 – 2020 at Detailed Analysis Unit by County (DAUCO), Detailed Analysis Unit (DAU), County, Planning Area (PA), Hydrological Region (HR), and Statewide spatial scales using the dropdown menu. Furthermore, throughout the whole process numerous computations and aggregation equations in various worksheets are included in this Excel application. And for obvious reasons all worksheets in this Excel application are hidden and password protected. So, accidentally they won’t be tampered with or changed/revised.

Following are definitions of terminology and listing of 20 crop categories used in this Excel application.

1. Study Area Maps
   The California Department of Water Resources (DWR) subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR),
   The next level of delineation is the planning area (PAS), which are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so the smallest study areas used by DWR is DAU/County. Many planning studies begin at the Dau or PA level, and the results are aggregated into hydrologic regions for presentation.

2. Irrigated Crop Area (ICA) in acres
   The total amount of land irrigated for the purpose of growing a crop (includes multi-cropping acres)

3- Multi-cropping (MC) in acres
A section of land that has more than one crop grown on it in a year, this included one crop being planted more than once in a season in the same field.
Please note that there are no double cropping acreages for 2017. Because on a normal year when Regional Offices (RO) receive data from Land IQ, they were able to provide double cropping acreages. Since the 2017 land use data was derived from average crop acres between water years 2016 and 2018,2019, & 2020, they lost spatial and temporal data necessary to calculate double cropping.

1. Evapotranspiration (ET)
   Combination of soil evaporation and transpiration is referred to as evapotranspiration or ET. The rate of evapotranspiration from the plant-soil environment is primarily dependent on the energy available from solar radiation but is also dependent on relative humidity, temperature, cloud cover, and wind speed. It is an indication for how much your crops, lawn, garden, and trees need for healthy growth and productivity.

2. Reference Evapotranspiration (ETo)
   Reference evapotranspiration (ETo) is an estimate of the evapotranspiration of a 10-15 cm tall cool season grass and not lacking for water. The daily Standardized Reference Evapotranspiration for short canopies is calculated using the Penman-Monteith (PM) equation (Monteith, 1965) as presented in the United Nations FAO Irrigation and Drainage Paper (FAO 56) by Allen et al. (1988).

3. Penman-Monteith Equation (PM)
   Equation is used to estimate ETo when daily solar radiation, maximum and minimum air temperature, dew point temperature, and wind speed data are available. It is recommended by both the America Society of Civil Engineers and United Nations FAO for estimating ETo.

4. Crop Evapotranspiration (ETc), both in unit value (acre feet per acre), & volume (acre feet)
   Commonly known as potential evapotranspiration, which is the amount of water used by plants in transpiration and evaporation of water from adjacent plants and soil surfaces during a specific time period. ETc is computed
   as the product of reference evapotranspiration (ETo) and a crop coefficient (Kc) value, i.e., ETc = ETo x Kc.
   One Acre foot equals about 325851 gallons, or enough water to cover an acre of land about the size of a football field, one foot deep.

5. Crop Coefficient (Kc)
   Relates ET of a given crop at a specific time in its growth stage to a reference ET. Incorporates effects of crop growth state, crop density, and other cultural factors affecting ET. The reference condition has been termed "potential" and relates to grass. The main sources of Kc information are the FAO 24 (Doorenbos and Pruitt 1977) and FAO 56 (Allen et al. 1988) papers on evapotranspiration.

6. Effective Precipitation (Ep), both in unit value (acre feet per acre), & volume (acre feet)
   Fraction of rainfall effectively used by a crop, rather than mobilized as runoff or deep percolation

7. Evapotranspiration of Applied Water (ETaw), both in unit value (acre feet per acre), & volume (acre feet)
   Net amount of irrigation water needed to produce a crop (not including irrigation application efficiency). Soil characteristic data and crop information with precipitation and ETc data are used to generate hypothetical water balance irrigation schedules to determine ETaw.

8. Applied Water (AW), both in unit value (acre feet per acre), & volume (acre feet)
   Estimated as the ETaw divided by the mean seasonal irrigation system application efficiency.

9. Consumed Fraction (CF) in percentage (%)
   An estimate of how irrigation water is efficiently applied on fields to meet crop water, frost protection, and leaching requirements for a whole season or full year.

10. Crop category numbers and descriptions
    Crop Category Crop category description.

1 Grain (wheat, wheat_winter, wheat_spring, barley, oats, misc._grain & hay)
2 Rice (rice, rice_wild, rice_flooded, rice-upland)
3 Cotton
4 Sugar beet (sugar-beet, sugar_beet_late, sugar_beet_early)
5 Corn
6 Dry beans
7 Safflower
8 Other field crops (flax, hops, grain_sorghum, sudan,castor-beans, misc._field, sunflower, sorghum/sudan_hybrid, millet, sugarcane
9 Alfalfa (alfalfa, alfalfa_mixtures, alfalfa_cut, alfalfa_annual)
10 Pasture (pasture, clover, pasture_mixed, pasture_native, misc._grasses, turf_farm, pasture_bermuda, pasture_rye, klein_grass, pasture_fescue)
11 Tomato processing (tomato_processing, tomato_processing_drip, tomato_processing_sfc)
12 Tomato fresh (tomato_fresh, tomato_fresh_drip, tomato_fresh_sfc)
13 Cucurbits (cucurbits, melons, squash, cucumbers, cucumbers_fresh_market, cucumbers_machine-harvest, watermelon)
14 Onion & garlic (onion & garlic, onions, onions_dry, onions_green, garlic)
15 Potatoes (potatoes, potatoes_sweet)
16 Truck_Crops_misc (artichokes, truck_crops, asparagus, beans_green, carrots, celery, lettuce, peas, spinach, bus h_berries, strawberries, peppers, broccoli, cabbage, cauliflower)
17 Almond & pistachios
18 Other Deciduous (apples, apricots, walnuts, cherries, peaches, nectarines, pears, plums, prunes, figs, kiwis)
19 Citrus & subtropical (grapefruit, lemons, oranges, dates, avocados, olives, jojoba)
20 Vineyards (grape_table, grape_raisin, grape_wine)

This EnviroAtlas dataset contains data on the mean cultivated biological nitrogen fixation (C-BNF) in cultivated crop and hay/pasture lands per 12-digit Hydrologic Unit (HUC) in 2006. Nitrogen (N) inputs from the cultivation of legumes, which possess a symbiotic relationship with N-fixing bacteria, were calculated with a recently developed model relating county-level yields of various leguminous crops with BNF rates. We accessed county-level data on annual crop yields for soybeans (Glycine max L.), alfalfa (Medicago sativa L.), peanuts (Arachis hypogaea L.), various dry beans (Phaseolus, Cicer, and Lens spp.), and dry peas (Pisum spp.) for 2006 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We estimated the yield of the non-alfalfa leguminous component of hay as 32% of the yield of total non-alfalfa hay (http://www.agcensus.usda.gov/index.php). Annual rates of C-BNF by crop type were calculated using a model that relates yield to C-BNF. We assume yield data reflect differences in soil properties, water availability, temperature, and other local and regional factors that can influence root nodulation and rate of N fixation. We distributed county-specific, C-BNF rates to cultivated crop and hay/pasture lands delineated in the 2006 National Land Cover Database (30 x 30 m pixels) within the corresponding county. C-BNF data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. 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 raster dataset represents the agricultural census data quality for yautia crop yields. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003.