This dataset consists of growth and yield data for each year when maize (Zea mays, L., also known as corn in the United States) was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Maize was grown for grain on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four square fields are themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Irrigation was by linear move sprinkler system in 1989, 1990, and 1994. In 2013, 2016, and 2018, two lysimeters and their respective fields (NE and SE) were irrigated using subsurface drip irrigation (SDI), and two lysimeters and their respective fields (NW and SW) were irrigated by a linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. The growth and yield data include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, ear mass (when present), kernel number, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on maize ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used by the Agricultural Model Intercomparison and Improvement Project (AgMIP), by OPENET, and by many others for testing, and calibrating models of ET that use satellite and/or weather data.Resources in this dataset:Resource Title: 1989 Bushland, TX, east maize growth and yield data. File Name: 1989_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: This dataset consists of growth and yield data for one of the seasons when maize was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Maize was grown for grain on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four square fields are themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Irrigation was by linear move sprinkler system in 1989, 1990, and 1994. In 2013, 2016, and 2018, two lysimeters and their respective fields (NE and SE) were irrigated using subsurface drip irrigation (SDI), and two lysimeters and their respective fields (NW and SW) were irrigated by a linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. The growth and yield data include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, ear mass (when present), kernel number, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. There are separate spreadsheets for the east (NE and SE) lysimeters and fields, and for the west (NW and SW) lysimeters and fields. The spreadsheets contain tabs for data and corresponding tabs for data dictionaries. Typically there are separate data tabs and corresponding dictionaries for plant growth during the season, crop growth stage, plant population, manual harvest from replicate plots in each field and from lysimeter surfaces, and machine (combine) harvest, An Introduction tab explains the tab names and contents, lists the authors, explains conventions, and lists some relevant references.Resource Title: 1990 Bushland, TX, east maize growth and yield data. File Name: 1990_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1990 East.Resource Title: 1994 Bushland, TX, east maize growth and yield data. File Name: 1994_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1994 East.Resource Title: 1994 Bushland, TX, west maize growth and yield data. File Name: 1994_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1994 West.Resource Title: 2013 Bushland, TX, west maize growth and yield data. File Name: 2013_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2013 West.Resource Title: 2016 Bushland, TX, east maize growth and yield data. File Name: 2016_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2016 East.Resource Title: 2016 Bushland, TX, west maize growth and yield data. File Name: 2016_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2016 West.Resource Title: 2018 Bushland, TX, west maize growth and yield data. File Name: 2018_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2018 West.Resource Title: 2013 Bushland, TX, east maize growth and yield data. File Name: 2013_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2013 East.Resource Title: 2018 Bushland, TX, east maize growth and yield data. File Name: 2018_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2018 East.

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes corn production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Corn ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States and HawaiiVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Operations with SalesSales in US DollarsGrain - Area Harvested in AcresGrain - Operations with Area HarvestedGrain - Production in BushelsGrain - Irrigated Area Harvested in AcresGrain - Operations with Irrigated Area HarvestedSilage - Area Harvested in AcresSilage - Operations with Area HarvestedSilage - Production in TonsSilage - Irrigated Area Harvested in AcresSilage - Operations with Area HarvestedTraditional or Indian - Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedTraditional or Indian - Production in PoundsTraditional or Indian - Irrigated Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

Dataset Abstract:

Field-level monitoring of crop types in the United States via the Cropland Data Layer (CDL) has played an important role in improving production forecasts and enabling large-scale study of agricultural inputs and outcomes. Although CDL offers crop type maps across the conterminous US from 2008 onward, such maps are missing in many Midwestern states or are uneven in quality before 2008. To fill these data gaps, we used the now-public Landsat archive and cloud computing services to map corn and soybean at 30m resolution across the US Midwest from 1999-2018. Our training data were CDL from 2008-2018, and we validated the predictions on CDL 1999-2007 where available, county-level crop acreage statistics, and state-level crop rotation statistics. The corn-soybean maps, which we call the Corn-Soy Data Layer (CSDL), are publicly hosted on Google Earth Engine and also available for download on Zenodo.

Summary of Methods:

Using Google Earth Engine, we trained a random forest classifier to classify each pixel of the study area into corn, soybean, and an aggregated "other crops" class. CDL 2008-2018 data were used as labels. The features input to the model were harmonic regression coefficients fit to the NIR, SWIR1, SWIR2, and GCVI bands/indices of time series from Landsat 5, 7, and 8 Surface Reflectance observations. Cloudy pixels were masked out using the pixel_qa band provided with Landsat Surface Reflectance products.

Map Legend:

• 0 = outside study area
• 1 = corn
• 5 = soy
• 9 = other crop
• 255 = non-crop (masked by NLCD)

Values were chosen to be consistent with CDL values when possible.

Usage Notes:

We recommend that users consider metrics such as (1) user's and producer's accuracy with CDL and (2) R² with NASS statistics across space and time to determine in which states/counties and years CSDL is of high quality. This can be done with the CSV file of user's and producer's accuracies included in this Zenodo, and annual county-level statistics and example code we have included in our repo at https://github.com/LobellLab/csdl.

USA Cropland is a time-enabled imagery layer of the USDA Cropland Data Layer dataset from the National Agricultural Statistics Service (NASS). The time series shows the crop grown during every growing season in the conterminous US since 2008. Use the time slider to select only one year to view, or press play to see every growing season displayed sequentially in an animated map.The USDA is now serving the Cropland Data Layer in their own application called CropCros which allows selection and display of a single product or growing season. This application will eventually replace their popular CropScape application.This dataset is GDA compliant. Compliancy information can be found here.Why USA Cropland masks out NLCD land cover in its default templateUSDA Cropland Data Layer, by default as downloaded from USDA, fills in the non-cultivated areas of the conterminous USA with land cover classes from the MRLC National Land Cover Dataset (NLCD). The default behavior for Esri's USA Cropland layer is a little bit different. By default the Esri USA Cropland layer uses the analytic renderer, which masks out this NLCD data. Why did we choose to mask out the NLCD land cover classes by default?While crops are updated every year from USDA NASS, the NLCD data changes every several years, and it can be quite a bit older than the crop data beside it. If analysis is conducted to quantify landscape change, the NLCD-derived pixels will skew the results of the analysis because NLCD land cover in a yearly time series may appear to remain the same class for several years in a row. This can be problematic because conclusions drawn from this dataset may underrepresent the amount of change happening to the landscape.Since the 2018 Cropland Data Layer was posted (early 2019), MRLC issued an update to the NLCD Land Cover dataset. The 2019 and 2020 cropland frames have this more current NLCD data, but the years before that contain NLCD land cover data from 2011 or older.To display the most current land cover available from both sources, add both the USA NLCD Land Cover service and USA Cropland time series to your map. Use the analytical template with the USA Cropland service, and draw it on top of the USA NLCD Land Cover service. When a time slider is used with these datasets together, the map user will see the most current land cover from both services in any given year.Variable mapped: Crop grown in each pixel since 2008.Data Projection: AlbersMosaic Projection: AlbersExtent: Conterminous USACell Size: 30mSource Type: ThematicVisible Scale: All scales are visibleSource: USDA NASSPublication Date: 2/2/2022This layer and the data making up the layer are in the Albers map projection. Albers is an equal area projection, and this allows users of this layer to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web Mercator, if that is the destination projection of the layer.Processing templates available with this layerTo help filter out and display just the crops and land use categories you are interested in showing, choose one of the thirteen processing templates that will help you tailor the symbols in the time series to suit your map application. The following are the processing templates that are available with this layer:Analytic RendererUSDA Analytic RendererThe analytic renderer is the default template. NLCD codes are masked when using analytic renderer processing templates. There is a default esri analytic renderer, but also an analytic renderer that uses the original USDA color scheme that was developed for the CropScape layers. This is useful if you have already built maps with the USDA color scheme or otherwise prefer the USDA color scheme.Cartographic RendererUSDA Cartographic RendererThese templates fill in with NLCD land cover types where crops are not cultivated, thereby filling the map with color from coast to coast. There is also a template using the USDA color scheme, which is identical to the datasets as downloaded from USDA NASS.In addition to different ways to display the whole dataset, some processing templates are included which help display the top 10 agricultural products in the United States. If these templates seem to overinclude crops in their category (for example, tomatoes are included in both the fruit and vegetables templates), this is because it's easier for a map user to remove a symbol from a template than it is to add one.Corn - Corn, sweet corn, popcorn or ornamental corn, plus double crops with corn and another crop.Cotton - Cotton and double crops, includes double crops with cotton and another crop.Fruit - Symbolized fruit crops include not only things like melons, apricots, and strawberries, but also olives, avocados, and tomatoes. Nuts - Peanuts, tree nuts, sunflower, etc.Oil Crops - Oil crops include rapeseed and canola, soybeans, avocado, peanut, corn, safflower, sunflower, also cotton and grapes.Rice - Rice crops.Sugar - Crops grown to make sugars. Sugar beets and cane are displayed of course, but so are corn and grapes.Soybeans - Soybean crops. Includes double crops where soybeans are grown at some time during the growing season.Vegetables - Vegetable crops, and yes this includes tomatoes. Wheat - Winter and spring wheat, durum wheat, triticale, spelt, and wheat double crops.In many places, two crops were grown in one growing season. Keep in mind that a double crop of corn and soybeans will display in both the corn and soybeans processing templates.Index to raster values in USA Cropland:0,Background (not a cultivated crop or no data)1,Corn2,Cotton3,Rice4,Sorghum5,Soybeans6,Sunflower10,Peanuts11,Tobacco12,Sweet Corn13,Popcorn or Ornamental Corn14,Mint21,Barley22,Durum Wheat23,Spring Wheat24,Winter Wheat25,Other Small Grains26,Double Crop Winter Wheat/Soybeans27,Rye28,Oats29,Millet30,Speltz31,Canola32,Flaxseed33,Safflower34,Rape Seed35,Mustard36,Alfalfa37,Other Hay/Non Alfalfa38,Camelina39,Buckwheat41,Sugarbeets42,Dry Beans43,Potatoes44,Other Crops45,Sugarcane46,Sweet Potatoes47,Miscellaneous Vegetables and Fruits48,Watermelons49,Onions50,Cucumbers51,Chick Peas52,Lentils53,Peas54,Tomatoes55,Caneberries56,Hops57,Herbs58,Clover/Wildflowers59,Sod/Grass Seed60,Switchgrass61,Fallow/Idle Cropland62,Pasture/Grass63,Forest64,Shrubland65,Barren66,Cherries67,Peaches68,Apples69,Grapes70,Christmas Trees71,Other Tree Crops72,Citrus74,Pecans75,Almonds76,Walnuts77,Pears81,Clouds/No Data82,Developed83,Water87,Wetlands88,Nonagricultural/Undefined92,Aquaculture111,Open Water112,Perennial Ice/Snow121,Developed/Open Space122,Developed/Low Intensity123,Developed/Med Intensity124,Developed/High Intensity131,Barren141,Deciduous Forest142,Evergreen Forest143,Mixed Forest152,Shrubland176,Grassland/Pasture190,Woody Wetlands195,Herbaceous Wetlands204,Pistachios205,Triticale206,Carrots207,Asparagus208,Garlic209,Cantaloupes210,Prunes211,Olives212,Oranges213,Honeydew Melons214,Broccoli215,Avocados216,Peppers217,Pomegranates218,Nectarines219,Greens220,Plums221,Strawberries222,Squash223,Apricots224,Vetch225,Double Crop Winter Wheat/Corn226,Double Crop Oats/Corn227,Lettuce228,Double Crop Triticale/Corn229,Pumpkins230,Double Crop Lettuce/Durum Wheat231,Double Crop Lettuce/Cantaloupe232,Double Crop Lettuce/Cotton233,Double Crop Lettuce/Barley234,Double Crop Durum Wheat/Sorghum235,Double Crop Barley/Sorghum236,Double Crop Winter Wheat/Sorghum237,Double Crop Barley/Corn238,Double Crop Winter Wheat/Cotton239,Double Crop Soybeans/Cotton240,Double Crop Soybeans/Oats241,Double Crop Corn/Soybeans242,Blueberries243,Cabbage244,Cauliflower245,Celery246,Radishes247,Turnips248,Eggplants249,Gourds250,Cranberries254,Double Crop Barley/Soybeans

Corn fell to 397.51 USd/BU on July 11, 2025, down 2.39% from the previous day. Over the past month, Corn's price has fallen 9.35%, and is down 4.16% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on July of 2025.

The Sustainable Corn CAP (Cropping Systems Coordinated Agricultural Project: Climate Change, Mitigation, and Adaptation in Corn-based Cropping Systems) was a multi-state transdisciplinary project supported by the USDA National Institute of Food and Agriculture (Award No. 2011-68002-30190). Research experiments were located through the U.S. Corn Belt and examined farm-level adaptation practices for corn-based cropping systems to current and predicted impacts of climate change.Research data were collected from 2011 to 2015 at research sites in 8 states: Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, and Wisconsin. The research coverage area spanned 95.3°W to 81.9°W and 38.5°N to 44.7°N. Research sites encompassed a varying set of management practices including crop rotation, cover crop, tillage, drainage, and nitrogen management, with several sites having landscape position incorporated as an additional treatment. These treatments were typically arranged in a randomized complete block design as a complete factorial or main-split plot with 3 to 4 replications per site. It should be noted that none of the sites were identical in terms of treatment structure or data collected as sites were a combination of previously and newly established experiments that aligned with project research goals.The dataset contains agronomic, soil, water, greenhouse gas, crop disease, and pest data collected from 30 sites. Standardized protocols were developed and followed by the project team for estimating C, N, and water footprints of corn production in the region and performing baseline monitoring. Variables measured during the five-year period include: grain and biomass yield, C and N content in crop grain and vegetation, soil water moisture and temperature, C and N concentration in soil, greenhouse gas fluxes, drainage water quality and quantity, groundwater table and others. Hourly or sub-hourly weather data are also provided for each location.In addition, the dataset includes site description (e.g. site location, plot area, soil type), field management information (e.g. planting, harvesting, tillage and fertilizer application dates, seeding rate, fertilizer and pesticide type and application rate), and experimental design (e.g. plot identifiers, experimental treatments, variables measured).Users can query and download data from the Sustainable Corn CAP research web-accessible application. At this website, users can also access site-specific weather data and select the time period of interest for water data (here they are uploaded at a daily interval). In addition to the research data, the web tool also provides a list of over 100 referred journal articles as well as theses and dissertations related to the dataset. Other project-related materials such as fact sheets, videos, and extension publications are available for free download through the Iowa State University Extension and Outreach website and project reports through the Iowa State University Digital Repository.A complete list of refereed journals, theses, dissertations, and reports published by the Sustainable Corn CAP project team members can be accessed at https://datateam.agron.iastate.edu/cscap/Refer to the ARDN Products 'child' dataset of this record for a subset of the parent data specifically developed for the Agricultural Research Data Network with csv and json files for easy ingestion into crop models. Resources in this dataset:Resource Title: Sustainable Corn CAP Research Data .File Name: Sustainable_Corn_Research_Data_2011-2015.xlsxResource Description: Data file contains: Plot Identifiers, Agronomic, Soil, GHG, IPM, Water_Tile Flow, Water_Water Quality, Water_Water Table, Field Operations, Pesticides, Site Metadata, Drainage Control Structure Mngt, and Notes.Resource Title: Sustainable Corn Research Data 2011-2015 Data Dictionary.File Name: Sustainable_Corn_Research_Data_2011-2015_DataDictionary.csv

Two datasets in the EOS-WEBSTER US County Data Collection provide county-level data for crop acreage, production and yield statistics. Crop data for 22 different field crops were acquired from the National Agricultural Statistical Service (NASS) for 1972 through 1998. One dataset provides data for individual varieties/types of each crop while the second dataset provides summary data by crop only. Data can be subset by irrigated and non-irrigated areas. Sucrose content, where applicable, is also included.

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

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.

Important Note: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Groundnut (Arachis hypogaea), also known as peanut, is grown around the world in a broad region between 40 degrees north and south latitude. Originally from South America, major producers of groundnut include China, India and the United States. Producing 30% of Africa"s total, Nigeria leads the continent"s production followed by Senegal, Sudan, Ghana, and Chad. Groundnut is a valuable source of protein and oil. It has the additional benefit of enriching depleted soils by converting nitrogen from the air into a form that is required by most plants. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofgroundnut harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by theInternational Food Policy Research Institutein 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing theSpatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of casava as a staple food see theHarvest Choice webpage. For data on other agricultural species in Africa see these layers:Groundnut (Peanut) Maize (Corn) Millet Potato Rice Sorghum Sweet Potato and Yam Wheat Data for important agricultural crops in South America are availablehere. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixelswhich allows access to the full dataset. The source data for this layer are availablehere. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started follow these links: Landscape Layers - a reintroductionLiving Atlas Discussion Group

A variety of factors shape farmers' views as they face the rising effects of climate change and consider a range of adaptation strategies to build the resilience of their farming systems. We examine a set of related questions to explore farmers' perspectives on risks and potential shifts to their operations: (1) Relative to other environmental factors, how salient of a challenge is climate change and climate-related impacts to farmers? (2) Do farmers intend to adapt to climate impacts generally?, and (3) What factors shape their use of a specific and underexplored adaptive response—farm product diversification? The data come from a survey of 179 operators within a 30-county region of Indiana, Michigan, and Ohio. The region spans various rural-urban gradients. Respondents generally represent smaller operations [median of 80 acres (32 hectares)]. Because our selection methods aimed to over-sample from food-producing farms, 60% of respondents produced some type of food or value-added product, and 40% produced only commodity feedstocks and biofuels. Although the group as a whole indicated only “somewhat” of a concern about changing weather patterns, and half did not anticipate adapting their farming practices to climate change, farmers' responses to a write-in question denoted regional climate effects as challenges to their farms. Analysis of subgroups among the respondents, according to their views of climate change, adaptation, and further diversifying their agricultural products, distinguished farmers' family considerations, and gender. Methods to elicit subgroups included correlation, regression, cluster analysis, and an examination of the many respondents (29%) who indicated uncertainty about adapting practices. Women, who participated in 29% of responses, indicated more concern with changing weather patterns and more openness to adapting farming practices compared to men. Farmers with the most family relationships to consider, and those with the greatest aspirations to employ descendants, were the most receptive to adapting their farming practices. This was the case even when respondents' concern over climate change was low. Results point to the importance of family relationships as a factor in farmers' openness to implementing adaptive and potentially mitigative actions.

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 EnviroAtlas dataset shows the number of major grains grown, yield in tons, and area in hectares for several major grains and for cotton by 12-digit Hydrologic Unit (HUC). It is based on the United States Department of Agriculture's 2010 Cropland Data Layer (CDL) and data on yields and sales from the National Agricultural Statistics Service (NASS). The grains included in this dataset are corn, barley, cotton, durum wheat, oats, rye, rice, sorghum, spring wheat, soybeans, and winter wheat; it does not include data on every grain. 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 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

Important Note: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Rice (Oryza sativaandO. glaberrima) is one of the world"s most important staple food crops. Over half of the world"s population relies on rice. The people in some parts of Africa have been cultivating rice for over 3,500 years. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofrice harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by theInternational Food Policy Research Institutein 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing theSpatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of rice as a staple food see theHarvest Choice webpage. For data on other agricultural species in Africa see these layers:Cassava Groundnut (Peanut) Maize (Corn) Millet PotatoSorghum Sweet Potato and Yam Wheat Data for important agricultural crops in South America are availablehere. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixelswhich allows access to the full dataset. The source data for this layer are availablehere. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started follow these links: Landscape Layers - a reintroductionLiving Atlas Discussion Group

High-resolution crop maps over large spatial extents are fundamental to many agricultural applications; however, generating high-quality crop maps consistently across space and time remains a challenge. In this study, we improved a workflow for operational crop mapping and developed the first openly available, annual, 10-m spatial resolution maize and soybean maps over the Contiguous United States (CONUS) from 2019 to 2022. We obtained all available Sentinel-2 surface reflectance data between May and October for every year, applied quality assurance, corrected the bidirectional reflectance distribution function (BRDF) effects, and generated 10-day analysis ready data (ARD) composites. We then derived multi-temporal metrics from the 10-day ARD as training features for the national-scale wall-to-wall mapping. We implemented a stratified, two-stage cluster sampling, and then conducted annual field surveys and collected ground data. Utilizing the training data with Sentinel-2 multi-temporal metrics, we trained random forest models generalized for annual maize and soybean classification separately. Validated using field data from the two-stage cluster sample, our annual maps achieved consistent overall accuracies (OA) greater than 95% with standard errors of less than 1%. User’s accuracies (UAs) and producer’s accuracies (PAs) for maize were higher than 91% and 84% across the years, and UAs and PAs for soybean were greater than 88% and 82%, respectively. To illustrate the substantial improvement of the 10-m map over existing datasets, e.g., the 30-m Cropland Data Layer (CDL), we aggregated the 10-m maps to 30-m spatial resolution and quantified the amount of 30-m mixed pixels that can be reduced at field, regional, and national levels. The counties with the most maize and soybean production in Iowa, Illinois and Nebraska had the lowest reduction in mixed pixels, ranging from 1% to 10%, whereas southern counties had a higher reduction in mixed pixels. Overall, the median percentages of mixed maize and soybean pixels across all counties were 14% and 16%, respectively, illustrating the substantial benefits of 10-m maps over 30-m maps. With more Sentinel-2-like data available from continuous observations and incoming satellite missions, we anticipate that 10-m crop maps will greatly benefit long-term monitoring for agricultural practices from the field to global scales.

Interseeding annual crops into existing alfalfa (Medicago sativa L.) stands is gaining interest, and one reason may be that alfalfa lowers nitrogen requirements for subsequent crops. However, little is known about the legacy impact of this practice on subsequent corn (Zea mays L.) production. An experiment involving interseeding annual cool‐season crops into alfalfa was conducted between 2017 and 2021, which serendipitously allowed us to evaluate the legacy impact of this practice on subsequent corn grain production. This follow‐up study compared corn grain yield and quality of corn planted subsequently on positive control plots (alfalfa monoculture), negative control plots (annual crop monoculture), and experimental treatment polyculture plots (annual crops planted into alfalfa). We found that corn yield was lower following annual monocultures compared to corn following alfalfa monoculture and polyculture plots. The treatments did not have a significant effect on grain protein or starch percentage, but grain oil percentage was higher following polyculture compared to annual monoculture. Corn grain zinc concentration was positively associated with previous alfalfa density and corn ear leaf chlorophyll concentration. These findings indicate that alfalfa monoculture and alfalfa‐annual crop polycultures can have different positive legacy effects on corn yield, near‐surface soil attributes, and grain quality. Future research aimed at evaluating the legacy of crop/alfalfa mixtures on subsequent corn crops in the northern Great Plains in multiple locations over several years are needed.

Maize Sackd

## Overview

Maize Sackd is a dataset for object detection tasks - it contains Sacks annotations for 243 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Important Note: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Sorghum (Sorghum bicolor) is an important grass species used for human and animal food. It was first cultivated in Africa and currently 53% of the world"s production is in sub-Saharan Africa. Millions of farmers in arid regions rely on this crop due to its drought tolerant qualities. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofsorghum harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by theInternational Food Policy Research Institutein 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing theSpatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of sorghum as a staple food see theHarvest Choice webpage. For data on other agricultural species in Africa see these layers:Cassava Groundnut (Peanut) Maize (Corn) Millet PotatoRice Sorghum Sweet Potato and Yam Wheat Data for important agricultural crops in South America are availablehere. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixelswhich allows access to the full dataset. The source data for this layer are availablehere. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started follow these links:Landscape Layers - a reintroductionLiving Atlas Discussion Group

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.

Maps of cropland conversion classes, year of conversion, and pre- and post-conversion land cover associated with Lark et al. (2020). This repository also includes maps of 'local' and 'national' yield differentials for corn, soybeans, and wheat that are associated with the same publication. Code used to generate these data can be found here.

• Lark, T.J., S.A. Spawn, M.F. Bougie, H.K. Gibbs. Cropland expansion in the United States produces marginal yields with disproportionate costs to wildlife. Nature Communications (In review)

Cropland conversion maps are included in a zipped ESRI Geodatabase titled "US_land_conversion_2008-16.gdb". Each feature layer encompasses all of the conterminous United States at a 30m spatial resolution. Feature layers include:

• mtr = "Multi-temporal results"; Classifies land as being one of five broad land use change classes during the 2008-16 study period:
  1. "stable non-cropland" -- areas of consistent non-cropland throughout the duration of the study period.
  2. "stable cropland" -- areas of consistent cropland throughout the duration of the study period.
  3. "cropland expansion" -- areas converted to crop production between 2008 and 2016.
  4. "cropland abandonment" -- areas converted away from crop production between 2008 and 2016.
  5. "intermittent cropland/confusion" -- areas that were cropped for at least two years but show no clear trend towards or away from cropland. These could include areas under a crop-pasture rotation, fallow rotations, or simply areas with repeated classifier confusion.
• ytc = "year to cropland"; Indicates the year in which pixels with an mtr classification of "3" (i.e. "cropland expansion") were converted from non-cropland to cropland. e.g., a value of 2009 represents land that was converted between the 2008 growing season and the 2009 growing season.
• yfc = "year from cropland"; Indicates the year in which pixels with an mtr classification of "4" (i.e. "cropland abandonment") were converted from cropland to non-cropland. e.g., a value of 2009 represents land that was still cropped in 2008 and no longer cropped during the 2009 growing season.
• bfc = "before first crop"; Indicates the last land cover class before a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.
• fc = "first crop"; Indicates the class of the first crop planted after a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.
• bfnc = "before first non-crop"; Indicates the last cropland class of a pixel before it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.
• fnc = "first non-crop"; Indicates the first non-crop class of a pixel after it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.

Yield differential maps are included in the "yieldDifferentials.zip" folder as GeoTIFF rasters with a ~10km spatial resolution. Raster values represent relative (%) differences between the representative yields of new croplands (mtr = 3) and those of stable croplands (mtr = 1) planted to that crop within either (i) the larger 10km x 10km gridcell in which those fields are situated ("local" differentials) or (ii) the entire nation ("national" differentials).

• corn_relDiff_local.tif = local yield differential (%) of corn grain.
• corn_relDiff_national.tif = national yield differential (%) of corn grain.
• soy_relDiff_local.tif = local yield differential (%) of soybeans.
• soy_relDiff_national.tif = national yield differential (%) of soybeans.
• wheat_relDiff_local.tif = local yield differential (%) of wheat.
• wheat_relDiff_national.tif = national yield differential (%) of wheat.