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 cattle 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 Cattle ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States, Alaska, 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.Cattle - Operations with SalesCattle - Sales in US DollarsCattle - Sales in HeadDairy - Operations with SalesDairy - Sales in US DollarsAdditionally 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.

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 hog 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 Hog 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.InventoryOperations with InventoryOperations with SalesSales in US DollarsSales in HeadAdditionally 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.

The Soil and Terrain Database for Northeastern Africa contains land resource information on soils, physiography, geology and vegetation for the following ten countries: Burundi, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Rwanda, Somalia, Sudan and Uganda. The information is accessible with an easy-to-use viewer program and is also stored in vector Arc/Info export format. Information on individual soil properties with class values is also given. A land suitability assessment for irrigated and upland crops for each unit is included. The scale ofthe source material is variable and ranges between 1:1 million and 1:2 million. A user manual for the viewer program and background information on the collected and correlated land resource materials are contained in filed documents.

Soils are classified in the Revised Legend; physiographic and lithology information was collected using an earlier draft version of the SOTER manual.

The Inter-Governmental Authority on Development (IGAD) -- Sudan, Kenya, Djibouti, Somalia, Uganda, Eritrea, Ethiopia -- Crop Production System Zones (CPSZ) software is a detailed database that provides background information about actual farming in the region. It comes with a program (CVIEW, a CPSZ viewer) that displays maps, zooms in and out, and provides export facilities for the maps in image format and for the actual data in text format. The elementary mapping unit is a compromise between administrative units and agro-ecological zones: whenever steep agro-ecological gradients exist, administrative units are subdivided, thus resulting in 1200 mapping units that are homogeneous from an agro-ecological point of view, while retaining the compatibility with the administrative units used for most socio-economic variables in agricultural planning.

The just over 500 mappable variables are subdivided into several categories covering the spectrum from agronomy and livestock to the physical environment. For each mapping unit, detailed information is also presented on the crop calendar, typical yields and main pests and diseases.

This CD-ROM contains a collection of land and natural resource information for Northeastern Africa, in particular for the IGAD countries bordering the Nile basin. It includes data on administrative boundaries, rivers and lakes, soil and terrain, climatology, land use, physiography, geology and natural vegetation in easily accessible format.

Soil and Terrain Database for Northeasterm Africa (1:1 Million Scale) and Crop Production System Zones of the IGAD Subregion is provided on CD-ROM by the FAO, Land and Water Digital Media Series (Number 2). The CD-ROM can be purchased (Price: US$40) from FAO, Sales and Marketing Group, Viale delle Terme di Caracalla 0100 Rome, Italy (Fax: +39-06-5705-3360 E-mail: publications-sales@fao.org).

The global production of monoammonium phosphate, commonly known as MAP, reached roughly 29.2 million metric tons in 2022, up from 16.5 million metric tons in 2009. MAP is commonly used as a source of phosphorous and nitrogen for plant growth.

This data package includes two shapefiles and their associated attribute tables. The two files, GFB_producers_2021-02-18.zip and GFB_retailers_2021-02-18.zip, contain all internet-discoverable (at the time of data collection, July-August 2020; with minor edits/additions circa June 2022) grass-fed beef producers and retailers in the Southwest and Southern Plains of the U.S. (Arizona, California, Colorado, Kansas, Nevada, New Mexico, Oklahoma, Texas, Utah), compiled through an internet search. The data were initially collected in August of 2020 using publicly available information from Google search engine and Google map searches with the intention of informing members of the Sustainable Southwest Beef Project (USDA NIFA grant #2019-69012-29853) team about existing grass-fed beef producers and retailers in the study area.

Northeast China is one of the most major grain production regions in China and has an overwhelming influence on regional and national food security. Crop type mapping is important for crop managements to maximize crop yield. We produced annual 10-m major crops (rice, maize and soybean) maps in Northeast China from 2017 to 2024, using a practical and transferable crop mapping approach by constructing crop-specific OIF knowledge graph (OIFKG). The overall accuracies higher than 90% (Kappa coefficient greater than 0.90), and the producer’s accuracies for rice, maize, and soybean are generally greater than 0.92 based on ground truth data. The crop maps have close agreement with the statistical data at the municipal level. This study provides a highly reliable long-term crop maps dataset, which can be helpful for regional agricultural production management.

This dataset is one of the key indicators of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area and yield, for 42 crops — disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. Unit of measure Production for each crop and technology: mt More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

This map displays drought, climate and manufacturing-related agriculture data for the United States. The map was created by the National Integrated Drought Information System (NIDIS) and is a component of the Manufacturing Sector web mapping application, a tool for exploring the relationship between drought, climate and the manufacturing sector in the United States.Data Sources for each layer are identified in the Layer section below as well as in the Layer and Legend sections of the web map. Additional information about the impact of drought on manufacturing can be found on the NIDIS Manufacturing Sector page.

Cropland Index

The Cropland Index evaluates lands used to produce crops based on the following input datasets: Revised Storie Index, California Important Farmland data, Electrical Conductivity (EC), and Sodium Adsorption Ratio (SAR). Together, these input layers were used in a suitability model to generate this raster. High values are associated with better Croplands

This raster dataset represents total cotton crop production in metric tons. Harvested area in hectares was multiplied by yield per hectare to create this data set. 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.

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 winter wheat 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 Winter Wheat ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United StatesVisible 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.Area Harvested in AcresOperations with Area HarvestedProduction in BushelsIrrigated Area Harvested in AcresAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.Additional information on wheat from the Census of Agriculture is available in the USDA Census of Agriculture 2017 - Wheat Production layer.Many other ready-to-use layers derived from the Census of Agriculture can be found in the Living Atlas Agriculture of the USA group.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.

Summary:

There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively.

The code, source data, and the simultaneously farm-size- and crop-specific harvested area datasets, including the GAEZv4 crop map based dataset and SPAM2010 crop map based dataset, are open-access, free, and available, which can be found below. The resulting dataset is available in *.csv and *.nc (netCDF) for each crop and farming system. For each crop, farming system, and farm size, we provide the gridded harvested area in the coordinate Systems of EPSG:4326 - WGS 84. Gridded summaries over crops and farming systems are also available.

How to cite this dataset:

Su, H., Willaarts, B., Luna-Gonzalez, D., Krol, M.S. and Hogeboom, R.J., 2022. Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries. Earth System Science Data, 14(9), pp.4397-4418.

Update history:

I am happy to receive any questions, comments, or potential collaboration on further dataset development. Please drop your email to Han Su (h.su@utwente.nl, han_su20@163.com)

Version 1.03: Fix bugs in data format; Netcdf didn't show properly before in QGIS. Data underlying the three versions are the same.

Version 1.02: New data summary, add Netcdf data format

Version 1: Initial dataset for peer-review, CSV format only

Note: please cite the original publications/sources if any data source based on which this dataset was developed is reused for your own study.

SPAM2010:

Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth System Science Data, 12, 3545-3572, 10.5194/essd-12-3545-2020, 2020.

GAEZv4:

FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN, Rome, Italy, 2021

The dataset of Ricciardi et al.'s:

Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.: How much of the world's food do smallholders produce?, Global Food Security, 17, 64-72, 2018.

The global dominant field size dataset:

Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.: Estimating the global distribution of field size using crowdsourcing, Glob Chang Biol, 25, 174-186, 10.1111/gcb.14492, 2019.

GLC-Share:

Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share (GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, 2014.

CAAS-IFPRI cropland extent map:

Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth System Science Data, 12, 1913-1928, 10.5194/essd-12-1913-2020, 2020.

Dataset Abstract:
Sugarcane is an important source of food, biofuel, and farmer income in many countries. At the same time, sugarcane is implicated in many social and environmental challenges, including water scarcity and nutrient pollution. Currently, few of the top sugar-producing countries generate reliable maps of where sugarcane is cultivated. To fill this gap, we introduce a dataset of detailed sugarcane maps for the top 13 producing countries in the world, comprising nearly 90% of global production. Maps were generated for the 2019-2022 period by combining data from the Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2). GEDI data were used to provide training data on where tall and short crops were growing each month, while S2 features were used to map tall crops for all cropland pixels each month. Sugarcane was then identified by leveraging the fact that sugar is typically the only tall crop growing for a substantial fraction of time during the study period. Comparisons with field data, pre-existing maps, and official government statistics all indicated high precision and recall of our maps. Agreement with field data at the pixel level exceeded 80% in most countries, and sub-national sugarcane areas from our maps were consistent with government statistics. Exceptions appeared mainly due to problems in underlying cropland masks, or to under-reporting of sugarcane area by governments.
The final maps should be useful in studying the various impacts of sugarcane cultivation and producing maps of related outcomes such as sugarcane yields.

USAGE: Users must mask the provided sugarcane map with the most appropriate crop mask from the ones provided. If none of the provided crop masks are suitable, users can use an external crop mask instead.

Validation results for the sugarcane maps are detailed in Section 4.3 of the paper. For Indonesia and Guatemala, no field-level data or raster datasets were available for validation of our sugarcane maps.

Dataset:
5 bands
b1: Number of tall months
b2: Sugarcane Map: 0 = non-sugarcane, 1 = sugarcane
b3: ESA crop mask: 0 = non-cropland, 1 = cropland
b4: ESRI crop mask: 0 = non-cropland, 1 = cropland
b5: GLAD crop mask: 0 = non-cropland, 1 = cropland

The dataset can be accessed on Google Earth Engine (GEE) at
https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD

Example GEE script for visualizing and masking the sugarcane maps by country available at:
https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2fl Bell-lab%2Fgedi_sugarcane%2 Maps%2FimgColl_10m_ESAESRIGLAD

This dataset provides mapped tidal wetland gross primary production (GPP) estimates (g C/m2/day) derived from multiple wetland types at 250-m resolution across the conterminous United States at 16-day intervals from March 5, 2000, through November 17, 2019. GPP was derived with the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field-based eddy covariance (EC) tower GPP data into a single Bayesian framework along with Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets. Tidal wetlands are a critical component of global climate regulation. Tidal wetland-based carbon, or "blue carbon," is a valued resource that is increasingly important for restoration and conservation purposes.

This dataset is one of the outputs of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area, production and yield, for 42 crops, disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. Production values in this dataset are given for each technology aggregated by categories - crops/food/non-food - with no information on individual crops.

Unit of measure: Production for each technology: mt

This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005.

More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

Data publication: 2019-10-09

Citation:

Citation: International Food Policy Research Institute, 2019, “Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1”, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V3.

Contact points:

Metadata Contact: Koo, Jawoo International Food Policy Research Institute (IFPRI)

Resource Contact: You, Liangzhi International Food Policy Research Institute (IFPRI)

Resource Contact: Harvard Dataverse

Data lineage:

Differences compared to SPAM 2010 V1r0 (Uploaded December 2018): - No more rounding errors of 0.1 ha or mt, ie areas and production in each pixel satisfy conditions: R=A-I and R=H+L+S. - CSV files do not have 'strange' entries for Yemen - admin names with "," and ";" were corrected. - Value of production only has one set of entries for Sudan - in previous version it had 2. - Missing values for maize in Nigeria/Osun (fisp1=NI31) now included.

Resource constraints:

IFPRI DATAVERSE TERMS OF USE This work is licensed under a Creative Commons Attribution 4.0 International License.

Online resources:

Map SPAM Data Center

ReadMe_V1r1_GeoTiff.txt

Download from Harvard Dataverse

Download - MapSpam Dimensions:Technology and Crops (CVS)

This raster dataset represents total mixed grains crop production in metric tons. Harvested area in hectares was multiplied by yield per hectare to create this data set. 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.

A central focus for governing bodies in Africa is the need to secure the necessary food sources to support their populations. It has been estimated that the current production of crops will need to double by 2050 to meet future needs for food production. Higher level crop-based products that can assist with managing food insecurity, such as cropping watering intensities, crop types, or crop productivity, require as a starting point precise and accurate cropland extent maps indicating where cropland occurs. Current cropland extent maps are either inaccurate, have coarse spatial resolutions, or are not updated regularly. An accurate, high-resolution, and regularly updated cropland area map for the African continent is therefore recognised as a gap in the current crop monitoring services. Key PropertiesGeographic Coverage: Continental Africa - approximately 37° North to 35 SouthTemporal Coverage: 2019Spatial Resolution: 10 x 10 meterUpdate Frequency: TBDNumber of Bands: 3 BandsParent Dataset: Digital Earth Africa's Sentinel-2 Semiannual GeoMADSource Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)

Digital Earth Africa’s cropland extent maps for Eastern, Western, and Northern Africa show the estimated location of croplands in these countries for the period of January to December 2019:

Eastern: Tanzania, Kenya, Uganda, Ethiopia, Rwanda and BurundiWestern: Nigeria, Benin, Togo, Ghana, Cote d'Ivoire, Liberia, Sierra Leone, Guinea and Guinea-BissauNorthern: Morocco, Algeria, Tunisia, Libya and EgyptSahel: Mauritania, Senegal, Gambia, Mali, Burkina Faso, Niger, Chad, Sudan, South Sudan, Somalia and DjiboutiSouthern: South Africa, Namibia, Botswana, Lesotho and Eswatini

Cropland is defined as:

"a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvestable at least once within the 12 months after the sowing/planting date."

This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation.

The provisional cropland extent maps have a resolution of 10 metres and were built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent maps were built separately using extensive training data from Eastern, Western, and Northern Africa, coupled with a Random Forest machine learning model. A detailed exploration of the methods used to produce the cropland extent map can be found in the Jupyter Notebooks in DE Africa’s crop-mask GitHub repository.

Independent validation datasets suggest the following accuracies:

The Eastern Africa cropland extent map has an overall accuracy of 90.3 %, and an f-score of 0.85 The Western Africa cropland extent map has an overall accuracy of 83.6 %, and an f-score of 0.75 The Northern Africa cropland extent map has an overall accuracy of 94.0 %, and an f-score of 0.91The Sahel Africa cropland extent map has an overall accuracy of 87.9 %, and an f-score of 0.78The Southern Africa cropland extent map has an overall accuracy of 86.4 %, and an f-score of 0.75

The algorithms for all regions tend to report more omission errors (labelling actual crops as non-crops) than commission errors (labelling non-crops as crops). Where commission errors occur, they tend to be focussed around wetlands and seasonal grasslands which spectrally resemble some kinds of cropping.

Available BandsBand IDDescriptionValue rangeData typeNoData/Fill valuemaskcrop extent (pixel)0 - 1uint80probcrop probability (pixel)0 - 100uint80filteredcrop extent (object-based)0 - 1uint80

mask: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is a pixel-based cropland extent map, meaning the map displays the raw output of the pixel-based Random Forest classification.

prob: This band displays the prediction probabilities for the ‘crop’ class. As this service uses a random forest classifier, the prediction probabilities refer to the percentage of trees that voted for the random forest classification. For example, if the model had 200 decision trees in the random forest, and 150 of the trees voted ‘crop’, the prediction probability is 150 / 200 x 100 = 75 %. Thresholding this band at > 50 % will produce a map identical to mask.

filtered: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has been filtered using an image segmentation algorithm (see this paper for details on the algorithm used). During this process, segments smaller than 1 Ha (100 10m x 10m pixels) are merged with neighbouring segments, resulting in a map where the smallest classified region is 1 Ha in size. The filtered dataset is provided as a complement to the mask band; small commission errors are removed by object-based filtering, and the ‘salt and pepper’ effect typical of classifying pixels is diminished.

More details on this dataset can be found here.

Russia Monoammonium Phosphate (MAP) Fertilizer Production increased 8.7% in 2019, from a year earlier.

This raster dataset represents total coconut crop production in metric tons. Harvested area in hectares was multiplied by yield per hectare to create this data set. 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.

description: This map service displays present and past oil and gas production in the United States, as well as the location and intensity of exploratory drilling outside producing areas. To construct this map, digital data were used from more than 3 million wells in IHS Inc.'s PI/Dwights PLUS Well Data on CD-ROM, current through 10/1/2005. In some areas, the PI/Dwights data tend not to be complete, particularly for pre-1920 production. IHS data was supplemented with state wells databases for Indiana, Pennsylvania, Kentucky, Illinois, and Ohio, (current as of 2004 to 2006). Because of the proprietary nature of many of these databases, the area of the United States was divided into cells one quarter-mile square and the production information of each well is aggregated in each cell. No proprietary data are displayed or included in the cell maps. The cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. The cell attributes also contain the latitude and longitude values of the center-cell coordinates.; abstract: This map service displays present and past oil and gas production in the United States, as well as the location and intensity of exploratory drilling outside producing areas. To construct this map, digital data were used from more than 3 million wells in IHS Inc.'s PI/Dwights PLUS Well Data on CD-ROM, current through 10/1/2005. In some areas, the PI/Dwights data tend not to be complete, particularly for pre-1920 production. IHS data was supplemented with state wells databases for Indiana, Pennsylvania, Kentucky, Illinois, and Ohio, (current as of 2004 to 2006). Because of the proprietary nature of many of these databases, the area of the United States was divided into cells one quarter-mile square and the production information of each well is aggregated in each cell. No proprietary data are displayed or included in the cell maps. The cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. The cell attributes also contain the latitude and longitude values of the center-cell coordinates.