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 rice 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 Rice 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.Operations with SalesOperations with Area HarvestedSales in US DollarsArea Harvested in AcresProduction in HundredweightAdditionally 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 United States Department of Agriculture (USDA), provides a complete count of Texas' farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishingThis layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.AlmondsAnimal TotalsBarleyCattleChickensCornCottonCrop TotalsGovt ProgramsGrainGrapesHayHogsLaborMachinery TotalsRiceSorghumSoybeanTractorsTrucksTurkeysWheatWinter Wheat

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).

Rice rose to 11.79 USD/cwt on August 29, 2025, up 2.34% from the previous day. Over the past month, Rice's price has fallen 4.57%, and is down 21.40% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Rice - values, historical data, forecasts and news - updated on September of 2025.

VCU's research station positioned on the north bank of the James River Estuary. This site houses many upland and wetland ecosystems. The marshes on the property were once damned (1920s) to create a recreational lake. In 2011 VCU removed the levy, and has been monitoring marsh restoration and recruitment.

The U.S. Borlaug Fellows in Global Food Security program is funded by the United States Agency for International Development (USAID) to expand the pool of U.S. food security professionals who have the scientific base needed to effectively study and manage the global landscapes in support of sustainable food systems. The intended objectives of the U.S. Borlaug Fellows in Global Food Security program are: a) To help train a new generation of interdisciplinary U.S. scientists with fluency in global food security and the skills to strengthen the capacity of developing countries to apply new innovations and technologies, b) To support the key research themes of the Feed the Future initiative and increase understanding of the links between agricultural production, nutritional status, natural resource conservation, and development, c) To foster cross-cultural understanding and dialog. These data show the quantities of imported maize and rice seed imported into Nigeria from the relevant source countries for the period 2008 to 2015. The data source is the Nigerian Customs Service.

Every year, USDA provides data, analysis, and support to the U.S. Environmental Protection Agency (EPA) for their Inventory of U.S. Greenhouse Gas Emissions and Sinks, an official submission to the United Nations Framework Convention on Climate Change. USDA provides the data and analysis for the land use, land-use change, and forestry and agriculture chapters as well as the agriculture portion of the energy chapter. Emission and sink estimates are reported in aggregate on a national basis. Periodically, USDA utilizes the same data and methods to produce the U.S. Agriculture and Forestry Greenhouse Gas Inventory, published quadrennially. While the data and methods in both GHG inventories are consistent, the USDA report is disaggregated in order to show specific trends by land use or by region.In an effort to provide users with more timely updates on national and state estimates of emissions from agriculture and forestry, USDA has developed an interactive dashboard that allows users to explore emissions estimates from those sectors which is accompanied by supplemental data that helps provide additional context about the drivers of those emissions. The dashboard was last updated on 14 May 2025 to add a glossary, change LULUC figures to improve readability, and add supplemental datasets to the LULUC sections for cropland and grasslands. Results presented in the dashboard are consistent with USDA's U.S. Agriculture and Forestry GHG Inventory, and EPA's Inventory of U.S. Greenhouse Gas Emissions and Sinks. Data are presented for Cropland Soils (N2O), Enteric Fermentation (CH4), Managed Livestock Waste (CH4 + N2O), Grazed Lands (CH4 + N2O), Rice Cultivation + Residue Burning (CH4 + N2O), Energy Use, Forests, Harvested Wood, Urban Trees, and Agricultural Soils.

Meeting increasing future electricity demand in the United States will require extensive and explorative planning due to advancing climatic, socioeconomic, and decarbonization policy drivers. Accounting for the response of changes in these drivers on the energy system are made even more complex when considering them in aggregate form with regionally relevant land and technology constraints that narrow where power plants capable of supporting increasing demand will be feasible to operate under uncertain futures. We offer the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package as a high-resolution product to readily evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States for alternative energy futures. GRIDCERF provides 269 suitability layers for use with 56 power plant technology configurations in a harmonized format readily ingestible by geospatially-enabled modeling software. GRIDCERF comes equipped with pre-compiled technology-specific suitability layers but also allows for user customization to robustly address science objectives when evaluating varying future conditions.

Contents:

Common Rasters:

Suitability Layer	GRIDCERF Raster Name
Agricultural Research Service Lands33	gridcerf_ars_lands_2020_conus.tif
Bureau of Indian Affairs (BIA) Land Area Representation Dataset34	cerf_bia_tribal_lands_2019.tif
Bureau of Land Management (BLM) National Landscape Conservation System (NLCS) - National Monuments35	gridcerf_blm_nlcs_national_monument_2021_conus.tif
BLM NLCS - Outstanding Natural Areas36	gridcerf_blm_nlcs_outstanding_natural_areas_2017_conus.tif
BLM NLCS - Trails Historic West37	gridcerf_blm_nlcs_trails_historic_west_buff_1km_2019_conus.tif
BLM NLCS System - Trails Scenic East37	gridcerf_blm_nlcs_trails_scenic_east_buff_1km_2019_conus.tif
BLM NLCS System – Wilderness38	gridcerf_blm_nlcs_wilderness_2021_conus.tif
BLM NLCS - Wilderness Study Areas38	gridcerf_blm_nlcs_wilderness_study_areas_2021_conus.tif
BLM NLCS - Scenic Rivers39	gridcerf_blm_scenic_rivers_1km_2009_conus.tif
National Park Service (NPS) Class 1 airsheds40	gridcerf_class1_airsheds_2015_conus.tif
BLM NLCS National Conservation Areas35	gridcerf_cons_monu_desig_2021_conus.tif
U.S. Fish and Wildlife Service (USFWS) - Critical Habitat41	gridcerf_fws_critical_habitat_2019_conus.tif
USFWS - Land Interests42	gridcerf_fws_land_interests_2019_conus.tif
USFWS - Lands43	gridcerf_fws_lands_2021_conus.tif
USFWS - National Wildlife Refuges42	gridcerf_fws_national_wildlife_refuges_2019_conus.tif
USFWS - Special Designation42	gridcerf_fws_special_designation_2019_conus.tif
National Land Cover Dataset (NLCD) Wetlands44	gridcerf_nlcd_wetlands_1km_2019_conus.tif
NPS Administrative Boundaries45	gridcerf_nps_administrative_boundaries_2020_conus.tif
NPS Lands46	gridcerf_nps_lands_2019_conus.tif
BLM NLCS - Wild & Scenic Rivers39	gridcerf_nwrs_buff_1km_2021_conus.tif
U.S. Forest Service (USFS) Administrative Boundaries47	gridcerf_usfs_administrative_boundaries_2021_conus.tif
USFS lands43	gridcerf_usfs_lands_2021_conus.tif
U.S. Geological Survey (USGS) National Wilderness Lands48	gridcerf_wilderness_lands_2021_conus.tif
USGS Protected Areas of the U.S - Class 1&249	gridcerf_usgs_padus_class_1_to_2_2018_conus.tif
U.S. State Protected Lands50	gridcerf_wdpa_state_protected_lands_2021_conus.tif
Nature Conservancy lands51	gridcerf_wdpa_tnc_managed_lands_2016_conus.tif
USFS Wilderness Areas52	gridcerf_usfs_wilderness_ares_2015_conus.tif

Technology-specific Rasters:

Suitability Layer	GRIDCERF Raster Name
Slope 10% or less suitable22	gridcerf_srtm_slope_5pct_or_less.tif
Slope 10% or less suitable22	gridcerf_srtm_slope_10pct_or_less.tif
Slope 12% or less suitable22	gridcerf_srtm_slope_12pct_or_less.tif
Slope 20% or less suitable22	gridcerf_srtm_slope_20pct_or_less.tif
Airports (10-mile buffer)53	gridcerf_airports_10mi_buffer_conus.tif
Airports (3-mile buffer)53	gridcerf_airports_3mi_buffer_conus.tif
Proximity to Railroad and Navigable Waters (< 5 km)54,55	gridcerf_railnodes5km_navwaters5km_conus.tif
Coal Supply54–56	gridcerf_coalmines20km_railnodes5km_navwaters5km_conus.tif
NTAD CO Non-attainment Areas57	gridcerf_naa_co_1km_2013_conus.tif
NTAD NOx Non-attainment Areas57	gridcerf_naa_nox_1km_2013_conus.tif
NTAD Ozone Non-attainment Areas57	gridcerf_naa_ozone_1km_2018_conus.tif
NTAD Lead Non-attainment Areas57	gridcerf_naa_pb_1km_2017_conus.tif
NTAD PM10 Non-attainment Areas57	gridcerf_naa_pm10_1km_2013_conus.tif
NTAD PM2.5 Non-attainment Areas57	gridcerf_naa_pm25_1km_2016_conus.tif
NTAD SOx Non-attainment Areas57	gridcerf_naa_sox_1km_2021_conus.tif
Earthquake Potential58	gridcerf_earthquake_pga_0.3g_at_2pct_in_50yrs_2016_conus.tif
Densely population areas12	gridcerf_densely_populated_ssp[2,3,5]_[year].tif
Densely population areas buffered by 25 miles12	gridcerf_densely_populated_ssp[2,3,5]_[year]_buff25mi.tif
Densely population areas – nuclear12	gridcerf_densely_populated_ssp[2,3,5]_[year]_nuclear.tif
National Hydrography Dataset (version 2;

The mineral concentrations in cereals are important for human health, especially for individuals who consume a cereal subsistence diet. A number of elements, such as zinc, are required within the diet, while some elements are toxic to humans, for example arsenic. In this study we carry out genome-wide association (GWA) mapping of grain concentrations of arsenic, copper, molybdenum and zinc in brown rice using an established rice diversity panel of ∼300 accessions and 36.9 k single nucleotide polymorphisms (SNPs). The study was performed across five environments: one field site in Bangladesh, one in China and two in the US, with one of the US sites repeated over two years. GWA mapping on the whole dataset and on separate subpopulations of rice revealed a large number of loci significantly associated with variation in grain arsenic, copper, molybdenum and zinc. Seventeen of these loci were detected in data obtained from grain cultivated in more than one field location, and six co-localise with previously identified quantitative trait loci. Additionally, a number of candidate genes for the uptake or transport of these elements were located near significantly associated SNPs (within 200 kb, the estimated global linkage disequilibrium previously employed in this rice panel). This analysis highlights a number of genomic regions and candidate genes for further analysis as well as the challenges faced when mapping environmentally-variable traits in a highly genetically structured diversity panel.

Germany Imports from Pakistan of Rice was US$29.07 Million during 2024, according to the United Nations COMTRADE database on international trade. Germany Imports from Pakistan of Rice - data, historical chart and statistics - was last updated on September of 2025.