Grid square estimates of agricultural census data for England Scotland and Wales supplied by EDINA. Request specific areas or national coverage.

The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them. Conducted by USDA’s National Agricultural Statistics Service, the 2012 Census of Agriculture collected more than six million data items directly from farmers. The Ag Census Web Maps application makes this information available at the county level through a few clicks. The maps and accompanying data help users visualize, download, and analyze Census of Agriculture data in a geospatial context.

The Census of Agriculture highlight key agricultural metrics for US states and counties. Percentage metrics included were calculated as follows: Percent of harvested cropland in cover crops = (cover crops acres)/((harvested cropland)+(failed crops)-(alfalfa))Percent of total tilled cropland using no-till = (no-till acreage)/(no till + reduced till + conventional till)Percent of tilled cropland using conservation tillage = (no till + reduced till acreage)/(no till + reduced till + conventional till)Percent of agricultural land in conservation easement = (conservation easement acres that excludes CRP)/((land in farms) – (CRP WRP FWP CREP acres))Percent of agricultural land in Conservation Reserve Program = (Conservation Reserve Program acres / cropland acres + Conservation Reserve Program acres ))*100Note, that counties for the Census of Agriculture are different than standard US Census Bureau counties; for example, cities in Virginia such as Harrisonburg, VA are rolled into the respective county and counties in Alaska are rolled into regions with their own district/region FIPS codes, etc. Also note, some counties have no data as one or more of the input variables included suppression.These data have been made publicly available from an authoritative source other than this Atlas and data should be obtained directly from that source for any re-use. See the original metadata from the authoritative source for more information about these data and use limitations. The authoritative source of these data can be found at the following location: https://www.nass.usda.gov/Publications/AgCensus/2017/Online_Resources/Ag_Census_Web_Maps/Data_download/index.php

description: This data set portrays the National Agricultural Statistics Service, U.S. Department of Agriculture's 1997 Census data for the Lake Michigan basin, presented by county. There are 25 categories of data which include information about farms, crops, livestock, values of products, and farm operator characteristics. This is a revised version of the April, 2000, data set.; abstract: This data set portrays the National Agricultural Statistics Service, U.S. Department of Agriculture's 1997 Census data for the Lake Michigan basin, presented by county. There are 25 categories of data which include information about farms, crops, livestock, values of products, and farm operator characteristics. This is a revised version of the April, 2000, data set.

EDINA agcensus data provides agricultural census data for England, Scotland and Wales at 2km, 5km or 10km grid square resolution. The Agricultural Census is conducted in June each year by the government departments dealing with Agriculture and Rural Affairs for Scotland, England, and Wales. Farmers are surveyed in each year via a postal questionnaire, with the farmer declaring the agricultural activity on their land. In Scotland the census covers all major agricultural holdings, but in England and Wales a stratified sample of holdings are surveyed. Data for non-surveyed farms is extrapolated from previous years and trends on comparable farms. The respective government departments publish information relating to farm holdings for recognised geographies for the 150 items of data. Algorithms developed by Edinburgh University Data Library convert small area data provided by the government department into grid squares of 2, 5 or 10 km. Dates covered: Great Britain: 1969-1994; England and Wales: 1969-1997; England: 2000-present; Wales: 2000-present; Scotland: 1969-present. The frequency of updating is dependent upon the respective government department collating and publishing the census data for each year prior to supplying EDINA with the data for their processing. EDINA data is a paid for service. There is a nominal fee per year for unlimited access for UK tertiary education institutions. Non-academic subscriptions are based on the number of potential users or on a per project basis.

Availability of EDINA agcensus data for each crop type in England.

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

The Census of Agriculture highlight key agricultural metrics for US states and counties. Percentage metrics included were calculated as follows: Percent of harvested cropland in cover crops = (cover crops acres)/((harvested cropland)+(failed crops)-(alfalfa))Percent of total tilled cropland using no-till = (no-till acreage)/(no till + reduced till + conventional till)Percent of tilled cropland using conservation tillage = (no till + reduced till acreage)/(no till + reduced till + conventional till)Percent of agricultural land in conservation easement = (conservation easement acres that excludes CRP)/((land in farms) – (CRP WRP FWP CREP acres))Percent of agricultural land in Conservation Reserve Program = (Conservation Reserve Program acres / cropland acres + Conservation Reserve Program acres ))*100Note, that counties for the Census of Agriculture are different than standard US Census Bureau counties; for example, cities in Virginia such as Harrisonburg, VA are rolled into the respective county and counties in Alaska are rolled into regions with their own district/region FIPS codes, etc. Also note, some counties have no data as one or more of the input variables included suppression.These data have been made publicly available from an authoritative source other than this Atlas and data should be obtained directly from that source for any re-use. See the original metadata from the authoritative source for more information about these data and use limitations. The authoritative source of these data can be found at the following location: https://www.nass.usda.gov/Publications/AgCensus/2017/Online_Resources/Ag_Census_Web_Maps/Data_download/index.php

This EnviroAtlas dataset contains data on the mean cultivated biological nitrogen fixation (C-BNF) in cultivated crop and hay/pasture lands per 12-digit Hydrologic Unit (HUC) in 2006. Nitrogen (N) inputs from the cultivation of legumes, which possess a symbiotic relationship with N-fixing bacteria, were calculated with a recently developed model relating county-level yields of various leguminous crops with BNF rates. We accessed county-level data on annual crop yields for soybeans (Glycine max L.), alfalfa (Medicago sativa L.), peanuts (Arachis hypogaea L.), various dry beans (Phaseolus, Cicer, and Lens spp.), and dry peas (Pisum spp.) for 2006 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We estimated the yield of the non-alfalfa leguminous component of hay as 32% of the yield of total non-alfalfa hay (http://www.agcensus.usda.gov/index.php). Annual rates of C-BNF by crop type were calculated using a model that relates yield to C-BNF. We assume yield data reflect differences in soil properties, water availability, temperature, and other local and regional factors that can influence root nodulation and rate of N fixation. We distributed county-specific, C-BNF rates to cultivated crop and hay/pasture lands delineated in the 2006 National Land Cover Database (30 x 30 m pixels) within the corresponding county. C-BNF data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The National Agricultural Statistics Service 2012 Census of AgriculturePrepared by Larry Heard, NMCDC, larryheard@gmail.comSource: United States Department of Agriculture 2012 Census of Agriculture, http://www.agcensus.usda.gov/The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them.Maps and statistics from the 2012 Census of Agriculture are organized into five broad categories:Crops and Plants – Data on harvested acreage for major field crops, hay, and other forage crops, as well as acreage data for vegetables, fruits, tree nuts, and berries.Economics – Data on agriculture sales, farm income, government payments from conservation and farm programs, amounts received from loans, a broad range of production expenses, and value of buildings and equipment.Farms – Information on farm size, ownership, and Internet access, as well as data on total land in farms, land use, irrigation, fertilized cropland, and enrollment in crop insurance programs.Livestock and Animals – Statistics on cattle and calves, cows and heifers, milk cows, and other cattle, as well as hogs, sheep, goats, horses, and broilers.Operators – Statistics on hired farm labor, tenure, land rented or leased, primary occupation of farm operator, and demographic characteristics such as age, sex, race/ethnicity, and residence location.

This EnviroAtlas dataset contains data on the mean livestock manure application to cultivated crop and hay/pasture lands by 12-digit Hydrologic Unit (HUC) in 2006. Livestock manure inputs to cultivated crop and hay/pasture lands were estimated using county-level estimates of recoverable animal manure from confined feeding operations compiled for 2007. Recoverable manure is defined as manure that is collected, stored, and available for land application from confined feeding operations. County-scale data on livestock populations -- needed to calculate manure inputs -- were only available for the year 2007 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We acquired county-level data describing total farm-level inputs (kg N/yr) of recoverable manure to individual counties in 2007 from the International Plant Nutrition Institute (IPNI) Nutrient Geographic Information System (NuGIS; http://www.ipni.net/nugis). These data were converted to per area rates (kg N/ha/yr) of manure N inputs by dividing the total N input by the land area (ha) of combined cultivated crop and hay/pasture (agricultural) lands within a county as determined from county-level summarization of the 2006 NLCD. We distributed county-specific, per area N inputs rates to cultivated crop and hay/pasture lands (30 x 30 m pixels) within the corresponding county. Manure data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The National Agricultural Statistics Service 2012 Census of Agriculture - Farm OperatorsPrepared by Larry Heard, NMCDC, larryheard@gmail.comSource: United States Department of Agriculture 2012 Census of Agriculture, http://www.agcensus.usda.gov/The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them.Maps and statistics from the 2012 Census of Agriculture are organized into five broad categories:Crops and Plants – Data on harvested acreage for major field crops, hay, and other forage crops, as well as acreage data for vegetables, fruits, tree nuts, and berries.Economics – Data on agriculture sales, farm income, government payments from conservation and farm programs, amounts received from loans, a broad range of production expenses, and value of buildings and equipment.Farms – Information on farm size, ownership, and Internet access, as well as data on total land in farms, land use, irrigation, fertilized cropland, and enrollment in crop insurance programs.Livestock and Animals – Statistics on cattle and calves, cows and heifers, milk cows, and other cattle, as well as hogs, sheep, goats, horses, and broilers.Operators – Statistics on hired farm labor, tenure, land rented or leased, primary occupation of farm operator, and demographic characteristics such as age, sex, race/ethnicity, and residence location.ArcGIS Map Service: http://arcgis-ersarcgism3xl-1157953884.us-east-1.elb.amazonaws.com/arcgis/rest/services/NASS/operators/MapServer

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.

What?

A dataset containing 315 total variables from 33 secondary sources. There are 262 unique variables, and 53 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 2.2?

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 v 2.2 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. Variable herbac22 for 55079, Milwaukee, WI, incorrectly had the value 2,049.612. That value was correctly changed to missing, with no data in the cell.

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.

Comprehensive population and demographic data for 23 Ag Village

Comprehensive population and demographic data for 7 Ag Village

Comprehensive population and demographic data for 3 Ag Village

Comprehensive population and demographic data for 10 Ag Village

According to Cognitive Market Research, The Non-Protein Nitrogen Market will be USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031. Asia Pacific held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX % from 2024 to 2031. The North American region is the fastest-growing market with a CAGR of XX% from 2024 to 2031 and is projected to grow at a CAGR of XX% in the future. Europe accounted for a market share of over XX% of the global revenue with a USD XX million market size. Latin America had a market share for more than XX% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031. The Non-Protein Nitrogen Market held the highest market revenue share in 2024. Market Dynamics of the Non-Protein Nitrogen Market

Key Drivers for The Non-Protein Nitrogen Market

Increased meat consumption will fuel market expansion of non-nitrogen proteins

The market for non-protein nitrogen is anticipated to increase at a faster rate in the future due to rising meat consumption. The term "meat" refers to the edible muscle tissue of animals—usually birds or mammals—that people eat. By enabling bacteria in the ruminant stomach to transform substances like urea, biuret, and ammonium phosphate into proteins, non-protein nitrogen in feed can increase the total protein content and nutritional value of meat products. For instance, in march 2023 The Australian Bureau of Statistics, an Australian government institution, reports that the output of beef climbed by 11.3% to 524,335 tons. In addition, mutton output rose to 58,662 tons, a 31.8% rise. Thus, the non-protein nitrogen market is driven by rising meat consumption. Source:(https://www.abs.gov.au/statistics/industry/agriculture/livestock-products-australia/mar-2023) Thus, the Increased meat consumption is poised to drive market expansion for non-protein nitrogen, as demand for livestock feed additives rises to optimize production efficiency and meet the nutritional needs of a growing meat industry, aligning with the broader trend of rising meat consumption worldwide.

The market for non-nitrogen proteins rises in response to a rising global population of livestock.

The market for non-protein nitrogen is anticipated to develop in the future because to the growing number of animals. Domesticated animals produced by people for commercial, recreational, or agricultural purposes are referred to as livestock. Non-protein nitrogen supplements, like urea, are essential for livestock nutrition because they offer a cost-effective substitute for conventional protein sources, improve feed efficiency, and meet the higher production needs that come with a bigger herd. For instance, in September 2022 A study published in 2021 by the Brazilian Institute of Geography and Statistics states that the number of hogs and pigs increased by 3.2% to 42.25 million. Thus, the market for non-protein nitrogen is expanding as a result of the growing number of cattle. Source:(https://www.census.gov/newsroom/press-releases/2022/2022-population-estimates.html) For instance, as of September 2022, the U.S. Census Bureau a US government agency reported that the number of residents in the US had grown by 0.4%, or 1,256,003, to 333,287,557. Consequently, the market for Non-Nitrogen Proteins is being driven by the rising food demands of a growing population. Source:(https://www.nass.usda.gov/AgCensus/) Thus, the growing population of livestock is expected to fuel market growth for non-protein nitrogen, as the need to sustainably feed and nourish an expanding livestock population drives up demand for supplements that enhance feed efficiency, promote animal health, and support increased productivity to meet the demands of a growing global population.

Restraint Factor for The Non-Protein Nitrogen Market

Ruminant toxicity of non-protein nitrogen restrains the market

In ruminant diets, non-protein nitrogen is used more frequently. A...

Comprehensive population and demographic data for 17 Ag Village