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

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

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.

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 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

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.

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

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).

Water Use for Crop Irrigation and Irrigated Vegetable Crops in New Mexico CountiesPrepared by: Larry Heard, NMCDC, larryheard@gmail.com Sources: The National Agricultural Statistics Service 2012 Census of Agriculture - Crops and PlantsUnited States Department of Agriculture 2012 Census of Agriculture, http://www.agcensus.usda.gov/Map Layer New Mexico Water Use, 2010 Source: USGS, http://water.usgs.gov/watuseMap Layer

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).

County-Level Pesticide Use Estimates - 1993

The NAWQA Pesticide National Synthesis Project, which began in 1992, is a national-scale assessment of the occurrence and behavior of pesticides in streams and ground water of the United States and the potential for pesticides to adversely affect drinking-water supplies or aquatic ecosystems.

The tables, maps, and graphs provided by Pesticide National Synthesis Project provide estimates of agricultural pesticide use in the conterminous United States for numerous pesticides. The tables report agricultural pesticide use at the county level and are based on farm surveys of pesticide use and estimates of harvested crop acres. The maps show agricultural pesticide use on a finer scale and are created by allocating the county-level estimates to agricultural land within each county. A graph accompanies each map and shows annual national use by major crop for the mapped pesticide for each year.

These pesticide-use estimates are suitable for evaluating national and regional patterns and trends of annual pesticide use. The reliability of estimates, however, generally decreases with scale and these estimates and maps are not intended for detailed evaluations, such as comparing within or between specific individual counties.

About The Pesticide National Synthesis Project

Estimated Annual Agricultural Pesticide Use

How the estimates and maps are made

For all States except California, proprietary farm survey pesticide-use data are aggregated and reported at the multi-county Crop Reporting District (CRD) level. Harvested-crop acreage data by county from the U.S. Department of Agriculture Census of Agriculture are used to calculate the median pesticide-by- crop use rates for each crop in each CRD. These rates are applied to the harvested acreage of each crop in a county to obtain pesticide-use estimates at a county level. Estimates for California are obtained from annual Department of Pesticide Regulation Pesticide Use Reports (California Department of Pesticide Regulation). Methods for generating county-level pesticide-use estimates are described in Estimation of Annual Agricultural Pesticide Use for Counties of the Conterminous United States, 1992–2009 (Thelin and Stone, 2013) and Estimated Annual Agricultural Pesticide Use for Counties of the Conterminous United States, 2008-12 (Baker and Stone, 2015).

Maps are created by allocating county-level use estimates to agricultural land within each county based on land classifications defined in the National Land Cover Database 2011 (NLCD11) (Jin and others, 2013; NLCD 2011 Data Download). The NLCD11 is used for the entire period of record because at a national level agricultural land use has not changed much during that time frame, and by using a single snapshot in time, changes in pesticide use are not obscured by changes in land use. NLCD11 Planted/Cultivated categories 81 (Pasture/Hay) and 82 (Cultivated Crops) were combined to differentiate agricultural land from non-agricultural land. The NLCD11 was then generalized to 1 square kilometer cell size and the percentage of agricultural land for each cell was calculated. The proportion of county agricultural land included in each 1 square kilometer cell was multiplied by the total county use for each pesticide to calculate the proportional amount of use allocated to each cell. To display pesticide use on the annual maps for each compound, the range of all of the cell values nationwide for the entire period are divided into quartiles and a color-coded map is generated for each year based on these quartiles. The quartile classes are converted to pounds per square mile.

Estimates of Pesticide Use – EPest-low and EPest-high methods

For all States except California, two different methods, EPest-low and EPest- high, are used to estimate a range of pesticide use. Both EPest-low and EPest-high methods incorporate proprietary surveyed rates for Crop Reporting Districts (CRDs), but EPest-low and EPest-high estimates differ in how they treat situations when a CRD was surveyed and pesticide use was not reported for a particular crop present in the CRD. In these situations, EPest-low assumes zero use in the CRD for that pesticide-by- crop combination. EPest-high, however, treats the unreported use for that pesticide-by- crop combination in the CRD as missing data. In this case, pesticide-by- crop use rates from neighboring CRDs or CRDs within the same region are used to estimate the pesticide-by- crop EPest-high rate for the CRD.

State-based restrictions on pesticide use were not incorporated into EPest- high or EPest-low estimates. However, EPest-low estimates are more likely to reflect these restrictions than EPest-high estimates. Users of the maps and data should consult the methods presented in Thelin and Stone (2013) and Baker and Stone (2015) to understand the details of how both estimates were determined. Maps are provided for both EPest-low and EPest-high estimates.

Use estimates for California are obtained from annual California Department of Pesticide Regulation pesticide use reports. Because these reports provide county-level use estimates, they are incorporated into the data without further processing and low and high rates are the same for counties in California. California county data are appended after the estimation process is completed for the rest of the Nation.

Pesticide Use-Trend Graphs by Crop

Graphs showing annual use by crop for each pesticide are created by summing the national pesticide use for each compound, for each crop or combination of crops. Combined crops are Pasture and Hay (cropland for pasture, fallow and idle cropland, pastureland, and other hay); Alfalfa; Orchards and grapes (stone fruit trees, citrus, nut trees, apples, pears, and grapevines); Vegetables and fruit (all vegetables and non-orchard fruit, including beans, peas, greens, berries, and melons); and Other (sorghum, non-wheat grains, tobacco, peanuts, sugarcane, sugarbeets, and other miscellaneous crops). The relations of graphed crops and combinations of crops to individual Epest Crop Names are shown in the following table. State-by crop estimates are available in tabular format.

Applications and limitations

Pesticide-use estimates from this study are suitable for making national, regional, and watershed estimates of annual pesticide use; however, the reliability of these estimates generally decreases with scale. For example, detailed interpretation of where and how much use occurs within a county is not appropriate. Although county-level estimates were used to create the maps and are provided in the dataset, it is important to understand that surveyed pesticide-by- crop use was not available for all CRDs and, therefore, extrapolation methods were used to estimate pesticide use for some counties. Moreover, surveyed pesticide-by- crop use may not reflect all agricultural use on all crops grown. In addition, State-based restrictions on pesticide use were not incorporated into EPest-high or EPest-low estimates. EPest-low estimates are more likely to reflect these restrictions than EPest- high estimates. With these caveats in mind, including other details discussed in Thelin and Stone (2013) and Baker and Stone (2015), the maps, graphs, and associated county-level use data are critical information for water- quality models and provide a comprehensive graphical overview of the geographic distribution and trends in agricultural pesticide use in the conterminous United States.

References

• Baker, N.T., and Stone, W.W., 2015, Estimated annual agricultural pesticide use for counties of the conterminous United States, 2008-12: U.S. Geological Survey Data Series 907, 9 p., http://dx.doi.org/10.3133/ds907.
• California Department of Pesticide Regulation, Variously dated, Pesticide use reporting: California Department of Pesticide Regulation pesticide use reporting (PUR) database, at http://www.cdpr.ca.gov/docs/pur/purmain.htm.
• Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., and Xian, G. 2013. A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132: 159-175.
• Thelin, G.P., and Stone, W.W., 2013, Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992–2009: U.S. Geological Survey Scientific Investigations Report 2013-5009, 54 p.
• U.S. Department of Agriculture, 2014, State and county data, Geographic area series parts 1–50, AC–12–A–1—AC–12–A–50: U.S. Department of Agriculture 2012 Census of Agriculture, v. 1, at http://www.agcensus.usda.gov/Publications/2012

References and Resources:

• 1993 County-Level Pesticide Use Estimates
• Estimated Annual Agricultural Pesticide Use - County Level

Comprehensive population and demographic data for 10 Ag Village

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.

Comprehensive population and demographic data for 8 Ag Village

Comprehensive population and demographic data for 23 Ag Village

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

Comprehensive population and demographic data for 3 Ag Village

Comprehensive population and demographic data for 12 Ag Village