Farmland classification identifies map units as prime farmland, farmland of statewide importance, farmland of local importance, or unique farmland. It identifies the location and extent of the soils that are best suited to food, feed, fiber, forage, and oilseed crops. NRCS policy and procedures on prime and unique farmlands are published in the "Federal Register," Vol. 43, No. 21, January 31, 1978.This data set is a digital soil survey and generally is the mostdetailed level of soil geographic data developed by the NationalCooperative Soil Survey. The information was prepared by digitizingmaps, by compiling information onto a planimetric correct baseand digitizing, or by revising digitized maps using remotelysensed and other information.This data set consists of georeferenced digital map data andcomputerized attribute data. The map data are in a soil survey areaextent format and include a detailed, field verified inventoryof soils and miscellaneous areas that normally occur in a repeatablepattern on the landscape and that can be cartographically shown atthe scale mapped. The soil map units are linked to attributes in theNational Soil Information System relational database, which givesthe proportionate extent of the component soils and their properties.

Improving the quality of water discharged from agricultural watersheds requires comprehensive and adaptive approaches for planning and implementing conservation practices. These measures will need to consider landscape hydrology, distributions of soil types, land cover, and crop distributions in an integrated manner. The two most consistent challenges to these efforts will be consistency and reliability of data, and the capacity to translate conservation planning from watershed to farm and field scales. The translation of scale is required because, while conservation practices can be planned based on a watershed scale framework, they must be implemented by landowners in specific fields and riparian sites that are under private ownership. To support these goals, it has been necessary to develop planning approaches, high-resolution spatial datasets, and conservation practice assessment tools that will allow the agricultural and conservation communities to characterize and mitigate these challenges. The field boundary dataset represents a spatial framework for assembling and maintaining geospatial data to support conservation planning at the scale where conservation practices are implemented. This field boundaries dataset has been assembled to support field-scale agricultural conservation planning using the USDA/ARS Agricultural Conservation Planning Framework (ACPF). The original data used to create this database are the pre-2008 Farm Bill FSA common land unit (CLU) datasets. A portion of metadata found herein pertains to the USDA FSA CLU. The remaining information has been developed to reflect the repurposing of the data in its aggregated form. It is important to note that all USDA programmatic and ownership information that was associated with the original data have been removed. Beyond that, these data has been extensively edited to reflect crop-specific land use consistent with 2015 land cover as derived from 2015 NASS Crop Data Layer datasets and 2015 aerial photography, and no longer reflects discrete ownership patterns. Resources in this dataset:Resource Title: Agricultural land use by field: Minnesota 2010-2019. File Name: MN_ACPFfields2019.zipResource Description: This field boundaries dataset has been assembled to support field-scale agricultural conservation planning using the USDA/ARS Agricultural Conservation Planning Framework (ACPF).Resource Software Recommended: ArcGIS,url: https://www.esri.com/en-us/home Resource Title: Minnesota Field Boundaries 2019. File Name: MN_ACPF_fieldBoundaries_2019.pdfResource Description: Minnesota Field Boundaries 2019Resource Title: Minnesota ACPF Crop History 2010-2019. File Name: MN_ACPFfields_CropHistory2010_2019.pdfResource Description: Minnesota ACPF Crop History 2010-2019Resource Title: Minnesota ACPF Land Use 2014-2019. File Name: MN_ACPFfields_LandUse2014_2019.pdfResource Description: Minnesota ACPF Land Use 2014-2019

The Farmland Protection Policy Act, part of the 1981 Farm Bill, is intended to limit federal activities that contribute to the unnecessary conversion of farmland to other uses. The law applies to construction projects funded by the federal government such as highways, airports, and dams, and to the management of federal lands. As part of the implementation of this law, the Natural Resources Conservation Service identifies high quality agricultural soils as prime farmland, unique farmland, and land of statewide or local importance. Each category may contain one or more limitations such as Prime Farmland if Irrigated. For more information of farmland classification see the National Soil Survey Handbook. Dataset SummaryPhenomenon Mapped: FarmlandGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). The value for farmland class is derived from the gSSURGO map unit table field Farm Class(farmlndcl). What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "farmland" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "farmland" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. The ArcGIS Living Atlas of the World provides an easy way to explore many otherbeautiful and authoritative maps on hundreds of topics like this one. Data Dictionary"All areas are prime farmland" 1;"Farmland of local importance" 2;"Farmland of statewide importance" 3;"Farmland of statewide importance, if drained" 4;"Farmland of statewide importance, if drained and either protected from flooding or not frequently flooded during the growing season" 5;"Farmland of statewide importance, if irrigated" 6;"Farmland of statewide importance, if irrigated and drained" 7;"Farmland of statewide importance, if irrigated and either protected from flooding or not frequently flooded during the growing season" 8;"Farmland of statewide importance, if irrigated and reclaimed of excess salts and sodium" 9;"Farmland of statewide importance, if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60" 10;"Farmland of statewide importance, if protected from flooding or not frequently flooded during the growing season" 11;"Farmland of statewide importance, if warm enough" 12;"Farmland of statewide importance, if warm enough, and either drained or either protected from flooding or not frequently flooded during the growing season" 13;"Farmland of unique importance" 14;"Not prime farmland" 15;"Prime farmland if drained" 16;"Prime farmland if drained and either protected from flooding or not frequently flooded during the growing season" 17;"Prime farmland if irrigated" 18;"Prime farmland if irrigated and drained" 19;"Prime farmland if irrigated and either protected from flooding or not frequently flooded during the growing season" 20;"Prime farmland if irrigated and reclaimed of excess salts and sodium" 21;"Prime farmland if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60" 22;"Prime farmland if protected from flooding or not frequently flooded during the growing season" 23;"Prime farmland if subsoiled, completely removing the root inhibiting soil layer" 24;"Farmland of local importance, if irrigated" 25" Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Improving the quality of water discharged from agricultural watersheds requires comprehensive and adaptive approaches for planning and implementing conservation practices. These measures will need to consider landscape hydrology, distributions of soil types, land cover, and crop distributions in an integrated manner. The two most consistent challenges to these efforts will be consistency and reliability of data, and the capacity to translate conservation planning from watershed to farm and field scales. The translation of scale is required because, while conservation practices can be planned based on a watershed scale framework, they must be implemented by landowners in specific fields and riparian sites that are under private ownership. To support these goals, it has been necessary to develop planning approaches, high-resolution spatial datasets, and conservation practice assessment tools that will allow the agricultural and conservation communities to characterize and mitigate these challenges. The field boundary dataset represents a spatial framework for assembling and maintaining geospatial data to support conservation planning at the scale where conservation practices are implemented. This field boundaries dataset has been assembled to support field-scale agricultural conservation planning using the USDA/ARS Agricultural Conservation Planning Framework (ACPF). The original data used to create this database are the pre-2008 Farm Bill FSA common land unit (CLU) datasets. A portion of metadata found herein pertains to the USDA FSA CLU. The remaining information has been developed to reflect the repurposing of the data in its aggregated form. It is important to note that all USDA programmatic and ownership information that was associated with the original data have been removed. Beyond that, these data has been extensively edited to reflect crop-specific land use consistent with 2009 land cover as derived from 2009 NASS Crop Data Layer datasets and 2009 aerial photography, and no longer reflects discrete ownership patterns. The ACPF field boundaries feature class incorporates two additional resources that form the Illinois ACPF Land Use database. The Illinois ACPF Fields Crop History table holds the dominant land use class, derived from the NASS CDL, for individual fields from 2010 to 2020. The Illinois ACPF Land Use table hold summary land use information for individual fields for 2015 to 2020 including an assigned General Land Use (GenLU) that represent the cropping system over that period. In lieu of a data dictionary for these resources, each dataset has a FGDC-compliant metadata file using the North American ISO 19115-2003 profile in .xml format. For more information about this dataset contact David E. James at davide.james@usda.gov or dejames@iastate.edu Resources in this dataset:Resource Title: Illinois ACPF Crop History 2010-2020. File Name: IL_ACPFfields_CropHistory2010_2020.pdfResource Description: Illinois ACPF Crop History 2010-2020Resource Software Recommended: Adobe Acrobat,url: www.adobe.com Resource Title: Illinois ACPF Land Use 2015-2020. File Name: IL_ACPFfields_LandUse2015_2020.pdfResource Description: Illinois ACPF Land Use 2015-2020Resource Software Recommended: Adobe Acrobat,url: www.esri.com Resource Title: Illinois Field Boundaries 2020. File Name: IL_ACPFfields2020.pdfResource Description: Illinois Field Boundaries 2020 metadataResource Software Recommended: Adobe Acrobat,url: www.adobe.com Resource Title: Agricultural land use by field: Illinois 2010-2020. File Name: Illinois_ACPFfields2020.zipResource Description: This field boundaries dataset has been assembled to support field-scale agricultural conservation planning using the USDA/ARS Agricultural Conservation Planning Framework (ACPF).Resource Software Recommended: ESRI's ArcGIS,url: www.esri.com

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Farmland. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Farmland, the median income for all workers aged 15 years and older, regardless of work hours, was $29,901 for males and $17,083 for females.

These income figures highlight a substantial gender-based income gap in Farmland. Women, regardless of work hours, earn 57 cents for each dollar earned by men. This significant gender pay gap, approximately 43%, underscores concerning gender-based income inequality in the town of Farmland.

- Full-time workers, aged 15 years and older: In Farmland, among full-time, year-round workers aged 15 years and older, males earned a median income of $53,167, while females earned $34,318, leading to a 35% gender pay gap among full-time workers. This illustrates that women earn 65 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Farmland, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Farmland median household income by race. You can refer the same here

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. Resources in this dataset:Resource Title: Ag Census Web Maps. File Name: Web Page, url: https://www.nass.usda.gov/Publications/AgCensus/2012/Online_Resources/Ag_Census_Web_Maps/Overview/index.php/ The interactive map application assembles maps and statistics from the 2012 Census of Agriculture in 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.

The Ag Census Web Maps application allows you to:

Select a map to display from a the above five general categories and associated subcategories. Zoom and pan to a specific area; use the inset buttons to center the map on the continental United States; zoom to a specific state; and show the state mask to fade areas surrounding the state. Create and print maps showing the variation in a single data item across the United States (for example, average value of agricultural products sold per farm). Select a county and view and download the county’s data for a general category. Download the U.S. county-level dataset of mapped values for all categories in Microsoft ® Excel format.

The Farmland Protection Policy Act, part of the 1981 Farm Bill, is intended to limit federal activities that contribute to the unnecessary conversion of farmland to other uses. The law applies to construction projects funded by the federal government such as highways, airports, and dams, and to the management of federal lands. As part of the implementation of this law, the Natural Resources Conservation Service identifies high quality agricultural soils as prime farmland, unique farmland, and land of statewide or local importance. Each category may contain one or more limitations such as Prime Farmland if Irrigated. For a complete list of categories and definitions, see the National Soil Survey Handbook.All areas are prime farmlandFarmland of local importanceFarmland of statewide importanceFarmland of statewide importance, if drainedFarmland of statewide importance, if drained and either protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if irrigatedFarmland of statewide importance, if irrigated and drainedFarmland of statewide importance, if irrigated and either protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if irrigated and reclaimed of excess salts and sodiumFarmland of statewide importance, if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60Farmland of statewide importance, if protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if warm enoughFarmland of statewide importance, if warm enough, and either drained or either protected from flooding or not frequently flooded during the growing seasonFarmland of unique importanceNot prime farmlandPrime farmland if drainedPrime farmland if drained and either protected from flooding or not frequently flooded during the growing seasonPrime farmland if irrigatedPrime farmland if irrigated and drainedPrime farmland if irrigated and either protected from flooding or not frequently flooded during the growing seasonPrime farmland if irrigated and reclaimed of excess salts and sodiumPrime farmland if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60Prime farmland if protected from flooding or not frequently flooded during the growing seasonPrime farmland if subsoiled, completely removing the root inhibiting soil layerDataset SummaryPhenomenon Mapped: FarmlandUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: December 2021ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for farmland class is derived from the gSSURGO map unit table field Farm Class (farmlndcl).What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "farmland" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "farmland" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Farmland, IN, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Farmland, IN reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Farmland households based on income levels.

Key observations

• For Family Households: In Farmland, the majority of family households, representing 24.75%, earn $35,000 to $39,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.49%, have incomes falling $125,000 to $149,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Farmland, the majority of non-family households, accounting for 13.08%, have income $20,000 to $24,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 0.0%, earn $125,000 to $149,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Farmland, IN (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Farmland, IN
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Farmland, IN
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Farmland, IN

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Farmland median household income. You can refer the same here

The Cropland Data Layer (CDL), hosted on CropScape, provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. The data is created annually using moderate resolution satellite imagery and extensive agricultural ground truth. Users can select a geographic area of interest or import one, then access acreage statistics for a specific year or view the change from one year to another. The data can be exported or added to the CDL. The information is useful for issues related to agricultural sustainability, biodiversity, and land cover monitoring, especially due to extreme weather events. Resources in this dataset:Resource Title: CropScape and Cropland Data Layer - National Download. File Name: Web Page, url: https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php Downloads available as zipped files at https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php --

National CDL's -- by year, 2008-2020. Cropland Data Layer provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. National Cultivated Layer -- based on the most recent five years (2013-2020). National Frequency Layer -- the 2017 Crop Frequency Layer identifies crop specific planting frequency and are based on land cover information derived from the 2008 through 2020CDL's. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat. National Confidence Layer -- the Confidence Layer spatially represents the predicted confidence that is associated with that output pixel, based upon the rule(s) that were used to classify it. Western/Eastern/Central U.S.

Visit https://nassgeodata.gmu.edu/CropScape/ for the interactive map including tutorials and basic instructions. These options include a "Demo Video", "Help", "Developer Guide", and "FAQ".

Representative dairy farms were modeled using the Integrated Farm System Model with 20 farms in each of 6 regions of the United States for the years of 1971 and 2020 to determine improvements made in reducing environmental impacts over the 50-year period. Important data and information describing these farms are documented in these tables. These data include the farm location, number of cows and heifers maintained, milk produced, feeds and nutrient contents fed, crop areas, crop yields, fertilizer and lime application rates, irrigation water applied, milking and housing facilities, manure collection, storage and application methods used, and soil characteristics. These data are published as supplementary information for the article “Fifty years of environmental progress for United States dairy farms” published in the Journal of Dairy Science.

Abstract (en): This collection includes county-level data from the United States Censuses of Agriculture for the years 1840 to 2012. The files provide data about the number, types, output, and prices of various agricultural products, as well as information on the amount, expenses, sales, values, and production of machinery. Most of the basic crop output data apply to the previous harvest year. Data collected also included the population and value of livestock, the number of animals slaughtered, and the size, type, and value of farms. Part 46 of this collection contains data from 1980 through 2010. Variables in part 46 include information such as the average value of farmland, number and value of buildings per acre, food services, resident population, composition of households, and unemployment rates. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: Not applicable. Datasets:DS0: Study-Level FilesDS1: Farm Land Value Data Set (County and State) 1850-1959DS2: 1840 County and StateDS3: 1850 County and StateDS4: 1860 County and StateDS5: 1870 County and StateDS6: 1880 County and StateDS7: 1890 County and StateDS8: 1900 County and StateDS9: 1910 County and StateDS10: 1920 County and State, Dataset 1DS11: 1920 County and State, Dataset 2DS12: 1925 County and StateDS13: 1930 County and State, Dataset 1DS14: 1930 County and State, Dataset 2DS15: 1935 County and StateDS16: 1940 County and State, Dataset 1DS17: 1940 County and State, Dataset 2DS18: 1940 County and State, Dataset 3DS19: 1940 County and State, Dataset 4 (Water)DS20: 1945 County and StateDS21: 1950 County and State, Dataset 1DS22: 1950 Crops, County and State, Dataset 2DS23: 1950 County, Dataset 3DS24: 1950 County and State, Dataset 4DS25: 1954 County and State, Dataset 1DS26: 1954 Crops, County and State, Dataset 2DS27: 1959 County and State, Dataset 1DS28: 1959 Crops, County and State, Dataset 2DS29: 1959 County, Dataset 3DS30: 1964 Dataset 1DS31: 1964 Crops, County and State, Dataset 2DS32: 1964 County, Dataset 3DS33: 1969 All Farms, County and State, Dataset 1DS34: 1969 Farms 2500, County and State, Dataset 2DS35: 1969 Crops, County and State, Dataset 3DS36: 1974 All Farms, County and State, Dataset 1DS37: 1974 Farms 2500, County and State, Dataset 2DS38: 1974 Crops, County and State, Dataset 3DS39: 1978 County and StateDS40: 1982 County and StateDS41: 1987 County and StateDS42: 1992 County and StateDS43: 1997 County and StateDS44: 2002 County and StateDS45: 2007 County and StateDS46: State and County Data, United States, 1980-2010DS47: 2012 County and State Farms within United States counties and states. Smallest Geographic Unit: FIPS code The sample was the universe of agricultural operating units. For 1969-2007, data were taken from computer files from the Census Bureau and the United States Department of Agriculture. 2018-08-20 The P.I. resupplied data and documentation for 1935 County and State (dataset 15) and 1997 County and State (dataset 43). Additionally, documentation updates and variable label revisions have been incorporated in datasets 22, 26, 28, 31, 35, and 38 at the request of the P.I.2016-06-29 The data and documentation for 2012 County and State (data set 47) have been added to this collection. The collection and documentation titles have been updated to reflect the new year.2015-08-05 The data, setup files, and documentation for 1964 Dataset 1 have been updated to reflect changes from the producer. Funding insitution(s): National Science Foundation (NSF-SES-0921732; 0648045). United States Department of Health and Human Services. National Institutes of Health (R01 HD057929).

This coverage contains estimates of land in agricultural production in counties in the conterminous United States as reported in the 1987 Census of Agriculture (U.S. Department of Commerce, 1989a). Land in agriculture data are reported as either a number (for example, number of Farms), acres, or as a percentage of county area. Land in agriculture estimates were generated from surveys of all farms where $1,000 or more of agricultural products were sold, or normally would have been sold, during the census year. Most of the attributes summarized represent 1987 data, but some information for the 1982 Census of Agriculture also was included. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). Agricultural land Census of Agriculture Counties United States

Abstract

This dataset, developed by the Native Lands Advocacy Project summarizes data from the USDA Cropland Data Layer for 552 unique native land areas in the coterminous United States. According to the the USDA, the Cropland Data Layer is a “raster, geo-referenced, crop-specific land cover data layer created annually for the continental United States. The CDL is created using moderate resolution satellite imagery and extensive agricultural ground truthing. The purpose of the Cropland Data Layer program is to use satellite imagery to provide acreage estimates to the Agricultural Statistics Board for major commodities and to produce digital, crop-specific, categorized geo-referenced output products.” The CDL data has been collected since 1997 making it an excellent tool for analyzing long-term land cover trends.

The data includes 130 categories ranging from specific types of crops, pasturelands, developed lands, wetlands, etc. According to Lark et al. (2017) “the CDL covers the conterminous 48 states with field-level resolution and crop classification accuracies typically upwards of 90% for major commodities like corn, cotton, rice, soybeans, and wheat.” The Cropland Data Layer has been used in hundreds of studies on a range of topics from agriculture productivity, crop variability, impacts of climate change, climate resiliency studies, estimates of carrying capacity, etc.Boundary data used to define summary areas was obtained from the 2019 US Census Bureau’s TIGER/Line Shapefile for American Indian areas in the United States which includes both American Indians and Alaskan Natives, a total of 845 unique features or land areas. From this we removed all features/areas outside of the conterminous United States leaving a total of 549 features/areas.

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

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Farmland, IN, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 19,954, while the mean income for the highest quintile (20% of households with the highest income) is 126,750. This indicates that the top earners earn 6 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 165,145, which is 130.29% higher compared to the highest quintile, and 827.63% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Farmland median household income. You can refer the same here

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

State fact sheets provide information on population, income, education, employment, federal funds, organic agriculture, farm characteristics, farm financial indicators, top commodities, and exports, for each State in the United States. Links to county-level data are included when available.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Query tool For complete information, please visit https://data.gov.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Farmland. Based on the latest 2018-2022 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Farmland. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2022

In terms of income distribution across age cohorts, in Farmland, householders within the 45 to 64 years age group have the highest median household income at $63,342, followed by those in the 65 years and over age group with an income of $40,869. Meanwhile householders within the under 25 years age group report the second lowest median household income of $40,595. Notably, householders within the 25 to 44 years age group, had the lowest median household income at $34,441.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Farmland median household income by age. You can refer the same here

The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.