About this dataset

Context

The dataset tabulates the Farmland population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Farmland. The dataset can be utilized to understand the population distribution of Farmland by age. For example, using this dataset, we can identify the largest age group in Farmland.

Key observations

The largest age group in Farmland, IN was for the group of age 20 to 24 years years with a population of 153 (11.38%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Farmland, IN was the 85 years and over years with a population of 10 (0.74%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Farmland is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Farmland total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Farmland by race. It includes the population of Farmland across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Farmland across relevant racial categories.

Key observations

The percent distribution of Farmland population by race (across all racial categories recognized by the U.S. Census Bureau): 96.28% are white, 1.41% are American Indian and Alaska Native, 0.37% are Asian, 0.15% are some other race and 1.78% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Farmland
• Population: The population of the racial category (excluding ethnicity) in the Farmland is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Farmland total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Population by Race & Ethnicity. You can refer the same here

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

"The U.S. Department of Agriculture's (USDA) Farm Service Agency (FSA) provides emergency loans to help farmers and ranchers who own or operate a farm/ranch located in a county declared by the President or designated by the Secretary of Agriculture as a primary disaster area or quarantine area. Emergency loan funds may be used to: Restore or replace essential property Pay all or part of production costs associated with the disaster year Pay essential family living expenses Reorganize the farming operation Refinance certain debts, excluding real estate Loan applicants may borrow up to 100 percent of their total actual production and/or physical losses. The maximum loan amount is $500,000. Loans for crops, livestock, and non-real estate losses have a repayment term usually between 1 to 7 years depending upon the loan purpose, collateral, and repayment ability. Loans for physical losses to real estate normally have a 30-year repayment term, not to exceed 40 years."

The proportion of agricultural land as percentage of Soil Landscape of Canada (SLC) polygon area data set depicts areas of greater than 2% of agriculture within agricultural extents of Canada.

These data report the percent of agricultural land, within each selected Chesapeake Bay watershed, that is enrolled in the Conservation Reserve Program (CRP), based on county-level data for each year from the U.S. Department of Agriculture Farm Service Agency.

This dataset provides values for ARABLE LAND PERCENT OF LAND AREA WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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

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

description: This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIS05), Combination of Irrigation Sources (CIS) on agricultural land by county. A combination of irrigation sources means one or more sources of irrigation, such as wells, ponds, or streams are used on agricultural land. (U.S. Department of Agriculture, 1995) This data set was created with geographic information systems (GIS) and database management tools. The acres on which CIS's are applied were totaled at the county level in the tabular NRI database and then apportioned to a raster coverage of agricultural land within the county based on the Enhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not record information on those lands.; abstract: This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIS05), Combination of Irrigation Sources (CIS) on agricultural land by county. A combination of irrigation sources means one or more sources of irrigation, such as wells, ponds, or streams are used on agricultural land. (U.S. Department of Agriculture, 1995) This data set was created with geographic information systems (GIS) and database management tools. The acres on which CIS's are applied were totaled at the county level in the tabular NRI database and then apportioned to a raster coverage of agricultural land within the county based on the Enhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not record information on those lands.

These data are from soil salinity surveys conducted on California irrigated farmland between 1991 and 2017. The data consist of: (i.) geospatial field survey measurements of bulk soil electrical conductivity (ECa) and (ii.) laboratory determinations of soil salinity (ECe) and saturation percentage (SP) made on soil core sections extracted from the surveyed fields. The data consist of 277,624 ECa measurements and 8,575 ECe and SP determinations. Soil bulk electrical conductivity (ECa) is relatively easy to measure in agricultural fields using electromagnetic induction (EMI) instrumentation. EMI instruments are readily mobilized and thus can be used to characterize in detail the spatial variability of ECa within fields (Corwin, 2005; 2008). ECa is a useful property because it often correlates with difficult-to-measure soil physical and chemical properties that affect crop production, including soil water content, clay percentage, bulk density, PH, and especially soil salinity. The standard quantitative measure of soil salinity is defined to be the electrical conductivity of the soil saturation paste extract, or ECe (U.S. Salinity Laboratory Staff, 1954). Saturation percentage (SP) is the dry-weight moisture percentage of the saturation paste. The data can be used to test and explore model relationships between ECe, SP, and ECa (EMv and EMh), as well as their spatial variability. In particular, the data may be useful for comparing and testing modeling approaches that account for both deterministic and random components of soil spatial variability at single-field and multi-field scales, and to support high-resolution digital soil mapping studies across irrigated lands. Data Files Data are stored column-wise in two comma-delimited text files, ECe_USDA_ARS_USSL_v01.csv and ECa_USDA_ARS_USSL_v01.csv. Joining the files on the 'ID' column returns data for geolocations at which field measurements of ECa and laboratory determinations of ECe and SP both exist. For example: ECe <- read.csv('ECe_USDA_ARS_USSL_v01.csv') ECa <- read.csv('ECa_USDA_ARS_USSL_v01.csv') dat <- plyr::join(ECe, ECa, 'ID') plot3D::scatter3D(dat$ECe, dat$EMv_grd, dat$EMh_grd, zlab='EMh (dS/m)', xlab='ECe (dS/m)', ylab='EMv (dS/m)', clab = c("dS/m"), bty = "b2") Salinity Survey Identifiers (DATASET) The DATASET label in each file indicates the survey or field campaign from which the data are taken. DATASET_1. Survey of the Broadview Water District in California performed by Corwin and co-workers in 1991 (Corwin et al, 1999). Data include: (i.) ECe and SP determinations on 1,889 soil samples (depths) from 315 soil cores (locations) and (ii.) 2613 ECa (EMv and EMh) field measurements. Data from this survey have been used previously for interpreting the spatial variability of soil salinity at the regional scale (Corwin, 2005). DATASET_2. Survey of Coachella Valley, California farmland conducted between 2005 and 2008 and led by the Coachella Water District. Data consist of: (i.) ECe and SP determinations on 2,088 samples from 476 soil cores and (ii.) 133,037 ECa (EMv and EMh) measurements across the Coachella Valley. This dataset has been used in previous work for validating linear approaches to regional-scale ECa and ECe calibration (Corwin and Lesch, 2014). DATASET_3. Survey led by Singh and colleagues across four fields in western San Joaquin Valley for the purpose of assessing environmental risk associated with saline drainage (Singh et al,. 2020). Data include: (i.) ECe and SP determinations on 1,080 samples from 273 soil cores and (ii.) 36,236 ECa (EMv and EMh) field measurements. DATASET_4. Soil salinity survey led by USDA-ARS U.S. Salinity Laboratory between 2012 and 2013. The survey covered 21 fields in San Joaquin Valley, California. Data consist of: (i.) ECe and SP determinations on 1,634 samples from 180 soil cores and (ii.) 63,225 ECa (EMv and EMh) field measurements. These data were used previously for large scale soil salinity assessments and is described in detail by Scudiero et al. (2014). DATASET_5. Data from surveys of 6 miscellaneous fields in California led by the USDA-ARS U.S. Salinity Laboratory. Data consist of: (i.) 244 determinations of ECe and SP on samples taken from 62 soil cores and (ii.) 62 corresponding ECa (EMv and EMh) field measurements. DATASET_6. Soil salinity surveys led by the USDA-ARS U.S. Salinity Laboratory between 1999 and 2012. One field in southern San Joaquin Valley was assessed several times over many years. Data consist of: (i.) ECe and SP determinations on 1,640 samples from 239 soil cores and (ii.) 42,458 ECa (EMv and EMh) field measurements. These data have been used in previous works focusing on long-term and short-term monitoring and mapping of the spatial and temporal variability of soil salinity (Corwin, 2008, Corwin, 2012, Scudiero et al., 2017). Majority funding provided by USDA-ARS Office of National Programs. Additional funding provided by Office of Naval Research (No. 3200001344), Coachella Valley Resource Conservation District (No. 09FG340003), and California Department of Water Resources (No. 4600011273). References Corwin, D.L. (2005). Geospatial Measurement of Apparent Soil Electrical Conductivity for Characterizing Soil Spatial Variability. doi: 10.1201/9781420032086 (Chapter 18) Corwin, D.L. (2008). Past, present, and future trends of soil electrical conductivity measurement using geophysical methods. Handbook of Agricultural Geophysics, CRC Press. Corwin, D.L. (2012). Field-scale monitoring of the long-term impact and sustainability of drainage water reuse on the west side of California's San Joaquin Valley. Journal of Environmental Monitoring 14(6), 1576-1596. doi: 10.1039/c2em10796a. Corwin, D.L., Carrillo, M.L.K., Vaughan, P.J., Rhoades, J.D., Cone, D.G. (1999). Evaluation of a GIS-linked model of salt loading to groundwater. Journal of Environmental Quality 28(2), 471-480. doi: 10.2134/jeq1999.00472425002800020012x. Corwin, D.L., Lesch, S. (2014). A simplified regional-scale electromagnetic induction: Salinity calibration model using ANOCOVA modeling techniques. Geoderma. s 230-231. 288-295. 10.1016/j.geoderma.2014.03.019. Scudiero, E., Skaggs, T., Corwin, D.L. (2014). Regional Scale Soil Salinity Evaluation Using Landsat 7, Western San Joaquin Valley, California, USA. Geoderma Regional. 2-3. 82-90. 10.1016/j.geodrs.2014.10.004. Scudiero, E., Skaggs, T. H., Corwin, D. L. (2017). Simplifying field-scale assessment of spatiotemporal changes of soil salinity. Sci. Total Environ., 587–588:273–281. doi:10.1016/j.scitotenv.2017.02.136. Singh, A., Quinn, N.W.T., Benes, S.E., Cassel, F. (2020). Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California. Sustainability 12(16), 6362. U.S. Salinity Laboratory Staff. 1954. Diagnosis and improvement of saline and alkali soils. USDA Agric. Handbook. 60. U.S. Gov. Print. Office, Washington, DC.

[ Note: This dataset supersedes Version 1, https://doi.org/10.15482/USDA.ADC/1520662 ] In February 2020, USDA launched the Agriculture Innovation Agenda (AIA), representing a Department-wide effort to align USDA's resources, programs, and research to provide farmers with the tools they need to position American agriculture as a global leader meeting future food, fiber, fuel, and feed demands. As part of the AIA, USDA set ambitious goals to increase agricultural production by 40 percent, while cutting the environmental footprint of U.S. agriculture in half by 2050. The Agriculture Innovation Agenda (AIA) is USDA's commitment to the continued success of American farmers, ranchers, producers, and foresters in the face of future challenges. The AIA is comprised of four main parts. The first is focused on developing a U.S. Agriculture Innovation Strategy that aligns public- and private-sector research. The second integrates innovative technologies and practices into USDA programs to help fast track producer adoption. The metrics component aims to review USDA productivity and conservation data. Finally, the USDA scorecard has benchmarks to improve accountability. As part of the research component, in 2020, USDA collected unstructured text feedback through a Request for Information (RFI) on the most important innovation opportunities for the next era of agriculture to be addressed in the near and long term. RFI responses were qualitatively analyzed to identify emergent themes and common aspirational goal statements that stakeholders shared across multiple RFI responses. USDA collected 223 responses through the RFI. Responses were grouped into several focus areas including commodity crops, specialty crops, livestock, aquaculture, forestry, and farming, general. This dataset supports a dashboard that represents the synthesized data collected through the RFI and stakeholder engagements. Resources in this dataset:Resource Title: Discovery Goals spreadsheet with text tables for use with AIS dashboard, Version 2. File Name: Discovery goal tables_FinalApproved_12.28.20_v2.xlsxResource Description: Text summaries are organized into tabs that support filters by theme (e.g. crop production, forestry, beef and range, dairy, and data) and within theme into Innovation clusters (genome design, digital/automation, prescriptive intervention, systems based farm management) and innovation horizon (Incremental solutions to accelerate, transformative solutions to create, Next era concepts, and gaps & barriers (including policies or supporting technologies needed)). For each combination of innovation cluster and innovation horizon for a theme there is a text summary of the stakeholder feedback, drawn from raw responses. Only minor punctuation and grammatical changes from Version 1.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

This metadata record describes a computed ratio of county acres of farmland reported in agricultural conservation programs to the total acres of farmland reported for the county. Acres in conservation programs are for the 2012 time period, and reported by survey from the U.S. Department of Agriculture, Farm Field Survey. Acres of total farmland were used from the U.S. Department of Agriculture, 2012 Census of Agriculture. The ratio is intended to provide an indication of the intensity of agricultural management practices.

The dataset contains information collected from 122 K-12 public school food service directors in Mississippi, USA, who completed an online survey designed for Mississippi school food service directors. The survey was created using Snap Surveys Desktop software. Information includes school size (number of enrolled students), percent of students participating in free or reduced-price lunch, foods sourced locally (defined as grown or produced in Mississippi), desire to purchase more or start purchasing locally sourced foods, fresh fruit and vegetable purchasing practices, experience purchasing fruits and vegetables from farmers, challenges purchasing from farmers, and interest in other farm to school (F2S) activities. School food service directors' demographic characteristics collected include gender, age, ethnicity/race, marital status, and education level. The data were collected from October 2021 to January 2022 using an online mobile and secure survey management system called Snap Online. The data were collected to obtain updated demographic and school purchasing characteristics from school food service directors in Mississippi and to determine their current abilities, experiences, and desires to engage in F2S activities. The dataset can be used to learn about K-12 public school food service directors in Mississippi but results should not be generalized to all school food service directors in Mississippi or elsewhere in the USA. Resources in this dataset:Resource Title: Mississippi Farm to School Food Service Director Dataset. File Name: MS F2S School Data Public.csvResource Description: The dataset contains information collected from 122 K-12 public school food service directors in Mississippi regarding their experience with and interest in farm to school, including purchasing local foods. It also contains demographic characteristics of the school food service directors and their fresh fruit and vegetable purchasing practices.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Mississippi Farm to School Food Service Director Data Dictionary. File Name: MS F2S School Data Dictionary Public.csvResource Description: The file contains information for variables contained in the associated dataset including names, brief descriptions, types, lengths, and values.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

This dataset tracks annual american indian student percentage from 2009 to 2023 for Many Farms Community School District vs. Arizona

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

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Farmland by race. It includes the distribution of the Non-Hispanic population of Farmland across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Farmland across relevant racial categories.

Key observations

Of the Non-Hispanic population in Farmland, the largest racial group is White alone with a population of 1,281 (96.46% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/farmland-in-population-by-race-and-ethnicity.jpeg" alt="Farmland Non-Hispanic population by race">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Farmland
• Population: The population of the racial category (for Non-Hispanic) in the Farmland is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Farmland total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Farmland population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Farmland. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 704 (54.24% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Farmland population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Farmland is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Farmland is shown in the following column.

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 Population by Age. You can refer the same here

This dataset tracks annual american indian student percentage from 2011 to 2022 for P.s. 6 West Farms vs. New York and New York City Geographic District #12 School District

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Farmland by race. It includes the distribution of the Non-Hispanic population of Farmland across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Farmland across relevant racial categories.

Key observations

Of the Non-Hispanic population in Farmland, the largest racial group is White alone with a population of 1,293 (97.73% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Farmland
• Population: The population of the racial category (for Non-Hispanic) in the Farmland is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Farmland total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Population by Race & Ethnicity. You can refer the same here