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.

Texas was by far the leading U.S. state in terms of total number of farms, with about 231 thousand farms by the end of 2024. Iowa was ranked second, among the leading ten states, with 86.7 thousand farms as of 2023. Farms classification In the United States farms are classified based on the farm income and government payments into six sales classes. According to the USDA, about half of all farms in the U.S. were classified in the 1,000 to 9,999 U.S. dollars sales class in 2023. Farming sector in the U.S. The total number of farms in the United States has decreased steadily since 2007. As of 2022, there were about 1.89 million farms in the U.S., down slightly from 1.9 million in the previous year. Contrastingly, the average farm acreage in the United States has increased in the past few years. The number of employees, including both part-time and full-time workers, in this sector was over 965 thousand as of 2019.

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.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Farmers Branch. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Farmers Branch. 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 2023

In terms of income distribution across age cohorts, in Farmers Branch, householders within the 45 to 64 years age group have the highest median household income at $99,317, followed by those in the 25 to 44 years age group with an income of $95,049. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $66,226. Notably, householders within the under 25 years age group, had the lowest median household income at $58,778.

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.

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 2023 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 Farmers Branch median household income by age. You can refer the same here

This Product provides information on Census of Agriculture, Alberta Farm Operators, 1996-2011. Total number of Census Farms and Total Farm Operators, Average Age (years) of Farm Operartors, Farm Operator Gender, Age on All Farms, Operators by Average Hours Per Week Worked for the Agricultural Operation, and Operators Reporting Paid Non-Farm Work, hours per week are included.

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

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Farmer City. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Farmer City. 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 2023

In terms of income distribution across age cohorts, in Farmer City, the median household income stands at $77,813 for householders within the 25 to 44 years age group, followed by $76,875 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $72,829.

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.

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 2023 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 Farmer City median household income by age. You can refer the same here

North America Agricultural Robots Market size was valued at USD 5.3 Billion in 2024 and is projected to reach USD 13.5 Billion by 2031, growing at a CAGR of 23.5% from 2024 to 2031.

North America Agricultural Robots Market Drivers

Labor Lack and Workforce Aging: North America's agriculture sector is facing a substantial labor deficit, which is being driven by an aging workforce and diminishing interest in traditional farming vocations. According to the United States Department of Agriculture (USDA), farmers' average age has continuously climbed, reaching 57.5 years in 2017. This demographic transition has created an urgent demand for robotic solutions to cover labor shortfalls. According to the American Farm Bureau Federation, 56% of farms are having difficulty finding enough personnel, making robots and automation more appealing for sustaining agricultural output. Precision Agriculture and Resource Optimization: Technological breakthroughs are increasing the usage of agricultural robots as a tool for improving precision farming operations. According to the National Agricultural Statistics Service, precision agricultural technology can help farmers save 15-20% on input costs while increasing crop yields. Agricultural robots with powerful sensors and AI can offer extremely precise data on crop health, soil conditions, and resource allocation. Increasing Food Demand and Climate Change Adaptation: The growing world population and the problems posed by climate change are putting unprecedented strain on agricultural output. The United Nations estimates that global food production must rise by 70% by 2050 to feed the world's expanding population. Agricultural robots are developing as a vital tool for meeting this problem. According to a National Resources Conservation Service assessment, robotic technology can help farmers adapt to changing climate conditions by improving crop management, water conservation, and yield optimization.

United States US: Employment In Agriculture: Modeled ILO Estimate: Male: % of Male Employment data was reported at 2.325 % in 2017. This records an increase from the previous number of 2.318 % for 2016. United States US: Employment In Agriculture: Modeled ILO Estimate: Male: % of Male Employment data is updated yearly, averaging 2.341 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 4.022 % in 1991 and a record low of 2.082 % in 2007. United States US: Employment In Agriculture: Modeled ILO Estimate: Male: % of Male Employment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Employment and Unemployment. Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

United States US: Employment In Agriculture: Modeled ILO Estimate: % of Total Employment data was reported at 1.663 % in 2017. This records an increase from the previous number of 1.660 % for 2016. United States US: Employment In Agriculture: Modeled ILO Estimate: % of Total Employment data is updated yearly, averaging 1.663 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 2.794 % in 1994 and a record low of 1.463 % in 2007. United States US: Employment In Agriculture: Modeled ILO Estimate: % of Total Employment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Employment and Unemployment. Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Meadowbrook Farm. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Meadowbrook Farm. 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 2023

In terms of income distribution across age cohorts, in Meadowbrook Farm, where there exist only two delineated age groups, the median household income is $106,250 for householders within the 45 to 64 years age group, compared to $86,875 for the 65 years and over age group.

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.

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 2023 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 Meadowbrook Farm median household income by age. You can refer the same here

Abstract

The Global Agriculture and Food Security Program Impact Evaluation proposes to study the impact of seed and tool distribution on the take-up of modern farming inputs and the use of productivity enhancing tools, thereby resulting in higher agricultural yields and improved nutritional outcomes, as measured by dietary diversity scores. The wide geographic scope of SAPEC and its focus on smallholder farmers offer a unique opportunity to generate data that can be more robustly extrapolated to the wider Liberian population. We will use data from a 2016 registration of Liberian farmers to randomly select 1,000 Liberian farmers from 100 randomly selected communities in Liberian districts serviced by SAPEC.

Using a randomization at multiple levels, we seek to determine whether the provision of 91%-subsidized improved seeds, tools, and fertilizer promote the take-up of modern farming inputs and improve diets. We will also study whether particular beneficiary sub-groups (by age and gender) are more likely to respond to SMS messaging with an agricultural focus and whether small adjustments to the content of these messages can result in relatively greater improvements in take-up by youth.

Geographic coverage

National

Sampling procedure

Sample size: 570 households

The core strategy for the evaluation is a cluster-randomized phase in of subsidy offers with individual assignment of input deliveries within treatment communities. What this means is that, because SAPEC can only provide a fixed quantity of inputs in a particular season due to capacity constraints, the study aims to randomly assign which communities are first in line to receive the inputs and which farmers in these communities are first in line to receive the inputs. Random selection of farmers and communities is a fair way to decide who receives inputs first that allows us to compare farmers who have been offered inputs with those who have not been offered yet without ultimately affecting which farmers receive benefits at the project closing date. Including some communities and not others in the first round allows to rule out spillovers of farmers who get inputs to other farmers in the same community by comparing to communities where no one has received any inputs. Randomly selecting farmers within communities in contrast also allows us to make comparisons between farmers who are very similar to each other. Finally, strategically make offers of inputs to youth vs older farmers in order to also make careful comparisons of impacts on these groups of particular interest.

There are two dimensions on which the offer of improved seeds, cuttings, and tools will be made in order to assess the impact of this offer on agricultural practices and outcomes, both in general and differentially by the age of the farmer.

First, we propose to compare households in communities where SAPEC will offer subsidized inputs against communities where subsidized inputs will not be offered during the evaluation year. Assignment to SAPEC input provision (treatment communities) or no provision (non-treatment) occurs at two levels. First, 100 communities in SAPEC treatment and control districts will be randomly selected to take part in the study, using the list of SAPEC-eligible communities in the LATA database as a sampling frame. Each community will have a minimum of 25 farmers. Of the selected communities, 50 will be treatment communities and 50 will be control communities. Since SAPEC can deliver inputs and technical assistance to a maximum of 5,000 farmers in a given year and the potential number of beneficiaries in all SAPEC-eligible communities is much larger than this 5,000, the random assignment of potential treatment communities to the set of controls only changes the order of who receives the benefits next, rather than withholding benefits from anyone in particular.

The second dimension to create a counterfactual for beneficiaries will be the random selection of specific beneficiaries within SAPEC treatment communities. From the list of all farmers from the randomly-selected treatment and control communities that have been registered in the e-platform system, we will randomly select an average of 10 per community to be SAPEC beneficiaries (or survey respondents, for control communities) in the upcoming round. This will allow us to sample farmers within treatment communities as well as farmers who were randomly selected to not receive them this year, allowing us to see the causal impact of this input provision on the delivery of tools.

Within the communities to be treated this year, we will start from the list of all households listed in the mobile phone registries who are eligible to receive SAPEC benefits in this year’s wave of benefit delivery. In advance of distribution, we will stratify these eligible farmers by age and gender, and randomly assign 1,500 of them to be invited to report to a SAPEC office in order to be registered as beneficiaries, with equal proportions of male and female and under- and over-35-year-old farmers receiving invitations. All of the farmers who appear to be registered will receive the package of benefits from SAPEC. If less than 1,500 farmers reply, a corresponding number of additional invitations will be sent to randomly selected households who were not selected in the first round until 1,500 beneficiaries have been registered. When invitations are sent, the invitee will be randomly assigned to receive one of 5 different types of messages which emphasis different features such as the potential to earn higher income or to meet other farmers the invitee as described in section 5. This will allow us to test whether different types of farmers (young vs. older) respond to different aspects of program benefits or design.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire is structured and available in English. It can be downloaded under the "Resources" tab.

Autonomous Orchard Mower Market Outlook

According to our latest research, the global autonomous orchard mower market size reached USD 624 million in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a compelling CAGR of 17.2% from 2025 to 2033, reaching a forecasted value of USD 2,154 million by 2033. This remarkable growth is primarily attributed to the rising adoption of automation in agriculture, increasing labor costs, and the urgent need for precision farming solutions in orchard management. As per our findings, technological advancements and the integration of AI-driven navigation systems are significantly accelerating the deployment of autonomous orchard mowers globally.

The surge in demand for autonomous orchard mowers is being driven by the growing need for efficient, cost-effective, and sustainable solutions in fruit and nut orchards, as well as vineyards. Traditional mowing methods are often labor-intensive and time-consuming, leading to higher operational costs and inefficiencies. The integration of advanced sensor technologies, machine learning algorithms, and real-time data analytics in autonomous mowers has enabled growers to achieve precise grass and weed management, reducing manual labor requirements and enhancing productivity. Furthermore, the increasing focus on environmental sustainability and the reduction of carbon footprints has encouraged orchard owners to transition towards electric and hybrid-powered autonomous mowers, further propelling market growth.

Another significant growth driver for the autonomous orchard mower market is the global shortage of agricultural labor, particularly in developed economies such as the United States, Germany, and Japan. As the average age of farm laborers continues to rise and younger generations show less interest in agricultural work, orchard owners are compelled to seek automated solutions to maintain productivity. Autonomous mowers address this challenge by offering round-the-clock operation, minimizing human intervention, and ensuring consistent mowing quality across large orchard landscapes. Additionally, government incentives and subsidies aimed at promoting agricultural mechanization and smart farming technologies are further accelerating market adoption.

Technological innovation remains at the heart of the autonomous orchard mower market’s expansion. The integration of GPS-based guidance systems, obstacle detection sensors, and wireless connectivity has revolutionized orchard maintenance by enabling seamless navigation even in complex terrains. Manufacturers are increasingly collaborating with AI and robotics companies to enhance the intelligence and adaptability of their mowers, allowing for dynamic route planning, real-time monitoring, and remote control via mobile applications. These advancements not only improve operational efficiency but also contribute to significant cost savings and higher yields, making autonomous orchard mowers an indispensable asset for commercial growers and research institutions alike.

From a regional perspective, North America and Europe collectively command the largest share of the autonomous orchard mower market, driven by the early adoption of precision agriculture technologies and the presence of large-scale commercial orchards. The Asia Pacific region, however, is poised for the fastest growth during the forecast period, fueled by the rapid expansion of the horticulture sector, increasing mechanization rates, and supportive government policies. Latin America and the Middle East & Africa are gradually embracing autonomous mowing solutions, with a focus on improving orchard productivity and sustainability. As a result, the global landscape of the autonomous orchard mower market is becoming increasingly competitive and dynamic, offering ample opportunities for innovation and growth.

Product Type Analysis

The product type segment in the autonomous orchard mower market is bifurcated into fully autonomous and semi-autonomous mowers. Fully autonomous orchard mowers represent the cutting edge of agricultural robotics, equipped with advanced navigation systems, AI-driven obstacle avoidance, and real-time data processing capabilities. These machines are designed to operate independently, requiring minimal human intervention, and are particularly favored in large-scale commercial orchards where operational efficiency and consisten

Autonomous Solar-Weeding Robot Market Outlook

According to our latest research, the global autonomous solar-weeding robot market size reached USD 548 million in 2024. The market is expanding rapidly, driven by the increasing need for sustainable and cost-effective agricultural solutions, and is expected to grow at a CAGR of 15.2% from 2025 to 2033. By the end of 2033, the market is projected to reach USD 2,051 million. This remarkable growth is largely attributed to advancements in robotics, the rising adoption of precision agriculture, and growing concerns over herbicide resistance and labor shortages in farming.

The primary growth factor for the autonomous solar-weeding robot market is the global shift towards sustainable agriculture. Farmers and agribusinesses are increasingly seeking alternatives to chemical herbicides due to their adverse environmental impacts and the growing prevalence of herbicide-resistant weeds. Autonomous solar-weeding robots offer a viable solution by mechanically removing weeds without the need for chemicals, aligning with eco-friendly farming practices. Additionally, these robots leverage renewable solar energy, further reducing the carbon footprint associated with traditional farming equipment. This dual benefit of chemical-free weed control and energy efficiency is fueling their adoption, especially among environmentally conscious growers and organizations aiming to meet stricter sustainability standards.

Another significant driver is the acute labor shortage in the agricultural sector, particularly in developed regions such as North America and Europe. As the average age of farmers rises and fewer young workers enter the industry, the demand for automation and robotics in agriculture is intensifying. Autonomous solar-weeding robots help address these labor gaps by operating continuously with minimal human intervention, thus ensuring timely weed management and reducing operational costs. The integration of artificial intelligence and advanced sensor technologies further enhances the efficiency and precision of these robots, making them indispensable tools for modern, large-scale farms striving for higher productivity and profitability.

The rapid advancement in robotics, sensor technology, and artificial intelligence is also catalyzing market growth. Modern autonomous solar-weeding robots are equipped with sophisticated machine vision systems that enable them to distinguish between crops and weeds with high accuracy. This technological leap minimizes crop damage and maximizes weed removal efficacy, providing a tangible return on investment for farmers. Moreover, the modular design and scalability of these robots make them suitable for a wide range of crops and farm sizes, from smallholder plots to expansive commercial operations. As research and development efforts continue, the cost of these robots is expected to decline, further broadening their accessibility and adoption across diverse agricultural landscapes.

Regionally, the market is witnessing robust growth in North America and Europe, where regulatory support for sustainable agriculture and high labor costs are driving early adoption. However, the Asia Pacific region is emerging as a lucrative market, fueled by the rapid modernization of agriculture, government initiatives to promote mechanization, and the presence of large arable land. Latin America and the Middle East & Africa are also showing promising potential, albeit at a slower pace, as awareness about the benefits of autonomous solar-weeding robots spreads and infrastructural challenges are gradually addressed. The regional outlook underscores a global trend towards smarter, greener farming practices, with autonomous solar-weeding robots at the forefront of this transformation.

Product Type Analysis

The autonomous solar-weeding robot market is segmented by product type into fully autonomous and semi-autonomous systems. Fully autonomous robots represent the cutting edge of agricultural robotic

The gross margin of beef cattle farming among smallholder beef cattle farmers (N = 388).

Socioeconomic characteristics of smallholder beef cattle farmers based on mean scores of continuous variables.

This interactive map of Ethiopia identifies the woredas (districts) where the AGP is active. GAFSP contributes about 23% of the total AGP financing, which is also supported by other development partners, including the Canadian International Development Agency (CIDA), the Spanish Agency for International Development Cooperation (AECID), the Kingdom of the Netherlands, the United Nations Development Program (UNDP), and the United States Agency for International Development (USAID). GAFSP funds are being channeled into a pooled AGP fund to increase donor coordination and to decrease project administrative costs. The map is broken down into 11 regions, 81 zones, and 550 woredas (districts). The 83 AGP project areas (at the woreda level) are spread across the four regions of Amhara, Oromiya, Tigray, and Southern Nations, Nationalities, and Peoples Region (SNNPR). AGP activities are primarily in the highlands temperate mixed zones, where the climatic conditions are relatively temperate and that, with AGP support, have considerable potential for agricultural growth. In these areas, small-scale farmers crop an average area of less than 1 hectare (ranging between 0.25 and 2.3 hectares). The interactive map shows sub-national poverty and population density data, as well as information on the predominant farming systems in the various regions. Data Sources: AGP Project LocationsSource: Project Appraisal Document (PAD). Africa Juice Project LocationSource: IFC - GAFSP Documents. Poverty (Proportion of population below the poverty line) (2005): Proportion of the population living on less than US$1.25 a day, measured at 2005 international prices, adjusted for purchasing power parity (PPP).Source: Harvest Choice / Multiple national household surveys; PovcalNet; The World Bank; and Centro Internacional de Agricultura Tropical (CIAT). 2011. Sub-national poverty headcount ratios derived from 23 nationally representative household surveys and population census information conducted in various years. Rates are for the $1.25/day (extreme poverty) expressed in 2005 international equivalent purchasing power parity (PPP) dollars. Rates are in percentages of total population. (Aggregation type: WGHTD). Poverty (Proportion of population below the poverty line) (2011): Proportion of the population living on less than 3,781 Birr per adult per year.Source: Ministry of Finance and Economic Development. “Ethiopia’s Progress Towards Eradicating Poverty: An Interim Report on Poverty Analysis Study (2010/11).” Malnutrition (Proportion of underweight children under 5 years) (2011): Prevalence of severely underweight children is the percentage of children aged 0-59 months whose weight-for-age is less than minus 3 standard deviations below the median weight for age of the international reference population.Source: “Demographic and Health Survey 2011.” Measure DHS.MEASURE DHS (Demographic and Health Surveys) Project is responsible for collecting and disseminating accurate, nationally representative data on health and population in developing countries. The project is implemented by Macro International, Inc. and is funded by the United States Agency for International Development (USAID) with contributions from other donors such as UNICEF, UNFPA, WHO, UNAIDS. Malnutrition (Proportion of underweight children under 5 years) (2016): Prevalence of severely underweight children is the percentage of children aged 0-59 months whose weight for age is less than minus 3 standard deviations below the median weight for age of the international reference population.Source: Central Statistical Agency CAS. “Demographic and Health Survey 2016.” Measure DHS. Population Density (Persons per 1 square kilometer) (2007): Population divided by land area in square kilometers.Total population (2015): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin.Source: Central Statistical Agency CAS. Projections based on the results of the May 2007 National Population and Housing Census of Ethiopia. Population Density (2015): Population divided by land area in square kilometers.Source: Central Statistical Agency CAS. Projections based on the results of the May 2007 National Population and Housing Census of Ethiopia. Market Centers: Key market centers for retail, assembly and/ or wholesale of agricultural products. FEWS NET reference markets.Source: FEWS Net. The Famine Early Warning Systems Network (FEWS NET) is a USAID-funded activity that collaborates with international, regional and national partners to provide timely and rigorous early warning and vulnerability information on emerging and evolving food security issues. Farming Systems (2001): Farming systems according to FAO methodology: Agro-pastoral land, millet-sorghum, highland perennial, maize mixed, irrigated land, pastoral land and sparse arid Land.Source: Harvest Choice / Dixon, J. and A. Gulliver with David Gibbon, Principal Editor: Malcolm Hall. Improving Farmers' Livelihoods in a Changing World. FAO/World Bank. 2001. (Aggregation type: NONE) Land cover (2009): Land cover defined as the physical material at the surface or earth, vegetation planted or man-made constructions (water, ice, bare rock, sand, grass, asphalt, trees, etc.). Land cover can be determined by analyzing satellite and aerial imagery.Source: 3R Initiative (RAIN, Acacia Water, MetaMeta, Aqua for all, BGR and IGRAC). “Global Land Cover.” www.hoefsloot.com/horn/ Sorghum Area (2015-16): Area in hectares of agriculture land used for sorghum.Source: Central Statistical Agency CAS. “Agricultural Sample Survey (AgSS) 2015/2016 (2008 E.C.) Report on Area and Production of Major Crops.” Sorghum Production (2015-16): Sorghum harvested expressed in tons.Source: Central Statistical Agency CAS. “Agricultural Sample Survey (AgSS) 2015/2016 (2008 E.C.). Report on Area and Production of Major Crops.” Maize Area (2015-16): Area in hectares of agriculture land used for Maize.Source: Central Statistical Agency CAS. “Agricultural Sample Survey (AgSS) 2015/2016 (2008 E.C.). Report on Area and Production of Major Crops.” Maize Production (2015-16): Maize harvested expressed in tons.

Source: Central Statistical Agency CAS. “Agricultural Sample Survey (AgSS) 2015/2016 (2008 E.C.). Report on Area and Production of Major Crops.”The maps displayed on this website are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of GAFSP (and the World Bank Group), any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Prairie Farm town. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Prairie Farm town. 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 2023

In terms of income distribution across age cohorts, in Prairie Farm town, the median household income stands at $101,875 for householders within the under 25 years age group, followed by $93,500 for the 25 to 44 years age group. Notably, householders within the 45 to 64 years age group, had the lowest median household income at $76,875.

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.

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 2023 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 Prairie Farm town median household income by age. You can refer the same here

Multiple linear regression estimates using ordinary least squares (OLS) on the factors influencing the profitability of beef cattle farming among smallholder beef cattle farmers N = 388).

According to Cognitive Market Research, the global Agricultural Tire Market size will be XX million by 2030, whereas its compound annual growth rate (CAGR) will be XX% from 2024 to 2031.

Asia Pacific held the largest share of the global Agricultural Tire market around XX% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
Europe accounted for a share of over XX% of the global market size of USD XX million.
North America held a market share of around XX% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
The Latin American market is around XX% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
Middle East and Africa held the major market of around XX% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.

Market Dynamics of the Agricultural Tire Market

Key Drivers of the Agricultural Tire Market

The Tire industry's emerging technological advancements are fueling market expansion

Improvements in Tire technology are contributing to increased agricultural output, sustainability, and efficiency. Furthermore, a major factor driving the market expansion is the incorporation of precision farming technologies, which enable real-time monitoring of Tire pressure and tread wear, enhancing Tire performance and prolonging their lifespan, ultimately lowering operating costs and minimizing soil compaction, which improves crop yields. Modern Tires with cutting-edge technology are being offered by several major producers to boost agricultural yields and improve crop quality.

• For Instance: On Mar 22, 2022 - CEAT Specialty, an Indian Tire manufacturer offered Sprayer Tires called SPRAYMAX. These Tires are made for larger self-propelled sprayers and are built to support 40% more weight than a typical radial Tire. Its rounded shoulders also minimize crop and soil damage.

https://www.ceatspecialty.com/gb/blog/Tire-care/specialist-Tires-for-self-propelled-sprayers-a-specialty-of-ceat-specialty

• For Instance: In May 2022 - Apollo Tires in India launched the Virat series, a collection of cutting-edge agriculture Tires.

https://corporate.apolloTires.com/press-and-media/news/product/apollo-Tires-launches-new-gen-agri-Tires/#:~:text=May%2006%2C%202022&text=Application%20wise%2C%20the%20new%20'VIRAT,both%2C%20front%20and%20rear%20fitments.

According to the manufacturer, these Tires offer "industry-best traction" and a special design that enhances performance for vehicles used in the haulage and agricultural industries. It is expected that these advancements will have a favorable effect on the agricultural Tire market forecast.

Market growth is being encouraged by the implementation of favorable government policies

Governments from various countries are enacting laws and launching programs to help and maintain the agriculture industry. Furthermore, the market is growing as a result of the implementation of various policies, such as grants, tax breaks, and subsidies, to encourage agricultural operations and lessen farmers' financial burdens.

• For Instance: The 'Macro-Management Scheme of Agriculture,' developed by the Indian government, offers a 25% subsidy on tractors with up to 35 PTO HP.

https://tractorbuyersguide.in/tractor-subsidy/Tractor-Subsidy-from-Government-of-India.html

• For Instance: Oct 03, 2022 - The Canadian government introduced the 'Canadian Agricultural Loans Act,' enabling farmers to borrow up to US$ 500,000 to buy a tractor or land. Concerned regulatory authorities are also concentrating on machinery operator training programs.

https://agriculture.canada.ca/en/programs/canadian-agricultural-loans-act

• For Instance: In Mar 2022 - Six companies benefited from nearly $860,000 in investments granted by the Canadian government under the Innovative Solutions Canada (ISC) program.

https://pembinavalleyonline.com/articles/government-of-canada-announces-investment-of-over-860k-in-six-...