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 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

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.

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.

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

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

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 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 environmental impacts of beef cattle production and their effects on the overall sustainability of beef have become a national and international concern. Our objective was to quantify important environmental impacts of beef cattle production in the United States. Surveys and visits of farms, ranches and feedlots were conducted throughout seven regions (Northeast, Southeast, Midwest, Northern Plains, Southern Plains, Northwest and Southwest) to determine common practices and characteristics of cattle production. These data along with other information sources were used to create about 150 representative production systems throughout the country, which were simulated with the Integrated Farm System Model using local soil and climate data. The simulations quantified the performance and environmental impacts of beef cattle production systems for each region. A farm-gate life cycle assessment was used to quantify resource use and emissions for all production systems including traditional beef breeds and cull animals from the dairy industry. Regional and national totals were determined as the sum of the production system outputs multiplied by the number of cattle represented by each simulated system. The average annual greenhouse gas and reactive N emissions associated with beef cattle production over the past five years were determined to be 243 ± 26 Tg carbon dioxide equivalents (CO2e) and 1760 ± 136 Gg N, respectively. Total fossil energy use was found to be 569 ± 53 PJ and blue water consumption was 23.2 ± 3.5 TL. Environmental intensities expressed per kg of carcass weight produced were 21.3 ± 2.3 kg CO2e, 155 ± 12 g N, 50.0 ± 4.7 MJ, and 2034 ± 309 L, respectively. These farm-gate values are being combined with post farm-gate sources of packing, processing, distribution, retail, consumption and waste handling to produce a full life cycle assessment of U.S. beef. This study is the most detailed, yet comprehensive, study conducted to date to provide baseline measures for the sustainability of U.S. beef. Resources in this dataset:Resource Title: Appendix A. Supplementary Data - Tables S1 to S8 (docx). File Name: Web Page, url: https://ars.els-cdn.com/content/image/1-s2.0-S0308521X18305675-mmc1.docx Direct download, docx.

Table S1. Important characteristics of farms and ranches simulated throughout seven regions of the U.S.

Table S2. Important characteristics of representative finishing facilities simulated in seven regions of the U.S.

Table S3. Important characteristics of dairy farms simulated throughout seven regions of the U.S.

Table S4. Summary of 25 years of weather data (daily solar radiation, daily mean temperature, annual precipitation and daily wind speed)1 used to simulate beef cattle operations in each area of the eastern regions.

Table S5. Soil characteristics used for locations simulated across the U.S.

Table S6. Cattle numbers by state and region as obtained or estimated from NASS (2017).

Table S7. Cattle numbers by state and region divided between traditional beef and dairy breeds as obtained or estimated from NASS (2017).

Table S8. Important resource inputs and emissions from representative cow-calf, stocker / background and feedlot operations expressed per unit of final carcass weight (CW) produced.

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

Autonomous Orchard Mowing Robot Market Outlook

According to our latest research, the global Autonomous Orchard Mowing Robot market size reached USD 624.7 million in 2024, with the sector expanding at a robust CAGR of 18.4% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 2,934.5 million, underscoring the rapid adoption of advanced automation solutions in orchard management worldwide. The primary growth factor driving this surge is the agricultural sector’s increasing need for efficient, labor-saving technologies in response to rising labor costs and the growing scale of commercial orchard operations.

The explosive growth of the Autonomous Orchard Mowing Robot market is primarily fueled by the global shift towards sustainable and precision agriculture. As orchard owners and commercial growers face mounting pressure to optimize operational efficiency and reduce environmental footprints, the adoption of autonomous mowing robots has emerged as a strategic solution. These robots offer precise grass and weed management, minimizing the need for chemical herbicides and manual labor. Furthermore, the integration of advanced sensors, AI-driven navigation systems, and IoT connectivity enables real-time monitoring and data-driven decision-making, which is crucial for maximizing yield and maintaining healthy orchard ecosystems. This trend is further amplified by government incentives and sustainability mandates, particularly in regions with intensive fruit and nut production.

Another significant growth factor is the acute labor shortage in agricultural sectors, especially in developed economies such as North America and Europe. The increasing average age of farm workers, coupled with the declining interest of younger generations in manual agricultural work, has created a pressing need for automation. Autonomous orchard mowing robots directly address this challenge by offering reliable, consistent, and scalable mowing solutions, thereby reducing dependency on seasonal labor. Additionally, these robots can operate for extended hours, even in challenging terrains and weather conditions, ensuring uninterrupted maintenance and contributing to higher productivity and cost savings in the long run. This operational resilience and efficiency are compelling more orchard managers to invest in autonomous solutions as part of their modernization strategies.

Technological advancements and the decreasing cost of robotics hardware are also pivotal in propelling market growth. Innovations in battery technology, machine learning algorithms, and lightweight materials have significantly enhanced the performance, durability, and affordability of autonomous mowing robots. The proliferation of electric and hybrid power sources has further improved the sustainability profile of these machines, aligning with the growing emphasis on green agriculture. Moreover, the emergence of user-friendly interfaces and remote management capabilities has lowered the technical barriers for adoption, enabling even small and medium-sized orchard operators to leverage these solutions. As manufacturers continue to innovate and scale production, the market is expected to witness greater accessibility and widespread deployment across diverse orchard types.

From a regional perspective, North America and Europe currently dominate the Autonomous Orchard Mowing Robot market due to their advanced agricultural infrastructure and early adoption of precision farming technologies. However, the Asia Pacific region is rapidly emerging as a high-growth market, driven by expanding commercial orchards in China, Australia, and India. The region’s increasing investment in agricultural automation, coupled with supportive government policies, is expected to accelerate the adoption of autonomous mowing robots over the next decade. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, primarily in export-oriented fruit and nut plantations. As global food demand continues to rise, the strategic importance of efficient orchard management solutions will further intensify across all regions.

How many cattle are in the world? The global live cattle population amounted to about 1.57 billion heads in 2023, up from approximately 1.51 million in 2021. Cows as livestock The domestication of cattle began as early as 10,000 to 5,000 years ago. From ancient times up to the present, cattle are bred to provide meat and dairy. Cattle are also employed as draft animals to plow the fields or transport heavy objects. Cattle hide is used for the production of leather, and dung for fuel and agricultural fertilizer. In 2022, India was home to the highest number of milk cows in the world. Cattle farming in the United States Cattle meat such as beef and veal is one of the most widely consumed types of meat across the globe, and is particularly popular in the United States. The United States is the top producer of beef and veal of any country worldwide. In 2021, beef production in the United States reached 12.6 million metric tons. Beef production appears to be following a positive trend in the United States. More than 33.07 million cattle were slaughtered both commercially and in farms annually in the United States in 2019, up from 33 million in the previous year.

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-...

The global agricultural solar pump market is experiencing robust growth, driven by the increasing need for efficient and sustainable irrigation solutions in agriculture. Factors such as rising energy costs, water scarcity, and government initiatives promoting renewable energy are significantly boosting market expansion. The market is segmented by application (irrigation, drinking water, others) and type (submersible pumps, surface pumps). Irrigation remains the dominant application, accounting for a significant portion of the market share due to the large-scale adoption of solar pumps in various agricultural practices. Submersible pumps are generally preferred over surface pumps due to their deeper well capabilities and increased efficiency. While the market shows strong potential in developing regions like Asia-Pacific and Africa, driven by rapid agricultural expansion and government support, mature markets in North America and Europe are also witnessing growth fueled by the transition towards environmentally-friendly farming methods. Major players like Lorentz, Grundfos, and Shakti Solar Pumping Systems are actively shaping market dynamics through product innovation, strategic partnerships, and technological advancements. The market is expected to maintain a steady CAGR (let's assume 8% based on typical renewable energy sector growth) for the forecast period (2025-2033), presenting lucrative opportunities for existing and new market entrants. Competition is expected to intensify with the introduction of more efficient and affordable solar pump technologies. Challenges, however, include high initial investment costs and the need for reliable grid infrastructure in some regions, which may pose barriers to broader adoption. The market's growth trajectory is further influenced by several key trends. The increasing adoption of smart irrigation technologies integrated with solar pumps enhances water usage efficiency and reduces operational costs, thus driving market demand. Government policies promoting renewable energy adoption in agriculture, alongside subsidies and financial incentives, significantly contribute to market growth. Furthermore, advancements in solar panel technology, resulting in improved efficiency and reduced costs, directly impact the affordability and attractiveness of solar-powered irrigation systems. Technological innovations in pump design, including improved durability and longevity, are extending the lifespan of these systems and enhancing their overall value proposition for farmers. As the focus on sustainable agriculture gains momentum globally, the demand for agricultural solar pumps is poised for continued expansion in the coming years.

The size of the U.S. Floriculture Market was valued at USD 7.01 billion in 2023 and is projected to reach USD 12.01 billion by 2032, with an expected CAGR of 8.0 % during the forecast period. Floriculture is the branch of horticulture that focuses on the cultivation, production, and management of flowering plants and ornamental plants for use in decorative displays, landscaping, and floral arrangements. It is a specialized field of agriculture that combines aspects of plant biology, design, and commercial production. The term "floriculture" derives from Latin, where "flor" means flower and "culture" means cultivation or care. The practice involves the growing, breeding, and marketing of plants primarily for aesthetic purposes, with flowers being the most significant product. The floriculture industry encompasses a wide variety of activities, ranging from the cultivation of cut flowers, potted plants, bedding plants, and landscaping plants to the growing of foliage plants, bulbs, and seeds. It is not just limited to flower production but also involves plant breeding, propagation, pest management, and post-harvest handling. Floriculture plays a key role in the agricultural sector, contributing to the economy, creating employment opportunities, and enhancing the quality of life through beautification of public and private spaces. Recent developments include: In April 2024, Costa Farms launched its latest Greenteriors collection, featuring a curated selection of eight plants. The Baltic Blue Pothos, Global Green Pothos, Mini Monstera, Monstera Little Swiss, Pink Princess Philodendron, Polka Dot Begonia, Raven ZZ Plant, and Silver Dragon Alocasia are among them. Through this release launch, Costa Farms aims to assist both consumers and designers in enhancing their environments and overall well-being. , In January 2024, Costa Farms acquired Battlefield Farms, a grower of over 700 bedding annuals and perennials. This strategic acquisition expands Costa Farms' geographical reach and strengthens its position within the industry. By integrating Battlefield Farms into its operations, Costa Farms strengthened its commitment to the All Thumbs are Green Mission, ensuring that it continues to provide top-quality plants and exceptional gardening experiences to its expanding customer base. , In February 2022, Royal Van Zanten partnered with The Queen's Flowers to develop a fresh flower variety, Astronova. Astronova flowers have a distinct appearance and longer-than-average vase life. This partnership is vital for consistently affirming and enhancing Astronova's demand in the market. .

The structure and function of fungal communities in the coffee rhizosphere is shaped by crop environment. Because coffee can be grown along a management continuum from conventional application of pesticides and fertilizers in full sun to organic management in a shaded understory, we used coffee fields to hold host constant while comparing rhizosphere fungal communities in markedly different environmental conditions with regard to shade and inputs. We characterized the shade and soil environment in 25 fields under conventional, organic or transitional management in two regions of Costa Rica. We amplified the ITS2 region of fungal DNA from coffee roots in these fields and characterized the rhizosphere fungal community via high-throughput sequencing. Sequences were assigned to guilds to determine differences in functional diversity and trophic structure among coffee field environments. Organic fields had more shade, a greater richness of shade tree species, more leaf litter, and were less acidic, with lower soil nitrate availability and higher soil copper, calcium, and magnesium than conventionally-managed fields, although differences between organic and conventionally-managed fields in shade, calcium and magnesium depended on region. Differences in richness and community composition of rhizosphere fungi between organic and conventionally-managed fields were also correlated with shade, soil acidity, nitrate, and copper. Trophic structure differed with coffee field management. Saprotrophs, plant pathogens, and mycoparasites were more diverse and plant pathogens were more abundant in organic than in conventionally-managed fields, while saprotroph-plant pathogens were more abundant in conventionally-managed fields. These differences reflected environmental differences and depended on region.

IMPORTANCE

Rhizosphere fungi play key roles in ecosystems, as nutrient cyclers, pathogens, and mutualists, yet little is currently known about which environmental factors and how agricultural management shape rhizosphere fungal communities and their functional diversity. This field study of the coffee agroecosystem suggests that organic management not only fosters a greater overall diversity of fungi, but also maintains a greater richness of saprotrophic, plant pathogenic and mycoparasitic fungi that has implications for efficiency of nutrient cycling and regulation of plant pathogen populations in agricultural systems. As well as influencing community composition and richness of rhizosphere fungi, shade management and use of fungicides and synthetic fertilizers altered the trophic structure of the coffee agroecosystem.

Methods Site description and study design. Two coffee-growing regions of Costa Rica with a premontane wet forest climate were selected for this study, Monteverde (10⁰ 19'27.8" N, 084⁰ 50'30.1" W) and San Vito (08⁰ 52'41.1" N, 082⁰ 57'03.1" W). Soils in both Monteverde and San Vito are Andisols, a volcanic soil type with high organic matter, high leaching capacity and pH of 5.6 - 5.8. Monteverde experiences slightly lower rainfall on average (300 cm yr-1 vs. 400 cm yr-1 in San Vito.

Twenty-five coffee fields were included in this study. Thirteen fields were sampled in Monteverde, six between 25-28 May 2011 and seven between 1-4 June 2012. In San Vito, six fields each were sampled between 31 May-3 June 2011 and 7-11 June 2012. At each site, the farmer or farm manager was interviewed to determine types of herbicides, pesticides, fungicides, and fertilizers used on the field, as well as the cultivars present, age of the field and coffee plants, prior land use and pruning regimen. Fields were designated as ‘conventionally-managed’ if farmers reported using synthetic fertilizers and pesticides, as ‘organic’ if farmers reported that fields were certified organic or reported no use of synthetic fertilizers and pesticides in the previous five years, and as ‘minimal conventional’ if farmers reported that they were in the process of transitioning from conventional to organic management or had not used synthetic fertilizers or pesticides in the preceding 1-3 years.

Field sampling. For each field, species richness of shade trees, type of windbreak, and phenological status of coffee plants (vegetative, flowering, green or mature fruit) were recorded. All fields except one, in which plants were vegetative, were producing green (immature) or green and red (mature) fruits at the time of sampling. In each field, a 20 m × 20 m plot was established > 5 m from the edge and representative of the shade tree density of the field. Approximate elevation was recorded with a Garmin eTrex Venture HC® (Garmin Corp., Schaffhausen, Switzerland). Percent canopy cover at the center of the plot was calculated using a spherical densiometer with convex mirror (Forestry Suppliers, Jackson, Mississippi, USA) according to manufacturer’s instructions. Plot aspect was measured by compass; plot slope was measured qualitatively in 2011 and using a clinometer in 2012. Coffee plant density was estimated by averaging the distance between rows for five rows and the distance between plants within a row for five pairs of plants.

Within each plot, one coffee plant was sampled every 5 m along every other row, for a total of 20 plants per plot. At each plant, leaf litter depth was measured at the dripline, and a soil sample was taken using a 2 cm in diameter corer to a depth of approximately 20 cm. From every other sampled plant, root samples were taken at 1-15 cm of depth from 3-5 sections of fine roots and combined, for a total of 10 plants per plot. Soil samples within a field were pooled, air-dried in paper bags and stored at room temperature.

In the lab, each root sample was rinsed with tap water and divided in two. One subsample from each plant was stored in 1% KOH (w/v) for analysis of root colonization by AM fungi (Aldrich-Wolfe et al., in review), while the second was dried in the presence of Drierite (W.A. Hammond Company, Xenia, Ohio, USA) for DNA extraction. Drying roots results in no reduction in DNA yield relative to isolation from fresh or frozen samples, although it may reduce the yield of fungal DNA (86), and eliminates the risk of DNA degradation when frozen samples thaw in transit (87). At the end of each year’s sampling period, soils and dried root samples for DNA extraction were transported to the United States and stored at room temperature. Two-three soil subsamples from each field were analyzed for soil nutrient availability, pH in water, and organic matter by LOI at the Soils Testing Laboratory, North Dakota State University, Fargo, North Dakota, USA. Means per field were subsequently used for all statistical analyses.

Molecular detection of root fungi. Dried root samples were pulverized using six 2.33-mm in diameter chrome-steel beads (Biospec Products, Bartlesville, Oklahoma, USA) in a vortex adapter (Mo Bio Laboratories, Carlsbad, California, USA) on a Vortex-Genie® 2 Mixer for 1 h (Scientific Industries, Inc., Bohemia, New York, USA). DNA was isolated from 20 mg of each sample for 8-10 root samples per field using the Qiagen DNeasy Plant Mini Kit (Qiagen, Germantown, Maryland, USA), following the manufacturer’s protocol (with two elution volumes of 50 μL each) and stored at -20 °C.

The internal transcribed spacer region 2 (ITS2) was amplified by polymerase chain reaction (PCR) for each DNA extract using 12.5 μL of 2× Kapa HiFi Hotstart Ready Mix (Kapa Biosystems, Wilmington, Massachusetts, USA), 10 μL nuclease-free water, 0.8 μL each of 10 mM fungal-specific HPLC-purified primers 5.8SR and ITS4 (88), and 1 μL of DNA template for a total reaction volume of 25.1 μL. Each extract was amplified in triplicate using an Eppendorf Mastercycler (Hamburg, Germany) with 3 min activation at 95 °C, 30 cycles of denaturing at 98 °C for 20 s, annealing at 65.7 °C for 15 s and elongation at 72 °C for 45 s, and a final elongation at 72 °C for 5 min. PCR products were confirmed by electrophoresis in 1% agarose and 0.5× TBE followed by staining with ethidium bromide. Extracts which failed to produce PCR products were diluted tenfold and amplified using the above reaction conditions with an annealing temperature of 64.4 °C. PCR products were stored overnight at 4 °C and for longer periods at -20 °C.

Triplicate PCR products were pooled and purified using the Agencourt® Ampure® XP system (Beckman Coulter, Indianapolis, Indiana, USA) following the manufacturer’s protocol, with two washes with ethanol and elution in 10 mM Tris. Concentration of dsDNA in each sample was measured using a Qubit 2.0 fluorimeter (Invitrogen, Carlsbad, California, USA). Eight (2011) or ten (2012) samples per field were pooled at equal DNA concentration in 10 mM Tris, and 3-5 ng of DNA per field was shipped frozen on dry ice for sequencing at the University of Minnesota Genomics Center (UMGC, St. Paul, Minnesota, USA).

PCR products from each field were amplified using Nextera™ indexing primers (Illumina, San Diego, California, USA) and 10 cycles of denaturation at 98 °C for 20 s, annealing at 55 °C for 15 s, and elongation at 72 °C for 1 min. Indexed PCR products were denatured with 8 pM NaOH in Illumina HTI buffer (20% PhiX) at 96 °C for 2 min prior to loading and sequencing on an Illumina Miseq® using Reagent Kit v3 with separate index reads. Preliminary quality control (QC) and demultiplexing were conducted by the UMGC.

Sequence data processing. Sequences were processed with the PIPITS 1.4.0 pipeline (Gweon et al, 2015), which employs a number of different software packages, using the standard settings. Briefly, forward and reverse reads were merged using PEAR 0.9.8 (http://www.exelixis-lab.org/pear), followed by quality filtering using FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/), and extraction of the fungal-specific ITS2 region using ITSx 1.0.11 (90). Dereplication, removal