In 2023, the average age of persons engaged in farming in Japan stood at **** years. The figure increased significantly throughout the past decade, compared to **** years in 2010. Japan's agricultural workforce is shrinking Japan's aging population and low birth rate have produced a labor shortage in many industries. Since agricultural work is physically demanding and barely profitable and few young people are willing to inherit their parent's farm or enter the sector as newcomers, the number of commercial farm households consequently continues to decrease. The younger generations often prefer to move to metropolitan areas which provide work, convenience, and a modern lifestyle. Further obstacles to the Japanese agricultural sector Its geography complicates agriculture in Japan as the island nation regularly suffers from natural disasters. Typhoons, earthquakes, and tsunamis cause high damage costs to the agriculture, forestry, and fishery industry every year.Furthermore, only about ** percent of the mountainous archipelago is suitable for cultivation, and the area of cultivated land keeps shrinking as more and more land is used for housing.

Census of Agriculture, 2021. Age and sex of farm operators classified by the number of operators reported on the farm.

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.

Average age of farmers retirement savings contributors, average male age, average female age - divided by withdrawal rate

In 2021, the age group with the highest number of farmers in Japan was those aged between 60 and 74 years old, with over ************ people. The second largest age group consisted of people aged 75 years and older, numbering over *** thousand people.

Census of Agriculture, 1991 to date. Characteristics of farm operators: sex, age and paid non-farm work.

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.

According to a survey among South Korean farmers on their age in 2020, roughly **** percent of respondents stated that they were between 60 and 69 years old. With the aging of the South Korean population and especially the aging of farmers, the succession of farms and the development of new technologies such as smart farming will be an important issue in the future for the agricultural sector.

The data describe the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock and labour force. They also describe production methods, rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.

The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies.

The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables. The data are presented at different geographical levels and over periods.

The data collections are organised in line with the EU legislation. For 2020 (the agricultural census), 2023 and 2026, they are organised in line with the Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The data are as comparable and coherent as possible across European countries.

This statistic shows the average age of people with farmers health insurance coverage in Taiwan from 2013 to 2021. In 2021, the average age of people with farmers health insurance coverage in Taiwan was around ***** years.

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

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.

** Farmers and employees of agricultural holdings by municipality** Agreste – Agricultural Censuses (1970, 1979, 1988, 2000, 2010) **** All farms (excluding collective grazing) Small farms Medium and large holdings Characteristics of people: Managers and co-operators Managers and co-operators – Men Managers and co-operators – Women Managers and co-operators – Under 40 Farm managers and co-operators – 40 to 60 years old Managers and co-operators – 60 years or older Farm managers and co-operators – multi-active Permanent employees outside the family Permanent employees outside the family – Men Permanent employees outside the family – Women Permanent employees outside the family – Gender not specified (mutual assistance DOM 2000) Permanent employees outside the family – Under 40 Permanent employees outside the family – 40 years or older Permanent employees outside the family – Age not specified (DOM employees) Farm description: Number of holdings Number of persons Annual Work Units (AWU) * * * * # The agricultural censuses, ten-year surveys, offer an instant, complete and detailed picture of a key sector of the French and European economy: agriculture (agricultural population, plant areas, including vineyards, livestock numbers, means of production, ancillary activities, etc.). It answers questions as diverse as it is varied, at all geographical levels, allowing comparisons at the finest level (canton, common) and takes into account the local specificities as well as the new challenges of agriculture, such as signs of quality, territorial farm contracts, cultivation practices, etc. It is also interested in smaller farms and the important local impact. To prepare for the future of the agricultural world: The agricultural census makes it possible to measure the impact of agricultural policies, in particular the Common Agricultural Policy (CAP), on agricultural practices and the environment.It gives political leaders, national elected officials and representatives of the profession keys to preparing future agricultural laws and regulations and international negotiations. It provides elected representatives of rural communes with valuable data for spatial management and spatial planning. The 2010 agricultural census follows the 1970, 1979, 1988 and 2000 censuses. The 2020 agricultural census will soon begin. The main data relate to: Crops and areas cultivated, Breeding and livestock, Methods of crop protection, Farm equipment, Diversification of activities (green tourism...), The marketing of products (AOC, direct sales to consumers, etc.), Employment (employee, family employment, etc.) and the level of training of the farmer, Management of the farm. There is no question about the financial results or the income of farmers. Who was identified? Investigators identified all production units meeting 3 criteria > producing agricultural products; > have an independent day-to-day management; > attain or exceed a certain threshold in area, production or number of animals. This threshold has been defined as follows: > a utilised agricultural area (UAA) of 1 hectare or more; > or a specialised crop area greater than or equal to 20 ares; > or sufficient agricultural production activity, estimated in terms of number of animals, production area or minimum production volume.

The geography of this dataset is Australian Statistical Local Area (SLA). This data relates to members of the population who classified themselves as having an occupation of aFarmera or aFarm Managera in the 1996 Population and Housing Census and have a Bachelor degree, Undergraduate diploma, Associate diploma, Higher degree or Post-graduate qualifications.The data is presented at a scale of 25000000. Projection -P Albers Equal-Area Conic -D WGS84 -m 132 -o 0 -p -18 -p -36 -e 0 -n 0The following attributes are contained within the dataset; Sla_code a unique code for Statistical Local Areas (SLA), Sla_name a the name of the Statistical Local Area (SLA) and,Bach_degre a median age of farmers and farm managers as at census night 1996. Ugrad_dip a hectares of agricultural land use in the Statistical Local Area (SLA).Post_grad a number of farmers/farm managers with postgraduate qualificationsHigher_deg a number of farmers/farm managers with a higher degreeAssoc_dip a number of farmers/farm managers with an associate diploma Totalqual a total number of farmers/farm managers with higher qualificationsTotalpop a total farmer population 1996%Pop a per cent of farmers who have higher qualificationsAg_land_ha a hectares of agricultural land use in the Statistical Local Area (SLA).

See further metadata for more detail.

In South Korea in 2024, approximately *********** people were working in the agricultural industry. The majority of these were farmers and families, with people in forestry and fishery numbering around ************. There were just little less than *********** farming households. Shrinking number of farmers The development of manufacturing and high-tech industries in Korea meant the number of people involved in agriculture, forestry, and fishing has been dropping steadily for years. The mainstay of the economy became automobiles, ships, semiconductors, petroleum products, and so on. People in agriculture accounted for approximately ************ of the total population. This rate saw a steady decrease and is expected to fall further. The aging of current farmers and the industry’s unpopularity among younger generations is also having a negative effect. Youths prefer better paying and less physical strenuous occupations in companies or government service, among others. Around half of all farmers are more than 65 years old; this is the typical retirement age in Korea. Possibilities for growth of Korean agriculture In recent years, an increased focus on healthy eating and the introduction of technology into agriculture have created the potential for further industry growth. Koreans today are showing greater concern with the food they consume, whether they be grains, vegetables, or meat products. Organic farming and so-called ‘wellbeing’ foods are popular, despite the higher price tags associated with such products, leading to a growing market for organic agriculture and food products.

Average economic size of farms by standard output, farm type and age of farm manager

For questions where responses were reported as a range, means were calculated using the middle of the range.

The data set presents the part of research results financed by the National Science Centre in Poland as part of the MINIATURA7 scientific activity. The main objective of the research was to identify the possibilities of using digital solutions to enable knowledge sharing in the operating conditions of farms from areas with high agrarian fragmentation. As part of the research objective, the first aim was to determine the technological, social, and economic limitations of using digital solutions in farms operating in areas with high agrarian fragmentation and also to identify differences in the potential for implementing individual digital solutions depending on the size of the farm and the type of activity conducted in the surveyed area. The area of the research was designated as the area of the Polish voivodeships with the highest level of agrarian fragmentation - the Małopolskie and Podkarpackie voivodeships, in which the average area of an agricultural farm in 2020 was 5.26 ha and 5.97 ha, respectively. Using the questionnaire method, 389 agricultural farms in this area were surveyed. The average size of the surveyed farms was 8.4 ha, and their area ranged from 1 ha to 390 ha. A stratified random sampling was used. First, six counties were drawn from the Małopolskie and Podkarpackie provinces, and then a questionnaire survey was conducted in the randomly selected farms. Proportionally to the number of farms operating in the surveyed provinces, 205 farms from the Małopolskie province and 184 farms from the Podkarpackie province were surveyed. In each case, the person answering the questions was the farm manager. The analysis included the division of the surveyed farms into groups according to their area. For this purpose, the quartile method was used, which allowed the following groups of farms to be identified: • Farms with less than or equal to 2.7 ha (106 farms with an average area of 1.98 ha). • Farms with more than 2.7 ha and less than or equal to 4 ha (93 farms with an average area of 3.39 ha). • Farms with an area of more than 4 ha and less than or equal to 7.8 ha (94 farms with an average area of 5.47 ha). • Farms with more than 7.8 ha (96 farms with an average area of 23.10 ha). Data were also compiled according to the characteristics of the farm manager, such as level and type of education, age, and gender. Data related to the topic of the Possibilities of using digital technologies in agriculture in areas with high agrarian fragmentation were presented in tabular form in five Excel files and their combined form in a PDF file. The individual tabular reports contain the following types of data: • Data set 1: Data on Age, Gender, Respondents' education, Employment, Farm Organizational form, Type of farm, Dominant production direction, Production directions, Digital technologies used in the surveyed farms in the surveyed group in general, and depending on the size of the farm • Data set 2: Data on the use of digital technologies in farms divided into groups according to the farmer's age- the farm's owner. Data on barriers to the use of Digital technologies in the group in general and depending on the age of the farm owner. • Data set 3: Data on the use of digital technologies and barriers to their implementation in groups of farms divided according to the farm owner's education type. • Data set 4: Directions of agricultural production implemented in farms using and not using digital technologies. • Data set 5: Barriers to implementing digital technologies depending on the size of the farm.

Robotics-as-a-Service in Agriculture Market Outlook

According to our latest research, the Robotics-as-a-Service (RaaS) in Agriculture market size reached USD 2.94 billion in 2024, demonstrating robust momentum driven by the growing adoption of automation in farming practices. The market is poised to expand at a CAGR of 19.7% from 2025 to 2033, with the forecasted market size expected to hit USD 14.23 billion by 2033. This rapid growth is fueled by the increasing need for precision agriculture, labor shortages, and the demand for higher crop yields, all of which are prompting farmers and agribusinesses to embrace innovative robotics solutions delivered through flexible service models.

One of the primary growth factors for the Robotics-as-a-Service in Agriculture market is the acute labor shortage affecting global agriculture. As the average age of farmers increases and fewer young people enter the profession, farms are struggling to meet the labor demands required for traditional crop cultivation and harvesting. RaaS offers a scalable and cost-effective solution, allowing farms of all sizes to access advanced robotics for tasks such as planting, weeding, and harvesting without the heavy upfront capital investment. This shift is particularly pronounced in developed economies, where labor costs are high, but is also gaining traction in emerging markets facing similar demographic challenges. The flexibility of RaaS models enables farms to adapt quickly to seasonal labor fluctuations and changing crop cycles, further enhancing operational efficiency.

Another significant driver is the increasing emphasis on precision agriculture and sustainable farming practices. Robotics-as-a-Service solutions leverage artificial intelligence, machine learning, and data analytics to enable targeted applications of water, fertilizers, and pesticides, reducing waste and environmental impact. Drones and autonomous tractors, for instance, can provide real-time data on crop health and soil conditions, allowing for more precise interventions. This not only improves yield quality and quantity but also aligns with global trends toward sustainability and resource conservation. As regulatory pressures mount to minimize chemical use and carbon emissions, RaaS platforms are becoming indispensable tools for compliance and environmental stewardship.

Technological advancements in robotics hardware and software are further propelling the growth of the Robotics-as-a-Service in Agriculture market. The integration of advanced sensors, machine vision, and cloud-based analytics has significantly enhanced the functionality and reliability of agricultural robots. These innovations have lowered the barriers to entry for small and medium-sized farms, making it feasible for them to adopt RaaS solutions. Additionally, the proliferation of high-speed internet connectivity in rural areas is enabling seamless remote monitoring and control of robotic fleets, further driving adoption. The convergence of these technologies is expected to unlock new applications and business models, accelerating the digital transformation of agriculture worldwide.

Regionally, North America and Europe are leading the adoption of Robotics-as-a-Service in Agriculture, accounting for a significant share of the global market in 2024. North America, in particular, benefits from a mature technological ecosystem, high labor costs, and strong investment in agricultural innovation. Europe is following closely, driven by stringent environmental regulations and a strong focus on sustainable farming practices. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by large-scale agricultural operations in China and India and increasing government support for agri-tech initiatives. Latin America and the Middle East & Africa are also witnessing rising adoption, albeit at a slower pace, as RaaS providers expand their reach to tap into the vast potential of these regions.

Robot Type Analysis

The robot type segment in the Robotics-as-a-Service in Agriculture market encompasses a diverse array of machines, each designed to address specific challenges in the farming value chain. Harvesting robots are among the most widely adopted, as they directly tackle one of the most labor-intensive and time-sensitive tasks in agriculture. These robots use advanced machine vision and gripping technology to identify and pick ripe produce with mini