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Agricultural Equipment Operators

Occupation · SOC 45-2091.00

Drive and control equipment to support agricultural activities such as tilling soil; planting, cultivating, and harvesting crops; feeding and herding livestock; or removing animal waste. May perform tasks such as crop baling or hay bucking. May operate stationary equipment to perform post-harvest tasks such as husking, shelling, threshing, and ginning.

Also called: Equipment Operator · Farm Equipment Operator · Loader Operator · Rake Operator · Baler Operator · Cutter Operator · Hay Baler · Packing Tractor Machine Operator · Sprayer · Windrower Operator · Agricultural Equipment Operator (Ag Equipment Operator) · Agricultural Farm Equipment Operator

Job family: Farming, Fishing, and Forestry Occupations

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A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-45-2091-00/context.md directly.

AI work map

A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.

9th-percentile task overlap — yet about 10,500 openings a year (+7.7% projected, BLS) . What exposure means →

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

Measure Rank vs all occupations Percentile Score
Overall AI exposure (Felten et al.) Low 13th -1.1
LLM task exposure, γ (OpenAI / Eloundou) Low 3rd 0.0
AI assistant applicability (Microsoft) Low 23rd 0.1

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.0), and including AI-powered software (γ 0.0). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook Growing fast · +7.7% by 2034
Projected annual openings 10,500
Employment 2024 → 2034 65,200 → 70,300

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international 6 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

17% mean task exposure (2025)
23rd percentile of 427 placed occupations
−0 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Mixed Crop and Animal Producers · 6130 19% Not exposed
Gardeners, Horticultural and Nursery Growers · 6113 18% Not exposed
Field Crop and Vegetable Growers · 6111 18% Not exposed
Mixed Crop Growers · 6114 17% Not exposed
Tree and Shrub Crop Growers · 6112 17% Not exposed
Mobile Farm and Forestry Plant Operators · 8341 12% Not exposed

Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.

Tasks

All 17 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Emerging tasks

Newer responsibilities O*NET has flagged as growing for this occupation.

  • Operate drones to monitor crop health, growth and pest infestations, and apply targeted treatments.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Transferable skills

Operation and Control 3.9
Operations Monitoring 3.8
Troubleshooting 3.1
Equipment Maintenance 3.0
Repairing 3.0
Quality Control Analysis 3.0
Social Perceptiveness 2.9

Abilities

Control Precision 3.9
Multilimb Coordination 3.9
Near Vision 3.6
Problem Sensitivity 3.4
Far Vision 3.4
Depth Perception 3.4
Oral Comprehension 3.3
Arm-Hand Steadiness 3.3
Response Orientation 3.3
Rate Control 3.3
Reaction Time 3.3
Oral Expression 3.1
Manual Dexterity 3.1
Static Strength 3.1
Trunk Strength 3.1
Hearing Sensitivity 3.1
Speech Clarity 3.1
Deductive Reasoning 3.0
Information Ordering 3.0
Finger Dexterity 3.0
Extent Flexibility 3.0
Speech Recognition 3.0
Inductive Reasoning 2.9
Perceptual Speed 2.9
Visualization 2.9
Selective Attention 2.9
Auditory Attention 2.9

Knowledge

English Language 3.4
Mechanical 2.9
Public Safety and Security 2.8
Mathematics 2.8

Essential skills

Active Listening 3.0
Critical Thinking 2.9

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Microsoft Access Data base user interface and query software Hot technology
Microsoft Excel Spreadsheet software Hot technology
Microsoft PowerPoint Presentation software Hot technology
Farm Management Software Hay and Crop Manager Enterprise resource planning ERP software
Martens Farms Farm Site Mate Map creation software
Martens Farms Farm Trac Data base user interface and query software

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

Outdoors, Exposed to All Weather Conditions 4.3
Face-to-Face Discussions with Individuals and Within Teams 4.2
In an Enclosed Vehicle or Operate Enclosed Equipment 4.1
Exposed to Very Hot or Cold Temperatures 3.9
Determine Tasks, Priorities and Goals 3.9
Work With or Contribute to a Work Group or Team 3.8
Importance of Being Exact or Accurate 3.7
Contact With Others 3.7
In an Open Vehicle or Operating Equipment 3.7
Exposed to Contaminants 3.6
Pace Determined by Speed of Equipment 3.6
Telephone Conversations 3.4
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 3.4
Time Pressure 3.4
Freedom to Make Decisions 3.2
Work Outcomes and Results of Other Workers 3.1
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 3.1
Outdoors, Under Cover 3.1
Spend Time Sitting 3.1
Impact of Decisions on Co-workers or Company Results 3.0
Level of Competition 3.0
Health and Safety of Other Workers 3.0
Spend Time Standing 2.9
Spend Time Walking or Running 2.9
Coordinate or Lead Others in Accomplishing Work Activities 2.9
Consequence of Error 2.8
Frequency of Decision Making 2.7
Physical Proximity 2.6
Indoors, Not Environmentally Controlled 2.5
Exposed to Hazardous Equipment 2.4
Importance of Repeating Same Tasks 2.4
Exposed to Minor Burns, Cuts, Bites, or Stings 2.4
E-Mail 2.2
Conflict Situations 2.2
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 2.2
Indoors, Environmentally Controlled 2.1
Spend Time Making Repetitive Motions 2.1
Spend Time Bending or Twisting Your Body 2.1
Degree of Automation 2.0
Spend Time Kneeling, Crouching, Stooping, or Crawling 1.9

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
No formal educational credential · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

What to study: Agriculture, Agriculture Operations, and Related Sciences . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

Share of people in this occupation at each level of education.

Less than a High School Diploma 56.8%
High School Diploma 19.1%
Post-Secondary Certificate 14.4%
Some College Courses 9.5%
Associate's Degree (or other 2-year degree) 0.1%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Realistic 7.0
Conventional 3.7
Investigative 2.5
Enterprising 1.3

Interest areas

Agriculture 6.3
Transportation/Machine Operation 6.3
Physical/Manual Labor 5.3
Mechanics/Electronics 4.3
Nature/Outdoors 3.5
Engineering 2.5
Life Science 1.4
Animal Service 1.4

Work styles

Dependability 2.3
Attention to Detail 1.8
Cautiousness 1.5
Perseverance 1.3

Wages & employment

U.S. · annual wages (BLS OEWS)

$31k10th$37k25th$43kMedian$49k75th$58k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
65k202470k2034 (proj.)+7.7% · Growing fast
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $31,240
25th percentile $36,640
Median (50th) $42,580
75th percentile $48,690
90th percentile $57,790
People employed 30,940

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Industry Workers National median pay
Agriculture, Forestry, Fishing and Hunting · Sector 16,020 $37,940
Wholesale Trade · Sector 9,560 $46,200
Manufacturing · Sector 1,820 $46,460
Transportation and Warehousing · Sector 930 $46,680
Administrative and Support and Waste Management and Remediation Services · Sector 700 $43,150
Retail Trade · Sector 670 $47,110
Landscaping Services · National industry 280 $39,580
Temporary Help Services · National industry 200 $41,600
Management of Companies and Enterprises · Sector 160 $41,790
Professional, Scientific, and Technical Services · Sector 120 $38,220
Real Estate and Rental and Leasing · Sector 40 $54,200
Construction · Sector 30 $52,870

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Industry Concentration Workers
Agriculture, Forestry, Fishing and Hunting · Sector 188.57× 16,020
Wholesale Trade · Sector 7.89× 9,560
Landscaping Services · National industry 1.52× 280
Manufacturing · Sector 0.71× 1,820
Transportation and Warehousing · Sector 0.63× 930
Administrative and Support and Waste Management and Remediation Services · Sector 0.39× 700
Temporary Help Services · National industry 0.38× 200
Management of Companies and Enterprises · Sector 0.28× 160

Part of the Agriculture career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Agricultural Equipment Operators sits at the 9th percentile of AI task-overlap and the 17th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Agricultural Equipment Operators Industrial Truck and Tractor Operators Laborers and Freight, Stock, and Material Movers, Hand Farmworkers and Laborers, Crop, Nursery, and Greenhouse Operating Engineers and Other Construction Equipment Operators Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders Industrial Machinery Mechanics Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Agricultural Equipment Operators — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Write a report on thisheadline · factoids · citation

Agricultural Equipment Operators show 9th-percentile AI task overlap — and about 10,500 annual U.S. openings

  • Agricultural Equipment Operators rank in the 9th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
  • The occupation is projected to see about 10,500 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
  • BLS projects employment to be growing fast (+7.7%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $42,580, across about 30,940 U.S. workers.BLS OEWS (May 2024)
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Agricultural Equipment Operators show 9th-percentile AI task overlap — and about 10,500 annual U.S. openings

• Agricultural Equipment Operators rank in the 9th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 10,500 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be growing fast (+7.7%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $42,580, across about 30,940 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Agricultural Equipment Operators". https://singulariki.com/roles/role-45-2091-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Agricultural Equipment Operators." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-45-2091-00

APA

Singulariki. (2026). Agricultural Equipment Operators. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-45-2091-00

BibTeX
@misc{singulariki-role-45-2091-00,
  title  = {Agricultural Equipment Operators},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-45-2091-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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