Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Logging Equipment Operators
Occupation · SOC 45-4022.00
Drive logging tractor or wheeled vehicle equipped with one or more accessories, such as bulldozer blade, frontal shear, grapple, logging arch, cable winches, hoisting rack, or crane boom, to fell tree; to skid, load, unload, or stack logs; or to pull stumps or clear brush. Includes operating stand-alone logging machines, such as log chippers.
Also called: Loader Operator · Logging Equipment Operator · Skidder Driver · Skidder Operator · Delimber Operator · Feller Buncher Operator · Harvester Operator · Log Processor Operator · Logging Shovel Operator · Yarder Operator · Buncher Operator · Chain Hooker
Job family: Farming, Fishing, and Forestry Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-45-4022-00/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.
Market signal
Independent published positions, read together — not a forecast.
- Declining employment outlook (-1.4% by 2034)
- 4,200 openings/yr
- Low AI exposure
- Median pay $49,210/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (64th); today's AI task-overlap is comparatively low (10th)
10th-percentile task overlap — yet about 4,200 openings a year (-1.4% 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 | 17th | -1.0 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 24th | 0.2 | |
| AI assistant applicability (Microsoft) Low | 1st | 0.0 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.2), with simple added tooling (β 0.2), and including AI-powered software (γ 0.2). 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.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.8 · 64th percentile among occupations · Moderate
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -1.4% by 2034 |
| Projected annual openings | 4,200 |
| Employment 2024 → 2034 | 30,900 → 30,500 |
“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 2 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.
Logging Equipment Operators sits at the 8th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure eased from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
12% mean task exposure (2025)
8th percentile of 427 placed occupations
−1 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Mobile Farm and Forestry Plant Operators · 8341 | 12% | Not exposed |
| Forestry and Related Workers · 6210 | 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 9 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Inspect equipment for safety prior to use, and perform necessary basic maintenance tasks. Core · importance 4.5
- Control hydraulic tractors equipped with tree clamps and booms to lift, swing, and bunch sheared trees. Core · importance 4.4
- Grade logs according to characteristics such as knot size and straightness, and according to established industry or company standards. Core · importance 4.3
- Drive and maneuver tractors and tree harvesters to shear the tops off of trees, cut and limb the trees, and cut the logs into desired lengths. Supplemental · importance 4.2
- Drive straight or articulated tractors equipped with accessories such as bulldozer blades, grapples, logging arches, cable winches, and crane booms to skid, load, unload, or stack logs, pull stumps, or clear brush. Core · importance 4.1
- Drive crawler or wheeled tractors to drag or transport logs from felling sites to log landing areas for processing and loading. Core · importance 3.8
- Fill out required job or shift report forms. Core · importance 3.6
- Calculate total board feet, cordage, or other wood measurement units, using conversion tables. Supplemental · importance 3.5
- Drive tractors for building or repairing logging and skid roads. Core · importance 3.2
Emerging tasks
Newer responsibilities O*NET has flagged as growing for this occupation.
- Operate remote-controlled logging machines and drones for dangerous or hard-to-reach tasks.
Work activities
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
| Mechanical | 4.2 | |
| Public Safety and Security | 3.5 | |
| Production and Processing | 3.4 |
Transferable skills
Abilities
| Control Precision | 4.0 | |
| Reaction Time | 3.9 | |
| Arm-Hand Steadiness | 3.8 | |
| Multilimb Coordination | 3.8 | |
| Depth Perception | 3.6 | |
| Problem Sensitivity | 3.5 | |
| Response Orientation | 3.5 | |
| Rate Control | 3.5 | |
| Far Vision | 3.5 | |
| Near Vision | 3.3 | |
| Perceptual Speed | 3.1 | |
| Visualization | 3.1 | |
| Manual Dexterity | 3.1 | |
| Visual Color Discrimination | 3.1 | |
| Oral Comprehension | 3.0 | |
| Oral Expression | 3.0 | |
| Information Ordering | 3.0 | |
| Category Flexibility | 3.0 | |
| Spatial Orientation | 3.0 | |
| Selective Attention | 3.0 | |
| Finger Dexterity | 3.0 | |
| Trunk Strength | 3.0 | |
| Deductive Reasoning | 2.9 | |
| Inductive Reasoning | 2.9 | |
| Flexibility of Closure | 2.9 |
Essential skills
| Active Listening | 3.0 | |
| Monitoring | 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 Excel | Spreadsheet software | Hot technology |
| Microsoft Office software | Office suite software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| SAP software | Enterprise resource planning ERP software | Hot technology |
| BCS Woodlands Systems The Logger Tracker | Data base user interface and query software | |
| TradeTec TallyWorks Logs | Inventory management software | |
| TradeTec TallyWorks TimeTracker | Human resources 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.
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
- High school diploma or equivalent · 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: Transportation and Materials Moving . 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 | 55.4% | |
| High School Diploma | 43.7% | |
| Post-Secondary Certificate | 0.9% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 6.8 | |
| Conventional | 4.4 | |
| Investigative | 1.8 |
Interest areas
| Transportation/Machine Operation | 6.6 | |
| Physical/Manual Labor | 4.7 | |
| Nature/Outdoors | 3.7 | |
| Mechanics/Electronics | 2.9 | |
| Construction/Woodwork | 2.3 | |
| Engineering | 2.2 | |
| Agriculture | 1.6 | |
| Accounting | 1.2 |
Work styles
| Dependability | 2.4 | |
| Cautiousness | 2.2 | |
| Stress Tolerance | 1.8 | |
| Attention to Detail | 1.6 | |
| Perseverance | 1.5 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $35,050 |
| 25th percentile | $43,750 |
| Median (50th) | $49,210 |
| 75th percentile | $60,640 |
| 90th percentile | $72,280 |
| People employed | 22,520 |
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 | 17,290 | $50,400 |
| Manufacturing · Sector | 3,330 | $43,680 |
| Transportation and Warehousing · Sector | 410 | $50,820 |
| Construction · Sector | 100 | $52,000 |
| Wholesale Trade · Sector | — | $65,620 |
| Administrative and Support and Waste Management and Remediation Services · Sector | — | $49,000 |
| Temporary Help Services · National industry | — | $47,840 |
| Landscaping Services · National industry | — | $49,000 |
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 | 279.61× | 17,290 |
| Manufacturing · Sector | 1.79× | 3,330 |
| Transportation and Warehousing · Sector | 0.38× | 410 |
| Construction · Sector | 0.08× | 100 |
Related occupations
Part of the Agriculture and Construction career clusters.
- Operating Engineers and Other Construction Equipment Operators · 47-2073.00 Compare →
- Continuous Mining Machine Operators · 47-5041.00 Compare →
- Loading and Moving Machine Operators, Underground Mining · 47-5044.00 Compare →
- Excavating and Loading Machine and Dragline Operators, Surface Mining · 47-5022.00 Compare →
- Hoist and Winch Operators · 53-7041.00 Compare →
- Industrial Truck and Tractor Operators · 53-7051.00 Compare →
- Agricultural Equipment Operators · 45-2091.00 Compare →
- Fallers · 45-4021.00 Compare →
- Crane and Tower Operators · 53-7021.00 Compare →
- Log Graders and Scalers · 45-4023.00 Compare →
- Mobile Heavy Equipment Mechanics, Except Engines · 49-3042.00 Compare →
- Tank Car, Truck, and Ship Loaders · 53-7121.00 Compare →
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 Logging Equipment Operators — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
- Operating Engineers and Other Construction Equipment Operators Compare →
- Continuous Mining Machine Operators Compare →
- Loading and Moving Machine Operators, Underground Mining Compare →
- Excavating and Loading Machine and Dragline Operators, Surface Mining Compare →
- Hoist and Winch Operators Compare →
Paths in
How people typically prepare for this work.
- High school diploma or equivalent · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
- Transportation and Materials Moving · field of study
Zoom out
On the global GenAI exposure gradient this work sits around the 8th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Logging Equipment Operators show 10th-percentile AI task overlap — and about 4,200 annual U.S. openings
- Logging Equipment Operators rank in the 10th 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 4,200 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 declining (-1.4%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $49,210, across about 22,520 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Logging Equipment Operators show 10th-percentile AI task overlap — and about 4,200 annual U.S. openings • Logging Equipment Operators rank in the 10th 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 4,200 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 declining (-1.4%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $49,210, across about 22,520 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Logging Equipment Operators". https://singulariki.com/roles/role-45-4022-00 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.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Logging 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; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-45-4022-00
APA
Singulariki. (2026). Logging Equipment Operators. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-45-4022-00
BibTeX
@misc{singulariki-role-45-4022-00,
title = {Logging 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; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-45-4022-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.