Share and intensity of work current AI systems can materially affect.
SOC 45-2092
Farmworkers and Laborers, Crop, Nursery, and Greenhouse AI displacement risk
Harvest robots and precision sprayers are real but cover specific crops in uniform conditions. Hand harvest of delicate produce, greenhouse work, and the sheer variety of farm tasks keep manual labor the backbone of agriculture.
Likely potential for exposed tasks to move to software after workflow integration.
Agricultural automation is decades old and crop-selective: combines mechanized grains long ago, yet fresh produce still moves through human hands. Labor shortage, not robot displacement, is the binding force in farm labor markets.
Distribution
Where Farmworkers and Laborers, Crop, Nursery, and Greenhouse sits across 620 tracked roles
Displacement pressure 22 — higher than 29% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
27 O*NET task statements matched to SOC 45-2092. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $35,660 (May 2025, US national). The latest BLS row matched SOC 45-2092.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Farmworkers and Laborers, Crop, Nursery, and Greenhouse
SOC 45-2092 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 22/100 role score and are not an occupation forecast.
+1.1% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +1.6% GDP and 3.9% unemployment.
+5.9% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +8.3% GDP and 4.6% unemployment.
+33.6% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Farmworkers and Laborers, Crop, Nursery, and Greenhouse
The current evidence import matched 27 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks27
SOC45-2092
- Core task / ID 23387
Record information about crops, such as pesticide use, yields, or costs.
- Core task / ID 23379
Direct and monitor the work of casual and seasonal help during planting and harvesting.
- Core task / ID 23392
Participate in the inspection, grading, sorting, storage, and post-harvest treatment of crops.
- Core task / ID 23370
Harvest plants, and transplant or pot and label them.
- Core task / ID 23373
Repair and maintain farm vehicles, implements, and mechanical equipment.
- Core task / ID 23374
Harvest fruits and vegetables by hand.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Harvest crops by hand
Exposure 26, automation 19%, augmentation 16%.
O*NET evidence: Participate in the inspection, grading, sorting, storage, and post-harvest treatment of... (ID 23392)
Plant, weed, and prune plants
Exposure 22, automation 14%, augmentation 20%.
O*NET evidence: Plant, spray, weed, fertilize, water, and prune plants, shrubs, and trees, using garden... (ID 23399)
Operate tractors and machinery
Exposure 34, automation 24%, augmentation 40%.
O*NET evidence: Operate tractors, tractor-drawn machinery, and self-propelled machinery to plow, harrow... (ID 23371)
Record crop and work information
Exposure 46, automation 27%, augmentation 52%.
O*NET evidence: Record information about crops, such as pesticide use, yields, or costs. (ID 23387)
TaskExposureAutomationAugmentation
Harvest crops by hand2619%16%
Plant, weed, and prune plants2214%20%
Operate tractors and machinery3424%40%
Record crop and work information4627%52%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowCrew Lead
Training horizon: 1-3 months. Skill overlap 76. Wage preservation signal 122.
- Coordinate harvest crews
- Track picking rates
- Enforce safety standards
credentialed transition
LowAgricultural Equipment Operator
Training horizon: 3-9 months. Skill overlap 62. Wage preservation signal 126.
- Learn precision agriculture systems
- Get equipment certifications
- Practice GPS-guided operation
Comparison guides
Compare the next move before you commit
1-3 months
Farmworkers and Laborers, Crop, Nursery, and Greenhouse to Crew Lead
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Farmworkers and Laborers, Crop, Nursery, and Greenhouse into Crew Lead.
3-9 months
Farmworkers and Laborers, Crop, Nursery, and Greenhouse to Agricultural Equipment Operator
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Farmworkers and Laborers, Crop, Nursery, and Greenhouse into Agricultural Equipment Operator.
What the AI risk score means for Farmworkers and Laborers, Crop, Nursery, and Greenhouse
The displacement pressure score for Farmworkers and Laborers, Crop, Nursery, and Greenhouse is 22. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Record crop and work information carries 27% automation pressure, while Record crop and work information carries 52% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $35,660 (May 2025, US national). Employment context: Large agricultural workforce with chronic labor shortages. Typical education: No formal educational credential.
Wage vulnerability is 76, while transition feasibility is 60. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Low to moderate displacement pressure
- Robots cover uniform crops only
- Farm labor shortage dominates
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Farmworkers and Laborers, Crop, Nursery, and Greenhouse, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Physical stamina
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Equipment operation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Crop and plant knowledge
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Reliability
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Crew Lead, such as coordinate harvest crews.
- By 90 days, compare internal openings and external postings for Crew Lead or Agricultural Equipment Operator and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Farmworkers and Laborers, Crop, Nursery, and Greenhouse
Will AI replace Farmworkers and Laborers, Crop, Nursery, and Greenhouse?
Harvest robots and precision sprayers are real but cover specific crops in uniform conditions. Hand harvest of delicate produce, greenhouse work, and the sheer variety of farm tasks keep manual labor the backbone of agriculture. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Farmworkers and Laborers, Crop, Nursery, and Greenhouse work are most exposed to AI?
Record crop and work information and Operate tractors and machinery show the strongest automation pressure in this model. Record crop and work information and Operate tractors and machinery are better treated as AI-augmented work.
What should Farmworkers and Laborers, Crop, Nursery, and Greenhouse learn next?
Start with Physical stamina, Equipment operation, Crop and plant knowledge. The most practical adjacent paths in this model are Crew Lead and Agricultural Equipment Operator.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics