Share and intensity of work current AI systems can materially affect.
SOC 19-1013
Soil and Plant Scientists AI displacement risk
Soil and plant scientists run field experiments on crop varieties, soil health, and land management. Sensor networks and satellite imagery now stream the data they once collected by hand, and AI phenotyping scores plots faster than any field crew, but experiment design and agronomic judgment still set the questions.
Likely potential for exposed tasks to move to software after workflow integration.
Precision-agriculture platforms automate measurement, not interpretation. Advising farmers on degraded soils, designing variety trials, and defending recommendations to landowners remain judgment work anchored in field observation.
Distribution
Where Soil and Plant Scientists sits across 620 tracked roles
Displacement pressure 28 — higher than 43% 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 19-1013. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $78,850 (May 2025, US national). The latest BLS row matched SOC 19-1013.
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 Soil and Plant Scientists
SOC 19-1013 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 28/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
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 Soil and Plant Scientists
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
SOC19-1013
- Core task / ID 9021
Communicate research or project results to other professionals or the public or teach related courses, seminars, or workshops.
- Core task / ID 9024
Develop methods of conserving or managing soil that can be applied by farmers or forestry companies.
- Core task / ID 9022
Provide information or recommendations to farmers or other landowners regarding ways in which they can best use land, promote plant growth, or avoid or correct problems such as erosion.
- Core task / ID 9025
Conduct experiments to develop new or improved varieties of field crops, focusing on characteristics such as yield, quality, disease resistance, nutritional value, or adaptation to specific soils or climates.
- Core task / ID 9026
Investigate soil problems or poor water quality to determine sources and effects.
- Core task / ID 9023
Investigate responses of soils to specific management practices to determine the use capabilities of soils and the effects of alternative practices on soil productivity.
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
Conduct experiments on new crop varieties
Exposure 42, automation 18%, augmentation 62%.
O*NET evidence: Conduct experiments to develop new or improved varieties of field crops, focusing on ch... (ID 9025)
Advise farmers on land use and erosion
Exposure 30, automation 10%, augmentation 52%.
O*NET evidence: Provide information or recommendations to farmers or other landowners regarding ways in... (ID 9022)
Investigate soil problems and water quality
Exposure 36, automation 14%, augmentation 58%.
O*NET evidence: Investigate soil problems or poor water quality to determine sources and effects. (ID 9026)
Communicate research results to professionals
Exposure 54, automation 28%, augmentation 66%.
O*NET evidence: Communicate research or project results to other professionals or the public or teach r... (ID 9021)
TaskExposureAutomationAugmentation
Conduct experiments on new crop varieties4218%62%
Advise farmers on land use and erosion3010%52%
Investigate soil problems and water quality3614%58%
Communicate research results to professionals5428%66%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowPrecision Agriculture Specialist
Training horizon: 3-6 months. Skill overlap 68. Wage preservation signal 112.
- Own sensor and imagery data
- Validate AI plot scoring
- Translate analytics for growers
credentialed transition
LowAgronomy Research Lead
Training horizon: 12-24 months. Skill overlap 60. Wage preservation signal 132.
- Complete a graduate degree
- Lead variety trial programs
- Publish applied research
Comparison guides
Compare the next move before you commit
3-6 months
Soil and Plant Scientists to Precision Agriculture Specialist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Soil and Plant Scientists into Precision Agriculture Specialist.
12-24 months
Soil and Plant Scientists to Agronomy Research Lead
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Soil and Plant Scientists into Agronomy Research Lead.
What the AI risk score means for Soil and Plant Scientists
The displacement pressure score for Soil and Plant Scientists is 28. 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. Communicate research results to professionals carries 28% automation pressure, while Communicate research results to professionals carries 66% 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: $78,850 (May 2025, US national). Employment context: Agricultural research role feeding precision-ag pipelines. Typical education: Bachelor degree minimum; doctoral degree for research.
Wage vulnerability is 34, while transition feasibility is 66. 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 displacement pressure
- AI phenotyping speeds plot scoring
- Farm advising stays relationship work
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Soil and Plant Scientists, 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.
Experimental design
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.
Field sampling
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.
Agronomic data analysis
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.
Landowner advisory
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 Precision Agriculture Specialist, such as own sensor and imagery data.
- By 90 days, compare internal openings and external postings for Precision Agriculture Specialist or Agronomy Research Lead and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Soil and Plant Scientists
Will AI replace Soil and Plant Scientists?
Soil and plant scientists run field experiments on crop varieties, soil health, and land management. Sensor networks and satellite imagery now stream the data they once collected by hand, and AI phenotyping scores plots faster than any field crew, but experiment design and agronomic judgment still set the questions. 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 Soil and Plant Scientists work are most exposed to AI?
Communicate research results to professionals and Conduct experiments on new crop varieties show the strongest automation pressure in this model. Communicate research results to professionals and Conduct experiments on new crop varieties are better treated as AI-augmented work.
What should Soil and Plant Scientists learn next?
Start with Experimental design, Field sampling, Agronomic data analysis. The most practical adjacent paths in this model are Precision Agriculture Specialist and Agronomy Research Lead.
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