# Pesticide Handlers, Sprayers, and Applicators, Vegetation

> Mix or apply pesticides, herbicides, fungicides, or insecticides through sprays, dusts, vapors, soil incorporation, or chemical application on trees, shrubs, lawns, or crops. Usually requires specific training and state or federal certification.

- **SOC code:** 37-3012.00
- **Canonical URL:** https://singulariki.com/roles/role-37-3012-00
- **Also known as:** Lawn Specialist, Lawn Technician, Licensed Pesticide Applicator, Spray Technician, Chemical Applicator, Integrated Pest Management Technician (IPM Technician), Pest Control Technician, Pesticide Applicator
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Mix pesticides, herbicides, or fungicides for application to trees, shrubs, lawns, or botanical crops.
- Fill sprayer tanks with water and chemicals, according to formulas.
- Lift, push, and swing nozzles, hoses, and tubes to direct spray over designated areas.
- Identify lawn or plant diseases to determine the appropriate course of treatment.
- Cover areas to specified depths with pesticides, applying knowledge of weather conditions, droplet sizes, elevation-to-distance ratios, and obstructions.
- Start motors and engage machinery, such as sprayer agitators or pumps or portable spray equipment.
- Connect hoses and nozzles selected according to terrain, distribution pattern requirements, types of infestations, and velocities.
- Clean or service machinery to ensure operating efficiency, using water, gasoline, lubricants, or hand tools.
- Provide driving instructions to truck drivers to ensure complete coverage of designated areas, using hand and horn signals.
- Plant grass with seed spreaders, and operate straw blowers to cover seeded areas with mixtures of asphalt and straw.

**Emerging tasks** (O*NET):
- Establish driving routes for pesticide applications.
- Record information about pesticide applications, such as the type used and amount applied.
- Use new technology and equipment, such as drones or GPS systems, to apply pesticides more accurately and efficiently.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Customer and Personal Service _(knowledge)_
- Biology _(knowledge)_
- Production and Processing _(knowledge)_
- Near Vision _(ability)_
- English Language _(knowledge)_
- Arm-Hand Steadiness _(ability)_
- Control Precision _(ability)_
- Administration and Management _(knowledge)_
- Active Listening _(essential_skill)_
- Oral Comprehension _(ability)_
- Problem Sensitivity _(ability)_
- Deductive Reasoning _(ability)_

**Skills in demand:**
- Biology _(Specialized Skill)_
- English Language _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Active Listening _(Common Skill)_
- Time Management _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Mathematics _(Common Skill)_
- Chemistry _(Specialized Skill)_
- Social Perceptiveness _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Microsoft Outlook _(Common Skill)_

**Tools & technology:**
- Facebook _(hot technology)_
- Google Android _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Customer database software
- Geographic information system GIS systems
- Materials inventory software
- Rate calculation software
- Unit conversion software

## AI exposure & outlook

- **AI task-overlap index:** 17th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 22nd percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 15th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 23rd percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 94th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 3.8% growth (About average); 4.1k annual openings; 29.6k → 30.7k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $45,200; 25,200 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-37-3012-00_