# Agricultural Engineers

> Apply knowledge of engineering technology and biological science to agricultural problems concerned with power and machinery, electrification, structures, soil and water conservation, and processing of agricultural products.

- **SOC code:** 17-2021.00
- **Canonical URL:** https://singulariki.com/roles/role-17-2021-00
- **Also known as:** Agricultural Engineer, Engineer, Project Engineer, Research Agricultural Engineer, Agricultural Systems Specialist, Conservation Engineer, Field Engineer, Product Engineer
- **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):
- Prepare reports, sketches, working drawings, specifications, proposals, and budgets for proposed sites or systems.
- Visit sites to observe environmental problems, to consult with contractors, or to monitor construction activities.
- Meet with clients, such as district or regional councils, farmers, and developers, to discuss their needs.
- Discuss plans with clients, contractors, consultants, and other engineers so that they can be evaluated and necessary changes made.
- Design food processing plants and related mechanical systems.
- Test agricultural machinery and equipment to ensure adequate performance.
- Plan and direct construction of rural electric-power distribution systems, and irrigation, drainage, and flood control systems for soil and water conservation.
- Provide advice on water quality and issues related to pollution management, river control, and ground and surface water resources.
- Design structures for crop storage, animal shelter and loading, and animal and crop processing, and supervise their construction.
- Conduct educational programs that provide farmers or farm cooperative members with information that can help them improve agricultural productivity.
- Design sensing, measuring, and recording devices, and other instrumentation used to study plant or animal life.
- Design agricultural machinery components and equipment, using computer-aided design (CAD) technology.

**Emerging tasks** (O*NET):
- Communicate results in peer-reviewed research articles or at workshops or conferences.
- Use agricultural drones for crop monitoring, irrigation management, and pest control.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Engineering and Technology _(knowledge)_
- Computers and Electronics _(knowledge)_
- Design _(knowledge)_
- Mathematics _(knowledge)_
- Physics _(knowledge)_
- Biology _(knowledge)_
- Reading Comprehension _(essential_skill)_
- Active Listening _(essential_skill)_
- Writing _(essential_skill)_
- Speaking _(essential_skill)_
- Oral Comprehension _(ability)_
- Written Comprehension _(ability)_

**Skills in demand:**
- Physics _(Specialized Skill)_
- Mathematics _(Common Skill)_
- Biology _(Specialized Skill)_
- Writing _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Active Listening _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Complex Problem Solving _(Common Skill)_
- English Language _(Common Skill)_
- Systems Analysis _(Specialized Skill)_

**Tools & technology:**
- Adobe InDesign _(hot technology)_
- Adobe Photoshop _(hot technology)_
- Autodesk AutoCAD _(hot technology)_
- Dassault Systemes SolidWorks _(hot technology)_
- Microsoft Access _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Project _(hot technology)_
- Microsoft SharePoint _(hot technology)_
- Microsoft Word _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 61st percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 72nd percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 62nd percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 52nd percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 47th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 5.9% growth (About average); 0.1k annual openings; 1.7k → 1.8k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $84,630; 1,680 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 24% automation, 55% augmentation (usage-weighted).
- **Autonomy median:** 4.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** task iteration.

**Tasks most handed to AI here:**
- Conduct educational programs that provide farmers or farm cooperative members with information that can help them improve agricultural productivity. _(1.0% of measured AI use; directive)_
- Prepare reports, sketches, working drawings, specifications, proposals, and budgets for proposed sites or systems. _(0.4% of measured AI use; task iteration)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me conduct educational programs that provide farmers or farm cooperative members with information that can help them improve agricultural productivity.
- Help me prepare reports, sketches, working drawings, specifications, proposals, and budgets for proposed sites or systems.

## 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/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **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

---
_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-17-2021-00_