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AI exposure report
Will AI replace Conservation Scientists?
Some of your tasks will be automated. People who use AI will have the edge.
Task exposure to today's AI
Moderate exposure
This is the occupation's typical task mix. Your own score can be up to 30 points higher or lower depending on how you work: routine share, people contact, judgment and how much you already use AI.
Get your personal scoreWill AI replace Conservation Scientists?
Not entirely. Today's AI tools can take on about 45% of the typical work in this occupation, mostly: compute design specifications for implementation of conservation practices, using survey or field information, technical guides or engineering manuals. The parts that stay human include: Advise land users, such as farmers or ranchers, on plans, problems, or alternative conservation solutions.; Implement soil or water management techniques, such as nutrient management, erosion control, buffers, or filter strips, in accordance with conservation plans.; Plan soil management or conservation practices, such as crop rotation, reforestation, permanent vegetation, contour plowing, or terracing, to maintain soil or conserve water..
People who learn to use AI for the automatable parts keep the edge. The 3-phase plan below shows where to start.
Which tasks AI can take on
The largest tasks in this occupation, their share of the working week, and how much of each today's AI tools can do.
+ 24 more tasks (57% of the week) in the full test.
Your human edge
- Advise land users, such as farmers or ranchers, on plans, problems, or alternative conservation solutions.
- Implement soil or water management techniques, such as nutrient management, erosion control, buffers, or filter strips, in accordance with conservation plans.
- Plan soil management or conservation practices, such as crop rotation, reforestation, permanent vegetation, contour plowing, or terracing, to maintain soil or conserve water.
AI tools to try
ChatGPT / Claude for literature and writingAI research tools (e.g. Elicit, Consensus, Semantic Scholar)Copilot for data analysis code (Python, R)Related roles
A 3-phase AI learning plan
Phase 1
Faster literature and writing
- Literature search and summaries with AI, always checked against the papers.
- Drafts of methods, reports and grant text.
- Reference management and formatting.
Project
Use AI to summarise 20 papers on cover-crop erosion control into a 4-page landowner briefing, verifying every claim and citation against the originals, and log hours saved versus your usual review.Phase 2
AI for data analysis
- Analysis code in Python or R written with AI help.
- Visualisation and statistics explained step by step.
- Reproducibility: version code and prompts.
Project
Build an AI-assisted R or Python script that processes three years of soil test and rainfall data into erosion-risk maps and statistics, versioned in Git with prompts recorded.Phase 3
AI as a research method
- Classification, extraction or modelling with AI on your data.
- Validate against human labels or ground truth.
- Ethics, data protection and disclosure of AI use.
Project
Train and validate an AI image or GIS classifier to map riparian buffer gaps on 50 field parcels, score accuracy against your own ground-truth checks, and document AI use and data handling.