EXPOSED
The modal ag tech splits time between field plots/greenhouses/labs — laying out trial blocks, tagging plants, pulling soil cores, running tissue and seed assays, calibrating instruments — and a screen tier of data entry, spreadsheet cleanup, and routine report writing that AI plus sensor networks eats first. Sampling and instrument handling in muddy, variable field conditions still needs hands and legs, but no license protects the role and the scientist above signs off on the science. Drone imagery, in-line soil sensors, and automated plate readers shrink the headcount needed per acre of trial rather than eliminating the job.
Headcount grew steadily across the period.
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing.
BLS projection, 2024–2034
+4.3%
Percentage only. The projection counts a different population from the 15,130 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +4.3% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~2,900 openings a year on average, including replacing people who leave.
AgronomistCow TesterSeed ExpertSeed TesterBlood TesterSeed AnalystBiotechnicianCattle TesterAcidity TesterBiological AideCrop ConsultantCrop SpecialistField AssistantMoisture TesterSeed SpecialistField AgronomistFowl Blood TesterDairy TechnologistFeed Research AidePlant Control AideAgronomy ConsultantAgronomy SpecialistChemical ApplicatorExtension Associate
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Hand-thinning a variety trial, tagging individual plants for phenotyping, and pulling soil cores on a grid still need a person walking the plot, but the yield-data transcription, plate-reader output cleanup, and standard-format trial reports are already machine work — that split is what puts it at 11 rather than 15.
Hands-on in uncontrolled environments You are in mud, greenhouses at 95°F, and grain bins on the same week, hauling augers and sample bags across uneven ground and handling live plant material and pesticide-treated plots — uncontrolled outdoor work, though the lab bench and instrument room half of the job keeps it off the 18-plus ladder-and-livestock tier.
No licence, no signature requirement No state licenses agricultural technicians; a pesticide applicator card or a two-year ag degree may be listed as preferred, and even the GLP-relevant signature on a trial record belongs to the study director above you, so the 3 reflects credentials that help you get hired but shield nothing.
Meaningful discretion Trials come with a written protocol specifying plot layout, sampling depth, and replication, so your discretion is real but bounded — deciding a plot is compromised by flooding and flagging it, or calling an instrument out of calibration mid-run, are the calls you own, and the protocol deviation still goes upstairs for approval.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (11/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (3/20) is whether the law requires a licensed human to sign. Trust premium (5/20) is whether buyers specifically pay for a person. Judgment and accountability (7/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 15 of this occupation's 40 points (38%).
Embodiment (14/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
Agricultural Engineers EXPOSED
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 53/100, still EXPOSED.
Task-mix shift: once sensor networks and LLM report drafting absorb the data-entry/spreadsheet/routine-report tier, the residual job is chain-of-custody sampling, anomaly triage on instrument drift, and salvaging trials when plots flood or a treatment fails — the genuine judgment tier. Watch for job postings that drop 'data entry' and add 'trial troubleshooting / QA' language.
GLP study-personnel requirements already name individuals responsible for raw data integrity; a formal certification requirement (e.g. state-mandated Certified Crop Adviser or a licensed 'sampling technician' credential tied to hemp THC compliance or PFAS/biosolids soil testing under state ag-department rules) that makes a named human sign the sample record and be personally sanctionable.
Regulatory sampling protocols that require physical human-collected specimens with documented chain of custody — e.g. EPA FIFRA Good Laboratory Practice (40 CFR 160) residue trials and USDA AMS pesticide data program sampling, plus state seed-certification field inspections (AOSCA) — being extended to hemp/THC compliance and PFAS soil testing, where a drone or in-line sensor reading is not an admissible sample.
If AI-generated agronomic prescriptions require a named human to accept or reject them before field application — analogous to insurer or seed-company contract terms requiring a signed technician deviation report when a trial protocol is broken — the role owns the consequential call rather than the scientist above.
The limit. No realistic route to a trust premium: buyers of trial data want defensible numbers, not human-collected ones, and the customer relationship sits with the PI or the seed/chemical company. Liability gains are capped because the supervising scientist or study director remains the legal signer in GLP work.
| Los Angeles-Long Beach-Anaheim, CA | 500 | $63,630 +28% |
| Sacramento-Roseville-Folsom, CA | 460 | $62,680 +26% |
| Atlanta-Sandy Springs-Roswell, GA | 350 | $54,380 +10% |
| Madison, WI | 250 | $47,030 -5% |
| Portland-Vancouver-Hillsboro, OR-WA | 220 | $46,800 -6% |
| Boise City, ID | 210 | $49,220 -1% |
| Minneapolis-St. Paul-Bloomington, MN-WI | 210 | $52,600 +6% |
| Kansas City, MO-KS | 170 | $51,380 +4% |
| Santa Rosa-Petaluma, CA | 30 | $66,540 +34% |
| Lansing-East Lansing, MI | 120 | $64,790 +31% |
| Los Angeles-Long Beach-Anaheim, CA | 500 | $63,630 +28% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 40. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.