EXPOSED
The modal conservation scientist — a soil or water conservationist or rangeland manager, often at NRCS or a state agency — splits time between on-site inspection of farms, pastures and watersheds and desk work writing conservation plans, grant paperwork and GIS-based erosion or grazing analyses. That desk half is squarely in reach of AI: remote-sensing classification, plan templating from field data, cost-share application drafting and technical report writing. What holds is walking the ground with a landowner, reading actual soil profiles and vegetation, and persuading a skeptical producer to change practices on their own land.
Headcount grew steadily across the period.
Median pay $62,660 → $73,010 -6.8% in real terms
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. The marked year is 2020.
BLS projection, 2024–2034
+3.4% 28,500 → 29,500 on the projections basis
Growing, and only partly exposed
The BLS expects +3.4% more of these jobs by 2034, and at 56/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
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.
~2,500 openings a year on average, including replacing people who leave.
RangerPark AideNaturalistPark GuideCamp RangerPark RangerPark WorkerFarm PlannerRange OfficerSoil SurveyorPark AttendantRefuge ManagerConservationistPark NaturalistPark SpecialistPreservationistRange EcologistRange ScientistField AgronomistLand Use PlannerRange TechnicianResource ManagerAquatic EcologistBotany Technician
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier A 12 rather than a 16 because half the job — drafting EQIP and CSP cost-share applications, running RUSLE2 and NRCS Field Office Technical Guide calculations, classifying NAIP or Sentinel imagery into cover maps, writing the plan narrative — is templated work AI already does faster, while the auger-hole soil texture calls, rangeland similarity-index transects and pasture walk-throughs still need someone standing on the site.
Hands-on in uncontrolled environments Field days are spent on unfenced rangeland, streambanks and cropland in whatever weather the season brings — digging pits, running line-point intercept, checking gully headcuts after a storm — but a meaningful share of the week is back in a field office at a screen, which keeps it at 14 instead of the high-teens territory of a wildland fire or logging crew.
Certification preferred, not legally required Nothing legally bars an unlicensed person from writing a conservation plan; the Society for Range Management CPRM and SSSA Certified Professional Soil Scientist credentials are resume assets, and the agency or the engineer who stamps a structural practice — not you — carries the sign-off, which is why this sits at 6 rather than in licensed-professional range.
Meaningful discretion You set stocking rates, decide whether a wetland determination is farmed wetland or prior converted, and pick which practice standard fits an odd field — real calls with money and resource consequences attached, but bounded by NRCS practice standards, ecological site descriptions and state technical guides that narrow the answer before you get to it.
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 (12/20 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 (6/20) is whether the law requires a licensed human to sign. Trust premium (12/20) is whether buyers specifically pay for a person. Judgment and accountability (12/20) 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 30 of this occupation's 56 points (54%).
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.
Chief Executives SAFE
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 72/100 — SAFE.
Carbon and water-quality credit markets are moving toward mandatory third-party verification with named, personally accountable verifiers (Verra, Climate Action Reserve, and state nutrient trading programs). If verification bodies bar model-only attestation and require a credentialed individual to certify field baselines and additionality — as Verra's VM0042 soil carbon methodology's sampling requirements point toward — signature demand attaches to this occupation.
Genuine two-tier structure: if remote-sensing classification, plan templating and cost-share drafting are automated, what remains is site-specific ground truthing, resolving conflicts between model output and observed soil profiles or vegetation state, and negotiating practice adoption. The residual job is the judgment tier, which scores higher than the current blended average.
NRCS conservation practice standards already require a certified Conservation Planner or a Professional Engineer to sign engineering job approvals (Job Approval Authority) for structural practices like waterways, terraces and waste storage facilities. If NRCS extends JAA-style personal certification requirements to AI-generated resource management system plans — i.e. a named certified planner must sign that they walked the site and concur with the plan — the shield rises. Similarly, state-level Certified Professional in Rangeland Management (SRM) or Certified Professional Soil Scientist licensure being written into state cost-share eligibility (as Texas and some Midwest states have debated for nutrient management plan writers) would do it.
If audit findings on cost-share fraud (USDA OIG has repeatedly flagged EQIP and CSP payment errors) push agencies to name an individual planner as accountable for eligibility determinations and practice-failure calls, the role owns consequential contested decisions rather than routing them upward.
Narrow route only: landowner-facing trust is real but rarely priced, since most work is free federal technical assistance. A raise would require private conservation planning and carbon-project origination to grow enough that producers pay a fee specifically for a person who walks their ground — visible in TSP (Technical Service Provider) contracting volume.
The limit. Federal employment is the dominant employer and a hiring or budget contraction at NRCS lowers headcount regardless of how any dimension scores; a signature requirement protects the task, not the job count. Trust premium is structurally capped because the service is mostly given away free.
| Fort Collins-Loveland, CO | 650 | $84,820 +16% |
| New York-Newark-Jersey City, NY-NJ | 590 | $64,020 -12% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 490 | $99,360 +36% |
| Boston-Cambridge-Newton, MA-NH | 480 | $78,580 +8% |
| Portland-Vancouver-Hillsboro, OR-WA | 450 | $83,580 +14% |
| Austin-Round Rock-San Marcos, TX | 430 | $68,620 -6% |
| San Francisco-Oakland-Fremont, CA | 410 | $97,810 +34% |
| Chicago-Naperville-Elgin, IL-IN | 370 | $62,710 -14% |
| San Jose-Sunnyvale-Santa Clara, CA | 70 | $110,200 +51% |
| Springfield, IL | 40 | $107,990 +48% |
| Boulder, CO | 70 | $101,380 +39% |
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 56. 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.