Soil scientist pay and the person everyone asks first
$170,820top of the range in Iowa · middle $78,850 / yr
AI augments this role
Soil Scientists in the United States earn a median of $78,850 a year. Pay starts near $48,680. Pay reaches $170,820 at the top of the range in Iowa, the best-paying state for this work among those with at least 500 people in the job.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Soil and Plant Scientists, SOC 19-1013). Last checked 9 September 2026.
Entry level
$48,680
Top of the range · Iowa
$170,820
Education
Bachelor's or Master's degree in Soil Science
Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Soil and Plant Scientists). Top of the range is the highest state-level figure among states with at least 500 people in the job. AI-impact rating is PayCrunch's editorial assessment. Updated September 2026.
🆕 New & Trending AI Tools for Soil ScientistReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Soil Scientist work right now.
Julius AINEWFree / $20 mo
AI data analyst that runs statistics and charts from plain-language prompts.
How a Soil Scientist uses it: analyze datasets and generate figures without writing code
NotebookLMNEWFree / $7.99 mo
Google tool that answers questions grounded only in the documents you give it — with citations.
How a Soil Scientist uses it: load your own manuals, policies, or PDFs and ask questions that stay accurate to the source
ElicitFree / $12 mo
AI research assistant that finds and summarizes papers.
How a Soil Scientist uses it: run a literature review and extract findings across dozens of papers fast
ConsensusFree / $9 mo
AI search that answers questions from peer-reviewed research.
How a Soil Scientist uses it: get evidence-backed answers with the studies behind them
SciSpaceFree / paid
AI that explains papers and helps with literature review.
How a Soil Scientist uses it: decode dense papers and trace citations quickly
SciteFree / $20 mo
Shows whether other studies support or contradict a paper's claims (Smart Citations).
How a Soil Scientist uses it: check if a finding is actually backed by the wider literature before you cite it
ChatGPTFree / $20 mo
The most-used AI assistant — writing, analysis, research, and images from a plain-language chat.
How a Soil Scientist uses it: draft emails and documents, summarize long files, and get instant answers to on-the-job questions
ClaudeFree / $20 mo
AI assistant known for careful writing, long-document analysis, and coding.
How a Soil Scientist uses it: analyze big reports or spreadsheets and turn messy notes into clean, finished writing
Google GeminiFree / $20 mo
Google's AI assistant, built into Gmail, Docs, and Search.
How a Soil Scientist uses it: draft and reply inside Google Workspace and research without leaving the page
A pit, a bag, and a decision about land
A soil scientist reads the ground the way other people read a file. You walk a field, a building site, or a restoration plot. You open a pit or you use a probe. You note color, texture, roots, and how water sits in the profile. Then you take samples the lab can trust, label them so they cannot be mixed up, and turn that record into advice someone can act on. The advice might be about crops, drainage, a septic layout, a road bed, or a wetland the map only hinted at.
The week splits between outdoors and a desk. Field days are weather, mud, and careful notes. Office days are maps, lab sheets, and a report a farmer, an engineer, or a planner can follow. You may sit in a meeting where your paragraph decides whether a project moves. You may spend the morning explaining why two fields that look alike will not behave alike after rain. The skill is translation. The soil does not speak in project deadlines. You do.
Employers are varied. A university extension office, a conservation district, a consulting firm, a farm cooperative, a government review desk, and a lab that serves builders all hire this craft. Some jobs are mostly sampling. Some are mostly interpretation for permits. Some mix research plots with grower visits. Ask which mix you are signing up for. A title that says scientist can still be a season of bags and trucks, or a season of hearings. Both are real. They tire you in different ways.
What the report has to carry
Your name on a report means someone may build, plant, or regulate from it. Describe what you observed, what the lab returned, and what you are willing to conclude. Separate observation from guess. If the site was too wet to sample properly, say so. If a recommendation depends on a follow-up season, say that too. Clients remember the person who prevented a bad install more than the person who sounded certain and was wrong in April.
Tools are ordinary and worth respecting. Maps, a probe, a Munsell-style color comparison, sample bags, a lab that runs texture and nutrient work, and software that keeps sites straight. You do not need a glamorous kit. You need habits that keep sample A from becoming sample B. In the lab, you learn what the numbers can support. In the field, you learn when the map is out of date because someone filled a low spot last year. The job lives in that mismatch.
Collaboration is constant. Agronomists, engineers, hydrologists, planners, and growers all want a piece of the answer. Your lane is the soil. When the topic is really crop variety, structural load, or a legal boundary, you hand it to the person who owns that lane. Staying in your lane makes your part more believable. Overclaiming makes the next report harder to sell, even when the sampling was excellent.
Seasons shape the craft more than outsiders expect. Spring can be a rush of site visits before planting or before a permit deadline. A wet week can shut the field down and stack the office with half-finished logs. Winter may be the time you finally write the long report, calibrate how you describe profiles, and fix the map layers that drifted during the busy months. If you only picture sunny pits, the first winter of deadlines will surprise you. Ask a hiring manager what the slow season looks like, and whether the slow season is actually slow.
When a state board licenses the title
Some states license soil scientists through a state board. Where that licence exists, the board grants it. The grant shows you met the board's rules for education and practice for work that state regulates. It is separate from the diploma alone. Other employers hire on a degree in soil science or a close field, plus seasons of field work and reports a reviewer can check. Do not assume the rule you learned in one place applies everywhere you might move.
People prepare with the degree, with supervised or mentored field seasons, and with the board's current path if they want the licence. Read that path on the board's own site before you pay for a course a vendor called required. A voluntary professional credential can sit beside state licensure in this field. Treat it as voluntary unless an employer or a board says otherwise. This page will not invent the shape of any exam. If a board uses one, its site is the only honest description.
What the licence proves, where it exists, is that the board recognizes you for regulated work. It does not prove you write clear reports or treat a grower with respect. Keep both. If your state does not license the title, a strong record of sites, maps, and recommendations still gets you hired. Say which kinds of sites you have worked: agricultural, onsite wastewater, construction, conservation. Specific beats a vague claim that you love the outdoors.
Board grant, where a board exists
Some states license soil scientists through a state board. The board grants the licence. Where no licence exists, hiring rests on the degree, the field record, and the reports.
Who hires, and what they want to hear
A hiring manager wants a site story with the judgment left in. Describe a pit that surprised you, what you changed in the recommendation, and how you documented it. Describe a time you told a client the site would not support what they hoped, and how you said it. They are listening for honesty and for clarity. A candidate who only talks about equipment, and never about a conclusion, sounds like a sampler who has not yet become a scientist.
Ask who signs the report, whether a licence is required for that signature, and how much of the year is field versus desk. Ask who mentors new staff through a first permit season. Ask whether the firm works for growers, builders, public agencies, or all three. Those mixes change the pressure. A public review job and a private consulting job can share a degree and still feel like different careers by November. Bring writing samples with private landowner details removed.
Sampler, author, then the person who signs
Early work is often sampling, logging, and drafting under someone else's signature. You learn not to rush a label. You learn which labs the office trusts. The next step is writing recommendations other people send out with light edits. Later you may sign, lead a crew, or specialize in agriculture, wastewater, or restoration. Signing is a responsibility shift. Your name is now the one a regulator or a client argues with. Take that step when your judgment is ready, not only when the title sounds larger.
Some scientists move into teaching, extension, or a research plot. Some open a small consulting practice and spend as much time on invoices as on pits. Pay moves when the signature, the specialty, or the client load moves. A new title on the same sampling route will not, by itself, remake your year. When you negotiate, describe the sites you can already carry and the ones you would still want reviewed. Then set that scope next to the wages below.
A useful mid-career test is whether other people seek your read before they commit. A grower calls before they tile a wet corner. An engineer sends a boring log because your note last year caught a layer the map missed. That reputation is built from small accurate calls, not from a single dramatic site. Protect it by writing what you saw, by answering when you were wrong, and by refusing to bless a plan the profile does not support. The signature means more after that habit is visible.
One series, and Iowa counted two ways
Soil scientists share the May 2025 Occupational Employment and Wage Statistics series for soil and plant scientists. Name that shared series here and then leave it. The national middle is $78,850. Early pay sits near $48,680, and the gap between those two is $30,170. The high end of the published range is $170,820 in Iowa. From the national middle to that high end is $91,970. From the national middle to the highest state median is $17,460.
State medians are a different statistic from the high end. In this order they are Iowa at $96,310, Idaho at $95,150, California at $91,930, Oregon at $85,150, and Washington at $79,970. The highest median is Iowa at $96,310. The lowest median is Michigan at $65,390. The gap between those medians is $30,920. Iowa's high end of $170,820 and Iowa's median of $96,310 are different statistics. One is the top of the published range. The other is the middle. Mixing them makes an ordinary Iowa offer sound like the top, or makes the top sound typical.
Same state, two statistics
Iowa holds both the high end, $170,820, and the highest median, $96,310. They are different statistics. Use the median for a typical Iowa wage and the high end only for the top of the published range.
Putting the offer next to the right figure
Compare an offer with $48,680 and $78,850 first. Near the early figure, the national middle is $30,170 away. Ask what closes that gap: signing reports, a licence the board requires, a specialty, or leading a crew. Near $78,850, you match the country. Iowa's median of $96,310 is $17,460 above that national middle. Idaho, California, Oregon, and Washington, in the order already listed, also sit at or above the national middle. If you work in one of those places, the state median is the local comparison. The national middle is the country's middle.
Use $170,820 only as Iowa's high end of the published range. Iowa's median remains $96,310. Those two numbers live in the same state and still describe different facts. A recruiter who calls $170,820 the typical Iowa wage is using the wrong statistic. Correct it with the median. Michigan's median of $65,390 sits between early national pay and the national middle. The gap from Michigan's median to Iowa's median is $30,920. Use it when you compare places, and pair it with the kind of employer, because a conservation district and a private permit practice are different weeks.
The climb from the national middle to Iowa's high end is $91,970. That distance belongs in a conversation about senior scope in a market that already pays at the top of the published range. It does not belong in a first field season as the wage you expect to see. If a posting waves the high end, ask whether the offer is near the national middle, near Iowa's median, or actually near that range top. Write the offer, $78,850, and the state median you are using. Keep $170,820 in the note only so it cannot be confused with $96,310.
Bring the licence into the same conversation only where a board actually grants one. If the employer wants you to pursue it, ask whether pay changes when the board grants it, and whether the supervised or mentored practice is supported. If no licence applies, negotiate on the reports you can sign and the sites you can carry. The May 2025 figures are the map. A firm that will say which statistic it is using is a firm that understands the work, and that clarity is part of what you are choosing.
The top of Soil Scientist pay — and how to get there with AI
$170,820what Soil Scientist pay reaches in Iowa
Highest state-level top-of-range annual wage for Soil and Plant Scientists, among states with at least 500 people in the job. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025.
And the role it leads to — Biological Scientists, All Other — reaches $211,910 in District of Columbia.
$48,680entry$78,850middle$170,820top end
Between a soil scientist in the middle of this range and one at its top end sits a quiet monopoly: being the person the whole group consults before running a classification, a model or a statistical test, because you are the one who worked out how the tool actually behaves.
Classifying soils by landscape position and properties, running chemical analyses of microorganism content, and testing how a soil responds to a given management practice all pass through software that changes faster than the underlying science does, including ESRI ArcGIS software, R, the Erosion Productivity Impact Calculator EPIC and the National Resources Conservation Service NRCS PEDON Description Program PDP. Most teams contain one person who reads the release notes and a dozen who copy last season's workflow. The one who reads them ends up writing the group's method, reviewing everyone else's outputs and being named on every project that needs a defensible number. Assistants make that position easier to hold, since a model will explain an unfamiliar error or draft a first training handout, which moves the bottleneck from your typing to your judgement about whether a result is credible.
Your playbook, by where you are now
Just startingLearn one tool past the point of comfort
Choose the package your group leans on most and read its documentation right through, not only the section your current task needs.
Rebuild an old project's soil classification from raw data in ESRI ArcGIS software so you know every step, including the ones somebody skipped.
Move your statistics out of a spreadsheet into R and keep the script, so a reviewer can rerun your erosion or productivity result.
Ask Claude to explain a function or error you do not recognise, then verify the behaviour on a dataset whose answer you already know.
What proves it: A rerunnable script or map project that reproduces a published result from your own group.
Realistic span: the first two years, alongside graduate work
A few years inWrite the method everyone copies
Turn your workflow into a short written procedure with screenshots, and hand it to the newest colleague to test against.
Run a lunchtime session on the PEDON description program or GAEA Technologies WinSieve for the field staff who avoid them.
Standardise how the group records chemical analyses of soil microorganism content so results from different people can be compared.
Take first interpretation of degraded or contaminated sites in Leica Geosystems ERDAS IMAGINE, and teach the reasoning you used.
Keep NotebookLM loaded with your group's methods, standards and past reports so newcomers can ask it before they ask you.
What proves it: A written method your organisation adopted, with your name attached to it.
Realistic span: years three through seven
ExperiencedTurn teaching into scope
Run sessions for the farmers and landowners you advise, since erosion and land use recommendations land better when someone can see the data behind them.
Join the group that drafts regulatory standards for land reclamation and soil conservation, because the method you taught becomes the standard.
Take responsibility for tool selection and licensing, so a purchase becomes a technical decision rather than a procurement one.
Look at where this work is funded most heavily, Iowa being one such place, when you are weighing your next post.
Move toward directing research programmes if you want the broader biological science route rather than deeper specialisation.
What proves it: A standard or training programme in use beyond your own team.
Realistic span: year eight and beyond
The next 90 days
Pick the single analysis your group repeats most, whether it is classifying soils by geographic position and properties or modelling productivity loss under a management practice, and rebuild it from raw data in the next ninety days without copying anyone's existing project file. Keep the script, comment every decision, and note each place the old workflow made an assumption nobody could defend. Then write it up as four pages any colleague could follow, and hand it to the person who has been avoiding that analysis. You will find out immediately whether your method survives contact with somebody else's hands, and you will have done the thing that makes a soil scientist indispensable: turned personal fluency into something the organisation depends on.
Wage figures: BLS OEWS, May 2025. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.
Every figure is the national median from the U.S. Bureau of Labor Statistics (OEWS) shown on that role’s own page.
Never used AI before? Start here (2 minutes).
Start with digital soil mapping — the biggest single leverage in the field. Open Google Earth Engine and pull satellite imagery and terrain for your site, and query SoilGrids for global property predictions as a first-pass surface. In an afternoon you can generate a working map that used to take a season of augering, then design your field sampling to confirm and refine it.
For analysis and writing, the toolkit is free or low-cost: R (with the ranger and caret packages) for the machine-learning maps, QGIS for cartography, ChatGPT or GitHub Copilot to write the code, and Perplexity for regulations. Always calibrate model output to real field samples before you report it.
The one rule, forever: Digital soil maps and spectral predictions are estimates that must be calibrated and ground-truthed with physical sampling and lab analysis — never certify contamination status, a remediation decision, or a regulatory conclusion on model output alone. Follow sampling and QA/QC protocols and Phase I/II ESA standards, document data provenance and model uncertainty, and remember that a wrong call on a contaminated site carries real legal liability.
The plays — exact steps, exact prompts
Do these in order. Each one is copy-paste ready. You do not need to know anything about AI going in.
1
Build digital soil maps with machine learning and satellite data
Why this pays: Predictive soil mapping over a whole property or watershed is a service clients pay well for, and it's far faster than traditional survey. Delivering ML-based maps calibrated to field data is the core capability that wins landscape-scale mapping and consulting contracts.
Google Earth EngineSoilGridsR (ranger/caret)
1
Pull covariates (terrain, NDVI, climate, parent material) in Google Earth Engine, use SoilGrids as a starting surface, then train a random-forest model in R to predict soil class or a property across the site.
2
Get a defensible digital-soil-mapping workflow and code.
Copy-paste this prompt
Write a reproducible R workflow for digital soil mapping of [topsoil organic carbon] across a [500 ha farm]. Cover: assembling terrain and remote-sensing covariates, joining my point samples, fitting a random forest with the ranger package, spatial cross-validation, mapping prediction and uncertainty, and how to report accuracy (RMSE, R2). Comment the code so I can adapt it.
A map is only as good as its calibration data — design field sampling to train and validate the model, and always publish the uncertainty layer, not just the prediction.
What you'll haveProperty- and landscape-scale soil maps produced fast and defensibly — the deliverable behind larger consulting contracts.
2
Predict soil properties from spectra instead of the lab queue
Why this pays: NIR/MIR spectroscopy plus a prediction model gives near-instant estimates of carbon, texture, and nutrients at a fraction of wet-chemistry cost. Running that pipeline yourself lets you characterize far more samples per project — the throughput that raises billable capacity.
Use the open OSSL reference data and your own scans to build calibration models in Python (scikit-learn) or R, so a spectrometer reading predicts the property directly.
2
Get a spectroscopy calibration plan that will hold up.
Copy-paste this prompt
I want to predict [soil organic carbon and clay content] from [MIR spectra]. Recommend a modeling approach: spectral preprocessing steps, whether to use PLS regression or a machine-learning model, how to combine my local samples with the OSSL library, how to validate (independent test set, RPD/RPIQ), and how to flag samples the model shouldn't be trusted on. Write starter Python I can adapt.
Spectral models drift across instruments and soil types — validate on an independent set and confirm outliers with wet chemistry before reporting a number.
What you'll haveFast, low-cost property estimates on many more samples — the capacity that lets you take on bigger sampling projects.
3
Turn soil data into precision-agriculture prescriptions
Why this pays: Growers and ag retailers pay for variable-rate maps that cut input costs and lift yield. A soil scientist who converts survey and sensor data into agronomic prescriptions taps the well-paid ag-tech and consulting market — a direct route toward the top of the band.
Climate FieldViewEOS Crop MonitoringArcGIS Pro
1
Combine your soil maps with satellite vegetation and yield data in EOS Crop Monitoring or Climate FieldView, and build management zones and variable-rate layers in ArcGIS Pro.
2
Draft an agronomically sound variable-rate plan.
Copy-paste this prompt
Using [soil organic matter, texture zones, and 3 years of NDVI and yield data] for a [corn field], propose a variable-rate approach for [nitrogen and lime]: how to delineate management zones, what soil and tissue tests to confirm rates, the agronomic logic per zone, and how to frame the expected input savings and yield effect for the grower. Note what field verification is required before applying anything.
Prescriptions affect a grower's crop and budget — ground every rate in confirmatory soil/tissue tests and local agronomic limits, not model output alone.
What you'll haveInput-saving prescription maps growers will pay for — an entry into the higher-paid precision-ag consulting market.
4
Map contamination and model remediation scenarios
Why this pays: Environmental site assessment and remediation is where consulting soil science pays best. AI-assisted geostatistics that map a contaminant plume and compare cleanup options let you deliver the technical core of a Phase II/remediation project faster — more billable projects.
ArcGIS ProPython (PyKrige)ChatGPT
1
Interpolate contaminant concentrations with kriging in ArcGIS Pro (Geostatistical Analyst) or Python (PyKrige) to map the plume and estimate volumes above action levels.
2
Structure the assessment and compare remedies.
Copy-paste this prompt
For a site with [lead and petroleum hydrocarbon] impacts in surface soil, outline a defensible Phase II approach: a sampling design to delineate the plume, how to interpolate and estimate impacted volume, and a comparison of remediation options (excavation, capping, in-situ) with rough pros, cons, and cost drivers. Reference the relevant regulatory framework and flag every step that requires a licensed professional sign-off.
Contamination conclusions carry legal liability — model outputs are estimates to confirm with adequate sampling, and regulatory decisions need qualified professional certification.
What you'll haveFaster, defensible plume maps and remedy comparisons — the technical deliverable that drives environmental consulting revenue.
5
Break into soil-carbon measurement and verification
Why this pays: Carbon markets need credible soil-carbon measurement, reporting, and verification (MRV), and qualified soil scientists are scarce. Building an MRV workflow now positions you in an emerging, well-funded niche — the kind of specialized expertise that commands premium rates.
Google Earth EngineRPerplexity
1
Combine field sampling design with remote-sensing covariates in Google Earth Engine and modeling in R to estimate soil-carbon stocks and change, and track protocol requirements with Perplexity.
2
Design a credible MRV sampling and modeling plan.
Copy-paste this prompt
Design a soil-organic-carbon MRV plan for a [regenerative grazing project on 2,000 ha] aiming to sell carbon credits. Cover: a statistically defensible baseline sampling design, stratification, how to estimate stock change with uncertainty, remote-sensing support, and what a major registry protocol requires for verification. List the biggest sources of error and how to control them.
Credit integrity depends on rigorous, protocol-compliant sampling — verify current registry requirements and never let modeled gains substitute for measured, uncertainty-bounded stock change.
What you'll haveA defensible soil-carbon MRV capability — entry into a scarce, premium-paying emerging market.
6
Draft environmental and soil reports faster
Why this pays: Consulting soil science runs on reports: soil surveys, ESAs, nutrient-management and land-suitability plans. AI that accelerates drafting and synthesis lets you turn projects around faster and take on more, directly lifting billable output.
ClaudeNotebookLMPerplexity
1
Load your field data, lab results, and the relevant standards into NotebookLM, then use Claude to draft report sections grounded strictly in those sources.
2
Draft a report section anchored to your data.
Copy-paste this prompt
Draft the [soil resources and limitations] section of a land-suitability report for [a proposed housing development on former pasture]. Structure it by soil map unit, summarize drainage, texture, and engineering limitations from my provided data, and identify constraints (shrink-swell, shallow water table, erosion risk). Flag every place where a site-specific test result must replace my placeholder before this is final.
Every conclusion must rest on your actual field and lab data, not AI-assumed typical values — verify each map-unit statement against your site.
What you'll haveReports drafted faster with your data and judgment on top — more projects delivered, the core of consulting income.
Your 12-month sequence to the top of the range
How the plays above stack into a path from median pay toward the $170,820 tier.
Month 1
Build your first digital soil map for a real site with Google Earth Engine, SoilGrids, and R; calibrate it to field samples.
Months 2-3
Add spectroscopy-based property prediction and adopt an AI coding partner for your geostatistics and stats.
Months 3-6
Move into a paid application — precision-ag prescriptions or contamination mapping — and speed report drafting with AI.
Months 6-12
Develop a specialization (soil-carbon MRV or environmental remediation) and package an AI-accelerated survey-to-report workflow.
Year 2
Target senior consulting or ag-tech roles/contracts where your AI-scaled mapping and modeling command pay at the top of the range.
Gear for this job
As an Amazon Associate, PayCrunch earns from qualifying purchases. Links to books and tools are for the job on this page; we only recommend what we’d use in the work.
Same live O’Reilly 3rd already on data-scientist / python-developer / market-research-analyst / agricultural-scientist. This leftover page names Python (scikit-learn) as a play tool for OSSL calibration models; Month 1 is Build your first digital soil map for a real site with Google Earth Engine, SoilGrids, and R. FAQ is Do I need to code to do digital soil mapping? Not CompTIA Data+ and not leftover Ross Exam P (that is actuary). Confirm 109810403X. Live page HTTP 200, no PC_GEAR / amazon.com/dp / tag=paycrunch-20 at 2026-09-17 4:34 PM PT.
Next steps for a Soil Scientist
Some links below are affiliate or partner links. PayCrunch may earn a commission if you enroll or subscribe through them, at no extra cost to you. Wage figures on this page still come from the Bureau of Labor Statistics, not from these programs.
Soil Scientist work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Soil and Plant Scientists (SOC 19-1013). O*NET Job Zone 5 is typical: graduate or professional school, so the honest next credential is a graduate-level or professional certificate — not a random catalog dump.
The occupation's listed knowledge areas include Biology and Chemistry; the links search those subjects, not a generic 'career courses' list.
Soil Scientists in this dataset list Autodesk AutoCAD among the tools in use, so a program that names that stack is a better fit than a survey course.
FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for Soil Scientist work, not a claim that they list a counted SOC 19-1013 inventory.
Write a Soil Scientist resume, or one aimed at Biological Scientists, All Other, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.
A Soil Scientist resume that names the actual tasks on this page, or the step-up title Biological Scientists, All Other, beats a blank template when you apply.
What Soil Scientists earn by state
These are the Bureau of Labor Statistics’ own figures for Soil and Plant Scientists, state by state — not a cost-of-living adjustment applied to the national number. Only states employing at least 500 people in the occupation are shown, because a state median drawn from a handful of workers is noise rather than a signal.
Iowa
$96,310
highest of them · +22% vs the national median
Michigan
$65,390
lowest of the 11 states that qualify · -17% vs the national median
The same job pays $30,920 more a year at the median in Iowa than in Michigan — 47% higher. That gap is what the Bureau measured, before any question of what it costs to live in either place. Iowa also carries the top of this job’s range, $170,820 — the figure quoted at the head of this page.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025, SOC 19-1013. 11 states clear the 500-employee reporting floor for this occupation; those below it are left out rather than shown with a wide error band.
Free data. Use any of it.
PayCrunch publishes verified, BLS-sourced salary + AI-playbook data on 1,000+ professions — free, no signup.
No. Someone has to design the sampling, dig and describe the pit, verify the model in the field, and take professional responsibility for a contamination or land-use conclusion. AI predicts soil from data, but it can't feel structure in a hand sample or be liable for a wrong call. The soil scientists who use AI map and report faster; the risk is a competitor who delivers more, not the software.
Can I trust a digital soil map or a spectral prediction?
Only after calibration and validation. Predictive maps and spectroscopy models are estimates with real error that varies by soil type and instrument, and SoilGrids at global resolution won't capture your field. Ground-truth with physical samples, validate on independent data, report the uncertainty, and never certify a regulatory decision on model output alone.
How does AI actually increase a soil scientist's pay?
By raising throughput and unlocking higher-value work. Digital mapping and spectroscopy let you characterize more land per project; precision-ag, remediation, and soil-carbon MRV are premium markets AI makes accessible; and faster reporting means more billable projects. More and higher-value work is the path from a mid-band salary to the $170,820 consulting tier.
Do I need to code to do digital soil mapping?
You need to run code, not write it from scratch. ChatGPT and Copilot will produce and explain the R, Python, and Earth Engine scripts step by step; your job is to understand the method, supply good field data, and check the results. Start by adapting one worked example to your own site.
Which AI tool should a soil scientist learn first?
Google Earth Engine paired with R for digital soil mapping, because it delivers the biggest immediate leverage and underpins mapping, precision ag, and carbon work alike. Add an AI coding assistant to move faster, then a specialization tool (spectroscopy or geostatistics) as your niche sharpens.
Methodology & sources
Salary (median, 10th, top of the range) — U.S. Bureau of Labor Statistics, OEWS.
By state — the Bureau of Labor Statistics’ own state medians, limited to states employing at least 500 people in the occupation. No cost-of-living arithmetic is applied to a wage anywhere on this page.
The plays — PayCrunch's own step-by-step guidance using publicly available AI tools. Tool names/URLs are real and current as of August 2026; prompts written to work as-is. Verify any professional output before relying on it.