The agricultural scientist who owns the office method
$170,820top of the range in Iowa · middle $78,850 / yr
AI augments this role
Agricultural 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
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 Agricultural ScientistReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Agricultural Scientist work right now.
Julius AINEWFree / $20 mo
AI data analyst that runs statistics and charts from plain-language prompts.
How an Agricultural 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 an Agricultural 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 an Agricultural 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 an Agricultural Scientist uses it: get evidence-backed answers with the studies behind them
SciSpaceFree / paid
AI that explains papers and helps with literature review.
How an Agricultural 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 an Agricultural 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 an Agricultural 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 an Agricultural 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 an Agricultural Scientist uses it: draft and reply inside Google Workspace and research without leaving the page
If your current days are lab assays, crop scouting, or advice from an extension desk, soil and plant science is the research craft that turns those observations into trials a grower can use. This letter is for that move. It covers the work itself, the fact that this page’s wages follow Soil and Plant Scientists, the degree research seats usually expect, the absence of a single national licence, the path from technician to scientist or industry agronomist, and the Iowa figures you can actually quote.
Experiments that have to survive a season
The work is experiments, field trials, and advice. You might design a trial on fertilizer rate, a variety comparison, a cover-crop mix, a soil amendment, or a pest practice, then lay it out so the comparison is fair. You collect soil cores, tissue samples, yield, and notes on weather and management. You analyze what the season actually did, and you write it so a grower, a company, or a journal can tell what to repeat. The tools are a plot plan, sampling kits, a lab method you can defend, a statistics package, and a notebook that still makes sense in November. The places are research farms, grower fields, greenhouses, and bench labs. The people are farm managers, technicians, graduate students, industry agronomists, and the grower who will ignore you if the result only works on a station with perfect equipment.
Soil scientists in this series lean toward classification, fertility, physics, and the way water and nutrients move. Plant scientists lean toward breeding support, physiology, pathology, and the crop’s response. Many jobs blend them, because a recommendation about nitrogen is a soil story and a plant story at once. Coming from a biology lab, your assays are an asset and your missing piece is often the field: plot borders, edge effects, a combine that cannot hit a tiny strip, a cooperator who applied the wrong product on the north half. Coming from production agronomy, your field sense is the asset and your missing piece is often the design: replicates, controls, and a written protocol someone else can follow.
Animal science and food science are neighboring scientific lives. This page’s Bureau series is Soil and Plant Scientists, so the day described here is soils and crops, not a herd nutrition trial and not a processing-plant microbiology seat. If your background is animals or food, the transferable piece is experimental discipline and the habit of giving advice people can audit. The job you are entering still has to be about soil, plants, or both, or you will be interviewing for a neighboring occupation with its own wage series. Say which crop or soil problem you want. “I like agriculture” is too wide for a hiring professor or a seed company.
Advice growers can use is a product, not a leftover. A result that lives only in a figure, with no sentence about rate, timing, soil type, or the cost of being wrong, has not finished the job. Extension-style writing, field days, and a short technical bulletin are part of many public roles. Industry roles turn the same evidence into a product recommendation or a placement decision. Learn to do both kinds of sentences. The scientific one states what the trial can support. The practical one states what a grower might do next season, including where you would not stretch the finding.
The series these wages belong to
The pay figures come from the Bureau of Labor Statistics Occupational Employment and Wage Statistics for May 2025, under Soil and Plant Scientists, SOC 19-1013. Name that series once in any salary conversation so you and the employer know which occupation’s wages you are holding. Crop consulting, animal research, and food science have their own labor markets. Quoting this page at one of those tables will confuse the offer. Quoting it for a soil or plant research seat, a university technician track that feeds that research, or an industry agronomist role grounded in crops and soils, matches the series the page chose.
An advanced degree for the research seat
What stands in for a licence
There is no single national licence for soil and plant scientists. Research seats commonly expect an advanced degree. Employers otherwise look at the trials you have run, the methods you can defend, and advice a grower or a product team has already used.
A bachelor’s degree in agronomy, soil science, plant science, crop science, or biology can open technician work and some industry agronomy roles. A master’s degree is a common door to independent trial leadership. A doctorate is the usual preparation for a principal investigator who designs the program, wins the funding, and supervises others. “Common” is the right word. Some industry scientists lead serious programs on a master’s plus a record of products and protocols. Public research faculty seats still cluster around the doctorate. Look at the posting’s degree line before you decide you must stop working for years. If the posting says doctorate, believe it. If it says master’s or equivalent experience, gather the equivalent in trials, not in adjectives.
Preparation is the degree, supervised research, and a portfolio of studies. Supervised practice here means a technician post, a graduate assistantship, or an industry trial program where a senior scientist signs the protocol. Your portfolio can be a thesis chapter, a poster, a bulletin, or an internal report scrubbed of confidential numbers. For each study, be ready to say what you controlled, what the field refused to control, and what you would change. That conversation is the interview. Memorizing crop facts without a study of your own is how strong technicians stay stuck, and how career changers from pure lab work get passed for someone who has lost a plot to flood and written it up honestly.
Certifications in crop advising exist in parts of the industry and can help a commercial agronomist signal competence to retailers and growers. They are optional signals, granted by the professional groups that run them, and they do not replace the degree a research seat asked for. If you pursue one, treat it as fluency with recommendations in your state, and keep the scientific record beside it. This page’s occupation does not hang on a single national licence, so lead with the degree and the trials.
Getting hired from the adjacent bench
Universities, federal and state research units, seed and fertilizer companies, food companies with grower programs, and nonprofit research farms all hire. The application should name a crop or a soil process and a method. Remote sensing, wet chemistry, greenhouse physiology, breeding support, and on-farm trials are different doors into the same series. Pick the door your hands already know, then show one step toward the door you want. A lab biologist who has added a summer of field sampling is easier to place than a lab biologist who wants the field in theory only.
Ask who you would report to and who owns the protocol. A technician hired into a healthy group gets training on methods and a path toward designing a piece of the next trial. A technician hired as extra hands, with no scientist who reads the data, learns speed and little judgment. For a scientist seat, ask what you would be expected to publish or to hand a grower in the first two seasons. For an industry agronomist seat, ask whether the job is trials, sales support, or both, because the week and the pay logic differ. Sales support can be a fine bridge. Call it what it is so the offer can be compared with the research figures honestly.
References should be people who have seen your data, not only your attitude. A professor, a farm manager, or a senior agronomist who can describe a mistake you caught in a spreadsheet will help more than a character reference. Bring a one-page diagram of a trial layout to interviews where you expect a whiteboard. Drawing the replicate is more persuasive than saying you are passionate about sustainability.
Technician, scientist, principal investigator, or industry agronomist
The path people can actually follow starts at technician, moves to scientist, and then either to principal investigator or across to industry agronomist. A technician runs the methods, keeps the plots alive, and protects the data. A scientist designs studies and answers for the interpretation. A principal investigator sets the program, mentors the group, and carries the relationship with funders or with the company’s research leadership. An industry agronomist may sit beside that ladder rather than on it: trials in growers’ fields, training for a sales force, and recommendations tied to a product portfolio. Some agronomists later move back into a research seat. Some scientists move out to industry because they want the advice to land inside a season rather than inside a journal.
Promotion inside research follows finished studies. A technician who can already write a method section and notice a confounded factor is the one scientists trust with more design. A scientist who can fund or justify the next season, and who can still walk a field, is the one considered for principal investigator. Keep a list of studies, your role in each, and the decision the result changed. That list is the promotion packet and the next resume. If the list is only tasks, you are still in the technician story, which is honorable and should be paid as such until the design responsibility is real.
Quoting Iowa without confusing the median and the high end
Entry on this page is $48,680. The national median is $78,850. The step from entry to median is $30,170. The high end is $170,820. Iowa is the place named for that high end. Iowa’s median, a different figure, is $96,310, which sits $17,460 above the national median. Idaho’s median is $95,150. California’s median is $91,930. Oregon’s median is $85,150. Washington’s median is $79,970. Use medians when you mean typical pay in that state. Use $170,820 only when you mean the high end of the range in Iowa.
From the national median to that Iowa high end is $91,970. That distance describes senior scientific scope: a principal investigator, a scarce specialty, or an industry scientist whose recommendations carry a product line. It is a poor opening number for a technician offer or for a new master’s-level hire. Open those conversations at entry or at the median. The $30,170 from entry to median is the right gap when you already run methods independently and the offer still prices you as a new pair of hands. The $17,460 from the national median to Iowa’s median is the right gap when the job is in Iowa and you are discussing typical pay, not the top of the range.
A practical script stays inside those dollars. For a technician role, compare the offer with $48,680 and ask what would move it toward $78,850: independent plots, a method only you can run, supervision of seasonal crews. For a scientist role, anchor on $78,850 and then on the state median if you are in Iowa, Idaho, California, Oregon, or Washington. For a principal investigator or a senior industry scientist in Iowa, you may discuss how far the role sits along the $91,970 between the national median and $170,820. Ask the employer which description they intend. Then stop adding numbers. The chart has given you a national entry, a national middle, five state medians, and one state high end. That is the whole set.
What to carry into the first season
Carry the habit of writing down what you did in the field the day you did it, and the humility to tell a grower when a result is local. Leave behind any habit of hiding a bad plot or of stretching a greenhouse result into a statewide recommendation. You are aiming at technician, then scientist, then principal investigator, or at an industry agronomist seat that still rests on trials. An advanced degree is the common key to the research chair. Employers treat the degree and the trials as the gate. When the offer arrives, set it next to $48,680, next to $78,850, and next to the Iowa median or the Iowa high end only when you know which of those two Iowa numbers you mean.
The top of Agricultural Scientist pay — and how to get there with AI
$170,820what Agricultural 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
Mid-range agricultural scientists answer the question they were handed; at the top of the range they return the sizing, the working drawings and the budget in the same week, because they built the thing that produces all three.
Much of this job is the same work with different ground under it: a drainage layout for one district, an irrigation plan for the next, each carrying its own report, sketches, specifications and budget. Scientists in the middle of the range reassemble that package by hand every time. The ones at the top of the range turn it into equipment — a locked Autodesk AutoCAD template, a Microsoft Excel workbook that sizes from the field survey, a proposal skeleton a model drafts from the meeting notes. What that buys back is time for the part no tool can do: standing on the site, watching where the water actually goes, and telling a farmer something true about it.
Your playbook, by where you are now
Just startingTurn one calculation into a sheet
Pick the sizing calculation you repeat most, whether that is drain spacing, pump duty, or storage volume for a crop shed, and rebuild it in Microsoft Excel with every assumption on the face of the sheet.
Record site visits identically each time: photograph the environmental problem, note the location, and drop both into Microsoft Access so a season of visits stays searchable.
Learn enough Autodesk AutoCAD layer and block discipline that your irrigation and drainage drawings leave the office looking like one hand drew them.
Have Claude convert your rough field notes into a first draft of the site report, then correct every technical statement yourself before a client sees it.
What proves it: A sizing workbook two colleagues used on their own jobs without asking you how it works.
Realistic span: the first twelve to eighteen months
A few years inWire the survey into the drawing
Hold survey, soil and catchment data in ESRI ArcView so a boundary, a contour and a proposed flood control alignment all come from one source instead of three.
Build the proposal skeleton — scope, specification clauses, drawing list, budget lines — with quantities pulled from the sizing workbook rather than retyped.
Set up a machinery test log so equipment performance is recorded against hours, crop and conditions, and the trend is readable after a single season.
Load the water quality and pollution management guidance you work under into NotebookLM so you can answer a discharge question inside the meeting instead of a week later.
Lay the report out once in Adobe InDesign and reuse it, so every district council and developer receives the same document structure.
What proves it: A live template set — drawings, specification and budget — that the office now issues from.
Realistic span: roughly years two to five
ExperiencedOwn the standard the office designs to
Publish the design standard: which method applies to each drainage case, what factor of safety, and what has to appear on the drawing.
Push sizing and cost history into Oracle Database or the ERP software so budgets for a new site start from what comparable sites really cost to build.
Take the instrumentation work — specify the sensing, measuring and recording devices for a trial, define what gets logged, and write the analysis before the first reading arrives.
Chair design reviews with contractors and other engineers using your own checklist, and revise the standard whenever a site proves it wrong.
Bring graduates onto the toolset and treat each question they ask as a hole in the documentation.
What proves it: A written office design standard under your name, plus a supervised build that followed it.
Realistic span: six years and beyond
The next 90 days
On the next site where you have to plan drainage or an irrigation system, refuse to start from a blank sheet. Spend the first day building the workbook instead: inputs at the top, the sizing method in the middle, and the quantities that feed the budget at the bottom. Draw from a fixed template. When you meet the farmer or the district council, type their requirements straight into that same file rather than a notebook. By the time the report is due, the drawing, the specification and the budget all come from one place, and the second job of that type costs a fraction of the effort. That is the whole shift: from a scientist who produces documents to the one whose method the office adopts.
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).
Open Google Earth Engine first. It's free for research and non-commercial use and puts decades of satellite imagery and climate data at your fingertips — you can pull NDVI time series, map field variability, and detect crop stress without buying a single image. Pair it with Python or R for the analysis.
For literature and experimental design, use Elicit or Consensus to synthesize agronomy papers fast, and Claude or ChatGPT to draft and debug your analysis code. Keep proprietary grower data and unpublished genetics out of consumer tools.
The one rule, forever: AI predictions are hypotheses, not results — always ground-truth model outputs against field observation and replicated trials before advising a grower or publishing. Never let a computer-vision disease ID or a yield model override scouting and lab confirmation, protect proprietary grower and genetic data, and keep reproducibility and biosecurity front of mind.
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
Turn satellite and drone imagery into yield and stress maps
Why this pays: Growers and agribusinesses pay for scientists who can pinpoint where and why a field underperforms. Remote-sensing analysis that guides variable-rate inputs and catches stress early is high-value work that anchors senior, role at the top of the ranges in precision agriculture.
Google Earth EngineClimate FieldViewSentera FieldAgent
1
In Google Earth Engine, build an NDVI/EVI time series for a field or region to see variability and trend across the season, then zone the field for targeted management.
2
Generate the analysis script and adapt it to your imagery source without writing it from scratch.
Copy-paste this prompt
Write a Google Earth Engine (JavaScript) script that loads Sentinel-2 surface reflectance for a field boundary I define, masks clouds, computes NDVI and NDRE for every image from [April] to [September 2026], and exports a monthly median composite plus a time-series chart of mean NDVI. Comment each step so I can adapt it.
Verify cloud masking and calibration on your own imagery; a clean-looking index can hide sensor or atmospheric artifacts. Ground-truth stress zones by scouting before advising inputs.
What you'll haveField-level stress and yield maps that drive variable-rate decisions — the deliverable that makes you indispensable to growers and agribusiness.
2
Detect crop disease and pests with computer vision
Why this pays: Early, accurate disease detection prevents yield loss worth far more than the scouting time it takes. Building and validating vision-based scouting workflows is exactly the applied-AI skill that agribusinesses pay senior scientists to lead.
PlantVillage NuruTaranisPlantix
1
Use PlantVillage Nuru or Plantix in-field to triage suspected disease and nutrient issues, then confirm the call with lab diagnostics or an extension pathologist.
2
Design a validation study so you know the tool's real accuracy on your crops and conditions before you trust it operationally.
Copy-paste this prompt
Design a validation protocol to test a computer-vision crop-disease tool against ground truth in [maize] for [gray leaf spot and northern corn leaf blight]. Specify sample size, how to establish ground truth (lab confirmation), field sampling design, metrics (sensitivity, specificity, per-disease confusion matrix), and how to report where the tool fails by growth stage and lighting.
Vision models confuse look-alike symptoms and struggle in poor light. Confirm any actionable diagnosis with lab or expert verification before recommending treatment.
What you'll haveA validated, fast scouting workflow that protects yield and marks you as the applied-AI expert on the team.
3
Squeeze insight out of multi-season trial data
Why this pays: The scientist who can extract a clean, defensible finding from years of messy trial data — and communicate it — gets the lead-author credit, the grant renewals, and the promotions. AI accelerates the modeling so you spend your time on interpretation.
RPythonJulius AI
1
Use Claude or Julius AI to draft a mixed-model analysis of your trial data, then run and check it yourself in R.
Copy-paste this prompt
I have multi-environment field trial data with columns: genotype, location, year, replicate, block, yield, and soil_type. Write R code using lme4 to fit a mixed model with genotype as fixed and location/year/block as random effects, extract BLUEs for genotype, test genotype-by-environment interaction, and produce diagnostic plots. Explain each modeling choice.
You own the statistics. Check model assumptions, confirm the design matches the model, and never report an effect the diagnostics don't support.
2
Have the tool draft plain-language summaries of the results for growers and a technical version for a paper, keeping the numbers under your control.
What you'll haveFaster, cleaner analysis and clear write-ups — the productivity that builds a publication and grant record leading to senior pay.
4
Accelerate breeding and genomic selection
Why this pays: Genomic selection and marker-assisted breeding are where the highest-paid agricultural science sits — seed companies and research institutes reward scientists who can shorten the breeding cycle. AI helps you manage the data pipeline and prioritize crosses.
R (rrBLUP / sommer)Python (scikit-learn)Claude
1
Build a genomic-prediction pipeline in R with rrBLUP or sommer, using Claude to scaffold the code and explain the model choices.
2
Prioritize which crosses or lines to advance using predicted breeding values plus your knowledge of the target environment.
Copy-paste this prompt
Explain, step by step, how to set up genomic selection for a [wheat] breeding program: quality control on marker data, choosing a prediction model (GBLUP vs. Bayesian), cross-validation to estimate prediction accuracy, and how to combine predicted breeding values with selection index weights for [yield, disease resistance, and grain protein]. Note the common pitfalls.
Prediction accuracy varies by trait and population — validate with cross-validation and never advance lines on model output alone without phenotypic confirmation.
What you'll haveA working genomic-selection workflow that shortens breeding cycles — the specialized skill behind top-of-range industry roles.
5
Win grants and publish faster with AI-assisted writing
Why this pays: In research-track ag science, funding and publications are the currency of advancement. Using AI to accelerate literature review, proposal drafting, and revision lets you submit more, stronger applications — the direct route to bigger budgets and senior appointments.
ElicitConsensusClaude
1
Use Elicit or Consensus to map the literature on your topic and extract findings across dozens of papers into a table you can verify.
2
Draft and tighten the proposal narrative, then fact-check every citation yourself.
Copy-paste this prompt
Act as a grant-writing partner. From this project summary, draft a one-page specific-aims section for a [USDA NIFA] proposal on [cover-crop effects on soil carbon and cash-crop yield]: a compelling significance paragraph, three testable aims with hypotheses and approaches, and expected outcomes. Summary: [PASTE NON-CONFIDENTIAL SUMMARY].
AI invents citations and can overstate significance. Verify every reference against the actual paper and keep unpublished data and collaborators' IP out of the tool.
What you'll haveMore competitive proposals and papers submitted per year — compounding into the funding and rank that pay at the top of the field.
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
Set up Google Earth Engine and Python/R, and reproduce an NDVI time series on a field you know so you trust the pipeline before you rely on it.
Months 2-3
Add a validated computer-vision scouting workflow and start using AI to speed your trial-data analysis in R.
Months 3-6
Build a genomic-prediction or precision-ag analysis pipeline end to end on a real project, ground-truthing every output.
Months 6-12
Use AI-accelerated literature review and writing to submit stronger grants and papers, and position yourself to lead your team's applied-AI work.
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. This leftover page opens with Pair it with Python or R for the analysis; Month 1 is Set up Google Earth Engine and Python/R; play 4 names Python (scikit-learn). 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 3:31 PM PT.
Next steps for an Agricultural 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.
Agricultural 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.
Agricultural 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 Agricultural Scientist work, not a claim that they list a counted SOC 19-1013 inventory.
Write an Agricultural 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.
An Agricultural 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 Agricultural 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. AI is a powerful analysis and detection layer, but it can't design a field trial, judge whether a model's output makes agronomic sense, or take responsibility for advice that affects a grower's season. The physical, biological, and contextual reality of a field still needs a scientist. Those who adopt remote sensing and ML will simply do far more, and better-paid, work than those who don't.
Can I trust a computer-vision disease diagnosis?
As a triage tool, yes; as a final answer, no. Vision models confuse look-alike symptoms, struggle in poor light, and were often trained on different regions and cultivars than yours. Use them to prioritize scouting and confirm any actionable diagnosis with lab diagnostics or an extension specialist before recommending treatment.
Is it safe to put grower or genetic data into ChatGPT?
Not proprietary data. Grower yield maps, unpublished trial results, and breeding-line genetics are valuable, confidential IP. Use general tools for public literature, code, and generic questions, and keep sensitive data in your institution's approved, access-controlled systems.
How does AI actually increase an agricultural scientist's pay?
It moves you toward the high-value work: precision-ag analytics, genomic selection, and applied-AI scouting are exactly what agribusinesses and research institutes pay senior scientists to lead. AI also lets you publish and win grants faster on the research track. It's about doing higher-leverage work, not cutting corners.
Do I need to code to use AI in agricultural science?
A little, and it's very learnable now. Tools like Claude and Julius AI will write and explain R and Python for you, so you can run real analyses while you build fluency. Google Earth Engine and platforms like Climate FieldView do a lot without deep coding. Start by adapting generated scripts and checking the output against data you understand.
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.