How we get our numbers
Every figure traces to a named public source and the date we read it.
That is the whole promise, and it is rare enough to need a page. Most land tools hand you a confident number with no way to check it. AgriTerra Scout holds no private data. It reads the public federal record for your exact parcel, puts it in plain language, and prints the source and the read date on every figure, for example "Source: USDA SSURGO, accessed 2026-06-03". This page is how it works and how we keep ourselves honest.
It is also self-checking: this page is rendered from the same data snapshot the product computes from, so what you read here cannot drift from the numbers a report ships.
Read the full per-dataset methodology on ScoutWhy we show a range, not a single number
A single number implies a precision the data does not have. A rating does worse. One farmer put the problem to us plainly:
"Very high wind. What does that mean in dollars?"
So we show a low, a midpoint, and a high, in dollars per acre per year, and we name what anchors them. Here is a real one from the model.
Example band
Solar lease income, OH mid-bucket parcel
Dollars per acre per year, operating phase
Low
$800
Midpoint
$1,000
High
$1,225
Anchored to Purdue Ag Economy Barometer solar-offer surveys and OSU Extension solar leasing guidance, modeled into a band rather than mirrored from any one rate card. As of 2026-08-01. The fuller modeled spread runs $650 to $1,500; the band above is the middle of it.
The band is AgriTerra-modeled, not an offer from any developer. It tells you whether solar income is worth pursuing on these acres and roughly at what scale, which is exactly what "very high" could not.
Every dataset, who publishes it, and how fresh it is
The report is built on 6 federal datasets, enumerated with agency parentage: USDA NASS, USDA NRCS SSURGO, NREL, FEMA, USGS, USFWS. Alongside them sit the lease and market bands we model from named surveys. This table is generated from the same snapshot the product runs on, so it cannot fall out of date with the code. Modeled data in all 50 states; pre-loaded parcel boundaries are Ohio and Indiana first.
| Dataset | Published by | What it powers | Refresh |
|---|---|---|---|
| USDA SSURGO | USDA NRCS | Soil productivity index, drainage class, hydric/farmland classification | Annual (USDA cycle) |
| USDA NASS QuickStats | USDA NASS | County cash-rent AVERAGES and marketing-year corn price | Annual |
| USDA AMS Market News (Daily Grain Bids) | USDA AMS | Daily state cash grain bids + basis vs CME futures | Live (daily) |
| NREL PVWatts v8 | NREL | Modeled solar output (kWh per kW per year) - buckets the solar income band | Live (queried per parcel) |
| NREL Wind Toolkit | NREL | 100 m wind-speed map behind the AgriTerra wind resource bucket (not a wind power class rating) | Static map lookup (v1) |
| AgriTerra Solar Lease Band (Purdue/OSU anchored) | AgriTerra (Purdue Ag Economy Barometer + OSU Extension anchored) | Solar lease income bands ($/ac/yr) | Annual re-verification, as of 2026-08-01 |
| AgriTerra Wind Lease Band (NDSU/ACP/NREL derived) | AgriTerra (NDSU/ACP payment ÷ NREL 45834 land use) | Wind lease income bands ($/ac/yr) | Annual re-verification, as of 2026-08-01 |
| USDA FSA Conservation Reserve Program - rental rates | USDA FSA | CRP rental rates - general-signup AND all-signup (continuous/CREP) state bands | Annual re-key (FSA publishes monthly), as of 2025-07-01 |
| OSU Extension Ohio Timber Price Report Ohio | Ohio State University Extension | Ohio stumpage prices ($/MBF, Doyle scale) for the timber estimate | Semi-annual (Jan + Jul), as of 2026-01-31 |
| Indiana Consulting Foresters Stumpage Timber Price Report Indiana | Indiana Consulting Foresters (Indiana Woodland Steward) | Indiana stumpage prices ($/MBF, Doyle scale) | Annual, as of 2026-01-31 |
| AgriTerra recreational-lease market indications (MSU Extension / brokerage) | AgriTerra (MSU Extension + brokerage market indications) | Regional recreational-lease bands ($/ac/yr) | Annual re-verification, as of 2026-08-01 |
| AgriTerra State Renewable Policy Gates | AgriTerra (statutes, IRS, OPSB/DLGF/FSA records) | State siting posture, landowner tax disclosures, and the federal OBBBA credit-vintage gate on energy scenarios | Semi-annual, as of 2026-08-02 |
| HIFLD Electric Power Transmission Lines Ohio | HIFLD (U.S. DHS) | Distance to the nearest transmission line and its voltage | Periodic snapshot |
| ISO/RTO Interconnection Queues | Lawrence Berkeley National Laboratory ("Queued Up") | County-level interconnection-queue activity signal (all 7 ISOs + non-ISO regions) | Annual, as of 2025-12-31 |
"Live" applies only to the layers we query per parcel (NREL PVWatts, USGS water) and the daily USDA AMS grain bids; the bundled tables refresh on the cadence shown and carry the date we last read them.
High, Modeled, or Estimate, and we never blur them
Not every number is equally certain, so we label how each one was produced. You always know whether you are looking at a reading or an estimate.
Read straight from a federal dataset.
Soil and productivity from USDA SSURGO, flood zones from FEMA, wetlands from USFWS, water and drought from USGS, county cash-rent averages from USDA NASS, and daily state grain bids from USDA AMS. The number is the source, carried through with its agency and the date we read it.
A federal input run through a documented formula.
Example: NREL PVWatts gives the modeled solar output for your exact parcel, and that output buckets which solar income band applies. The farm scenario runs the SSURGO soil productivity index through a published yield curve. The inputs are federal; the arithmetic is ours, and it is written down.
A market band anchored to named surveys, not a single published rate.
The recreational-lease bands are AgriTerra estimates anchored to sources we name in every cell. Solar and wind lease bands are Modeled because a federal resource input or documented land-use formula decides which band applies. All of them tell you whether an option is worth pursuing and roughly at what scale, not what one developer will quote.
The step-down rule, worked through
USDA NASS publishes cash rent as county averages. If NASS has an average for your county, that is the number you see, labeled as a county figure. If it only has a state average, you get the state number, labeled as a state average, never dressed up to look county-specific. A state average never wears a county label. That one rule is the difference between a figure you can defend and one that quietly overstates what is known about your acres.
The part almost nobody does
How we verify our own sources
A citation is only worth something if someone checked it. Ours are checked in four ways, and the checks run in the build, not in a promise. We built them the hard way, after a 2026 self-audit caught citations of our own that did not hold up: the story of what we found and fixed.
A person opened the document
Every dataset edition must have a verification record confirming a human opened the cited document; the tombstone gate blocks any purged fabrication from returning. The record is appended to a log with the edition and the date, so a citation is a thing that happened, not a string someone typed.
An automated check probes every cited link
npm run check:citations runs over every citation in the data layer and fails the build if a cited source cannot be reached. An automated check permanently blocks any citation we have removed as unverifiable from coming back, so a fabrication cannot be reintroduced by accident.
Every dataset carries a machine-readable read date
Each source records the date it was last read, in a form the build can check. If a dataset goes stale relative to the code that depends on it, a freshness check in the build catches it. That is the same date you see in the refresh column above and on the figure in your report.
The writing step cannot invent or alter a number
The numbers are computed from the datasets first. The step that writes the sentences can only reuse those computed numbers, and every figure it emits is checked back against the computed value before it ships. Where a value is missing, the report says "data unavailable" rather than guessing. A section that cannot be produced on-spec falls back to a fixed, human-written template and the report is marked partial. The prose can describe the numbers; it cannot move them.
We also check whether a lease can actually be built
Since the 2025 federal energy law, an energy band is not enough on its own, so we run it through a policy gate: county siting restrictions, the state posture, and the federal tax-credit vintage, and we demote income a developer would not actually offer today rather than show a number you cannot collect. As of 2026-08-02 the gate covers Ohio, Indiana, Iowa.
The rest of the series, in plain language: why we will tell you a lease won't happen on your land, plus the state deep dives on Ohio CAUV recoupment, the Indiana solar property-tax jump, and Iowa wind repowering.
Where we draw the line
What we deliberately do not do
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No land valuations. We do not tell you what your land is worth. We assemble and source the data; the opinion of value is not ours to give.
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No guaranteed-income claims. A band is what the market has paid, not a promise of what you will be offered. We label estimates as estimates.
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No treating queue presence as a promise. A project sitting in an interconnection queue is a signal, not a guarantee it gets built. We say so, and the policy gate says more.
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No single-number precision theater. We will not dress up an estimate as a precise figure to look more confident than the data allows. The range is the honest answer.
Challenge any number in your report
Every figure carries its formula and its source, and we will show either on request. If you can point to a better source or a number that is off, that is the most useful thing you can send us, and we would rather hear it from you than from the person across the negotiating table. See the full per-dataset methodology on Scout, read the sourced numbers on the sample report, or check what it costs on the pricing page.
Staking a report on the citation? Rural appraisers use Land Scout Pro because every figure carries its agency and vintage, with CSV and shapefile export into the software they already run.
Weighing AgriTerra against another tool? This per-figure provenance is the heart of the difference. See how it compares to AcreValue and AcreTrader.
See it on your own parcel.
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