How AgriTerra calculates this

1. Data sources and how fresh they are

Each row below is a public dataset we read to build a report. The “Powers” column tells you which part of the report depends on it. Where a source is currently live in only certain states, its badge names them (for example Ohio or Indiana); the federal datasets (USDA, NREL, USGS, FEMA, USFWS) work nationwide.

Some sources are read live at report time (PVWatts, NWIS); others are bundled snapshots we refresh on the cadence shown. Parcel boundaries are live in Ohio (via OGRIP) and Indiana (via the live IndianaMap / IGIO statewide FeatureServer); the other state-specific datasets above are live in Ohio first, and coverage is rolling out state by state. Where a dataset has no entry for your location, the report says “data unavailable” rather than filling the gap with a guess.

2. How we estimate solar & wind income

For energy leases we report a dollar band — low / mid / high per acre per year — instead of a single number, because a categorical rating like “very high wind” tells a landowner nothing they can price. The band is the smallest unit you can actually act on.

Where the dollar bands come from

The payment bands are mirrored from two annually published Lawrence Berkeley National Laboratory reports — the figures developers and landowners cite when negotiating real leases:

• Wind: LBNL Land-Based Wind Market Report (2024 edition), landowner-payment tables.
• Solar: LBNL Utility-Scale Solar (2024 edition), reported land-lease payment ranges.

We store the full low-to-high spread and surface the middle (25th–75th percentile) range on your report, with the wider spread retained for the PDF. The mid value also drives the plain-language rating:

Wind rating from the mid band

Solar rating from the mid band

How a parcel lands in a band

• Solar: we query NREL PVWatts v8 at the parcel for modeled annual output (kWh per kW), then compare it to that state’s 25th/50th/75th-percentile output to pick the low / mid / high bucket.
• Wind: we assign a coarse NREL Wind Toolkit resource class from the state and latitude, then read the matching state-and-class band.
• Grid access: a parcel within ~1 mile of a transmission line of 69 kV or higher (per the HIFLD data above) is flagged as more attractive to a developer.

What these numbers are — and are not

These are defensible seed bands, not lease offers. They are deliberately coarse in v1: the wind class comes from latitude rather than a full wind-resource raster, and the solar percentiles are sampled rather than computed from a statewide irradiance grid. They tell you whether energy income is worth pursuing and roughly what scale to expect — not what any single developer will pay you. Full per-cell citations live in our internal methodology notes and are refreshed when each report publishes a new annual edition.

3. Mineral rights, timber, and hunting leases

Mineral rights Ohio

In Ohio we read the Ohio DNR Oil & Gas Wells (RBDMS) dataset for well presence inside the parcel and the nearest-well distance. This shows drilling activity; it does not establish who owns the minerals or whether rights have been severed. The report points you to the county recorder for ownership and severance — the only authoritative record. Outside Ohio we provide the relevant state recorder link rather than a data layer.

Timber

We count forest acres from USDA CropScape (CDL) forest pixels and multiply by a per-acre stumpage value. In Ohio the stumpage prices come from the OSU Extension Ohio Timber Price Report ($/MBF, Doyle scale), and in Indiana from the Indiana Consulting Foresters Stumpage Timber Price Report; other states use a clearly flagged generic fallback. This is a one-time standing-timber value estimate, not an annual income figure, and it assumes a merchantable stand — a forester’s cruise is the next step before acting on it.

Recreational (hunting) lease

Recreational-lease bands are mirrored from the Mossy Oak Properties Hunting Lease Guide and routed by state and county to a regional band. If the parcel centroid is within about 500 m of a USFWS National Wildlife Refuge / Wildlife Management Area, we apply a flat +15% adjacency premium and name the refuge. These are advisory regional midpoints, not transactional comps — an outfitter-quality block beats them and a fragmented parcel falls short.

4. Multiple parcels and partial parcels

A single report can cover several parcels at once. When it does, each parcel is analyzed on its own data — its own soil, its own crop history, its own energy and lease bands — and the report adds a combined summary on top. Nothing is averaged across parcels in a way that would hide a weak one behind a strong one.

You can also draw a partial parcel — a slice of a larger field — directly on the map. When you do, the slice becomes the unit of analysis: its acreage is measured from the shape you drew, and the report opens by stating plainly that it covers an N-acre slice of the larger parcel rather than the whole thing. Every figure that follows is scaled to the slice, not the parent.

5. How the written analysis is produced

The numbers in your report are fetched and calculated by the steps above. The sentences that explain those numbers are assembled by a writing step, under strict constraints. We want to be completely transparent about how that works:

In practice that means:

• Every dollar amount, acreage, percentage, year, county, soil class, and crop label in the prose must match a value already computed from the datasets above. The writing step is told, in its hard rules, not to compute, estimate, round further, or interpolate.
• If a value is missing, the report says “data unavailable” — it never invents a placeholder to keep a sentence flowing.
• It may not introduce place names, agencies, programs, neighbors, comparable sales, prices, or legal instruments that are not in the data. v1 makes no land-value or comparable-sale claims at all.
• If the writing step can’t produce a valid, on-spec section, that section falls back to a fixed, human-written template, and the report is marked as partial rather than shipping unverified prose.

The goal is simple: the explanation is just words wrapped around audited numbers. You can ignore the prose entirely and check every figure against its source line.

6. How to challenge a number we got wrong

You know your land better than any dataset does. If a figure looks off — a cash rent that’s low for irrigated ground, a soil class that doesn’t match what you farm — we want to hear it, and every number in your report is built to be checkable: it carries the source it came from and the date we read it.