Agricultural Products & Services Data: From County Acres to Global Balances · Head-to-head
USDA NASS Quick Stats vs BTS Data Inventory - Air Category
Which agricultural products & services data: from county acres to global balances data fits your job: USDA NASS Quick Stats, or BTS Data Inventory - Air Category. API, files, or your warehouse. Daily, weekly, or hourly.
USDA NASS Quick Stats
BTS Data Inventory - Air Category
Where the fields line up
1 shared field — join on these.
| Field | USDA NASS Quick Stats | BTS Data Inventory - Air Category |
|---|---|---|
YEAR | Reference year of the estimate. | Calendar year of the summary period. |
Coverage, side by side
| USDA NASS Quick Stats | BTS Data Inventory - Air Category | |
|---|---|---|
| Geographic | United States and territories: NATIONAL to STATE, COUNTY, AG DISTRICT, WATERSHED, CONGRESSIONAL DISTRICT, AMERICAN INDIAN RESERVATION and ZIP CODE, plus international aggregates | US carriers and airports plus foreign carriers serving the US; airport-level views cover individual origin airports worldwide |
| Temporal | 1850 to present depending on program; census years every 5 years, surveys weekly, monthly and annual | Annual summaries back to at least 2014; monthly and preliminary views extend into the current year |
What each contains
Pick by fit, not by loyalty.
| USDA NASS Quick Stats | BTS Data Inventory - Air Category | |
|---|---|---|
| Publisher | USDA National Agricultural Statistics Service | U.S. DOT Bureau of Transportation Statistics |
| Subject lens | Ad-hoc estimates bank spanning crops, livestock, demographics, economics and environment from the Census of Agriculture and NASS surveys | Catalog of 155 transportation datasets whose air category centers on T-100 segment summaries of passengers, seats, freight and mail |
| Geographic coverage | United States and territories: NATIONAL to STATE, COUNTY, AG DISTRICT, WATERSHED, CONGRESSIONAL DISTRICT, AMERICAN INDIAN RESERVATION and ZIP CODE, plus international aggregates | US carriers and airports plus foreign carriers serving the US; airport-level views cover individual origin airports worldwide |
| Temporal coverage | 1850 to present depending on program; census years every 5 years, surveys weekly, monthly and annual | Annual summaries back to at least 2014; monthly and preliminary views extend into the current year |
| Detail level | Aggregated estimates per commodity x statistic x geography x period; no farm-level microdata | One row per period x carrier x airport x segment type depending on the view |
| Formats | JSON, CSV, XML and plain-text tabular records | CSV, JSON and GeoJSON tabular extracts |
| Scale | Millions of estimate records across all sectors | 155 datasets in the catalog; the largest T-100 views hold hundreds of thousands of rows |
| Best for | County-level acreage, yield and price questions; long-horizon supply history | Air cargo tonnage, carrier shares and airport utilization questions |
What each does better
USDA NASS Quick Stats
Depth measured in centuries. Year selectors run from 2027 back to 1850 depending on program, with census vintages every five years layered against weekly, monthly and annual survey collections - freq_desc alone spans ANNUAL, MONTHLY, WEEKLY, SEASON and POINT IN TIME. Nothing in the air-cargo shelf reaches within shouting distance of that spine.
A geographic ladder nobody else in this pairing attempts. Estimates resolve to ZIP code, watershed, congressional district, agricultural district and American Indian reservation, not just state and county - which means a corn-belt question can be answered at the elevation the question was actually asked.
Across all sectors the corpus holds millions of estimate records, each normalized to that single dimension stack. See area harvested, production and yield for how these series are typically read.
BTS Data Inventory - Air Category
A verified April 2026 row for Los Angeles reads 21,793 revenue departures, 2,875,734 enplaned passengers against 3,571,137 available seats (an 80.5 percent load factor), 130,984,289 pounds of freight and 5,108,018 pounds of mail - one cell of a grid that splits every tonne into domestic, outbound international and inbound international.
An annual layer with real mass behind it. The yearly summaries reach back to at least 2014 - a verified 2014 row shows 800,407 departures, 64,085,200 passengers and 3,388,255,244 pounds of freight - and monthly plus preliminary views extend into the current year.
Shelf breadth beyond the T-100 core. The air category sits inside a 155-dataset inventory that also carries air-carrier fuel-efficiency, aircraft fleet age and average air fare series, plus preliminary estimate tables reconciling checkpoint counts against market enplanements. Cargo questions route through freight and mail tonnage; utilization questions through load factor.
Where they're equivalent
More than their different subjects suggest. Both field dictionaries were verified during research, a bar most of the slice clears. Both publish tabular records in documented structures - JSON and CSV among the output layouts on each side - rather than narrative PDFs alone. Both support month-grain observations: MONTHLY sits inside NASS's freq_desc, reporting_month keys the BTS monthly variants, placing each among the catalog's month-grain grids. And both are aggregates all the way down: Quick Stats explicitly holds no farm-level microdata, and the T-100 summaries stop at segment level with no passenger-itinerary detail. Two federal statistical agencies, two disciplined dictionaries, zero gossip about individual farms or individual travelers.
The verdict
Verdict: sample both - they are different instruments pointed at the same industry, and neither substitutes for the other.
Take USDA NASS Quick Stats if your question names land.
Take BTS Data Inventory - Air Category if your question names lift. Freight tonnage through a named airport, carrier shares of a cargo market, domestic versus transborder splits, seat capacity and load factor by month - anything answered by a segment ledger keyed to carriers and airports.
Investors and quants tend toward Quick Stats for supply-side screens and the BTS tables for logistics exposure; market researchers and consultants usually want both orders reversed depending on whether the client sells seed or sells lift.
Sample both, pick by fit. See USDA NASS Quick Stats · See BTS Data Inventory - Air Category
Or take both in one feed
Yes - they stack because they occupy different layers of one supply chain.
Two alignments decide whether the merge holds. First, identifiers: NASS names counties, states and ZIP codes while the T-100 views name IATA airports and carriers, so a geography crosswalk is mandatory - there is no shared key. Second, units: acres, bushels and head do not convert to pounds of payload without an explicit assumption sheet, and summing across domestic and international freight columns carelessly double-counts the total. Align on calendar years last - both grids carry annual periods cleanly, while NASS weekly observations and BTS monthly rows meet only at the year boundary.
Browse the rest of the shelf at the agricultural products & services data hub. Datadory ships either record alone or both merged onto one calendar, delivered daily, weekly, or hourly - your call. Or take both in one feed.
API, files, or your warehouse. Daily, weekly, or hourly.
Fair questions
Is USDA NASS Quick Stats better than BTS Data Inventory - Air Category?
Better at different jobs. The BTS air inventory owns the lift ledger: freight and mail tonnage in pounds by carrier, airport and month, split domestic against outbound and inbound international.
Do the two datasets cover the same ground?
Only at the widest zoom - both describe US production-and-logistics activity in aggregate. Quick Stats keys every number to an administrative geography and a commodity; the T-100 views key every number to a carrier, an airport and a segment type. One has no airports in it, the other has no commodities in it.
Which dataset reaches further back?
Quick Stats, decisively: its year selectors run from 2027 back to 1850 depending on program, with census vintages every five years beneath weekly and monthly survey collections. The BTS air holdings begin their annual summaries around 2014, though monthly and preliminary views extend into the current year.
Which should an agricultural exporter studying air-freight capacity sample first?
Start with the BTS air inventory - the verified Los Angeles row for April 2026 shows 21,793 departures, an 80.5 percent load factor, and roughly 131 million pounds of freight alongside 5.1 million pounds of mail, exactly the capacity read the question asks for. Then add Quick Stats to size the underlying production by state and county.
Can Datadory deliver both datasets together?
Yes - alone or merged onto one calendar, delivered daily, weekly, or hourly, your call. Each arrives normalized to its verified field dictionary (sixteen documented fields on the NASS side, fourteen on the BTS side) with sample rows for inspection before anything ships.