Agricultural & Farm Machinery Data: From Tractor Stocks to Live Listing Prices · Head-to-head
FAOSTAT – Agricultural Machinery vs FRED – Industrial Production: Farm Machinery and Equipment
Which agricultural & farm machinery data: from tractor stocks to live listing prices data fits your job: FAOSTAT – Agricultural Machinery, or FRED – Industrial Production: Farm Machinery and Equipment. API, files, or your warehouse. Daily, weekly, or hourly.
FAOSTAT – Agricultural Machinery
FRED – Industrial Production: Farm Machinery and Equipment
Where the fields line up
No shared field names. These two answer different questions.
| Field | FAOSTAT – Agricultural Machinery | FRED – Industrial Production: Farm Machinery and Equipment |
|---|---|---|
Area Code (M49) | Numeric code identifying the country, territory or aggregate - the stable join key that keeps regional aggregates from bleeding into country panels. | not in this set |
Area | Name of the country or territory reported; 258 distinct area labels including aggregates such as China, mainland carried beside their parent groupings. | not in this set |
Item Code / Item | Machine or equipment category measured: agricultural tractors (two-axle), track-laying tractors, pedestrian-controlled single-axle tractors, agricultural tractors total, combine harvester-threshers, threshing machines, balers, ploughs, seeders planters and transplanters, manure spreaders and fertiliser distributors, root or tuber harvesting machines, milking machines, plus four traded machinery lines. | not in this set |
Element Code / Element | Measure reported: In Use (equipment stock), Import Quantity, Import Value, Export Quantity, Export Value. | not in this set |
Unit | Unit of measure - number of machines for stock and quantity elements, thousand USD for value elements. | not in this set |
Y1961 ... Y2009 | Annual observation for each calendar year in the series; blank where nothing was reported, which is itself coverage information. | not in this set |
Y*F / Y*N flag columns | Per-observation provenance codes documenting how each figure was obtained - questionnaire return, publication or international compilation - so reported and compiled values stay separable. | not in this set |
observation_date | not in this set | documented |
IPG33311S | not in this set | documented |
Coverage, side by side
| FAOSTAT – Agricultural Machinery | FRED – Industrial Production: Farm Machinery and Equipment | |
|---|---|---|
| Geographic | `Area Code (M49)` plus `Area` - 258 labels from the United States of America to China, mainland | Absent - every row is implicitly the same United States aggregate |
What each contains
Pick by fit, not by loyalty.
| FAOSTAT – Agricultural Machinery | FRED – Industrial Production: Farm Machinery and Equipment | |
|---|---|---|
| Time axis | `Y1961... Y2009` - forty-nine annual columns per row; China, mainland tractors read 52239 (1961), 813512 (1990), 3515757 (2009) | `observation_date` - one ISO stamp per row, keyed to the first of the month (2026-07-01) |
| Value measure | `Element` chooses the measure: `In Use` equipment stock, import or export quantity, import or export value | `IPG33311S` - one fixed measure, real output of NAICS 33311 carried as an index value (109.6809 in July 2026) |
| Units | `Unit` column declares `No` (number of machines) for stock and quantities, 1000 USD for values | None declared per row - index points on a seasonally adjusted 2017=100 base, stated once in the series identity |
| Geography key | `Area Code (M49)` plus `Area` - 258 labels from the United States of America to China, mainland | Absent - every row is implicitly the same United States aggregate |
| Category dimension | `Item Code / Item` - twelve In-Use categories from two-axle tractors to milking machines, plus four traded machinery lines | None - one subject, NAICS 33311 agricultural implement manufacturing, named in the series itself |
| Row identity | Seven documented fields make each row self-describing: area, item, element, unit, then the annual values | Two fields make each row minimal: a month stamp and an index value, 655 rows in total |
| Adjustment and provenance | Raw reported counts with `Y*F / Y*N` flag columns documenting how each observation was obtained | Seasonally adjusted by construction - harvest-driven factory-calendar noise already removed from the index |
What each does better
FAOSTAT – Agricultural Machinery
A map, not a point. Roughly 258 areas report into the panel, and the contrasts are the analysis: China, mainland climbs from 52,239 agricultural tractors in 1961 to 813,512 by 1990 and 3,515,757 by 2009, while separately logging 17,509,031 pedestrian-controlled single-axle units that final year - mechanisation arriving on foot before it arrives on wheels. The United States drifts from 4,690,000 tractors in 1961 to 4,503,625 in 2000, a saturated fleet. Japan falls from 3,075,463 in 2000 to 1,910,724 in 2005. Any question whose answer starts with the word where starts here; see cross-country panel for how these grids are used.
Twelve machine categories on one schema. Two-axle tractors, track-laying crawlers, pedestrian single-axle units, combine harvester-threshers, threshing machines, balers, ploughs, seeders/planters/transplanters, manure spreaders and fertiliser distributors, root or tuber harvesting machines and milking machines all sit on the same country-year grid, so fleet-mix questions ride along with the totals at no extra work.
A trade block beside the stock block. Four traded machinery lines carry import and export quantities and thousand-dollar values alongside the In-Use counts, so a fleet study picks up its equipment-trade context without leaving the domain.
FRED – Industrial Production: Farm Machinery and Equipment
Recency. The FAOSTAT stock panel closes with 2009; the FRED series runs to the current month - July 2026 prints 109.6809, easing from 110.3253 in April through 106.6745 in June before rebounding. For any question touching the present US cycle there is no contest: one record stops exactly where the other keeps going.
Monthly grain, cycle-ready shape. Forty-nine annual columns cannot resolve a downturn; 655 monthly observations can. The series is seasonally adjusted, which strips harvest-driven factory-calendar noise and leaves the cycle you actually want to model - see seasonal adjustment for why that matters. And because it tracks one four-digit industry definition, NAICS 33311 agricultural implement manufacturing, it isolates farm machinery from the rest of durable-goods manufacturing by construction rather than by cleaning.
Two fields, zero friction. Every row carries exactly a date and a value: 655 rows, no missing values, no reshaping, a baseline trend model in seconds. The anchor arithmetic tells the long story by itself - the panel opens at 76.3386 in January 1972 against the 2017=100 base, so the whole half-century lives in the swings between rather than in the drift.
The verdict
Verdict: sample both, pick by fit - they tie at 8 out of 10, so the question decides, not the leaderboard.
Take FAOSTAT – Agricultural Machinery when the question names places and fleets: cross-country mechanisation comparisons, long-run diffusion studies, tractor-park or harvester-fleet sizing, machinery-mix questions across twelve equipment categories, or trade context around equipment flows.
Take FRED – Industrial Production: Farm Machinery and Equipment when the question names the US production cycle: tracking factory output month by month, dating turning points, feeding a nowcast or a demand forecasting model with a seasonally adjusted series, or benchmarking any US machinery-market claim against the NAICS 33311 definition.
Three quick tests settle most cases. Need more than one country? Only the FAOSTAT side has them. Need observations after 2009? Only the FRED side has them. Need machine-type detail behind a national number? FAOSTAT again - and if the need is a monthly frequency instead, FRED is the only instrument in the pair that keeps it.
Sample both, pick by fit. See FAOSTAT – Agricultural Machinery · See FRED – Industrial Production: Farm Machinery and Equipment
Fair questions
Is FAOSTAT – Agricultural Machinery better than FRED – Industrial Production: Farm Machinery and Equipment?
Different instruments, tied on craft - both score 8 out of 10. FAOSTAT wins whenever the question spans countries or machine types: about 10,700 rows covering roughly 258 areas, twelve equipment categories and a four-line trade block, annually from 1961 through 2009. FRED wins whenever the question needs the current US cycle: 655 monthly observations of the NAICS 33311 output index through July 2026. Sample both and match each to the question.
Do the two datasets overlap?
Only conceptually, on the theme of farm mechanisation. There is no shared key - FAOSTAT keys rows by area code, machine category and element against year columns, while FRED keys rows by month alone. The United States appears inside the FAOSTAT panel as one of roughly 258 areas, but no field, unit or individual observation coincides between the two records.
Which dataset covers more history?
It depends which end of the clock you count from. FAOSTAT reaches back furthest, with annual observations from 1961, but closes in 2009. FRED starts eleven years later, in January 1972, and extends to the current month - July 2026 on the latest print. Depth before 2009 belongs to FAOSTAT; recency and monthly resolution belong to FRED.
Which dataset is bigger?
FAOSTAT, decisively and in both directions: about 10,700 rows spread across a seven-field schema with up to forty-nine year columns, against 655 rows carrying two fields apiece. Neither extreme is accidental - FRED's narrowness is the point, one industry reduced to one index, while FAOSTAT spends its width on machine categories, measures and a trade block.
Can Datadory deliver both datasets together?
Yes. Either record arrives alone or both arrive merged onto one calendar, delivered daily, weekly, or hourly - your call. Name the countries, machine categories and year windows when you request the sample and it lands pre-cut, with field definitions and coverage profiles attached. Or take both in one feed.