Agricultural & Farm Machinery Data: From Tractor Stocks to Live Listing Prices · Head-to-head
Data.gov - Agriculture-Tagged Datasets Collection vs World Bank WDI - Agricultural Machinery, Tractors
Which agricultural & farm machinery data: from tractor stocks to live listing prices data fits your job: Data.gov - Agriculture-Tagged Datasets Collection, or World Bank WDI - Agricultural machinery, tractors. API, files, or your warehouse. Daily, weekly, or hourly.
Data.gov - Agriculture-Tagged Datasets Collection
World Bank WDI - Agricultural machinery, tractors
Where the fields line up
No shared field names. These two answer different questions.
| Field | Data.gov - Agriculture-Tagged Datasets Collection | World Bank WDI - Agricultural machinery, tractors |
|---|---|---|
title | Dataset title exactly as submitted by the publishing agency - the human-readable handle every downstream filter starts from. | not in this set |
description | Dataset abstract summarizing what the agency collected, over what area and period, and why. | not in this set |
identifier | Unique UUID assigned to the record at harvest - the stable join key that keeps consecutive pulls diffable. | not in this set |
slug | URL-friendly identifier behind the catalog's permalinks for the record. | not in this set |
publisher | Name of the organization behind the dataset - a federal agency, a state agriculture department, a university extension. | not in this set |
keyword | Array of keyword tags applied to the dataset; the agriculture term selects this slice. | not in this set |
theme | Array of thematic taxonomy categories assigned alongside the free-form keywords. | not in this set |
has_spatial | Whether the underlying dataset carries a spatial component at all. | not in this set |
spatial_centroid | Geographic center point when the dataset is geospatial. | not in this set |
popularity | Relative popularity score of the dataset inside the catalog. | not in this set |
last_harvested_date | ISO 8601 timestamp of the most recent catalog ingestion of the record - freshness evidence per row rather than per catalog. | not in this set |
distribution_titles | Titles of the available file distributions - the named entries pointing at the publisher-hosted files. | not in this set |
Coverage, side by side
| Data.gov - Agriculture-Tagged Datasets Collection | World Bank WDI - Agricultural machinery, tractors | |
|---|---|---|
| Geographic | `has_spatial` flag plus centroid when the dataset is geospatial | Implicit in the row - one economy per row, no sub-national split |
What each contains
Pick by fit, not by loyalty.
| Data.gov - Agriculture-Tagged Datasets Collection | World Bank WDI - Agricultural machinery, tractors | |
|---|---|---|
| Named subject | `title` written by the publishing agency ("Census of Agriculture", a county conservation register) | `Country Name`: United States, Euro area, High income - nations beside aggregate groupings |
| Stable identifier | `identifier` UUID assigned at harvest plus the catalog permalink `slug` | `Country Code`: ISO three-letter code or World Bank aggregate code |
| Measured value | None - catalog cards describe datasets rather than measure anything | `<year>` columns, 66 of them, holding tractor counts such as 417,032,140 |
| Series label | — | `Indicator Name` and `Indicator Code`: Agricultural machinery, tractors / AG.AGR.TRAC.NO |
| Publisher attribution | `publisher` naming the owning organization - USDA NASS, a state agriculture department | None per row - FAO compilation credited once in the indicator documentation |
| Time anchor | `modified` and `issued` stamps marking when each record last changed | The year column itself, 1960 through 2025 with observations from 1961 |
| Geography fields | `has_spatial` flag plus centroid when the dataset is geospatial | Implicit in the row - one economy per row, no sub-national split |
| Delivery declarations | `distribution_titles` naming the agency's files across seven formats | None - one flat wide table shape throughout |
Fair questions
Is Data.gov better than World Bank WDI for agricultural machinery data?
Different instruments, so better depends on the question. The Data.gov collection wins whenever the question discovers - which agencies publish which farming statistics, from USDA NASS Quick Stats extracts down to municipal conservation registers. The WDI tractor indicator wins whenever the question compares fleets across borders: 292 economies on one country-year schema under code AG.AGR.TRAC.NO. Both score 7 of 10 on the Datadory rubric. Sample both, pick by fit.
Which dataset covers more geography?
The WDI panel reaches further by count - 292 countries, territories and aggregate groupings worldwide, with 262 carrying observations. The Data.gov collection is US-first but layered: federal agencies dominate, yet state agriculture departments, county governments, cities, tribal authorities and universities publish into the same index. One measures the whole world thinly at national level; the other measures one country deeply across every level of its government.
Do the two datasets overlap anywhere?
Only in spirit. Both sit in the Agricultural & Farm Machinery slice of the 1,744 datasets Datadory catalogs, and both ultimately describe machines in agriculture. But one row is a metadata record about someone else's dataset, and the other is a number of tractors per economy per year. Nothing in a catalog card reproduces AG.AGR.TRAC.NO's counts, and nothing in the tractor panel enumerates publishers. They complement far more than they compete.
Which one should an equipment-market analyst pick first?
Start with the WDI tractor panel when sizing markets: installed-base counts for up to 292 economies are the denominator parts, attachments and service contracts sell into, with the 1961 to 2007-2009 window stated up front.
Can Datadory deliver both datasets together?
Yes. Request them separately or merged onto one calendar, delivered daily, weekly, or hourly - your call. Every delivery arrives as clean, documented rows with the field dictionary included, so your team evaluates real records before committing. Or take both in one feed.