EU FADN Public Database

It is the European Commission's official dissemination layer for the Farm Accountancy Data Network - operating today as the Farm Sustainability Data Network - the survey that turns audited farm books into standard results. Around 75,000 farms a year keep accounts to one EU-wide template; weighted, they represent roughly 3.5 million holdings and about 90% of EU agricultural output.

Its role in this industry is singular. Every other record in the agricultural-farm-machinery slice counts hardware - tractor fleets, combine stocks, listing volumes. This record prices hardware: the measure pane carries machines and equipment asset values, gross investment and depreciation taken straight from validated balance sheets, flanked by outputs, input costs, subsidies and income indicators. If the question is "what does European farm machinery cost and what is it worth afterward", this is the only official answer in the slice.

Three editions ship. The main series weights on Standard Output from 2004 onward; a legacy edition keeps the older Standard Gross Margin methodology for 1989-2009; and a Medians view re-runs the same reports on median-farm basis across eleven layouts, from Year*Country down to Year*Country*SIZ6*TF14. Datadory delivers all three as one governed feed, normalized to the field dictionary below.

Every cell answers the same shape: fix the filters, get one figure. The report theme decides how wide the slice is, the measure decides what is being valued, and the result arrives as a single weighted average or aggregate in EUR:

row_shape    : standard result - national cut
report_theme : Year*Country
dimensions   : accounting_year=2023 | country=France
measure      : machines and equipment
value        : one weighted-average figure in EUR per cell
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row_shape    : standard result - regional + farm-type cut
report_theme : Year*Country*Region*TF8
dimensions   : accounting_year=2023 | country=Spain |
               region=NUTS 2 | tf8=specialist field crops
measure      : gross investment in machinery and equipment
value        : one weighted-average figure in EUR per cell
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row_shape    : median-farm variant
report_theme : Year*Country*SIZ6*TF14
basis        : Medians view - median farm instead of weighted average
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row_shape    : weighting reference
accounting_year_2024 : FSS 2020 | SO 2017
note         : reference regimes rotate; the pair in force moves
               reported levels between years

These are the documented structures, populated per cut rather than illustrated with borrowed numbers - the weighting-reference row is real, because the FSS and SO reference regimes genuinely rotate and genuinely move reported levels between years. Get a sample of this dataset and the cells land in exactly this shape for whichever countries, regions, farm types, size classes and accounting years you name.

Seven fields make up the confirmed core, and they divide cleanly: four select the slice (accounting year, country, NUTS region, and the TF8/TF14-plus-SIZ6 pair that pins farm type and economic size), one picks the measure from the standard-result catalogue, and one returns the euro value. Definitions map to the interface's own filter panes and methodology pages gathered during the August 2026 research pass - no upstream data dictionary was shipped with the source, so none was invented here, and the remainder of the catalogue folds into the explicit request note above.

Geography - the EU Member States, with United Kingdom figures present only through accounting year 2020. Regional detail runs to NUTS level where the reporting country publishes it; some members publish nationally only, which the delivery preserves rather than papering over.

Temporal - the main Standard Output edition runs from 2004 onward, with the 2017 Standard Output regime applied to accounting years 2018-2024; the legacy Standard Gross Margin edition reaches back to 1989-2009. Figures are final to 2023 and preliminary for 2024, with per-country caveats carried explicitly: Romania 2021-2023 stays preliminary and Malta 2022-2024 is unavailable.

Granularity - weighted averages and aggregates per country, NUTS region, farm type and size class. The underlying farm-level records never leave the network; the published layer is the statistically defensible aggregation of them, and the Medians view adds a robustness check for skewed segments.

API, files, or your warehouse. Daily, weekly, or hourly.

Your cadence is decoupled from the survey rhythm behind the figures - take one snapshot for a benchmarking engagement, or land every edition in the warehouse as it settles so dashboards never wait on anyone. Rows arrive normalized to the field dictionary above with each dimension typed as its own key: country, region, tf8, tf14, siz6 and measure stay separate columns, so joins against your own segmentation hold without parsing concatenated labels. Edition and weighting regime ride along on every row, and median-basis variants are flagged as such - averages and medians never mix silently in the same column.

• Machinery capex benchmarking - gross investment and depreciation per farm by type and size class, Member State against Member State, on one template.
• Equipment demand modelling - asset values and investment flows by segment are the spending signal behind Europe's fleet-replacement cycle.
• Market sizing by segment - roughly 28 countries times farm types times six size classes times years of pre-built cells, ready to weight into addressable-market estimates.
• Policy and subsidy analysis - subsidy lines share the accounts with investment behaviour, so support-regime effects on capital spending become measurable rather than anecdotal.
• Citation-grade sector work - one harmonised methodology lets a single methods footnote carry a whole comparative table.
• Long-run series construction - the legacy 1989-2009 edition joins the modern one for two-decade views, handled with splice care at the seam.

Market researchers and consultants top the ranking at 3/3 - benchmarking income and machinery investment across Member States is precisely what this record exists for; see market researchers use cases. Investors and quant researchers read machinery asset values and gross investment by size class as an ag-equipment demand series - a structural benchmark rather than a monitoring feed; see investors and quants use cases. Journalists, academics and students get official figures with final-versus-preliminary status attached to every number; see journalists and academics use cases. Competitive intelligence teams watch capex inflections by size class ahead of purchase cycles; see competitive intel use cases. Sales and growth teams segment European equipment prospects by the purchasing power implied by country, type and size class; see sales and growth teams use cases. Data scientists and ML engineers use the aggregates as exogenous features - there is no microdata to train on, and pretending otherwise would be the fastest way to waste a quarter; see data scientists use cases.

Within agricultural-farm-machinery data, this record owns the money layer. Its neighbours count metal: the Eurostat Agriculture & Farm Structure database tallies tractors by NUTS 2 region, FAOSTAT Agricultural Machinery tracks machine stocks across roughly 258 countries from 1961 to 2009, and World Bank WDI tractor series carries country-level counts for cross-region screens.

The trade-off is breadth versus balance sheets. Those sources sweep wider geographies; none of them can tell you what a specialist dairy farm in a given size class actually spent on equipment or what its machines are now worth. Run this record for value-per-farm economics and the counting records for fleet geography, and the two layers reconcile into a complete picture of the European equipment market.

Three things worth knowing upfront. First, this is an aggregated panel - weighted averages and totals by segment, never farm-level rows, which is exactly what makes cross-country benchmarking defensible. Second, the field dictionary above is the confirmed core; the complete standard-result catalogue, labour and enterprise detail lines and the legacy 1989-2009 methodology series confirm with your sample rather than being guessed at here. Third, mind the seams: weighting reference regimes rotate between years, and national size thresholds move - Hungary lifted its threshold from EUR 4,000 to EUR 8,000 in 2023 - so long splices deserve a break test before they get a chart. Name the countries, farm types, size classes and years, and the sample returns in exactly the schema shown above.