Global Crop Production & Yield
Sixty-plus years of FAOSTAT crop statistics, cleaned into one continuous table: harvested area, yield, and production for 143 single-commodity crops across ~200 countries - grains (wheat, maize, rice, barley, sorghum, millet, oats, rye, triticale, quinoa, fonio), the full oilseed complex (soya, rapeseed/canola, sunflower, oil palm, groundnuts, sesame, linseed), soft commodities (coffee, cocoa, tea, tobacco, natural rubber), the entire fruit & nut estate (apples, grapes, bananas, citrus, berries, mangoes, avocados, almonds, walnuts, pistachios…), vegetables & pulses (tomatoes, onions, cabbages, beans, lentils, chickpeas, peas), roots (potatoes, cassava, yams, sweet potatoes), sugar (cane + beet) and fibres/spices. The raw FAO drop is a 545 MB normalized dump of every item back to 1961 - we stream it down to real single commodities (FAO super-aggregates dropped, no double-counting) and serve a tidy per-country, per-series Parquet with country names correctly UTF-8 encoded.
- Source
- FAOSTAT
- Licence
- Redistributable open data
- Updates
- Annual
- Data through
- 2024-01-01
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- 143 crops × 3 elements (Area harvested, Yield, Production) = 429 series
- ~200 countries, annual 1961-present; FAO super-aggregates & n.e.c. buckets dropped (real single commodities only)
- Long/tidy format: zone, year, series, value, unit - ready for pandas/SQL
- Source: FAO - FAOSTAT (CC-BY-4.0)
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| zone | str | Country (FAOSTAT Area name) | 100.0% | 210 |
| ts | timestamp (UTC) | Reference year | 100.0% | 64 |
| series | str | Crop - Element (e.g. 'Wheat - Yield') | 100.0% | 429 |
| value | float | Measured value in the series' unit | 100.0% | 383,713 |
| unit | str | ha / kg/ha / t | 100.0% | 3 |
| res_min | int | Native resolution (annual) | 100.0% | 1 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: FAOSTAT (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("global-crop-production-yield_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
Coverage: 210 zones
Afghanistan · Albania · Algeria · Angola · Antigua and Barbuda · Argentina · Armenia · Australia · Austria · Azerbaijan · Bahamas · Bahrain · Bangladesh · Barbados · Belarus · Belgium · Belgium-Luxembourg · Belize · Benin · Bhutan · Bolivia (Plurinational State of) · Bosnia and Herzegovina · Botswana · Brazil · Brunei Darussalam · Bulgaria · Burkina Faso · Burundi · Cabo Verde · Cambodia · Cameroon · Canada · Central African Republic · Chad · Chile · China · China, Hong Kong SAR · China, Macao SAR · China, Taiwan Province of · China, mainland · Colombia · Comoros · Congo · Cook Islands · Costa Rica · Croatia · Cuba · Cyprus · Czechia · Czechoslovakia · Côte d'Ivoire · Democratic People's Republic of Korea · Democratic Republic of the Congo · Denmark · Djibouti · Dominica · Dominican Republic · Ecuador · Egypt · El Salvador · Equatorial Guinea · Eritrea · Estonia · Eswatini · Ethiopia · Ethiopia PDR · Faroe Islands · Fiji · Finland · France · French Guiana · French Polynesia · Gabon · Gambia · Georgia · Germany · Ghana · Greece · Grenada · Guadeloupe · Guatemala · Guinea · Guinea-Bissau · Guyana · Haiti · Honduras · Hungary · Iceland · India · Indonesia · Iran (Islamic Republic of) · Iraq · Ireland · Israel · Italy · Jamaica · Japan · Jordan · Kazakhstan · Kenya · Kiribati · Kuwait · Kyrgyzstan · Lao People's Democratic Republic · Latvia · Lebanon · Lesotho · Liberia · Libya · Lithuania · Luxembourg · Madagascar · Malawi · Malaysia · Maldives · Mali · Malta · Marshall Islands · Martinique · Mauritania · Mauritius · Mexico · Micronesia (Federated States of) · Mongolia · Montenegro · Morocco · Mozambique · Myanmar · Namibia · Nauru · Nepal · Netherlands (Kingdom of the) · New Caledonia · New Zealand · Nicaragua · Niger · Nigeria · Niue · North Macedonia · Norway · Oman · Pakistan · Palestine · Panama · Papua New Guinea · Paraguay · Peru · Philippines · Poland · Portugal · Puerto Rico · Qatar · Republic of Korea · Republic of Moldova · Romania · Russian Federation · Rwanda · Réunion · Saint Kitts and Nevis · Saint Lucia · Saint Vincent and the Grenadines · Samoa · Sao Tome and Principe · Saudi Arabia · Senegal · Serbia · Serbia and Montenegro · Seychelles · Sierra Leone · Singapore · Slovakia · Slovenia · Solomon Islands · Somalia · South Africa · South Sudan · Spain · Sri Lanka · Sudan · Sudan (former) · Suriname · Sweden · Switzerland · Syrian Arab Republic · Tajikistan · Thailand · Timor-Leste · Togo · Tonga · Trinidad and Tobago · Tunisia · Turkmenistan · Tuvalu · Türkiye · USSR · Uganda · Ukraine · United Arab Emirates · United Kingdom of Great Britain and Northern Ireland · United Republic of Tanzania · United States of America · Uruguay · Uzbekistan · Vanuatu · Venezuela (Bolivarian Republic of) · Viet Nam · Yemen · Yugoslav SFR · Zambia · Zimbabwe
Related datasets
Agri Positioning Matrix (crop condition × COT)
Physical crop reality vs paper positioning: USDA Good+Excellent condition momentum joined to CFTC speculative-positioning z-score per crop, weekly, across 8 crops - corn, soybeans, wheat, cotton, rice, oats (all with both condition and the dominant CFTC futures market) plus barley and sorghum (condition only, no listed futures). Crops deteriorating while funds are net-short flags short-squeeze setups in food commodities.
EU CAP Subsidy Flows
Common Agricultural Policy expenditure, cleaned to analysis-ready Parquet: EUR paid per EU member state x CAP instrument (direct payments, market measures, rural development) x year, 2003-2022. Fully aggregated (member-state level, no beneficiary individuals) - the GDPR-safe view of where CAP money goes. Source: DG AGRI 'Financing the CAP' indicators.
European Agricultural Output Prices
The real output-price index of Europe's major crops - cereals, wheat, barley, grain maize, industrial crops and oilseeds - deflated to 2020=100, per country, annual. Cleaned from Eurostat's agricultural price statistics into one tidy panel: the farm-gate price signal, in real terms, that agri-commodity traders, food manufacturers and ag-lenders track. Distinct from our global crop-production volumes - this is PRICE, not tonnage.