Datasets:

electricsheepeurope
/

europe-owid-yields-of-important-staple-crops

license: cc-by-4.0
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
multilinguality: monolingual
size_categories:
  - 1K<n<10K
tags:
  - tabular
  - europe
  - our-world-in-data
  - yields-of-important-staple-crops
  - owid
  - long-run-series
  - time-series
pretty_name: Yields Of Important Staple Crops | Europe (Our World in Data)

Yields Of Important Staple Crops | Europe (Our World in Data)

🇪🇺 1,995 observations · 39 Europe countries · 1275–2024 · Repackaged by Electric Sheep Europe

TL;DR

This dataset contains 1,995 observations of Yields Of Important Staple Crops data across 39 Europe countries, spanning 1275–2024.

About the source

Source: Our World in Data
Publisher: Our World in Data
License: cc-by-4.0
Topic: Yields Of Important Staple Crops

Geographic coverage

39 Europe countries · top rows shown below, sorted by row count:

Country	Rows	First year	Last year
`GBR`	81	1275	2024
`GRC`	68	1850	2024
`AUT`	68	1850	2024
`DNK`	68	1850	2024
`ESP`	68	1850	2024
`DEU`	68	1850	2024
`FRA`	68	1850	2024
`NLD`	68	1850	2024
`NOR`	68	1850	2024
`ITA`	68	1850	2024
`ROU`	67	1911	2024
`HUN`	67	1911	2024
`BGR`	67	1911	2024
`ALB`	64	1961	2024
`CHE`	64	1961	2024
...	24 more countries

Schema

Column	Type	Description	Example
`country_name`	`string`	—	`Albania`
`country_iso3`	`string`	—	`ALB`
`year`	`int64`	—	`1961`
`Wheat`	`float64`	—	`0.7731`
`Rice`	`float64`	—	`1.5514001`
`Barley`	`float64`	—	`1.0224`
`Maize`	`float64`	—	`0.9541`
`Rye`	`float64`	—	`0.6626`
`Oats`	`float64`	—	`0.60450006`
`Millet`	`float64`	—	`1.5315001`
`Sorghum`	`float64`	—	`0.53790003`
`Potatoes`	`float64`	—	`7.2134004`

Data quality & caveats

Wheat column has 3.6% null values (filtered to non-null in this dataset).

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepeurope/europe-owid-yields-of-important-staple-crops")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

germany = df[df["country_iso3"] == "DEU"]

Time-series for a single indicator

sample = df.sort_values("year")
sample.plot(x="year", y="Wheat")

Citation

@misc{europe_owid_yields_of_important_staple_crops_2024,
  title        = {Yields Of Important Staple Crops | Europe (Our World in Data)},
  author       = {Our World in Data},
  year         = {2024},
  url          = {https://ourworldindata.org/grapher/yields-of-important-staple-crops},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-owid-yields-of-important-staple-crops}}
}

License

Released under cc-by-4.0.

Original data © Our World in Data. When using this dataset, please cite both the original source above and the Electric Sheep Europe repackaging.

About Electric Sheep

Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepeurope

Provenance: ingested 2026-06-13 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/yields-of-important-staple-crops