Datasetpublic

electricsheepasia/asia-owid-projected-cropland-by-2050

Projected Cropland By 2050 | Asia (Our World in Data) 🌏 41 observations · 41 Asia countries · 2050–2050 · Repackaged by Electric Sheep Asia TL;DR This dataset contains 41 observations of Projected Cropland By 2050 data across 41 Asia countries, spanning 2050–2050. About the source Source: Our World in Data Publisher: Our World in Data License: cc-by-4.0 Topic: Projected Cropland By 2050 Geographic coverage 41 Asia countries ·… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-owid-projected-cropland-by-2050.

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Dataset Card

Projected Cropland By 2050 | Asia (Our World in Data)

🌏 41 observations · 41 Asia countries · 2050–2050 · Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

TL;DR

This dataset contains 41 observations of Projected Cropland By 2050 data across 41 Asia countries, spanning 2050–2050.

About the source

—Source: Our World in Data
—Publisher: Our World in Data
—License: cc-by-4.0
—Topic: Projected Cropland By 2050

Geographic coverage

41 Asia countries · top rows shown below, sorted by row count:

Country	Rows	First year	Last year
`AFG`	1	2050	2050
`ARM`	1	2050	2050
`AZE`	1	2050	2050
`BGD`	1	2050	2050
`BRN`	1	2050	2050
`BTN`	1	2050	2050
`CHN`	1	2050	2050
`CYP`	1	2050	2050
`GEO`	1	2050	2050
`IDN`	1	2050	2050
`IND`	1	2050	2050
`IRN`	1	2050	2050
`IRQ`	1	2050	2050
`ISR`	1	2050	2050
`JOR`	1	2050	2050
...	26 more countries

Schema

Column	Type	Description	Example
`country_name`	`string`	—	`Afghanistan`
`country_iso3`	`string`	—	`AFG`
`year`	`int64`	—	`2050`
`Business-as-usual`	`float64`	—	`6993.2705`
`Optimize trade`	`float64`	—	`10267.549`
`Close yield gaps`	`float64`	—	`-18645.773`
`Adopt healthy diets`	`float64`	—	`25886.025`
`Halve food waste`	`float64`	—	`177.25023`
`Combined interventions`	`float64`	—	`-9233.35`

Usage

python

from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-owid-projected-cropland-by-2050")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python

indonesia = df[df["country_iso3"] == "IDN"]

Time-series for a single indicator

python

sample = df.sort_values("year")
sample.plot(x="year", y="Business-as-usual")

Citation

bibtex

@misc{asia_owid_projected_cropland_by_2050_2050,
  title        = {Projected Cropland By 2050 | Asia (Our World in Data)},
  author       = {Our World in Data},
  year         = {2050},
  url          = {https://ourworldindata.org/grapher/projected-cropland-by-2050},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-owid-projected-cropland-by-2050}}
}

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 Asia repackaging.

About Electric Sheep

Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia 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/electricsheepasia

Provenance: ingested 2026-06-07 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/projected-cropland-by-2050