electricsheepafrica/africa-world-bank-agriculture-and-rural-development-indicators-for-south-africa

South Africa - Agriculture and Rural Development

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27

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Abstract

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

For the 70 percent of the world's poor who live in rural areas, agriculture is the main source of income and employment. But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations. Data presented here include measures of agricultural inputs, outputs, and productivity compiled by the UN's Food and Agriculture Organization.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: ZAF.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Country-level aggregates
Rows (total)	1,761
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	1,408 rows
Test split	352 rows
Geographic scope	ZAF
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (South Africa), country_iso3 (ZAF), year (range 1960.0–2025.0).

Outcome / Measurement — value (range -0.9385–11276969219.4595).

Identifier / Metadata — indicator_name (Rural population (% of total population), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added (% of GDP)), indicator_code (SP.RUR.TOTL.ZS, NV.AGR.TOTL.CD, NV.AGR.TOTL.ZS), esa_source (HDX), esa_processed (2026-04-10).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-agriculture-and-rural-development-indicators-for-south-africa")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
country_name	object	0.0%	South Africa
country_iso3	object	0.0%	ZAF
year	int64	0.0%	1960.0 – 2025.0 (mean 1994.5974)
indicator_name	object	0.0%	Rural population (% of total population), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added (% of GDP)
indicator_code	object	0.0%	SP.RUR.TOTL.ZS, NV.AGR.TOTL.CD, NV.AGR.TOTL.ZS
value	float64	0.0%	-0.9385 – 11276969219.4595 (mean 182793295.1279)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-10

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Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2025.0	1994.5974	1996.0
value	-0.9385	11276969219.4595	182793295.1279	79.4179

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from World Bank Group and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_world_bank_agriculture_and_rural_development_indicators_for_south_africa,
  title     = {South Africa - Agriculture and Rural Development},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-agriculture-and-rural-development-indicators-for-south-africa},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.