Dataset Viewer
Auto-converted to Parquet Duplicate
name
string
description
string
version
string
created
string
license
string
scenarios
dict
countries
list
crop_types
list
insurance_types
list
seasons
list
sources
list
variables
dict
parameter_sources
dict
african-crop-insurance-payouts
Synthetic dataset of agricultural index-insurance payouts across 12 Sub-Saharan African countries. Parameters calibrated from IBLI (Kenya/Ethiopia), WFP R4, and ACRE Africa programs.
1.0.0
2026-03-21T00:31:40.633780Z
CC-BY-4.0
{ "baseline": "Current-state parameters based on existing IBLI/R4/ACRE programs", "expanded_index_insurance": "Scaled enrollment via subsidy and mobile distribution (ACRE model)", "climate_shock": "Increased drought frequency reflecting IPCC AR6 projections for SSA" }
[ "Kenya", "Ethiopia", "Tanzania", "Malawi", "Zambia", "Zimbabwe", "Mozambique", "Senegal", "Ghana", "Nigeria", "Rwanda", "Uganda" ]
[ "maize", "wheat", "sorghum", "rice", "cassava" ]
[ "index_based", "area_yield", "weather_derivative" ]
[ "long_rains", "short_rains" ]
[ "Jensen & Barrett (2016) AJAE - IBLI basis risk analysis", "WFP R4 Rural Resilience Initiative reports (2018-2023)", "ACRE Africa scaling data (GIIF 2016, IFC 2014)", "Ntukamazina et al. (2017) J. Agr. Rural Develop. Trop. Subtrop.", "IPCC AR6 WGII Chapter 9 - Africa climate projections" ]
{ "record_id": "Unique integer identifier", "country": "Country name", "year": "Calendar year (2015-2024)", "season": "Growing season (long_rains / short_rains)", "crop_type": "Insured crop (maize/wheat/sorghum/rice/cassava)", "insurance_type": "Product type (index_based/area_yield/weather_derivative)", "...
{ "premium_rates": "IBLI: 5.5%/3.25% of insured value; R4: 10-15% farmer contribution", "payout_trigger": "IBLI: NDVI < 15th percentile; ACRE: yield < 80% of average", "basis_risk": "Jensen et al. 2016: IBLI reduces covariate risk by 63%, residual 69%", "enrollment": "R4: ~180K farmers (2020); ACRE: ~400K farme...

African Crop Insurance Payouts | Africa (Electric Sheep Africa metadata inventory)

Size category: n<1K - Formats: json - Sector: agriculture_food - Engineered by Electric Sheep Africa

size sector downloads license

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. African Crop Insurance Payouts Synthetic dataset of agricultural index-insurance payouts across 12 Sub-Saharan African countries (30,000 records). Parameters are calibrated from real-world programs including IBLI (Kenya/Ethiopia), WFP R4 Rural Resilience Initiative, and ACRE Africa. Dataset Description Scenarios… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-crop-insurance-payouts-all.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-synth-agriculture-crop-insurance-payouts-all
Sector agriculture_food
Topic tags insurance, agriculture, crop, climate, sub-saharan-africa, synthetic, agritech, index-insurance, drought, food-security
Modalities text
Formats json
Size category n<1K
Countries Africa-wide or source-defined African coverage
ISO3 coverage not declared
Last modified on HF 2026-04-14 22:58:29+00:00
Inventory snapshot 2026-07-16T16:00:34Z

How To Read This Dataset

  • Start from the repository files and the dataset viewer when available.
  • Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
  • Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
  • Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-synth-agriculture-crop-insurance-payouts-all")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

  • This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
  • Exact schema, row counts, and source files should be inspected in the repository data files.
  • Metadata gaps from the inventory: country, upstream_publisher.
  • Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Suggested Analyses

  • Inspect schema and missingness before modeling.
  • Profile variables by geography, time, and subgroup columns where present.
  • Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
  • Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_synth_agriculture_crop_insurance_payouts_all_2026,
  title        = {African Crop Insurance Payouts | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-crop-insurance-payouts-all},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-crop-insurance-payouts-all}}
}

License

Released under CC BY 4.0.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.


Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

Downloads last month
16