electricsheepasia/asia-drought-asap-hotspots-monthly

Anomaly Hotspots of Agricultural Production

Publisher: Joint Research Centre of the European Commission · Source: HDX · License: cc-by-igo · Updated: 2026-05-04

Abstract

ASAP is an online decision support system for early warning about hotspots of agricultural production anomaly (crop and rangeland), developed by the JRC for food security crises prevention and response planning.

The monthly hotspots data set is available below, but you can explore the hotspots on the ASAP Warning Explorer and access more contextual data on the downloads page. To learn more about the hotspots, refer to the warning classification methodology document.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the date column(s). Geographic scope: AFG, DZA, AGO, BGD, BEN, BOL, BWA, BFA, and 73 others.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

Variables

Geographic — g1_w_any (range 0.0–92.0), iso3 (KGZ, BGD, TJK).

Temporal — date.

Identifier / Metadata — asap0_id (range 13.0–221.0), asap0_name (Kyrgyzstan, Bangladesh, Tajikistan), hs_code (range 0.0–3.0), hs_name (No hotspot, Hotspot, Major hotspot), esa_source (HDX) and 1 others.

Other — comment (Not a hotspot country according to the latest ASAP analysis., Not a hotspot country according to the latest ASAP analysis., During the last three dekads the country was characterized by average rainfall and biomass conditions for both crop and rangeland areas.), g1_w_crop (range 0.0–69.0), g1_w_range (range 0.0–89.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-drought-all")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

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. 1 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

Limitations

• —Data originates from Joint Research Centre of the European Commission and has not been independently validated by ESA.
• —Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• —This dataset spans 81 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_drought_all,
  title     = {Anomaly Hotspots of Agricultural Production},
  author    = {Joint Research Centre of the European Commission},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/asap-hotspots-monthly},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.