electricsheepafrica/nigerian_agriculture_crop_disease_pest_incidents

Nigeria Agriculture – Crop Disease & Pest Incidents

Dataset Description

Pest/disease outbreaks: severity, area affected, yield loss, treatment, outcome.

Category: Crop Production & Yields
Rows: 120,000
Format: CSV, Parquet
License: MIT
Synthetic: Yes (generated using reference data from FAO, NBS, NiMet, FMARD)

Dataset Structure

Schema

• farm_id: string
• incident_date: string
• crop: string
• pest_disease: string
• severity: integer
• area_affected_ha: float
• yield_loss_pct: float
• treatment: string
• outcome: string

Sample Data

| farm_id     | incident_date   | crop    | pest_disease   |   severity |   area_affected_ha |   yield_loss_pct | treatment   | outcome              |
|:------------|:----------------|:--------|:---------------|-----------:|-------------------:|-----------------:|:------------|:---------------------|
| FARM-007631 | 2022-05-18      | cocoa   | black_pod      |          4 |               1.98 |             15.5 | chemical    | controlled           |
| FARM-027954 | 2022-05-08      | onion   | aphids         |          1 |               0.74 |             24   | chemical    | not_controlled       |
| FARM-010969 | 2023-02-15      | cocoa   | black_pod      |          1 |               0.45 |              0   | chemical    | partially_controlled |
| FARM-002885 | 2023-06-09      | cocoa   | black_pod      |          2 |               2.6  |             15.2 | none        | partially_controlled |
| FARM-015044 | 2024-02-24      | sorghum | stem_borer     |          1 |               1.39 |             10.7 | cultural    | controlled           |

Data Generation Methodology

This dataset was synthetically generated using:

1. Reference Sources:

   • FAO (Food and Agriculture Organization) - crop yields, production data
   • NBS (National Bureau of Statistics, Nigeria) - farm characteristics, surveys
   • NiMet (Nigerian Meteorological Agency) - weather patterns
   • FMARD (Federal Ministry of Agriculture and Rural Development) - extension guides
   • IITA (International Institute of Tropical Agriculture) - agronomic research

2. Domain Constraints:

   • Crop calendars and phenology (planting/harvest windows)
   • Agro-ecological zone characteristics (Sahel, Sudan Savanna, Guinea Savanna, Rainforest)
   • Nigeria-specific realities (smallholder dominance, market dynamics, conflict zones)
   • Statistical distributions matching national agricultural patterns

3. Quality Assurance:

   • Distribution testing (KS test, chi-square)
   • Correlation validation (rainfall-yield, fertilizer-yield, yield-price)
   • Causal consistency (DAG-based generation)
   • Multi-scale coherence (farm → state aggregations)
   • Ethical considerations (representative, unbiased)

See QUALITY_ASSURANCE.md in the repository for full methodology.

Use Cases

• Machine Learning: Yield prediction, price forecasting, pest detection, supply chain optimization
• Policy Analysis: Agricultural program evaluation, subsidy impact assessment, food security planning
• Research: Climate-agriculture interactions, market dynamics, technology adoption patterns
• Education: Teaching agricultural economics, data science applications in agriculture

Limitations

• Synthetic data: While grounded in real distributions, individual records are not real observations
• Simplified dynamics: Some complex interactions (e.g., multi-generational pest populations) are simplified
• Temporal scope: Covers 2022-2025; may not reflect longer-term trends or future climate scenarios
• Spatial resolution: State/LGA level; does not capture micro-level heterogeneity within localities

Citation

If you use this dataset, please cite:

@dataset{nigeria_agriculture_2025,
  title = {Nigeria Agriculture – Crop Disease & Pest Incidents},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_agriculture_crop_disease_pest_incidents}
}

Related Datasets

This dataset is part of the Nigeria Agriculture & Food Systems collection:

• https://huggingface.co/collections/electricsheepafrica/nigeria-agriculture-and-food-systems

Contact

For questions, feedback, or collaboration:

• Organization: Electric Sheep Africa
• Collection: Nigeria Agriculture & Food Systems
• Repository: https://github.com/electricsheepafrica/nigerian-datasets

Changelog

Version 1.0.0 (October 2025)

• Initial release
• 120,000 synthetic records
• Quality-assured using FAO/NBS/NiMet reference data