Datasets:
metadata
license: mit
task_categories:
- time-series-forecasting
tags:
- nigeria
- agriculture
- food-systems
- synthetic
- agricultural-markets-and-pricing
size_categories:
- 100K<n<1M
data_type: synthetic
⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
Nigeria Agriculture – Commodity Market Prices
Dataset Description
Daily/weekly market prices by crop, location, volume.
Category: Agricultural Markets & Pricing
Rows: 180,000
Format: CSV, Parquet
License: MIT
Synthetic: Yes (generated using reference data from FAO, NBS, NiMet, FMARD)
Dataset Structure
Schema
- market: string
- commodity: string
- date: string
- price_ngn_kg: float
- volume_kg: float
Sample Data
| market | commodity | date | price_ngn_kg | volume_kg |
|:---------|:------------|:-----------|---------------:|------------:|
| Dawanau | groundnut | 2023-10-02 | 841.2 | 3900.3 |
| Ariaria | oil_palm | 2023-07-31 | 50 | 4506.9 |
| Dawanau | cocoa | 2022-07-15 | 347.71 | 6406.7 |
| Ariaria | rice | 2023-10-25 | 747.14 | 3621.6 |
| Mile 12 | sorghum | 2022-07-18 | 650.7 | 2689.1 |
Data Generation Methodology
This dataset was synthetically generated using:
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
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
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 – Commodity Market Prices},
author = {Electric Sheep Africa},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_agriculture_commodity_market_prices}
}
Related Datasets
This dataset is part of the Nigeria Agriculture & Food Systems collection:
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
- 180,000 synthetic records
- Quality-assured using FAO/NBS/NiMet reference data