--- license: mit task_categories: - time-series-forecasting tags: - nigeria - agriculture - food-systems - synthetic - agricultural-markets-and-pricing size_categories: - 100K ⚠️ **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: 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: ```bibtex @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: - 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 - 180,000 synthetic records - Quality-assured using FAO/NBS/NiMet reference data