Nigeria Agriculture & Food Systems
Collection
A collection of Nigerian agriculture & food systems datasets. • 42 items • Updated
Datasets:
state stringclasses 37
values | date stringdate 2022-01-01 00:00:00 2025-03-30 00:00:00 | temp_c float64 22 38 | rainfall_mm float64 0 57.1 | humidity_pct float64 20 100 | wind_kmh float64 0 25.9 | solar_mj_m2 float64 5 30 |
|---|---|---|---|---|---|---|
Rivers | 2022-09-26 | 24.9 | 9.5 | 92 | 12.2 | 16.7 |
Ondo | 2022-08-05 | 24.5 | 15.8 | 61.2 | 3.6 | 20.5 |
Yobe | 2023-10-30 | 29.2 | 2.1 | 80.4 | 1.7 | 23.1 |
Oyo | 2023-03-26 | 27.6 | 1.2 | 60 | 3.4 | 17.9 |
Ebonyi | 2024-08-09 | 27.9 | 1.8 | 66.8 | 9.7 | 19.5 |
Edo | 2022-08-22 | 26.6 | 1 | 36.2 | 9.9 | 19.7 |
Benue | 2023-07-02 | 28 | 5 | 43.5 | 8.5 | 10.4 |
Borno | 2022-03-20 | 33.3 | 12.2 | 74.9 | 9.1 | 18.9 |
Kebbi | 2024-04-16 | 25.8 | 5.4 | 65.6 | 9 | 9 |
Anambra | 2024-12-07 | 27.9 | 1.9 | 91.5 | 12.7 | 21.7 |
Taraba | 2024-08-20 | 29.5 | 3.7 | 68 | 13.1 | 16.3 |
Zamfara | 2025-03-02 | 34.2 | 8.1 | 46 | 11.9 | 18.8 |
Kogi | 2022-07-15 | 30.8 | 7.2 | 52.3 | 8.3 | 15.8 |
Taraba | 2024-07-21 | 27.9 | 12.7 | 56.5 | 8.5 | 12.8 |
Imo | 2025-02-03 | 28.1 | 2.7 | 67.6 | 6.1 | 18.4 |
Yobe | 2024-02-11 | 33.1 | 2.6 | 60.8 | 8.6 | 11 |
Anambra | 2024-04-30 | 28.5 | 24 | 53.4 | 5.2 | 12.4 |
Osun | 2023-10-18 | 27.4 | 13.3 | 70.7 | 8.2 | 20.7 |
Delta | 2023-12-11 | 25.5 | 1.3 | 53.8 | 11.2 | 15.3 |
Yobe | 2024-12-27 | 26.2 | 5.4 | 71.8 | 8.8 | 9.1 |
Kano | 2022-11-06 | 32.4 | 10.7 | 66.1 | 7 | 21.1 |
Kogi | 2023-09-24 | 26.4 | 1.9 | 68.9 | 13.4 | 12.9 |
FCT | 2024-09-05 | 23.2 | 0.4 | 81.2 | 16.9 | 16.3 |
Ekiti | 2024-10-23 | 28 | 4.9 | 52.9 | 6.1 | 25.6 |
Katsina | 2022-07-25 | 33.6 | 1.8 | 52.9 | 8 | 22.9 |
Benue | 2025-02-08 | 29.4 | 7.4 | 52 | 14.8 | 15.5 |
Anambra | 2024-06-14 | 27.8 | 3.4 | 36.4 | 1.8 | 13.4 |
Kano | 2024-09-12 | 29 | 5.6 | 67.9 | 6.7 | 14.7 |
Jigawa | 2023-01-13 | 28.3 | 1 | 64.3 | 1.3 | 21.2 |
Anambra | 2022-11-15 | 26.6 | 0.1 | 68.8 | 13.3 | 16.3 |
Ebonyi | 2022-11-15 | 24.4 | 2.7 | 55 | 9.1 | 21.5 |
Sokoto | 2024-04-18 | 33.9 | 4 | 54.6 | 4.7 | 14.2 |
FCT | 2022-01-22 | 24.6 | 1.3 | 72.3 | 2.8 | 25 |
Cross River | 2024-03-28 | 27.1 | 8.7 | 72.4 | 6.4 | 26.8 |
Ebonyi | 2024-10-02 | 24.9 | 8.5 | 64.5 | 7.6 | 13.5 |
Yobe | 2024-11-19 | 37.9 | 1.9 | 93 | 8.2 | 21.3 |
Ogun | 2025-03-03 | 28.6 | 1.3 | 65.2 | 9.4 | 24.4 |
Ekiti | 2023-08-04 | 26.1 | 1.8 | 53.1 | 17.3 | 14.4 |
Kogi | 2024-11-08 | 26.7 | 12.3 | 52.1 | 6.2 | 14.2 |
Bayelsa | 2024-03-30 | 28.7 | 5.7 | 53.8 | 1.1 | 21.3 |
Plateau | 2025-01-02 | 28.3 | 4.9 | 60.7 | 7.2 | 19.2 |
Abia | 2023-01-04 | 24.2 | 0.1 | 61.8 | 8.3 | 17.6 |
FCT | 2024-11-12 | 23 | 7 | 71.1 | 9.1 | 18.6 |
FCT | 2023-07-29 | 29.9 | 5.1 | 75.9 | 8 | 14.2 |
Borno | 2023-03-23 | 35.1 | 8.5 | 53 | 6.1 | 13.6 |
Kwara | 2023-04-12 | 26.4 | 22 | 55.5 | 9 | 18.2 |
Borno | 2023-05-02 | 29.4 | 3.8 | 80 | 17.5 | 16.6 |
Oyo | 2022-05-10 | 25.8 | 8.3 | 77.7 | 8.4 | 12.4 |
Katsina | 2024-03-12 | 26.3 | 1.4 | 82.6 | 4.2 | 19.3 |
FCT | 2022-05-28 | 28.1 | 3.3 | 77 | 8 | 17.1 |
Taraba | 2023-08-17 | 24.2 | 9.7 | 96.9 | 14.8 | 25.5 |
Borno | 2023-09-08 | 25.2 | 2.7 | 50.7 | 6.2 | 22.5 |
Zamfara | 2024-02-02 | 28.9 | 6.8 | 47.6 | 13.4 | 7.7 |
Ekiti | 2022-07-21 | 24.2 | 11.6 | 77.3 | 7.7 | 22.3 |
Ebonyi | 2022-04-28 | 29.7 | 9.2 | 74 | 7.8 | 16.2 |
Enugu | 2023-11-27 | 24.7 | 0.5 | 66.3 | 14.6 | 26.6 |
Sokoto | 2022-02-21 | 32.6 | 2.1 | 94.2 | 4 | 23.2 |
Kebbi | 2022-10-22 | 25.1 | 0 | 74.6 | 9.7 | 19.9 |
Katsina | 2023-03-05 | 30.2 | 11.9 | 57.5 | 9.7 | 16.4 |
Ogun | 2023-04-08 | 24.1 | 0.2 | 54.9 | 4.4 | 14.9 |
Yobe | 2022-01-08 | 25.8 | 0.2 | 62.5 | 1.7 | 20.3 |
Enugu | 2023-10-02 | 24.6 | 1.8 | 77.4 | 10.4 | 20.3 |
Kogi | 2024-10-28 | 26.9 | 4.3 | 73.7 | 12.5 | 24.5 |
Benue | 2024-04-29 | 28.1 | 0.3 | 90.4 | 9.6 | 15.5 |
Lagos | 2022-07-16 | 28.3 | 2.1 | 50.1 | 12.6 | 21.6 |
Sokoto | 2022-04-25 | 23.2 | 13.2 | 54.5 | 10.2 | 17.4 |
FCT | 2022-06-24 | 24.5 | 2.4 | 68.3 | 6.5 | 23.8 |
FCT | 2022-05-28 | 30.8 | 5.7 | 36.6 | 5.6 | 23.3 |
Sokoto | 2023-11-29 | 32.6 | 11 | 80.3 | 7.6 | 26.8 |
Anambra | 2024-12-10 | 27 | 11.8 | 63.5 | 7.3 | 15 |
Edo | 2024-10-10 | 26.8 | 9.2 | 56 | 8.8 | 20 |
Kaduna | 2023-05-15 | 23.7 | 3.8 | 36.8 | 5.5 | 17.8 |
Gombe | 2024-12-08 | 23.7 | 1.3 | 72.4 | 8.8 | 18.6 |
Lagos | 2023-12-17 | 27.4 | 3.6 | 70.4 | 5.7 | 12.5 |
Benue | 2022-01-21 | 26.8 | 1.7 | 64.7 | 11.3 | 12.3 |
Rivers | 2023-04-22 | 25.4 | 3.8 | 53.8 | 6 | 12.7 |
Niger | 2022-04-26 | 27 | 3.1 | 64.3 | 7.8 | 14.6 |
Benue | 2025-02-16 | 29.7 | 1.4 | 75.3 | 3.8 | 10.9 |
Edo | 2024-07-23 | 28.8 | 0.7 | 74 | 14.4 | 9.3 |
Kaduna | 2024-03-13 | 27 | 11.5 | 56.6 | 17.9 | 14 |
Rivers | 2023-08-28 | 26.5 | 0.4 | 81.3 | 13.5 | 17.9 |
Osun | 2023-07-28 | 26.5 | 5.7 | 75.5 | 6.4 | 13.9 |
Kwara | 2024-08-15 | 25.9 | 2.9 | 43.5 | 11 | 24.3 |
Taraba | 2023-04-22 | 27.6 | 2.7 | 62.9 | 6.7 | 19.9 |
Sokoto | 2023-03-12 | 31.5 | 8.3 | 88.9 | 12.6 | 23.6 |
Delta | 2024-10-05 | 24.6 | 2.3 | 72.3 | 7.6 | 20.7 |
Adamawa | 2024-10-19 | 22.5 | 0.8 | 70.7 | 8 | 25 |
Niger | 2022-09-03 | 26.3 | 13.4 | 60.6 | 7.6 | 23.7 |
Delta | 2022-01-16 | 28.1 | 5.5 | 73.7 | 6.5 | 19.1 |
Osun | 2023-02-12 | 27.9 | 4.5 | 27.3 | 9.3 | 18.1 |
Ondo | 2024-08-19 | 26.7 | 0.2 | 73.4 | 10.5 | 14.5 |
Yobe | 2024-01-15 | 26.6 | 4.4 | 63.9 | 1.9 | 16.8 |
Lagos | 2024-09-23 | 26.2 | 4.6 | 56 | 11.1 | 21 |
Katsina | 2023-07-24 | 23.4 | 0.6 | 32.4 | 7.1 | 12 |
Taraba | 2024-01-09 | 24.5 | 4.9 | 76.7 | 7.5 | 14.4 |
Plateau | 2024-09-23 | 23.7 | 1.5 | 51.9 | 5.5 | 25.2 |
Katsina | 2024-07-24 | 24.2 | 2.8 | 87.5 | 10.7 | 12.5 |
Edo | 2022-05-31 | 24.5 | 6.1 | 52.1 | 11.4 | 29.1 |
Akwa Ibom | 2024-03-25 | 25.5 | 0.5 | 69.1 | 8.2 | 22 |
Oyo | 2022-06-24 | 29.7 | 9.8 | 63.3 | 10 | 13 |
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⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
Nigeria Agriculture – Farm Weather Stations
Dataset Description
Daily weather: temp, rainfall, humidity, wind, solar radiation.
Category: Weather & Climate
Rows: 150,000
Format: CSV, Parquet
License: MIT
Synthetic: Yes (generated using reference data from FAO, NBS, NiMet, FMARD)
Dataset Structure
Schema
- state: string
- date: string
- temp_c: float
- rainfall_mm: float
- humidity_pct: float
- wind_kmh: float
- solar_mj_m2: float
Sample Data
| state | date | temp_c | rainfall_mm | humidity_pct | wind_kmh | solar_mj_m2 |
|:--------|:-----------|---------:|--------------:|---------------:|-----------:|--------------:|
| Rivers | 2022-09-26 | 24.9 | 9.5 | 92 | 12.2 | 16.7 |
| Ondo | 2022-08-05 | 24.5 | 15.8 | 61.2 | 3.6 | 20.5 |
| Yobe | 2023-10-30 | 29.2 | 2.1 | 80.4 | 1.7 | 23.1 |
| Oyo | 2023-03-26 | 27.6 | 1.2 | 60 | 3.4 | 17.9 |
| Ebonyi | 2024-08-09 | 27.9 | 1.8 | 66.8 | 9.7 | 19.5 |
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 – Farm Weather Stations},
author = {Electric Sheep Africa},
year = {2025},
publisher = {Hugging Face},
url = {/datasets/electricsheepafrica/nigerian_agriculture_farm_weather_stations}
}
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
- 150,000 synthetic records
- Quality-assured using FAO/NBS/NiMet reference data
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