---
license: mit
task_categories:
- tabular-classification
tags:
- nigeria
- agriculture
- food-systems
- synthetic
- extension-services-and-technology
size_categories:
- 10K<n<100K
---

# Nigeria Agriculture – Training Programs

## Dataset Description

Synthetic Extension Services & Technology data for Nigeria agriculture sector.

**Category**: Extension Services & Technology  
**Rows**: 90,000  
**Format**: CSV, Parquet  
**License**: MIT  
**Synthetic**: Yes (generated using reference data from FAO, NBS, NiMet, FMARD)

## Dataset Structure

### Schema

- **id**: string
- **date**: string
- **state**: string
- **value**: float
- **category**: string

### Sample Data

```
| id           | date       | state   |   value | category   |
|:-------------|:-----------|:--------|--------:|:-----------|
| REC-00851488 | 2022-03-20 | Ebonyi  |  118.42 | B          |
| REC-00387964 | 2023-12-07 | Enugu   |  212.94 | B          |
| REC-00511133 | 2024-08-11 | Katsina |  113.15 | B          |
| REC-00563931 | 2024-06-18 | Osun    |   15.07 | A          |
| REC-00980604 | 2022-05-28 | Niger   |  107.23 | A          |
```

## 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 – Training Programs},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_agriculture_training_programs}
}
```

## 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
- 90,000 synthetic records
- Quality-assured using FAO/NBS/NiMet reference data