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whale3il/nigerian_agriculture_soil_health_testing

Nigeria Agriculture – Soil Health Testing Dataset Description Soil test results: pH, NPK, organic matter, moisture, recommendations. Category: Crop Production & YieldsRows: 80,000Format: CSV, ParquetLicense: MITSynthetic: Yes (generated using reference data from FAO, NBS, NiMet, FMARD) Dataset Structure Schema farm_id: string state: string test_date: string ph: float nitrogen_ppm: float phosphorus_ppm: float potassium_ppm: float… See the full description on the dataset page: https://huggingface.co/datasets/whale3il/nigerian_agriculture_soil_health_testing.

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Nigeria Agriculture – Soil Health Testing

Dataset Description

Soil test results: pH, NPK, organic matter, moisture, recommendations.

Category: Crop Production & Yields Rows: 80,000 Format: CSV, Parquet License: MIT Synthetic: Yes (generated using reference data from FAO, NBS, NiMet, FMARD)

Dataset Structure

Schema

  • —farm_id: string
  • —state: string
  • —test_date: string
  • —ph: float
  • —nitrogen_ppm: float
  • —phosphorus_ppm: float
  • —potassium_ppm: float
  • —organic_matter_pct: float
  • —moisture_pct: float
  • —recommendation: string

Sample Data

| farm_id     | state   | test_date   |   ph |   nitrogen_ppm |   phosphorus_ppm |   potassium_ppm |   organic_matter_pct |   moisture_pct | recommendation             |
|:------------|:--------|:------------|-----:|---------------:|-----------------:|----------------:|---------------------:|---------------:|:---------------------------|
| FARM-035513 | Lagos   | 2024-12-26  | 5.63 |           33.4 |             22.5 |            86.5 |                 1.13 |           30.6 | Maintain current practices |
| FARM-040855 | Kebbi   | 2022-02-04  | 6.37 |           15.5 |             43.2 |           189   |                 2.54 |           13.2 | Maintain current practices |
| FARM-030826 | Kwara   | 2024-11-06  | 5.29 |           19.3 |             35.5 |           149.7 |                 3.09 |           24   | Apply lime to raise pH     |
| FARM-027792 | Ebonyi  | 2022-04-30  | 5.15 |           50.4 |             14.4 |           163.3 |                 2.1  |           14.7 | Apply lime to raise pH     |
| FARM-019148 | Ekiti   | 2022-07-11  | 4.41 |           17.6 |             22.9 |            54.7 |                 1.16 |           17.9 | Apply potassium fertilizer |

Data Generation Methodology

This dataset was synthetically generated using:

  1. 1.Reference Sources:
  2. 2.FAO (Food and Agriculture Organization) - crop yields, production data
  3. 3.NBS (National Bureau of Statistics, Nigeria) - farm characteristics, surveys
  4. 4.NiMet (Nigerian Meteorological Agency) - weather patterns
  5. 5.FMARD (Federal Ministry of Agriculture and Rural Development) - extension guides
  6. 6.IITA (International Institute of Tropical Agriculture) - agronomic research
  1. 1.Domain Constraints:
  2. 2.Crop calendars and phenology (planting/harvest windows)
  3. 3.Agro-ecological zone characteristics (Sahel, Sudan Savanna, Guinea Savanna, Rainforest)
  4. 4.Nigeria-specific realities (smallholder dominance, market dynamics, conflict zones)
  5. 5.Statistical distributions matching national agricultural patterns
  1. 1.Quality Assurance:
  2. 2.Distribution testing (KS test, chi-square)
  3. 3.Correlation validation (rainfall-yield, fertilizer-yield, yield-price)
  4. 4.Causal consistency (DAG-based generation)
  5. 5.Multi-scale coherence (farm → state aggregations)
  6. 6.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 – Soil Health Testing},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_agriculture_soil_health_testing}
}

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
  • —80,000 synthetic records
  • —Quality-assured using FAO/NBS/NiMet reference data