This project analyses crop yield data from 50 smallholder cassava and yam farmers supervised by JPIL Farms Ltd in Edo State, Nigeria between 2022 and 2024.
The analysis investigates whether a structured fertilizer training programme produced a measurable improvement in farmer productivity and identifies the structural constraints limiting smallholder output in the region.
- Trained farmers achieved 16.5% average yield increase
- Untrained farmers achieved only 1.0% average yield increase
- The 15.5 percentage point gap is statistically significant (T-test p-value < 0.05)
- 62% of farmers experienced pest and disease incidence
- 66% of farmers operate on leased land (tenure insecurity)
- Only 20% of farmers have access to formal credit
- Python 3 — Pandas, NumPy, SciPy, Matplotlib, Seaborn
- Microsoft Excel
- Power BI Desktop
- Jupyter Notebook
- GitHub
| File | Description |
|---|---|
jpil_farmers_dataset.xlsx |
Raw dataset — 50 farmers, 13 columns |
farmer_analysis.py |
Full Python analysis script |
farmer_analysis.ipynb |
Jupyter Notebook — portfolio version |
jpil_farms_dashboard.pdf |
Power BI dashboard export |
charts/ |
All 5 visualisation charts |
The independent T-test produced a p-value of 0.000 — confirming that the yield improvement from JPIL Farms training is statistically real and not due to chance.
Unity Osagie-Aaron B.Sc. Agricultural Economics and Extension Best Graduating Student — Ambrose Alli University, Ekpoma, Edo State, Nigeria
Agricultural Consultant and Economic Development Coordinator JPIL Farms Ltd, Edo State, Nigeria
GitHub: github.com/unityaaron