ikigai-hub/maji-ndogo-farm-analysis

What factors influence crop yield across 5,654 farms in the Maji Ndogo region, and why do some farms fail to meet their standard yield? # 🌾 Maji Ndogo Farm Survey Analysis ## Overview An exploratory data analysis of 5,654 farms in the Maji Ndogo region, investigating what factors influence crop yield and why some farms fail to meet their standard yield targets. ## Questions Investigated - Which crops are most and least cultivated across the region? - Are farms meeting their standard yield expectations? - Does pollution level affect yield performance? - Does rainfall influence how much a farm can produce? - What drives underperformance in the 11% of farms that fall below standard yield? ## Key Findings - Most farms (89%) exceed their standard yield - High pollution suppresses yield potential — extreme overperformance only occurs at low pollution levels - Peak yields only appear at high rainfall levels - Rice has the highest median yield; potato has the lowest - No single factor explains underperformance — it is likely driven by a combination of conditions ## Recommendations - Prioritise pollution control as a first-line intervention - Expand rice cultivation in suitable areas - Investigate maize farms specifically — highest underperformance rate at 13% - Target investment toward high rainfall zones for maximum yield returns ## Tools Used - Python (Pandas, NumPy, Matplotlib, Seaborn) - SQLite - Jupyter Notebook ## Dataset Maji Ndogo Farm Survey — 5,654 farms across 4 related tables: geographic features, weather features, soil & crop features, and farm management features. ## How to Run 1. Clone the repo 2. Ensure the `.db` file is in the same directory as the notebook 3. Run `maji_ndogo_analysis.ipynb` top to bottom