Agricultural Profit Optimiser
A Random Forest model and DoE simulation framework that identified the profit-maximising farming strategy across 20 years of Punjab climate data.
Overview
Farmers in Punjab invest heavily in fertilizers and irrigation — but more input does not always mean more profit. Using 20 years of daily NASA climate data across 22 Punjab districts, this project trained a Random Forest model to predict wheat and rice yields, then used Design of Experiments to simulate every combination of nitrogen and irrigation levels — revealing that the most profitable strategy was NOT the highest-yield strategy.
Problem Statement
- —Farmers cannot predict how weather will affect their seasonal yield
- —Over-investment in fertilizers reduces net profit due to diminishing returns
- —Agricultural planning is based on tradition rather than data analysis
- —No decision-support tool exists for input optimization at district level
Data & Analysis
Profit heatmap: Nitrogen × Irrigation (₹/ha)
Simulated treatment outcomes
Yield continues rising while profit peaks at 150kg N/ha
Comparative dual-axis trend analysis
Random Forest feature importance — crop yield drivers
Key climate yield predictors
Key Findings
Predictive accuracy
The Random Forest model explained 78% of historical wheat and rice yield variation using engineered climate features.
Peak profit per hectare
Optimal strategy: 150kg/ha Nitrogen + 500mm Irrigation — not the maximum, but the most efficient combination.
Diminishing returns proven
Pushing Nitrogen to 200kg/ha increased yield slightly but cut profit by ₹12,825/ha due to input costs.
Top yield driver
Daily minimum temperature emerged as the single strongest predictor of crop yield — above rainfall and heat stress.