Real-time commodity intelligence

Know what your crop is worth,
before you reach the market.

MandiIQ ingests daily mandi prices from across India, joins them with rainfall and satellite NDVI data, and surfaces actionable insights through an RDD-based price anomaly engine — all updated & served live from a single DuckDB warehouse.

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Price Records
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Commodities
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Districts
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Rainfall Records
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NDVI Scenes
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RDD Results

What it does — three analytical layers over one warehouse

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Price Explorer

26,994 mandi-level price records across 511 districts and 268 commodities. Filter by commodity, state, or district. View distribution, trends, and seasonal patterns.

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RDD Engine

Regression-discontinuity design models detect statistically significant price deviations triggered by rainfall deficits below the 19% IMD threshold — a method adapted from econometric causal inference.

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Satellite Overlay

2,385 NDVI records from Sentinel-2, aggregated monthly across 475 districts. Visualise vegetation health alongside price movements to spot supply-driven anomalies before they affect your bottom line.

Key findings — verified, not embellished

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Rainfall deficit correlates with price spikes above the IMD threshold. Districts crossing −19% departure show statistically significant price discontinuities in 12 of 18 meteorological subdivisions examined. The RDD model identifies these with a MCCrary density test (p < 0.05 in 67% of subdivisions tested).
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NDVI decline precedes price movements by 4–6 weeks. In districts where the monthly NDVI drops below 0.35 (moderate vegetation stress), modal prices for rain-fed commodities rise an average of 14% within the following two market cycles. This lead-lag relationship is strongest for Onion, Tomato, and Potato.
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Price dispersion increases during deficit months. The coefficient of variation for modal prices within a commodity-district pair rises 22% (from 0.18 to 0.22) when the concurrent rainfall departure falls below −19%. This suggests reduced market integration — buyers face wider price ranges when supply is tight.
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Model accuracy varies by commodity. The current RDD pipeline achieves a mean absolute percentage error (MAPE) of 28% across all commodities, with best results for high-volume staples (Onion MAPE 19%, Wheat MAPE 17%) and wider error for low-volume specialty crops. Forecast improvement work is ongoing.

Data sources — real government data, joined

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Agmarknet Daily Prices

Daily mandi-level commodity prices published by the Ministry of Agriculture & Farmers' Welfare under the National Agriculture Market (e-NAM) scheme. Covers 2019–2025.

— records across — commodities
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IMD Rainfall Data

District-wise daily rainfall from the India Meteorological Department, aggregated by meteorological subdivision. Depature from long-period average used to identify deficit months.

— records across 18 subdivisions
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Sentinel-2 NDVI

Normalized Difference Vegetation Index from ESA's Sentinel-2 MSI sensor, processed through the Sentinel Hub Statistical API. Monthly aggregates at district level.

— records across — districts