Agriculture & Commodity Data Scraping Services

Fundamentally different. Futures reflect a standardised contract at a standardised delivery point for a forward month, aggregating global financial positioning. Our data reflects what physical product of a specific grade actually fetched at a named market on a specific date.

The two diverge, sometimes substantially and for long periods, because physical prices respond to local arrivals, transport, storage and regional demand. If your exposure is physical — buying, processing, or lending against actual crop — the physical series is what you need. If you need both, we deliver physical alongside your existing exchange feed for basis calculation.

Variable, and this is the main operational challenge in this category. Some portals are well maintained with consistent daily publication; others go offline for days, publish late, or restate figures without notice.

We handle it three ways: fallback collection from state-level or alternative sources where the primary portal fails; a source-attribution field on every record so you always know where a figure came from; and restatement detection, which flags when a previously published figure changes. Silent restatements corrupt time-series analysis, and catching them is one of the more valuable things we do here.

India is our deepest coverage, drawing on Agmarknet, e-NAM and state marketing board sources across thousands of APMC mandis. Coverage completeness follows source publication: some mandis report daily and comprehensively, others intermittently, and a few not at all.

During scoping we give you a market-by-market assessment for your commodities of interest, including typical reporting frequency and historical gap rates. That matters more than a headline market count, because a mandi that reports twice a month is not usable for daily basis work.

We deliver an explicit no-trade record with a reason where one is given — holiday, closure, no arrivals. This is deliberate and it matters more than it might appear.

If a row is simply absent, your pipeline cannot distinguish 'the market did not trade' from 'our collection failed' from 'the portal was down'. Those are three different facts with three different implications for a model. Explicit no-trade records remove that ambiguity, and any commodity dataset that omits them should be treated with suspicion.

Yes, with varying depth. Where government portals maintain public archives — India's Agmarknet among them — we can backfill multiple years. Our own collection archive adds depth for markets we already cover. Some markets have no accessible history at all.

We provide exact date ranges and gap profiles per market during scoping. This matters more here than in most categories, because agricultural analysis is inherently seasonal: a series with an unacknowledged gap covering one monsoon produces conclusions that look robust and aren't.

Yes. Fertiliser, seed and crop protection retail pricing is extracted from dealer sites, agri e-commerce platforms and government price monitoring portals, with brand, formulation and pack size captured, plus availability and stock-out signals.

Input pricing refreshes weekly rather than daily, which matches how it moves. It is bought both by input brands monitoring their own price realisation across channels, and by lenders and researchers modelling farm-level margin, where input cost is half the equation.

Yes, where the source publishes in time. Delivery timing is configured to your trading time zone, and for Indian mandi data we typically deliver in the early morning window after overnight portal publication.

The constraint is source publication, not our processing. Where a portal publishes at 11am local, we cannot deliver it at 6am. During scoping we tell you the realistic delivery window per source so your morning process is built around what is actually achievable.

We retain the reported grade and variety strings exactly as published, and additionally supply a mapped grade tier with a confidence indicator where a defensible mapping exists.

Where it doesn't, we leave the mapped field null rather than forcing it. Indian mandi grades like FAQ are not equivalent to European milling specifications, and pretending otherwise creates false comparability — which in commodity work leads to real trading losses, not just bad charts.

We quote every commodity data engagement individually, because a real number depends on scope: source count, record volume, refresh frequency and delivery method. Anyone quoting you a price before understanding those four things is guessing.

Market count and refresh frequency drive cost. Daily coverage across thousands of markets sits well above a focused commodity set.

The process is short: one scoping call, a free pilot on your own sources within 48 hours, then a fixed monthly quote. No per-request metering, no overage billing, and field or source additions are handled inside the retainer rather than re-quoted. Request a quote.