We extract cotton production statistics, global price indices, supply-demand forecasts, and trade metrics from the International Cotton Advisory Committee. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Supply & Demand objects from icac.org. All fields typed and schema-versioned.
"country": "India", "season": "2025/26", "production_mt": 5820000, "consumption_mt": 5400000, "exports": 350000, "ending_stocks": 1850000, "stock_to_use_ratio": 34.2
| # | country | season | production_mt | consumption_mt | imports | exports |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Price Indices objects from icac.org. All fields typed and schema-versioned.
"index_name": "Cotlook A Index", "date": "2026-04-12", "price": 94.25, "currency": "USD", "unit": "cents/lb", "percent_change": 1.2
| # | index_name | date | price | currency | unit | percent_change |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Government Support objects from icac.org. All fields typed and schema-versioned.
"country": "USA", "year": "2025", "direct_subsidy": 450000000, "crop_insurance": 820000000, "total_assistance": 1270000000, "assistance_per_kg": 0.32
| # | country | year | direct_subsidy | border_protection | crop_insurance | min_support_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Yield Data objects from icac.org. All fields typed and schema-versioned.
"country": "Brazil", "season": "2025/26", "area_harvested_ha": 1750000, "yield_kg_ha": 1920, "lint_production": 3360000, "gm_cotton_pct": 98.5
| # | country | region | season | area_harvested_ha | yield_kg_ha | lint_production |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Trade Matrices objects from icac.org. All fields typed and schema-versioned.
"exporter_country": "Australia", "importer_country": "Vietnam", "season": "2025/26", "volume_mt": 420000, "tariff_rate": 0.0, "actual_shipped": 185000
| # | exporter_country | importer_country | season | volume_mt | value_usd | transport_mode |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our ICAC scraper handles the complexity of agricultural data extraction: parsing PDF statistical reports, reconstructing chart data, and normalising decades of historical time-series.
Extract area harvested, yield per hectare, and total lint production by country and season, normalised into standard units.
Capture the Cotlook A Index and other regional pricing metrics, maintaining a continuous time-series database.
Automated extraction of tabular data from ICAC monthly and annual PDF reports using advanced OCR and table boundary detection.
Reconstruct underlying datasets from embedded web charts and visualisations published on the ICAC portal.
Monitor changes in consumption, ending stocks, and stock-to-use ratios across major textile manufacturing hubs.
Map bilateral cotton trade flows, tracking import and export volumes between producing and consuming nations.
Quantify state intervention, tracking direct subsidies, minimum support prices, and border protection measures.
Backfill your database with decades of historical cotton statistics, mapped to a consistent modern schema.
Configure pipelines to run immediately after ICAC publishes new monthly bulletins or quarterly forecasts.
Brief in. Clean data out.
Select the datasets you need: production yields, trade matrices, price indices, or historical archives.
We configure web crawlers and PDF parsing pipelines to extract and normalise the target statistics.
Unit normalisation checks, null-rate monitoring, and time-series continuity validation.
Structured data pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Agricultural bodies often publish critical data in hostile formats. Here is how we convert static reports into queryable databases.
Much of ICAC's historical data exists only in PDF format. We deploy Camelot and Tesseract OCR to detect table boundaries, extract cell values, and reconstruct the tabular structure into machine-readable JSON.
Over decades, ICAC has changed its reporting columns, country names, and unit metrics. Our pipeline applies normalisation rules to map legacy data structures into a unified, consistent schema.
For data presented purely as web charts, we intercept the underlying network requests or parse the JavaScript data objects to extract the raw coordinates and values before they are rendered.
We maintain a hash index of last-seen values per statistical category. Subsequent runs only push diffs, reducing storage bloat and downstream processing load for your data engineering team.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, unit anomalies, and missing reports, responding before you notice a gap in your data.
Quantitative funds feed ICAC supply and demand forecasts into pricing models to predict movements in global cotton futures.
Textile manufacturers track yield forecasts and ending stocks to optimise raw material procurement and hedge against price volatility.
Agricultural economists analyse government subsidy data and trade matrices to evaluate the impact of state intervention on global markets.
Machine learning teams correlate historical ICAC yield data with satellite imagery and weather patterns to train predictive crop models.
Consultancies track the long-term shift in mill use and consumption metrics to advise clients on factory location strategy.
Private equity firms evaluate macroeconomic cotton trends before investing in regional spinning mills or apparel manufacturing hubs.
"The ICAC holds the definitive dataset on global cotton economics, but accessing historical time-series often requires manually extracting tables from decades of PDF reports."
Most data teams underestimate the complexity of parsing unstructured statistical reports. Reliable ICAC extraction requires OCR, table-boundary detection, PDF parsing, and strict normalisation rules to handle shifting statistical methodologies over time. DataFlirt absorbs that complexity so your analysts can focus on forecasting.
Everything supported by our icac.org scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We utilise Camelot and Tesseract OCR to accurately detect table structures within unstructured PDF documents, converting visual grids into structured JSON arrays.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering for interactive charts and dynamic portal navigation.
Pipelines run on AWS ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About icac.org scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available statistical data from government and intergovernmental portals is generally permissible. DataFlirt targets only public, non-authenticated agricultural reports and indices. We do not extract gated member-only content or circumvent authentication walls. Clients should consult legal counsel for specific use cases.
We deploy specialised PDF parsing libraries like Camelot alongside Tesseract OCR to identify table boundaries, extract text, and reconstruct the data into structured JSON formats. We also apply strict validation rules to ensure accuracy.
ICAC typically publishes major statistical updates on a monthly or quarterly basis. We configure pipelines to monitor the portal daily and trigger extraction immediately upon the publication of new reports.
Yes. ICAC reporting formats have evolved over the years. We map legacy column headers, country names, and measurement units to a consistent modern schema, providing a clean time-series database.
We extract the price indices published directly on the ICAC portal, such as the Cotlook A Index. However, live tick-by-tick market data requires direct integration with commodity exchanges.
Our packages start at defined statistical sets (e.g., global production and mill use) with monthly delivery. For full historical archive extraction and custom normalisation, we price based on pipeline complexity.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical archive dump or a continuous feed of monthly cotton statistics - we scope, build, and operate the pipeline. Tell us what you need.
