Sugar · Thailand
Thailand Sugar
How we predicted Thailand sugar, with over 95% accuracy, at 90% lower costs, verified by thousands of field surveys.
Read case study →
Problems we solve
Crop Forecasting: Why It's So Hard.
Crop forecasting is often wrong due to poor data, incomplete data and constantly changing data — economics, policies and weather. We have solved this, with over 95% accuracy across every major crop and country.
Weather Volatility
We map weather to crops, and do not use generic data. We have 20 years of data for every crop and country — and monitor the weather every 12 hours.
Integrated Intelligence
We do not depend on any one data source — we do not rely solely on satellite data or weather data or field visits. We combine all data sources, including macro economic data.
Bias & Error
We do not use "best fit lines". All of our data is rigorously tested and validated, using multiple methods of validation. We are evidence based.
95%+
Typical accuracy rate
24 Hours
Frequency of updates
20 Years
Back testing and validation
Client success stories
How our forecasts have played out in the real world.
Barley · Spain
Spain Barley
How we predicted Spain barley during a drought, months ahead of the market.
Read case study →
Cocoa · Côte d'Ivoire & Ghana
Cocoa Ivory Coast
How we predicted cocoa months ahead of the harvest for Ivory Coast and Ghana — tracking disease, production and even smuggling.
Read case study →
Why existing tools fail
Most forecasting tools were never built for modern commodity markets.
01
Limited data integration
Single-source models — satellite only, or weather only — miss the market drivers and ground truth that actually move production.
02
Static model assumptions
Best-fit lines calibrated to the last decade fail when climate, policy or input costs shift the baseline.
03
Poor scalability
Solutions that work for one crop in one country can't stretch to 700+ crop-country combinations without breaking.
04
Lack of real-time updates
Monthly USDA-style updates miss the 72-hour windows when weather, disease or policy actually change the forecast.
Ready to transform your forecasting?
From cocoa in Ivory Coast to cotton in Pakistan — we have never failed to deliver an accurate prediction.
We can provide high-accuracy crop predictions for any crop in any country. Talk to the team to see a live forecast for your book.
Talk to the team
Frequently asked questions
Crop forecasting, answered.
How accurate are HSAT forecasts?
- HSAT yield forecasts typically achieve over 95% accuracy for major crops.
- Accuracy is proven with the past decade's performance records for every crop and country.
- Forecasts combine satellite, weather, economic and ground-truth data for cross-validated results.
What data sources do you use?
Satellite imagery (NDVI, soil moisture, surface temperature), 20+ years of crop-specific weather (temperature, rainfall, GDD, drought indices), commodity prices and FX, policy and trade data, and thousands of ground-truth field visits per year — all mapped to crops rather than generic admin boundaries.
How often are forecasts updated?
The underlying data refreshes every 30 minutes. Standard forecasts update weekly. Enterprise clients can receive daily or hourly updates delivered via dashboard, API, S3 or email.
Which crops can you forecast?
We publish forecasts for 700+ crop-country combinations — cocoa, sugar, cotton, coffee, palm oil, wheat, soy, corn, canola, barley, rice and more. If it's a commercially traded crop, we can almost certainly forecast it.
What is the setup time?
Dashboard access is typically live within 24 hours of contract. Custom enterprise forecasts and API integrations usually go live within 2–4 weeks, depending on scope and validation requirements.
Do you offer custom solutions?
Yes. CropGPT Enterprise delivers bespoke forecasts for specific crops, regions, refresh frequencies and delivery formats. We also build custom models for unusual commodities or private supply chains on request.