Treefera launched Market Intelligence, a new data product delivering field- and plantation-level yield and production area forecasts across seasonal and multi-year horizons.
“In financial markets, risk is continuously modelled and uncertainty is explicit. In ag and soft commodities, markets still rely heavily on survey-based data compiled after the fact,” says Jonathan Horn, CEO and founder of Treefera. “We combine satellite observation, crop-stage scientific modelling and deep learning to directly measure what is happening at field level and translate it into probabilistic supply forecasts. That allows traders to assess probability distributions, quantify uncertainty and position with greater confidence ahead of consensus. The result is measurable information advantage in an imperfect market.”
Key takeaways:
· Market Intelligence is purpose-built for ag and soft commodity traders, analysts and procurement teams who need earlier visibility into yield, planted area and supply shifts.
· Treefera AI takes price discovery in ag and softs to the next level, leveraging deep learning models trained on single-crop ground truth data to detect crop-specific production areas with 92% accuracy; generate in-season yield forecasts with over 90% accuracy; and update forecasts weekly as crop conditions evolve.
· Treefera aggregates from the field level upward. Millions of satellite observations, weather variables and biological growth indicators are synthesized into structured, quant-ready datasets designed for ingestion, back-testing and integration into existing trading workflows.
· Phenology-aware modelling: Yield sensitivity is weighted toward critical growth windows, such as the VT–R1 tasseling and silking stage in corn. This avoids false bullish or bearish signals and aligns forecasts with how yield is biologically determined.
· Single-crop canopy isolation: Treefera isolates crop-specific canopy signals rather than relying on blended vegetation indices across multiple crops. The model is trained from the ground up using single-crop detection and crop-specific ground truth data.
· Treefera’s deep learning crop detection models identify planted area shifts weeks or months before official statistics are published. Early visibility into production area changes materially impacts supply expectations, particularly in weather-sensitive markets and tree crops such as coffee and cocoa.
· For tree crops and multi-year biological systems, forward modelling enables earlier structural supply assessment, supporting longer-dated positioning strategies.
