Zema Global Integrates SatYield Crop Intelligence into Commodity Data Infrastructure

Zema Global has expanded its partnership with SatYield, integrating the satellite-based agricultural intelligence provider’s crop forecasts into its Decisioning Infrastructure to give commodity traders and risk managers access to forward-looking supply data alongside conventional market and fundamental information.

SatYield uses Earth observation data and biophysical modelling to generate forecasts of crop yields, production, acreage and growing conditions. Its approach uses physics-based crop modelling rather than relying primarily on historical statistical relationships, with the aim of identifying changes in crop development ahead of official agricultural estimates.

“More and more clients have these highly complex multi-asset portfolios, and those are getting harder to manage with all the volatility out there,” says Eric Steimel, Chief Data Officer at Zema Global, in conversation with Market & Alt Data Insight. “The sooner they can gain insights into their investments and portfolios, the greater the advantage. What SatYield provides is that early, forward-looking view that a lot of traditional publications don’t. It gives our clients insights ahead of the official estimates coming out. We have clients across power trading, agricultural trading and agribusiness, and they’re always looking for innovative datasets.”

Bringing Physical Intelligence into Trading Workflows

Zema’s infrastructure aggregates information from more than 1,000 sources and over 10,000 individual data feeds, spanning exchanges, brokers, price reporting agencies and specialist providers. The SatYield integration brings model-generated agricultural forecasts into an environment already used to process market prices, fundamental data and other commodity information.

The arrangement allows customers to incorporate SatYield’s estimates into existing analytical processes without having to develop separate data collection and delivery infrastructure. It also extends access beyond the marketplace to Zema’s enterprise tools, where datasets can be combined for more complex modelling and valuation.

“We have reports from USDA WASDE, Brazil’s CONAB, weather providers and a number of other sources,” says Steimel. “SatYield adds another piece to that collection, giving clients a fuller view. Within our Decisioning Infrastructure, we offer analytics and curve-based tools that can integrate datasets from different providers for portfolio evaluation and forward curve creation. We provide normalised data in formats that work together, and clients can then feed that into downstream systems, whether that’s an ETRM, CTRM, Databricks or Snowflake. We create flexible adapters to get the data where clients need it.”

Customers continue to license SatYield’s data directly from the provider, with Zema’s partnerships team coordinating the integration and onboarding process. Subscriptions can be tailored by geography and crop type, allowing firms to select US or Brazilian coverage and individual agricultural commodities according to their requirements.

Zema’s professional services teams can also help customers develop forward curves and analytical models using the integrated information. Steimel acknowledges that detailed visibility into individual trading applications sits largely with those teams, rather than his data operations function.

Managing Data Quality and Revisions

Incorporating predictive agricultural intelligence into commodity analytics introduces operational requirements around data availability, consistency and revisions. Crop forecasts evolve throughout the growing season, while official statistics and other supporting datasets arrive at different frequencies and may subsequently be corrected.

Zema’s collection infrastructure is designed to accommodate sources ranging from sophisticated APIs to publicly available reports and files delivered by email. Its monitoring processes apply different validation rules according to the characteristics and publication schedules of individual datasets.

“Data quality is baked into our collection process,” says Steimel. “We have a multi-step process with detailed logging at each stage, so we can see exactly what’s being collected and extracted. Independent QA runs validation tests for completeness, extreme values and spikes, with different checks depending on the type of data. Every feed has error logging, and we build redundancy into the collection process to handle late postings and corrections. Clients need to know when a value has changed, what the latest version is and when it was updated. They can track that through our products.”

The collection environment is largely configurable through low-code tools, giving operations staff visibility into individual feeds without requiring developer intervention. Direct relationships with specialist providers such as SatYield also give Zema clearer delivery arrangements and channels for resolving problems than are typically available when collecting information from public sources.

AI-Assisted Data Operations

Zema is also extending its use of AI within data collection and monitoring, building on existing automated processes to help operations teams identify and resolve issues across its expanding range of feeds.

“We’re getting to the point where AI agents can provide more targeted insights when data collection fails,” says Steimel. “If we’re trying to fetch a file and it’s not there, agents can look at what’s happened in the past and recommend what to do next. Keeping humans in the loop is essential to assess those recommendations, apply judgment and intervene when necessary. We can also be smarter about when to run quality checks, what parameters to monitor and when to react. That makes us more efficient and helps us deliver data that’s timely, accurate and complete.”

On the analytical side, Zema’s CQuant business, acquired approximately 18 months ago, develops quantitative models for commodity and multi-asset valuation. Steimel’s data team works with CQuant to supply the information required for those models, including new specialist datasets.

SatYield’s biophysical approach, which effectively creates a simulated representation of crop development, adds another potential input for models combining physical commodity fundamentals with financial market information.

The expanded partnership gives Zema customers a route to incorporating those forecasts into established trading and risk infrastructure, while providing its analytical teams with additional information for developing models that draw on both observed physical conditions and conventional market data.