Weather Data for Smarter Farming Decisions: Accuracy, Productivity, Resilience

Presented by: Meteomatics, British Sugar and Nave Analytics

Hosted by: Ag-Tech Navigator

Date: June 3, 2025

Duration: 32 minutes

Participants:

• Meteomatics experts: Alex Sakal (Account Executive, Agriculture and Insurance), Candice Thompson (Customer Success Manager)
• British Sugar: Alec McNulty (Senior Contract Manager), Tom Dale (Research Manager AB Vista)
• Nave Analytics: Val Kovalsky (Co-founder and CTO)
• Moderation: Jim Robinson (Director of Marketing and Communications North America)

The webinar explored how precise, hyperlocal weather intelligence can improve productivity, resilience, and profitability across the farming value chain. After setting the context of climate volatility, operational uncertainty, and rising sustainability demands, the session outlined common pitfalls in using weather data, treating all sources as equal, relying on regional averages, and making binary decisions from single deterministic runs. The solution proposed was a single, reliable source of truth that unifies forecasts and historical data, delivers hyperlocal accuracy, and integrates directly into existing agricultural workflows.

Meteomatics presented its approach to “scalable predictability” built around the Weather API, which aggregates more than 1,800 parameters from global and local sources and supports real-time downscaling to field-level resolution. The platform combines observations (stations, satellites, radar, lightning, ocean data) and models via a mixed-model method, and further sharpens guidance with high-resolution models such as EURO1k and 90-meter downscaling. Examples in MetX illustrated the operational impact of hyperlocal nowcasts and forecasts, including a spring frost event and a localized hail outbreak, emphasizing why lead time and spatial detail are critical for actions like spraying, irrigation, and harvest planning.

Two customer stories demonstrated practical outcomes. Nave Analytics described a sensor-free irrigation decision system that fuses a deterministic hydrological framework with satellite observations, using Meteomatics forecasts to anticipate water demand and storage. Benefits included water and pumping cost savings and reduced greenhouse-gas emissions. British Sugar detailed a digitization effort for crop-yield modeling and forecasting, shifting from coarse regional views to farm/field-level insights using the Meteomatics Weather API for real-time data, multiple resolutions, and extensive historical archives spanning 10+ campaigns.

British Sugar highlighted why granularity matters: regional rainfall might average 50 mm while local pockets vary from 0 to 80 mm, materially affecting yield. With larger data sets and machine-learning methods, the team can quantify which weather events drive yield outcomes and how different thresholds (e.g., 15 °C vs. 27 °C) influence performance. The session closed with guidance on defining data needs, choosing appropriate resolution and delivery methods, and future-proofing workflows with flexible data formats and comprehensive historical coverage.

Key Takeaways

• Hyperlocal, rapid-refresh weather intelligence enables proactive decisions across planting, spraying, irrigation, harvesting, and logistics.

• A single, standardized Weather API from Meteomatics provides >1,800 parameters, real-time downscaling, and connectors to integrate directly into ag workflows.

• High-resolution modeling (e.g., EURO1k with 90 m downscaling) captures field-level microclimates, improving timing for narrow spray windows, frost response, and harvest adjustments.

• MetX visualizations of severe frost and localized hail show why spatial detail and lead time materially change outcomes and loss prevention.

• Nave Analytics’ sensor-free irrigation decisions (hydrological model + satellite data) leverage Meteomatics forecasts to anticipate water demand/storage, saving water, pumping costs, and emissions.

• British Sugar’s crop-yield modeling moved from coarse regional averages to farm/field-level insights using the Meteomatics API, enabling near-real-time updates instead of monthly summaries.

• Extensive historical archives (10+ campaigns) combined with machine learning help quantify which weather events truly drive yield variation and by how much.

• Practical implementation advice: define required resolution and delivery (API vs. summaries), ensure flexible formats, and plan for evolving use cases to future-proof analytics and operations.