Jim Robinson:
I’m Jim Robinson, and today we’re talking about how precise weather insights empower agribusinesses. I’ve invited my colleagues Candice and Alex, along with leaders from Nave Analytics and British Sugar. Alex, what’s on the agenda?
Alex Sakal:
Thanks, Jim, and welcome, everyone. Today’s webinar covers how weather data can unlock productivity, enhance resilience, and drive profitability across the farming value chain. Whether you’re deciding on planting, irrigation, harvesting, or logistics, timely, accurate, and actionable weather intelligence can be the difference between risk and reward.
We’ll explore how advanced weather solutions enable smarter, data-driven decisions from field to fleet—so weather becomes something you plan with, not just react to. Picture a world where every farming decision is guided by precise insights. In reality, uncertainty persists, and many producers still rely on the sky instead of dashboards. It’s time to optimize the path from cloud to crop: manage risk, optimize timing, and make every decision count. Weather can erase months of effort in minutes; hope is not a strategy. We need visibility and precision to act before events happen.
What holds agriculture back from becoming truly data-driven and resilient? First, a changing climate brings more severe and less predictable events—from hailstorms to heat waves, flooding, and late frosts. Second, many still lack access to consistent, high-quality weather data at farm scale. Third, rising costs and sustainability demands require optimizing land, water, labor, and time—conditions that change hourly.
Weather-related mistakes drain profits hectare by hectare. Sowing at the wrong time can cut yields by up to 10%. Spraying before rain or in high winds wastes inputs. Harvesting too early or after unexpected rain degrades quality. Each misstep chips away at margins; together, the losses can exceed thousands of euros per hectare. Conversely, a single weather-smart decision can save or earn hundreds of euros per hectare. Farming is changing; those who adapt first win.
Many teams fall into common weather-data traps: assuming all sources are equal, applying regional trends to local operations, making binary decisions from a single deterministic run, and treating weather as an afterthought. The goal is a single, reliable source of truth; hyperlocal accuracy (not regional averages); and both forecasts and history in one place—integrated into existing systems so weather becomes action via automation.
At Meteomatics, our solution ecosystem is built around scalable predictability: delivering precise, hyperlocal weather data across time scales and spatial resolutions—from today’s spray window to next season’s sowing plan. Whether you run a 100-hectare wheat farm in Poland, a vineyard in rural Argentina, or thousands of sugar-beet fields in the UK, you get global data access—even in remote areas. Our platform unifies historical data, real-time insights, and forecasts into a single source of truth ready to integrate with your tools. Modern agriculture needs weather intelligence you can act on.
What do we offer? Meteomatics provides an advanced Weather API with more than 1,800 parameters aggregated from global and local sources. Our system enables real-time downscaling so you get hyperlocal resolution fast, delivered in major formats with connectors for common programming languages—ready to plug into your models, platforms, or analytics. This isn’t just a weather feed; it’s a scalable intelligence system for agricultural decision-making.
We bring it all together with comprehensive data—weather stations, satellites, radar, lightning, ocean data, and atmospheric models—accessed through the Weather API, a single gateway to hyperlocal forecasts, decades of historical weather, and real-time satellite and radar. A mixed-model approach blends global weather models and observations into a harmonized forecast, refined to farm and field level. Forecasts are further enhanced with EURO1k and US1k high-resolution models, fine-tuned with 90-meter terrain data and real-time calibration using nearby station observations. Whether planning a spray window, adjusting irrigation, or predicting yields, you get precision forecasts you can trust worldwide.
Resolution and update speed matter. EURO1k updates rapidly so you can catch narrow spray windows, adapt harvest timing, and react to frost within the hour. With advanced downscaling we deliver temperature forecasts at 90-meter resolution, capturing field-level microclimates—so you manage the actual, not the average.
Let’s look at the cost of extreme weather and how precise intelligence mitigates risk. In MetX, the first animation shows a severe spring frost on April 22–23, 2024, in eastern France and western Germany. Temperatures dropped sharply overnight, causing catastrophic vineyard losses—nearly €500 million—especially in Burgundy and the Rhineland. Many had no early warning at a useful resolution or lead time to respond.
Fast-forward to May 3, 2025: MetX captures a localized hailstorm outbreak in France that triggered €7 million in agricultural insurance claims and over €200 million in wider losses (vehicles and property). With hyperlocal resolution, MetX shows what happened, where, when, and how fast you need to act.
We’ve seen the challenges and opportunities—and how the right weather intelligence transforms agricultural decisions. To bring this to life, I’ll hand over to Jim and Candice for two customer stories: a large-scale sugar-beet operation improving crop-yield modeling, and how an AgTech solution uses Meteomatics weather intelligence to help farmers make faster, more profitable, and more resilient choices. Jim, Candice—over to you.
Jim Robinson:
Let’s start with Nave Analytics. Dr. Val Kovalsky, your work is unique and exciting. What’s Nave’s “claim to fame”?
Val Kovalsky:
We deliver a new generation of irrigation-support products that work completely sensor-free and can be deployed anywhere in minutes. We provide cost-effective irrigation decision data so farmers can apply water efficiently for healthy, plentiful crops.
Jim Robinson:
Traditional soil-moisture monitoring relies on physical sensors that are costly and labor-intensive. Your NaveGrow solution is sensor-free. How does it work?
Val Kovalsky:
NaveGrow is our main product for in-season, efficient, sustainable irrigation. We use data assimilation—combining a deterministic hydrological framework with satellite observations in real time—so satellites stand in for sensors. Farmers avoid installing, revisiting, and recalibrating hardware while staying close to reality.
Jim Robinson:
How has integrating Meteomatics weather data improved your precipitation forecasts and services?
Val Kovalsky:
It’s had a major impact. To be “in the game,” farmers must be ahead of it. Meteomatics’ forecasts help anticipate water demand and available storage for crops. That saves significant water and pumping costs and reduces greenhouse-gas emissions, saving money and the planet at the same time.
Jim Robinson:
Let’s talk planting. How does NavePlant assist optimal planting decisions, especially where weather is unpredictable?
Val Kovalsky:
We keep farmers ahead of two main risks. First, water shortage: without sufficient moisture, seeds won’t germinate. NavePlant shows how much water is available and whether it’s sufficient to start the season. Second, excess moisture: fields can be too wet, risking stuck equipment and costly delays. We quantify this risk so farmers pick the right planting window, get seeds in, and set up the season for the best yields.
Jim Robinson:
Thank you, Val. Let’s move to crop-yield modeling. Candice is standing by with British Sugar—experts in growing sugar beets. Candice?
Candice Thompson:
Thanks, Jim. British Sugar has been a key agricultural player for over a century. Today I’m joined by Alec McNulty and Tom Dale, who are helping drive a digital transformation. Alec, tell us how British Sugar is moving into digital.
Alec McNulty:
I work in crop forecasting. We estimate likely sugar production early in the season to support better business decisions. Before Meteomatics, we worked at a broad regional scale (East Anglia or even all of England) without specific weather information or usable totals and averages. We couldn’t get to farm or field level.
Candice Thompson:
What challenges did you face before using Meteomatics weather data?
Alec McNulty:
Given the UK’s variability, we needed local-level understanding of temperature, rainfall, and sunshine. With detailed local data, we can build to farm, local, and field-level views—something we couldn’t do before.
Candice Thompson:
Has Meteomatics enabled better or faster decisions?
Alec McNulty:
Yes. With the Weather API we get near-real-time data daily instead of monthly summaries. That’s made a big difference for in-month crop forecasting, especially at key times of year. We see impacts immediately rather than waiting for month-end.
Candice Thompson:
You mentioned crop forecasting. Tom, you work closely on crop-yield modeling. How important is accurate weather data?
Tom Dale:
Very important. Yield variability comes from management practices, grower practices, soils, and quality—but a large portion (roughly 40–80%) is weather-driven, according to the literature. No yield model is complete without weather data. With large data sets we can analyze which weather events truly matter and which matter less.
Candice Thompson:
Could you outline your approach and how you use weather data?
Tom Dale:
We predict a future event (yield) based on past events using extensive historical data. We infer from trends and patterns to forecast outcomes. Integrating Meteomatics required some IT setup and testing to ensure data came through correctly. It wasn’t difficult—just careful validation. Our own internal data can be challenging when switching from 2024 to 2025 fields; we’re moving from last year’s fields to current ones, which adds some complexity. Otherwise, setup was straightforward.
Once configured, the API worked smoothly. We can easily change field locations by uploading a file and have it live the next day. The API provides ample parameters and multiple resolutions; we chose a resolution appropriate to our needs.
Granularity is critical. A regional figure might show 50 mm of rainfall, while local pockets in that region had 0 mm or 80 mm. These differences can materially influence yield. High-resolution, local data helps capture those effects.
Historical depth matters, too. Meteomatics provides extensive archives—we have data spanning more than 10 campaigns. With only one season, you can’t see trends. With many, you can apply statistics and machine-learning methods to uncover underlying relationships between weather and yield. With that, when real-time weather arrives during the current campaign, we can quantify impacts—for example, the difference between 15 °C and 27 °C—and understand likely outcomes.
Looking forward, this granularity supports planning amid increasing weather volatility. Local-level detail ensures we recognize when specific thresholds are met—signals that can be lost when data is averaged.
Candice Thompson:
You’ve started this digitization journey. How will weather data support broader agricultural decisions beyond yield modeling?
Tom Dale:
Weather drives many outcomes—for instance, mycotoxin risk or optimal harvest timing. With large data sets and machine learning, we can quantify which events occurred, how much each contributed, and how they interacted. Without that level of data, you finish a campaign unsure why yield landed where it did.
Candice Thompson:
Any advice for teams adopting high-resolution weather data?
Tom Dale:
Define what you want from the data: required detail (field-level or more aggregated), delivery method (daily API or summaries), and future-proofing (keep historical data in formats that allow flexibility). Needs will evolve—resolution, area, or use case—so choose a product that lets you adapt. Internally, you might need both an overall company number for beet production and views at factory, local, farm, or field levels. Think ahead about how granular you want to go and how you’ll work with the data as insights change.
Candice Thompson:
Thank you, Alec and Tom, for sharing valuable insights on crop-yield modeling and agricultural decision-making from a major industry player like British Sugar.
