Adviser: A Long Awaited Digital Boost for Smarter Forage Production

In northernmost Europe, forage production is the dominant use of agricultural land, and the economic importance of grassland is enormous.
“It’s easy to overlook just how crucial grasslands really are. It’s not just grass. It is the most important crop we have. It forms the basis of all feed production, whether you’re producing milk or meat,” says Benjamin Bollhöner of the Rural Economy and Agricultural Society in Norrbotten–Västerbotten.

Despite short summers, the long, bright summer nights and abundant water resources make the northern regions exceptionally suitable for grass production. Grass grows almost around the clock, allowing farmers to harvest three cuts per season.

But the farmers do not have an are decision-support tools tailored to help determine the optimal harvest time. Harvesting too early or too late can significantly reduce both yield and feed quality—losses that quickly become expensive.

A need for better forecasting tools

Today’s main decision-support system, Vallprognos.se, works by summing daily mean temperatures from weather stations and graphing when the optimal first‑cut harvest window closes. It works reasonably well for the first cut—though accuracy declines if a field is far from a weather station.

For the second and third harvests, the situation becomes more complex: each field begins to regrow at different times, and temperature alone is no longer the key factor determining growth and feed quality. As a result, these later cuts currently lack reliable forecasting support.

Using satellites to predict yield

This is exactly where CyberGrass 2.0 comes in. The central goal of the project is to develop a functional tool driven by satellite imagery to predict forage yield, feed quality, and the optimal harvest time.

“That means it could be used not only for the first cut, but also for the second—and possibly even the third cut, which today has no forecasting support at all,” says Benjamin Bollhöner.

He highlights an additional benefit: the tool aims to predict both the amount of feed a farmer will harvest and its nutritional composition. This means farmers can better estimate whether their feed supply will be sufficient for the coming year—or if they need to chase an extra cut.

“With this tool, the idea is that on the day you’re out harvesting, you’ll also receive a prediction of what will end up in your bales or silage pit,” says Benjamin Bollhöner.

The EU-funded research project Cybergrass 2.0 is led by SLU. In the project, the partners include, in addition to Rural Economy and Agricultural Society from Sweden, Natural Resources Institute Finland, Finnish Geospatial Research Institute, as well as the advisor Icelandic Agricultural Advisory Center and the digital company Svarmi from Finland.