Yield Data
A key foundation for effective application maps

Many farmers are already using it — often without knowing it: yield mapping!

Many manufacturers have been offering yield mapping technology for years. But only a few farmers actually use the data. Often, the data is incomplete, inaccurate, or missing for certain fields entirely.
Yet yield maps are valuable indicators for assessing overall performance:

  • Did a field perform similarly to its neighbor compared to last year?
  • Was the yield higher on subfield 1 with variety A or with variety B?
  • Did my fertilizer strip trial have any effect on yield?
  • Did weed infestation reduce yield — and if so, by how much?
  • ...
Many of these questions can be answered more clearly with yield maps. And you don’t need absolute precision — even relative differences (i.e., high vs. low yield zones) can provide valuable insights.

Single-season yield maps, statistically cleaned
Example of a single-year yield map calibrated by weighed harvest data and statistically processed.

A major challenge in practice is calibrating multiple combines. According to manufacturers, this should ideally be done several times a day — but who really has the time for that? Especially during tight harvest windows, no one wants to send grain carts home half full just to weigh bunker loads from each machine.

We’ve developed a tool that allows you to recalibrate yield data from multiple machines after harvest, based on total weighed yields. It also cleans outliers and empty runs. With this cleaned data, you can create multi-year yield maps, which are an ideal foundation for planning fertilizer or seeding maps for the next season.

Based on this data, nitrogen fertilizer can be reduced in low-performing zones or redistributed to higher-performing areas — helping to reduce total usage or optimize effectiveness.

Multi-year yield map for fertilizer planning
Example of a multi-year yield map. These are ideal for creating future application maps. In our experience, 3–4 years of data are enough to reveal strong patterns in expected harvest performance — allowing for easier and more precise nutrient planning.