Most field data starts life as points. A yield monitor logs a reading every few feet. A soil probe samples one spot per acre. A scout counts clusters at a handful of vines per row. Each point is real, but the spaces between the points are where most of your farm actually is.
Magic Map fills in those spaces. It takes any point dataset in Every.Farm and produces a smooth, continuous map that estimates values everywhere in the field — not just where you happened to stand. One button, no GIS degree required.
What it actually does
When you run Magic Map, five things happen behind the scenes:
It draws a boundary. If you choose blocks, the boundary is your block edges — including fields made of several blocks. If not, it wraps a snug outline around your data points automatically.
It trims to your blocks (optional). Points outside the boundary — the row-end turnarounds, the reading taken from the truck — get left out.
It cleans up outliers (optional). Sensors glitch. A yield monitor that briefly reads zero in the middle of a healthy block will drag your map down with it. Magic Map can detect and drop statistical outliers before they distort the picture, and it tells you how many points it removed.
It builds a grid. The field is divided into a regular grid (3-meter squares by default). Every Magic Map on the platform uses the same universal grid system, so maps made from different datasets line up with each other square-for-square — which is what makes later comparisons and joins possible.
It estimates a value for every square. Each grid square looks at the sample points nearest to it and computes a distance-weighted average — nearby points count more, distant ones less. This is a standard technique called inverse-distance weighting (IDW).
The result is saved as a brand-new dataset on your farm. Your original point data is never modified.
Using it, step by step
- Open a point dataset — yield data, soil samples, a data collector, anything with points and a numeric column.
- Click Magic Map.
- Pick the header you want to map (the numeric column — yield, pH, cluster count…).
- Optionally select blocks. Click the blocks that make up the field to clip the map to their boundaries. Multiple blocks are fine — they're treated as one field.
- Optionally adjust settings. Grid resolution, outlier removal, and how the result gets colored into zones. The defaults are sensible; you can ignore all of this on a first run.
- Run it. Processing takes a moment — bigger datasets take longer. When it finishes, the new interpolated dataset appears in your datasets drawer, already colored.
From there it's a normal dataset: recolor it, filter it, download it, or layer it under another dataset for comparison.
Tips from the field
Resolution is a tradeoff. A 3 m grid gives you fine detail; a 10 m grid processes faster and produces a much smaller dataset. For a quick whole-farm look, coarser is often plenty. For variable-rate work, stay fine.
Trust the outlier filter. It's conservative — it only removes points that are statistically far outside the rest of your data, and it reports exactly how many it dropped. If your map has a suspicious bullseye in it, re-run with outlier removal on.
More points beat more smoothing. Interpolation estimates between samples — it can't invent information you didn't collect. Twenty well-spread soil samples will always make a better map than five, no matter the settings.
Multi-block fields just work. Select all the blocks that make up the field and Magic Map treats them as one boundary — points are kept inside any of them, and the grid covers them all.
Line it up with satellite data. Because Magic Maps share the universal grid, an interpolated yield map and an NDVI dataset from the satellite browser can be compared square-by-square. That's where the interesting questions start: where is vigor high but yield low?
When not to use it
Magic Map is for values that vary continuously across ground — yield, moisture, nutrients, vigor, counts. It's not meant for categorical data (variety names, pass/fail scouting notes) — interpolating between "Concord" and "Niagara" doesn't mean anything. For those, color your points by category instead.
That's it. Collect points, press the button, get the whole picture.
Want to try this on your own farm? Open Every.Farm — it's free while we're in research-platform mode.