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Farmer experiments with protein monitor

In farming, there are late adopters of technology, there are early adopters and then there’s Rick Rutherford.

As an example of his eagerness to try new things, Rutherford was ahead of the curve on yield mapping. He began using yield monitors and producing yield maps more than 25 years ago.

“I’ve got one in the office — 1997 was the first yield map that we generated,” he said.

Now, on his farm northwest of Winnipeg, Rutherford could be one of the first producers in Manitoba to experiment with a protein monitor in his combine.

John Deere offers the HarvestLab 3000 on its S700 Series combines. The sensor can measure the protein levels in wheat and barley on the go. It can also measure oil content in canola.

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Seeing the Whole Season: How Continuous Crop Modeling Is Changing Breeding

Video: Seeing the Whole Season: How Continuous Crop Modeling Is Changing Breeding

Plant breeding has long been shaped by snapshots. A walk through a plot. A single set of notes. A yield check at the end of the season. But crops do not grow in moments. They change every day.

In this conversation, Gary Nijak of AerialPLOT explains how continuous crop modeling is changing the way breeders see, measure, and select plants by capturing growth, stress, and recovery across the entire season, not just at isolated points in time.

Nijak breaks down why point-in-time observations can miss critical performance signals, how repeated, season-long data collection removes the human bottleneck in breeding, and what becomes possible when every plot is treated as a living data set. He also explores how continuous modeling allows breeding programs to move beyond vague descriptors and toward measurable, repeatable insights that connect directly to on-farm outcomes.

This conversation explores:

• What continuous crop modeling is and how it works

• Why traditional field observations fall short over a full growing season

• How scale and repeated measurement change breeding decisions

• What “digital twins” of plots mean for selection and performance

• Why data, not hardware, is driving the next shift in breeding innovation As data-driven breeding moves from research into real-world programs, this discussion offers a clear look at how seeing the whole season is reshaping value for breeders, seed companies, and farmers, and why this may be only the beginning.