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Researchers attempt to turn weed into a crop

Early adopters might want to take a little time to explain to the neighbours why their entire field is covered by what looks like, and from a genetic standpoint, is mostly stinkweed.

What may resemble the worst weed infestation ever may be just a new cover crop and oilseed called domestic pennycress, or formally, Thlaspi arvense.

John Sedbrook is a professor of genetics at Illinois State University and one of the researchers working to turn this weed into a crop. In some ways it echoes the development of its plant relative, canola.

Sedbrook and his colleagues have been using plant-breeding tools such as CRISPR gene editing to modify pennycress.

“We’ve gotten to the point where this crop can be economical,” he said.

Like canola’s rapeseed ancestors, pennycress suffers from two problems: high levels of antinutritional erucic acid in the oil and high levels of glucosinalates, particularly one called sinigrin, in the meal. If you’ve experienced the sinus-clearing effects of a good horseradish sauce, you are familiar with sinigrin.

This limits pennycress’s value as animal feed. Raw glucosinalate-containing plants contain enzymes that break them down into toxic products in the body. While heating deactivates this enzyme, the glucosinalates can add an off taste to products such as milk, for example, if dairy cattle are fed rapeseed meal.

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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.