For decades, plant breeding has relied on moments: a walk through a plot, a snapshot in time, a yield measurement at the end of the season. But crops don’t grow in moments. They respond, adapt, and change every day of the season.
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.
