Bio-pesticides bloom in greenhouses

Jan 03, 2023

Canada’s floriculture industry is not small.

In 2020, sales of flowers, bedding plants and potted plants was $1.7 billion. Most of those plants are grown inside southern Ontario and British Columbia greenhouses.

Like other Canadian farmers, floriculture producers have problems with insects and disease. But to solve those problems, they don’t rely on traditional pesticides.

“In floriculture, there are a few pests that are resistant against almost every insecticide that’s available in Canada,” said Rose Buitenhuis, program leader for biological crop protection at the Vineland Research Centre in Niagara, Ont.

“The pests are just not killed anymore by the products that the growers apply.”

Some are applying bio-pesticides in rotation with chemicals because it’s become obvious that applying the same pesticide, to the same pest, is not sustainable.

“The greenhouse growers have seen very clearly that the use of pesticides is a short-term solution,” Buitenhuis said.

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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.