Objectives for better beetle management

Apr 04, 2023

Flea beetles are the pest of greatest economic risk to canola production, according to a 2022 Canola Council of Canada survey of canola growers.

With more striped species, which emerge earlier in the spring and seem more tolerant of common seed treatments, and with spring weather conditions that challenge rapid crop emergence, flea beetle damage seems worse than ever.

Objective A: Rapid canola emergence

The ideal flea beetle buster is a canola crop that establishes quickly with five to eight plants per square foot. More plants mean more food for the flea beetles, which limits the damage per plant.

Scenarios that require multiple in-season foliar sprays are often the result of a slow-establishing, non-competitive crop. Many factors can cause this, including moisture, temperature, plant populations, seed treatment and overall flea beetle numbers.

Management steps to reduce the risk include:

Seed shallow into warm, moist soil. Consider seeding cereals first as they can tolerate cooler spring soils. Seed canola after soils have warmed up and ideally just before or after a spring rain.

Use an advanced seed treatment to improve flea beetle protection in high-risk areas. These include Buteo Start, Lumiderm, Fortenza and Fortenza Advanced.

Use safe rates of seed-placed fertilizer. The recommendation is to use only phosphorus in the seed row at rates of 20 lb./ac. of actual phosphate. Higher rates of seed-placed fertilizer can add more stress, slow the pace of growth and reduce the stand.

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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.