Use an Integrated Disease Management Plan to Control Blackleg in Canola

Dec 16, 2022

For canola growers, blackleg can cause yield and quality losses, impact profitability and may create market risk.To help protect your investment and manage this disease, Keep it Clean encourages growers to employ an integrated blackleg management strategy. This includes growing resistant varieties, rotating crops and pre-harvest scouting for the disease.

It Starts with Seed Selection

Selecting blackleg-resistant canola seed is an essential part of an integrated blackleg management strategy.Only plant varieties that are rated R (resistant) or MR (moderately resistant) to blackleg and rotate varieties to bring a mix of blackleg resistance genes and sources to the field over time. Use a blackleg race identification test to determine predominant races in the field and help match appropriate major gene resistance.Consider fungicide options: a fungicide seed treatment is available for many canola varieties to protect plants when they are most susceptible; an early season foliar fungicide application can help to prevent yield losses in higher risk situations.During non-canola years, control volunteer canola and other Brassica weeds (e.g. stinkweed, shepherd’s purse, wild mustard and flixweed) to prevent build up of the blackleg pathogen in the field.Scout canola fields regularly for blackleg symptoms and incidence to help determine the effectiveness of your blackleg management plan.

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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.