How Do Snow And Winter Affect Canola Quality?

Nov 11, 2016

We don’t have a lot of data showing what happens to yield and quality for canola that overwinters in the field. Canola harvested in the spring can have lower weights, lower oil content, higher free fatty acids in the oil and more animal excrement in the harvested sample, which could pose significant challenges from a feed safety perspective. Because the degree of yield or quality degradation is difficult to predict and likely depends on conditions, oilseed processors will assess the physical and intrinsic quality attributes and make a decision as to whether to accept spring-threshed canola at that time.

Quality and yield loss can also occur throughout the fall if crops have cured and harvest is delayed due to moisture. We encourage producers to harvest their canola this fall if at all possible and remind them to monitor and manage bins containing crops that have been harvested with elevated grain moisture.

To learn more about how snow delays or overwintering can affect canola quality, the Canadian Grain Commission (CGC) encourages growers to send in samples from canola harvested late in 2016 and canola harvested next spring. Growers interested in getting information on the quality of their canola crop can contact Dr. Veronique Barthet at veronqiue.barthet@grainscanada.gc.ca or Twylla McKendry at Twylla.mcKendry@grainscanada.g.ca to receive a harvest survey envelope and a consent card.

Source : Albertacanola

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.