Updated Saskatchewan Yield Estimates Mixed

Oct 06, 2022

Oats, canola, and lentils were among those crops seeing upward yield revisions in the latest weekly Saskatchewan crop report, while barley and mustard saw declines.

Updated yield estimates from the province on Thursday pegged this year’s average Saskatchewan canola yield at 35 bu/acre, up 1 bu from the early September estimate although still below the current Statistics Canada projection of 37.8 bu. The average oat yield was bumped up 9 bu from September to 89 bu, still well off the StatsCan estimate of 96 bu, while the average lentil yield was increased to 1,204 lbs/acre from 1,174 lbs, but below the StatsCan estimate of 1,362 lbs.

On the other hand, the average Saskatchewan barley yield was trimmed 2 bu from early September to 62 bu/acre (versus 64.1 bu for StatsCan), while mustard was dropped to just 821 lbs/acre from 1,102 lbs but closer to StatsCan’s 713 lbs.

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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.