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Canadian Wheat, Canola Production Drops on Dry Conditions

Canadian farmers produced less wheat, canola, barley and oats in 2023 due to lower yields in Western Canada where growing conditions were generally drier than in 2022, the production of principal field crops for November 2023 from Statistics Canada said.

“Throughout the growing season in 2023, dry conditions across much of Western Canada contributed to lower yields compared with 2022. Warm and dry conditions allowed farm operators to complete harvests in a timely manner across most of the prairies,” a news release on the report said.

Canadian wheat production fell by 6.9 per cent to 32 million tonnes with lower yields offsetting the higher harvested area, the report said. In Alberta, wheat yields decreased by 19.6 per cent to 44.7 bushels per acre due to dry conditions in parts of the province. Harvested area rose by 2.4 per cent to 7.7 million acres, resulting in a 17.8 per cent decrease in wheat production to 9.3 million tonnes.

Nationally canola production decreased by two per cent to 18.3 million tonnes driven by lower yields. Harvested area rose three per cent to 21.9 million acres. The report noted in Alberta, canola production fell 3.5 per cent to 5.4 million tonnes. The decrease was attributable to lower harvested area while yields edged down 0.5 per cent to 37.9 bushels per acre.

Canadian barley production fell 10.9 per cent to 8.9 million tonnes. Total oat production decreased by 49.6 per cent nationally to 2.6 million tonnes in 2023 — the lowest production in more than a decade.

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Seeing the Whole Season: How Continuous Crop Modeling Is Changing Breeding

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.