Farms.com Home   News

USDA Expects Upward Revision in Canadian Canola Production Estimate

A USDA official said he expects the latest 2022 Canadian canola production estimate to be revised higher, up to around 19 million tonnes. 

Aaron Mulhollen, USDA crop assessment specialist for Canada and the Caribbean, said the US government considers the Statistics Canada canola production estimate of 18.17 million to be preliminary rather than final. 

“We expect an increase to the current StatCan estimate will occur in the coming year, and, if average, that increase will result in a final production estimate for the 2022 season to be near 19 million tonnes,” Mulhollen wrote in an email. 

In its last crop production report for the 2022 growing season earlier this month, StatsCan surprised traders and analysts by lowering its canola production forecast by roughly 900,000 from its model-based projection in September. In its subsequent monthly supply-demand update, the USDA failed to follow StatsCan’s lead and trimmed its own estimate of the Canadian canola crop by 500,000 tonnes from November to 19 million. 

Click here to see more...

Trending Video

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.