Discovering New Opportunities For Conventional Soybeans In Manitoba

Aug 14, 2018

Sevita International hopes to increase production of non-GMO, or conventional, soybeans in Manitoba.

John Van Herk said the company, in conjunction with Delmar Commodities, is in the early stages of discovering new opportunities for conventional soybeans in Manitoba. Van Herk said the hope is to ultimately offer I.P. contracts with an associated premium for the production of conventional soybeans.

He explained that essentially the genetics of non-GMO soybeans are similar to the trader products that have herbicide tolerance, noting they're just a continuation of the original soybean.

"The agronomy is fairly similar. The big difference generally speaking is the weed control issues, that's where it gets a little bit tricker because there's a few less products available and we're relying on some of the older chemistries to bear the brunt of the weed control programs."

Van Herk added Sevita's primary destinations for the contracted product include the Pacific Rim countries where soybeans are a staple in most diets.

"Most of the work for Sevita, the products are going for tofu, miso and soya sauce manufacturing. We're also doing some work in natto soybeans...those are into primarily food-use. Some of them would make it into other venues like soybean flour but our primary interest is tofu and miso."

Source : Steinbachonline

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.