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Manitoba Agriculture Crop Report

Oct 17, 2017

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Seeding progress was slower than normal in April, but in May progress was very quick with 95% of the main field crops seeded by the fourth week of May. The 5 year average to achieve 95% seeded is the first week of June.

Dry conditions persisted throughout most of the province until mid-September. Crop yields for spring cereals and canola are better than average in many areas; however, lower yields were reported for soybeans due to dry conditions during pod filling. Dry weather conditions and low disease pressure resulted in good crop quality.

Harvest in Manitoba is nearing completion. Harvest of cereal crops, field peas, and canola is essentially complete. Soybean and flax harvest is close to complete, grain corn and sunflower harvest is ongoing.

Germination and stand establishment of winter cereal crops is good; seeded acres are down across the province.

Fall field work including tillage, soil testing, post-harvest weed control, and fertilizer applications of anhydrous ammonia is on-going.

Source : Manitoba Agriculture

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.