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U.S. Soybean Crushings Slip in November

The National Oilseed Processor Association (NOPA) issued November soybean crush and stocks data on Monday, December 16. According to the data, NOPA member soybean processing in the second month of the 2019/20 product (October-September) marketing year totaled 4.488 million tonnes. This was off 0.285 million tonnes from October and compares to last November’s crushings of 4.544 million tonnes.
 
 
According to analysts’ expectations published by Reuters, the trade was looking for crush to come in near 4.681 million tonnes with the lowest published guess slightly above the actual crushings at 4.589 million tonnes. Again, this month, there was a wide range of guessing from polled analysts, with some respondents suggesting that crush could come in as high as 4.845 million tonnes. It is not uncommon for crushings to slip from October to November, given the lower number of calendar days, but there were some ideas in the marketplace that crushings would rise as harvest advanced and brought new-crop soybeans into crushers’ supply chains. Instead, some analysts believed that low farm prices and depressed margins were factors that limited crushings for the month.
 
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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.