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Argentina Rains Help Pump Up Soybean, Corn Crop Forecasts

Abundant rains in recent weeks in Argentina have lifted forecasts for soybean and corn harvests, according to a revised outlook by the Rosario grains exchange, a major boost for the grains producer amid a global food price rally.

The exchange raised its forecast for the 2020/21 soybean harvest to 49 million tonnes, versus 47 million previously, and for corn to 48.5 million tonnes from 46 million earlier, when dry conditions had raised concerns over crops.

The exchange said in the monthly report sent to Reuters early on Thursday that the rains in recent weeks had been a "turning point" in the season and banished any fears about a repeat of the 2018 drought that caused heavy crop losses.

"The specter of the 2018 productive disaster remains behind us," the exchange said in the report.

Argentina is the world's top exporter of soybean oil and meal, and the third largest of corn, but lower rainfall in recent months due to a moderate La Niña weather phenomenon had caused uncertainty over harvests from the country.

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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.