For Fields With High Yields

Mar 13, 2013

Arkansas soybean yields are higher than the national average, but University of Arkansas doctoral student Ryan Van Roekel has research that shows farmers can do even better. Last year, Van Roekel achieved 115-bushel-per-acre yields in a test plot.

The Arkansas Soybean Promotion Board (ASPB) funded Van Roekel’s project to help farmers in that state achieve maximum yields.

“When we fund projects like Ryan’s, we’re investing in research that will put money back into the pockets of Arkansas farmers,” says Shannon Davis, chairman of ASPB, the soy checkoff board in Arkansas. “Our research investments are critical to the success of farmers and we’re committed to that investment every year.”

Van Roekel cautions against expecting 115-bushel yields, but he believes many farmers can use these five tips to boost their yields.

Plant Early – In Van Roekel’s test plots, soybeans planted in the first half of April had a better chance of success because podset will more likely correspond with the longest days of summer. Bonus: this method costs nothing extra.

Choose the Right Soybean Varieties – Several criteria will lead farmers to water regarding variety selection. Be certain to consider each variety’s traits with regard to the growing environment of your beans.

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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.