Hail Damage To Soybean

Aug 06, 2014

Laura Lindsey, Grace Bluck, Harold Watters, CPAg/CCA, Mark Badertscher

On July 27, Hardin County experienced hail that damaged soybean at the R3 growth stage (beginning pod). The R1 (beginning bloom) to R5 (beginning seed fill) stages are the most sensitive to defoliation. At 50% defoliation when soybeans are at the R3 growth stage, we expect a 9-18% reduction in yield. (See table for expected yield losses due to defoliation at several growth stages.)

Last year, we conducted a hail simulation trial at the Western Agricultural Research Station in South Charleston. Hail was simulated (via weed whacker) on July 18, 2013 when soybeans were at the R3 growth stage. Defoliation was 40-45%. Soybean yield was 74 bu/ac without hail simulation and 62 bu/ac with hail simulation. (Yes, this was a statistically significant yield reduction.)

Despite cool temperatures this year, soybeans are adding a 1-2 new trifoliates per week which should help the plant recover. In last year’s trial, there was very limited evidence of defoliation 12 days after the simulated hail.

Soybean Hail Damage Study (poster)

Source : osu.edu

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.