Nematode has potential to reduce cotton yields by 50 percent

Jul 07, 2020

The reniform nematode is one of the most commonly found pests of cotton, with the ability to cause severe economic damage. In order to assess exactly how much damage the reniform nematode can cause, plant pathologists at Auburn University conducted a field trial comparing a clean field to a reniform-infested field.

To get the most accurate data, the plant pathologists began with one field experiencing the same conditions, including soil type and irrigation system. They then split the field in half, leaving a 10-foot grass strip in the center, and inoculated one side with the reniform nematode and left the other half clean. They planted ten cotton varieties on each half. They found that, averaged over two years, the cotton yields were 50 percent lower in the reniform field compared to the clean field.

They also experimented with the nematicide Velum Total and found it to be effective dependent on the environment. The nematicide supported a 55 percent increase in yield in 2017 but only 6 percent in 2018, in part due to the dry spring.

Click here to see more...

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.