Soybean Cyst Nematode Management And Sampling

Aug 17, 2022

By Marisol Quintanilla

The soybean cyst nematode (SCN) causes more yield loss than any other soybean disease in North America. In Michigan alone, SCN causes more than $40 million of economic losses per year.

There are multiple tactics for managing SCN

Because of the significant damage that SCN causes, effective solutions are essential. There are several management practices, with some practices more effective than others:

Test soil and know SCN numbers and SCN type (ability to reproduce on a source of resistance).
Have good sanitation practices (prevent soil movement) if a field does not have SCN (prevention).
Rotate with non-host crops such as corn, wheat, and most non-leguminous crops.
Rotate sources of resistance (i.e., Peking and PI 88788).
Promote soil health with inputs such as manures applied during years of non-host rotations (i.e., corn or wheat), non-host cover crops.
Consider using seed treatments.

Soil testing tips

Farmers in soybean producing areas of Michigan can receive free SCN?diagnosis thanks to farmer checkoff funds from the Michigan Soybean Committee.

Use a cylindrical soil probe to collect samples.
Collect samples at a depth of 6-8 inches deep, making sure to sample in the row.
Collect 20 samples in a zigzag pattern. Place the samples in a plastic bucket and mix thoroughly for a composite sample. Research shows that 20 well-taken samples per composite will result in more repeatable test results compared to samples with five to 10 samplings per composite.
Sample fields according to soil texture zones.
Take the composite soil sample and place about 1 quart of it in a plastic bag. Store in a cool area away from sunlight until shipment can be made to the laboratory.

The Michigan Soybean Committee completely covers the costs of laboratory analyses for soybean cyst nematode monitoring through Michigan State University Plant & Pest Diagnostics. Please use the form when you submit your sample. You can also see instructions on how to take and submit soil or root samples.

In addition, you can request for an evaluation of your SCN type since many SCN populations overcame the resistance found in commonly used PI 88788 varieties. Evaluation of SCN type can be requested in the form you will submit with your sample.

Crop rotation

What to know about rotating different sources of resistance:

It is most effective to rotate sources of resistance (i.e., Peking and PI88788).

What to know about rotating to non-host crops:

It is important to rotate with non-host crops such as corn and wheat.? Most non-leguminous crops are non-host.?Do not rotate with dry beans or other legumes.
The first year of non-host rotation is the most effective in reducing SCN.?It has been observed that farms that have both corn and wheat (compared with corn alone) in the rotation have lower incidence and numbers of SCN.

Nematode-protectant seed treatments

If they are used, they need to be used in conjunction with other management strategies (i.e., non-host crops rotation, and rotation of sources of SCN resistance).
They are a tool but based on literature reviews and trials, they generally do not reduce numbers enough to use as a stand-alone management practice. Rotation with a non-host crop and rotating sources of resistance are both more effective management strategies, so a combination of these tools is important.

Source : msu.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.