Farms.com Home   Ag Industry News

Managing White Mold in Soybean Fields

Managing White Mold in Soybean Fields
Feb 06, 2026
By Farms.com

Research Outlines Spacing, Seeding, and Fungicide Strategies

Research conducted by North Dakota State University, White mold remains a major disease concern for soybean growers, particularly during seasons with cool temperatures and frequent moisture. Weather conditions that favor extended canopy moisture increase the likelihood of disease development, not only in soybeans but also in crops such as dry beans and sunflowers.

Long-term research offers practical guidance to help producers manage disease risk while maintaining yield and grain quality.

Planting row spacing is one of the most effective tools for reducing white mold pressure. Wider row spacing allows more air movement and light penetration, which lowers humidity within the crop canopy.

However, research showed that wide rows only provide a yield advantage when disease pressure is extremely high. In most situations, intermediate row spacing of approximately 21 to 22.5 inches delivers the best balance between disease suppression and yield potential.

Seeding rates also play a critical role in disease management. Higher plant populations lead to denser canopies that favor white mold development. Research indicated that reducing seeding rates can significantly lower disease levels.

Planting roughly 100,000 viable seeds per acre is most effective when white mold symptoms appear at least 15 percent of the canopy later in the growing season.

Soybean variety selection further influences disease risk. Varieties with longer maturity periods tend to face greater exposure to favorable white mold conditions.

Selecting varieties with appropriate maturity groups and improved disease tolerance can reduce overall risk. Fields planted with longer-maturing varieties are more likely to benefit from fungicide applications.

Proper fungicide timing is essential for effective disease control. When environmental conditions favor white mold during early flowering, a single fungicide application is most effective when nearly all plants reach the R2 growth stage or when the canopy begins to close.

In fields with higher disease risk, two fungicide applications provide stronger protection. The first spray should be applied early in bloom, followed by a second application timed according to the variety’s maturity.

Spray coverage also affects fungicide performance. Smaller droplets provide better coverage when the canopy is open, while medium to coarse droplets improve penetration as the canopy closes. Canopy closure can be assessed visually or with mobile applications that assist with spray decisions.

By applying these research-backed strategies, soybean producers can reduce white mold losses, protect grain quality, and improve long-term farm profitability through smarter planting and disease management decisions.

Photo Credit: istock-ds70


Trending Video

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.