By Carolyn Bernhardt
June 15, 2026
For roughly 25 years, one tiny, efficient adversary has threatened to topple Minnesota’s major soybean industry with its insatiable appetite: the soybean aphid. According to the US Department of Agriculture, the United States produces over a quarter of the world’s soybeans. And the American Soybean Association ranked Minnesota third in US soybean production in 2023, behind Illinois and Iowa.
Experts across the Midwest have gathered crucial information about the soybean aphid’s life cycle and biology over the years, driving advances such as management tactics that help producers limit pesticide use. Still, one team of MITPPC experts has noted a crucial missing tool essential to managing soybean aphids: a systematic effort to collect, analyze, and report estimates of yield losses. Without knowing how often aphid outbreaks occur, where losses are greatest, and how much damage farmers absorb across the landscape, it is difficult to decide where pest-management investments will have the greatest impact.
So now, Philip Pardey, PhD, and Yuan Chai, PhD, from the Department of Applied Economics and GEMS Informatics, have teamed up with Robert Koch, PhD, a professor in the Department of Entomology, to lead a team of experts developing new approaches to more effectively quantify the long-term economic and geographic impacts of soybean aphid damage in Minnesota and beyond.
Moving past worst-case thinking
A major reason for this research is the growing problem of pesticide resistance. Soybean aphids are mainly controlled with insecticides, but studies over the past decade show they are becoming more resistant to commonly used chemicals. As these pest control tools lose effectiveness, understanding the true scale and timing of future outbreaks becomes even more important.
However, current approaches to studying aphid outbreaks often focus on worst-case scenarios of crop loss rather than on how damage actually unfolds over time and place. Rather than asking how much damage soybean aphids can cause under the worst conditions, the team asks how often different levels of damage occur across years and locations. “Relying on worst-case, maximal loss framings is of limited use for calibrating how much money to invest in crop loss avoidance research,” says Pardey.
This approach, he says, overlooks key real-world patterns and factors, such as where and when aphid outbreaks are likely to happen, how frequent and how severe they are over time, and the fact that today’s pest-management tools may stop working as well in the future.
After all, maximal-loss scenarios occur unevenly across locations and years.
This team’s approach takes a different tack. Designed to reflect what farmers actually experience in the field, the system tracks how pest risk changes over time and across locations. And it can work in areas with limited data, helping researchers better understand how common, widespread, and severe soybean aphid damage is across entire regions.
Fine-tuned data for a clearer view
MITPPC funding for this effort began in 2024, and as of mid-2026 the team is preparing its first manuscript for publication. However, a key preliminary finding is that despite Minnesota farmers’ mitigation efforts, they still suffer considerable losses in potential soybean production due to aphid damage. “Nonetheless, these losses—at scale over time—fall well below previous claims that soybean aphids can cause up to 40% yield loss,” says Chai.
Because few researchers had studied this issue before, the team has not had access to reliable, consistent long-term data on where and when soybean aphid outbreaks occurred. To address this problem the team worked with colleagues within the University of Minnesota, as well as at North Dakota State University and the Wisconsin Department of Agriculture, Trade and Consumer Protection, to compile an extensive dataset on soybean aphid abundance in soybean fields across Minnesota, North Dakota and Wisconsin for nine years.
To strengthen the dataset, the team uses a predictive modeling approach called Random Forest. The model combines aphid observations with historical weather data to estimate pest risk in areas where no field data existed previously, creating a more complete picture across the landscape.
Another challenge, the team says, has been combining data from many field studies conducted under different environmental conditions. Here again, the experts have built a model. This one can separate environmental effects, such as weather or location, from the actual impact of soybean aphids, leading to more accurate estimates of crop damage and risk.
From reactive to proactive
This project was inspired in part by previous research led by Chai and Pardey, which showed that using probability-based models—which account for how often pest outbreaks occur, when and where they occur, and the variable losses associated with them—provides a more realistic picture of pest-related crop losses. Taken together with this recent study, a new, twofold way of thinking about pest control emerges.
Firstly, instead of treating pest-control tools as something that directly increases crop yields, experts can treat them as tools that prevent pest-induced yield losses. And secondly, the framework now accounts for the fact that tools don’t stay effective forever, as pests adapt or technologies become outdated. As a result, Chai says the framework helps decision-makers anticipate how much spending on new pest-management tools is likely to pay off.
The researchers are also trying to predict future risks and determine how to prioritize new pest-management research before problems become severe. “This includes exploring how future variables like climate change, pesticide resistance, and regulatory restrictions might degrade the effectiveness of current control technologies,” Pardey says.
The approach isn’t exclusive to soybean aphids—it can apply to different crop pests and diseases. “We use soybean aphids as an informative case study of a more extensive problem set,” says Chai.
By moving away from planning only for worst-case scenarios and toward an approach that addresses the many variables that affect pest occurrence and yields, scientists, policy makers, and farmers gain use of a forward-looking tool. This research lays the foundation for smarter, more strategic investments in pest-management research and technologies in the decades ahead.
More information
- Linking soybean aphid losses to technology investment decisions, research project
- Innovation in assessing soybean aphid risk - GEMS Informatics Blog
- Soybean aphid pest information - UMN Extension