Regional Pest Monitoring as Agricultural Information Infrastructure: Evidence from Two Decades of Suction Trap Network Data

by Hyewon Lee, Doris M. Lagos-Kutz, and David A. Hennessy

That a red sunset precedes good weather ahead has been common knowledge in much of the world for millennia. Consistent with the earth’s rotation, weather systems tend to move from west to east so sunset provides a picture of the future. Systematic forecasting built on that intuition, and as observations were gathered across space, processed centrally, and shared broadly, created an effective weather forecasting system that became an important asset for a country’s crop sector. The US National Weather Service draws on publicly owned stations, volunteer associations, satellite operators, and private weather stations, and its outputs serve many users and purposes, from short-run planning to the study of climate change.

Weather forecasting is publicly provided in large part because the information it produces is a public good, a feature it shares with other monitoring information. Such information can be used by many people at once, use by one does not reduce its availability to others, and excluding any user is difficult. While sampling, identification, data management, and reporting can be costly, once the information is collected and released the cost of serving another user is close to zero. Public institutions can be well-suited to systematic data collection because they often have a physical presence throughout their jurisdiction with communication systems already in place, and may have their own reasons to collect the information. When users are many and their needs disparate, the resulting transaction costs can also deter private provision.

Weather is not the only hazard nature poses for agriculture. As crops and livestock are produced in open, living systems, agricultural production is exposed to biological pests such as insects and pathogens that can move across farms, counties, and states before losses are visible in any one field, orchard, or herd. Soybean rust, which reached the United States in 2004, and citrus greening, confirmed in Florida in 2005 along with its vector the Asian citrus psyllid, are crop pest examples. The coordinated framework built to monitor soybean rust after its arrival has been credited with substantial value to US soybean producers (Roberts et al. 2006). The recent confirmation of New World screwworm in Texas in June 2026 is a livestock pest example.¹ The spatial movement of such hazards is now tracked through public-private partnerships, often involving industry and commodity groups with a stake in the outcome. These outbreaks share the same broad features: biological risks tend to move fast and are a threat well beyond the place where they first appear. Consequently, managing them requires a systematic, coordinated response, and timely information is essential.

Mobile pests are constrained by geography and biology, but not by the field, county, or state lines that delineate private actions and policy responses. Detection in one location can help nearby farms update expectations, help advisers interpret regional movement, and help public agencies judge whether an unusual season is emerging. The relevant information set is larger than any single farm. Regional monitoring also offers economies of scope, because one collection protocol, identification system, and historical archive can generate information on many species at once. For these reasons, US agriculture has built shared monitoring systems for crop insects, crop pathogens, invasive plant pests, and livestock pests. Table 1 gives several illustrative, but not exhaustive, examples. They differ in both the pest targeted and the monitoring function served.

The focus here is the Suction Trap Network (STN), a regional monitoring system for flying insects in crop landscapes. The network emerged in Illinois in 2001 and expanded across the Midwest in 2005, with early efforts tied closely to soybean aphid, an invasive pest that had become a major concern for soybean production. Most traps sit on university research farms, with a few on private farmland. The network draws support from universities, commodity groups, and regional Integrated Pest Management (IPM) programs. The STN now records many insect taxa and contributes to invasive-species monitoring as well. Iowa’s record offers a focused look at what such infrastructure has produced over two decades: seasonal timing, long-run baselines, and pest movement within a heavily managed crop landscape.

Suction Trap Network (STN) in Iowa

We use STN observations from 2005 to 2025 in Iowa for nine aphid species included in the curated insecticide-sensitive aphid sample. Suction traps catch winged, dispersing aphids, so the record reflects regional movement and flight activity rather than aphid density on any one plant, and it does not replace field scouting. Our analysis uses catch per trap-week, calculated as each year’s total catch divided by its number of trap-by-date samples, to adjust for changes in sampling effort across years. Iowa has operated five traps over the 21 sampled years, in Boone, Floyd, Hancock, Lucas, and O’Brien Counties.

The species considered are bird cherry-oat aphid, corn leaf aphid, English grain aphid, greenbug, green peach aphid, potato aphid, rice root aphid, soybean aphid, and spotted alfalfa aphid. Pest relevance varies across these species and crops, so the sample is best interpreted as a common STN-based record of insecticide-sensitive aphid movement through Iowa’s agricultural landscape.

The monitored aphids differ in commercial relevance and management. Soybean aphid is the main soybean pest among the aphids in this list, managed through scouting, economic thresholds, conservation of natural enemies, and foliar insecticides when thresholds are exceeded. Corn leaf aphid colonizes corn and is the clearest corn-associated aphid in the sample, but economic injury in Iowa field corn is context dependent. Bird cherry-oat aphid, English grain aphid, greenbug, and rice root aphid are associated with grass, cereal, and corn-related host systems, where they can cause feeding injury, plant stress, or virus transmission depending on crop and timing. Green peach aphid, potato aphid, and spotted alfalfa aphid are not major direct pests of Iowa field corn or soybeans, but are tied to other crops or host systems. Recording these distinct signals on one platform produces a repeated, multi-species record across the sampled years.

What two decades of Iowa monitoring show

The Iowa STN record illustrates three outputs of regional pest-information infrastructure: seasonal timing, long-run abundance baselines, and interpretation within a managed crop landscape. Figure 1 speaks to the first by comparing the within-season timing of Iowa aphid captures between 2005–2015 and 2016–2025. For each species, two horizontal barlines span the mean annual 10th to 90th percentile capture dates, and the dot marks the mean annual median date. Sustained monitoring of this kind can describe both whether a species is present and whether its seasonal activity window is shifting.

Figure 1 shows mixed changes over time and across species in the capture barlines. Several aphids shift modestly earlier in the later period, most visibly greenbug and spotted alfalfa aphid, while others show little change or have shifted differently across the early, median, and late portions of the annual STN catch. Timing matters for crop management because scouting, natural enemy activity, and insecticide decisions all depend on when pests are active.

Figure 2, which has its vertical axis on a log scale, turns to the second output, long-run abundance, focusing on six aphids with stronger annual coverage and clearer relevance to the corn-soybean landscape. Over 2005 to 2025, annual catch per trap-week declined for soybean aphid, corn leaf aphid, bird cherry-oat aphid, and rice root aphid, with the largest fitted decrease for soybean aphid and more gradual declines for bird cherry-oat aphid and rice root aphid. English grain aphid and greenbug showed no clear long-run decline.

The fitted trends summarize broad changes over the full record rather than a smooth year-to-year process. The mixed pattern across species is itself informative: Iowa pest pressure cannot be captured well by a single aggregate index; and, a standing, multi-species record lets institutions distinguish broad regional change from species-specific movement. It also gives the region a baseline for reading such changes. Extension specialists, researchers, growers, and policymakers can compare current conditions with earlier years, check whether established aphids are moving outside recent patterns, and spot shifts in the pest community. 

The third output, interpretation, requires reading these signals within the managed landscape they come from. Iowa corn and soybean acres are routinely and widely exposed to insecticides through seed treatments and foliar sprays, and figure 3 shows the scale of that exposure. Insecticide use is one among several components of that landscape, alongside weather, host availability, and natural enemies, and isolating its effect on aphid abundance would require a separate research design. The point is not to explain the trends here, but to make clear that the STN records these signals from a system that is already heavily managed, and its baseline should be read with that context in mind.

Discussion

Iowa’s suction-trap record shows one part of what regional pest-monitoring infrastructure can produce. The broader STN spans much of the Midwest and records many insect taxa, while the Iowa record given here follows a focused set of insecticide-sensitive aphids tied to several host systems. The figures describe three outputs of that infrastructure: shifts in seasonal timing, long-run abundance baselines, and interpretation within a heavily managed corn-soybean landscape.

The network’s relevance is not tied to any single aphid or any single year. Its value rests on continuity, breadth, and comparability. Because the record is sustained, the state and region can set current seasons against earlier ones and notice when the pest community shifts or when a rare or newly relevant signal begins to appear. Sorghum aphid (Melanaphis sorghi) is a useful reminder. It is not an established concern in Iowa, yet after expanding as a US sorghum pest in 2013 the species began appearing in the Iowa record around 2016, still too sparsely for trend analysis but early enough to show how a standing network registers emerging signals before anyone looks for them.² A record like this pays off only when it is maintained ahead of the signal that matters, which makes the STN a shared investment in the information base for extension, research, and public pest-management planning.

Footnotes

1. The first confirmed US detection in decades was a calf in Zavala County, Texas, on June 3, 2026 (USDA-APHIS 2026).

2. The sorghum aphid, the sorghum-feeding form once called the sugarcane aphid, erupted on southern sorghum in 2013 and spread north across 17 states within two years (Bowling et al. 2016).

References

Bowling, R.D., Brewer, D.L. Kerns, J. Gordy, N. Seiter, N.E. Elliott, G.D. Buntin, M.O. Way, T.A. Royer, S. Biles, and E. Maxson. 2016. “Sugarcane Aphid (Hemiptera: Aphididae): A New Pest on Sorghum in North America.” Journal of Integrated Pest Management 7(1):12. https://doi.org/10.1093/jipm/pmw011

Roberts, M.J., D. Schimmelpfennig, E. Ashley, M. Livingston, M. Ash, and U. Vasavada. 2006. “The Value of Plant Disease Early-Warning Systems: A Case Study of USDA’s Soybean Rust Coordinated Framework.” Economic Research Report No. 18. USDA Economic Research Service. https://www.ers.usda.gov/publications/45314

US Department of Agriculture Animal and Plant Health Inspection Service (USDA-APHIS). 2026. “USDA Confirms Presence of New World Screwworm in the United States.” APHIS agency announcement, June 3. https://www.aphis.usda.gov/news/agency-announcements/usda-confirms-presence-new-world-screwworm-united-states

Suggested citation

Lee, H., Lagos-Kutz, D.M., and D.A. Hennessy. 2026. “Regional Pest Monitoring as Agricultural Information Infrastructure: Evidence from two decades of Suction Trap Network data.” Agricultural Policy Review, Spring 2026. Center for Agricultural and Rural Development, Iowa State University. https://agpolicyreview.card.iastate.edu/spring-2026/regional-pest-monitoring-agricultural-information-infrastructure-evidence-two-decades