Pest Watch

Western Bean Cutworm Trap Comparisons

Smart Trap powered by DTN, Image by Scott Williams

Studying agricultural pests helps DTN understand insect behavior and informs our products and customer service. As the resident entomologist, I’m out in the field collecting data, which has largely focused on the corn pest western bean cutworm (WBC). My work helped develop the algorithms we use in our Smart Traps and inform how we deploy them, and how we interpret the data we collect. Now, in its third season, I’d like to share some of my research findings.


When adopting a new technology, growers are right to expect that DTN’s trapping solutions perform as well as other traps available in the market. To meet this expectation, we deployed DTN Smart Traps alongside green bucket (GB) traps and observed each one’s ability to track trends in the WBC population.

There are differences in how the traps capture pests. GB traps restrain moths with an inverted funnel, while DTN Smart Traps use a glue board. As a result, we did not expect the traps to catch equivalent numbers of moths. What we did expect--and observed--was that the two trap types would detect similar trends in the moth population. As capture numbers changed in one trap type, we observed a proportional change in the other.

With the traps’ sensitivity to changes in the moth population being equal, is there an advantage to using DTN Smart Traps? Yes. We compared the resolution (time between data points) of the data between GB and DTN Smart Traps to determine when the first WBC observation was made. We found the Smart Traps could alert growers to the start of the WBC flight six days earlier than conventional bucket traps. That can make a big difference.


If you don’t set up your own traps, there are two ways to estimate when WBC is flying in your area: a degree-day (DD) model (day-temperature metric) or weekly reports from a university Extension Service. However, the most commonly applied DD model for WBC was not developed in Indiana where I’m based and may not apply perfectly. Also, university reports may not have the resolution needed to catch issues early enough for your specific situation. That’s why we compared DTN trap performance against these standard practices.

In 2017, automated traps detected the start of the WBC flight 17 days ahead of the predicted threshold for the DD model and nine days ahead of the local university newsletter. Scouting coordinated with trap data found damaging levels of WBC eggs and larvae within six to 34 days (average 17 days) of the start of the flight. In 2018, traps detected the start of the WBC flight 13 days ahead of the DD model and just two days following the first university report. That year, damaging numbers of larvae were found 21 to 29 days following the start of the flight.

DTN Smart Traps provided actionable information on par with university reports. While there is some lack of clarity in the data, DTN Smart Traps can be relied on to provide actionable insights into a specific grower’s operation. And, their sensitivity to incoming WBC moths is well ahead of the DD model, getting scouts out earlier to spot infestations.

Based on our research, DTN Smart Traps perform equally well to conventional alternative traps and provide earlier detection of the target pest, WBC, than other methods. The traps’ reliability means growers can have confidence that what they trap is a fair representation of the activity in the field. And, the early detection gives growers plenty of time to prepare how to address these pest issues.


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