Smart trap study points to more precise codling moth management
Researchers from Michigan State University's Department of Entomology found that automated camera traps often detected codling moth activity in orchards several days earlier than standard monitoring methods, potentially helping growers make more precise pest management decisions. The study is published in the journal Crop Protection.
A recent study from Michigan State University reveals that automated camera traps are able to detect codling moth activity several days earlier than traditional monitoring methods. This earlier detection could help growers make more precise decisions about pest management. Codling moth, a major pest of apples, can cause severe crop losses if not managed effectively.
Currently, growers use biofix to determine when to start degree-day models guiding insecticide applications. Biofix is estimated using weekly pheromone traps and weather-based prediction models, but these methods do not provide continuous observations. The research team, including Heather Leach and Julianna Wilson from Michigan State University and Frank Becker, Arnol Gomez, and Ashley Leach from The Ohio State University, evaluated automated camera traps in commercial apple orchards and compared their performance with conventional pheromone traps and weather-based predictive models.
The study found that automated camera traps detected codling moth activity 3-7 days earlier than standard traps, and weather-based models ranged from 7 days early to 12 days late compared to field observations. The researchers concluded that higher-resolution monitoring provided by camera traps may improve pest forecasting systems in the future.
By detecting codling moth activity more precisely, growers can better align management decisions with what is happening in the field, potentially reducing unnecessary insecticide applications and improving the effectiveness of reduced-risk insecticides.
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