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ARS Home » Southeast Area » Stoneville, Mississippi » Southern Insect Management Research » Research » Publications at this Location » Publication #189100
Title: PREDICTION OF MASKED CHAFER (COLEOPTERA: SCARABAEIDAE) CAPTURE IN LIGHT TRAPS THROUGH A DEGREE-DAY MODEL

Author
item Blanco, Carlos
item HERNANDEZ, GERARDO - CINVESTAV

Submitted to: Journal of Insect Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/29/2006
Publication Date: 10/27/2006
Citation: Blanco, C.A., Hernandez, G. 2006. Prediction of masked chafer (coleoptera: scarabaeidae) capture in light traps through a degree-day model. Journal of Insect Science. 6:36

Interpretive Summary: Pests that are buried into the soil (for example white grubs) are very difficult to study and control. The damage done to turfgrass while scouting for this pest sometimes is more severe than that done by the insects. To reduce the need for this intensive sampling and predict the occurrence of the adult stage (masked chafers) of this pest, a mathematical model based on field-data rather than laboratory controlled experiments, was elaborated and predicted the occurrence of these masked chafers within 1-4 days of the actual date when they were observed in the field. The application of degree-day theory to field-collected data can also be implemented to different pests. The model described here utilized air temperature data obtained from a weather station and can greatly reduce the need for an intensive sample method. The model of this study adapts very well to integrated pest management programs.

Technical Abstract: In order to obtain information on the biology of the masked chafer Cyclocephala pasadenae, and determine the date when 50% of the population is captured in light traps, field data were obtained during 4 years in Albuquerque, New Mexico. Capture of the 50% of the masked chafer population occurred approximately during the third week of July, of this one-generation per year insect. To reduce the need for this intensive sampling and obtain a predictable model for the capture of these pests, these data were analyzed using trapezoidal numerical integration to estimate both a lower threshold and degree-days to predict the 50% capture date. A mathematical model based on field-data rather than laboratory controlled experiments, accounted for the influence of natural environmental conditions on development, and predicted 50% capture dates within 1-4 days of what was actually observed from the field. The difference between our predictions with field-data is smaller than using estimates from laboratory controlled experiments. The model presented here could serve as an accurate estimator of the appropriate timing to implement control measures of this important turfgrass pest.