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ARS Home » Plains Area » Fargo, North Dakota » Edward T. Schafer Agricultural Research Center » Sunflower and Plant Biology Research » Research » Publications at this Location » Publication #116335
Title: SUNFLOWER MIDGE: MONITORING, EMERGENCY PATTERN, DEGREE-DAY MODELS, EDGE EFFECT, AND ECONOMIC INJURY LEVELS

Author
item TATTA, V - NDSU, ENTOMOLOGY DEPT
item Charlet, Laurence
item BREWER, G - NDSU, ENTOMOLOGY DEPT

Submitted to: Great Plains Sunflower Insect Workshop Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 9/15/2000
Publication Date: N/A
Citation: N/A

Interpretive Summary: The sunflower midge has two generations per year. Adults from the overwintering generation lay eggs in sunflower buds and the resultant larval feeding causes head growth deformities and reduced yields. Chemical control measures have not been successful because of inaccurate timing to adult flight. Developing a predictive model of adult emergence based on air or soil temperature or soil moisture would help in timing control measures. Our objectives were to identify which parameter most accurately predicts overwintering generation emergence. Temperature and emergence data from 1993-97 were used to develop the model. Air and soil temperature, in addition to soil moisture, were then tested against midge adult emergence in 1999 to validate the model. We also studied population densities of midge eggs and larvae in fields to determine if midge populations exhibited an edge effect. Our analysis showed the best fit of the data to the model for air temperature was at a 57 deg. F base temperature with a model predicting the first emergence of sunflower midge adults at 488 +/- 95 degree days. In 1999, based on air temperature at base 57 deg. F, the first emergence of midge adults at two locations occurred within the estimated degree days. Actual and predicted emergence using soil temperature was not as accurate as air temperature. For first generation sunflower midge, adult emergence occurred at 12-18% soil moisture. A second generation emergence occurred at 18-27% soil moisture. For both generations, there was no linear relationship between soil moisture and midge emergence. An edge effect was detected, with midge populations declining with distance into the field. This data can be used to predict midge population density at locations away from field edges.

Technical Abstract: The sunflower midge has two generations per year. Adults from the overwintering generation lay eggs in sunflower buds and the resultant larval feeding causes head growth deformities and reduced yields. Chemical control measures have not been successful because of inaccurate timing to adult flight. Developing a predictive model of adult emergence based on air or soil temperature or soil moisture would help in timing control measures. Our objectives were to identify which parameter most accurately predicts overwintering generation emergence. Temperature and emergence data from 1993-97 were used to develop the model. Air and soil temperature, in addition to soil moisture, were then tested against midge adult emergence in 1999 to validate the model. We also studied population densities of midge eggs and larvae in fields to determine if midge populations exhibited an edge effect. Our analysis showed the best fit of the data to the model for air temperature was at a 57 deg. F base temperature with a model predicting the first emergence of sunflower midge adults at 488 +/- 95 degree days. In 1999, based on air temperature at base 57 deg. F, the first emergence of midge adults at two locations occurred within the estimated degree days. Actual and predicted emergence using soil temperature was not as accurate as air temperature. For first generation sunflower midge, adult emergence occurred at 12-18% soil moisture. A second generation emergence occurred at 18-27% soil moisture. For both generations, there was no linear relationship between soil moisture and midge emergence. An edge effect was detected, with midge populations declining with distance into the field. This data can be used to predict midge population density at locations away from field edges.