72-2 El Niņo-Southern Oscillation (ENSO) Effects on Hessian Fly Infestation in Wheat in the Southeastern USA.
See more from this Division: ASA Section: Climatology & ModelingSee more from this Session: General Agroclimatology and Agronomic Modeling: I
Monday, November 3, 2014: 1:15 PM
Hyatt Regency Long Beach, Seaview A
Climate variability is expected to have an influence on the population of Hessian fly, Mayetiola destructor Say (Diptera: Cecidomyiidae), a serious insect pest of winter wheat in the southeastern USA. This study had two objectives: (i) to examine the effects of El Niño-Southern Oscillation (ENSO) on Hessian fly infestation and (ii) to develop a weather-based Hessian fly infestation model for wheat yield loss prediction. At least twenty years of Hessian fly infestation and wheat yield records from two locations in South Georgia were used for this study. The yearly values of infestation were separated by ENSO phase and tested to assess the infestation differences across ENSO phases. Each year, yield losses from infestation were calculated by subtracting the yields of resistant varieties from those of susceptible ones. The yield losses were then separated by ENSO phase and tested. Multiple regression analyses were conducted to identify the contribution of monthly weather variables and changes in wheat acreage to Hessian fly infestation. Results showed that Hessian fly infestation and yield losses were greatest during the La Niña and least during the El Niño phase. The weather conditions that significantly increased the risk for infestation were those of the August-February period. The risk of infestation was higher during August-September under wetter, cooler conditions and during October-February under drier, warmer conditions. These findings could help wheat growers reduce the risk of infestation in the years that are expected to have more infestation through the adoption of necessary mitigation measures prior to the crop season.
See more from this Division: ASA Section: Climatology & ModelingSee more from this Session: General Agroclimatology and Agronomic Modeling: I