Overwintering ecology of the Russian wheat aphid, Diuraphis noxia (Kurdjumov), in eastern Colorado

Abstract

The Russian wheat aphid (RWA), Diuraphis noxia (Kurdjumov), is a serious pest of small-grains throughout much of the United States. This body of work integrates many components of research directed towards elucidating overwintering RWA population dynamics. Components include: 1) a literature review, 2) meta-analyses of RWA reproductive and developmental rates as functions of temperature, 3) a RWA density model based on weather conditions, 4) a high-resolution, spatially explicit RWA density model that incorporates the techniques of Geographic Information Systems and spatial statistical modeling, 5) a cross-validation analysis that calculates prediction errors for the spatially explicit RWA density model, and 6) a study of the spatially explicit correlation between fall and spring RWA densities. The literature review and meta-analysis chapters provide background for the formulation of hypotheses and augment our general understanding of RWA overwintering population dynamics. A spatially implicit RWA density model was developed using precipitation and temperature variables, which yielded boundary conditions on spring RWA densities. Additionally, a spatially explicit RWA density model was developed from Landsat 7 Enhanced Thematic Mapper Plus imagery, topographic variables, and soil information. Cross-validation statistics indicate that the spatially explicit RWA density model, which relies entirely on remotely sensed data, is successfully predicting RWA densities as well or better than traditional field scouting. RWA density models were used to develop spatially explicit RWA density maps, which can be used for a variety of purposes including risk assessment. Unsurprisingly, fall RWA densities explained a significant but limited amount of variation in spring RWA densities. Spatially explicit RWA density models will enable management actions to be focused on locations and times when the risk of economic damage is high. Use of this research for forecasting spatially explicit RWA densities, as well as increasing knowledge on initial and boundary conditions, will facilitate directed scouting, precision agricultural techniques, and within-field management practices for control of RWA. Adoption of these practices and techniques has the potential to reduce pesticide inputs, allow for more judicious use of resistant cultivars (when they become available), increase natural enemy refuges, and reduce pesticide exposure to agricultural workers and consumers.