Modeling Spatial Variation of Russian Wheat Aphid Overwintering Population Densities in Colorado Winter Wheat

Abstract

The Russian wheat aphid, Diuraphis noxia (Kurdjumov), is a pest of small grain crops that has caused hundreds of millions of dollars of damage since it was first reported in the United States in 1986. Much is known about D. noxia population dynamics during the spring and early summer when most of the crop damage occurs, whereas little is known about the system during the overwintering period. Using a spatially explicit model developed from field observations in a wheat/fallow agro-ecosystem, we sought for predictable variation in overwintering success of D. noxia based on environmental factors such as topography and soil type. Successful modeling of densities of D. noxia would facilitate early control efforts targeting locations where D. noxia successfully overwintered. D. noxia density data were collected over 3 yr at two sites in eastern Colorado. The model incorporates georeferenced data from soil surveys, topography, and satellite imagery as predictor variables. Our approach links an information theoretic approach for model inference and model selection to landscape ecology, allowing for the examination of multiple candidate models and variables within each of the candidate models. Results were used to create trend surface models for D. noxia density in winter wheat agroecosystems. The model has the potential for use in site specific pesticide applications. Using site specific pesticide applications, pesticide inputs could be reduced by an estimated 30%, reducing input costs to the producer, increasing natural enemy refuges, reducing environmental contamination, augmenting pesticide resistance management practices, and reducing exposure of agricultural workers.

Assessing the impact of site-specific spraying on control of Eurygaster integriceps (Hemiptera: Scutelleridae) damage and natural enemies

“…Whole-field spraying will be economically feasible if The means with the same letter in each column are not significantly different (P \ 0.01) the pest population is distributed uniformly (Merrill et al 2009). Since the spatial distribution of E. integriceps was aggregated, site-specific application of pesticide would be more economical.…”

“…Determining the spatial distribution patterns of pests is a pre-requisite for SSIPM programs. SSIPM is applicable in cases where pest population distribution is aggregated in space Merrill et al 2009). Availability of new technologies and methods including the global positioning system (GPS), geographic information systems (GIS) and geostatistics allows pest managers to analyze and map the spatial distribution of pests and to use this information in SSIPM (Liebhold et al 1993;Plant 2001).…”

Examining the Competitive Advantage of <I>Diuraphis noxia</I> (Hemiptera: <I>Aphididae</I>) Biotype 2 Over Biotype 1

“…RWA2 rapidly expanded throughout Colorado at a rate in excess of the rate expected based on its competitive advantage in RWA1-resistant wheat cultivars (Merrill et al 2008a). D. noxia population growth rates change based on many factors, such as plant host (Webster et al 1996, Merrill et al 2008b), plant host condition or age (Girma et al 1990, Hein 1992, Brewer and Noma 2002, physical environment (e.g., slope or soil type; Merrill et al 2009b), and temperature Behle 1988, 1989;Legg and Brewer 1995;Merrill et al 2009a). In addition, differences in aphid population growth rates by biotype have been observed: Large competitive advantages have been found within host plant (Jyoti et al 2006, Randolph et al 2008 with the obvious extreme observed when plant host resistance is overcome by one biotype.…”

Spatial Variability of Western Bean Cutworm (Lepidoptera: Noctuidae) Pheromone Trap Captures in Sprinkler Irrigated Corn in Eastern Colorado

“…Like many aspects of cropping systems, spatial dynamics of pest densities are heterogeneous (Taylor 1984). Spatial patterns may be caused by numerous factors including aggregation pheromones or landscape covariates such as Þeld slope (Merrill et al 2009). Precision pest management is deÞned as the use of spatially-explicit pest ecology to directed scouting efforts and inform precision agricultural strategy.…”

“…Precision pest management is deÞned as the use of spatially-explicit pest ecology to directed scouting efforts and inform precision agricultural strategy. For example, Merrill et al (2009) estimated that adequate control of the Russian wheat aphid could be achieved by only spraying within-site locations modeled to have medium to high aphid incidence thereby reducing pesticide inputs by 30%. In Pennsylvania, treating only the infested portions of potato (Solanum tuberosum L.) Þelds reduced the amount of pesticide needed to control the Colorado potato beetle by 30 Ð 40% (Weisz et al 1996).…”