title: Validating spatiotemporal predictions of an important pest of small grains.
abstract: BACKGROUND
  Arthropod pests are typically managed using tactics applied uniformly to the whole field. Precision pest management applies tactics under the assumption that within-field pest pressure differences exist. This approach allows for more precise and judicious use of scouting resources and management tactics. For example, a portion of a field delineated as attractive to pests may be selected to receive extra monitoring attention. Likely because of the high variability in pest dynamics, little attention has been given to developing precision pest prediction models. Here, multimodel synthesis was used to develop a spatiotemporal model predicting the density of a key pest of wheat, the Russian wheat aphid, Diuraphis noxia (Kurdjumov).
  
  
  RESULTS
  Spatially implicit and spatially explicit models were synthesized to generate spatiotemporal pest pressure predictions. Cross-validation and field validation were used to confirm model efficacy. A strong within-field signal depicting aphid density was confirmed with low prediction errors.
  
  
  CONCLUSIONS
  Results show that the within-field model predictions will provide higher-quality information than would be provided by traditional field scouting. With improvements to the broad-scale model component, the model synthesis approach and resulting tool could improve pest management strategy and provide a template for the development of spatially explicit pest pressure models.
authors.affiliation: Department of Plant and Soil Science, University of Vermont, Burlington, VT, USA.
authors.forename: Scott C
authors.initials: SC
authors.lastname: Merrill
authors.forename: Thomas O
authors.initials: TO
authors.lastname: Holtzer
authors.forename: Frank B
authors.initials: FB
authors.lastname: Peairs
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Lester
mesh_terms: D000818:Animals; D001042:Aphids; D005544:Forecasting; D007300:Insect Control; D008954:Models, Biological; D011156:Population Density
keywords: Diuraphis noxia; cross-validation; invasive species; model synthesis; predictive model; spatial model