Conference on Applied Statistics in Agriculture

ESTIMATING THE LIKELIHOOD OF YELLOW STARTHISTLE OCCURRENCE USING AN EMPIRICALLY DERIVED NONLINEAR REGRESSION MODEL

Yellow starthistle is a noxious weed common in the semiarid climate of Central Idaho and other western states. Early detection of yellow starthistle and predicting its infestation potential have important scientific and managerial implications. Weed detection and delineation are often carried out by visual observation or survey techniques. However, such methods may be ineffective in detecting sparse infestations. The distribution of yellow starthistle over a large region may be affected by various exogenous variables such as elevation, slope and aspect. These landscape variables can be used to develop prediction models to estimate the potential invasion of yellow starthistle into new areas. A nonlinear prediction model has been developed based on a polar coordinate transformation to investigate the ability of landscape characteristics to predict the likelihood of yellow starthistle occurrence in North Central Idaho. The study region included the lower Snake river and parts of the Salmon and Clearwater basins encompassing various land use categories. The model provided accurate estimates of incidence of yellow starthistle within each specified land use category and performed well in subsequent statistical validations.