Barley yellow dwarf disease risk assessment based on Bayesian modelling of aphid population dynamics

Abstract

A stochastic population dynamics model is proposed to improve integrated pest management strategies against the aphid Rhopalosiphum padi, the main Barley yellow dwarf virus (BYDV) vector in winter cereals during autumn in Europe. The model is based on a temperature-dependent simulation of R. padi population dynamics. The model requires a single early assessment of the proportion of plants infested by aphids. To account for sampling errors and for uncertainty caused by the numerous factors acting on aphid population dynamics under field conditions, Bayesian statistical inference was used. The model allows assessment of the probability distribution of the area under the curve of the percentage of plants infested by R. padi during autumn, a predictor of the need for insecticide sprays against BYDV vectors. The accuracy of model predictions was tested on an independent data set collected from 1995 to 1998 in the main French small grain production areas. The use of this model as a basis for a user-friendly decision support system improving BYDV management is discussed.