摘要
Long-term forecasts of pest pressure are central to the effective managementof many agricultural insect pests. In the eastern cropping regions of Australia, seriousinfestations of Helicoverpa punctigera (Wallengren) and H. armigera (Hiibner)(Lepidoptera:Noctuidae) are experienced annually. Regression analyses of a long series of light-trap catches ofadult moths were used to describe the seasonal dynamics of both species. The size of the springgeneration in eastern cropping zones could be related to rainfall in putative source areas in inlandAustralia. Subsequent generations could be related to the abundance of various crops inagricultural areas, rainfall and the magnitude of the spring population peak. As rainfall figuredprominently as a predictor variable, and can itself be predicted using the Southern OscillationIndex (SOI), trap catches were also related to this variable. The geographic distribution of eachspecies was modelled in relation to climate and CLIMEX was used to predict temporal variation inabundance at given putative source sites in inland Australia using historical meteorological data.These predictions were then correlated with subsequent pest abundance data in a major croppingregion. The regression-based and bio-climatic-based approaches to predicting pest abundance arecompared and their utility in predicting and interpreting pest dynamics are discussed.
Long-term forecasts of pest pressure are central to the effective management of many agricultural insect pests. In the eastern cropping regions of Australia, serious infestations of Helicoverpa punctigera (Wallenglen) and H. armigera (Hübner)(Lepidoptera:Noctuidae) are experienced annually. Regression analyses of a long series of light-trap catches of adult moths were used to describe the seasonal dynamics of both species. The size of the spring generation in eastern cropping zones could be related to rainfall in putative source areas in inland Australia. Subsequent generations could be related to the abundance of various crops in agricultural areas, rainfall and the magnitude of the spring population peak. As rainfall figured prominently as a predictor variable, and can itself be predicted using the Southern Oscillation Index (SOI), trap catches were also related to this variable. The geographic distribution of each species was modelled in relation to climate and CLIMEX was used to predict temporal variation in abundance at given putative source sites in inland Australia using historical meteorological data. These predictions were then correlated with subsequent pest abundance data in a major cropping region. The regression-based and bioclimatic-based approaches to predicting pest abundance are compared and their utility in predicting and interpreting pest dynamics are discussed.
