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Probability models for pine twisting rust (Melampsora pinitorqua) damage in Scots pine (Pinus sylvestris) stands in Finland
Authors:U Mattila
Institution:1. Faculty of Forestry, University of Joensuu, PO Box 111, FIN‐80101, Joensuu, Finland;2. Present address: The Finnish Forest Research Institute, Joensuu Research Centre, PO Box 68, FIN‐80101, Joensuu, Finland. E‐mail: ulla.mattila@metla.fi
Abstract:Factors affecting the probability that pine twisting rust (Melampsora pinitorqua) damage occur in a Scots pine (Pinus sylvestris) stand were analysed using the 7th Finnish National Forest Inventory data (NFI7) from southern Finland in 1977–1983. The inventory was based on systematic sampling. The NFI7 data was measured in clusters, each of which consisted of 21 sample plots. In addition to the stand and site characteristics measured for forest management planning purposes, the data included records of damage by pine twisting rust and occurrence of aspens (Populus tremula, the other host plant of the pathogen) in the stands. Two multilevel logit models were developed for predicting the overall probability of pine twisting rust damage and the probability of severe pine twisting rust damage. Site and stand characteristics were used as explanatory variables in the models. Residual variance in the models was studied on the inventory crew, cluster and year levels. The occurrence of aspens and site fertility were the most important factors increasing the probability that pine twisting rust damage will occur in a stand. The damage probability also decreased with increasing effective temperature sum calculated for the location. The overall damage probability was equally high on peatlands and on mineral soil if there were aspens in the stand. If, however, there were no aspens in the stand, the probability of damage was higher on mineral soils than on peatlands. In addition, the overall probability was lower in naturally regenerated stands than in planted or sown stands, and it decreased with increasing mean age of pines. In both models, the residual variance was significant on the both the inventory crew and the cluster levels.
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