Swiss needle cast (SNC), caused by the fungus Phaeocryptopus gaeumannii, is producing extensive defoliation and growth reduction in Douglas-fir forest plantations along the Pacific Northwest coast. An SNC disease prediction model for the coastal area of Oregon was built by establishing the relationship between the distribution of disease and the environment. A ground-based disease survey (220 plots) was used to study this relationship. Two types of regression approaches, multiple linear regression and regression tree, were used to study the relationship between disease severity and climate, topography, soil, and forest stand characteristics. Fog occurrence, precipitation, temperature, elevation, and slope aspect were the variables that contributed to explain most of the variability in disease severity, as indicated by both the multiple regression (r 2 = 0.57) and regression tree (RMD = 0.27) analyses. The resulting regression model was used to construct a disease prediction map. Findings agree with and formalize our previous understanding of the ecology of SNC: warmer and wetter conditions, provided that summer temperatures are relatively low, appear to increase disease severity. Both regression approaches have characteristics that can be useful in helping to improve our understanding of the ecology of SNC. The prediction model is able to produce a continuous prediction surface, suitable for hypothesis testing and assisting in disease management and research.
Incorporation of Genetic Gain into Growth Projections of Douglas-Fir Using ORGANON and the Forest Vegetation Simulator
