Hybrid growth models for Norway spruce and Scots pine: Using leaf area and light use efficiency for predicting stemwood production
Sammanfattning: With a changing climate, it has become more challenging to make long-term sustainable forest management decisions because many models and planning systems have difficulties accounting for the effects of climate change. One way to improve these tools is to include ecophysiological variables that respond to changes in weather and climate. The aim of this thesis was to investigate the use and implementation of ecophysiological concepts around light interception and light use in models for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) forests in Sweden. Different methods for estimating leaf area were compared in eight established forest experiments and showed that tree- and stand-level leaf area could be estimated using stand and tree variables together with indirect optical canopy measurements (Paper I). The higher leaf area for Norway spruce showed the importance of species-specific models. When using leaf area models in practice, measures of stand heterogeneity improve accuracy (Paper II). New species-specific mensurational basal area models were developed using permanent sample plot data (Paper III). There were minor differences in precision between these new models and an existing stand basal area growth model. Climate-sensitive hybrid models were developed from the mensurational models by replacing time with potentially usable light sums modified by climate factors (Paper IV). The hybrid models did not improve prediction precision compared to the mensurational models. However, tests with different climate scenarios demonstrated the ability of the hybrid models to capture both positive and negative effect from changes in temperature and precipitation, with a sizeable local variation in growth response. This thesis shows that it is possible to include climate sensitivity in forest models through light interception and light use. These models allow for better predictions of forest development and better-informed decision making for sustainable forest management.
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