![]() Traditionally, large efforts have been placed to design and retrieve the necessary data, often from large databases or long-term observations in permanent plots. This preliminary model can be updated in a Bayesian framework when growth data from tree cores or remeasurements were obtained.Įfficient and accurate models for growth and yield are a fundamental tool in forest sciences, playing a key role in forest management, forest planning, ecological studies, or in fact any discipline within the field. Our virtual-data-based modelling approach only requires one-time observations from temporary plots, but provide reliable predictions of stand- and tree-level growth when validated with tree cores and whole-stand models. Although it underestimated the growth of suppressed trees compared with tree cores and remeasurements, this bias was negligible when aggregating tree-level simulations into stand-level growth predictions. ![]() The virtual-data-based diameter increment model provided logical patterns and reliable performances in both tree- and stand-level predictions. The final stage is to improve the reliability of individual tree diameter estimates by updating parameters with Bayesian calibration based on a likelihood of diameter distributions. The second stage is to simulate diameter distribution at 5-year intervals using a Weibull function based on tree-level research inventory data. The first stage is to predict stand dynamics at 5-year intervals based on stand-level resource inventory data. The purpose of this study was to propose a three-stage approach for modelling individual tree diameter growth based on temporary plots. In some cases, however, sufficient inventory data from remeasured permanent or semitemporary plots are unavailable or difficult to access. ContextĪ key component of tree-level growth and yield prediction is the diameter increment model that requires at least two different points in time with individual tree measurements. The individual tree model was parameterized using Bayesian calibration and a likelihood of diameter distributions. A virtual dataset was generated by linking the whole stand and diameter distribution models. This modelling approach predicts individual tree growth using only one-time observations from temporary plots. We propose a methodology to develop a preliminary version of a growth model when tree-level growth data are unavailable.
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