Overview

The ability to accurately predict future forest growth and structure, and the yields and quality of diverse products from these forests, is an essential part of forest management.  With changes in global climate patterns, rapid genetic gains in commercial forest sectors around the world and the serious risks posed internationally by pests and diseases, forecasting how forests will develop is increasingly challenging.  New innovations in statistical modelling techniques, as well as significant advances in process-based modelling approaches are leading, however, to major improvements in modelling success.  In the context of challenges facing forestry professionals around the world, this conference will provide an opportunity to bring together scientists, modelers and managers in a focused forum to present and discuss advances in models predicting future attributes of forests.  The conference will be organised as plenary sessions to bring stakeholders together around four key themes that pertain to the broader topic.  The four themes have been identified as:

  • Understanding and evaluating uncertainties in models predicting future growth, yield and wood properties
  • The nexus between models of tree growth, wood formation and product properties
  • Model application, integration and accessibility for forest management, planning and product development
  • The cutting edge in forest measurements and models

Research considering/taking empirical, process-based or mixed approaches to modelling at any scale is acceptable.

The three main objectives of the meeting are to

  • benchmark the state-of-the-art,
  • identify emerging frontiers and
  • explore new innovations and technologies

in models predicting future forest growth, yield and wood properties.

An important aspect of the meeting will be to explore and discuss how new frontiers in forest modelling research can be implemented to empower decision makers and deliver impact, particularly in the context of changing international climate and market conditions.

 



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