About this Research Topic
Climate change is recognized as one of the most complex global environmental issues that directly affect biological systems, including forest resources. Among these, forest plantations have been established and are managed worldwide for providing raw material to satisfy a growing global scale demand for timber production, fibre and other forest products.
Sustainable management of forest plantations in the long-term necessarily requires quantifying the impact of climate change on wood production. Expected changes in climate conditions in different regions have generated uncertainty about how productivity and growth of forests at the tree and stand level will be affected. To support forest plantation management, various dynamic modelling approaches have been developed for valuable commercial tree species around the world. They are validated and updated based on an extensive and long-term network of permanent plots and silvicultural trials that represent current knowledge.
Due to the accelerated climate change, forest managers need more than ever practical tools to anticipate changes in forest productivity and make accurate projections of tree and stand growth. This information is essential to evaluate adaptive management options in planted forests. For instance, some crucial decisions such as planting density and intensity and timing of silvicultural interventions, such as thinning and pruning, need to be evaluated under various climate change scenarios.
Recent research has shown that empirical models can be improved by including additional climate predictor variables to account for changes in climate conditions. Parameterization of mechanistic models can provide a more flexible way of studying the effects of changes in environmental conditions on tree growth. Some research has also focused on the development of hybrid modelling approaches that need to be validated for various tree species. Advances in remote sensing, computer science and statistical methods, and the availability of climate variables provide a framework to explore new modelling approaches. Currently, the application of machine learning and non-parametric statistical approaches open opportunities and challenges to expand research in growth and yield modelling.
This Research Topic aims to invite contributors that explore new statistical methods (parametric and non-parametric) and machine learning procedures to identify key climate factors affecting site productivity and new tree- and stand-level modelling approaches that consider the effects of climate change.
We invite submissions that may include, but are not limited to:
- Procedures for mapping current and future site productivity under diverse climate change scenarios;
- Modelling of patterns of changes in site productivity over time;
- Procedures to incorporate environmental effects on existing empirical growth models;
- Parameterization and validation of various types of mechanistic models;
- Development of new hybrid models to support management decisions.
Sustainable management of forest plantations in the long-term necessarily requires quantifying the impact of climate change on wood production. Expected changes in climate conditions in different regions have generated uncertainty about how productivity and growth of forests at the tree and stand level will be affected. To support forest plantation management, various dynamic modelling approaches have been developed for valuable commercial tree species around the world. They are validated and updated based on an extensive and long-term network of permanent plots and silvicultural trials that represent current knowledge.
Due to the accelerated climate change, forest managers need more than ever practical tools to anticipate changes in forest productivity and make accurate projections of tree and stand growth. This information is essential to evaluate adaptive management options in planted forests. For instance, some crucial decisions such as planting density and intensity and timing of silvicultural interventions, such as thinning and pruning, need to be evaluated under various climate change scenarios.
Recent research has shown that empirical models can be improved by including additional climate predictor variables to account for changes in climate conditions. Parameterization of mechanistic models can provide a more flexible way of studying the effects of changes in environmental conditions on tree growth. Some research has also focused on the development of hybrid modelling approaches that need to be validated for various tree species. Advances in remote sensing, computer science and statistical methods, and the availability of climate variables provide a framework to explore new modelling approaches. Currently, the application of machine learning and non-parametric statistical approaches open opportunities and challenges to expand research in growth and yield modelling.
This Research Topic aims to invite contributors that explore new statistical methods (parametric and non-parametric) and machine learning procedures to identify key climate factors affecting site productivity and new tree- and stand-level modelling approaches that consider the effects of climate change.
We invite submissions that may include, but are not limited to:
- Procedures for mapping current and future site productivity under diverse climate change scenarios;
- Modelling of patterns of changes in site productivity over time;
- Procedures to incorporate environmental effects on existing empirical growth models;
- Parameterization and validation of various types of mechanistic models;
- Development of new hybrid models to support management decisions.
Keywords: Plantation forestry, forest management, dynamic models, biophysical effects, adaptation strategies
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
