Forest Growth and Yield Modeling synthesizes current scientific literature and provides insights in how models are constructed. PDF | Completely updated and expanded new edition of this widely cited book, Modelling Forest Growth and Yield, 2nd Edition synthesizes. PDF | Forest Growth and Yield Modelingsynthesizes current scientific literature and provides insights in how models are constructed.
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A few other books on the subject. – Compilations or conference proceedings. – Focused on 1 region. – Focused on 1 model type. Forest growth and yield modeling. Forest growth models attempt to quantify the growth of a forest, and are commonly used for two principal purposes: to predict. Forest Growth and Yield Modeling. Jerome K Vanclay. Aaron Weiskittel. John A Kershaw. Jerome K Vanclay. Aaron Weiskittel. John A Kershaw. Loading.
Profile Link: Description Typically, when different forms of growth and yield models are considered, they are grouped into convenient discrete classes. As a heuristic device, I chose to use a contrasting perspective, that all growth and yield models are diameter distribution models that merely differ in regard to which diameter distribution is employed and how the distribution is projected to future conditions.
I describe different diameter distributions, whether they are the classical contmuous diameter distributions, the implied distributions of whole-stand models, or the discrete diameter distributions of size-class or individual tree models. There are also intermediates between these types of diameter distributions.
Aggregation vs. There are intermediates between these extremes, as well. There are several alternatives that vary from the classical paradigms. One alternative is a continuous analog to stand table projection that employs a "distribution modifying function" to project diameter distributions in time. Evidence from many sources indicates that climate change is getting stronger and this upward trend is expected to amplify into the future [ 2 , 3 ]. Global climate models GCMs predict that there will be significant change in climate by the end of the current century.
A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change
Climatic factors have a direct impact on tree growth and overall forest productivity, as well as indirect impacts on forest ecosystem functioning [ 5 , 6 ]. Climate change will modify tree-growing environments by altering site conditions, such as soil water content, atmospheric humidity, soil and air temperatures, and the length of growing seasons.
The forest industry is expected to be the most affected by climate change, compared to other resource-based industries.
This is because normal harvesting rotations for timber range from 50 to years for most tree species and present-day forests are expected to experience a period of transition from current to future growing conditions over the next years. Forests provide important socio-economic and ecological services, and existing forests cannot be replaced quickly over the short term to accommodate the anticipated changes in tree-growing environment.
Therefore, existing forests need to be managed during this period of transition with well-informed management plans that account for the effects of climate change. These models usually require simple inputs and predict with relatively low bias at regional scales [ 9 , 10 ].
Growth models are available for even-aged and uneven-aged stands as well as single and mixed species forests. These models have the capability to produce information from individual-tree to stand and forest levels. However, these models are empirical in nature and as a result are applicable only to stand growing conditions similar to the conditions for which they were developed [ 10 , 11 ]. Also, these empirical models rely on a core assumption that climatic and environmental conditions significant to tree growth are not violated [ 10 , 12 , 13 ].
Process-based models are widely used to investigate forest response to climatic change because they predict tree growth based on biological cause-and-effect associations [ 12 , 14 , 18 , 19 ]. Gap models are a special type of process-based forest ecosystem models that can take into account climatic and environmental influences based on eco-physiological principles [ 20 ]. Gap models are valuable tools to study tree growth, species composition, and stand-structure dynamics under diverse climatic conditions [ 21 , 22 ].
Considerable attempts had been made to use gap models to evaluate climate change impacts on forests [ 23 — 29 ]. Process-based models do not have the same accuracy at regional scales compared to conventional models empirical , because they are not developed to account for site differences across regions.
The preferred models should be based on empirical data, but should not be as complicated as process-based models. The new model was developed by combining tree growth information generated with a process-based model and historical data acquired from permanent sample plots PSPs.
A review of stand basal area growth models
Materials and Methods 2. Kershaw Jerome K.
First published: Print ISBN: About this book Forest Growth and Yield Modeling synthesizes current scientific literature and provides insights in how models are constructed. Giving suggestions for future developments, and outlining keys for successful implementation of models the book provides a thorough and up-to-date, single source reference for students, researchers and practitioners requiring a current digest of research and methods in the field.
Single source reference providing an evaluation and synthesis of current scientific literature Detailed descriptions of example models Covers statistical techniques used in forest model construction Accessible, reader-friendly style. Free Access.
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Summary PDF Request permissions. PDF Request permissions.Height growth and competitive relationship between paper birch and Douglas-fir in coast and interior of British Columbia.
Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations.
A density-dependent matrix model for bottomland hardwood stands in the lower Mississippi alluvial valley. Modeling type 1 and type 2 growth responses in plantations after application of fertilizer or other silvicultural treatments. NS DNR. Modeling forest dynamics: moving from description to explanation.
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