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8/3/2019 Common Growth Models and Their Applications in Plantation
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Bernard AKOLI
&
Adewole OLAGOKE
Common Growth Models and their
Applications in Plantation Forestry
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Introduction
ResourceInventory
Environmental
Databases
Experiments &Dynamic inventory
Growth Models &
Other Decision Aids
Policy
Predictions
Tests
Prescri-ptionsFEEDBACK FEEDBACK
Feedback
Growth modelling processes
Growth models in decision making, forest management and theformulation of forest policy (after Nix and Gillison 1985).
y Growth Models generally
refers to a system of functionswhich can predict the growthand yield of a forest standunder a wide variety of conditions
y Forest plantations managementrequire large amount of supporting informationwhich sometimes difficult, ifnot impossible to measuredirectly.
y Growth models are credibletools to assist forest managersand researchers to estimategrowth variables, essential fordecision making
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Classification of Growth and Yield Models
Source: Vanclay (1999)
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Description of some models applicable to forest
plantation management
yWhole Stand Modelsy Growth and Yield Tables
y Growth and Yield Equations
y Stand Distribution Models
y Size Class Models
y Stand Table Projection
y Transition Matrix models
y Cohort Models
y Individual Tree Models
y Distance Dependent Models
y Distance Independent Models
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Growth and Yield Tables
y
Describe the forest stand characteristics at varying ages per hectarey Data Source (mainly, Age and Site quality)
y permanent sample plots with optimal management
y
set of temporary sample plots which describe regular management
y 3 Basic Types
y Normal: assume fully stock density
y Empirical: average stocking, more accurate
y Variable-density tables: additional variable, Stand Density
y Ideal conditions for application: constant objectives
Old Concepts
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Typical Variable-density Yield
Table (Knoebel et al, 1986)
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Growth and Yield Equations
y no plain distinction between yield tables and equations since equations
can be presented as tables when evaluated
y provide more concise way to express growth and yield relationships
y can accommodate more variables
y possibility for repeatability in estimation
y vary from simple linear regression to a more complex polynomial or
higher order functions
y Limitation: assume a certain management regime throughout the
prediction
Typical Growth and Yield Equation
E(lnV1) = bO + b1S + b2A1 +b3lnB1
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Stand Table Projections
y Forecast future stand table from the present stand data, using
estimated diameter increments for each classy Growth-index ratio controls trees movement in-between
during the growth period
y Growth-index ratio = diameter growth/ diameter class interval
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Transition Matrix Models
y Predict final number of trees using:
y
Matrix multiplication of a state vector, comprising the initial treedensity per unit area by diameter classes
y Markov matrix, containing probability of movement
y Weakness: competition, mortality and regeneration cannot be
varied.
Cohort Models
y Cohorts represent the groups of trees with unifying characteristics suchas size or species
y mean tree growth can be modeled within each group
y cohorts are set in deciles or percentiles with boundaries determinedby tree distribution within stand
y Group identity, the mean size and number of stems is monitored
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Individual Tree Models
y
Simulation by growing characteristics ofindividual tree
y aggregating the trees to give estimates
of stand growth and yield
y Classified into 2 classes:
yDistance dependent models (DDM)
y Distance independent models
(DIM)
y DDM use data on inter-tree distance, and individual tree diameter, height
or crown class to calculate a competition index (CI)y Use CI to determine which trees live or die, and the growth
y DIM works similarly, but requires no information on spatial positions of trees
(expensive and sparsely available coordinates)
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Common application of growth models in plantation forestry
y Yield prediction : Needed for planning purposes and calculation of costs and
profits.
y Health monitoring: Can provide predictions on the specific critical time of the
damage caused by fires, insect pests and diseases so that prevention methods can
effectively be applied.
y Long term productivity monitoring: Provides a mechanism of identifying
changes and options for improvement in site quality for a long term higherproductivity.
y Socio economic analysis of Management Options: Aid decisions of
sustainable harvesting quota (the quantity of products to harvest without inducing a
negative response on the production potential of the forest ecosystem).
y Marketing of products:
y Produce projections of potential supply of a particular products based on a more
detailed information about area, species, yield and plantation age structure,
y and to examine periods to expect higher demand for product so that harvest can be
directed at that time for profit maximization.
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Selected Readingsy Avery, T.E. and Burkhart, H.E. (2002). Forest Measurements (5th
Edition). NewYork: McGraw-Hill. 456 p.y Husch B, BeersT.W. and Kershaw J.A., Jr. (2003).Forest
Mensuration. (4th Edition). New York:Wiley 456p.
y Nix, H.A. and Gillison, A.N. (1985). Towards an operationalframework for habitat and wildlife management. In J. Kikkawa (ed.)
Wildlife Management in the Forests and Forestry-controlled Lands inthe Tropics and the Southern Hemisphere. Proc. Workshop, Univ. ofQld, 1618 July 1984, p. 3955.
y Subasinge, S.M.C.U.P (2008). Growth models and their use inplantation forestry. Originally published in the Silver JubileeProceedings of the Department of Forestry and Environmental Science,University ofSri Jayewardenepura ( Dec. 2008)
y Vanclay,J.K. (1995). Growth models for tropical forests: a synthesisof models and methods. For. Sci. 41 (1), 742.