Common Growth Models and Their Applications in Plantation

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.

Common Growth Models and Their Applications in Plantation

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