Assessing the quality of permanent sample plot databases for growth modelling in forest plantations

Informed plantation management requires a good database, since the quality of information depends on the quality of data, growth models and other planning tools. There are several important questions concerning permanent plots: how many plots, where to put them, and how to manage them. Plot measurem...

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Bibliographic Details
Published in:Forest ecology and management 1995-02, Vol.71 (3), p.177-186
Main Authors: Vanclay, J.K., Skovsgaard, J.P., Pilegaard Hansen, C.
Format: Article
Language:English
Subjects:
BANQUE DE DONNEES
BASES DE DATOS
Biological and medical sciences
CALIDAD
CAMPO
CHAMP
Database quality
Dendrometry. Forest inventory
ECHANTILLONNAGE
FIELDS
Forest plantation
FOREST PLANTATIONS
Forestry
Fundamental and applied biological sciences. Psychology
Growth modelling
MODELE
MODELOS
MUESTREO
Permanent sample plot
PLANTACION FORESTAL
PLANTATION FORESTIERE
PREVISION DE RENDEMENT
PRONOSTICO DEL RENDIMIENTO
QUALITE
QUALITY
SAMPLING
Supplementary sampling
YIELD FORECASTING
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Summary:Informed plantation management requires a good database, since the quality of information depends on the quality of data, growth models and other planning tools. There are several important questions concerning permanent plots: how many plots, where to put them, and how to manage them. Plot measurement procedures are also important. This paper illustrates graphical procedures to evaluate existing databases, to identify areas of weakness, and to plan remedial sampling. Two graphs, one of site index versus age, another with stocking versus tree size, may provide a good summary of the site and stand conditions represented in the database. However, it is important that these variables, especially site index, can be determined reliably. Where there is doubt about the efficacy of site index estimates, it is prudent to stratify the database according to geography, soil/geology or yield level (total basal area or volume production). Established permanent plot systems may sample a limited range of stand conditions, and clinal designs are an efficient way to supplement such data to provide a better basis for silvicultural inference. Procedures are illustrated with three data sets: teak plantations in Burma, Norway spruce in Denmark, and a clinal spacing experiment in India.
ISSN:0378-1127
1872-7042
DOI:10.1016/0378-1127(94)06097-3