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 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.