Changes of branch and crown characteristics with stem parameters and age of Swietenia macrophylla (mahogany) even-aged monocultures
B.Sc. Dissertation
Shyanika Lakmali and Upul Subasinghe
Growth predictions in the plantation forestry play a vital role in order to maximize the future gains specially in the field of economy. Plantations of mahogany monocultures in private sector directly focus in the timber production and thereby the financial gain. Tree growth is accompanied with the photosynthesis process and it increases the stem parameters of the trees. Therefore there is a close relationship between crown development and tree growth. In addition to that, crown growth causes a competition after the canopy closure in the forest plantations.
The main objective of the present study was to establish a series of empirical models for predicting the relationships between stem, crown and branch parameters. In addition to that an attempt was taken to predict the above parameters with age. It was expected to use those relationships to prepare a pruning schedule for mahogany monoculture plantations in
In order to achieve the objectives, data were collected from 16 mahogany monocultures in Kalutara, Ratnapura and Matale districts. In order to represent the whole plantation, due to growth differences, sixty trees were selected from each of plantation as twenty trees from each good, moderate, and poor growing areas. The measurements were taken including dbh, total tree height, crown diameter, height to the first branch, height to the second branch, branch lengths and base diameters up to two branches.
Regression analysis was employed to build the suitable relationships between related variables. Both linear and non-linear regression equations were tested for each relationship using MINITAB and GENSTAT statistical software. In order to obtain the best equations, both qualitative (R2 ) and quantitative (residual distribution and fitted line plots) were used. Initially, the data were grouped as good, moderate and poor growth types. Then different theoretical model structures (linear with untransformed and transformed variables, exponential and logistic) were separately fitted to those data. After that the best model was selected for each relationship for each growth type. At this point, it was a must to select the similar model structure (with different regression parameter sets) for each relationship in each growth type.
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