Timber production in high density planting of Rubber

Timber production in high density planting of Hevea brasiliensis (Rubber)

Kelum Silva, Upul Subasinghe and Lakshman Rodrigo

Paper presented for the 14th International Forestry and Environmental Symposium 2009

The demand of natural rubber has increased continuously with the increase of population and living standards of the human being. Rubber plantations are also a major source of timber and fuels wood. In order to meet the continuous increase in demand for latex, timber and fuelwood, the productivity of rubber plantations should be increased. Whilst producing high yielding clones for improved latex and timber yield per tree which is a long-term process in perennial crops, planting density could be adjusted to obtain high productivity in rubber plantations. The present level of planting density of rubber in Sri Lanka has been decided on the experiments conducted with the genotypes which are not in common use at the moment. Therefore the present study was aimed to identify the suitable planting density for the recently developed and commonly used genotypes of rubber. This paper is focused to assess the timber production of rubber with respect to high density planting.

The experiment was set up in Rathnapura District of Sri Lanka in 1992. Rubber was planted in three high densities, i.e., 600, 700 and 800 trees per hectare. Also three genotypes (clones), i.e., RRIC 100, RRIC 110 and RRIC 121 were incorporated with the statistical design of split plot where the planting densities were laid as the main plot whilst clones were in the sub-plots. Five trees in each sub-plot were randomly selected and used for the measurements of total tree height (TH), crown height (CH), thickness of the untapped bark (BT) and tree diameter at breast height. Thereafter, stem volumes were determined using the Newton’s formula.

Both TH and CH did not vary significantly among the planting densities tested. Although not statistically significant, there was a marginal decrease in tree diameter with the increase in planting density. Irrespective of the clone used, BT and mean merchantable timber volume per tree decreased significantly with increase in planting density. Nevertheless, this decrease was compensated by the increased number of trees in high densities resulting in comparable level of merchantable volume per hectare among different densities. Total stem volume per tree remained same among four densities tested with that total stem per hectare increased significantly with the increase of planting density. Therefore, higher densities are more useful in the industries of fuelwood, pulp, MDF boards etc. Among the clones tested, the clone RRIC 212outperformed in growth and timber production. The clone RRIC 110 was infected with the Corynespora disease hence showed poor performance in all densities. Despite the increase in total timber production with the increase in planting density, overall financial viability of different densities is to be assessed considering all cost components and valuing both timber and latex produced before making any firm recommendation.

Construction of a stem volume prediction model for E. grandis

Construction of a stem volume prediction model for mature Eucalyptus grandis plantation in Pidurutalagala of Nuwara Eliya, Sri Lanka
by
Kandiah Selvarathnam and Upul Subasinghe

Timber volume is the most crucial variable in commercial forest plantations. Therefore a precise volume prediction model was constructed for the mature Eucalyptus grandis plantation located in the Pidurutalagala mountain of Nuwara Eliya District, Sri Lanka.

0.02 ha circular samples were used for the data collection. Breast height diameter (dbh) and total height (h) were measured as the preliminary measurements. Then the stems of the standing trees in the sample plots were divided into sections and section lengths, end-diameters and mid-diameters were measured in order to use the Newton's formular for the stem volume estimations. Altogether 14 samples were used for the data collection.

A basic relationship was then developed by assuming the stem volume (v) can be predicted as a function of h, basal area (g) which is calculated using dbh and site quality as given below.

v = f (g, h, site quality)

In order to represent the site quality, top height and top height / age functions were used. Regression analysis (linear) was used to quantify the relationships. In order to obtain the best models, the variables in the above equation were transformed into different forms that can biologically be accepted. R2 and standard residual distrinbution were used to evaluate the model quality. After a careful study, two models were selected to predict the stem volume of E. grandis trees in the selected plantation.

Prediction of individual tree volume of Alstonia macrophylla

Construction of a precise growth model to predict the individual stem volume of Alstonia macrophylla
by
Rangika Bandara and Upul Subasinghe

In order to reduce the pressure on existing natural forests of Sri Lanka, Forest Department promoted growing timber species as plantations and in homegardens. Among the suggested species, Alstonia macrophylla (Hawari nuga) has recently become popular due to its faster growth rate, ease of estabishment and timber value.

However, at present, there is no method for estimating the stem volume of this species. Therefore mathematical models were built in this study to predict the individual stem volume of the selected species grown as plantations and in homegardens.

Hawari nuga is sidely found in wet zone of Sri Lanka and therefore the study sites were selected from Kalutara and Galle Districts. For the non-forest areas, sampling was carried out in individual basis and ten 0.02 ha circular plots with slope correction were randomly laid out for each plantation in order to measure the necessary parameters.

Dbh, total height and height to the crown base of the trees were measured. Necessary measurements were also taken to calculate the stem volume using Newton's formula.

A theoritical model structure was developed using the relationship between form factor, volume, basal area and total height. Regression analysis was used to fit the data into the model. Untransformed as well as transformed combinations of the model structures were tested in order to select the best models. After examining the statistical performances of the resultant models, two models were finally selected for plantation grown trees and open grown trees. Those models had very high modelling efficiencies and negligible bias. When validated with the reserved data at the beginning of the modelling procedure, the selected models proved the capability of predicting stem volumes of Alstonia macrophylla grown in plantations and homegardens.