Prediction of stem biomass of Pinus caribaea growing in the low country wet zone of Sri Lanka

Forests are important ecosystems as they reduce the atmospheric CO2 amounts and thereby control the global warming. Estimation of biomass values are vital to determine the carbon contents stored in trees. However, biomass estimation is not an easy task as the trees should be felled or uprooted which are time consuming and expensive procedures. As a solution to this problem, construction of mathematical relationships to predict biomass from easily measurable variables can be used.   The present study attempted to construct a mathematical model to predict the stem biomass of Pinus caribaea using the data collected from a 26 year old plantation located in Yagirala Forest Reserve in the low country wet zone of Sri Lanka. Due to the geographical undulations of this forest, two 0.05 ha sample plots were randomly established in each of valley, slope and ridge-top areas. In order to construct the model, stem wood density values were calculated by using stem core samples extracted at the breast height point. Stem volume was estimated for each tree using Newton’s formula and the stem biomass was then estimated by converting the weight of the known volume of core samples to the weight of the stem volume. Prior to pool the data for model construction, the density variations along the stem and between geographical locations were also tested.   It was attempted to predict the biomass using both dbh and tree height. Apart from the untransformed variables, four biologically acceptable transformations were also used for model construction to obtain the best model. All possible combinations of model structures were fitted to the data. The preliminary model selection for further analysis was done based on higher R2 values and compatibility with the biological reality. Out of those preliminary selected models, the final selection was done using the average model bias and modeling efficiency quantitatively and using standard residual distribution qualitatively. After the final evaluation the following model was selected as the best model to use in the field.    

Construction of Allometric Relationships to Predict Growth Parameters, Stem Biomass and Carbon of Eucalyptus grandis Growing in Sri Lanka

            Enhancement of carbon storage through the establishment of man-made forests has been considered as a mitigation option to reduce increasing atmospheric CO2 levels. Therefore the present study was carried out to estimate the biomass and carbon storages of the main stem of Eucalyptus grandis using allometric relationships using the plantations of Nuwara Eliya and Badulla districts in Sri Lanka. Tree diameter and total height were measured for the samples trees and stem volume was estimated using a previously built individual model for the same species. Stem biomass was estimated using core samples and carbon was determined using Walkley-Black method. Finally the biomass values were converted separately to the carbon values. Non-liner regression analysis was employed for the construction of models which had age as the explanatory variable. Linear regression was used in order to build the models to predict the above ground and stem biomass and carbon using volume as the explanatory variable. For both linear and non-linear types, the model quality was tested using R2 and fitted line plots. According to the results, stem biomass and carbon values at the 7th year were 110.8 kg and 68.7 kg respectively. Stem biomass and carbon values at the 40th year were 1,095.8 kg and 679.4 kg respectively. Carbon content at the age 20 was 62.0% from the stem biomass. Exponential models were proven to be better than the logistic models to predict the diameter, height, stem volume, biomass and carbon with age. R2 values and the fitted line plots indicated that the selected models are of high quality. Linear models built to predict the stem biomass and carbon using stem volume also showed the high accuracy of these models which had R2 values above 97.9%

Construction of allometric relationships to predict growth parameters, stem biomass and carbon of Eucalyptus grandis growing in Sri Lanka

Enhancement of carbon storage through the establishment of man-made forests has been considered as a mitigation option to reduce increasing atmospheric CO2 levels. Therefore the present study was carried out to estimate the biomass and carbon storages of the main stem of Eucalyptus grandis using allometric relationships using the plantations of Nuwara Eliya and Badulla districts. Tree diameter and total were measured for the samples trees and stem volume was estimated using a previously built individual model for the same species. Stem biomass was estimated using core samples and carbon was determined using Walkley-Black method. Finally the biomass values were converted separately to the carbon values.   Non-liner regression analysis was employed for the construction of models which had age as the explanatory variable. Linear regression was used in order to build the models to predict the above ground and stem biomass and carbon using volume as the explanatory variable. For both linear and non-linear types, the model quality was tested using R2 and fitted line plots.   According to the results, stem biomass and carbon values at the 7th year were 110.8 kg and 68.7 kg respectively. Stem biomass and carbon values at the 40th year were 1,095.8 kg and 679.4 kg respectively. The carbon content at the age 20 was 62.0% from the stem biomass.   Exponential models were proven to be better than the logistic models to predict the diameter, height, stem volume, biomass and carbon with age. R2 values and the fitted line plots indicated that the selected models are of high quality. Linear models built to predict the stem biomass and carbon using stem volume also showed the high accuracy of these models which had R2 values above 97.9%