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Allometric Relationship for Estimating Above-ground Biomass of Aegiali-tis Rotundifolia Roxb. of Sundarbans Mangrove Forest, in Bangladesh

Mohammad Raqibul Hasan Siddique
Forestry and Wood Technology Discipline,Khulna University, Khulna -9208, Bangladesh.

Mahmood Hossain
Forestry and Wood Technology Discipline, Khulna University, Khulna -9208, Bangladesh.

M d. Rezaul Karim Chowdhury
Forestry and Wood Technology Discipline, Khulna University, Khulna -9208, Bangladesh.

Abstract/ Executive Summary:

Tree biomass plays a key role in sustainable management by providing different aspects of ecosystem. Estimation of above ground biomass by non-destructive means requires the development of allometric equations. Most researchers used DBH (diameter at breast height) and TH (total height) to develop allometric equation for a tree. Very few species-specific allometric equations are currently available for shrubs to estimate of biomass from measured plant attributes. Therefore, we used some of readily measurable variables to develop allometric equations such as girth at collar-height (GCH) and height of girth measuring point(GMH) with total height (TH) for A. rotundifolia, a mangrove species of Sundarbans of Bangladesh, as it is too dwarf to take DBH and tooirregular in base to take Girth at a fixed height. Linear, non-linear and logarithmic regression techniques were tried to determine the best regression model to estimate the above-ground biomass of stem, branch and leaf. A total of 186 regression equations were generated from the combination of independent variables. Best fit regression equations were determined by examining co-efficient of determination (R2), co-efficient of variation (CV), mean-square of the error (MSerror), residual mean error (Rsme), and F-value. Multiple linear regression models showed more efficient over other types of regression equation. The performance of regression equations was increased by inclusion of GMHas an independent variable along with total height and GCH.

Keywords: Aegialitis rotundifolia; Allometry; Biomass; Mangroves; Sundarbans

Location:

Start Date:

End Date:

Program Area: Conservation and Biodiversity

Commodity: Plant

Objectives:

The pre-sent study aims to derive the allometric models for estimating the above-ground biomass of A. rotundifolia,which may contribute to the sustainable management of this species.

Methodology:

Study area The study area (N 21°45'03'' and E 89°33'11'') is bordering the Indian Sundarbans in the West and the Bay of Bengal in the South. Yearly rainfall of the area varies from 1200 to 1500 mm and the average temperature varies from 28°C to 30°C in sum-mer and 18°C to 20°C in winter. The forest is influenced by comparatively higher salinity (2.2%), (Siddique 2001). The vegetation in the study area is dwarf and the average height of the vegetation was about 3−4 m. The major species of this area were Avicennia alba, A. officinalis, Xylocarpus granatum, X. mekon-gensis, A. rotundifolia, Lumnitzera racemosa, Rhizophora api-culata, R. mucronata, Excoecaria agallocha and Ceriops decandra (Sididique 2001). Sample collecting and processing Fifty individuals of A. rotundifolia were selected (avoiding mechanicaland insect damage) with girth (>5 cm to <20 cm) of collar region from its dominated vegetation sites. Total height (TH) and girth at collar height (GCH) of the selected individuals were measured and recorded. Beside these measurements, height of girth measuring point (GMH) (on an average 0.70 m above from the ground) was also taken because the trunk of this species is conically broad based with gradual tapering towards the apex. Girth, rather than diameter, was used here because the cross-section of the stem is irregular in shape. In such cases, the use of diameter tapes or the division by pi (p) does not give the exact diameter. Moreover, the plant’s height was too small (maximum total height 2.89 m and average total height 2.04 m) to take DBH (diameter at breast height) for each stem. After taking these measurements, the individual plants were harvested at the ground level and separated into different parts, such as leaf, branch and stem. All parts of plant were weighed (fresh weight) in the field and sub-samples from each component (about 1 kg) were brought back to the laboratory to get conversion factor (ratio of oven dry weight at 80°C to fresh weight). Oven weights of different parts were calculated from the conversion factor. Allometric Relationship Equations of linear, nonlinear and logarithmic regression were used to develop allometric models for predicting above-ground biomass of plant parts (leaves, branches and stems). Significant test of regression equations was used by using SAS (6.12) statistical software. The Best fit regression equation was determined by having the highest R2, with lowest CV, Rsme, MSerror, F-value.

Source of Information: Journal of Forestry Research (2012) 23(1): 23?28

Article Link (Online Journal): DOI 10.1007/s11676-012-0229-5

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

The selected allometric models to estimate the biomass of different parts of A. rotundifolia were multiple regression equations. This technique provides some multiple linear models as the best-found estimators. Multiple regression model includes more independent variables that improve the estimation of a dependent variable by decreasing random error and is a good alternative to avoid logarithmic transformation (Wonnacott et al.1980; Payandeh 1981; Ross et al. 2001). Selection of best fit regression equation is the major problem to estimate plant bio-mass by allometric equation (Whittaker et al. 1969; Brown et al. 1989; Grundy 1995; Steinke et al. 1995; Tam et al. 1995; Mahmood et al. 2008). Among the enormous number of multiple regression equations, the best allometric models were selected by considering the values of R2, CV, Rsme, MSerror and F-value (Table 4). The use of R2 to select best fit equation was opposed by number of authors (Payandeh 1981; West et al. 1990; Zar 1996). More precise regression equation can be obtained by considering the other parameters of estimation values such as CV, Rsme, MSerror and F-value with R2 (Schacht et al. 1988; Zak et al. 1989; Busing et al. 1993; Slim et al. 1993; Ibrahima 1995; Zar 1996; Soares et al. 2005). The findings from this study will be useful to assess the present stocking of this species in the Sundarbans mangrove forest for the future management and commercial exploitation in a sustainable way.

Knowledge Management: Journal