iForest - Biogeosciences and Forestry


Allometric models for estimating biomass, carbon and nutrient stock in the Sal zone of Bangladesh

Hossain Mahmood (1)   , Mohammad RH Siddique (1), Liam Costello (2), Luca Birigazzi (3), SM Rubaiot Abdullah (1), Matieu Henry (3), Baktiar Nur Siddiqui (4), Tariq Aziz (4), Sayed Ali (4), Abdullah Al Mamun (4), Mofizul IK Forhad (5), Mariam Akhter (4-6), Zaheer Iqbal (4), Falgoonee Kumar Mondol (6)

iForest - Biogeosciences and Forestry, Volume 12, Issue 1, Pages 69-75 (2019)
doi: https://doi.org/10.3832/ifor2758-011
Published: Jan 24, 2019 - Copyright © 2019 SISEF

Research Articles

Allometric models are commonly used to estimate biomass, nutrients and carbon stocks in trees, and contribute to an understanding of forest status and resource dynamics. The selection of appropriate and robust models, therefore, have considerable influence on the accuracy of estimates obtained. Allometric models can be developed for individual species or to represent a community or bioregion. In Bangladesh, the nation forest inventory classifies tree and forest resources into five zones (Sal, Hill, Coastal, Sundarbans and Village), based on their floristic composition and soil type. This study has developed allometric biomass models for multi-species of the Sal zone. The forest of Sal zone is dominated by Shorea robusta Roth. The study also investigates the concentrations of Nitrogen, Phosphorus, Potassium and Carbon in different tree components. A total of 161 individual trees from 20 different species were harvested across a range of tree size classes. Diameter at breast height (DBH), total height (H) and wood density (WD) were considered as predictor variables, while total above-ground biomass (TAGB), stem, bark, branch and leaf biomass were the output variables of the allometric models. The best fit allometric biomass model for TAGB, stem, bark, branch and leaf were: ln (TAGB) = -2.460 + 2.171 ln (DBH) + 0.367 ln (H) + 0.161 ln (WD); ln (Stem) = -3.373 + 1.934 ln (DBH) + 0.833 ln (H) + 0.452 ln (WD); ln (Bark) = -5.87 + 2.103 ln (DBH) + 0.926 ln (H) + 0.587 ln (WD); ln (Branch) = -3.154 + 2.798 ln (DBH) - 0.729 ln (H) - 0.355 ln (WD); and ln (Leaf) = -4.713 + 2.066 ln (DBH), respectively. Nutrients and carbon concentration in tree components varied according to tree species and component. A comparison to frequently used regional and pan-tropical biomass models showed a wide range of model prediction error (35.48 to 85.51%) when the observed TAGB of sampled trees were compared with the estimated TAGB of the models developed in this study. The improved accuracy of the best fit model obtained in this study can therefore be used for more accurate estimation of TAGB and carbon and nutrients in TAGB for the Sal zone of Bangladesh.


Common Model, Forest Inventory, Phytomass, Tropical Forest

Authors’ address

Hossain Mahmood
Mohammad RH Siddique
SM Rubaiot Abdullah
Forestry and Wood Technology Discipline, Khulna University, Khulna (Bangladesh)
Liam Costello
School of Ecosystem and Forest Sciences, The University of Melbourne, Victoria 3010 (Australia)
Luca Birigazzi
Matieu Henry
Food and Agriculture Organization, Rome (Italy)
Mofizul IK Forhad
Bangladesh Forest Research Institute, Chittagong (Bangladesh)
Mariam Akhter
Falgoonee Kumar Mondol
Food and Agriculture Organization, Dhaka (Bangladesh)

Corresponding author

Hossain Mahmood


Mahmood H, Siddique MRH, Costello L, Birigazzi L, Abdullah SMR, Henry M, Siddiqui BN, Aziz T, Ali S, Al Mamun A, Forhad MIK, Akhter M, Iqbal Z, Mondol FK (2019). Allometric models for estimating biomass, carbon and nutrient stock in the Sal zone of Bangladesh. iForest 12: 69-75. - doi: 10.3832/ifor2758-011

Academic Editor

Tomás Vrska

Paper history

Received: Feb 10, 2018
Accepted: Nov 10, 2018

First online: Jan 24, 2019
Publication Date: Feb 28, 2019
Publication Time: 2.50 months

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 15422
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 10276
Abstract Page Views: 1601
PDF Downloads: 2858
Citation/Reference Downloads: 9
XML Downloads: 678

Web Metrics
Days since publication: 1973
Overall contacts: 15422
Avg. contacts per week: 54.72

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Nov 2020)

Total number of cites (since 2019): 5
Average cites per year: 2.50


Publication Metrics

by Dimensions ©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

Akhter M, Jalal R, Costello L, Rahman L, Tasnuva U (2016)
Zoning for tree and forest assessment in Bangladesh. Bangladesh Forest Department and Food and Agricultural Organization of the United Nations, Dhaka, Bangladesh, pp. 36.
Allen SE (1989)
Chemical analysis of ecological materials. Blackwell Scientific Publications, Oxford, UK, pp. 565.
Alvarez E, Rodríguez L, Duque A, Saldarriaga J, Cabrera K, De Las Salas G, Del Valle I, Lema A, Moreno F, Orrego S (2012)
Tree above-ground biomass allometries for carbon stocks estimation in the natural forests of Colombia. Forest Ecology and Management 267: 297-308.
CrossRef | Gscholar
Basuki TM, Van Laake PE, Skidmore AK, Hussin YA (2009)
Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests. Forest Ecology and Management 257: 1684-1694.
CrossRef | Gscholar
Beathgen WE, Alley MM (1989)
A manual colorimetric procedure for measuring ammonium Nitrogen in soil and plant Kjeldahl digests. Soil Science and Plant Analysis 20 (9-10): 961-969.
CrossRef | Gscholar
BFD (2016)
Field instructions for the Bangladesh forest inventory. Bangladesh Forest Department (BFD) and Food and Agricultural Organization of the United Nations (FAO), Dhaka, Bangladesh, pp. 137.
Binkley D (1986)
Forest nutrition management. John Wiley and Sons, New York, USA, pp. 290.
Online | Gscholar
Brown S, Gillespie AJR, Lugo AE (1989)
Biomass estimation method for tropical forests with applications to forest inventory data. Forest Science 35: 881-902.
Brown S, Lugo AE (1992)
Above ground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia 17: 8-18.
Brown S (1997)
Estimating biomass and biomass change of tropical forests: a primer. FAO Forestry Paper no. 134, Rome, Italy, pp. 55.
Online | Gscholar
Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T, Lescure JP, Nelson BW, Ogawa H, Puig H, Riera B, Yamakura T (2005)
Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145: 87-99.
CrossRef | Gscholar
Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WB, Duque A, Eid T, Fearnside PM, Goodman RC, Henry M, Martínez-Yrízar A, Mugasha WA, Muller-Landau HC, Mencuccini M, Nelson BW, Ngomanda A, Nogueira EM, Ortiz-Malavassi E, Pélissier R, Ploton P, Ryan CM, Saldarriaga JG, Vieilledent G (2014)
Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology 10 (10): 3177-3190.
CrossRef | Gscholar
Djomo AN, Ibrahimab A, Saborowskic J, Gravenhorst G (2010)
Allometric equations for biomass estimations in Cameroon and pan moist tropical equations including biomass data from Africa. Forest Ecology and Management 260: 1873-1885.
CrossRef | Gscholar
FD (2007)
National forest and tree resources assessment 2005-2007 Bangladesh. Bangladesh Forest Department (BFD), Dhaka, Bangladesh, pp. 118.
FD (2017)
National and sub-national forest inventory. Web site.
Online | Gscholar
Gibbs HK, Brown S, Niles JO, Foley JA (2007)
Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letter 2: 1-13.
Online | Gscholar
Hoque AE, Nazrul-Islam AKM, Imamul Huq SM (2008)
Seasonal variation of edaphic features of Madhupur Sal forest, Bangladesh. Ecoprint 15: 7-14.
CrossRef | Gscholar
Iftekhar MS, Saenger P (2008)
Vegetation dynamics in the Bangladesh Sundarbans mangroves: a review of forest inventories. Wetlands Ecology and Management 6: 291-312.
CrossRef | Gscholar
Islam KK, Sato N (2012)
Deforestation, land conversion and illegal logging in Bangladesh: the case of the Sal (Shorea robusta) forest. iForest 5: 171-178.
CrossRef | Gscholar
Kenzo T, Furutani R, Hattori D, Kendawang JJ, Tanaka S, Sakurai K, Ninomiya I (2009)
Allometric equations for accurate estimation of above-ground biomass in logged-over tropical rainforests in Sarawak, Malaysia. Journal of Forest Research 14: 365-372.
CrossRef | Gscholar
Ketterings QM, Coe R, Noordwijk MV, Amagau Y, Palm CA (2001)
Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forest. Forest Ecology and Management 146: 199-209.
CrossRef | Gscholar
Koch B (2010)
Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS Journal of Photogrammetry and Remote Sensing 65: 581-590.
CrossRef | Gscholar
Komiyama A, Poungparn S, Kato S (2005)
Common allometric equations for estimating the tree weight of mangroves. Journal of Tropical Ecology 21: 471-477.
CrossRef | Gscholar
Kumar L, Mutanga O (2017)
Remote sensing of above-ground biomass. Remote Sensing 9: 935.
CrossRef | Gscholar
Litton CM (2008)
Allometric models for predicting aboveground biomass in two widespread woody plants in Hawaii. Biotropica 40 (3): 313-320.
CrossRef | Gscholar
Mahmood H (2004)
Biomass, litter production and selected nutrients in Bruguiera parviflora (Roxb.) Wight and Arn. dominated mangrove forest ecosystem at Kuala Selangor, Malaysia. PhD thesis, Biology Department, University Putra Malaysia, Serdang, Malaysia, pp. 333.
Online | Gscholar
Mahmood H, Saberi O, Japar Sidik B, Misri K, Rajagopal S (2004)
Allometric relationships for estimating above and below-ground biomass of saplings and trees of Bruguiera parviflora (Wight and Arnold). Malaysian Applied Biology 33 (1): 37-45.
Mahmood H, Saberi O, Japar Sidik B, Misri K (2008)
Net primary productivity of Bruguiera parviflora (Wight and Arn.) dominated mangrove forest at Kuala Selangor, Malaysia. Forest Ecology and Management 255: 179-182.
CrossRef | Gscholar
Mahmood H (2014)
Carbon pools and fluxes in Bruguiera parviflora dominated naturally growing mangrove forest of Peninsular Malaysia. Wetland Ecology and Management 22 (1): 15-23.
CrossRef | Gscholar
Mahmood H, Siddique MRH, Saha S, Abdullah SMR (2015)
Allometric models for biomass, nutrients and carbon stock in Excoecaria agallocha of the Sundarbans, Bangladesh. Wetlands Ecology and Management 23 (4): 765-774.
CrossRef | Gscholar
Mahmood H, Siddique MRH, Akhter M (2016)
A critical review and database of biomass and volume allometric equation for trees and shrubs of Bangladesh. IOP Conference Series, Earth and Environmental Science 39: 012057.
CrossRef | Gscholar
Manuri S, Brack C, Nugroho NP, Hergoualc’h K, Novita N, Dotzauer H, Verchot L, Putra CAS, Widyasari E (2014)
Tree biomass equations for tropical peat swamp forest ecosystems in Indonesia. Forest Ecology and Management 334: 241-253.
CrossRef | Gscholar
Marschner H (1995)
Mineral nutrition of higher plants. Academic Press, New York, USA, pp. 889.
Maulana SI, Wibisono Y, Utomo S (2016)
Development of local allometric equation to estimate total aboveground biomass in Papua tropical forest. Indonesian Journal of Forest Research 3 (2): 107-118.
CrossRef | Gscholar
Mayer D, Butler D (1993)
Statistical validation. Ecological Modeling 68 (1): 21-32.
CrossRef | Gscholar
Murphy J, Riley JP (1962)
A modified single solution method for the determination of phosphate in natural waters. Analytica Chimica Acta 27: 31-36.
CrossRef | Gscholar
Nam VT, Van Kuijk M, Anten NPR (2016)
Allometric equations for aboveground and belowground biomass estimations in an evergreen forest in Vietnam. PLoS ONE 11 (6): e0156827.
CrossRef | Gscholar
Nelson BW, Mesquita R, Pereira JLG, Souza SGAD, Batista GT, Couto LB (1999)
Allometric regressions for improved estimate of secondary forest biomass in the central Amazon. Forest Ecology and Management 117: 149-167.
CrossRef | Gscholar
Ngomanda A, Engone Obiang NL, Lebamba J, Moundounga Mavouroulou Q, Gomat H, Mankou GS, Loumeto J, Midoko Iponga D, Kossi Ditsouga F, Zinga Koumba R (2014)
Site-specific versus pantropical allometric equations: which option to estimate the biomass of a moist central African forest? Forest Ecology and Management 312: 1-9.
CrossRef | Gscholar
Picard N, Rutishauser E, Ploton P, Ngomanda A, Henry M (2015)
Should tree biomass allometry be restricted to power models? Forest Ecology and Management 353: 156-163.
CrossRef | Gscholar
Piñeiro G, Perelman S, Guerschman JP, Paruelo JM (2008)
How to evaluate models: observed vs. predicted or predicted vs. observed. Ecological Modeling 216: 316-322.
CrossRef | Gscholar
Rahman MM, Motiur MR, Guogang Z, Islam KS (2010)
A review of the present threats to tropical moist deciduous Sal (Shorea robusta) forest ecosystem of central Bangladesh. Tropical Conservation Science 3 (1): 90-102.
CrossRef | Gscholar
Rahman MM, Khan MNI, Hoque AKF, Ahmed I (2015)
Carbon stock in the Sundarbans mangrove forest: spatial variations in vegetation types and salinity zones. Wetlands Ecology and Management 23: 269-283.
CrossRef | Gscholar
Sattar MA (1981)
Some physical properties of 116 Bangladeshi timbers. Bulletin no. 7, Wood seasoning series, Forest Research Institute, Chittagong, Bangladesh, pp. 15.
Online | Gscholar
Sileshi GW (2014)
A critical review of forest biomass estimation models, common mistakes and corrective measures. Forest Ecology and Management 329: 237-254.
CrossRef | Gscholar
Sprugel DG (1983)
Correcting for bias in log-transformed allometric equations. Ecology 64 (1): 209-210.
CrossRef | Gscholar
Van Breugel M, Ransijn J, Craven D, Bongers F, Hall JS (2011)
Estimating carbon stock in secondary forests: decisions and uncertainties associated with allometric biomass models. Forest Ecology and Management 262: 1648-1657.
CrossRef | Gscholar
Wagenmakers EJ, Farrell S (2004)
AIC model selection using Akaike weights. Psychonomic Bulletin and Review 11 (1): 192-196.
CrossRef | Gscholar

This website uses cookies to ensure you get the best experience on our website. More info