Allometric models for estimation of aboveground biomass of Combretum molle R.Br. ex G.Don. and Terminalia schimperiana Hochst. in Tulu Lafto Forest, Horo Guduru Wollega zone, Ethiopia
DOI:
https://doi.org/10.20372/star.V14.i3.03Keywords:
Allometric biomass equation, Combretum molle, Terminalia schimperiana, Tulu Lafto ForestAbstract
Forests play a key role in the global carbon cycle. Hence, accurate forest biomass estimation is crucial in climate change mitigation efforts and for monitoring carbon stock dynamics. This study developed allometric models used to estimate AGB of two indigenous tree species, namely, Combretum molle and Terminalia schimperiana, using a semi-destructive method. Diameter at breast height (DBH) and total height were measured for 40 selected trees per species across 10 plots (each 0.1 ha). Selected branches with leaves were trimmed for fresh and dry weight analysis. Samples were taken to the laboratory for dry-to-fresh weight ratio determination and further analysis. Wood density was calculated for both species. The results showed that C. molle had a significantly higher mean wood density (0.573 g/cm³) than T. schimperiana (0.476 g/cm³, p = 0.000), a significantly lower mean biomass (34.57) compared to T. schimperiana (266.13, p = 0.000). Linear regression analysis revealed that DBH was the most reliable predictor for AGB for both species. The study recommends using Model 2 for C. molle (DBH- 5-43cm) and Model 5 for T. schimperiana (DBH-5-60cm) for AGB estimation. It also emphasizes conserving and incorporating C. molle and T. schimperiana in national afforestation programs for carbon sequestration projects.
Downloads
Metrics
References
Abich, A., Negash, M., Alemu, A., & Gashaw, T. (2022). Aboveground biomass models in the Combretum-Terminalia Woodlands of Ethiopia: Testing species and site variation effects. Land, 11(6), 811. https://doi.org/10.3390/land11060811
Bastin, J., Fayolle, A., Tarelkin, Y., Van Den Bulcke, J., De Haulleville, T., Mortier, F., Beeckman, H., Van Acker, J., Serckx, A., Bogaert, J., & De Cannière, C. (2015). Wood specific gravity variations and biomass of Central African tree species: the simple choice of the outer wood. PLoS ONE, 10(11), e0142146.https://doi.org/10.1371/journal.pone.0142146
Brown, S. (1997). Estimating biomass and biomass change of tropical forests: A primer. http://ci.nii.ac.jp/ncid/BA52417799
Brown, S. (2002). Measuring, monitoring, and verification of carbon benefits for forest-based projects. Philosophical Transactions of the Royal Society a Mathematical Physical and Engineering Sciences, 360(1797), 1669–1683. https://doi.org/10.1098/rsta.2002.1026
Cairns, M. A., Olmsted, I., Granados, J., & Argaez, J. (2003). Composition and aboveground tree biomass of a dry semi-evergreen forest on Mexico’s Yucatan Peninsula. Forest Ecology and Management, 186(1–3), 125–132. https://doi.org/10.1016 /s0 378-1127(03)00229-9
Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J., Nelson, B. W., Ogawa, H., Puig, H., Riéra, B., & Yamakura, T. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145(1), 87–99. https://doi.org/10.1007/s00442-005-0100-x
Chave, J., Muller-Landau, H. C., Baker, T. R., Easdale, T. A., Ter Steege, H., & Webb, C. O. (2006). Regional and phylogenetic variation of wood density across 2456 neotropical tree species. Ecological Applications, 16(6), 2356–2367.https://doi.org/10.1890/1051-0761(200 6)016
Chave, J., Réjou‐Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B., Duque, A., Eid, T., Fearnside, P. M., Goodman, R. C., Henry, M., Martínez‐Yrízar, A., Mugasha, W. A., Muller‐Landau, H. C., Mencuccini, M., Nelson, B. W., Ngomanda, A., Nogueira, E. M., Ortiz‐Malavassi, E., . . . Vieilledent, G. (2014). Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology, 20(10), 3177–3190. https://doi.org/10.111 1/gcb.12629
Clark, D. A., Brown, S., Kicklighter, D. W., Chambers, J. Q., Thomlinson, J. R., & Ni, J. (2001). Measuring net primary production in forests: concepts and field methods. Ecological Applications, 11(2), 356–370. https://doi.org/10.1890/1051-0761(2001)011
Eggleston, H S, Buendia, L, Miwa, K, Ngara, T, & Tanabe, K. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Japan. http://www.ipcc-nggip.iges.or.jp/public/ 2006gl/index.htm
Fayolle, A., Doucet, J., Gillet, J., Bourland, N., & Lejeune, P. (2013). Tree allometry in Central Africa: Testing the validity of pantropical multi-species allometric equations for estimating biomass and carbon stocks. Forest Ecology and Management, 305, 29–37. https://doi.org/10.1016/j.foreco.2013.05.036
FDRE (Federal Democratic Republic of Ethiopia) (2017). Ethiopia’s forest reference level submission to the United Nations Framework Convention on Climate Change (UNFCCC). Addis Ababa: Ministry of Environment, Forest and Climate Change. https:// redd.unfccc.int/files/ethiopia_frel_3.2_final_ modified_submission.pdf
Freer,S.P.H., Broadmeadow, M., & Lynch, J. (2007). Forestry & climate change. In CABI Publishing eBooks (Issue 1). http://fipak.areeo.ac.ir/site/catalogue/18342944
Gurmessa, F., Warkineh, B., Soromessa, T., & Demissew, S. (2022). Species diversity and plant community distribution along environmental gradient in Tulu Lafto Forest, western Ethiopia. Phytocoenologia. https://doi.org/10.1127/phyto/2022/0376
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Journal of Wildlife Management, 67(3), 655. https://doi.org/10.2307/3802723
Henry, M., Besnard, A., Asante, W., Eshun, J., Adu-Bredu, S., Valentini, R., Bernoux, M., & Saint-André, L. (2010). Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. Forest Ecology and Management, 260(8), 1375–1388. https://doi.org/10.1016/j.foreco.2010.07.040
IBC (2014). Ethiopia’s Fifth National Report to the Convention on Biological Diversity. Addis Ababa, Ethiopia. Pp. 72. https://ebi.gov.et/biodiversity/conservation/projects/cbd-4th-country-report/
Kershaw, J. A., Ducey, M. J., Beers, T. W., & Husch, B. (2016). Forest mensuration. https://doi.org/10.1002/9781118902028
Kunneke, A., Van Aardt, J., Roberts, W., & Seifert, T. (2013). Localisation of biomass potentials. In Managing forest ecosystems (pp. 11–41). https://doi.org/10.1007/978-94-007-7448-3_2
Kuyah, S., Sileshi, G., & Rosenstock, T. (2016). Allometric models based on Bayesian frameworks give better estimates of aboveground biomass in the Miombo woodlands. Forests, 7(2), 13. https://doi.org/10.3390/f7020013
Lung, M., & Espira, A. (2015). The influence of stand variables and human use on biomass and carbon stocks of a transitional African forest: Implications for forest carbon projects. Forest Ecology and Management, 351, 36–46. https://doi.org/10.1016/j.foreco.2015.04.032
Mensah, S., Veldtman, R., Du Toit, B., Kakaï, R. G., & Seifert, T. (2016). Aboveground Biomass and Carbon in a South African Mistbelt Forest and the Relationships with Tree Species Diversity and Forest Structures. Forests, 7(4), 79. https://doi.org/10.3390/f7040079
Muller, L. H. C. (2004). Interspecific and inter‐site variation in wood specific gravity of tropical trees. Biotropica, 36(1), 20–32. https://doi.org/10.1111/j.1744-7429.2004.tb00292.x
Mwakalukwa, E. E., Meilby, H., & Treue, T. (2014). Volume and aboveground biomass models for dry miombo woodland in Tanzania. International Journal of Forestry Research, 2014, 1–11. https://doi.org/10.1155/2014/531256
Ngomanda, A., Obiang, N. L. E., Lebamba, J., Mavouroulou, Q. M., Gomat, H., Mankou, G. S., Loumeto, J., Iponga, D. M., Ditsouga, F. K., Koumba, R. Z., Bobé, K. H. B., Okouyi, C. M., Nyangadouma, R., Lépengué, N., Mbatchi, B., & Picard, N. (2013). 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. https://doi.org/10.1016/j.foreco.2013.10.029
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. https://doi.org/10.1016/j.foreco.2015.05.035
Picard, N., Saint-Andre, L., & Henry, M. (2012). Manual for building tree volume and biomass allometric equations: from field measurement to prediction. Montpellier. Pp 213. https://www.fao.org/4/i3058e/i3058e.pdf
R Development Core Team, (2018). A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at https://www.R-project.org/
Reyes, G., Brown, S., Chapman, J., & Lugo, A. E. (1992). Wood densities of tropical tree species, 1, 1-18. https://doi.org/10.2737/so-gtr-88
Seifert, T., & Seifert, S. (2013). Modelling and simulation of tree biomass. In Managing forest ecosystems, 1, 43–65. https://doi.org/10.1007/ 978-94-007-7448-3_3
Shirima, D. D., Munishi, P. K. T., Lewis, S. L., Burgess, N. D., Marshall, A. R., Balmford, A., Swetnam, R. D., & Zahabu, E. M. (2011). Carbon storage, structure and composition of miombo woodlands in Tanzania’s Eastern Arc Mountains. African Journal of Ecology, 49(3), 332–342. https://doi.org/10.1111/j.1365-2028.2011.01269.x
Tesfaye, M. A., Bravo-Oviedo, A., Bravo, F., & Ruiz-Peinado, R. (2015). Aboveground biomass equations for sustainable production of fuelwood in a native dry tropical afro-montane forest of Ethiopia. Annals of Forest Science, 73(2), 411–423. https://doi.org/10.1007/s13595-015-0533-2
WBISPP, (2004). A National Strategic Plan for the Biomass Energy Sector. Addis Ababa, Ethiopia. https://www.fao.org/4/aj012E/aj012E00.pdf
Worku, E. (2015). Allometric Equation for Biomass Determination in Juniperus procera Endl. and Podocarpus falcatus Mirb of Wof-Washa Forest: Implication for Climate Change Mitigation. American Journal of Life Sciences, 3(3), 190. https://doi.org/10.11648/j.ajl s.20150303.20
Zar, J. H. (2010). 24.8 Confidence limits for a population proportion. Biostatistical Analysis Fifth Edition, Pearson Education, Inc., Upper Saddle River, New Jersey, USA, 543-548. https://bayesmath.com/wp-content/uploads/20 21/05/Jerrold-H.-Zar-Biostatistical-Analysis-5th-Edition-Prentice-Hall-2009.pdf
Downloads
Published
How to Cite
License
Copyright (c) 2025 Journal of Science, Technology and Arts Research

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
STAR © 2023 Copyright; All rights reserved