Journal of Ecology and Environment

The Ecological Society of Korea (한국생태학회)
권호 표지
DOI QR코드

DOI QR Code

Allometric equation for estimating aboveground biomass of Acacia-Commiphora forest, southern Ethiopia

  • Wondimagegn Amanuel (Ethiopian Forestry Development (EFD), Hawassa Center) ;
  • Chala Tadesse (Ethiopian Forestry Development (EFD)) ;
  • Moges Molla (Ethiopian Forestry Development (EFD), Hawassa Center) ;
  • Desalegn Getinet (Ethiopian Forestry Development (EFD)) ;
  • Zenebe Mekonnen (Ethiopian Forestry Development (EFD))
  • Received : 2024.03.20
  • Accepted : 2024.05.13
  • Published : 2024.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Background: Most of the biomass equations were developed using sample trees collected mainly from pan-tropical and tropical regions that may over- or underestimate biomass. Site-specific models would improve the accuracy of the biomass estimates and enhance the country's measurement, reporting, and verification activities. The aim of the study is to develop site-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 140 trees was harvested from each diameter class biomass model development. Data was analyzed using SAS procedures. All relevant statistical tests (normality, multicollinearity, and heteroscedasticity) were performed. Data was transformed to logarithmic functions and multiple linear regression techniques were used to develop model to estimate aboveground biomass (AGB). The root mean square error (RMSE) was used for measuring model bias, precision, and accuracy. The coefficient of determination (R2 and adjusted [adj]-R2), the Akaike Information Criterion (AIC) and the Schwarz Bayesian information Criterion was employed to select most appropriate models. Results: For the general total AGB models, adj-R2 ranged from 0.71 to 0.85, and model 9 with diameter at stump height at 10 cm (DSH10), ρ and crown width (CW) as predictor variables, performed best according to RMSE and AIC. For the merchantable stem models, adj-R2 varied from 0.73 to 0.82, and model 8) with combination of ρ, diameter at breast height and height (H), CW and DSH10 as predictor variables, was best in terms of RMSE and AIC. The results showed that a best-fit model for above-ground biomass of tree components was developed. AGBStem = exp {-1.8296 + 0.4814 natural logarithm (Ln) (ρD2H) + 0.1751 Ln (CW) + 0.4059 Ln (DSH30)} AGBBranch = exp {-131.6 + 15.0013 Ln (ρD2H) + 13.176 Ln (CW) + 21.8506 Ln (DSH30)} AGBFoliage = exp {-0.9496 + 0.5282 Ln (DSH30) + 2.3492 Ln (ρ) + 0.4286 Ln (CW)} AGBTotal = exp {-1.8245 + 1.4358 Ln (DSH30) + 1.9921 Ln (ρ) + 0.6154 Ln (CW)} Conclusions: The results demonstrated that the development of local models derived from an appropriate sample of representative species can greatly improve the estimation of total AGB.

Keywords

Acknowledgement

We have grateful to the South Omo Zone Environmental Protection and Forest Development office members who participated through field data collection. The authors are grateful to the reviewers and the academic editor for the constructive and insightful comments.

References

  1. Andersson F. Ecological studies in a Scanian woodland and meadow area, southern Sweden. II. Plant biomass, primary production and turnover of organic matter. Bot Not. 1970;123(1):8-51.
  2. Basuki TM, van Laake PE, Skidmore AK, Hussin YA. Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests. For Ecol Manag. 2009;257(8):1684-94. https://doi.org/10.1016/j.foreco.2009.01.027.
  3. Brown S. Estimating biomass and biomass change of tropical forest: a primer. Rome: Food and Agriculture Organization of the United Nations (FAO); 1997.
  4. Brown S. Measuring carbon in forests: current status and future challenges. Environ Pollut. 2002;116(3):363-72. https://doi.org/10.1016/s0269-7491(01)00212-3.
  5. Brown S, Gillespie AJR, Lugo AE. Biomass estimation methods for tropical forests with applications to forest inventory data. For Sci. 1989;35(4):881-902. https://doi.org/10.1093/forestscience/35.4.881.
  6. Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia. 2005;145(1):87-99. https://doi.org/10.1007/s00442-005-0100-x.
  7. Chave J, Rejou-Mechain M, Burquez A, Chidumayo E, Colgan MS, Delitti WB, et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol. 2014;20(10):3177-90. https://doi.org/10.1111/gcb.12629.
  8. Curtis JT, McIntosh RP. The interrelations of certain analytic and synthetic phytosociological characters. Ecology. 1950;31(3):434-55. https://doi.org/10.2307/1931497.
  9. Djomo AN, Ibrahima A, Saborowski J, Gravenhorst G. Allometric equations for biomass estimations in Cameroon and pan moist tropical equations including biomass data from Africa. For Ecol Manag. 2010;260(10):1873-85. https://doi.org/10.1016/j.foreco.2010.08.034.
  10. Dovrat G, Meron E, Shachak M, Golodets C, Osem Y. Plant size is related to biomass partitioning and stress resistance in water-limited annual plant communities. J Arid Environ. 2019;165:1-9. https://doi.org/10.1016/j.jaridenv.2019.04.006.
  11. Federal Democratic Republic of Ethiopia. Ethiopia's climate-resilient green economy: green economy strategy. Addis Ababa: Federal Democratic Republic of Ethiopia; 2011.
  12. Fonseca W, Alice FE, Rey-Benayas JM. Carbon accumulation in aboveground and belowground biomass and soil of different age native forest plantations in the humid tropical lowlands of Costa Rica. New For. 2012;43(2):197-211. https://doi.org/10.1007/s11056-011-9273-9.
  13. Friis I, Demissew S, van Breugel P. Atlas of the potential vegetation of Ethiopia. Copenhagen: The Royal Danish Academy of Sciences and Letters; 2010.
  14. Gibbs HK, Brown S, Niles JO, Foley JA. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett. 2007;2(4):045023. https://doi.org/10.1088/1748-9326/2/4/045023.
  15. Henry M, Besnard A, Asante WA, Eshun J, Adu-Bredu S, Valentini R, et al. Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. For Ecol Manag. 2010;260(8);1375-88. https://doi.org/10.1016/j.foreco.2010.07.040.
  16. Kachamba DJ, Eid T, Gobakken T. Above- and belowground biomass models for trees in the miombo woodlands of Malawi. Forests. 2016;7(2):38. https://doi.org/10.3390/f7020038.
  17. Kangas A, Maltamo M. Forest inventory: methodology and applications. Dordrecht: Springer; 2006.
  18. Kent M, Coker P. Vegetation description and data analysis: a practical approach. Boca Raton: CRC Press; 1992.
  19. Kenzo T, Furutani R, Hattori D, Kendawang JJ, Tanaka S, Sakurai K, et al. Allometric equations for accurate estimation of above-ground biomass in logged-over tropical rainforests in Sarawak, Malaysia. J For Res. 2009;14(6):365-372. https://doi.org/10.1007/s10310-009-0149-1.
  20. Kuyah S, Dietz J, Muthuri C, Jamnadass R, Mwangi P, Coe R, et al. Allometric equations for estimating biomass in agricultural landscapes: I. aboveground biomass. Agric Ecosyst Environ. 2012;158:216-24. https://doi.org/10.1016/j.agee.2012.05.011.
  21. Litton CM, Kauffman JB. Allometric models for predicting aboveground biomass in two widespread woody plants in Hawaii. Biotropica. 2008;40(3):313-20. https://doi.org/10.1111/j.1744-7429.2007.00383.x.
  22. Mokria M, Mekuria W, Gebrekirstos A, Aynekulu E, Belay B, Gashaw T, et al. Mixed-species allometric equations and estimation of aboveground biomass and carbon stocks in restoring degraded landscape in northern Ethiopia. Environ Res Lett. 2018;13(2):024022. http://doi.org/10.1088/1748-9326/aaa495.
  23. Mugasha WA, Eid T, Bollandsas OM, Malimbwi RE, Chamshama SAO, Zahabu E, et al. Allometric models for prediction of above- and belowground biomass of trees in the miombo woodlands of Tanzania. For Ecol Manag. 2013;310:87-101. https://doi.org/10.1016/j.foreco.2013.08.003.
  24. Nelson BW, Mesquita R, Pereira JLG, Souza SGA, Batista GT, Couto LB. Allometric regressions for improved estimate of secondary forest biomass in the central Amazon. For Ecol Manag. 1999;117(1-3):149-67. https://doi.org/10.1016/S0378-1127(98)00475-7.
  25. Ngomanda A, Obiang NLE, Lebamba J, Mavouroulou QM, Gomat H, Mankou GS, et al. Site-specific versus pantropical allometric equations: which option to estimate the biomass of a moist central African forest? For Ecol Manag. 2014;312:1-9. https://doi.org/10.1016/j.foreco.2013.10.029.
  26. Parresol BR. Assessing tree and stand biomass: a review with examples and critical comparisons. For Sci. 1999;45(4):573-93. https://doi.org/10.1093/forestscience/45.4.573.
  27. Pascal JP, Pelissier R. Structure and floristic composition of a tropical evergreen forest in south-west India. J Trop Ecol. 1996;12(2):191-214. https://doi.org/10.1017/S026646740000941X.
  28. Picard N, Saint-Andre L, Henry M. Manual for building tree volume and biomass allometric equations: from field measurement to prediction. Rome: Montpellier: Food and Agriculture Organization of the United Nations (FAO); Centre de Cooperation Internationale en Recherche Agronomique pour le Developpement (CIRAD); 2012.
  29. Pilli R, Anfodillo T, Carrer M. Towards a functional and simplified allometry for estimating forest biomass. For Ecol Manag. 2006;237(1-3):583-93. https://doi.org/10.1016/j.foreco.2006.10.004.
  30. Pothong T, Elliott S, Chairuangsri S, Chanthorn W, Shannon DP, Wangpakapattanawong P. New allometric equations for quantifying tree biomass and carbon sequestration in seasonally dry secondary forest in northern Thailand. New For. 2022;53(1):17-36. https://doi.org/10.1007/s11056-021-09844-3.
  31. Readiness Preparation Proposal (R-PP). Readiness Preparation Proposal (R-PP) Socialist Republic of Vietnam. Ha Noi: Forest Carbon Partnership Facility (FCPF); 2011.
  32. Roxburgh SH, Paul KI, Clifford D, England JR, Raison RJ. Guidelines for constructing allometric models for the prediction of woody biomass: how many individuals to harvest? Ecosphere. 2015;6(3):1-27. https://doi.org/10.1890/ES14-00251.1.
  33. Salis SM, Assis MA, Mattos PP, Piao ACS. Estimating the aboveground biomass and wood volume of savanna woodlands in Brazil's Pantanal wetlands based on allometric correlations. For Ecol Manag. 2006;228(1-3):61-8. https://doi.org/10.1016/j.foreco.2006.02.025.
  34. Smith RJ. Logarithmic transformation bias in allometry. Am J Phys Anthropol. 1993;90(2):215-28. https://doi.org/10.1002/ajpa.1330900208.
  35. Ubuy MH, Eid T, Bollandsas OM, Birhane E. Aboveground biomass models for trees and shrubs of exclosures in the drylands of Tigray, northern Ethiopia. J Arid Environ. 2018;156:9-18. https://doi.org/10.1016/j.jaridenv.2018.05.007.
  36. van Breugel M, Ransijn J, Craven D, Bongers F, Hall JS. Estimating carbon stock in secondary forests: decisions and uncertainties associated with allometric biomass models. For Ecol Manag. 2011;262(8):1648-57. https://doi.org/10.1016/j.foreco.2011.07.018.
  37. Vieilledent G, Vaudry R, Andriamanohisoa SF, Rakotonarivo OS, Randrianasolo HZ, Razafindrabe HN, et al. A universal approach to estimate biomass and carbon stock in tropical forests using generic allometric models. Ecol Appl. 2012;22(2):572-83. https://doi.org/10.1890/11-0039.1.
  38. Whittaker RH, Woodwell GM. Dimension and production relations of trees and shrubs in the Brookhaven Forest, New York. J Ecol. 1968;56(1):1-25. https://doi.org/10.2307/2258063.
  39. Williamson GB, Wiemann MC. Measuring wood specific gravity...correctly. Am J Bot. 2010;97(3):519-24. https://doi.org/10.3732/ajb.0900243.
  40. Zeleke G, Dejene T, Tadesse W, Martin-Pinto P. Gum Arabic production and population status of Senegalia senegal (L.) britton in dryland forests in South Omo Zone, Ethiopia. Sustainability. 2021;13(21):11671. https://doi.org/10.3390/su132111671.