Journal of Forestry Research

, Volume 21, Issue 4, pp 475–481 | Cite as

Allometric prediction of above-ground biomass of eleven woody tree species in the Sudanian savanna-woodland of West Africa

  • Louis Sawadogo
  • Patrice Savadogo
  • Daniel Tiveau
  • Sidzabda Djibril Dayamba
  • Didier Zida
  • Yves Nouvellet
  • Per Christer Oden
  • Sita Guinko
Original Paper

Abstract

Allometric models are necessary for estimating biomass in terrestrial ecosystems. Generalized allometric relationship exists for many tropical trees, but species- and region-specific models are often lacking. We developed species-specific allometric models to predict aboveground biomass for 11 native tree species of the Sudanian savanna-woodlands. Diameters at the base and at breast height, with species means ranging respectively from 11 to 28 cm and 9 to 19 cm, and the height of the trees were used as predictor variables. Sampled trees spanned a wide range of sizes including the largest sizes these species can reach. As a response variable, the biomass of the trees was obtained through destructive sampling of 4 754 trees during wood harvesting. We used a stepwise multiple regression analysis with backward elimination procedure to develop models separately predicting, total biomass of the trees, stem biomass, and biomass of branches and twigs. All species-specific regression models relating biomass with measured tree dimensions were highly significant (p < 0.001). The biomass of branches and twigs was less predictable compared to stem biomass and total biomass, although their models required fewer predictors and predictor interactions. The best-fit equations for total above-ground biomass and stem biomass had R2 > 0.70, except for the Acacia species; for branches including twig biomass, R2-values varied from 0.749 for Anogeissus leiocarpa to 0.183 for Acacia macrostachya. The use of these equations in estimating available biomass will avoid destructive sampling, and aid in planning for sustainable use of these species.

Keywords

allometry above-ground biomass indigenous woody species linear regression site specific equation 

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References

  1. Aboal JR, Arevalo JR, Fernandez A. 2005. Allometric relationships of different tree species and stand above ground biomass in the Gomera laurel forest (Canary Islands). Flora, 200: 264–274.Google Scholar
  2. Arbonnier M. 2004. Trees, shrubs and lianas of West African dry zones. CIRAD Margraf, GMBH, MNHN, Paris, France.Google Scholar
  3. Baskerville GL. 1965. Estimation of dry weight of tree components and total standing crop in conifer stands. Ecology, 46: 867–869.CrossRefGoogle Scholar
  4. Bellefontaine R, Gaston A, Petrucci Y. 2000. Management of natural forests of dry tropical zones. Food and Agriculture Organization of the United Nations, Rome.Google Scholar
  5. Brocard D, Lacaux JP, Eva H. 1998. Domestic biomass combustion and associated atmospheric emissions in West Africa. Global Biogeochemical Cycles, 12: 127–139.CrossRefGoogle Scholar
  6. Brown S, Gillespie AJ, Lugo AE. 1989. Biomass estimation methods for tropical forests with applications to forest inventory data. Forest Science, 35: 881–902.Google Scholar
  7. 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.CrossRefPubMedGoogle Scholar
  8. Chidumayo EN. 1990. Aboveground woody biomass structure and productivity in a Zambezian woodland. Forest Ecology and Management, 36: 33–46.CrossRefGoogle Scholar
  9. Cole TG, Ewel JJ. 2006. Allometric equations for four valuable tropical tree species. Forest Ecology and Management, 229: 351–360.CrossRefGoogle Scholar
  10. Crawley MJ. 2005. Statistics: An introduction using R. Chister, England: John Wiley & Sons,.Google Scholar
  11. Faraway JJ. 2005. Extending Linear Model With R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Ltd, Boca Raton, Fla USA.Google Scholar
  12. Fearnside PM. 1997. Wood density for estimating forest biomass in Brazilian Amazonia. Forest Ecology and Management, 90: 59–87.CrossRefGoogle Scholar
  13. Grundy IM. 1995. Wood biomass estimation in dry miombo woodland in Zimbabwe. Forest Ecology and Management, 72: 109–117.CrossRefGoogle Scholar
  14. Ketterings QM, Coe R, van Noordwijk M, Ambagau Y, Palm CA. 2001. Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. Forest Ecology and Management, 146: 199–209.CrossRefGoogle Scholar
  15. Komiyama A, Jintana V, Sangtiean T, Kato S. 2002. A common allometric equation for predicting stem weight of mangroves growing in secondary forests. Ecological Research, 17: 415–418.CrossRefGoogle Scholar
  16. Litton CM, Kauffman JB. 2008. Allometric models for predicting aboveground biomass in two widespread woody plants in Hawaii. Biotropica, 40: 313–320.CrossRefGoogle Scholar
  17. Navar J. 2002. Biomass estimation equations in the Tamaulipan thornscrub of north-eastern Mexico. Journal of Arid Environments, 52: 167–179.CrossRefGoogle Scholar
  18. Navar J. 2009a. Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. Forest Ecology and Management, 257: 427–434.CrossRefGoogle Scholar
  19. Navar J. 2009b. Biomass component equations for Latin American species and groups of species. Annals of Forest Sciences, 66: 208–216.CrossRefGoogle Scholar
  20. Nouvellet Y. 1993. Evolution d’un taillis de formation naturelle en zone soudano-sahélienne au Burkina Faso. Bois et Forêts des Tropiques, 237: 45–60.Google Scholar
  21. Nygard R, Elfving B. 2000. Stem basic density and bark proportion of 45 woody species in young savanna coppice forests in Burkina Faso. Annals of Forest Science, 57: 143–153.CrossRefGoogle Scholar
  22. Overman JPM, Witte HJL, Saldarriaga JG. 1994. Evaluation of regression models for above-ground biomass determination in Amazon rainforest. Journal of Tropical Ecology, 10: 207–218.CrossRefGoogle Scholar
  23. R Development Core Team. 2009. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.Google Scholar
  24. Savadogo P, Tiveau D, Sawadogo L, Tigabu M. 2008. Herbaceous species responses to long-term effects of prescribed fire, grazing and selective tree cutting in the savanna-woodlands of West Africa. Perspectives in Plant Ecology Evolution and Systematics, 10: 179–195.CrossRefGoogle Scholar
  25. Smektala G, Hautdidier B, Gautier D, Peltier R, Njiemoun A. 2002. Construction de tarifs de biomasse pour l’évaluation de la disponibilité ligneuse en zone de savanes au Nord-Cameroun. Actes du colloque, 27–31Google Scholar
  26. Mai 2002, Garoua, Cameroun. Stromgaard P. 1985. Biomass estimation equations for miombo woodland, Zambia. Agroforestry Systems, 3: 3–13.Google Scholar
  27. Tabachnick BG, Fidell LS. 1996. Using multivariate statistics. New York: Harper Collins College Publishers.Google Scholar
  28. Ter-Mikaelian MT, Korzukhin MD. 1997. Biomass equations for 65 North American tree species. Forest Ecology and Management, 97: 1–24.CrossRefGoogle Scholar
  29. Whittaker RH. 1968. Dimension and production relations of trees and shrubs in the Brookhaven forest, New York. Journal of Ecology, 56: 1–25.CrossRefGoogle Scholar
  30. Wiemann MC, Williamson GB. 1989. Wood specific-gravity gradients in tropical dry and montane rain-forest trees. American Journal of Botany, 76: 924–928.CrossRefGoogle Scholar
  31. Willebrand E, Ledin S, Verwijst T. 1993. Willow coppice systems in short rotation forestry: effects of plant spacing, rotation length and clonal composition on biomass production. Biomass and Energy, 4: 323–331.CrossRefGoogle Scholar
  32. Zianis D, Mencuccini M. 2004. On simplifying allometric analyses of forest biomass. Forest Ecology and Management, 187: 311–332.CrossRefGoogle Scholar
  33. Zuur AF, Leno EN, Walker NJ, Saveliev AA, Smith GM. 2009. Mixed effects models and extensions in ecology with R. New York: Springer-Verlag,.CrossRefGoogle Scholar

Copyright information

© Northeast Forestry University and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Louis Sawadogo
    • 1
  • Patrice Savadogo
    • 1
    • 2
  • Daniel Tiveau
    • 3
  • Sidzabda Djibril Dayamba
    • 2
  • Didier Zida
    • 1
  • Yves Nouvellet
    • 4
  • Per Christer Oden
    • 2
  • Sita Guinko
    • 5
  1. 1.Département Productions ForestièresInstitut de l’Environnement et de Recherches AgricolesKoudougouBurkina Faso
  2. 2.Faculty of Forest Sciences, Southern Swedish Forest Research CentreSwedish University of Agricultural SciencesAlnarpSweden
  3. 3.Ambassade de SuèdeKinshasa-GombeRépublique Démocratique du Congo
  4. 4.Consulat Général de France à Pointe Noire (Congo) Valise DiplomatiqueParis 07 SPFrance
  5. 5.Unité de Formation et Recherche en Sciences de la Vie et de la TerreUniversité de OuagadougouOuagadougou 03Burkina Faso

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