Advertisement

Annals of Forest Science

, Volume 71, Issue 1, pp 101–112 | Cite as

Intra-specific differences in allometric equations for aboveground biomass of eastern Mediterranean Pinus brutia

  • Sergio de-MiguelEmail author
  • Timo Pukkala
  • Nabil Assaf
  • Zuheir Shater
Original Paper

Abstract

Context

Biomass prediction is important when dealing for instance with carbon sequestration, wildfire modeling, or bioenergy supply. Although allometric models based on destructive sampling provide accurate estimates, alternative species-specific equations often yield considerably different biomass predictions. An important source of intra-specific variability remains unexplained.

Aims

The aims of the study were to inspect and assess intra-specific differences in aboveground biomass of Pinus brutia Ten. and to fill the gap in knowledge on biomass prediction for this species.

Methods

Two hundred one trees between 2.3 and 55.8 cm in diameter at breast height were sampled throughout the eastern- and southernmost natural distribution area of P. brutia, in Middle East, where it forms different stand structures. Allometric equations were fitted separately for two countries. The differences in biomass prediction at tree, stand, and forest level were analyzed. The effect of stand structure and past forest management was discussed.

Results

Between-country differences in total aboveground biomass were not large. However, differences in biomass stock were large when tree components were analyzed separately. Trees had higher stem biomass and lower crown biomass in dense even-aged stands than in more uneven-aged and sparse stands.

Conclusion

Biomass and carbon predictions could be improved by taking into account stand structure in biomass models.

Keywords

Allometry Biomass allocation Allometric models Carbon sequestration Biomass prediction Pine Stand structure 

Notes

Acknowledgments

Data collection was supported by Agencia Española de Cooperación Internacional para el Desarrollo (AECID) and Fundación Biodiversidad. The authors wish to thank the Ministries of Agriculture of the Governments of Lebanon and Syria as well as the Forest Sciences Centre of Catalonia (CTFC) for their precious collaboration.

References

  1. António N, Tomé M, Tomé J, Soares P, Fontes L (2007) Effect of tree, stand and site variables on the allometry of Eucalyptus globulus tree biomass. Can J For Res 37:895–906CrossRefGoogle Scholar
  2. Balderas Torres A, Lovett JC (2012) Using basal area to estimate aboveground carbon stocks in forests: La Primavera Biosphere’s Reserve, Mexico. Forestry 86:267–281CrossRefGoogle Scholar
  3. Baskerville GL (1972) Use of logarithmic regression in the estimation of plant biomass. Can J For Res 2:49–53CrossRefGoogle Scholar
  4. Bilgili E, Kucuk O (2009) Estimating above-ground fuel biomass in young calabrian pine (Pinus brutia Ten.). Energy Fuel 23:1797–1800CrossRefGoogle Scholar
  5. Bragg DC, Guldin JM (2010) Estimating long-term carbon sequestration patterns in even- and uneven-aged southern pine stands. In: Jain TB, Graham RT, Sandquist J (eds) Integrated management of carbon sequestration and biomass utilization opportunities in a changing climate: Proceedings of the 2009 National Silviculture Workshop; 2009 June 15–18; Boise, ID. Proceedings RMRS-P-61. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, pp 111–123Google Scholar
  6. Bravo F, Bravo-Oviedo A, Diaz-Balteiro B (2008) Carbon sequestration in Spanish Mediterranean forests under two management alternatives: a modeling approach. Eur J For Res 127:225–234CrossRefGoogle Scholar
  7. Burrows WH, Henry BK, Back PV, Hoffmann MB, Tait LJ, Anderson ER, Menke N, Danaher T, Carter JO, McKeon GM (2002) Growth and carbon stock change in eucalypt woodlands in northeast Australia: ecological and greenhouse sink implications. Glob Chang Biol 8:769–784CrossRefGoogle Scholar
  8. 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, Riéra B, Yamakura T (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99PubMedCrossRefGoogle Scholar
  9. Chave J, Condit R, Aguilar S, Hernandez A, Lao S, Perez R (2004) Error propagation and scaling for tropical forest biomass estimates. Phil Trans R Soc Lond B 359:409–420CrossRefGoogle Scholar
  10. Crow TR (1978) Common regressions to estimate tree biomass in tropical stands. For Sci 24:110–114Google Scholar
  11. de-Miguel S, Mehtätalo L, Shater Z, Kraid B, Pukkala T (2012a) Evaluating marginal and conditional predictions of taper models in the absence of calibration data. Can J For Res 42:1383–1394Google Scholar
  12. de-Miguel S, Pukkala T, Assaf N, Bonet JA (2012b) Even-aged or uneven-aged modelling approach? A case for Pinus brutia. Ann For Sci 69:455–465Google Scholar
  13. de-Miguel S, Pukkala T, Shater Z, Assaf N, Kraid B, Palahí M (2010) Models for simulating the development of even-aged Pinus brutia stands in Middle East. Forest Syst 19:449–457Google Scholar
  14. del Río M, Barbeito I, Bravo-Oviedo A, Calama R, Cañellas I, Herrero C, Bravo F (2008) Carbon sequestration in Mediterranean pines forests. In: Bravo F, LeMay V, Jandl R, von Gadow K (eds) Managing forest ecosystems: the challenge of climate change. Springer, Berlin, pp 221–246, 338 pCrossRefGoogle Scholar
  15. Durkaya A, Durkaya B, Ünsal A (2009) Predicting the above-ground biomass of calabrian pine (Pinus brutia Ten.) stands in Turkey. Afr J Biotechnol 8:2483–2488Google Scholar
  16. Enquist BJ, Niklas KJ (2001) Invariant scaling relations across tree-dominated communities. Nature 410:655–660PubMedCrossRefGoogle Scholar
  17. Feldpausch TR, Banin L, Phillips OL, Baker TR, Lewis SL, Quesada CA, Affum-Baffoe K, Arets EJMM, Berry NJ, Bird M, Brondizio ES, de Camargo P, Chave J, Djagbletey G, Domingues TF, Drescher M, Fearnside PM, França MB, Fyllas NM, Lopez-Gonzalez G, Hladik A, Higuchi N, Hunter MO, Iida Y, Salim KA, Kassim AR, Keller M, Kemp J, King DA, Lovett JC, Marimon BS, Marimon-Junior BH, Lenza E, Marshall AR, Metcalfe DJ, Mitchard ETA, Moran EF, Nelson BW, Nilus R, Nogueira EM, Palace M, Patiño S, Peh KSH, Raventos MT, Reitsma JM, Saiz G, Schrodt F, Sonké B, Taedoumg HE, Tan S, White L, Wöll H, Lloyd J (2011) Height–diameter allometry for tropical forest trees. Biogeosciences 8:1081–1106CrossRefGoogle Scholar
  18. Gauch HG, Hwang JTG, Fick GW (2003) Model evaluation by comparison of model-based predictions and measured values. Agron J 95:1442–1446CrossRefGoogle Scholar
  19. Gray KL, Reinhardt ED (2003) Analysis of Algorithms for predicting canopy fuel. In: Proceedings of the Second International Wildland Fire Ecology and Fire Management Congress and Fifth Symposium on Fire and Forest Meteorology, November 16–20, 2003, Orlando, FL. American Meteorological Society, p 5.8Google Scholar
  20. Guller B (2007) The effects of thinning treatments on density, MOE, MOR and maximum crushing strength of Pinus brutia Ten. wood. Ann For Sci 64:467–475CrossRefGoogle Scholar
  21. Hasenauer H, Monserud RA (1996) A crown ratio model for Austrian forests. For Ecol Manage 84:49–60CrossRefGoogle Scholar
  22. Hemery GE, Savill PS, Pryor SN (2005) Applications of the crown diameter-stem diameter relationship for different species of broadleaved trees. For Ecol Manage 215:285–294CrossRefGoogle Scholar
  23. Henry M, Picard N, Trotta C, Manlay RJ, Valentini R, Bernoux M, Saint-André L (2011) Estimating tree biomass of Sub-Saharan African forests: a review of available allometric equations. Silva Fenn 45:477–569Google Scholar
  24. Hynynen J (1995) Predicting tree crown ratio for unthinned and thinned Scots pine stands. Can J For Res 25:57–62CrossRefGoogle Scholar
  25. Intergovernmental Panel on Climate Change (IPCC) (2006) IPCC guidelines for national greenhouse gas inventories, prepared by the National Greenhouse Gas Inventories Programme, Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (eds). IGES, JapanGoogle Scholar
  26. Isik F, Isik K, Lee SJ (1999) Genetic variation in Pinus brutia Ten. in Turkey: I. growth, biomass and stem quality traits. For Genet 6:89–99Google Scholar
  27. Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2003) National-scale biomass estimators for United States tree species. For Sci 49:12–35Google Scholar
  28. Ketterings QM, Coe R, van Noordwijk M, Ambagau Y, Palm CA (2001) Reducing uncertainty in the use of allometric biomass equations for predicting aboveground tree biomass in mixed secondary forests. For Ecol Manage 146:199–209CrossRefGoogle Scholar
  29. Kuuluvainen T (1991) Relationships between crown projected area and components of above-ground biomass in Norway spruce trees in even-aged stands: empirical results and their interpretation. For Ecol Manage 40:243–260CrossRefGoogle Scholar
  30. Küçük Ö, Bilgili E (2007) Crown fuel load for young calabrian pine (Pinus brutia Ten.) trees. J For Fac Kastamonu Uni-Kastamonu 7:180–189Google Scholar
  31. Küçük Ö, Bilgili E, Saglam B (2008) Estimating crown fuel loading for Calabrian pine and Anatolian black pine. Int J Wildland Fire 17:147–154CrossRefGoogle Scholar
  32. Marklund LG (1987) Biomass functions for Norway spruce (Picea abies (L.) Karst.) in Sweden. Sveriges lantbruksuniversitet Rapporter–Skog 43:1–127Google Scholar
  33. Marklund LG (1988) Biomassafunktioner för tall, gran och björk i Sverige. Sveriges lantbruksuniversitet Rapporter–Skog 45:1–73Google Scholar
  34. Montero G, Ruiz-Peinado R, Muñóz M (2005) Producción de biomasa y fijación de CO2 por los bosques españoles. Monografías INIA nO 13Google Scholar
  35. Muukkonen P (2007) Generalized allometric volume and biomass equations for some tree species in Europe. Eur J For Res 126:157–166CrossRefGoogle Scholar
  36. Mäkelä A (1986) Implications of the pipe model theory on dry matter partitioning and height growth in trees. J Theor Biol 123:103–120CrossRefGoogle Scholar
  37. Naidu SL, DeLucia EH, Thomas RB (1998) Contrasting patterns of biomass allocation in dominant and suppressed loblolly pine. Can J For Res 28:1116–1124CrossRefGoogle Scholar
  38. Návar J (2009) Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. For Ecol Manage 257:427–434CrossRefGoogle Scholar
  39. Návar J (2010) Measurement and assessment methods of forest aboveground biomass: a literature review and the challenges ahead. In: Momba M, Bux F (eds) Biomass. Sciyo, Croatia, pp 27–64Google Scholar
  40. Palahí M, Pukkala T, Kasimiadis D, Poirazidis K, Papageorgiou AC (2008) Modelling site quality and individual-tree growth in pure and mixed Pinus brutia stands in north-east Greece. Ann For Sci 65:501CrossRefGoogle Scholar
  41. Palumets YK (1988) Distribution of Norway spruce phytomass fractions as a function of age and climatic factors. Soviet Forest Sci 2:34–40Google Scholar
  42. Parresol BR (2001) Additivity of non-linear biomass equations. Can J For Res 31:865–878CrossRefGoogle Scholar
  43. Peichl M, Arain MA (2007) Allometry and partitioning of above- and belowground tree biomass in an age-sequence of white pine forests. For Ecol Manage 253:68–80CrossRefGoogle Scholar
  44. Porté A, Trichet P, Bert D, Loustau D (2002) Allometric relationships fro branch and tree woody biomass of maritime pine (Pinus pinaster Ait.). For Ecol Manage 158:71–83CrossRefGoogle Scholar
  45. Pukkala T, Karsikko J, Kolström T (1992) A spatial model for the diameter of thickest branch of Scots pine. Silva Fenn 26:219–230Google Scholar
  46. Pukkala T, Lähde E, Laiho O, Salo K, Hotanen JP (2011) A multifunctional comparison of even-aged and uneven-aged forest management in a boreal region. Can J For Res 41:851–862CrossRefGoogle Scholar
  47. R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0. http://www.R-project.org/
  48. Repola J (2009) Biomass equations for Scots pine and Norway spruce in Finland. Silva Fenn 43:625–647Google Scholar
  49. Shater Z, de-Miguel S, Kraid B, Pukkala T, Palahí M (2011) A growth and yield model for even-aged Pinus brutia stands in Syria. Ann For Sci 68:149–157CrossRefGoogle Scholar
  50. Snowdon P, Eamus D, Gibbons P, Khanna PK, Keith H, Raison RJ, Kirschbaum MUF (2000) Synthesis of allometrics, review of root biomass and design of future woody biomass sampling strategies. National Carbon Accounting System Technical Report 17. Australian Greenhouse Office, Canberra, 114 pGoogle Scholar
  51. Ter-Mikaelian MT, Korzukhin MD (1997) Biomass equations for sixty-five North American tree species. For Ecol Manage 97:1–24CrossRefGoogle Scholar
  52. Tinker D, Stakes GK, Arcano RM (2010) Allometric equation development, biomass, and aboveground productivity in Ponderosa pine forests, Black Hills, Wyoming. West J Appl For 25:112–119Google Scholar
  53. 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. For Ecol Manage 262:1648–1657CrossRefGoogle Scholar
  54. West GB, Brown JH, Enquist BJ (1999) A general model for the structure and allometry of plant vascular systems. Nature 400:664–667CrossRefGoogle Scholar
  55. Zianis D, Mencuccini M (2004) On simplifying allometric analyses of forest biomass. For Ecol Manage 187:311–332CrossRefGoogle Scholar
  56. Zianis D, Muukkonen P, Mäkipää R, Mencuccini M (2005) Biomass and stem volume equations for tree species in Europe. Silva Fenn Monographs 4, 63 pGoogle Scholar
  57. Zianis D, Xanthopoulos G, Kalabodikis K, Kazakis G, Ghosn D, Roussou O (2011) Allometric equations for aboveground biomass estimation by size class for Pinus brutia Ten. trees growing in North and South Aegean Islands, Greece. Eur J For Res 130:145–160CrossRefGoogle Scholar

Copyright information

© INRA and Springer-Verlag France 2013

Authors and Affiliations

  • Sergio de-Miguel
    • 1
    Email author
  • Timo Pukkala
    • 1
  • Nabil Assaf
    • 2
  • Zuheir Shater
    • 3
  1. 1.Faculty of Science and ForestryUniversity of Eastern FinlandJoensuuFinland
  2. 2.Food and Agriculture Organization of the United Nations (FAO)AlgiersAlgeria
  3. 3.Department of Forestry and Ecology, Faculty of AgricultureUniversity of TishreenLatakiaSyria

Personalised recommendations