Skip to main content
Log in

Bayesian and classical biomass allometries for open grown valonian oaks (Q. ithaburensis subs. macrolepis L.) in a silvopastoral system

  • Published:
Agroforestry Systems Aims and scope Submit manuscript

Abstract

Allometric models predicting aboveground woody biomass for open grown valonian oak (Q. ithaburensis subs. macrolepis L.) trees growing in a Mediterranean silvopastoral system were built based on Bayesian and classical statistical techniques. The simple power model M = aDb was used for predicting aboveground woody biomass (M), stem (MS) and branch (MB) biomass through tree diameter (D). An informative Bayesian approach (IB) based on prior information about a and b and increasing variance of predicted values in relation to D was applied on 25 destructively sampled trees for estimating M. Non-informative Bayesian (NB), log-linear regression (LR) and non-linear regression were also built for M, MS and MB. Quite similar M distribution was derived from LR and NB across the D range, totally different from IB predictions which provided biologically sound estimates. Tree height, stem length and crown length did not substantially improve predictions for M, MS and MB. Comparisons to oak trees growing in closed stands indicated that open-grown oaks sustain much less stem biomass but maintain larger branch biomass than forest-grown counterparts. Comparisons to published values for open-grown green ash trees supported the hypothesis that open grown broadleaved specimens may sustain similar M values, irrespectively of species, growth conditions and tree size. On the contrary, allocation pattern of organic matter to stem and branches seems to vary by species and/or site conditions. Finally, predictions for b = 2.67 derived from a theoretical model was not supported by this dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Brown S (1997) Estimating biomass and biomass change of tropical forests. A primer. Forestry paper, vol 134. FAO, Rome

    Google Scholar 

  • Chiyenda SS, Kozak A (1982) Some comments on choosing regression models for biomass prediction equations. For Chron 58:203–204

    Article  Google Scholar 

  • Ducey MJ, Zarin DJ, Vasconcelos SS, Araújo MM (2009) Biomass equations for forest regrowth in the eastern Amazon using randomized branch sampling. Acta Amazon 39:349–360

    Article  Google Scholar 

  • Eamus D, McGuinness K, Burrows W (2000) Review of allometric relationships for estimating woody biomass for Queensland, The Northern Territory and Western Australia. National Carbon Accounting System Technical Report 5b. Australian Greenhouse Office, Canberra

  • Ellison AM (2004) Bayesian inference in ecology. Ecol Lett 7:509–520

    Article  Google Scholar 

  • Enquist BJ, Niklas KJ (2001) Invariant scaling relations across tree-dominated communities. Nature 5:655–660

    Article  Google Scholar 

  • Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2014) Bayesian data analysis, 3rd edn. Chapman and Hall, Boca Raton

    Google Scholar 

  • Gilks W, Richardson S, Spiegelhalter D (eds) (1995) Markov chain Monte Carlo in practice. Springer, Berlin

    Google Scholar 

  • Gitay H, Suarez A, Watson R, Dokken DJ (eds) (2002) Climate change and biodiversity—IPCC Technical Paper V. Intergovernmental panel on climate change. Available at: https://www.ipcc.ch/pdf/technical-papers/climate-changes-biodiversity-en.pdf. Accessed 5 Aug 2016

  • 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(3B):477–569

    Article  Google Scholar 

  • Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2004) Comprehensive database of diameter-based biomass regressions for North American tree species. GTR NE-319 USDA

  • Ketterings QM, Noordwijk CMY, Ambagau R, Palm CA (2001) Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. For Ecol Manag 146:199–209

    Article  Google Scholar 

  • Kort J, Turnock R (1999) Carbon reservoir and biomass in Canadian prairie shelterbelts. Agrofor Syst 44:175–186

    Article  Google Scholar 

  • Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10:325–337

    Article  Google Scholar 

  • Makungwa SD, Chittock A, Skole DL, Kanyama-Phiri GY, Woodhouse IH (2013) Allometry for biomass estimation in jatropha trees planted as boundary hedge in farmers’ fields. Forests 4:218–233

    Article  Google Scholar 

  • McCarthy MA (2007) Bayesian methods for ecology. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Montagnini F, Nair PKR (2004) Carbon sequestration: an underexploited environmental benefit of agroforestry systems. Agrofor Syst 61:281–295

    Google Scholar 

  • Muller-Landau HC, Condit RS, Chave J, Thomas SC, Bohlman SA, Bunyavejchewin S et al (2006) Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests. Ecol Lett 9:575–588

    Article  Google Scholar 

  • Murthy IK, Gupta M, Tomar S, Munsi M, Tiwari R et al (2013) Carbon sequestration potential of agroforestry systems in India. J Earth Sci Clim Change 4:1–7

    Article  Google Scholar 

  • Navar J (2009) Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. For Ecol Manag 257:427–434

    Article  Google Scholar 

  • Navar J (2010) Methods of assessment of aboveground tree biomass. In: Maggy Ndombo M, Momba B. (eds) Biomass InTech. Available: http://www.intechopen.com/books/biomass/methods-of-assessment-of-aboveground-tree-biomass. Accessed 09 Feb 2015

  • Parresol BR (1999) Assessing tree and stand biomass: a review with examples and critical comparisons. For Sci 45:573–593

    Google Scholar 

  • Pastor J, Aber JD, Melillo JM (1984) Biomass prediction using generalised allometric regressions for some Northeast tree species. For Ecol Manag 7:265–274

    Article  Google Scholar 

  • Payandeh B (1981) Choosing regression models for biomass prediction equations. For Chron 57:229–232

    Article  Google Scholar 

  • Pilli R, Anfodillo T, Carrer M (2006) Towards a functional and simplified allometry for estimating forest biomass. For Ecol Manag 237:583–593

    Article  Google Scholar 

  • R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org/

  • Schoeneberger MM (2009) Agroforestry: working trees for sequestering carbon on agricultural lands. Agroforest Syst 75:27–37

    Article  Google Scholar 

  • Sileshi GW (2014) A critical review of forest biomass estimation models, common mistakes and corrective measures. For Ecol Manag 329:237–254

    Article  Google Scholar 

  • Spiegelhalter DJ, Thomas A, Best NG, Lunn D (2007) OpenBUGS User Manual version 3.0.2. MRC Biostatistics Unit, Cambridge

    Google Scholar 

  • Sprugel DG (1983) Correcting for bias in log-transformed allometric equations. Ecology 64:209–210

    Article  Google Scholar 

  • Ter-Mikaelian MT, Korzukhin MD (1997) Biomass equations for sixty-five North American tree species. For Ecol Manag 97:1–24

    Article  Google Scholar 

  • Thomopoulos NT, Johnson AC (2003) Tables and characteristics of the standardized lognormal distribution. In: Proceedings of the Decision Sciences Institute (ed. Decision Sciences Institute) pp 1031–1036

  • Tredennick AT, Lisa Patrick Bentley LP, Hanan NP (2013) Allometric convergence in savanna trees and implications for the use of plant scaling models in variable ecosystems. PLoS ONE 8:e5824

    Article  Google Scholar 

  • West BG, Brown HJ, Enquist JB (1997) A general model for the origin of allometric scaling laws in biology. Science 276:122–126

    Article  CAS  Google Scholar 

  • Xiao X, White EP, Hooten MB, Durham SL (2011) On the use of log transformation versus nonlinear regression for analysing biological power laws. Ecology 92:1887–1894

    Article  Google Scholar 

  • Zapata-Cuartas M, Sierra AC, Alleman L (2012) Probability distribution of allometric coefficients and Bayesian estimation of aboveground tree biomass. For Ecol Manag 277:173–179

    Article  Google Scholar 

  • Zell J, Bösch B, Kändler G (2014) Estimating above-ground biomass of trees: comparing Bayesian calibration with regression technique. Eur J For Res 133:649–660

    Article  Google Scholar 

  • Zhou X, Brandle JR, Awada TN, Schoeneberger MM, Martin DL, Xin Y, Tang ZH (2011) The use of forest-derived specific gravity for the conversion of volume to biomass for open-grown trees on agricultural land. Biomass Bioenerg 35:1721–1731

    Article  Google Scholar 

  • Zhou X, Schoeneberger MM, Brandle JR, Awada TN, Chu J et al (2014) Analyzing the uncertainties in use of forest-derived biomass equations for open-grown trees in agricultural land. For Sci 60:1–18

    CAS  Google Scholar 

  • Zianis D (2008) Predicting mean aboveground forest biomass and its associated variance. For Ecol Manag 256:1400–1407

    Article  Google Scholar 

  • Zianis D, Mencuccini M (2003) Aboveground biomass relationships for beech (Fagus moesiaca Cz.) trees in Vermio Mountain, Northern Greece, and generalised equations for Fagus spp. An For Sci 60:439–448

    Article  Google Scholar 

  • Zianis D, Radoglou K (2006) Comparison between empirical and theoretical biomass allometric models and statistical implications in stem volume predictions. Forestry 79:477–487

    Article  Google Scholar 

  • Zianis D, Muukkonen P, Mäkipää R, Mencuccini M (2005) Biomass and stem volume equations for tree species in Europe. Silva Fenn Monogr 4:5–63

    Google Scholar 

  • Zianis D, Spyroglou G, Tiakas E, Radoglou K (2016) Bayesian and classical models to predict aboveground tree biomass allometry. For Sci 62:247–259

    Google Scholar 

  • Zou GY, Taleban J, Huo CY (2009) Confidence interval estimation for log-normal data with application to health economics. Comput Stat Data Anal 53:3755–3764

    Article  Google Scholar 

Download references

Acknowledgements

This research was co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: ARCHIMEDES III. Investing in knowledge society through the European Social Fund, MIS 380360. Special permits for the destructive tree sampling were granted by the Special Secretariat for Forests, Ministry of Environment, Energy and Climate Change. Three anonymous referees substantially contributed to the improvement of the submitted manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dimitrios Zianis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zianis, D., Pantera, A., Papadopoulos, A. et al. Bayesian and classical biomass allometries for open grown valonian oaks (Q. ithaburensis subs. macrolepis L.) in a silvopastoral system. Agroforest Syst 93, 241–253 (2019). https://doi.org/10.1007/s10457-016-0060-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10457-016-0060-7

Keywords

Navigation