New Forests

, Volume 49, Issue 3, pp 363–382 | Cite as

Sapling biomass allometry and carbon content in five afforestation species on marginal farmland in semi-arid Benin

  • Florent Noulèkoun
  • Jesse B. Naab
  • John P. A. Lamers
  • Sophia Baumert
  • Asia Khamzina


Allometric equations are routinely used in the estimation of biomass stocks for carbon accounting within forest ecosystems. However, generic equations may not reflect the growth trajectories of afforestation species that are introduced to degraded farmland characterized by water and nutrient limitations. Using sequential measurements of the height, basal diameter, and above- and belowground biomass of juvenile trees, we developed allometric equations for five woody species (Moringa oleifera Lam., Leucaena leucocephala Lam., Jatropha curcas L., Anacardium occidentale L. and Parkia biglobosa Jacq.) subjected to a gradient of water and nutrient availability in an afforestation trial on degraded cropland in the semi-arid zone of Benin, West Africa. For three of the species studied, the allometric relationships between basal diameter and biomass components (i.e. leaves, stems and roots) were described best by a simple power-law model (R2 > 0.93). The incorporation of species-specific height–diameter relationships and total height as additional predictors in the power-law function also produced reasonable estimates of biomass. Fifteen months after planting, roots accounted for 10–58% of the total biomass while the root-to-shoot ratio ranged between 0.16 and 0.73. The total biomass of the saplings ranged between 1.4 and 10.3 Mg ha−1, yielding 0.6–4.3 Mg C ha−1, far exceeding the biomass in the traditional fallow system. The rate of stem carbon sequestration measured ca. 0.62 Mg C ha−1 year−1. Overall, the allometric equations developed in this study are generally useful for assessing the standing shoot and root biomass of the five afforestation species during the juvenile growth stage and can help in reporting and verifying carbon stocks in young forests.


Carbon stock Height–diameter allometry Jatropha curcas Power function Roots West Africa 



The authors are grateful to the German Federal Ministry of Education and Research (BMBF) who funded this study under the WASCAL (West African Science Service Center on Climate Change and Adapted Land Use) program, Project No. 00100218. We thank Dr. Nadine Worou, the technical staff of the WASCAL study catchment in Dassari, and the numerous workers who facilitated the field activities. We greatly appreciate the statistical guidance by Dr. Guido Lüchters.

Supplementary material

11056_2017_9624_MOESM1_ESM.docx (29 kb)
Supplementary material 1 (DOCX 29 kb)


  1. Achten WMJ, Maes WH, Reubens B, Mathijs E, Singh VP, Verchot L et al (2010) Biomass production and allocation in Jatropha curcas L. seedlings under different levels of drought stress. Biomass Bioenerg 34:667–676. CrossRefGoogle Scholar
  2. Baumert S, Khamzina A (2015) Allometric relations in Jatropha curcas production systems of Burkina Faso. J Arid Environ 120:95–104. CrossRefGoogle Scholar
  3. Baumert S, Khamzina A, Vlek PLG (2018) Greenhouse gas and energy balance of Jatropha biofuel production systems of Burkina Faso. Energy Sustain Dev 42:14–23. CrossRefGoogle Scholar
  4. Blujdea VNB, Pilli R, Dutca I, Ciuvat L, Abrudan IV (2012) Allometric biomass equations for young broadleaved trees in plantations in Romania. For Ecol Manag 264:172–184. CrossRefGoogle Scholar
  5. Brown S (1997) Estimating biomass and biomass change of tropical forests: a primer. UN FAO forestry paper 134, Food and Agriculture Organization, RomeGoogle Scholar
  6. Brown S, Gillespie AJ, Lugo AE (1989) Biomass estimation methods for tropical forests with applications to forest inventory data. For Sci 35(4):881–902Google Scholar
  7. Cairns MA, Brown S, Helmer EH, Baumgardner GA (1997) Root biomass allocation in the world’s upland forests. Oecologia 111(1):1–11CrossRefPubMedGoogle Scholar
  8. Chave J, Condit R, Aguilar S, Hernandez A, Lao S, Rolando P (2004) Error propagation and scaling for tropical forest biomass estimates. Philos Trans R Soc Lond 359:409–420CrossRefGoogle Scholar
  9. Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WB et al (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Change Biol 20:3177–3190. CrossRefGoogle Scholar
  10. Delagrange S, Messier C, Lechowicz MJ, Dizengremel P (2004) Physiological, morphological and allocational plasticity in understory deciduous trees: importance of plant size and light availability. Tree Physiol 24:775–784CrossRefPubMedGoogle Scholar
  11. Diédhiou I, Diallo D, Mbengue A, Hernandez RR, Bayala R, Diéme R, Diédhiou PM, Sène A (2017) Allometric equations and carbon stocks in tree biomass of Jatropha curcas L. in Senegal’s Peanut Basin. Glob Ecol Conserv 9:61–69CrossRefGoogle Scholar
  12. Djanibekov U, Djanibekov N, Khamzina A, Bhaduri A, Lamers JPA, Berg E (2013) Impacts of innovative forestry land use on rural livelihood in a bimodal agricultural system in irrigated drylands. Land Use Policy 35:95–106. CrossRefGoogle Scholar
  13. Ghezehei SB, Annandale JG, Everson CS (2009) Shoot allometry of Jatropha curcas. South For 71:279–286Google Scholar
  14. Hellings BF, Romijn HA, Franken YJ (2012) Carbon storage in Jatropha curcas tress in Northern Tanzania. FACT Foundation, EindhovenGoogle Scholar
  15. 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 Fennica 45(3B):477–569CrossRefGoogle Scholar
  16. Hosonuma N, Herold M, De Sy V, De Fries RS, Brockhaus M, Verchot L, Angelsen A, Romijn E (2012) An assessment of deforestation and forest degradation drivers in developing countries. Environ Res Lett 7(4):1–12. CrossRefGoogle Scholar
  17. IPCC (2003) Good practice guidance for land use, land-use change and forestry. Intergovernmental Panel on Climate Change Geneva, SeelisbergGoogle Scholar
  18. Kalliovirta J, Tokola T (2005) Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information. Silva Fennica 39(2):227–248CrossRefGoogle Scholar
  19. 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 Manag 146:199–209CrossRefGoogle Scholar
  20. Kuyah S, Dietz J, Muthuri C, Jamnadass R, Mwangi P, Coe R, Neufeldt H (2012a) Allometric equations for estimating biomass in agricultural landscapes: I. Aboveground biomass. Agric Ecosyst Environ 158:225–234. CrossRefGoogle Scholar
  21. Kuyah S, Dietz J, Muthuri C, Jamnadass R, Mwangi P, Coe R, Neufeldt H (2012b) Allometric equations for estimating biomass in agricultural landscapes: II. Belowground biomass. Agric Ecosyst Environ 158:225–234. CrossRefGoogle Scholar
  22. Kuyah S, Dietz J, Muthuri C, van Noordwijk M, Neufeldt H (2013) Allometry and partitioning of above-and below-ground biomass in farmed eucalyptus species dominant in Western Kenyan agricultural landscapes. Biomass Bioenerg 55:276–284. CrossRefGoogle Scholar
  23. Kuyah S, Sileshi GW, Rosenstock TS (2016) Allometric models based on Bayesian frameworks give better estimates of aboveground biomass in the Miombo woodlands. Forests 7(2):13. CrossRefGoogle Scholar
  24. Lewis SL, Lopez-Gonzalez G, Sonké B, Affum-Baffoe K, Baker TR, Ojo LO et al (2009) Increasing carbon storage in intact African tropical forests. Nature 457(7232):1003–1006. CrossRefPubMedGoogle Scholar
  25. Lin K, Lyu M, Jiang M, Chen Y, Li Y, Chen G, Xie J, Yang Y (2017) Improved allometric equations for estimating biomass of the three Castanopsis carlesii H. forest types in subtropical China. New For 48(1):115–135. CrossRefGoogle Scholar
  26. Lupi C, Larocque GR, DesRochers A, Labrecque M, Mosseler A, Major J et al (2017) Biomass from young hardwood stands on marginal lands: allometric equations and sampling methods. Biomass Bioenerg 98:172–181. CrossRefGoogle Scholar
  27. Mokany K, Raison JR, Prokushkin A (2006) Critical analysis of root: shoot ratios in terrestrial biomes. Glob Change Biol 12:84–96. CrossRefGoogle Scholar
  28. Nelson AS, Weiskittel AR, Wagner RG, Saunders MR (2014) Development and evaluation of aboveground small tree biomass models for naturally regenerated and planted species in eastern Maine, USA. Biomass Bioenerg 68:215–227. CrossRefGoogle Scholar
  29. Niklas KJ, Enquist BJ (2002) On the vegetative biomass partitioning of seed plant leaves, stems, and roots. Am Nat 159:482–497. CrossRefPubMedGoogle Scholar
  30. Noulèkoun F, Lamers JPA, Naab J, Khamzina A (2017a) Shoot and root responses of woody species to silvicultural management for afforestation of degraded croplands in the Sudano-Sahelian zone of Benin. For Ecol Manag 385:254–263. CrossRefGoogle Scholar
  31. Noulèkoun F, Khamzina A, Naab J, Lamers JPA (2017b) Biomass allocation in five semi-arid afforestation species is driven mainly by ontogeny rather than resource availability. Ann For Sci 74:78. CrossRefGoogle Scholar
  32. Orwa C, Mutua A, Kindt R, Jamnadass R, Simons A (2009) Agroforestry database: a tree reference and selection guide version 4.0. World Agroforestry Centre ICRAF, NairobiGoogle Scholar
  33. Packard GC, Birchard GF, Boardman TJ (2011) Fitting statistical models in bivariate allometry. Biol Rev 86(3):549–563. CrossRefPubMedGoogle Scholar
  34. Paul KI, Roxburgh SH, Chave J, England JR, Zerihun A, Specht A et al (2016) Testing the generality of above-ground biomass allometry across plant functional types at the continent scale. Glob Change Biol 22(6):2106–2124CrossRefGoogle Scholar
  35. Pearson TR, Brown S, Murray L, Sidman G (2017) Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag 12(1):3. CrossRefPubMedCentralGoogle Scholar
  36. Picard N, Rutishauser E, Ploton P, Ngomanda A, Henry M (2015) Should tree biomass allometry be restricted to power models? For Ecol Manag 353:156–163. CrossRefGoogle Scholar
  37. Pilli R, Anfodillo T, Carrer M (2006) Towards a functional and simplified allometry for estimating forest biomass. For Ecol Manag 237:583–593. CrossRefGoogle Scholar
  38. Piñeiro G, Perelman S, Guerschman JP, Paruelo JM (2008) How to evaluate models: observed vs predicted or predicted vs observed. Ecol Model 216:316–322. CrossRefGoogle Scholar
  39. Rothman KJ (2002) Epidemiology: an introduction, 2nd edn. Oxford University Press, New YorkGoogle Scholar
  40. Rubilar RA, Allen HL, Alvarez JS, Albaugh TJ, Fox TR, Stape JL (2010) Silvicultural manipulation and site effect on above and belowground biomass equations for young Pinus radiata. Biomass Bioenergy 34(12):1825–1837. CrossRefGoogle Scholar
  41. Sileshi GW (2014) A critical review of forest biomass estimation models, common mistakes and corrective measures. For Ecol Manag 329:237–254CrossRefGoogle Scholar
  42. Sit V (1994) Catalog of curves for curve fitting. Ministry of Forest, Victoria, British Columbia (Canada), Biometrics Information Handbook Series 4Google Scholar
  43. Snowdon P, Eamus D, Gibbons P et al (2000) Synthesis of allometrics, review of root biomass and design of future woody biomass sampling strategies. NCAS technical report 17. Australian Greenhouse Office, CanberraGoogle Scholar
  44. StataCorp (2015) Stata 14 base reference manual. Stata Press, College StationGoogle Scholar
  45. Tjeuw J, Mulia R, Slingerland M, van Noordwijk M (2015) Tree or shrub: a functional branch analysis of Jatropha curcas L. Agroforest Syst 89(5):841–856. CrossRefGoogle Scholar
  46. UNFCCC (1997) Kyoto protocol to the United Nations framework convention on climate changeGoogle Scholar
  47. UNFCCC (2010) Outcome of the work of the ad hoc working group on long-term cooperative action under the convention—policy approaches and positive incentives on issues relating to reducing emissions from deforestation and forest degradation in developing countries: and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries UNFCCC COP 16 Cancun.
  48. Weber JC, Sotelo Montes C, Abasse T, Sanquetta CR, Silva DA, Mayer S, Muñiz GI, Garcia RA (2017) Variation in growth, wood density and carbon concentration in five tree and shrub species in Niger. New For. Google Scholar
  49. Yeboah D, Burton AJ, Storer AJ, Opuni-Frimpong E (2014) Variation in wood density and carbon content of tropical plantation tree species from Ghana. New For 45(1):35–52. CrossRefGoogle Scholar
  50. Youkhana AH, Idol TW (2011) Allometric models for predicting above-and belowground biomass of Leucaena-KX2 in a shaded coffee agroecosystem in Hawaii. Agroforest Syst 83(3):331–345CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Center for Development Research (ZEF)Bonn UniversityBonnGermany
  2. 2.WASCAL Competence CenterOuagadougouBurkina Faso
  3. 3.AFC Agriculture and Finance Consultants GmbHBonnGermany
  4. 4.Division of Environmental Science and Ecological EngineeringKorea UniversitySeoulKorea

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