Skip to main content

Shrub biomass estimation in the semiarid Chaco forest: a contribution to the quantification of an underrated carbon stock

Abstract

Context

The quantification of biomass of woody plants is at the basis of calculations of forest biomass and carbon stocks. Although there are well-developed allometric models for trees, they do not apply well to shrubs, and shrub-specific allometric models are scarce. There is therefore a need for a standardized methodology to quantify biomass and carbon stocks in open forests and woodlands.

Aims

To develop species-specific biomass estimation models for common shrubs, as well as a multispecies shrub model, for the subtropical semiarid Chaco forest of central Argentina.

Methods

Eight shrub species (Acacia aroma, Acacia gilliesii, Aloysia gratissima, Capparis atamisquea, Celtis ehrenbergiana, Larrea divaricata, Mimozyganthus carinatus, and Moya spinosa) were selected, and, on average, 30 individuals per species were harvested. Their total individual dry biomass was related with morphometric variables using regression analysis.

Results

Crown area as well as crown-shaped variables proved to be the variables with the best performance for both species-specific and multispecies shrub models. These allometric variables are thus recommended for standardized shrub biomass assessments.

Conclusion

By accounting for the shrub component of the vegetation, our models provide a way to improve the quantification of biomass and carbon in semiarid open forest and woodlands.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

References

  1. Baskerville GL (1972) Use of logarithmic regression in the estimation of plant biomass. Can J For Res 2:49–53. doi:10.1139/x72-009

    Article  Google Scholar 

  2. Brown S (1997) Estimating biomass and biomass change of tropical forests: a primer, vol 134. FAO Forestry Paper. A forest resource assessment publication. FAO, Rome

  3. Burnham KP, Anderson DR (2002) Model selection and inference. A practical information-theoretic approach, 2nd edn. Springer, Berlin–Heidelberg–New York

    Google Scholar 

  4. Cabido M, Acosta A, Carranza ML, Diaz S (1992) La vegetación del Chaco Árido en el W de la provincia de Córdoba, Argentina. Doc Phytosociol XIV:447–456

    Google Scholar 

  5. Capitanelli R (1979) Clima. In: Vázquez J, Miatello R, Roque M (eds) Geografía física de la provincia de Córdoba. Ed. Boldt, Buenos Aires, pp 45–138

    Google Scholar 

  6. Castro H, Freitas H (2009) Above-ground biomass and productivity in the Montado: from herbaceous to shrub dominated communities. J Arid Environ 73:506–511. doi:10.1016/j.jaridenv.2008.12.009

    Article  Google Scholar 

  7. Chapin FS III, Matson PA, Vitousek PM (2011) Principles of terrestrial ecosystem ecology, 2nd edn. Springer, New York

    Google Scholar 

  8. Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T, Lescure J-P, 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 145:87–99. doi:10.1007/s00442-005-0100-x

    PubMed  Article  CAS  Google Scholar 

  9. Chojnacky DC, Milton M (2008) Measuring carbon in shrubs. In: Hoover CM (ed) Field measurements for forest carbon monitoring. Springer, New York, pp 45–72

    Chapter  Google Scholar 

  10. Conti G, Díaz S (2013) Plant functional diversity and carbon storage—an empirical test in semi-arid forest ecosystems. J Ecol 101:18–28. doi:10.1111/1365-2745.12012

    Article  CAS  Google Scholar 

  11. Di Rienzo JA, Casanoves F, Balzarini MG, Gonzalez L, Tablada M, Robledo CW (2011) InfoStat. Statistical software. Grupo Infostat FCA UNC, Córdoba

    Google Scholar 

  12. Gaillard de Benitez C, Pece M, Juárez de Galíndez M, Vélez S, Gómez A, Zárate M (2002) Determinación de funciones para la estimación de biomasa aérea individual de jarilla (Larrea divaricata) de la provincia de Santiago del Estero, Argentina. For Ver 4:23–28

    Article  Google Scholar 

  13. Gorgas J, Tassile J (2003) Recursos naturales de la provincia de Córdoba. Los suelos. Agencia Córdoba Ambiente S.E. - INTA EEA Manfredi, Córdoba

    Google Scholar 

  14. GTOS (2010) A framework for terrestrial climate-related observations and development of standards for the terrestrial essential climate variables: proposed workplan. FAO, ICSU, UNEP, UNESCO, WMO. http://www.fao.org/gtos/doc/pub78.pdf. Accessed 13 Nov 2012

  15. Hierro JL, Branch LC, Villarreal D, Clark KL (2000) Predictive equations for biomass and fuel characteristics of Argentine shrubs. J Range Manage 53:617–621. doi:10.2307/4003156

    Article  Google Scholar 

  16. Hofstad O (2005) Review of biomass and volume functions for individual trees and shrubs in southeast Africa. J Trop For Sci 17:151–162

    Google Scholar 

  17. Iglesias MR, Barchuk AH (2010) Estimación de la biomasa aérea de seis leguminosas leñosas del Chaco Árido (Argentina). Ecol Austral 20:71–79

    Google Scholar 

  18. Iglesias MR, Barchuk A, Grilli MP (2012) Carbon storage, community structure and canopy cover: a comparison along a precipitation gradient. For Ecol Manage 265:218–229. doi:10.1016/j.foreco.2011.10.036

    Article  Google Scholar 

  19. Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2003) National-scale biomass estimators for United States tree species. For Sci 49:12–35

    Google Scholar 

  20. Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2004) Comprehensive database of diameter-based biomass regressions for North American tree species. US Department of Agriculture, Forest Service, Northeastern Research Station, Delaware

    Google Scholar 

  21. Johnson JB, Omland KS (2004) Model selection in ecology and evolution. Trends Ecol Evol 19:101–108. doi:10.1016/j.tree.2003.10.013

    PubMed  Article  Google Scholar 

  22. 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. For Ecol Manage 146:199–209. doi:10.1016/s0378-1127(00)00460-6

    Article  Google Scholar 

  23. Logan M (2010) Biostatistical design and analysis using R. A practical guide. Wiley-Blackwell, UK

    Book  Google Scholar 

  24. Ludwig JA, Reynolds JF, Whitson PD (1975) Size-biomass relationships of several Chihuahuan desert shrubs. Am Midl Nat 94:451–461. doi:10.2307/2424437

    Article  Google Scholar 

  25. Lufafa A, Diédhiou I, Ndiaye NAS, Séné M, Kizito F, Dick RP, Noller JS (2009) Allometric relationships and peak-season community biomass stocks of native shrubs in Senegal’s Peanut Basin. J Arid Environ 73:260–266. doi:10.1016/j.jaridenv.2008.09.020

    Article  Google Scholar 

  26. Morello JH, Sancholuz LA, Blanco CA (1977) Estudio macroecológico de los Llanos de la Rioja. IDIA 34:242–248

    Google Scholar 

  27. Murray RB, Jacobson MQ (1982) An evaluation of dimension analysis for predicting shrub biomass. J Range Manage 35:451–454. doi:10.2307/3898603

    Article  Google Scholar 

  28. Nelson BW, Mesquita R, Pereira JLG, Aquino G, de Souza S, Teixeira Batista G, Bovino Couto L (1999) Allometric regressions for improved estimate of secondary forest biomass in the central Amazon. For Ecol Manage 117:149–167. doi:10.1016/s0378-1127(98)00475-7

    Article  Google Scholar 

  29. Northup BK, Zitzer SF, Archer S, McMurtry CR, Boutton TW (2005) Above-ground biomass and carbon and nitrogen content of woody species in a subtropical thornscrub parkland. J Arid Environ 62:23–43. doi:10.1016/j.jaridenv.2004.09.019

    Article  Google Scholar 

  30. Oñatibia GR, Aguiar MR, Cipriotti PA, Troiano F (2010) Individual plant and population biomass of dominant shrubs in Patagonian grazed fields. Ecol Austral 20:269–279

    Google Scholar 

  31. Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG, Ciais P, Jackson RB, Pacala SW, McGuire AD, Piao S, Rautiainen A, Sitch S, Hayes D (2011) A large and persistent carbon sink in the world’s forests. Science 333:988–993. doi:10.1126/science.1201609

    PubMed  Article  CAS  Google Scholar 

  32. Paton D, Nuñez J, Bao D, Muñoz A (2002) Forage biomass of 22 shrub species from Monfragüe Natural Park (SW Spain) assessed by log–log regression models. J Arid Environ 52:223–231. doi:10.1006/jare.2001.0993

    Article  Google Scholar 

  33. Pérez Harguindeguy N, Díaz S, Garnier E, Lavorel S, Poorter H, Jaureguiberry P, Bret-Harte MS, Cornwell WK, Craine JM, Gurvich DE, Urcelay C, Veneklaas EJ, Reich PB, Poorter L, Wright IJ, Ray P, Enrico L, Pausas JG, De Vos A, Buchmann N, Funes G, Quetier F, Hodgson JG, Thompson K, Morgan HD, Ter Steege H, Van Der Heijden MGA, Blonder B, Poschlod P, Vaieretti MV, Conti G, Staver MC, Aquino S, Cornelissen JHC (2013) New handbook for standardised measurement of plant functional traits worldwide. Aust J Bot. doi:10.1071/BT12225

    Google Scholar 

  34. Phua M-H, Saito H (2003) Estimation of biomass of a mountainous tropical forest using Landsat TM data. Can J Remote Sens 29:429–440. doi:10.5589/m03-005

    Article  Google Scholar 

  35. Pilli R, Anfodillo T, Carrer M (2006) Towards a functional and simplified allometry for estimating forest biomass. For Ecol Manage 237:583–593. doi:10.1016/j.foreco.2006.10.004

    Article  Google Scholar 

  36. Sah JP, Ross MS, Koptur S, Snyder JR (2004) Estimating aboveground biomass of broadleaved woody plants in the understory of Florida Keys pine forests. For Ecol Manage 203:319–329. doi:10.1016/j.foreco.2004.07.059

    Article  Google Scholar 

  37. Sampaio EVSB, Silva GC (2005) Biomass equations for Brazilian semiarid caatinga plants. Acta Bot Bras 19:935–943

    Article  Google Scholar 

  38. Smith WB, Brand GJ (1983) Allometric biomass equations for 98 species of herbs, shrubs and small trees. US Department of Agriculture, Forest Service, North Central Forest Experiment Station, St. Paul

    Google Scholar 

  39. Tietema T (1993) Possibilities for the management of indigenous woodlands in Southern Africa: a case study from Botswana. In: Pierce GD, Gumbo DJ (eds) The Ecology and management of indigenous forest in Southern Africa. Zimbabwe Forest Commission & SAREC, Harare, pp 134–142

    Google Scholar 

  40. Vilà M (1993) The use of dimensional analysis to estimate plant resprout biomass. Sci Gerundensis Univ Gerona 19:47–51

    Google Scholar 

  41. Vora RS (1988) Predicting biomass of five shrub species in northeastern California. J Range Manage 41:63–65. doi:10.2307/3898792

    Article  Google Scholar 

  42. Whittaker RH, Woodwell GM (1968) Dimension and production relations of trees and shrubs in the Brookhaven forest, New York. J Ecol 56:1–25. doi:10.2307/2258063

    Article  Google Scholar 

  43. Zeng H-Q, Liu Q-J, Feng Z-W, Ma Z-Q (2010) Biomass equations for four shrub species in subtropical China. J For Res 15:83–90. doi:10.1007/s10310-009-0150-8

    Article  CAS  Google Scholar 

  44. Zuloaga FO, Morrone O (1996) Catálogo de las Plantas Vasculares de la República Argentina I, vol 60. Monographs in systematic botany. Missouri Botanical Garden, Missouri

    Google Scholar 

  45. Zuloaga FO, Morrone O (1999) Catálogo de las Plantas Vasculares de la República Argentina II, vol 74. Monographs in systematic botany. Missouri Botanical Garden, Missouri

    Google Scholar 

Download references

Acknowledgments

We are grateful to C. Rodriguez, G. Bertone, P. Jaureguiberry, and M. Bonino for their valuable field assistance during the development of this work.

Funding

This study was funded by FONCyT, CONICET, Universidad Nacional de Córdoba, and the DiverSus programme through Inter-American Institute for Global Change Research (IAI) CRN 2015 and SGP-CRA2015, which were supported by the US National Science Foundation grants GEO-0452325 and GEO-1138881. GC and LE student grants are from CONICET and Fundación Bunge y Born.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Georgina Conti.

Additional information

Contribution of the co-authors

Georgina Conti conducted sampling design, field work, and data analysis and wrote the manuscript. Lucas Enrico participated in field work and in writing the manuscript. Fernando Casanoves helped in data analysis and manuscript revision. Sandra Díaz coordinated the research project and participated in writing the manuscript.

Handling Editor: Shuqing Zhao

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

Scientific, common as well as synonyms for the scientific names of the shrub species considered. (PDF 93 kb)

ESM 2

Calculations of the crown-shaped variables used to test the size–biomass relationship of shrub species. (PDF 665 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Conti, G., Enrico, L., Casanoves, F. et al. Shrub biomass estimation in the semiarid Chaco forest: a contribution to the quantification of an underrated carbon stock. Annals of Forest Science 70, 515–524 (2013). https://doi.org/10.1007/s13595-013-0285-9

Download citation

Keywords

  • Allometric models
  • Biomass quantification
  • Carbon inventories
  • Chaco
  • Dimensional relationships
  • Shrub