Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-25T23:28:54.588Z Has data issue: false hasContentIssue false
  • English
  • Français

Modelling tree growth to determine the sustainability of current off-take from miombo woodland: a case study from rural villages in Malawi

Published online by Cambridge University Press:  05 December 2016

EMMA L. GREEN ,
FELIX EIGENBROD ,
KATE SCHRECKENBERG  and
SIMON WILLCOCK Open the ORCID record for SIMON WILLCOCK [Opens in a new window]
EMMA L. GREEN
Affiliation:
Centre for Environmental Sciences, Faculty of Engineering and the Environment, University of Southampton, SO17 1BJ, UK
FELIX EIGENBROD
Affiliation:
Centre for Biological Sciences, Faculty of Natural and Environmental Sciences, University of Southampton, SO17 1BJ, UK
KATE SCHRECKENBERG
Affiliation:
Centre for Environmental Sciences, Faculty of Engineering and the Environment, University of Southampton, SO17 1BJ, UK
SIMON WILLCOCK*
Affiliation:
Centre for Biological Sciences, Faculty of Natural and Environmental Sciences, University of Southampton, SO17 1BJ, UK Scotland's Rural College, Edinburgh, EH9 3FH, UK School of Environment, Natural Resources and Geography, College of Natural Sciences, University of Bangor, LL57 2UW, UK
*
*Correspondence: Dr. Simon Willcock Tel: +44 23 8059 4221 e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Miombo woodlands supply ecosystem services to support livelihoods in southern Africa, however, rapid deforestation has necessitated greater knowledge of tree growth and off-take rates to understand the sustainability of miombo exploitation. We established 48 tree inventory plots within four villages in southern Malawi, interviewed representatives in these same villages about tree management practices and investigated the impact of climate on vegetation dynamics in the region using the ecosystem modelling framework LPJ-GUESS. Combining our data with the forest yield model MYRLIN revealed considerable variation in growth rates across different land uses; forested lands showed the highest growth rates (1639 [95% confidence interval 1594–1684] kg ha–1 year–1), followed by settlement areas (1453 [95% confidence interval 1376–1530] kg ha–1 year–1). Based on the modelled MYRLIN results, we found that 50% of the villages had insufficient growth rates to meet estimated off-take. Furthermore, the results from LPJ-GUESS indicated that sustainable off-take approaches zero in drought years. Local people have recognized the unsustainable use of natural resources and have begun planting activities in order to ensure that ecosystem services derived from miombo woodlands are available for future generations. Future models should incorporate the impacts of human disturbance and climatic variation on vegetation dynamics; such models should be used to support the development and implementation of sustainable forest management.

Keywords

Africaecosystem serviceforestmodellingsustainable forest managementyieldZomba
Type
Papers
Information
Environmental Conservation , Volume 44 , Issue 1: Thematic section: Forest Ecosystem Services , March 2017 , pp. 66 - 73
Copyright
Copyright © Foundation for Environmental Conservation 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Supplementary material can be found online at http://dx.doi.org/10.1017/S0376892916000485

References

Alder, D., Baker, N. & Wright, H. L. (2002) MYRLIN: Methods of Yield Regulation with Limited Information. Oxford, UK: Oxford Forestry Institute.Google Scholar
Brown, S. (1997) Estimating Biomass and Biomass Change of Tropical Forests: A Primer, pp. 1–55. Rome: FAO – Food and Agriculture Organization of the United Nations.Google Scholar
Burley, J. (2012) The changing role of forestry in conservation and development: why trees are good for Malawi [www document]. URL http://www.istf-bethesda.org/specialreports/Burley/Nyika_Vwaza.pdf Google Scholar
Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D. et al. (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145: 87–99.CrossRefGoogle ScholarPubMed
Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G. & Zanne, A. E. (2009) Towards a worldwide wood economics spectrum. Ecology Letters 12: 351–366.CrossRefGoogle ScholarPubMed
de Foresta, H., Somarriba, E., Temu, A., Boulanger, D., Feuilly, H. & Gauthier, M. (2013) Towards the Assessment of Trees Outside Forests. Rome, Italy: Food and Agriculture Organisation.Google Scholar
Dewees, P. A. (1995) Forest policy and woodfuel markets in Malawi. Natural Resources Forum 19: 143–152.Google Scholar
Dewees, P. A., Campbell, B. M., Katerere, Y., Sitoe, A., Cunningham, A. B., Angelsen, A. & Wunder, S. (2010) Managing the Miombo woodlands of Southern Africa: policies, incentives and options for the rural poor. Journal of Natural Resources Policy Research 2: 57–73.Google Scholar
Eggleston, S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K. (2006) IPCC Guidelines for National Greenhouse Gas Inventories, Vol. 4 – Agriculture, Forestry and Other Land Uses. Hayama, Japan: Intergovernmental Panel on Climate Change.Google Scholar
European Space Agency (2010) Global Land Cover Map: GlobCover 2009 [www document]. URL http://due.esrin.esa.int/page_globcover.php Google Scholar
Fisher, M., Chaudhury, M. & McCusker, B. (2010) Do Forests help rural households adapt to climate variability? Evidence from southern Malawi. World Development 38: 1241–1250.CrossRefGoogle Scholar
Flores, O. & Coomes, D. A. (2011) Estimating the wood density of species for carbon stock assessments. Methods in Ecology and Evolution 2: 214–220.Google Scholar
Government of Malawi (2010) Malawi State of the Environment and Outlook Report: Environment for Sustainable Economic Growth. Lilongwe, Malawi: Malawi Government, Ministry of Natural Resources, Energy and Environment.Google Scholar
Grainger, A. (2008) Difficulties in tracking the long-term global trend in tropical forest area. Proceedings of the National Academy of Sciences 105: 818–823.Google Scholar
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. (2014) Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset. International Journal of Climatology 34: 623–642.Google Scholar
Hickler, T., Vohland, K., Feehan, J., Miller, P. A., Smith, B., Costa, L. et al. (2012) Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model. Global Ecology and Biogeography 21: 50–63.CrossRefGoogle Scholar
Kuyah, S., Sileshi, G. W., Njoloma, J., Mng'omba, S. & Neufeldt, H. (2014) Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi. Biomass and Bioenergy 66: 214–222.Google Scholar
Lehmann, C. E. R., Anderson, T. M., Sankaran, M., Higgins, S. I., Archibald, S., Hoffmann, W. A. et al. (2014) Savanna vegetation–fire–climate relationships differ among continents. Science (New York, N.Y.) 343: 548–552.Google Scholar
Lewis, S. L., Lopez-Gonzalez, G., Sonke, B., Affum-Baffoe, K., Baker, T. R., Ojo, L. O. et al. (2009) Increasing carbon storage in intact African tropical forests. Nature 457: 1003–1006.Google Scholar
Malhi, Y., Gardner, T. A., Goldsmith, G. R., Silman, M. R. & Zelazowski, P. (2014) Tropical forests in the Anthropocene. Annual Review of Environment and Resources 39: 125–159.Google Scholar
MASDAP (2013) Malawi Spatial Data Portal [www document]. URL http://www.masdap.mw/ Google Scholar
Monserud, R. A. (2003) Evaluating forest models in a sustainable forest management context. Forest Biometry, Modelling and Information Sciences 1: 35–47 Google Scholar
Morales, P., Hickler, T., Rowell, D. P., Smith, B. & Sykes, M. T. (2007) Changes in European ecosystem productivity and carbon balance driven by regional climate model output. Global Change Biology 13: 108–122.Google Scholar
Ngongondo, C. S., Xu, C.-Y., Tallaksen, L. M., Alemaw, B. & Chirwa, T. (2011) Regional frequency analysis of rainfall extremes in southern Malawi using the index rainfall and L-moments approaches. Stochastic Environmental Research and Risk Assessment 25: 939–955.Google Scholar
Pfeiffer, M., Spessa, A. & Kaplan, J. O. (2013) A model for global biomass burning in preindustrial time: LPJ-LMfire (v1.0). Geoscientific Model Development 6: 643–685.CrossRefGoogle Scholar
Phillips, O., Baker, T., Feldpausch, T., Brienen, R., Almeida, S., Arroyo, L. et al. (2009) RAINFOR Field Manual for Plot Establishment and Remeasurement. Leeds, UK: RAINFOR.Google Scholar
Phillips, P. D., Brash, T. E., Yasman, I., Subagyo, P. & van Gardingen, P. R. (2003) An individual-based spatially explicit tree growth model for forests in East Kalimantan (Indonesian Borneo). Ecological Modelling 159: 1–26.Google Scholar
Porté, A. & Bartelink, H. H. (2002) Modelling mixed forest growth: a review of models for forest management. Ecological Modelling 150: 141–188.Google Scholar
Ryan, C. M. & Williams, M. (2011) How does fire intensity and frequency affect miombo woodland tree populations and biomass? Ecological Applications 21: 48–60.Google Scholar
Ryan, C. M., Williams, M. & Grace, J. (2011) Above- and Belowground carbon stocks in a miombo woodland landscape of Mozambique. Biotropica 43: 423–432.Google Scholar
Saint-Andre, L., Nouvellon, Y., Laclau, J. P., Ranger, J., Bouillet, J. P., Nzila, J. D. et al. (2003) Modelling nutrient cycling and integrating nutrient cycling into growth models. In: Site Management and Productivity in Tropical Plantation Forest: Proceedings of Workshops in Congo, pp. 171–185. Bogor, Indonesia: Centre for International Forestry Research (CIFOR).Google Scholar
Shirima, D. D., Munishi, P. K. T., Lewis, S. L., Burgess, N. D., Marshall, A. R., Balmford, A. et al. (2011) Carbon storage, structure and composition of miombo woodlands in Tanzania's Eastern Arc Mountains. African Journal of Ecology 49: 332–342.Google Scholar
Sileshi, G. W. (2014) A critical review of forest biomass estimation models, common mistakes and corrective measures. Forest Ecology and Management 329: 237–254.Google Scholar
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W. et al. (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology 9: 161–185.Google Scholar
Smith, B., Prentice, I. C. & Sykes, M. T. (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Global Ecology and Biogeography 10: 621–637.Google Scholar
Vanclay, J. K. (2003) Growth modelling and yield prediction for sustainable forest management. Malaysian Forester 66: 58–69.Google Scholar
Willcock, S., Phillips, O. L., Platts, P. J., Balmford, A., Burgess, N. D., Lovett, J. C. et al. (2012) Towards Regional, error-bounded landscape carbon storage estimates for data-deficient areas of the world. PLoS ONE 7: e44795.Google Scholar
Willcock, S., Phillips, O. L., Platts, P. J., Balmford, A., Burgess, N. D., Lovett, J. C. et al. (2014) Quantifying and understanding carbon storage and sequestration within the Eastern Arc Mountains of Tanzania, a tropical biodiversity hotspot. Carbon Balance and Management 9: 1–17.CrossRefGoogle ScholarPubMed
Willcock, S., Phillips, O. L., Platts, P. J., Swetnam, R. D., Balmford, A., Burgess, N. D. et al. (2016) Land cover change and carbon emissions over 100 years in an African biodiversity hotspot. Global Change Biology 22: 2787–2800.Google Scholar
Zanne, A. E., Lopez-Gonzalez, G., Coomes, D. A., Ilic, J., Jansen, S., Lewis, S. L. et al. (2009) Data from: Towards a worldwide wood economics spectrum. Dryad Digital Repository [www document]. URL http://hdl.handle.net/10255/dryad.235 Google Scholar
Zulu, L. C. (2010) The forbidden fuel: charcoal, urban woodfuel demand and supply dynamics, community forest management and woodfuel policy in Malawi. Energy Policy 38: 3717–3730.CrossRefGoogle Scholar
Supplementary material: File

Green supplementary material

Appendix S1

Download Green supplementary material(File)
File 32.8 KB
Supplementary material: File

Green supplementary material

Appendix S2

Download Green supplementary material(File)
File 26.6 KB
Supplementary material: File

Green supplementary material

Appendix S3

Download Green supplementary material(File)
File 295.3 KB
Supplementary material: File

Green supplementary material

Appendix S4

Download Green supplementary material(File)
File 13.9 KB
Supplementary material: File

Green supplementary material

Appendix S5

Download Green supplementary material(File)
File 16.4 KB
Supplementary material: File

Green supplementary material

Appendix S6

Download Green supplementary material(File)
File 14.4 KB
Supplementary material: File

Green supplementary material

Appendix S7

Download Green supplementary material(File)
File 15.3 KB
Supplementary material: File

Green supplementary material

Appendix S8

Download Green supplementary material(File)
File 17.7 KB