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Forests 2016, 7(2), 38; doi:10.3390/f7020038

Above- and Belowground Biomass Models for Trees in the Miombo Woodlands of Malawi

Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway
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Academic Editors: Shibu Jose and Eric J. Jokela
Received: 25 October 2015 / Revised: 29 December 2015 / Accepted: 25 January 2016 / Published: 5 February 2016
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Abstract

In this study we present general (multiple tree species from several sites) above- and belowground biomass models for trees in the miombo woodlands of Malawi. Such models are currently lacking in the country. The modelling was based on 74 trees comprising 33 different species with diameters at breast height (dbh) and total tree height (ht) ranging from 5.3 to 2 cm and from 3.0 to 25.0 m, respectively. Trees were collected from four silvicultural zones covering a wide range of conditions. We tested different models including dbh, ht and wood specific gravity ( ρ ) as independent variables. We evaluated model performance using pseudo-R2, root mean square error (RMSE), a covariance matrix for the parameter estimates, mean prediction error (MPE) and relative mean prediction error (MPE%). Computation of MPE% was based on leave-one-out cross-validation. Values of pseudo-R2 and MPE% ranged 0.82–0.97 and 0.9%–2.8%, respectively. Model performance indicated that the models can be used over a wide range of geographical and ecological conditions in Malawi. View Full-Text
Keywords: root to shoot ratio; root sampling; tropical dry forest; error estimation; carbon root to shoot ratio; root sampling; tropical dry forest; error estimation; carbon
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kachamba, D.J.; Eid, T.; Gobakken, T. Above- and Belowground Biomass Models for Trees in the Miombo Woodlands of Malawi. Forests 2016, 7, 38.

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