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Open AccessArticle

Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands

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Department of Botany, Jomo Kenyatta University of Agriculture and Technology (JKUAT), P.O. Box 62000-00200, Nairobi, Kenya
2
World Agroforestry Centre (ICRAF), United Nations Avenue, P.O. Box 30677-00100, Nairobi, Kenya
3
Plot 1244, Ibex Hill, Lusaka, Zambia
*
Author to whom correspondence should be addressed.
Academic Editors: P. K. Ramachandran Nair and Eric J. Jokela
Forests 2016, 7(2), 13; https://doi.org/10.3390/f7020013
Received: 27 August 2015 / Revised: 2 November 2015 / Accepted: 19 November 2015 / Published: 3 February 2016
The miombo woodland is the most extensive dry forest in the world, with the potential to store substantial amounts of biomass carbon. Efforts to obtain accurate estimates of carbon stocks in the miombo woodlands are limited by a general lack of biomass estimation models (BEMs). This study aimed to evaluate the accuracy of most commonly employed allometric models for estimating aboveground biomass (AGB) in miombo woodlands, and to develop new models that enable more accurate estimation of biomass in the miombo woodlands. A generalizable mixed-species allometric model was developed from 88 trees belonging to 33 species ranging in diameter at breast height (DBH) from 5 to 105 cm using Bayesian estimation. A power law model with DBH alone performed better than both a polynomial model with DBH and the square of DBH, and models including height and crown area as additional variables along with DBH. The accuracy of estimates from published models varied across different sites and trees of different diameter classes, and was lower than estimates from our model. The model developed in this study can be used to establish conservative carbon stocks required to determine avoided emissions in performance-based payment schemes, for example in afforestation and reforestation activities. View Full-Text
Keywords: biomass estimation models; carbon stocks; Southern Africa biomass estimation models; carbon stocks; Southern Africa
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MDPI and ACS Style

Kuyah, S.; Sileshi, G.W.; Rosenstock, T.S. Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands. Forests 2016, 7, 13. https://doi.org/10.3390/f7020013

AMA Style

Kuyah S, Sileshi GW, Rosenstock TS. Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands. Forests. 2016; 7(2):13. https://doi.org/10.3390/f7020013

Chicago/Turabian Style

Kuyah, Shem; Sileshi, Gudeta W.; Rosenstock, Todd S. 2016. "Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands" Forests 7, no. 2: 13. https://doi.org/10.3390/f7020013

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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