Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands
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 e...
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doaj-6195cb5496a24bc38939438780e3f54a2020-11-25T00:36:43ZengMDPI AGForests1999-49072016-02-01721310.3390/f7020013f7020013Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo WoodlandsShem Kuyah0Gudeta W. Sileshi1Todd S. Rosenstock2Department of Botany, Jomo Kenyatta University of Agriculture and Technology (JKUAT), P.O. Box 62000-00200, Nairobi, KenyaWorld Agroforestry Centre (ICRAF), United Nations Avenue, P.O. Box 30677-00100, Nairobi, KenyaPlot 1244, Ibex Hill, Lusaka, ZambiaThe 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.http://www.mdpi.com/1999-4907/7/2/13biomass estimation modelscarbon stocksSouthern Africa |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shem Kuyah Gudeta W. Sileshi Todd S. Rosenstock |
spellingShingle |
Shem Kuyah Gudeta W. Sileshi Todd S. Rosenstock Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands Forests biomass estimation models carbon stocks Southern Africa |
author_facet |
Shem Kuyah Gudeta W. Sileshi Todd S. Rosenstock |
author_sort |
Shem Kuyah |
title |
Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands |
title_short |
Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands |
title_full |
Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands |
title_fullStr |
Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands |
title_full_unstemmed |
Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands |
title_sort |
allometric models based on bayesian frameworks give better estimates of aboveground biomass in the miombo woodlands |
publisher |
MDPI AG |
series |
Forests |
issn |
1999-4907 |
publishDate |
2016-02-01 |
description |
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. |
topic |
biomass estimation models carbon stocks Southern Africa |
url |
http://www.mdpi.com/1999-4907/7/2/13 |
work_keys_str_mv |
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