Biomass Modelling of<i>Androstachys johnsonii</i>Prain: A Comparison of Three Methods to Enforce Additivity

Magalhães, Tarquinio Mateus; Seifert, Thomas

doi:10.1155/2015/878402

Cited by 13 publications

(18 citation statements)

References 33 publications

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“…Overman et al 1994;Grundy 1995;Eshete and Ståhl 1998;Pilli et al 2006;Salis et al 2006;Návar-Cháidez 2010;Suganuma et al 2012;Mason et al 2014), ignoring the fact that different tree components have distinguished uses and decomposition rates, affecting differently the storage time of carbon and nutrients (Magalhães and Seifert 2015a). Aware of that, here, the AGB is divided into 6 tree components (foliage, branches, crown, stem wood, stem bark, and stem).…”

Section: Discussionmentioning

confidence: 99%

Live above- and belowground biomass of a Mozambican evergreen forest: a comparison of estimates based on regression equations and biomass expansion factors

Magalhães

2015

For. Ecosyst.

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Background: Biomass regression equations are claimed to yield the most accurate biomass estimates than biomass expansion factors (BEFs). Yet, national and regional biomass estimates are generally calculated based on BEFs, especially when using national forest inventory data. Comparison of regression equations based and BEF-based biomass estimates are scarce. Thus, this study was intended to compare these two commonly used methods for estimating tree and forest biomass with regard to errors and biases. Methods: The data were collected in 2012 and 2014. In 2012, a two-phase sampling design was used to fit tree component biomass regression models and determine tree BEFs. In 2014, additional trees were felled outside sampling plots to estimate the biases associated with regression equation based and BEF-based biomass estimates; those estimates were then compared in terms of the following sources of error: plot selection and variability, biomass model, model parameter estimates, and residual variability around model prediction. Results: The regression equation based below-, aboveground and whole tree biomass stocks were, approximately, 7.7, 8.5 and 8.3 % larger than the BEF-based ones. For the whole tree biomass stock, the percentage of the total error attributed to first phase (random plot selection and variability) was 90 and 88 % for regression-and BEF-based estimates, respectively, being the remaining attributed to biomass models (regression and BEF models, respectively). The percent bias of regression equation based and BEF-based biomass estimates for the whole tree biomass stock were −2.7 and 5.4 %, respectively. The errors due to model parameter estimates, those due to residual variability around model prediction, and the percentage of the total error attributed to biomass model were larger for BEF models (than for regression models), except for stem and stem wood components. Conclusions: The regression equation based biomass stocks were found to be slightly larger, associated with relatively smaller errors and least biased than the BEF-based ones. For stem and stem wood, the percentages of their total errors (as total variance) attributed to BEF model were considerably smaller than those attributed to biomass regression equations.

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Section: Discussionmentioning

confidence: 99%

Live above- and belowground biomass of a Mozambican evergreen forest: a comparison of estimates based on regression equations and biomass expansion factors

Magalhães

2015

For. Ecosyst.

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“…Considering the fact that 90% of the lateral roots of A. johnsonii trees are located in the first node, which is located close to the ground level [ 16 , 17 , 38 , 50 ] it can be inferred that the 81.20% of the root system (found up to 61.2 cm in depth) is composed by root collar and lateral roots and therefore, the remaining portion of the taproot constitutes less than 20% of the root system biomass. This can be verified by summing the average taproot and lateral roots biomasses, which is equal to 82.43%, very close to the percentage of the root system biomass found at 20% of the taproot depth (81.20%).…”

Section: Discussionmentioning

confidence: 99%

“…Very few allometric biomass models exist for Mozambican forests; exceptions include Magalhães and Seifert [ 16 , 17 ], Ryan et al [ 33 ], Mate et al [ 36 ], and Sitoe et al [ 37 ]. As is best present known, the only studies that have included BGB are those by Magalhães and Seifert [ 16 , 17 ], Ryan et al [ 33 ], and Magalhães and Seifert [ 38 ]. However, the study by Ryan et al [ 33 ] was based on only several sample trees (23) within a limited geographical range (27 ha) and the root system was not completely excavated (fine roots were not included).…”

Section: Introductionmentioning

confidence: 99%

“…However, the study by Ryan et al [ 33 ] was based on only several sample trees (23) within a limited geographical range (27 ha) and the root system was not completely excavated (fine roots were not included). Although Magalhães and Seifert [ 16 , 17 , 38 ] considered relatively large sample trees and an expanded geographical area (93 trees harvested in 5 districts), besides including the entire root system, their allometric models were limited by not considering the different root components (e.g. taproot, root crown, lateral roots) and, therefore, the BGB allocation patterns were not analysed.…”

Section: Introductionmentioning

confidence: 99%

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Allometric equations for estimating belowground biomass of Androstachys johnsonii Prain

Magalhães

2015

Carbon Balance Manage

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BackgroundThe belowground component of the trees is still poorly known because it needs labour- and time-intensive in situ measurements. However, belowground biomass (BGB) constitutes a significant share of the total forest biomass. I analysed the BGB allocation patterns, fitted models for estimating root components and root system biomasses, and called attention for its possible use in predicting anchoring functions of the different root components.ResultsMore than half and almost one third of BGB is allocated to the lateral roots and to the root collar, respectively. More than 80% of the BGB is found at a depth range of 9.6–61.2 cm. As the tree size increased, the proportion of BGB allocated to taproots decreased and that allocated to lateral roots increased. All independent models performed almost equally, with the predictors explaining, on average, 98% of the variation in the BGB.ConclusionsIt was hypothesised that BGB allocation patterns are a response of the anchoring functions of the tap and lateral roots and therefore, root component biomass models can be used as a methodology to predict anchoring functions of the different root components. Based on the fact that all models performed almost equally, the models using either diameter at breast height (DBH) exclusively as a predictor should be preferred, as tree height is difficult to measure. Models using the root collar diameter (RCD) only should be preferred when the tree is found cut down, as sometimes the RCD is affected by root buttress. Given the large sample size, the validation results, and the coverage of a wide geographical, soil and climatic range, the models fitted can be applied in all A. johnsonii stands in Mozambique.

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“…The climate throughout the study region is dry tropical, with the exception of humid tropical areas in western Panda and southwestern Mandlakaze districts [22][23][24][25][26][27]; their warm or rainy season occurs from October to March, and their cool or dry season occurs from March to September [23][24][25][26][27]. More description on Androstachys johnsonii Prain and mecrusse woodlands can be foud in Magalhães [28] and Magalhães and Seifert [29][30][31][32].…”

Section: Study Areamentioning

confidence: 99%

Estimates of Tree Biomass, and Its Uncertainties Through Mean-of-Ratios, Ratio-of-Means, and Regression Estimators in Double Sampling: A Comparative Study of Mecrusse Woodlands

Magalhães¹,

Seifert

2015

AJAF

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Frequently, biomass expansion factors (BEFs), the respective biomass densities, and their uncertainties are computed without taking into account the appropriate estimators. The objective of this study was to compare the estimates of BEF, BEF-based biomass densities, and their uncertainties using different estimators (mean-of-ratios, ratio-of-means, and regression estimators) in double sampling. Our results demonstrated that increased uncertainty is associated with regressionbased biomass densities, and that the computation of BEF using merchantable timber volume should utilize regression estimators, not the usual ratio estimators, which preferably, should be avoided altogether, as they are found to be subjective and more susceptible to errors and personal judgment.

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Biomass Modelling ofAndrostachys johnsoniiPrain: A Comparison of Three Methods to Enforce Additivity

Cited by 13 publications

References 33 publications

Live above- and belowground biomass of a Mozambican evergreen forest: a comparison of estimates based on regression equations and biomass expansion factors

Live above- and belowground biomass of a Mozambican evergreen forest: a comparison of estimates based on regression equations and biomass expansion factors

Allometric equations for estimating belowground biomass of Androstachys johnsonii Prain

Estimates of Tree Biomass, and Its Uncertainties Through Mean-of-Ratios, Ratio-of-Means, and Regression Estimators in Double Sampling: A Comparative Study of Mecrusse Woodlands

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