Allometric relationships for the estimation of dry mass of aboveground organs in young highland Norway spruce stand

Marková, Irena; Pokorný, Radek

doi:10.11118/actaun201159060217

Cited by 10 publications

(15 citation statements)

References 24 publications

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“…On other hand, when they are fitted in such a way that specify tree component biomass as directly proportional to stem volume, the ratios of proportionality are then called component biomass expansion factors (BEFs). However, biomass equation (either regressions or BEFs) are developed from destructively sampled trees (Carvalho and Parresol 2003;Carvalho 2003;Dutca et al 2010;Marková and Pokorný 2011;Sanquetta et al 2011;Mate et al 2014;Magalhães and Seifert 2015 a, b, c).…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…High adjusted coefficient of determination (Adj R 2 > 95%) found for both types of models (one and two variables) highlighted that the models accounted for a high proportion of variability in predictions, indicating that most of the variance was explained by the models. This was partly due to the stratified sampling approach used that collected observations on tree size in each existing diameter class (Marková and Pokorný 2011;Cienciala et al 2013;Guendehou et al 2012;Sileshi 2014).…”

Section: Stem Volume and Stem Biomass Modelsmentioning

confidence: 99%

Specific and generic stem biomass and volume models of tree species in a West African tropical semi-deciduous forest

Goussanou¹,

Guendehou²,

Assogbadjo³

et al. 2016

Silva Fenn.

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Highlights• Non-destructive sampling approach applied to derive ground truth observations and generate robust basic wood densities.• Species-specific and generic allometric equations.• Specific equations have better predictive capabilities than generic models. AbstractThe quantification of the contribution of tropical forests to global carbon stocks and climate change mitigation requires availability of data and tools such as allometric equations. This study made available volume and biomass models for eighteen tree species in a semi-deciduous tropical forest in West Africa. Generic models were also developed for the forest ecosystem, and basic wood density determined for the tree species. Non-destructive sampling approach was carried out on five hundred and one sample trees to analyse stem volume and biomass. From the modelling of volume and biomass as functions of diameter at breast height (Dbh) and stem height, logarithmic models had better predictive capabilities. The model validation showed that in absence of data on height, models using Dbh only as variable was an alternative. The comparison of basic wood densities to data published in literature enabled to conclude that the non-destructive sampling was a good approach to determining reliable basic wood density. The comparative analysis of species-specific models in this study with selected generic models for tropical forests indicated low probability to identify effective generic models with good predictive ability for biomass. Given tree species richness of tropical forests, the study demonstrated the hypothesis that species-specific models are preferred to generic models, and concluded that further research should be oriented towards development of specific models to cover the full range of dominant tree species of African forests.

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“…2). In relationship between soil CO 2 effl ux and soil According to Marková and Pokorný (2011) temperature was found tightness dependencies, with the coeffi cient of determination (R 2 ) equal 0.40 for the stand with bellow thinning and 0.41 for the plot with above thinning. Was found exponential dependence.…”

Section: Resultsmentioning

confidence: 99%

Soil CO<sub>2</sub> efflux in young Norway spruce stands with different silviculture practices

Rosík¹,

Fabiánek²,

Marková³

2013

Acta Univ. Agric. Silvic. Mendelianae Brun.

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Allometric relationships for the estimation of dry mass of aboveground organs in young highland Norway spruce stand

Cited by 10 publications

References 24 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

Specific and generic stem biomass and volume models of tree species in a West African tropical semi-deciduous forest

Soil CO<sub>2</sub> efflux in young Norway spruce stands with different silviculture practices

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