Use of Logarithmic Regression in the Estimation of Plant Biomass: Discussion

Munro, Donald D.

doi:10.1139/x74-023

Cited by 5 publications

(3 citation statements)

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“…Typical values of s2 vary between 0.02 and 0.30, indicating a biased underestimate of stand biomass of the order of 1%-16% if correction is omitted and the underlying model is correct. This information appears to have escaped the notice of forest ecologists for 30 yr (Madgwick 1970) but has been the basis of anumber of recent publications (Baskerville 1972, Beauchamp and· Olson 1973, Baskerville 1974, Munro 1974.…”

Section: Introductionmentioning

confidence: 98%

On Estimating the Aboveground Weights of Tree Stands

Madgwick

Satoo

1975

Ecology

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Abstract. Simulated sampling of nine tree stands was used to compare estimated stand component weights with known weights based on complete harvest. On average, estimates based on regressions of logarithm weight on either logarithm tree diameter or on logarithm (diameter)2 X height overestimated weights of stems, branches, and leaves by 3% when the inherent bias of such regressions was accounted for. Ninety-five percent confidence intervals based on random sampling encompassed the measured stand weights 79%-100% of the time. Similar confidence intervals using stratified random sampling based on tree diameter encompassed the measured stand weights only 44%-98% of the time. The small average bias in estimates of stand weights using logarithmic regressions is of minor importance compared with the variation in estimates among replicated samples.

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Section: Introductionmentioning

confidence: 98%

On Estimating the Aboveground Weights of Tree Stands

Madgwick

Satoo

1975

Ecology

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“…There were 8-15 such 'quadrats' in each sampled community. A bias in biomass estimates is introduced if antilogs of the previously transformed data are simply taken because the geometric mean rather than the true mean of the estimated value is obtained (Munro 1974). To avoid this problem the steps outlined by Beauchamp and Olson (1973) were adopted.…”

Section: Determination Of Standing Biomass Relationshipsmentioning

confidence: 99%

Allometric relationships and community biomass estimates for some dominant eucalypts in Central Queensland woodlands

Burrows¹,

Hoffmann²,

Compton³

et al. 2000

Aust. J. Bot.

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Allometric equations are presented relating stem circumference to branch, leaf, trunk, bark, total above-ground and lignotuber biomass for Eucalyptus crebra F.Muell. (woodland trees), E. melanophloia Sol. Ex Gaerth. (both woodland and regrowth community trees) and E. populnea F.Muell. (woodland trees). There were no significant differences (P > 0.05) between the slopes of individual lognormal regression lines plotting stem circumference against total above-ground biomass for E. crebra, E. melanophloia and E. populnea. Root-to-shoot ratios and leaf area indices were also determined for the stands contributing to each regression. The regressions were then applied to measured eucalypt stems in the associated plant community to give estimates of each stand’s component (eucalypt tree fraction only) biomass per hectare. These eucalypt regressions were next applied to measured stems of each species on a total of 33 woodland sites in which these eucalypts individually contributed > 75% of total site basal area. Above-ground biomass/basal area relationships averaged 6.74 0.29 t m–2 basal area for 11 E. crebra sites, 5.11 0.28 t m–2 for 12 E. melanophloia sites and 5.81 0.11 t m–2 for 10 E. populnea sites. The mean relationship for all sites was 5.86 0.18 t m–2 basal area. The allometric relationships presented at both individual tree and stand levels, along with calculated biomass : basal area relationships, enable ready estimates to be made of above-ground biomass (carbon stocks) in woodlands dominated by these eucalypts in Queensland, assuming individual stem circumferences or community basal areas are known. However, to document changes in carbon stocks (e.g. for Greenhouse Gas Inventory or Carbon Offset trading purposes), more attention needs to be placed on monitoring fluxes in the independent variables (predictors) of these allometric equations.

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“…Draper and Smith 1966, Furnival 1961, Newnham 1967, Kozak 1970, Baskerville 1972, Munro 1974 and others), many researchers continue to use logarithmic models in biomass and other similar studies. Tree or stand biomass is usually expressed as a function of diameter or diameter and height.…”

Section: Introductionmentioning

confidence: 99%

Choosing Regression Models for Biomass Prediction Equations

Payandeh¹

1981

The Forestry Chronicle

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This paper briefly describes the underlying assumptions and inherent drawbacks of logarithmic regression models commonly used to develop biomass prediction equations. Superiority of simple nonlinear models over both the logarithmic and multiple linear regression models is discussed and demonstrated on two data sets.Ce document decrit brievement les suppositions sous-jacentes et les inconvknients rattaches aux modeles de regression logarithmique employes pour developper des equations de prkdic-. tion touchant la biomasse. L'auteur 6tudie et demontre, au moyen de deux series de donnees, la superiorite des modeles simples non linkaires tant sur les modeles de regression' logarithmiques que lineaires multiples.

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Use of Logarithmic Regression in the Estimation of Plant Biomass: Discussion

Cited by 5 publications

References 0 publications

On Estimating the Aboveground Weights of Tree Stands

On Estimating the Aboveground Weights of Tree Stands

Allometric relationships and community biomass estimates for some dominant eucalypts in Central Queensland woodlands

Choosing Regression Models for Biomass Prediction Equations

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