Methods for Ensuring Additivity of Biomass Components by Regression Analysis

Kozak, A.

doi:10.5558/tfc46402-5

Cited by 81 publications

(67 citation statements)

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“…The best prediction models were chosen on the basis of the following statistical criteria: goodness of fit (characterised by four statistics: R 2 , bias, residual mean square and most importantly, the Furnival index) and the absence of multicollinearity, as indicated by the condition number. In the second stage, selected equations for each biomass component were fitted simultaneously in order to ensure compatibility between the estimates of total biomass and each of their fractions [25,37], that is, the estimates of the different components should have the property of additivity. The regression methodology proposed by Zellner [54] known as weighted SUR (Seemingly Unrelated Regression), was then applied using the SAS statistical package [44].…”

Section: Tree Biomass Modelsmentioning

confidence: 99%

Carbon and nutrient stocks in matureQuercus roburL. stands in NW Spain

et al. 2006

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-In the present study, the distribution of C and nutrients in biomass (above-and below-ground) and soil were estimated in native Quercus robur stands in NW Spain. This information can be used to design future strategies to preserve and improve these forests, which are subjected to severe degradation as a consequence of poor management and fire damage. Biomass equations for tree components were fitted simultaneously using the data corresponding to 31 trees felled in 4 representative mature stands. The C accumulated in aerial biomass, roots, litter layer and mineral soil made up 40, 10, 8 and 42% respectively, of the C in the whole forest system. Tree biomass contained higher amounts of macronutrients than the available reserves in the soil. Stem wood was the major tree compartment for most nutrients, with the exception of Ca, which was mainly accumulated in stem bark. In comparison with commercial forest plantations in the region, particularly high amounts of nutrients were accumulated in the litter layer because of the high amounts of litterfall and higher foliar concentrations of nutrients. Quercus robur L. / biomasse / nutrition forestière / carbone / régression apparemment sans rapport

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Section: Tree Biomass Modelsmentioning

confidence: 99%

Carbon and nutrient stocks in matureQuercus roburL. stands in NW Spain

et al. 2006

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“…Los modelos se ajustaron empleando el paquete estadístico SAS/STAT ® (SAS Institute Inc. 2009). En un segundo paso se realizó un ajuste simultáneo para garantizar la aditividad (Kozak 1970, Parresol 1999, en el cual no se incluyó la ecuación de biomasa de hojas debido a que había menor número de observaciones para este componente. Debido a la presencia de heterocedasticidad el ajuste se realizó con la metodología GMM (generalized method of moments), con lo cual se obtuvieron estimaciones eficientes de los parámetros (Greene 2000, SAS Institute Inc. 2009).…”

Section: Métodosunclassified

Evolución de la biomasa y del carbono acumulado por Quercus robur en Galicia (España)

Gómez-García¹,

Rodríguez²

2015

Bosque (Valdivia)

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Evolución de la biomasa y del carbono acumulado por Quercus robur en Galicia (España)Changes in biomass and carbon storage in Quercus robur trees in Galicia (Spain) Esteban Gómez-García*, Fernando Pérez-Rodríguez Key words: pedunculate oak, carbon sequestration, national forest inventory (NFI). RESUMENEn este trabajo se ha estudiado la evolución de la biomasa y del carbono acumulado en los árboles de Quercus robur presentes en Galicia (noroeste de España). Se desarrollaron ecuaciones de estimación de biomasa para árbol individual, las cuales utilizan como variable independiente el diámetro normal. Se realizó un ajuste simultáneo para garantizar la aditividad, y se empleó la metodología GMM (generalized method of moments) debido a la presencia de heterocedasticidad. Para estimar la evolución de la biomasa de Q. robur en Galicia, las ecuaciones desarrolladas se aplicaron a los datos del número de árboles por clase diamétrica de los cuatro inventarios forestales nacionales (IFN) realizados hasta el momento. La concentración media de carbono en los diferentes componentes arbóreos considerados permitió estimar el carbono acumulado a partir de la biomasa. Aunque en Galicia ha disminuido la superficie de bosques puros de Q. robur, la evolución del número de árboles ha sido positiva en el periodo considerado

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“…Studies by Kozak (1970), Chiyenda and Kozak (1984) and Briggs (1984, 1985) resulted in three different methods for forcing additivity in a lineal system of biomass equations. Parresol (1999) concluded that the most flexible and general is the method based on simultaneous adjustment using Zellner's (1962) seemingly unrelated regression (SUR).…”

Section: Simultaneous Adjustment To Ensure Additivitymentioning

confidence: 99%

Above-ground biomass equations for Pinus radiata D. Don in Asturias

et al. 2013

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Aim of the study: The aim of this study was to develop a model for above-ground biomass estimation for Pinus radiata D. Don in Asturias.Area of study: Asturias (NE of Spain). Material and methods: Different models were fitted for the different above-ground components and weighted regression was used to correct heteroscedasticity. Finally, all the models were refitted simultaneously by use of Nonlinear Seemingly Unrelated Regressions (NSUR) to ensure the additivity of biomass equations.Research highlights: A system of four biomass equations (wood, bark, crown and total biomass) was develop, such that the sum of the estimations of the three biomass components is equal to the estimate of total biomass. Total and stem biomass equations explained more than 92% of observed variability, while crown and bark biomass equations explained 77% and 89% respectively.

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Methods for Ensuring Additivity of Biomass Components by Regression Analysis

Abstract: In many forestry problems additive regression equations are expected because of the nature of the data. This paper discusses the reasons why one does or does not have these additive equations. Mathematical proofs of the theory are given, with a numerical example.

Cited by 81 publications

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Carbon and nutrient stocks in matureQuercus roburL. stands in NW Spain

Carbon and nutrient stocks in matureQuercus roburL. stands in NW Spain

Evolución de la biomasa y del carbono acumulado por Quercus robur en Galicia (España)

Above-ground biomass equations for Pinus radiata D. Don in Asturias

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