Developing above-ground woody biomass equations for open-grown, multiple-stemmed tree species: Shelterbelt-grown Russian-olive

Zhou, Xinhua; Brandle, James R.; Schoeneberger, Michele; Awada, Tala

doi:10.1016/j.ecolmodel.2006.10.024

Cited by 34 publications

(19 citation statements)

References 19 publications

(17 reference statements)

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“…The power function using D as the only predictor is simple in equation form, easy to fit to biomass data, requires only basic forest inventory data to apply in practice, and usually provides reasonably accurate predictions for many species and regions (Ter-Mikaelian and Korzukhin 1997;Jenkins et al 2003;Wang 2006;Sierra et al 2007;Basuki et al 2009). However, adding tree height or height classes as an additional predictor into biomass equations can significantly improve the model fitting and performance (Bi et al 2004;Wang et al 2006;Li and Zhao 2013), especially for some tree component models such as branch and foliage biomass (Wang 2006;Zhou et al 2007). Our results demonstrated that adding tree height into the system improved most of the biomass equations for the nine hardwood species, which was consistent with the literature (Ketterings et al 2001;Bi et al 2004;Cole and Ewel 2006;António et al 2007;Battulga et al 2013).…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, tree height (H) can be measured relatively easily, and in fact the different tree heights at the same diameter obviously influence tree-level biomass equations . Studies show that adding tree height into biomass equations can significantly improve model fitting and performance (António et al 2007;Zhou et al 2007;Li and Zhao 2013).…”

Section: Introductionmentioning

confidence: 99%

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Developing additive systems of biomass equations for nine hardwood species in Northeast China

Dong

Zhang

2015

Trees

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Key message We developed two additive systems of biomass equations based on diameter and tree height for nine hardwood species by SUR, and used a likelihood analysis to evaluate the model error structures. Abstract In this study, a total of 472 trees were harvested and measured for stem, root, branch, and foliage biomass from nine hardwood species in Northeast China. Two additive systems of biomass equations were developed, one based on tree diameter (D) only and one based on both tree diameter (D) and height (H). For each system, three constraints were set up to account for the cross-equation error correlations between four tree component biomass, two sub-total biomass, and total biomass. The model coefficients were simultaneously estimated using seemly unrelated regression (SUR). Likelihood analysis was used to verify the error structures of power functions in order to determine if logarithmic transformation should be applied on both sides of biomass equations. Jackknifing model residuals were used to validate the prediction performance of biomass equations. The results indicated that (1) stem biomass accounted for the largest proportion (62 %) of the total tree biomass; (2) the two additive systems of biomass equations obtained good model fitting and prediction, of which the model R a 2 was [0.89, and the mean absolute percent bias (MAB %) was \35 %; (3) the system of biomass equations based on both D and H significantly improved model fitting and performance, especially for total, aboveground, and stem biomass; and (4) the anti-log correction was not necessary in this study. The established additive systems of biomass equations can provide reliable and accurate estimation for individual tree biomass of the nine hardwood species in Chinese National Forest Inventory.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developing additive systems of biomass equations for nine hardwood species in Northeast China

Dong

Zhang

2015

Trees

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“…For these trees, diameters of individual stems (dbh i ) were combined and a surrogate for dbh was determined as dbh ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffi ∑dbh i 2 p (e.g., Zhou et al 2007) while we used the heights of individual stems to determine basal area-weighted mean heights that were used as surrogate for ht. Table 2 summarizes statistics for plot (i.e., for trees ≥ 5 cm) and sample tree variables.…”

Section: Tree Sampling and Measurement Proceduresmentioning

confidence: 99%

Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach

Njana

Bollandsås

Eid

et al. 2015

Annals of Forest Science

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Abstract& Key message Tested on data from Tanzania, both existing species-specific and common biomass models developed elsewhere revealed statistically significant large prediction errors. Species-specific and common above-and belowground biomass models for three mangrove species were therefore developed. The species-specific models fitted better to data than the common models. The former models are recommended for accurate estimation of biomass stored in mangrove forests of Tanzania. & Context Mangroves are essential for climate change mitigation through carbon storage and sequestration. Biomass models are important tools for quantifying biomass and carbon stock. While numerous aboveground biomass models exist, very few studies have focused on belowground biomass, and among these, mangroves of Africa are hardly or not represented. & Aims The aims of the study were to develop above-and belowground biomass models and to evaluate the predictive accuracy of existing aboveground biomass models developed for mangroves in other regions and neighboring countries when applied on data from Tanzania. & Methods Data was collected through destructive sampling of 120 trees (aboveground biomass), among these 30 trees were sampled for belowground biomass. The data originated from four sites along the Tanzanian coastline covering three dominant species: Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith, and Rhizophora mucronata Lam. The biomass models were developed through mixed modelling leading to fixed effects/common models and random effects/speciesspecific models. & Results Both the above-and belowground biomass models improved when random effects (species) were considered. Inclusion of total tree height as predictor variable, in addition to diameter at breast height alone, further improved the model predictive accuracy. The tests of existing models from other regions on our data generally showed large and significant prediction errors for aboveground tree biomass. Handling Editor: Shuqing ZhaoContribution of co-authors: Njana: Took part in design of research, responsible for data collection and preparation, and for data analysis and manuscript development. O. M. Bollandsås: Supervising the work including providing technical guidance on data analysis and commenting on the manuscript. T. Eid: Took part in design of the research, supervising the work, commenting, and manuscript development. E. Zahabu: Supervising the work and commenting on the manuscript. R. Malimbwi: Coordinating the design of research project and commenting on the manuscript.

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“…Compared to forests, agroforestry plantings have a more open environment, resulting in trees with greater branch production and greater specific gravity (Zhou et al 2011). These differences indicate that use of existing forest-derived equations may not accurately estimate woody biomass C. Ongoing work in the United States and Canada is providing the basis for determining if existing forest-derived equations for North American trees can be adjusted for better estimation of this C in agroforestry-generated woody biomass, as well as developing equations for multiplestemmed tree species (Zhou et al 2007) and shrub species that contribute to this C stock in agroforestry plantings.…”

Section: Greenhouse Gas Mitigationmentioning

confidence: 99%

Branching out: Agroforestry as a climate change mitigation and adaptation tool for agriculture

Schoeneberger¹,

Bentrup²,

Gooijer³

et al. 2012

Journal of Soil and Water Conservation

164

107

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Developing above-ground woody biomass equations for open-grown, multiple-stemmed tree species: Shelterbelt-grown Russian-olive

Cited by 34 publications

References 19 publications

Developing additive systems of biomass equations for nine hardwood species in Northeast China

Developing additive systems of biomass equations for nine hardwood species in Northeast China

Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach

Branching out: Agroforestry as a climate change mitigation and adaptation tool for agriculture

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