Development and testing of allometric equations for estimating above-ground biomass of mixed-species environmental plantings

Paul, Keryn I.; Roxburgh, Stephen H.; England, Jacqueline R.; Ritson, Peter; Hobbs, Trevor; Brooksbank, Kim; Raison, R. J.; Larmour, John S.; Murphy, Simon; Norris, Jaymie; Neumann, C.; Lewis, Tom; Jonson, Justin; Carter, Jennifer L.; McArthur, Geoff; Barton, Craig V. M.; Rose, Ben

doi:10.1016/j.foreco.2013.08.054

Cited by 127 publications

(117 citation statements)

References 41 publications

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“…Second, analyses were based on a simple power function, and do not include additional terms such as tree height, D 2 , wood density, or additional parameters that account for changing allometry as individuals increase in size (Parresol 1999, Chave et al 2004, and that could lead to increased precision. When applied to our datasets, the simple power model provided robust allometric relationships, with previous work concluding that the inclusion of other factors such as height or wood density would be unlikely to yield significant improvements over the simpler model (Paul et al 2013b). However for other datasets the inclusion of additional variables might be required.…”

Section: Recommendationsmentioning

confidence: 82%

“…An equally diverse array of functional models has been used to relate these measurements to biomass, with a corresponding broad range of methods for estimating model parameters (Picard et al 2012). Here we focus on simple power functions that relate stem diameter to biomass (Equation 1), as these have been shown to adequately capture the allometry of a broad range of species (Kuyah et al 2012, Paul et al 2013b. The power function can be linearized by logarithmic transformation (Eq.…”

Section: Introductionmentioning

confidence: 99%

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Guidelines for constructing allometric models for the prediction of woody biomass: How many individuals to harvest?

et al. 2015

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Abstract. The recent development of biomass markets and carbon trading has led to increasing interest in obtaining accurate estimates of woody biomass production. Aboveground woody biomass (B) is often estimated indirectly using allometric models, where representative individuals are harvested and weighed, and regression analyses used to generalise the relationship between individual mass and more readily measured non-destructive attributes such as plant height and stem diameter (D). To satisfy regulatory requirements and/or to provide market confidence, allometric models must be based on sufficient data to ensure predictions are accurate, whilst at the same time being practically and financially achievable.Using computer resampling experiments and allometric models of the form B ¼ aD b the trade-off between increasing the sample size of individuals to construct an allometric model and the accuracy of the resulting biomass predictions was assessed. A range of algorithms for selecting individuals across the stem diameter size-class range were also explored. The results showed marked variability across allometric models in the required number of individuals to satisfy a given level of precision. A range of 17-95 individuals were required to achieve biomass predictions with a standard deviation within 5% of the mean for the best performing stem diameter selection algorithm, while 25-166 individuals were required for the poorest. This variability arises from (a) inherent uncertainty in the relationship between diameter and biomass across allometric models, and (b) differences between the diameter size-class distribution of individuals used to construct a model, and the diameter size-class distribution of the population to which the model is applied. Allometric models are a key component of quantifying land-based sequestration activities, but despite their importance little attention has been given to ensuring the methods used in their development will yield sufficiently accurate biomass predictions. The results from this study address this gap and will be of use in guiding the development of new allometric models; in assessing the suitability of existing allometric models; and in facilitating the estimation of uncertainty in biomass predictions.

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Section: Recommendationsmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Guidelines for constructing allometric models for the prediction of woody biomass: How many individuals to harvest?

et al. 2015

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“…However, generic species-specific models were used for Acacia spp., Eucalyptus spp., Pinus spp. and palms [37][38][39]; for the rest of the species, the AGB (kg) was computed based on wood-specific density ( , g cm −3 ), D (cm) and H (m) by the pantropical allometric model of [40]. Values of were obtained from the global wood density databases [41,42].…”

Section: Field Measurements and Aboveground Biomass Computationsmentioning

confidence: 99%

Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya

et al. 2017

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Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were (1) to generate a map of AGB for the Taita Hills, in Kenya, based on field measurements and airborne laser scanning (ALS), and (2) to examine determinants of AGB using geospatial data and statistical modelling. The study area is located in the northernmost part of the Eastern Arc Mountains, with an elevation range of approximately 600-2200 m. The field measurements were carried out in 215 plots in 2013-2015 and ALS flights conducted in 2014-2015. Multiple linear regression was used for predicting AGB at a 30 m × 30 m resolution based on canopy cover and the 25th percentile height derived from ALS returns (R 2 = 0.88, RMSE = 52.9 Mg ha −1 ). Boosted regression trees (BRT) were used for examining the relationship between AGB and explanatory variables at a 250 m × 250 m resolution. According to the results, AGB patterns were controlled mainly by mean annual precipitation (MAP), the distribution of croplands and slope, which explained together 69.8% of the AGB variation. The highest AGB densities have been retained in the semi-natural vegetation in the higher elevations receiving more rainfall and in the steep slope, which is less suitable for agriculture. AGB was also relatively high in the eastern slopes as indicated by the strong interaction between slope and aspect. Furthermore, plantation forests, topographic position and the density of buildings had a minor influence on AGB. The findings demonstrate the utility of ALS-based AGB maps and BRT for describing AGB distributions across Afromontane landscapes, which is important for making sustainable land management decisions in the region.

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“…However, these shifts of biomass from one vegetative organ to another may not affect the total biomass allometry. It has been found that biomass allocation pattern can be modified by soil type (Wolters and Stickan, 1991), altitude (Cienciala et al, 2006), precipitations (Paul et al, 2013) and light regime (Naidu et al, 1998, Kennedy et al, 2007. However, the response of the species to different environmental conditions may be not general.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Age, Location and Soil Conditions on the Allometry of Young Norway Spruce (<i>Picea abies</i> L. Karst.) Trees

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Negruţiu

Ioraş

et al. 2014

Not Bot Hort Agrobot Cluj

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In this study the influence of tree's age, location (i.e. latitude and altitude) and soil conditions (i.e. pH, humus content, carbon to nitrogen ratio, cation exchange capacity and percent base saturation) on tree allometry was investigated. The data was collected from 22 Norway spruce (Picea abies L. Karst) plantations located in Eastern Carpathians of Romania, aged between 4 and 15. From each plantation a soil sample and 10 trees were collected for soil chemical properties and biomass measurements, respectively. Root collar diameter (RCD) and height (H) based allometric equations were developed for total tree and vegetative organs of the tree (i.e. stem, branches, needles and roots). Furthermore, the interaction between the standardised residuals of these models and the tested factors was analysed. In order to account for the random effect of the clustered data, the mixed-effect modelling procedure was used. The results have shown no influence of these factors (age, location and soil conditions) on RCD based models, except for branches biomass model which was linked to soil carbon/nitrogen ratio. The H based models, however, were significantly influenced by latitude and soil cation exchange capacity as a consequence of H/RCD ratio change with these factors. The trees were more likely to allocate more to height growth when growing in higher latitudes or on soils with higher values of cation exchange capacity.

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Development and testing of allometric equations for estimating above-ground biomass of mixed-species environmental plantings

Cited by 127 publications

References 41 publications

Guidelines for constructing allometric models for the prediction of woody biomass: How many individuals to harvest?

Guidelines for constructing allometric models for the prediction of woody biomass: How many individuals to harvest?

Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya

The Influence of Age, Location and Soil Conditions on the Allometry of Young Norway Spruce (<i>Picea abies</i> L. Karst.) Trees

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