Jacqueline R. England

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

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 = aDb 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.

Using measured stocks of biomass and litter carbon to constrain modelled estimates of sequestration of soil organic carbon under contrasting mixed-species environmental plantings

Reforestation of agricultural land with mixed-species environmental plantings of native trees and shrubs contributes to abatement of greenhouse gas emissions through sequestration of carbon, and to landscape remediation and biodiversity enhancement. Although accumulation of carbon in biomass is relatively well understood, less is known about associated changes in soil organic carbon (SOC) following different types of reforestation. Direct measurement of SOC may not be cost effective where rates of SOC sequestration are relatively small and/or highly spatially-variable, thereby requiring intensive sampling. Hence, our objective was to develop a verified modelling approach for determining changes in SOC to facilitate the inclusion of SOC in the carbon accounts of reforestation projects. We measured carbon stocks of biomass, litter and SOC (0-30cm) in 125 environmental plantings (often paired to adjacent agricultural sites), representing sites of varying productivity across the Australian continent. After constraining a carbon accounting model to observed measures of growth, allocation of biomass, and rates of litterfall and litter decomposition, the model was calibrated to maximise the efficiency of prediction of SOC and its fractions. Uncertainties in both measured and modelled results meant that efficiencies of prediction of SOC across the 125 contrasting plantings were only moderate, at 39-68%. Data-informed modelling nonetheless improved confidence in outputs from scenario analyses, confirming that: (i) reforestation on agricultural land highly depleted in SOC (i.e. previously under cropping) had the highest capacity to sequester SOC, particularly where rainfall was relatively high (>600mmyear-1), and; (ii) decreased planting width and increased stand density and the proportion of eucalypts enhanced rates of SOC sequestration. These results improve confidence in predictions of SOC following environmental reforestation under varying conditions. The calibrated model will be a useful tool for informing land managers and policy makers seeking to understand the dynamics of SOC following such reforestation.