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Highlights • Refinement of the IPCC equation for estimating greenhouse gas emission from forest fires using country specific information. • Provides key recommendations for improving forest fire emissions estimations. • Highlights the complexity of using data from multiple sources and provides possible solutions. Abstract Forest fires are a significant contributor to global greenhouse gas (GHG) emissions. Accurate reporting of GHG emissions from forest fires requires development of detailed methodologies and country specific data for estimating emissions. In recent years, Australia has updated its national methodology for reporting GHG emissions from fires on temperate forested lands, using a Tier 2 approach of the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories. This involved refinement of the equation for estimating GHG emissions from fires provided in the Guidance, and the revision of country specific data which was derived from a comprehensive literature review. The refinements were key to transparent reporting and evaluation of the climatic impacts of mitigation actions such as forest fire management. In this paper we describe the steps required to develop a Tier 2 method in reporting fire emissions using this Australian example, the lessons learnt, and the steps required to reduce uncertainties in estimates. This paper may assist other countries seeking to estimate and report GHG emissions from forest fires by moving from the default Tier 1 method to Tier 2 using country-specific information. [ABSTRACT FROM AUTHOR]

Highlights • Maximum above-ground biomass is a key parameter in Australia's national carbon accounting model. • The Random Forest algorithm was used to create revised continental maps of this parameter. • The analysis was based on data collected from 5739 field survey plots. • The new biomass predictions for woodlands were similar to existing model predictions. • The new biomass predictions for forests increased significantly. Abstract The carbon accounting model FullCAM is used in Australia's National Greenhous Gas Inventory to provide estimates of carbon stock changes and emissions in response to deforestation and afforestation/reforestation. FullCAM-predicted above-ground woody biomass is heavily influenced by the parameter M , which defines the maximum upper limit to biomass accumulation for any location within the Australian continent. In this study we update FullCAM's M spatial input layer through combining an extensive database of 5739 site-based records of above-ground biomass (AGB) with the Random Forest ensemble machine learning algorithm, with model predictions of AGB based on 23 environmental predictor covariates. A Monte-Carlo approach was used, allowing estimates of uncertainty to be calculated. Overall, the new biomass predictions for woodlands, with 20–50% canopy cover, were on average 49.5 ± 1.3 (s.d.) t DM ha−1, and very similar to existing model predictions of 48.5 t DM ha−1. This validates the original FullCAM model calibrations, which had a particular focus on accounting for greenhouse gas emissions in Australian woodlands. In contrast, the prediction of biomass of forests with a canopy cover >50% increased significantly, from 172.1 t DM ha−1, to 234.4 ± 5.1 t DM ha−1. The change in forest biomass was most pronounced at sub-continental scales, with the largest increases in the states of Tasmania (166 to 351 ± 22 t DM ha−1), Victoria (201 to 333 ± 14 t DM ha−1), New South Wales (210 to 287 ± 9 t DM ha−1), and Western Australia (103 to 264 ± 14 s.d. t DM ha−1). Testing of model predictions against independent data from the savanna woodlands of northern Australia, and from the high biomass Eucalyptus regnans forests of Victoria, provided confidence in the predictions across a wide range of forest types and standing biomass. When applied to the Australian Government's National Inventory land clearing accounts there was an overall increase of 6% in continental emissions over the period 1970–2016. Greater changes were seen at sub-continental scales calculated within 6° × 4° analysis tiles, with differences in emissions varying from −21% to +35%. Further testing is required to assess the impacts on other land management activities covered by the National Inventory, such as reforestation; and at more local scales for sequestration projects that utilise FullCAM for determining abatement credits. [ABSTRACT FROM AUTHOR]

Highlights • Over 2000 measurements of plant belowground biomass were compiled. • Accurate generic allometric models could be developed. • These models were validated across stands where whole-plot excavation data were available. • Gains in prediction efficiencies using species-specific models were negligible. Abstract Accurate quantification of below-ground biomass (BGB) of woody vegetation is critical to understanding ecosystem function and potential for climate change mitigation from sequestration of biomass carbon. We compiled 2054 measurements of planted and natural individual tree and shrub biomass from across different regions of Australia (arid shrublands to tropical rainforests) to develop allometric models for prediction of BGB. We found that the relationship between BGB and stem diameter was generic, with a simple power-law model having a BGB prediction efficiency of 72–93% for four broad plant functional types: (i) shrubs and Acacia trees, (ii) multi-stemmed mallee eucalypts, (iii) other trees of relatively high wood density, and; (iv) a species of relatively low wood density, Pinus radiata D. Don. There was little improvement in accuracy of model prediction by including variables (e.g. climatic characteristics, stand age or management) in addition to stem diameter alone. We further assessed the generality of the plant functional type models across 11 contrasting stands where data from whole-plot excavation of BGB were available. The efficiency of model prediction of stand-based BGB was 93%, with a mean absolute prediction error of only 6.5%, and with no improvements in validation results when species-specific models were applied. Given the high prediction performance of the generalised models, we suggest that additional costs associated with the development of new species-specific models for estimating BGB are only warranted when gains in accuracy of stand-based predictions are justifiable, such as for a high-biomass stand comprising only one or two dominant species. However, generic models based on plant functional type should not be applied where stands are dominated by species that are unusual in their morphology and unlikely to conform to the generalised plant functional group models. [ABSTRACT FROM AUTHOR]

Reforestation schemes, which encompass environmental plantings and natural regeneration of vegetation on cleared land, are increasingly being established for the purposes of mitigating anthropogenic carbon emissions. However, these schemes are themselves at risk from climate change and associated changes in disturbance regimes. Simultaneously, there is increasing pressure on reforested areas to achieve multiple co-benefits, e.g. maximizing carbon storage, ameliorating environmental degradation and promoting biodiversity objectives, all while not adversely affecting community values, such as agricultural production. Here, we review the myriad of biophysical risks posed by climate change to reforested areas while documenting management actions and policies that can enhance both the resistance and resilience of reforested areas to such risks. While it is difficult to buffer vegetation against the direct effects of climate change, such as elevated temperature and changed precipitation patterns, it is possible to manage some of the indirect effects, such as wildfire, drought and insect defoliation. Methods for reducing the vulnerability of reforested areas range from site-specific management actions, particularly around design and location, through to regional and national scale initiatives, such as vulnerability assessments and decision support tools. The complexity of objectives and risks posed to reforested areas means that it is vitally important to evaluate outcomes from across the current estate of reforested areas. However, there is currently no national protocol in place in Australia to track, monitor or evaluate the outcomes of reforestation. Thus, we recommend the establishment of a national framework for analyzing and supporting the growing range of reforestation activities. [ABSTRACT FROM AUTHOR]

The development of biomass estimation models is highly resource intensive as it generally entails harvesting (or excavating) trees of a range of sizes to determine dry weight of above-ground (or below-ground) biomass. To maximise the cost effectiveness of such sampling, guidance is required on whether an allometric model that already exists is suitable for a new site or species, or whether further sampling and model development is necessary. With the aim to provide such guidance, we collated 12 pairs of well-sampled ( N  > 50) data sets of the same species at two sites, or two species at the same site. These provided case studies for: (i) assessing alternative statistical approaches to validate the application of a model developed using one data set to predict biomass of independent data from another site or species, and (ii) applying scenario analyses to explore the impact of sample size on uncertainty of validation, e.g. minimising type I and type II errors. Our results indicate that although an allometric model for a given species or plant functional type may be applied across multiple sites, validation will be important when an existing generic multi-site and multi-species model is applied to a new species. Results obtained demonstrated that an independent sample size of N  ≤ 15 frequently (37–46% of the time) provides insufficient power to avoid incorrectly accepting “validation” (type II errors). Hence, to ensure a useful outcome from resources spent in sampling biomass, it is recommended that at least 50 trees be sampled for each species. An equivalence test may then be applied to determine if the minimum detectable negligible difference between the existing model and the new independent data is <25% (or whichever threshold is deemed acceptable). If so, the new data set may then be combined with existing data to refine a generalised model, which may then be applied with confidence. If not, then the resources expended need not be wasted as the sample size is sufficient to develop a new model suitable for application to the specific species sampled. [ABSTRACT FROM AUTHOR]

Accurate assessment of environmental variables is vital to understanding the global issues of land-use change and climate change, but is hindered by their high spatial and temporal heterogeneity. Extensive surveys are needed to model such large-scale problems, with their success dependent on adequate sampling protocols. We present a robust method for designing efficient sampling protocols for environmental variables. The SIMAP method involves the following steps: 1) Selecting sites that cover a representative range of spatial variability, 2) Intensive and spatially-accurate surveys within sites, 3) construction of continuous Maps that replicate the spatial and statistical variation of the surveys, 4) Accuracy simulations based on sampling of these maps and 5) determining a sampling Protocol for subsequent broader surveys. To illustrate the method, we used estimation of soil C stocks in mixed-species tree plantings and pastures to estimate carbon sequestration following reforestation. Soil C was surveyed intensively from these two land uses at several farms that covered a large rainfall gradient to provide contrasting datasets. In this example, sampling simulations showed that a systematic design generally required one less sample than a restricted-random design to achieve the same accuracy, while a simple-random design required substantially more samples. We found taking a minimum of 30 soil samples was needed for both bulk density and C concentration to accurately estimate soil C content within a 1-ha plot in a pasture or tree planting, which suggests many previous surveys of soil C were sampled inadequately. The SIMAP method could be readily applied to a range of abiotic and biotic variables, with the construction of maps allowing most sampling intensities and designs to be tested. Adequate sampling intensities differ widely among environmental variables, so the SIMAP method enables researchers to determine which variables require more investment. For many variables, costs may be minimised while maintaining a high accuracy of the sampling design via bulking of well-mixed samples prior to analysis. [ABSTRACT FROM AUTHOR]

Accurate estimates of biomass are required for relating ecosystem functioning to atmospheric carbon regulation. Biomass may be directly measured through field sampling, which can then be used to calibrate biomass predictions from remote sensing and/or modelling. Field sampling generally entails measuring the fresh mass of individual trees or shrubs and then estimating the moisture contents of a representative sub-samples, which are then used to calculate dry mass. Because any errors in the estimation of the moisture content (MC) correction are translated proportionally to the biomass prediction of an individual tree or shrub, care is required to ensure MC estimates are unbiased and as precise as possible. There are numerous different protocols currently applied to attain MC, with these differing in accuracy (bias and precision) and cost of implementation. A dataset of MC of above-ground biomass (AGB) of 1396 individuals (trees or shrubs) was used to assess which protocols for within- and among-individual sampling are likely to provide the most cost-effective estimates of MC within acceptable bounds of accuracy. Monte-Carlo analysis was used to explore key sources of error in within-individual MC estimation. Results suggest these MC estimates may be based on at least the bole and crown components of AGB, with bias resulting if MC is based on stem wood only, particularly in young (or small) individuals. Little gain in accuracy was attained with more intensive sub-sampling (e.g. into foliage, twig, branches, bark, and stem wood components). Moreover, further efficiencies may be gained by applying existing empirical models to estimate the proportion of AGB that is crown based on easily measured variables such as stem diameter, thereby avoiding the resource-intensive process of partitioning to obtain fresh weights measurements of components. However to minimise bias, it is important to undertake MC sampling at each study site, and to stratify sampling among-individuals by both appropriate taxonomic grouping (e.g. plant functional type) and age-class. For a given plant functional type-by-size (or age) strata at a given site a precision of about 4% coefficient of variation of the average MC estimate can be achieved with intensive within- and among-individual sampling. However a precision of 8–10% is achievable using our recommended less intensive but more efficient protocol; derive an average MC for at least six individuals, and for each individual, intensively sub-sample bole and crown components for MC, which is then applied to the fresh weights of these components. This latter estimate may be obtained from partitioning of the AGB, or for the highest efficiency, from predictions obtained from the application of existing representative empirical relationships of partitioning based on the size of the individual. [ABSTRACT FROM AUTHOR]

Reforestation will have important consequences for the global challenges of mitigating climate change, arresting habitat decline and ensuring food security. We examined field-scale trade-offs between carbon sequestration of tree plantings and biodiversity potential and loss of agricultural land. Extensive surveys of reforestation across temperate and tropical Australia ( N = 1491 plantings) were used to determine how planting width and species mix affect carbon sequestration during early development (< 15 year). Carbon accumulation per area increased significantly with decreasing planting width and with increasing proportion of eucalypts (the predominant over-storey genus). Highest biodiversity potential was achieved through block plantings (width > 40 m) with about 25% of planted individuals being eucalypts. Carbon and biodiversity goals were balanced in mixed-species plantings by establishing narrow belts (width < 20 m) with a high proportion (>75%) of eucalypts, and in monocultures of mallee eucalypt plantings by using the widest belts (ca. 6–20 m). Impacts on agriculture were minimized by planting narrow belts (ca. 4 m) of mallee eucalypt monocultures, which had the highest carbon sequestering efficiency. A plausible scenario where only 5% of highly-cleared areas (<30% native vegetation cover remaining) of temperate Australia are reforested showed substantial mitigation potential. Total carbon sequestration after 15 years was up to 25 Mt CO 2 -e year −1 when carbon and biodiversity goals were balanced and 13 Mt CO 2 -e year −1 if block plantings of highest biodiversity potential were established. Even when reforestation was restricted to marginal agricultural land (<$2000 ha −1 land value, 28% of the land under agriculture in Australia), total mitigation potential after 15 years was 17–26 Mt CO 2 -e year −1 using narrow belts of mallee plantings. This work provides guidance on land use to governments and planners. We show that the multiple benefits of young tree plantings can be balanced by manipulating planting width and species choice at establishment. In highly-cleared areas, such plantings can sequester substantial biomass carbon while improving biodiversity and causing negligible loss of agricultural land. [ABSTRACT FROM AUTHOR]

• Biomass, litterfall, decomposition and soil carbon data were collated. • Data was sourced from decades of research in plantations and farm forestry. • These data informed the model predicting carbon in tree plantings across Australia. • Biomass, litterfall and decomposition data first constrained the model. • The model was then efficiently calibrated for prediction of litter and soil carbon. Understanding carbon sequestration from tree planting and management activities is important for contributing to the mitigation of climate change. Although rates of growth and resulting sequestration of carbon in live above-ground biomass are relatively well understood in various woody plantings and commercial plantations in Australia, less is known about associated changes in live below-ground biomass, and the turnover and decomposition of biomass, and hence, resulting changes in carbon stocks of litter and soil. Our objective was to develop a modelling approach for predicting these processes and the resulting changes in litter and soil carbon following the establishment and/or management of tree plantings (EMTP) to facilitate their inclusion in carbon abatement projects. We reviewed and collated data on allocation of biomass, litterfall, litter decomposition, litter/harvest residue mass and soil organic carbon (N = 5,655 estimates). Data were sourced from existing field studies of woody plantings (mixed-species environmental and mallee eucalypt), commercial plantations (hardwood and softwood) and in some cases, eucalypt-dominant native forests. 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 litter and soil pools. Although large uncertainties in 'observed' results meant that precision of prediction in any given data source was relatively poor, model predictions had negligible overall bias. The size and diversity of datasets applied in the process of calibration improved overall confidence in model predictions of litter and soil carbon pools. The calibrated model will therefore be a useful tool for informing land managers and policy makers seeking to understand on-site dynamics of all carbon pools, and hence, the total contribution to carbon abatement of different EMTP projects in Australia. [ABSTRACT FROM AUTHOR]

Plantings of mixed native species (termed ‘environmental plantings’) are increasingly being established for carbon sequestration whilst providing additional environmental benefits such as biodiversity and water quality. In Australia, they are currently one of the most common forms of reforestation. Investment in establishing and maintaining such plantings relies on having a cost-effective modelling approach to providing unbiased estimates of biomass production and carbon sequestration rates. In Australia, the Full Carbon Accounting Model (FullCAM) is used for both national greenhouse gas accounting and project-scale sequestration activities. Prior to undertaking the work presented here, the FullCAM tree growth curve was not calibrated specifically for environmental plantings and generally under-estimated their biomass. Here we collected and analysed above-ground biomass data from 605 mixed-species environmental plantings, and tested the effects of several planting characteristics on growth rates. Plantings were then categorised based on significant differences in growth rates. Growth of plantings differed between temperate and tropical regions. Tropical plantings were relatively uniform in terms of planting methods and their growth was largely related to stand age, consistent with the un-calibrated growth curve. However, in temperate regions where plantings were more variable, key factors influencing growth were planting width, stand density and species-mix (proportion of individuals that were trees). These categories provided the basis for FullCAM calibration. Although the overall model efficiency was only 39–46%, there was nonetheless no significant bias when the model was applied to the various planting categories. Thus, modelled estimates of biomass accumulation will be reliable on average, but estimates at any particular location will be uncertain, with either under- or over-prediction possible. When compared with the un-calibrated yield curves, predictions using the new calibrations show that early growth is likely to be more rapid and total above-ground biomass may be higher for many plantings at maturity. This study has considerably improved understanding of the patterns of growth in different types of environmental plantings, and in modelling biomass accumulation in young (<25 years old) plantings. However, significant challenges remain to understand longer-term stand dynamics, particularly with temporal changes in stand density and species composition. [ABSTRACT FROM AUTHOR]

Establishment of mallee eucalypt plantings on cleared agricultural land is currently the predominant method of reforestation for carbon sequestration in Australia. Investment in establishing and maintaining such plantings relies on having a cost-effective approach for providing un-biased estimates of yield in biomass and carbon sequestration. The Australian Government’s forest carbon accounting model (FullCAM) had not previously been calibrated for mallee eucalypt plantings and, in many circumstances, substantially under-estimated of biomass for these plantings. Our objective was to improve model applicability and reliability of estimates of carbon sequestration. To achieve this, we first collected and analysed above-ground biomass data from 257 mallee eucalypt plantings (or 744 observations, when including the multiple measurements made at some planting sites) to determine the key factors influencing growth. Plantings were categorised according to species, planting configuration (block or belt plantings) and stand density. Each category of planting had significantly different rates of growth, with the rates of sequestration of above-ground biomass carbon being relatively high when established in densely-stocked, two-row belts. These categories of plantings then provided the basis for calibration (estimation of appropriate modifiers) of FullCAM growth curves. Overall model efficiency was 63%, and there was no apparent bias when the model was applied to the various planting categories. Thus, modelled estimates of biomass accumulation will be reliable on average but at any particular location will be highly uncertain, with either substantial under- or over-prediction possible. For some categories of mallee eucalypt plantings, and for plantings with access to ground-water or established in non-productive soils, there were insufficient observations to provide confidence in new calibrations specific for these circumstances. Moreover, application of the calibrations provided here are applicable for prediction of sequestration of biomass carbon of relatively young (<15-year-old) stands, with more data needed for prediction of longer-term rates of growth. [ABSTRACT FROM AUTHOR]

Reducing uncertainty in forest carbon estimates at local and regional scales has become increasingly important due to the centrality of the terrestrial carbon cycle in issues of climate change. Despite relatively limited geographical extent, temperate forests are among the most carbon dense forests in the world. Estimates of carbon in key understorey life forms and belowground components of these forests have often been excluded from previous studies in south-eastern Australia. We estimated above- and belowground carbon stocks (including soil to 0.3 m depth) in stands of cool temperate rainforest (‘rainforest’), wet sclerophyll forest (‘eucalypt forest’), and mixed rainforest–wet sclerophyll stands (‘ecotone forest’) across a wide range of environmental conditions and forest structures. We examined relationships among component carbon stocks and a range of environmental variables (edaphic, climatic, spatial) and present the first allometric equations and carbon stock estimates for south-eastern Australian tree ferns. Component carbon stocks were within the range of published values for these stand types. Using multivariate analyses of all component stocks, we detected significantly more carbon in total above- and belowground components in ecotone (697 Mg ha −1 , 95% confidence interval 575–819 Mg ha −1 ) and eucalypt forests (689 Mg ha −1 , 605–773 Mg ha −1 ) than rainforest (550 Mg ha −1 , 453–647 Mg ha −1 ). However, we found no significant differences among the stand types in the proportional distribution of carbon among components despite significant differences in structural composition as indicated by size class distributions of the main genera. Of total carbon, ∼48% was stored in trees (>2 cm over-bark diameter), and ∼72% of tree carbon was stored in the largest 10% of all trees. The most important environmental variables associated with carbon stocks irrespective of stand type were edaphic variables, most commonly total and available soil nitrogen. Tree fern carbon was the only component stock more strongly associated with climatic and spatial than edaphic variables. Our findings indicate that disturbance mediated changes in stand dynamics could significantly alter total carbon stocks, particularly if more frequent fires limit tree recruitment and increase large tree mortality. Monitoring of these forests for carbon could place greater emphasis on key structural elements associated with the largest proportion of total carbon, the largest trees. By reducing uncertainties associated with estimates of carbon in key stocks, we can better understand potential future changes to the carbon cycle from altered stand dynamics under climate change. [ABSTRACT FROM AUTHOR]

Highlights: [•] Generalised root allometrics were developed for environmental and mallee plantings. [•] Monte Carlo analysis was used to demonstrate uncertainties in allometry. [•] To test predictions, root biomass was directly measured at the site-level at 11 sites. [•] Bias was relatively low for allometry-predictions of root biomass. [ABSTRACT FROM AUTHOR]

Highlights: [•] Specific and generalised allometrics were developed for mallee eucalypts. [•] To test predictions, biomass was directly measured at the site-level at 22 sites. [•] Bias was relatively low for generalised allometry-predictions of biomass. [•] Precision and accuracy increased with the level of specificity of allometry. [Copyright &y& Elsevier]

Highlights: [•] Specific and generalised allometrics were developed for mixed-species plantings. [•] To test predictions, biomass was directly measured at the site-level at 8 sites. [•] Bias was relatively low for generalised allometry-predictions of biomass. [•] Precision and accuracy increased with the level of specificity of allometry. [Copyright &y& Elsevier]

Highlights: [•] We review nine alternatives for correcting bias in log–log allometrics. [•] We use simulations to evaluate the ability of these to estimate average biomass. [•] We evaluate their ability to predict biomass of new trees. [•] Methods not commonly used in forest science performed best. [Copyright &y& Elsevier]

Abstract: Employing rangelands for climate change mitigation is hindered by conflicting reports on the direction and magnitude of change in soil organic carbon (ΔSOC) following changes in woody cover. Publications on woody thickening and deforestation, which had led to uncertainty in ΔSOC, were re-evaluated, and the dimensional-dependence of their data was determined. To model the fundamentals of SOC flux, linked SOC pools were simulated with first-order kinetics. Influences from forest development timelines and location of mature trees, with a potential for deep-set roots, were considered. We show that controversy or uncertainty has arisen when ΔSOC data were not measured along sufficient lengths of the three Cartesian axes and the time axis, i.e. in 4D. Thickening and deforestation experiments have particularly neglected factors affecting the time and depth axes, and sometimes neglected all four axes. Measurements of thickening must use time-spans beyond the calculable breakeven date – when thickening just recovers the SOC lost through land degradation: then all ecosystems are likely to incur net sequestration. The similarity between half-life of carbon pools, and the half-time required for sequestration, mandates that millennial time-spans must be considered in design of SOC experiments. Spatial and temporal averaging of ΔSOC data that accounted for environmentally dependent decomposition rates, revealed that deforestation to pasture incurred a higher and longer-term net emission than earlier reported. Published reports on thickening or deforestation appear no longer contradictory when one considers that they only presented views from lengths of the 4D axes that were too limited. Adoption of this understanding into carbon accounting will allow more precise estimates of carbon fluxes for emission trading schemes and national reports. [Copyright &y& Elsevier]

Abstract: In this study, we sought to address the issue of how to derive an extended synthetic record of fire incidence and timing at regional scale that would be representative of a short remotely-sensed calibration record. We used annual rainfall and simulated annual ground stratum growth to develop multiple regression relationships for prediction of annual fire probability and proportion of late (August–November) fires from AVHRR NDVI fire footprint data across Australian tropical savannas. Relationships were examined using spatial averaging in moving windows varying from 3×3 to 61×61 pixels in size. Model fits as measured by R 2 improved as window size increased, but output layers became smoother and less representative of natural heterogeneity. A 25×25 pixel window was selected as the best compromise between model fit and smoothing. A 113-year synthetic record of annual fire probability and proportion of late fires was generated using the spatially explicit layers of model coefficients. The statistical properties of the synthetic fire probabilities were compared with those derived from the available fire footprint record, using a simple vegetation classification based on ground stratum type for spatial stratification. The two data sets showed a strong correspondence for both burned area and fire probability; spatial variation in mean and coefficient of variation of fire probability was representative of that observed in the historical record. There was significant temporal variation in the synthetic annual fire probability for different vegetation zones across the tropical savanna region for the full 113-year length of record. This simple approach could readily be applied to other areas of the world provided rainfall data are available and annual ground stratum growth can be simulated with a suitable model or estimated with remote sensing. [Copyright &y& Elsevier]

Abstract: This paper uses a scenario analysis system – Range-ASSESS – to examine the potential for gains and losses of soil carbon in the Australian rangelands as affected by grazing and climate. The analysis involves a factorial examination of the effect of stocking rates and all possible 5-year historical climates between 1889 and 1999. The analysis also looks at the sensitivity of results to the method of calculation of safe carrying capacity, and to the thresholds used to calculate grazing and dryness indices that drive transitions in state and transition models. The analysis showed that different vegetation zones produced different responses to changes in stocking depending upon the spatial distribution of dryness index, nature of carbon state and transition model, rules governing transitions, and relative significance of soil carbon. At a stocking density equivalent to 100% of 1997 levels, the soil carbon loss from rangelands was about 400 Mt C in 40% of the 5-year periods using a sensitive growth deviation threshold to determine dryness index. If a less sensitive threshold was used, potential loss was reduced to about 200 Mt C. If the grazing pressure threshold for a grazing index of four is adjusted to a more generous level, then potential losses in the dry periods are substantially reduced. The analysis is intended to be indicative of a likely approximate outcome rather than a quantitative measure of system response. The results indicate that the interpretation of the effect of the drought-grazing pressure interaction on perennial plant survival, and consequent organic carbon input to soils, is a major source of uncertainty and a critical area for more experimental measurement. [Copyright &y& Elsevier]

Land restoration through planting native species or facilitating natural regeneration may provide opportunities for sequestration of biomass carbon in many regions where woody vegetation has been cleared or largely supressed. Australia provides a good case study of how sequestration from these activities may be simulated at national- and project-scales using an empirical full carbon accounting model; FullCAM. Recent work has resulted in 1.6-fold increase in calibration sites available, while the underlying input layer of site productivity has also been recently refined. These developments provided an opportunity to expand the domain of application and capability of the model. We collated 2340 observations of above-ground biomass in planted or naturally regenerated stands across Australia and applied a novel technique to correct these observations for a baseline biomass of existing remnant trees or shrubs within these stands (typically < 5 Mg DM ha−1 and < 10% of individuals). Five different sets of parameters were estimated based on five categories of stands for the FullCAM model, based on landscape position, land use, and configuration and/or density of the stand: (i) stands accessing water in addition to rainfall; (ii) belt plantings of high (>1500 stems per hectare) stand density; (iii) belt plantings of lower stand density; (iv) blocks of plantings or natural regeneration on land used to deliver environmental services, and; (v) blocks of natural regeneration occurring on land used for livestock production. Compared to blocks of plantings or natural regeneration, yields of biomass after 30 years of growth were 1.3- to 2.2-times higher in belt plantings, and stands of mesic regions accessing ground or surface water, e.g. riparian or floodplain zones. After 30 years of natural regeneration, when compared to stands fenced-off for conservation, yields of biomass were 1.5-times lower on stands that continued to be managed for livestock production. In revising the model calibrations, the trade-off between model accuracy and utility was considered. By accounting for only stand age, site productivity and the five stand categories, the overall model efficiency of biomass prediction was 68%. Additional explanatory variables such as stocking densities of stands with a block configuration, stand establishment method, species or species mix, and belt width were also tested, but their addition to the model was considered unwarranted, given the additional resources required to account for these inputs would be substantial, and they would deliver <10% improvement in prediction efficiencies. Although predictions were unbiased overall, they may nonetheless provide erroneous predictions at a given site. Nonetheless, given the absence of bias, individual over- and under-predictions will tend to cancel out for carbon accounting at the national-scale; while at the project-scale where carbon offsets are monetarised, discounting for uncertainty may be implemented. [ABSTRACT FROM AUTHOR]

The Full Carbon Accounting Model (FullCAM) is used by the Australian Government for international reporting of greenhouse gas emissions and to predict carbon stock changes for carbon abatement projects. It is also used by landowners, companies and researchers, and is therefore regularly applied at local to continental scales. FullCAM is a stand level model with daily, monthly or annual temporal resolutions, depending on the users' preference. FullCAM has been developed over the past 20 years, however, no studies have reviewed and described the current structure, parameters, calibration, and uses of FullCAM, which was the objective of this study. We also discuss the potential for further developments that could improve FullCAM's ability to model responses to disturbances and climate, while also increasing its local accuracy. • FullCAM simulates carbon follows between live biomass, standing dead, debris and soil. • FullCAM is used at local, regional and continental scales, and daily, monthly or annual steps. • FullCAM is used for native forests, woodlands and savannas, as well as plantations. [ABSTRACT FROM AUTHOR]

• We measured soil organic carbon that is typically ignored close to large trees. • 90% of the total soil organic carbon was within ~2.6 m of the surface. • Under large tree trunks soil organic carbon was four times more concentrated. • The stand-level had ~7% [95% CI: 3–12%] more soil carbon than previously estimated. • There may be higher emissions from past forest disturbances than have been inferred. Typical soil organic carbon (SOC) measurements do not account for the higher SOC concentration adjacent to, inside and under the trunks of large trees, or for the root volume which displaces soil and thereby reduces spatial density of SOC. Any net difference between these two omissions could have a significant impact on carbon accounts for the conversion of a primary forest with large trees to a type of land cover with much smaller trees, or no trees, such as to a secondary forest on short harvest cycles, or to deforested land, respectively. To improve knowledge of carbon stocks in primary forests, for better carbon management and climate change modelling, we sampled SOC and soil bulk density directly under large tree trunks, inside tree trunks, in the humus mounds in the buttress region, and under the humus mounds. The measurements were in primary Eucalyptus regnans mixed-forest. SOC was formulated as a function of depth. Adjacent to the trees, 90% of the total cumulative SOC was estimated to be within ~2.6 m of the mineral soil surface. That SOC was compared with an earlier measurement in the same locality of SOC in-between trees, away from the trunk and buttress. The SOC under large tree trunks was about four times more concentrated than in-between trees. Formulae that link SOC, root volume, and buttress shape, to tree diameter and ground slope were applied to forest stands within 54.4 ha of primary forest. When the under-trunk SOC was tallied with the organic soils associated with the buttress region and in nearly decomposed logs, SOC at the unit-area-level increased by ~7% [95% CI: 3–12%] relative to the in-between-tree SOC alone, and the absolute increase was 21 Mg ha−1 [95% CI: 10–37 Mg ha−1] of SOC. Our results suggest that, at least for land use change that fells mature trees >1 m in diameter, there may have been higher greenhouse gas emissions from past forest attrition than have been inferred. Globally, we identified 50 example tree species, other than E. regnans , that may also have extra SOC at the stand-level in the absence of fire. Additional SOC per hectare was positively correlated with basal area of trees, which increases with the number of large trees in a stand. The maintenance of large trees will help ensure higher levels of forest carbon. The protection of medium-sized trees will be necessary to ensure existence of large trees in the future. [ABSTRACT FROM AUTHOR]