technical report no. 41

Transcription

1 national carbon accounting system technical report no. 41 Calibration of the FullCAM Model to Eucalyptus globulus and Pinus radiata and Uncertainty Analysis Phil Polglase, Peter Snowdon, Tivi Theiveyanathan, Keryn Paul, John Raison, Tim Grove, and Stan Rance

2 The National Carbon Accounting System: Supports Australia's position in the international development of policy and guidelines on sinks activity and greenhouse gas emissions mitigation from land based systems. Reduces the scientific uncertainties that surround estimates of land based greenhouse gas emissions and sequestration in the Australian context. Provides monitoring capabilities for existing land based emissions and sinks, and scenario development and modelling capabilities that support greenhouse gas mitigation and the sinks development agenda through to 212 and beyond. Provides the scientific and technical basis for international negotiations and promotes Australia's national interests in international fora. For additional copies of this report phone

3 CALIBRATION OF THE FULLCAM MODEL TO EUCALYPTUS GLOBULUS AND PINUS RADIATA AND UNCERTAINTY ANALYSIS Phil Polglase, Peter Snowdon, Tivi Theiveyanathan, Keryn Paul, John Raison, Tim Grove, and Stan Rance CSIRO Forestry and Forest Products National Carbon Accounting System Technical Report No. 41 January 24

4 Printed in Australia for the Australian Greenhouse Office Australian Government 23 This work is copyright. It may be reproduced in whole or part for study or training purposes subject to the inclusion of an acknowledgement of the source and no commercial usage or sale results. Reproduction for purposes other than those listed above requires the written permission of the Communications Team, Australian Greenhouse Office. Requests and enquiries concerning reproduction and rights should be addressed to the Communications Team, Australian Greenhouse Office, GPO Box 621, CANBERRA ACT 261. For additional copies of this document please contact the Australian Greenhouse Office Publications Hotline on For further information please contact the National Carbon Accounting System at Neither the Australian Government nor the Consultants responsible for undertaking this project accepts liability for the accuracy of or inferences from the material contained in this publication, or for any action as a result of any person s or group s interpretations, deductions, conclusions or actions in reliance on this material. January 24 Environment Australia Cataloguing-in-Publication Calibration of the FullCAM model to Eucalyptus globulus and Pinus radiata and uncertainty analysis / Phil Polglase [et al.]. p. cm. (National Carbon Accounting System technical report ; no. 41) ISSN: Eucalyptus globulus-carbon content-measurement. 2. Pinus radiata-carbon content- Measurement. 3. Forest biomass-measurement. I. Polglase, Philip John. II. CSIRO Forestry and Forest Products. III. Australian Greenhouse Office. IV. Series dc dc21 ii Australian Greenhouse Office

5 EXECUTIVE SUMMARY The three main objectives of this study were to: i. calibrate the CAMFor sub-model within FullCAM to growth and partitioning between tree components in Pinus radiata and Eucalyptus globulus plantations. This enabled default equations to be derived relating basic stem wood density to age, and the biomass of stand components to biomass of the stem; ii. test calibrations using three case studies of P. radiata and E. globulus where data on volume yield curves and biomass of stand components were available for various stages of tree growth, and; iii. use Monte Carlo simulations for case studies of P. radiata and E. globulus to determine parameters to which the CAMFor sub-model is sensitive, and the accuracy and confidence with which the sub-model predicts patterns and amounts of carbon (C) storage in plantations. Calibration of basic density and partitioning coefficients. Using collated datasets of between 73 and 187 observations, CAMFor was calibrated for P. radiata and E. globulus plantations by developing linear and non-linear relationships between: i. basic density of stem wood (excluding bark) and plantation age. Basic density is then multiplied by stem wood volume to give stem wood mass; ii. mass of stem wood and stem bark, and; iii. mass of stem wood+bark and the mass of foliage or branches (sum of live and dead). All calibrations were satisfactory, explaining between 35 and 89% (average 7%) of the variance. Case studies. Three case studies of second rotation plantations were used for testing CAMFor: a P. radiata stand at the Biology of Forest Growth (BFG) experiment, Australian Capital Territory (ACT); and two E. globulus stands in south-west Western Australia (WA). Assuming that the volume yield curve (estimated from measurements of tree height and diameter) was accurate, rates of C storage in above-ground biomass components could be predicted with reasonable confidence. This is because calibrations for basic density, calculation of stem wood mass and its relationship to bark mass, and C concentrations in tree components were reasonably well constrained. The exception was accumulation of biomass in foliage and branches, which were not well predicted by the model. This was partly due to drought in the P. radiata case study limiting needle growth and increasing needle shed, and partly due to the fact that the site was thinned, which would have stimulated growth of foliage and branches to a greater extent than predicted given that partitioning to foliage and branches was based on data from unthinned sites. Predicted branch mass was overestimated in the two WA case studies because dead branch mass had not been measured. Nevertheless, accumulation of C in leaves and branches is relatively unimportant to the total C storage in the long-term because of the dominance of the stem. In all case studies, FullCAM was able to explain 99% of the variance in stem mass, and thus, above-ground biomass. Sensitivity and uncertainty analysis. Sensitivity analysis showed that the most important parameters for prediction depended upon stage of stand development, but were generally those predicting accumulation of biomass in the stem (i.e. stem volume, basic density, and C concentration of stem wood). For example, in young stands, parameters related to prediction of mass of foliage and decomposition of coarse dead roots (remaining following the harvest of the previous stand) were initially important as these components dominated the amount of stored C. However, canopy biomass becomes relatively stable, and accumulation of C in stem wood (and the root bole) soon dominates. Uncertainty analysis showed that, despite uncertainty in the estimates of default values used for calculation of basic density, partitioning National Carbon Accounting System Technical Report iii

6 coefficients, and fraction of C in tree components, rates of sequestration were predicted with a reasonable level of confidence (± 4. t C ha -1 yr -1, or generally within 24% of the average predicted), provided that volume yield curves are known. Further work. There are five main aspects of CAMFor that require further work: i. Calibration of partitioning coefficient was based on data collated from unthinned stands. Depending on stocking density and site quality, thinning may result in a stimulation of growth rates, particularly for foliage and branches. Such growth responses to thinning need to be described and incorporated into FullCAM. v. The model has been calibrated to the two most important commercial species in P. radiata and E. globulus. This was facilitated by the relative abundance of data available. It is likely that calibrations for some components will differ among species, particularly species growing in low rainfall zones. However, it is yet to be determined whether differences would be large enough in the context of total C sequestration by the stand, to warrant the use of separate default values for basic density and biomass partitioning. ii. iii. iv. Due to data available, stem wood density was related to age of the stand. However, it would be preferable to relate stem wood density to stem volume rather than the age of the stand, given that like density, stem volume will be influenced by site quality and management factors. Further work is required to test the validity of these relationships. Dead branches may contribute up to 5% of the total branch mass. Although we calibrated partitioning of C separately to live and dead branches, data are not shown because accuracy of predictions of total above-ground biomass was not improved over that using total branch mass. However, algorithms are needed to describe the rate of production of dead branches, their period of retention on the stem, and rate of turnover (branch fall). Thus far calibrations have only considered above-ground components of trees. Roots can be important for accumulation of C, as evidenced by the sensitivity of the model to this component. Although data for root biomass have been reviewed for Australia (Snowdon et al. 2), the relative paucity of good information and matching to growth in stem biomass, is likely to make calibrations difficult. iv Australian Greenhouse Office

7 TABLE OF CONTENTS Page No. Executive Summary iii Acknowledgements viii 1. Introduction 1 2. Methods The CAMFor Sub-model Datasets Basic Density and Partitioning Coefficients Wood and Wood Density Partitioning Coefficients for Bark, Foliage and Branches Case Studies Biology of Forest Growth (BFG) Manjimup and Busselton Model Assumptions Used in all Case Studies Sensitivity and Uncertainty Analysis 7 3. Pinus radiata Basic Density and Partitioning Coefficients The BFG Case Study Sensitivity and Uncertainty Analysis Eucalyptus globulus Basic Density and Partitioning Coefficients The Manjimup and Busselton Case Studies Sensitivity and Uncertainty Analysis 2 5. Synthesis and Further Work References 3 Appendix 1: Datasets used to calibrate CAMFor to basic density and above-ground biomass components. 35 LIST OF TABLES Table 1. Input values used in CAMFor for initial stem volume and initial mass of C in 7 components of trees and debris in the three case studies. Table 2. Default values used in case studies of P. radiata and E. globulus. Values in 8 parenthesis are given when defaults differed between species, with values in parenthesis being values used for E. globulus. Taken from Paul et al. (24). Table 3. Equations used, and the value, standard error and correlations of parameters in 1 these equations estimating basic density (D, kg DM m -3 ) from stand age (A, years) or the mass of bark (K, t DM ha -1 ), foliage (F, t DM ha -1 ) or branches (B, t DM ha -1 ) from the mass of stem (S, t DM ha -1 ) or mass of stem+bark (SK, t DM ha -1 ) and the site index (SI) of P. radiata. Table 4. Accuracy of the relationship between observed and predicted stem volume, biomass 11 of tree components, litterfall and mass at BFG. National Carbon Accounting System Technical Report v

8 LIST OF TABLES continued Page No. Table 5. Predicted amounts of C sequestered during 5-year intervals for P. radiata. 13 Table 6. Equations used, and the value, standard error and correlations of parameters in 18 these equations estimating basic density (D, kg DM m -3 ) from stand age (A, years) or the mass of bark (K, t DM ha -1 ), foliage (F, t DM ha -1 ) or branches (B, t DM ha -1 ) from the mass of stem (S, t DM ha -1 ) or mass of stem+bark (SK, t DM ha -1 ) of E. globulus. Table 7. Accuracy of the relationship between observed and predicted stem volume and 22 biomass of tree components at Manjimup and Busselton. Table 8. Predicted amounts of C sequestered during 5-year intervals for E. globulus. 27 LIST OF FIGURES Table 1. Generalised relationship for deriving partitioning coefficients (Dx) in the CAMFor 2 model (a), and the way in which relationships would change as trees grew (b). Figure 2. Pathways for calculating change in biomass in CAMFor. 3 Figure 3. Methodology used for deriving and testing default values in CAMFor. 4 Figure 4. Relationship between tree weighted basic stem wood density and age of P. radiata. 11 Figure 5. Relationship between the ratio of bark:stem wood and stem wood mass for P. radiata. 12 Figure 6. Relationship between mass of foliage and mass of stem wood+bark for P. radiata. 13 Figure 7. Relationship between mass of branches (live+dead) and mass of stem wood+bark 14 for P. radiata. Figure 8. Relationship between dead branches and the total mass of branches (live+dead) 15 for P. radiata. Figure 9. Comparison between predicted (lines) and observed (symbols) rates of biomass 16 accumulation in components of P. radiata at BFG. Figure 1. Correlation between parameters and inputs (varied between ±1% of their default 17 values) and amounts of C sequestered in tree+debris pools during 5-year intervals at BFG. Figure 11. Probability distributions for the amount of C sequestered in tree+debris pools during 17 5-year intervals at BFG. Figure 12. Average amount of C sequestered in trees and debris at BFG. 18 Figure 13. Relationship between basic density of stem wood and age for E. globulus. 19 Figure 14. Relationship between mass of bark and mass of stem for E. globulus. 2 Figure 15. Relationship between mass of foliage and mass of stem wood+bark for E. globulus. 21 Figure 16. Relationship between mass of branches (live+dead) and mass of stem wood+bark 22 for E. globulus. Figure 17. Relationship between dead branches and the total mass of branches (live+dead) for 23 E. globulus. Figure 18. Comparison between predicted (lines) and observed (symbols) rates of biomass 24 accumulation in components of E. globulus at Manjimup. Figure 19. Comparison between predicted (lines) and observed (symbols) rates of biomass 25 accumulation in components of E. globulus at Busselton. Figure 2. Correlation between parameters and inputs (varied between ±1% of their default 26 values) and amounts of C sequestered in the tree+debris pools during -5 and 5-1 years at Manjimup and Busselton. vi Australian Greenhouse Office

9 LIST OF FIGURES continued Page No. Figure 21. Probability distributions for the amount of C sequestered in the tree+debris pools 26 during -5 and 5-1 years at Manjimup. Figure 22. Probability distributions for the amount of C sequestered in the tree+debris pools 27 during -5 and 5-1 years at Busselton. Figure 23. Predicted amounts of C sequestered during the first 9 years for E. globulus at Manjimup 28 and Busselton (black line). National Carbon Accounting System Technical Report vii

10 ACKNOWLEDGEMENTS For provision of data we thank Tony O Connell (CSIRO Forestry and Forest Products) and Nuno Borralho (RAIZ Forest and Paper Research Institute, Portugal). Michael Battaglia, Kris Jacobsen and Rob Waterworth contributed to editing and many useful discussions on this work. Funding for field trials used in this study was initially provided through Australian Government Industry Statement Funds, Australian Centre for International Agricultural Research, the Rural Industries Research and Development Corporation, Bunnings Treefarms (now WA Plantation Resources) and the Western Australian Department of Resources Development. viii Australian Greenhouse Office

11 1. INTRODUCTION Carbon accounting methods are needed to underpin national carbon (C) inventories and investment and trade in forest C. Directly measuring storage of C in forests, particularly the amounts of C below-ground and the changes in these pools, is difficult, time consuming and costly. Soil C can make up a large proportion of the total C in a forest, but most of it is comprised of old organic matter that changes little over time (Paul et al. 22). That is, the challenge would be to try and detect relatively small changes in soil C against a large and variable initial amount. Given this, and the fact that there is a need for predictive capability so that amounts of C sequestered some time in the future by plantations can be estimated, a modelling approach is required for C accounting. Such a model needs to be accurate, include a measure of uncertainty, be easy to apply, and verifiable. The FullCAM model (Full Carbon Accounting Model) developed by the Australian Greenhouse Office (AGO) meets these needs (Richards 21). FullCAM is comprised of two empirical sub-models that track C in live vegetation, debris, soil and products, and account for the effects of site management; CAMAg is used to predict change in C in agricultural systems; and CAMFor to predict change in C in forest systems. FullCAM can be configured so that process-based submodels are linked to CAMAg or CAMFor, including three independently validated models to estimate: (i) plantation growth (3-PG, Landsberg and Waring 1997); (ii) above-ground litter decomposition (GENDEC, Moorhead and Reynolds 1999), and; (iii) turnover of soil C (RothC, Jenkinson 199). Therefore, FullCAM can be used empirically where there is good historical information available for rates of forest growth in the region to which it is applied, or as an entirely predictive tool in the absence of data. It also has the capacity for sensitivity and uncertainty analysis through program (Palisade Corporation, 1997), which uses repeated simulations to derive an estimate of error surrounding predictions. FullCAM has been shown to give realistic predictions for forest growth, litter fall, decomposition, and turnover of soil C under a range of different forest types (Paul et al. 21; 23a,b), and has been calibrated for decomposition of litter and turnover of soil C in both agricultural (Janik et al. 23) and forest (Paul et al. 24) soils. However, these workers tuned predictions of biomass accumulation to match those observed by altering partitioning coefficients. Hence, FullCAM has not been independently calibrated for estimates of partitioning of C between tree components. In FullCAM, the CAMFor sub-model takes speciesspecific input data for stem volume yield, and multiplies this by stem wood density to calculate stem wood mass. The mass of other tree components (foliage, bark, and coarse and fine roots) is then estimated as the increase in mass relative to the increase in mass of stem wood for each year of tree growth. The objectives of this report were to: (i) calibrate the CAMFor sub-model for stem wood density and partitioning of C between tree components in Pinus radiata and Eucalyptus globulus plantations, (ii) test these calibrations using three case studies, and (iii) use Monte Carlo simulations for these case studies to determine parameters and inputs to which the model is sensitive, and the accuracy and confidence with which it predicts changes in C stocks in plantations. National Carbon Accounting System Technical Report 1

12 2. METHODS 2.1 THE CAMFOR SUB-MODEL The main driver for the CAMFor model is userentered annual increment in volume of stem wood, estimated using a variety of methods. For example, for radiata pine, volume increment can be estimated from: (i) periodic measurement of stand height and diameter, and applied to a look-up table of stand volumes (e.g. Bary and Borough 198), (ii) measurement of diameter and height followed by application of a stand growth model (e.g. West et al. 1988) to estimate volume at other times, or (iii) estimation of volume growth from published volume yield curves (e.g. Lewis et al. 1976, Turner and James 21). Care needs to be taken that the volume estimate used is under bark and is total (rather than merchantable) stem volume. In CAMFor, the volume of stem wood is multiplied by its basic density to calculate mass of stem wood. The model user then enters, for each year of a rotation, the expected annual increment in each tree component (bark, branch, foliage, and coarse and fine roots) relative to the annual increment in stem mass (Figure 1). Annual sequestration of C in tree biomass is then calculated after adjusting for concentrations of C in each tree component and rates of turnover (i.e. litter fall and root slough) (Figure 2). Although C concentrations and turnover coefficients may vary little, basic density and, particularly partitioning coefficients, will vary greatly with species, age (more importantly, degree of stand development), and stand management. 2.2 DATASETS To properly calibrate CAMFor for partitioning coefficients requires data from individual stands where above-ground biomass and its component parts have been measured (from destructive sampling) repeatedly over time, or for point-in-time measurements across a range of sites. In Australia, P. radiata and E. globulus are two of the most commercially important species. We collated the extensive data sets available for these species in Australia and overseas (Appendix 1). Only standlevel data (t ha -1, or m 3 ha -1 ), where component biomass had been derived from allometric equations established from destructive sampling on the same stand, were used for calibration. That is, we excluded the application of allometric equations to stands from which they were not derived to ensure the accuracy and robustness of data. In many of these studies, bark had not been separated from wood. Therefore, we derived allometric equations relating mass of foliage and branches to the mass of stem wood + bark. The form of these relationships is often an accumulating growth curve rising to a maximum (asymptote). Component mass (t ha -1 ) (a) Ratio, change in component: change in stem wood mass (t ha -1 ) (b) stem mass (t ha -1 ) Age (years) Figure 1. Generalised relationship for deriving partitioning coefficients (Dx) in the CAMFor model (a), and the way in which relationships would change as trees grew (b). 2 Australian Greenhouse Office

13 Change in volume of: Stem vol (Vollnc) stem density (BasicDens) Change in mass of: Stem wood (Ds) Partitioning coefficients (Dx) Change in mass of: leaf (DI) bark (Dk) branch (Db) c. root (Dc) f. root (Df) Add to previous mass Current mass of: leaf bark branch c. root f. root Turnover TI Tk Tb Tc Tf Figure 2. Pathways for calculating change in biomass in CAMFor. 2.3 BASIC DENSITY AND PARTITIONING COEFFICIENTS Figure 3 shows the methods used to develop default parameters for basic density and partitioning coefficients. These steps are briefly described in turn Wood and Wood Density Within stems of P. radiata and E. globulus wood density increases with tree height and from the pith to the outer stem (Borough 1993; Raymond et al. 1998). This age-related density pattern is so pronounced that it tends to dominate all other effects (Cown and McConchie 1983; Beets and Brownlie 1987). Mitchell (1987) estimated the whole-tree wood density of 133 first rotation P. radiata trees (obtained from 34 sites subjected to standard thinning regimes) near Mt. Gambier, South Australia. Sample trees were taken at thinning at a stand age of between 1 and 47 years. Their dataset was used to derive a relationship between basic density and stand age for P. radiata. For E. globulus, a new equation was developed by regressing basic density against stand age for the 184 datasets collated (Appendix 1). The current mass of stem wood is then calculated from basic density and stem wood volume: S = D Stem volume (1) where, S is the mass of stem wood (t ha -1 ), D is the basic density of wood (t DM m 3, which was allowed to vary with age), and stem volume is the volume of stem wood (under bark, m 3 DM ha -1 ). Here we are assuming that data on stem volume yield is available from either published volume yield curves or from periodic measurements of stand height and diameter, which are then applied to a look-up table of stand volumes or a stand growth model Partitioning Coefficients for Bark, Foliage and Branches The mass of bark was calculated via a partitioning coefficient as: K = Dk S (2) National Carbon Accounting System Technical Report 3

14 Stand Level Data (review of global information) derive mean stem wood density (species, age) density defaults derive partitioning coefficients (species, stem size) partitioning defaults volume yield curve for case study apply density defaults stem + bark biomass apply partitioning defaults calculated total above-ground biomass compare measured total above-ground biomass aboveground biomass measured calculated from CAMFor time (years) Figure 3. Methodology used for deriving and testing default values in CAMFor. where, K is the current mass of bark (t DM ha -1 ), and Dk is the partitioning coefficient for bark that relates the mass of bark to that of stem wood (unitless). The masses of stem wood and bark are then summed to give the total stem mass (SK, t DM ha -1 ): SK = S + K (3) Masses of all other plantation components (branches and foliage) were then calculated as: X = Dx SK (4) where, X is the mass of component (x, t DM ha -1 ), and Dx is the partitioning coefficient for component (x). Note that, for equations 2 and 4, the relationship between mass of components and that of the stem is not linear because the parameter Dx will vary with time (Figure 1). It should be noted that only stands with less than 5, stems ha -1 were used to estimate Dk, while only unthinned stands were used to estimate Dx for foliage and branches. In long rotation P. radiata stands, the relationship between biomass of foliage and branch and that of the stem is likely to depend on site index (SI). The SI may be estimated by: (i) direct measurement at a prescribed age, commonly 2 years (Leech 1993), (ii) measurement at some other age followed by application of a height growth model (e.g. West et al. 1988), or (iii) application of an empiric model based on other information (e.g. Lewis et al. 1976). In this study, SI was determined from either the authors 4 Australian Greenhouse Office

15 estimate, stand height at 2 years, or calculated from the relationship derived from data by Lewis et al. (1976) which related SI to a measure of gross merchantable volume. Below-ground biomass (root mass) is difficult and expensive to sample and is rarely measured, but contributes substantially to total C sequestration. Root biomass is usually expressed as an amount relative to above-ground biomass through the term root:shoot ratio. We did not calibrate partitioning to roots in this study, but instead used a constant value for root:shoot ratio of.25, which was consistent with the Intergovernmental Panel on Climate Change (IPCC) default value. Snowdon et al. (2) found that the ratio for Australian vegetation types varied between.4 and 8.57, but is affected by vegetation age, species, nutrition, and management. The mean value calculated by Snowdon et al. (2) for productive forests was.26, close to the IPCC default value. Parameters for the fitted equations, their errors, and their correlations, were estimated by standard regression algorithms using the GENSTAT or S-Plus programs. In addition to regression analysis, we assessed the goodness of fit of these relationships by calculating model efficiency (EF, Soares et al. 1995). EF is analogous to R 2 and provides a simple index of performance on a relative scale where a value of 1 indicates a perfect fit and a value of indicates than the model is no better than a simple average. 2.4 CASE STUDIES Case studies were used to independently test model performance in predicting amounts of C sequestered in tree (total above- and below-ground biomass) and debris pools. At present, few studies in P. radiata or E. globulus have data available that meet all the criteria required for independent testing of CAMFor, that is, a yield curve of stand volume over the rotation and above-ground biomass and components estimated from destructive sampling on two occasions or more. Three of the better data-sets come from the case studies described below Biology of Forest Growth (BFG) The first case study was the control plot in a P. radiata plantation (second rotation) in the longterm (2-year) Biology of Forest Growth (BFG) experiment (Benson et al. 1992). The BFG site is located in Pierces Creek Forest, about 2 km south west of Canberra in the Australian Capital Territory (25 o 21 S, 148 o 5 E) where the climate is cool temperate. Long-term average annual rainfall and temperature are 791 mm and 13 o C. Soil is relatively low in both organic matter and nutrient reserves. Based on measurements of yield plots in stands adjacent to the experimental site, it was estimated that the height, diameter and above-ground biomass of the previous stand was about 32 m, 45 cm, and 122 t DM ha -1, respectively (Benson et al. 1992; Snowdon and Benson 1992). Following harvest, the slash was heaped and burned, the area was ripped to about 4 cm depth, and the site was replanted in winter 1973 at a stocking rate of 625 stems ha -1. The BFG experiment began in 1983 when trees were pruned to about 2 m height prior to the first biomass harvest at age 1 years. This would have removed about 1.25 t DM ha -1 (or 3% of mass) of foliage and about 2.63 t DM ha -1 of branches (calculations based on Forrest and Ovington 197 and Benson et al. 1992). The stand was subsequently thinned by 55% (stems per hectare) at age 15 years (where slash was removed from the site). The site index at BFG was 22.6 (standard deviation of.1). This was obtained by measurement, at age 2 years, of the tallest five stems per plot, which was equivalent to 5 stems ha -1 (Leech 1993). The volume yield curve used at BFG was estimated by applying the equation provided by Bary and Borough (198) for P. radiata grown in Pierces Creek plantation to annual height and diameter measurements made when the stand was between 1 and 3 years old. Earlier data on stem volume were obtained from height and diameter measurements made on adjacent P. radiata stands that were aged between 4 and 9 years (Snowdon, National Carbon Accounting System Technical Report 5

16 P. pers. comm., 23). Data were extrapolated to obtain stem volume yield in stands younger than 4 years. Using values for basic density and partitioning coefficients derived for P. radiata from the collated dataset (Appendix 1), growth increments were calculated and used as input to CAMFor. Predictions of biomass (stem, branches, bark, foliage, coarse and fine roots), litter fall and litter mass were then compared with that observed at given points in time at a stand age of between 1 and 3 years (Snowdon and Benson 1992; Ryan et al. 1996; Pongracic 21; Paul et al. 24) Manjimup and Busselton The Manjimup and Busselton case studies were second rotation E. globulus plantations established in 1995 in the Mediterranean climatic region of southwest Western Australia (O Connell et al. 2). The site near Manjimup (34 o 2 S, 116 o E) was on relatively fertile soil with high rainfall (1,23 mm), and thus had a high productivity, with the previous stand having an above-ground biomass of 275 t DM ha -1 at harvest. At Busselton (33 o 45 S, 115 o 7 E), given that soil was less fertile and rainfall was only 825 mm, the previous stand had an above-ground biomass of only 98 t DM ha -1 at harvest. Both sites were replanted with seedlings in winter 1995 at a stocking rate of 1,25 stems ha -1. These sites were not thinned, although all harvest residues were retained on the sites following harvest of previous tree crops. Data were available from the Manjimup and Busselton sites on stem volume yield up to a stand age of 6 years (O Connell et al. 2). Stem volume was linearly extrapolated to a stand age of 1 years using data available from 6 to 1 year old E. globulus plantations grown in regions of similar rainfall to that at Manjimup and Busselton (Hingston and Galbraith 1998). Using values for basic density and partitioning coefficients derived for E. globulus from the calibrated dataset (Appendix 1), growth increments for each tree component were calculated and used as input to CAMFor. Predictions of biomass (stem, branches, bark and foliage) were then compared with that observed at given points in time at a stand age of between.25 and 6 years (O Connell et al. 2). Statistical analysis was required to test the accuracy with which CAMFor predicted biomass of tree components, litter fall and litter mass in the three case studies. This was determined using regression analysis and the calculation of EF Model Assumptions Used in all Case Studies Although it was assumed that the root:shoot ratio was.25 (Section 2.3.2), estimates of growth of both coarse and fine root components are required in CAMFor. There were some data available on biomass of coarse and fine roots at the BFG site. Based on these data we assumed that, for all case studies, the fraction of coarse roots increased linearly from 8 to 9% between age 1 and 3 years. In all case studies, simulations commenced at the establishment of a second rotation. Although data on mass of tree components were available at establishment for the Manjimup and Busselton sites (O Connell and Grove 1999; O Connell et al. 2), for the BFG case study, they were estimated from data on a young P. radiata stand adjacent to the BFG site (Snowdon, P., pers. comm., 23) (Table 1). Data on the initial mass of foliage, bark and woody harvest residues from the previous tree crop were available for the Manjimup and Busselton sites (O Connell and Grove 1999; O Connell et al. 2; Mendham et al. 23). At BFG the initial mass of litter was assumed to be less than 1 t C ha -1 since slash was heaped and burned following harvest of the previous P. radiata stand. Using data on aboveground biomass of the previous stand at the time of harvest at Manjimup (275 t DM ha -1 ), Busselton (98 t DM ha -1 ) and BFG (122 t DM ha -1 ), we calculated the initial mass of dead roots by assuming a root:shoot ratio of.25, and that 8% of these roots were coarse. 6 Australian Greenhouse Office

17 Table 1. Input values used in CAMFor for initial stem volume and initial mass of C in components of trees and debris in the three case studies. Input values for initial conditions Case study BFG Manjimup Busselton Volume of stems (m 3 ha -1 ) Mass of branches (t C ha -1 ) Mass of bark (t C ha -1 ) Mass of foliage (t C ha -1 ) Mass of coarse roots (t C ha -1 ) Mass of fine roots (t C ha -1 ) Mass of decomposable wood debris (t C ha -1 )... Mass of resistant wood debris (t C ha -1 ) * Mass of decomposable bark litter (t C ha -1 )... Mass of resistant bark litter (t C ha -1 ) Mass of decomposable leaf litter (t C ha -1 ) Mass of resistant leaf litter (t C ha -1 ) Mass of decomposable dead coarse roots (t C ha -1 )... Mass of resistant dead coarse roots (t C ha -1 ) Mass of decomposable dead fine roots (t C ha -1 ) Mass of resistant dead fine roots (t C ha -1 ) * This fresh branch litter included bark material. However, even if we assumed that up to 4% of this material was bark, there was little effect (<.36 t C ha -1 yr -1 ) of this assumption on our predicted rates of sequestration of C. Material categorised as bark at the Manjimup site was bark stripped from the stem. Paul et al. (24) recently calibrated CAMFor for decomposition of litter under pine and eucalypt species. Thus we were able to use, for P. radiata and E. globulus, default values they derived for C fractions of tree components, their rates of turnover to pools of debris, the decomposable fractions of each of these pools of debris, their rates of breakdown, and ratios of CO 2 to total weight in breakdown products (Table 2). For example, all initial pools of harvest residues, litter and dead roots (Table 1) were split into decomposable and resistant pools of debris based on their assumed decomposable fractions, taken from Paul et al. (24). Based on previous work (Paul et al. 21), it was assumed that the change in soil C was relatively small and was therefore not included in this analysis. 2.5 SENSITIVITY AND UNCERTAINTY ANALYSIS In developing and applying FullCAM, it is useful to identify those parameters and inputs to which the model is most sensitive. This information can then be used to ensure that calibrations are robust for parameters that most affect predicted rates of sequestration. In the current work, sensitivity analysis was used to determine the effect of a ± 1% variation in the values of parameters and inputs on predicted rates of change in C sequestered in tree and debris pools. For the sensitivity analysis, the (Palisade Corporation 1997) was used to assign triangular probability distribution functions for each input and parameter value between their default value, and the default value ±1%. Parameters and inputs altered in the sensitivity analysis included: National Carbon Accounting System Technical Report 7

18 Table 2. Default values used in case studies of P. radiata and E. globulus. Values in parenthesis are given when defaults differed between species, with values in parenthesis being values used for E. globulus. Taken from Paul et al. (24). Parameter Assumed value C fraction of stem.49 C fraction of branch.47 C fraction of bark.49 C fraction of foliage.5 C fraction of coarse roots.49 C fraction of fine roots.46 Turnover of branches (1/yr).3 (.5) Turnover of bark (1/yr).5 (.7) Turnover of foliage (1/yr).3 (.5) Turnover of coarse roots (1/yr).7 (.1) Turnover of fine roots (1/yr) 1.25 Decomposable fraction of deadwood. Decomposable fraction of dead bark. Decomposable fraction of dead foliage.17 (.2) Decomposable fraction of dead coarse roots. Decomposable fraction of dead fine roots.38 Rate of breakdown of deadwood (1/yr)*.13 (.11) Rate of breakdown of bark and coarse roots (1/yr)*.18 Rate of breakdown of resistant foliage and fine roots (1/yr)*.11 (.26) Rate of breakdown of decomposable foliage and fine roots (1/yr)* 1. Fractions of resistant breakdown C emitted as CO 2.39 Fractions of decomposable breakdown C emitted as CO 2.77 * Rates of breakdown were taken as 1 e -k, where k is the rate of decomposition calibrated for each pine and eucalypt litter component by Paul et al. (24). In the present study, it was assumed that the average annual temperature and moisture modifiers of k that were used by Paul et al. (24) were equal to 1.. i. stem volume; vii. breakdown fractions of pools of debris; and ii. stem wood density; iii. mass of branches, bark, foliage, coarse roots and fine roots; iv. C concentration in stem, branches, bark, foliage, coarse roots and fine roots; v. turnover fractions of branches, bark, foliage, coarse roots and fine roots; vi. decomposable fractions of stem, branches, bark, foliage, coarse roots and fine roots; viii. proportions of C lost to CO 2 or transferred to soil during decomposition of decomposable and resistant pools of debris. Monte Carlo simulations were used to re-calculate amounts of C sequestered during 5-year intervals for 1, iterations, using random values sampled from assumed probability distribution functions. During each iteration, temporal inputs (i.e. stem volume, stem wood density, and the calculated mass of branches, bark, foliage, coarse and fine roots) had a concurrent proportional adjustment of observed 8 Australian Greenhouse Office

19 values for each year of simulation. Rank-order correlations between the value of each parameter or input and the predicted C sequestered were then calculated. Using this information, Tornado graphs were constructed to rank the relative sensitivity of C sequestered to the various parameters and inputs. Once we determined which parameters or inputs most affect the predicted rates of C sequestration, we conducted uncertainty analysis using the expected variation in these parameter and input values. This provided us with an upper and lower limit for C storage, together with the degree of confidence for the mean estimate within that range. Uncertainty analysis was entailed assigning a normal probability distribution function to each of the most important parameters or inputs, taking into account correlations between parameters. Using Monte Carlo recalculated, during 1, iterations, the amount of C sequestered during each 5-year period, using random values sampled from the assumed distribution functions. Probability distribution functions, standard deviations and the confidence intervals of predicted C sequestration were then calculated, based on the uncertainty in the important parameters and inputs. 3. PINUS RADIATA 3.1 BASIC DENSITY AND PARTITIONING COEFFICIENTS For P. radiata, the equations derived to explain the observed relationships between basic wood density and age was significant (P<.1) but accounted for only 49% of the variance, with an EF of.46, and a standard error of observations of 29 kg DM m -3 (Table 3, Figure 4). Although Mitchell (1987) found that more of the variance could be explained if site quality was taken into account, we did not account for this here since the site quality rankings available for the basic density datasets were applicable only to the Mount Gambier region. The equations derived to explain the observed relationships between biomass of tree components were also significant (P>.1), accounting for between 6 and 84% of the variance, with an EF of at least.6, and a standard error of observations less than 5.48 t DM ha -1 (Figures 5-7). Consistent with Mitchell (1987), we observed that the ratio of bark:wood tended to increase slightly with a decrease in site quality, or index (Figure 5). Although Mitchell (1987) also observed that the ratio of bark:wood tended to decrease with age, this relationship was not evident from the dataset we collated (Appendix 1). We note that dead branches can make a significant contribution to the total mass of branches (Figure 8). For P. radiata, there are few dead branches in young stands, but in older stands they may contribute up to 5% of the total mass of branches. At present CAMFor has not been modified to accommodate production and retention of dead branches on trees, but this clearly would be an area for further work. This would also require the model to be changed so that turnover of branches (branch fall) comes only from the dead pool. National Carbon Accounting System Technical Report 9

20 Table 3. Equations used, and the value, standard error and correlations of parameters in these equations estimating basic density (D, kg DM m -3 ) from stand age (A, years) or the mass of bark (K, t DM ha -1 ), foliage (F, t DM ha -1 ) or branches (B, t DM ha -1 ) from the mass of stem (S, t DM ha -1 ) or mass of stem+bark (SK, t DM ha -1 ) and the site index (SI) of P. radiata. N is the number of observations available (Appendix 1) and R 2, EF and SE indicate the accuracy with which the fitted equations explain the data. Relationship* Parameter SE Correlations N R 2 EF SE a b c D = a + b.a + c.a 2 a b c d f g K/ S = d + f / (1 + g.s) d f g h i j F = (h + i.si ) (1- j SK ) h i j k m n p B = (k + p.si) (1 - m SK ) + n.sk k m n p *Only stands with less than 5, stems ha -1 were used to estimate K/S, while only unthinned stands were used to estimate F and B. 3.2 THE BFG CASE STUDY Output from CAMFor for the BFG site is shown in Figure 9. The relationship between observed and predicted mass of stem and bark was highly significant (P<.1), giving R 2 values greater than.93 and an EF greater than.8 (Table 4). Mass of foliage and branches were less well predicted, with the relationship between observed and predicted values being significant (P<.5), but having an R 2 and EF values of less than.82 and.61, respectively. There are two possible reasons for the relatively poor predictions of mass of foliage and branches. Firstly, growing conditions were drier than average for the four years prior to the first measurement of biomass with the last season being a severe drought. As a consequence, seasonal needle growth was only about one quarter of that normally expected (Snowdon and Benson 1992), and it is also likely that branch growth was similarly reduced during the dry period prior to sampling, and that a considerable proportion of older foliage may have been shed. Secondly, partitioning to foliage and branches was calibrated for unthinned stands yet in the BFG case study, the stand was pruned at age 1 years (to obtain a 3% decrease in foliage mass), and thinned at age 15 years (to remove 55% of stems). It is likely that growth of foliage and branches is stimulated in response to pruning or thinning. However, this growth response is not accounted for in the model. Further work is required to incorporate into FullCAM the growth responses to thinning, and their dependence on site quality and stand density. The discrepancy between predicted and observed mass of foliage and branches is not serious because, 1 Australian Greenhouse Office

21 5 Basic density (kg DM m -3 ) Stand age (yr) Figure 4. Relationship between tree weighted basic stem wood density and age of P. radiata. Symbols are data collected by Mitchell (1987), and the solid line represents line of best-fit to these data (N=133, R 2 =.49, EF=.46). Table 4. Accuracy of the relationship between observed and predicted stem volume, biomass of tree components, litterfall and mass at BFG. Coefficients are based on linear regressions where N is the number of observations, s is the slope, i is the intercept, R 2 is the correlation coefficient squared, EF is model efficiency and P indicates the level of statistical significance. Model output N s i R 2 EF P Stem mass (t C ha -1 ) <.1 Branch mass (t C ha -1 ) <.5 Bark mass (t C ha -1 ) <.1 Foliage mass (t C ha -1 ) <.5 Total above-ground mass (t C ha -1 ) <.1 National Carbon Accounting System Technical Report 11

22 .5.4 Bark:stem wood Stem wood (t DM ha -1 ) Site index: < >22.5 Unknown Figure 5. Relationship between the ratio of bark:stem wood and stem wood mass for P. radiata. Symbols are collated data given in Appendix 1, and the solid line represents line of best-fit to these data (N=187, R 2 =.84, EF=.84). Site index was estimated for some data-points, and was categorised as either <17.5 (black symbols), (grey symbols), or >22.5 (light orange symbols). Open orange symbols are data-points for which site index was unknown. in the long-term, C accumulation in the stem dominates the total amount of C sequestered, and this was predicted adequately. As a result, the relationship between observed and predicted aboveground biomass was highly significant (P<.1), giving R 2 values of.99 and an EF of.93. There were too few data points to statistically assess the accuracy of prediction of coarse and fine root mass in the BFG case study (Figure 9). However, predictions of coarse and fine root mass were within.96 t C ha -1 of those observed. The relationship between observed and predicted mass of litter was significant (P<.1, R 2 =.97). This was not the case for litter fall (predominantly needle fall) despite the fact that predictions were within 1 t C ha -1 y -1 of that observed. This was mainly due to the poor prediction of mass of foliage. 3.3 SENSITIVITY AND UNCERTAINTY ANALYSIS Sensitivity analysis showed that, for P. radiata, parameters to which the model was most sensitive depended upon age of the stand (Figure 1). In young stands (<5 years), foliage, and particularly rates of breakdown of dead coarse roots left from the previous crop, had the greatest influence on the predicted change in C storage. Thus, for that period it is important to predict the correct rates of breakdown and mass of foliage through its partitioning coefficient and C concentration. In the long-term, incorrect prediction of sequestration in foliage and branches is relatively unimportant because of the dominance of the stem component. It is therefore most important to have good estimates for basic density, C concentration in wood and the stem volume. 12 Australian Greenhouse Office

23 Total foliage (t DM ha -1 ) Stem wood + bark (t DM ha -1 ) Site index: Unknown > <17.5 Figure 6. Relationship between mass of foliage and mass of stem wood+bark for P. radiata. Symbols are data collated in Appendix 1, and the solid lines represent estimates for site indices of 15 (black), 2 (grey) and 3 (orange). If a SI of 22 was assumed for sites where SI was not given, across all data-sets (N=164), the relationship of best-fit to these data gave R 2 =.6 and EF=.6, else N=98, R 2 =.57 and EF=.58. Site index was estimated for some data-points, and was categorised as either <17.5 (black symbols), (grey symbols), or >22.5 (light orange symbols). Open orange symbols are data-points for which SI was unknown. Table 5. Predicted amounts of C sequestered during 5-year intervals for P. radiata. Period (yr) Sequestration (t C ha -1 ) Mean Minimum Maximum Standard deviation % % Annual average (t C ha -1 yr -1 ) -1.1±.4 4.5± ±1. 8.1± ±1.7 National Carbon Accounting System Technical Report 13

24 7 6 Total branches (t DM ha -1 ) Stem wood + bark (t DM ha -1 ) Site index: Unknown > <17.5 Figure 7. Relationship between mass of branches (live+dead) and mass of stem wood+bark for P. radiata. Symbols are data collated in Appendix 1, and the solid lines represent estimates for site indices of 15 (black), 2 (grey) and 3 (orange). If a SI of 22 was assumed for sites where SI was not given, across all data-sets (N=161), the relationship of best-fit to these data gave R 2 =.78 and EF=.76, else N=98, R 2 =.69 and EF=.67. Site index was estimated for some data-points, and was categorised as either <17.5 (black symbols), (grey symbols), or >22.5 (light orange symbols). Open orange symbols are data-points for which SI was unknown. Probability distribution curves for the amount of C sequestered in each 5-year interval at BFG are shown in Figure 11, and mean rates and errors are summarised in Table 5. Changes in storage of C ranged from a net emission of 1.1±.4 t C ha -1 yr -1 during the -5 year interval when dead roots were decomposing and there was relatively little stem growth, to a net sequestration of 9.4±1.7 t C ha -1 yr -1 in the 2-25 year interval when accumulation of C in the stem is maximum, and accumulation in foliage and branches is comparatively small. Low rates of sequestration were predicted in the 1-15 year interval because the stand had been thinned and we did not attempt to predict the flow of C in products taken off-site. Figure 12 shows the predicted pattern of accumulation of C at BFG, with associated errors of prediction given the uncertainty in estimates of parameters used to determine basic density and partitioning coefficients, and uncertainty in the estimation of C fractions of tree components. It was assumed that there was no error in the estimation of stem volume. This clearly would not be the case and constraining the error in volume estimations would be necessary. However, given that assumption, the error associated with all other variables is relatively small, indicating that rates of C sequestration could be predicted with acceptable accuracy and precision. 14 Australian Greenhouse Office

25 4 Dead branches (t DM ha -1 ) Total branches (t DM ha -1 ) < 5 yrs old 5-1 yrs old > 1 yrs old Figure 8. Relationship between dead branches and the total mass of branches (live+dead) for P. radiata where trees are less than 5 years old (orange symbols), between 5 and 1 years old (dark grey symbols), or older than 1 years (black symbols). Solid line represents line of best-fit to data (N=179, R 2 =.67, EF=.67). Data taken from Will (1964, 1966, 1976), Ovington et al. (1967), Gadgil (1976), Wiliams (1976), Madgwick et al. (1977), Stewart et al. (1981), Feller (1983), Mead et al. (1984), Baker and Attiwill (1985), Cromer et al. (1985), Frederick et al. (1985), Madgwick and Oliver (1985), Beets and Pollock (1987), Dyck and Beets (1987), Beets and Madgwick (1988), Birk (1992), Neilsen et al. (1992), Snowdon and Benson (1992), Smith et al. (1994), and from personal communication with Beets, P.N., Carlyle, J.C., Jarvis, P.G., Shepherd, K.R., and Stewart, H.T.L. National Carbon Accounting System Technical Report 15

26 Stem volume (m 3 ha -1 ) Stem (t C ha -1 ) Bark (t C ha -1 ) 1 5 Branch (t C ha -1 ) Foliage (t C ha -1 ) Above-ground (t C ha -1 ) Coarse roots (t C ha -1 ) Fine roots (t C ha -1 ) Leaf drop (t C ha -1 yr -1 ) Stand age (yrs) Lit. layer mass (t C ha -1 ) Stand age (yrs) Figure 9. Comparison between predicted (lines) and observed (symbols) rates of biomass accumulation in components of P. radiata at BFG. 16 Australian Greenhouse Office

27 Stem volume Density of stem wood C frac. of stem C frac. of coarse roots Mass of coarse roots Mass of foliage C frac. of foliage C frac. of branches Rate of breakdown of coarse roots Correlation coefficients -5 yr 5-1 yr 1-15 yr 15-2 yr 2-25 yr Figure 1. Correlation between parameters and inputs (varied between ±1% of their default values) and amounts of C sequestered in tree+debris pools during 5-year intervals at BFG..5.4 Probability Carbon sequested (t C ha -1 ) -5 yr 5-1 yr 1-15 yr 15-2 yr 2-25 yr Figure 11. Probability distributions for the amount of C sequestered in tree+debris pools during 5-year intervals at BFG. National Carbon Accounting System Technical Report 17

28 25 Total C sequestered (t ha -1 ) % confidence intervals of prediction Average sequestration plus or minus the standard deviation Average amount of C sequestered in trees and debris at BFG Age of stand (yrs) Figure 12. Average amount of C sequestered in trees and debris at BFG (solid black line). Light orange lines show the average sequestration plus or minus the standard deviation, while the darker orange lines show 95% confidence intervals of prediction. Table 6. Equations used, and the value, standard error and correlations of parameters in these equations estimating basic density (D, kg DM m -3 ) from stand age (A, years) or the mass of bark (K, t DM ha -1 ), foliage (F, t DM ha -1 ) or branches (B, t DM ha -1 ) from the mass of stem (S, t DM ha -1 ) or mass of stem+bark (SK, t DM ha -1 ) of E. globulus. N is the number of observations available (Appendix 1) and R 2, EF and SE indicate the accuracy with which the fitted equations explain the data. Relationship Parameter SE Correlations N R 2 EF SE a b D = a / [1 + exp(b.a)] a b d f K = d [1-exp(f.S)] d f h i F = (h.i.sk) / (h + i.sk) h i k m B = k [1 exp(m.sk)] k m Australian Greenhouse Office

29 8 Basic density (kg DM m -3 ) Stand age (yr) Figure 13. Relationship between basic density of stem wood and age for E. globulus. Symbols are collated data given in Appendix 1, and the solid line represents line of best-fit to these data (N=184, R 2 =.35, EF=.32). 4. EUCALYPTUS GLOBULUS 4.1 BASIC DENSITY AND PARTITIONING COEFFICIENTS The equation derived to explain the observed relationships between basic wood density and age was significant (P<.1) but accounted for only 35% of the variance, with an EF of.32, and a standard error of observations of 41 kg DM m -3 (Table 6, Figure 13). As for P. radiata, it is possible that more of the variance could be explained had site quality been taken into account. A stand on a relatively high quality site may have a greater basic density for a given age than a stand on a lower site quality. Further work should entail investigating whether predictions of basic density could be improved by relating basic density to stem volume rather than to the age of the stand. However, in Tasmania, Raymond (pers. comm.) measured densities in many stands using non-destructive techniques and found that density was not well correlated with tree diameter. Consistent with this, in the regressions we attempted, incorporating tree size also did not improve the strength of the basic density relationship shown in Table 6. The equations derived to explain the observed relationships between biomass of tree components were also significant (P>.1), accounting for between 82 and 89% of the variance, with an EF of at least.79, and a standard error of observations less than 2.34 t DM ha -1 (Figures 14-16). As for P. radiata, there was a significant contribution by dead branches to total branch mass in E. globulus stands, averaging about 43% for all stands with little indication that the proportion of dead branches was related to tree size (Figure 17). Future improvements in the model would incorporate the production, retention on the stem, and turnover (branch fall) of dead branches. National Carbon Accounting System Technical Report 19

30 3 25 Bark (t DM ha -1 ) Stem wood (t DM ha -1 ) Figure 14. Relationship between mass of bark and mass of stem for E. globulus. Symbols are collated data given in Appendix 1, and the solid line represents line of best-fit to these data (N=73, R 2 =.86, EF=.84). 4.2 THE MANJIMUP AND BUSSELTON CASE STUDIES Figures 18 and 19 show a comparison between observed and predicted stem volume and biomass of above-ground tree components at Manjimup and Busselton. For both case studies, the relationship between observed and predicted mass of stem and mass of bark were highly significant (P<.1), giving R 2 values greater than.99 and an EF greater than.89 (Table 7). As was the case for P. radiata in the BFG case study, predictions were less satisfactory for biomass of foliage and branches. Actual values for mass of foliage, and particularly branches, were less than that predicted in stands older than four years. For both case studies, the relationship between observed and predicted mass of foliage or branches was significant (P<.1), but accounted for between only 77 and 85% of the variance, and had an EF of between 6.59 and.75 (Table 7). Predicted branch mass was overestimated in the two E. globulus case studies because dead branch mass had not been measured and yet allocation of C to both dead and live branches was assumed in the calibration of branch partitioning coefficients. However, because of the dominance of the stem, prediction for the amount of biomass accumulated above-ground was satisfactory, accounting for 99% of the variance with an EF of SENSITIVITY AND UNCERTAINTY ANALYSIS The importance of the stem in C sequestration of E. globulus plantations at both Manjimup and Busselton was shown in sensitivity analysis (Figure 2). The model was most sensitive to parameters related to estimating mass of stem: stem volume, basic density, and C concentration of stem wood. In general, coarse roots were next in importance. As observed with P. radiata in the BFG case study, rates of breakdown of coarse roots and 2 Australian Greenhouse Office

31 Foliage (t DM ha -1 ) Stem wood (t DM ha -1 ) Figure 15. Relationship between mass of foliage and mass of stem wood+bark for E. globulus. Symbols are collated data given in Appendix 1, and the solid line represents line of best-fit to these data (N=176, R 2 =.82, EF=.79). foliage mass and C content were important mainly during the first five years following establishment. This was due to a large mass of dead roots available for decomposition during this time (69 t DM ha -1 at Manjimup, and 25 t DM ha -1 at Busselton) and stem growth was relatively small. Uncertainty analysis (Figures 21 and 22) showed that rates of sequestration were high for E. globulus plantations at the Busselton and particularly Manjimup sites, and could be predicted with a reasonable level of confidence, even with the poor prediction of sequestration in foliage and branches. Mean annual rates of sequestration of C at Manjimup ranged from 4.5±2. t C ha -1 yr -1 during the -5 year interval to 2±4. t C ha -1 yr -1 in the 5-1 year interval (Table 8). Rates of sequestration of C were much lower at Busselton. At this less productive site there was a net C emission (being equivalent to -.2±.6 t C ha -1 yr -1 ) during the first five years following establishment. This was because slash from harvest residues decomposed but little biomass was produced. In the subsequent 5-year interval, there was a sequestration of only 4.8±1.1 t C ha -1 yr -1. Figure 23 shows that for both sites, the absolute level of uncertainty increased with age of the stand, but again this analysis excluded any uncertainty associated with estimating volume yield. Since C sequestered includes C in both debris and tree biomass pools, the initial negative rate of sequestration was attributable to the rapid decomposition of harvest residues during a time period when biomass and litter input rates are generally low. National Carbon Accounting System Technical Report 21

32 25 Total branches (t DM ha -1 ) Stem wood (t DM ha -1 ) Figure 16. Relationship between mass of branches (live+dead) and mass of stem wood+bark for E. globulus. Symbols are collated data given in Appendix 1, and the solid line represents line of best-fit to these data (N=179, R 2 =.89, EF=.89). Table 7. Accuracy of the relationship between observed and predicted stem volume and biomass of tree components at Manjimup and Busselton. Coefficients are based on linear regressions where N is the number of observations, s is the slope, i is the intercept, R 2 is the correlation coefficient squared, EF is model efficiency and P indicates the level of statistical significance. Model output N s i R 2 EF P Manjimup Stem mass (t C ha -1 ) <.1 Branch mass (t C ha -1 ) <.1 Bark mass (t C ha -1 ) <.1 Foliage mass (t C ha -1 ) <.1 Total above-ground mass (t C ha -1 ) <.1 Busselton Stem mass (t C ha -1 ) <.1 Branch mass (t C ha -1 ) <.1 Bark mass (t C ha -1 ) <.1 Foliage mass (t C ha -1 ) <.1 Total above-ground mass (t C ha -1 ) <.1 22 Australian Greenhouse Office

33 25 Dead branches (t DM ha-1) Total branches (t DM ha-1) Figure 17. Relationship between dead branches and the total mass of branches (live+dead) for E. globulus. Solid line represents line of best-fit to data (N=56, R 2 =.5, EF= -.87, slope=.43). Data taken from Cromer et al. (1975), Schonau and Boden (1982), Bennett et al. (1997), and Borralho, N. (pers. comm.). 5. SYNTHESIS AND FURTHER WORK The empirical C accounting model, CAMFor, was calibrated to P. radiata and E. globulus, and involved developing non-linear relationships between: i. age and basic stem density, the latter being multiplied by user-entered values for stem volume (under bark) to calculate mass of stem wood; ii. mass of bark and stem wood, and; iii. mass of other components of above-ground biomass (foliage, branches) and the mass of stem wood+bark. The sequence of developing relationships between bark to stem wood, then foliage and branches (separately) to stem wood+bark, was chosen because some studies used in the calibration data set did not separate stem wood from bark. Bark was a significant component of biomass, and its relationship to stem wood was well predicted, for example being a constant 13% of the mass of stem wood in all but the youngest P. radiata stands. In P. radiata, prediction of biomass accumulation in foliage and branches was related to site index of the stand (SI), being a measure or estimate of height at age 2 years. Most calibrations were adequate having low-tomoderate estimates of errors for regression equations. However, testing in case studies showed that temporal patterns of biomass accumulation in foliage and branches were not well predicted. For the P. radiata case study (control plot at the BFG experiment, ACT) a drought caused shedding of foliage and branches that was not predicted by the model. Furthermore, there may have been a growth response to thinning that was not predicted by the model. For the two E. globulus case studies, dead National Carbon Accounting System Technical Report 23

34 Stem volume (m 3 ha -1 ) Stem (t C ha -1 ) Bark (t C ha -1 ) Branch (t C ha -1 ) Foliage (t C ha -1 ) Above-ground (t C ha -1 ) Coarse roots (t C ha -1 ) Fine roots (t C ha -1 ) Leaf drop (t C ha -1 yr -1 ) Stand age (yrs) Lit. layer mass (t C ha -1 ) Stand age (yrs) Figure 18. Comparison between predicted (lines) and observed (symbols) rates of biomass accumulation in components of E. globulus at Manjimup. Closed symbols represent data obtained from the BFG site, while open symbols represent stem volume data obtained from a nearby high rainfall site (Northcliffe, Hingston and Galbraith 1998). 24 Australian Greenhouse Office

35 Stem volume (m 3 ha -1 ) Stem (t C ha -1 ) Bark (t C ha -1 ) 2 1 Branch (t C ha -1 ) Foliage (t C ha -1 ) Above ground (t C ha -1 ) Coarse roots (t C ha -1 ) Fine roots (t C ha -1 ) Leaf drop (t C ha -1 yr -1 ) Stand age (yrs) Lit. layer mass (t C ha -1 ) Stand age (yrs) Figure 19. Comparison between predicted (lines) and observed (symbols) rates of biomass accumulation in components of E. globulus at Busselton. Closed symbols represent data obtained from the BFG site, while open symbols represent stem volume data obtained from a nearby low rainfall site (Darkan, Hingston and Galbraith 1998). National Carbon Accounting System Technical Report 25

36 Density of stem wood Stem volume C frac. of stem C frac. of coarse roots Mass of coarse roots C frac. of foliage Mass of foliage Mass of branches Rate of breakdown of coarse roots Correlation coefficients -5 yr Manjimup 5-1 yr Manjimup -5 yr Busselton 5-1 yr Busselton Figure 2. Correlation between parameters and inputs (varied between ±1% of their default values) and amounts of C sequestered in the tree+debris pools during -5 and 5-1 years at Manjimup and Busselton Probability Carbon sequestered (t C ha -1 ) -5 yr 5-1 yr Figure 21. Probability distributions for the amount of C sequestered in the tree+debris pools during -5 and 5-1 years at Manjimup. 26 Australian Greenhouse Office

37 .2.16 Probability Carbon sequestered (t C ha -1 ) -5 yr 5-1 yr Figure 22. Probability distributions for the amount of C sequestered in the tree+debris pools during -5 and 5-1 years at Busselton. Table 8. Predicted amounts of C sequestered during 5-year intervals for E. globulus. Manjimup Manjimup Busselton Busselton Sequestration (t ha -1 ) -5 yr 5-1 yr -5 yr 5-1 yr Mean Minimum Maximum Standard deviation % % Annual average (t C ha -1 yr -1 ) 4.5±2. 2±4. -.2±.6 4.8±1.1 National Carbon Accounting System Technical Report 27

38 25 Total C sequestered (t ha -1 ) % confidence intervals of prediction Average sequestration plus or minus the standard deviation Predicted amounts of C sequestered during the first 9 years for E. globulus at Manjimup and Busselton Age of stand (yrs) Figure 23. Predicted amounts of C sequestered during the first 9 years for E. globulus at Manjimup and Busselton (black line). Light orange lines show the average sequestration plus or minus the standard deviation, while the darker orange lines show 95% confidence intervals of prediction. Lower plots are for the Busselton site and the upper plots for the Manjimup site. branch mass was not included in the observed branch biomass measurements and this contributed to the apparent overestimation of branch mass in these studies. Sensitivity analysis showed that the most important parameters for prediction depended on stage of stand development. In young stands, parameters related to prediction of foliage biomass and the decomposition of coarse roots and were initially important as these components dominated C sequestration. However, absolute rates of sequestration were low in young stands and so, in the long-term, accumulation in stems (and probably the root bole) dominated. Parameters related to estimation of accumulation in stem wood were stem volume, basic density, and C concentration of stem wood. Lack of prediction of biomass accumulation in leaves and branches was thus relatively unimportant in the long-term. Uncertainty analysis showed that rates of sequestration of C, particularly in above-ground biomass components could be predicted for P. radiata and E. globulus with reasonable confidence, with the assumption that the volume yield curve was correct. The reason for this is that calibrations for basic density for the two species, derivation for stem wood mass, its relationship to bark biomass, and C concentrations in stem wood and bark can be reasonably constrained. Although current calibration of CAMFor has been satisfactory for the two species examined, further improvements in the model would include: Growth response to thinning. Depending on stocking density and site quality, thinning may result in a stimulation of growth rates, particularly for foliage and branches. Such growth responses to thinning need to be 28 Australian Greenhouse Office

39 described and incorporated into FullCAM since this study only calibrated the partitioning coefficient for unthinned stands. Relating stem wood density to stem volume rather than stand age. In this study, stem wood density was related to the age of the stand. However, these relationships were relatively poor and furthermore, they are not applicable to native forests and woodlands comprised of trees of various ages. It would be better to relate stem wood density to the field measurements of stem volume per hectare for each species. Further work is required to test the validity of these relationships. Dead branches. Dead branches may contribute up to 5% of the total branch mass. Although we calibrated C partitioning separately to live and dead branches, data are not shown because accuracy of predictions of total aboveground biomass was not improved over that using total branch mass. However, algorithms are needed to describe the rate of production of dead branches, their period of retention on the stem, and rate of turnover (branch fall). E. camaldulensis, P. pinaster, and a variety of other plantation pines and eucalypts grown in Australia and overseas. It may be possible to develop a generic suite of calibration curves for eucalypts, at least for some parameters such as basic density or within groups of species. For components such as foliage, it is likely that calibrations will differ among species. However, whether or not differences would be large enough in the context of total C sequestration by the stand, to warrant the use of separate curves, would need to be determined. The model also needs to be calibrated to species growing in low rainfall zones, but this is a large task which would be best undertaken as a separate, but related, research program. Further testing and verification. The calibrations developed here for P. radiata and E. globulus are satisfactory but only three case studies were used to test the model. Further testing against other case studies for these and other species would increase the confidence in calibrations. Calibration to roots. Thus far, calibrations have only considered above-ground components of trees. Roots can be important for C accumulation, as evidenced by the sensitivity of the model to this component. Although data for root biomass have been reviewed for Australia (Snowdon et al. 2), the relative paucity of good information and matching to growth in stem biomass, is likely to make calibration difficult. Calibration to other species. The model has been calibrated to the currently two most important commercial species in P. radiata and E. globulus, and this was facilitated by the relative abundance of data available. Data are available, but on a lesser scale, for species such as E. nitens, E. pilularis, E. grandis, National Carbon Accounting System Technical Report 29

40 6. REFERENCES Baker, T.G., and Attiwill, P.M. (1985). Above-ground nutrient distribution and cycling in Pinus radiata D. Don and Eucalyptus obliqua L Herit. forests in south-eastern Australia. Forest Ecology and Management 13, Banham, P.W., Orme, K., Russell, S.L. (1995). Pulpwood qualities required for the cold soda pulping process. In: Eucalypt Plantations: Improving Fibre Yield and Quality. (Eds. B.M. Potts, N.M.G. Borralho, J.B. Reid, R.N. Cromer, W.N. Tibbits and C.A. Raymond). pp Proc. CRC-IUFRO Conf., Hobart, Feb. CRC for Temperate Hardwood Forestry: Hobart. Bary, G.A.V. and Borough, C.J. (198). Tree volume tables for Pinus radiata in the Australian Capital Territory. CSIRO Division of Forest Research, Internal Report 11: 19 pp. Beets, P.N., and Brownlie, R.K. (1987). Puruki experimental catchment: site, climate, forest management, and research. New Zealand Journal of Forestry Science 17, Beets, P.N., and Pollock, D.S. (1987). Accumulation and partitioning of dry matter in Pinus radiata as related to stand age and thinning. New Zealand Journal of Forestry Science 17, Beets, P.N., and Madgwick, H.A.I. (1988). Aboveground dry matter and nutrient content of Pinus radiata as affected by lupin, fertiliser, thinning, and stand age. New Zealand Journal of Forestry Science 18, Bennett, L.T., Weston, C.J., Attiwill, P.M. (1997). Biomass, nutrient content and growth response to fertilisers of six-year-old Eucalyptus globulus plantations at three contrasting sites in Gippsland, Victoria. Australian Journal of Botany 45, Benson, M.L., Landsberg, J.J., Borough, C.J. (1992). The Biology of Forest Growth experiment: an introduction. Forest Ecology and Management 52, Birk, E.M. (1992). Biomass and nutrient distribution in radiata pine in relation to previous land use. 1. Biomass. Australian Forestry 55, Borough, C. (1993). Know your product. In: Wood density of radiata pine. Australian Forest Grower 16 (2), 5 pp. Cown, D.J., and McConchie, D.L. (1983). Radiata wood properties survey ( ). FRI Bulletin No. 5, Forest Research Institute, New Zealand Forest Service. Cromer, R.N., Raupach, M., Clarke, A.R.P., and Cameron, J.N. (1975). Eucalypt plantations in Australia-The potential for intensive production and utilization. Appita 29, Cromer, R.N. (198). Irrigation of radiata pine with waste water: a review of the potential for tree growth and water renovation. Australian Forestry 42, Cromer, R.N., and Williams, E.R. (1982). Biomass and nutrient accumulation in a planted E. globulus (Labill.) fertilizer trial. Australian Journal of Botany 3, Cromer, R.N., Barr, N.J., Williams, E.R., and McNaught, A.M. (1985). Response to fertiliser in a Pinus radiata plantation. 1: Above-ground biomass and wood density. New Zealand Journal of Forestry 15, Dalianis, C, Christou, M, Kyritsis, C, Zatiris, C and Samiotakis, G. (1994). Growth yields and energy potential of densely planted Eucalyptus globulus in a two year rotation cycle. In: Eucalyptus for biomass production. Ed. J.S. Pereira and H. Pereira. Commission of the European Communities, pp Dyck, W.J. and Beets, P.N. (1987). Managing for longterm site productivity. New Zealand Forestry 32, Feller, M.C. (1983). Effects of an exotic conifer (Pinus radiata) plantation on forest nutrient cycling in southestern Australia. Forest Ecology and Management 7, Australian Greenhouse Office

41 Forrest, W.G. and Ovington, J.D. (197). Organic matter changes in an age series of Pinus radiata plantations. Journal of Applied Ecology 7, Frederick, D.J., Madgwick, H.A.I., Jurgensen, M.F., and Oliver, G.R. (1985). Dry matter, energy, and nutrient contents of 8-year-old stands of Eucalyptus regnans, Acacia dealbata, and Pinus radiata in New Zealand. New Zealand Journal of Forestry Science 15, Gadgil, R. (1976). Nitrogen distribution in stands of Pinus radiata with and without lupin in the understorey. New Zealand Journal of Forestry Science 6, Gadgil, R. L. (1979). The nutritional role of Lupinus arboreus in coastal sand dune forestry. IV. Nitrogen distribution in the ecosystem for the first 5 years after tree planting. New Zealand Journal of Forestry Science 9, George, M., and Varghese, G. (199). Nutrient cycling in Eucalyptus globulus plantation. I. Organic matter production, nutrients accumulation in standing crop and nutrient removal through harvest. Indian Forester 116, Gifford, R. M. (2a). Carbon contents of above-ground tissues of forest and woodland trees. National Carbon Accounting System Technical Report No 22. Australian Greenhouse Office, Canberra. Gifford, R.M. (2b). Carbon contents of woody roots. National Carbon Accounting System Technical Report No 7. Australian Greenhouse Office, Canberra. Giulimondi, G., and Duranti, G. (1975). Dry-matter production and nutrient contents of a 4-year-old Pinus radiata plantation. Pubblicazioni del Centro di Sperimentazione Agricola e Forestale 13, Hingston, F.J., and Galbraith, J.H. (1998). Application of the process-based model BIOMASS to Eucalyptus globulus plantations on ex-farmland in south western Australia II. Stemwood production and seasonal growth. Forest Ecology and Management 16, Hopmans, P., Stewart, H.T.L., Flinn, D.W., Hilman, T.J. (199). Growth, biomass production and nutrient accumulation by seven tree species irrigated with municipal effluent at Wodonga, Australia. Forest Ecology and Management 3, 1-4. Janik, L, Spouncer, L., Correll, R., and Skjemstad, J. (23). Sensitivity analysis of the Roth-C soil carbon model (ver Excel ). National Carbon Accounting System Technical Report No. 3. Australian Greenhouse Office, Canberra. Jenkinson, D.S. (199). The turnover of organic carbon and nitrogen in soil. Philosophical Transactions of The Royal Society. Lond. 329, Kingston, R.S.T. and Risdon, C.J.E. (1961). Shrinkage and density of Australian and other South-west Pacific woods. CSIRO Division of Forest Products Technological Paper No 13. Landsberg, J.J. (1977). Some useful equations for biological studies. Experimental Agriculture 13, Landsberg, J.J., Waring, R.H. (1997). A generalized model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance, and partitioning. Forest Ecology and Management 95, Leech, J.W. (1993). Terminology for forest mensuration and management in Australia. Australian Forestry 56: Lewis, N.B., Keeves, A. and Leech, J.W. (1976). Yield regulation in South Australian Pinus radiata plantations. South Australia, Woods and Forests Department, Bulletin No pp. Madgwick, H.A.I., Jackson, D.S., and Knight, P.J. (1977). Above-ground dry matter, energy, and nutrient contents of trees in an age series of Pinus radiata plantations. New Zealand Journal of Forestry Science 7, Madgwick, H.A.I. (1983). Seasonal changes in the biomass of a young Pinus radiata stand. New Zealand Journal of Forestry Science 13, National Carbon Accounting System Technical Report 31

42 Madgwick, H.A.I. and Oliver, G.R. (1985). Dry matter content and production of close-spaced Pinus radiata. New Zealand Journal of Forestry Science 15, Madgwick, H.A.I., and Webber, B. (1987). Nutrient removal in harvesting mature Pinus radiata. New Zealand Forestry 32, Madgwick, H.A.I. (1994). Pinus radiata-biomass, form and growth. Rotorua, New Zealand. 5 pp. Mead, D.J., Draper, D., and Madgwick, H.A.I. (1984). Dry matter production of a young stand of Pinus radiata: some effects of nitrogen fertilizer and thinning. New Zealand Journal of Forestry Science 14, Mendham, D.S., O Connell, A.M., Grove, T.S. and Rance, S.J. (23). Residue management effects on soil carbon and nutrient contents and growth of second rotation eucalypts. Forest Ecology and Management. 181, Miranda, I. Almeida, M.H., and Pereira, H. (21). Provenance and site variation of wood density in Eucalyptus globulus Labill. at harvest age and its relation to a non-destructive early assessment. Forest Ecology and Management 149, Mitchell, B.A. (1987). Nutrient losses from harvesting radiata pine plantations on podzolized sands on south eastern South Australia. DFR Users Series No. 5., CSIRO, Division of Forest Research. Moorhead, D.L. and Reynolds, J.F. (1991). A general model of litter decomposition in the northern Chihuahuan desert. Ecological Modelling 56, Muneri, A. and Raymond, C.A. (2). Genetic parameters and genotype-by-environment interactions for basic density, pilodyn penetration and stem diameter in Eucalyptus globulus. Forest Genetics 7, Neilsen, W.A., Pataczek, W., Lynch, T., Pyrke, P. (1992). Growth response of Pinus radiata to multiple applications of nitrogen fertilizer and evaluation of the quantity of added nitrogen remaining in the forest system. Plant and Soil 144, Negi, J.D.S., Bora, N.K.S., Tandon, V.N., Thapliyal, H.D. (1984). Organic matter production in an age series of Eucalyptus globulus plantations in Tamil Nadu. Indian Forester 11, O Brien ND (1998) Nutritional physiology of Eucalyptus grandis and Pinus radiata irrigated with municipal effluent. PhD Thesis. The University of Melbourne. O Connell, A.M. and Grove, T.S. (1999). Eucalypt plantations in south-western Australia. In: Site Management and Productivity in Tropical Plantation Forests (Eds. EKS Nambiar, C. Cossalter, A Tiarks.) pp Center for International Forestry Research: Pietermaritzburg, South Africa. O Connell, A.M., Grove, T.S., Mendham, D. and Rance, S.J. (2). Effects of Site Management in Eucalypt Plantations in South-Western Australia. In: Site Management and Productivity in Tropical Plantation Forests: A Progress Report (Eds EKS Nambiar, A Tiarks, C Cossalter, J Ranger.) pp Center for International Forestry Research: Bogor, Indonesia. Ouro, G., Perez-Batallon, P. and Merino, A. (21). Effects of silvicultural practices on nutrient status in a Pinus radiata plantation: nutrient export by tree removal and nutrient dynamics in decomposing logging residues. Annals of Forest Science 58, Orman, H.R. and Will, G.M. (196). The nutrient content of Pinus radiata trees. New Zealand Journal of Science.3, Ovington, J.D., Forrest, W.G. and Armstrong, J.S. (1967). Tree biomass estimation. Symposium on Primary Productivity and Mineral Cycling in Natural Ecosystems. University of Maine Press. Pp Paul, K., Polglase, P., Coops, N., O Connell, A., Grove, T., Mendham, D., Carlyle, C., May, B., Smethurst, P., and Baillie, C. (21). Modelling change in soil carbon following afforestation or reforestation: Preliminary simulations using GRC3 and sensitivity analysis. National Carbon Accounting System Technical Report No. 28. Australian Greenhouse Office, Canberra. 32 Australian Greenhouse Office

43 Paul, K.I., Polglase, P.J., Nyakuengama, N.J. and Khanna, P.K. (22) Change in soil carbon following afforestation. Forest Ecology and Management 168, Paul, K.I., Polglase, P.J. and Richards, G.P. (23a). Predicted change in soil carbon following afforestation or reforestation, and analysis of controlling factors by linking a C accounting model (CAMFor) to models of forest growth (3PG), litter decomposition (GENDEC), and soil C turnover (RothC). Forest Ecology and Management 177, Paul, K.I., Polglase, P.J. and Richards, G.P. (23b). Sensitivity analysis of predicted change in soil carbon following afforestation. Ecological Modelling 164, Paul, K., Polglase, P., Bauhus, J. and Raison, J. (24). Modelling change in litter and soil carbon following afforestation or reforestation: Calibration of the FullCAM model. National Carbon Accounting System Technical Report. No. 4. Australian Greenhouse Office, Canberra. Pongracic, S. (21). Influence of irrigation and fertilization on the belowground carbon allocation in a pine plantation. PhD thesis. University of New South Wales. Rance, S.J., Cromer, R.N., Cameron, D.M., Williams, E.R., Ryan, P., Brown, M., Johnston, J.B., Borschmann, G.R. (199). Response of young Eucalyptus grandis to fertilizer treatments. I. Biomass, leaf area, volume and wood density. In: Shell hardwood plantation development project. Final Report. CSIRO Forestry and Forest Products, Tasmania. Raymond, C.A. and MacDonald, A.C. (1998). Where to shoot your piodyn: within tree variation in basic density in plantation Eucalyptus globulus and E. nitens in Tasmania. New Forests 15, Raymond, C.A., Muner, A. and MacDonald, A.C. (1998). Non-destructive sampling for basic density in Eucalyptus globulus and Eucalyptus nitens. Appita Journal 51, Raymond, C.A. and Muneri, A. (2). Effect of fertilizer on wood properties of Eucalyptus globulus. Canadian Journal of Forest Research 3, Raymond, C.A. and Muneri, A. (21). Nondestructive sampling of Eucalyptus globulus and E. nitens for wood properties. I. Basic density. Wood Science and Technology 3, Richards, G.P. (21). The FullCAM Carbon Accounting Model: Development, Calibration and Implementation for the National Carbon Accounting System. National Carbon Accounting System Technical Report No. 28. Australian Greenhouse Office, Canberra. Ryan, M.G., Hubbard, R.M., Pongracic, S., Raison, R.J., and McMurtrie, R.E. (1996). Foliage, fine-root, woody-tissue and stand respiration in Pinus radiata in relation to nitrogen status. Tree Physiology 16, Schonau, A.P.G. and Boden, D.I. (1982). Preliminary biomass studies in young eucalypts. South African Forestry Journal 12, Smith, C.T., Lowe, A.T., Beets, P.N. and Dyck, W.J. (1994). Nutrient accumulation in a second-rotation Pinus radiata after harvest residues management and fertiliser treatment of coastal sand dunes. New Zealand Journal of Forest Science 24, Snowdon, P. and Benson, M.L. (1992). Effects of combinations of irrigation and fertilization on the growth and above-ground biomass production of Pinus radiata. Forest Ecology and Management 52, Snowdon, P, Eamus, D., Gibbons, P., Khanna, P., Keith, H., Raison, J., Kirschbaum, M. (2). Synthesis of Allometrics, Review of Root Biomass and Design of Future Woody Biomass Sampling Strategies. National Carbon Accounting System Technical Report No. 17. Australian Greenhouse Office, Canberra. National Carbon Accounting System Technical Report 33

44 Soares, P., Tome, M., Skovsgaard, J.P. and Vanclay, J.K. (1995). Evaluating a growth model for forest management using continuous forest inventory data. Forest Ecology and Management 71, Stevens, C.G. and Bond, R.D. (1957). Nitrogen economy in plantations. Australian Forestry 21, Stewart, H.T.L., Flinn, D.W. and James, J.M. (1981). Biomass and nutrient distribution in radiata pine. Australia, Australian Forestry Council: Proceedings, Australian Forest Nutrition Workshop, Productivity in perpetuity, 1981, pp CSIRO Division of Forest Research, Canberra. Turner, B. and James, R. (21). Chapter 5. Derivation of indicative yields for major plantation species. In Richards, G.P. (Ed.) Biomass estimation: approaches for assessment of stocks and stock change. National Carbon Accounting System Technical Report No. 27. Australian Greenhouse Office. Pp van Laar, A. (1983). Sampling for above-ground biomass for Pinus radiata in the Bosboukloof catchment at Jonkershoek. South African Forestry Journal 123, Webber, B. and Madgwick, H.A.I. (1983). Biomass and nutrient content of a 29-year-old Pinus radiata stand. New Zealand Journal of Forestry Science 13, West, P.W., Candy, S.G. and Osborn, T.E. (1988). Developments to a simulation model for prediction of wood volume of forests of Pinus radiata D. Don in the Australian Capital Territory. Consultant s Report for A.C.T. Forests Branch. 134 pp. Will, G.M. (1964). Dry matter production and nutrient uptake by Pinus radiata in New Zealand. Commonwealth Forestry Review 43, Will, G.M. (1966). Root growth and dry matter production in a high-producing stand of Pinus radiata. New Zealand Forestry Service Research Note 44. Wiliams, D.F. (1976). Forest fuels in unthinned radiata pine stands. Australian Forestry 39, Australian Greenhouse Office

45 APPENDIX 1 DATASETS USED TO CALIBRATE CAMFor TO BASIC DENSITY AND ABOVE-GROUND BIOMASS COMPONENTS. P. radiata Number of observations A Reference Basic density and age 134 Mitchell (1987) Biomass components 228 Total 1 Baker and Attiwill (1985) 28 Beets and Madgwick (1988) 39 Beets and Pollock (1987) 1 Beets, P.N. (pers. comm.) 3 Birk (1992) 7 Carlyle, J.C. (pers. comm.) 2 Cromer (198) 3 Cromer (1982) 2 Cromer et al. (1985) 1 Dyck and Beets (1987) 1 Feller (1983) 5 Forrest and Ovington (197) 1 Frederick et al. (1985) 7 Gadgil (1976) 5 Gadgil (1979) 1 Giulimondi and Duranti (1975) 1 Hopmans et al. (199) 3 Jarvis, P.G. (pers. comm.) 2 Madgwick (1983) 3 Madgwick (1994) 9 Madgwick and Oliver (1985) 1 Madgwick and Webber (1987) 11 Madgwick et al. (1977) 8 Mead et al. (1984) 2 Neilsen et al. (1992) 24 O Brien (1998) 1 Orman and Will (196) 1 Ouro et al. (21) 1 Ovington et al. (1967) 3 Shepherd, K.R. (pers. comm.) 8 Smith et al. (1994) 12 Snowdon, P. (pers. comm.) 25 Snowdon and Benson (1992) National Carbon Accounting System Technical Report 35

46 APPENDIX 1 continued DATASETS USED TO CALIBRATE CAMFor TO BASIC DENSITY AND ABOVE-GROUND BIOMASS COMPONENTS. P. radiata Number of observations A Reference 1 Stevens and Bond (1957) 1 Stewart et al. (1981) 1 van Laar (1983) 1 Webber and Madgwick (1983) 1 Wiliams (1976) 1 Will (1966) E. globulus Basic density and age 184 Total Biomass components 18 Total 1 Banham et al. (1995) 6 Hingston and Galbraith (1998) 6 Kingston and Risdon (1961) 71 Miranda et al. (21) 21 Muneri and Raymond (2) 2 Myers, B. (pers. comm.) 1 Periera and Araujo (199) 1 Rance et al. (199) 53 Raymond and MacDonald (1998) 16 Raymond and Muneri (2) 5 Raymond and Muneri (21) 1 Schonau and Boden (1982) 6 Bennett et al. (1997) 142 Borralho, N. pers. comm. (22) 4 Cromer and Williams (1982) 8 Cromer et al. (1975) 1 Dalianis et al. (1994) 1 George and Varghese (199) 4 Hingston and Galbraith (1998) 6 Negi et al. (1984) 2 O Connell et al. (2) 6 Schonau and Boden (1982) A All biomass components were not measured in all stands. Data from closely spaced or thinned stands were not used for regressions with crown components. 36 Australian Greenhouse Office

47 Series 1 Publications Set the framework for development of the National Carbon Accounting System (NCAS) and document initial NCAS-related technical activities (see publications). Series 2 Publications Provide targeted technical information aimed at improving carbon accounting for Australian land based systems (see Series 3 Publications Detail protocols for biomass estimation and the development of integrated carbon accounting models for Australia (see /publications). Of particular note is Technical Report No. 28. The FullCAM Carbon Accounting Model: Development, Calibration and Implementation for the National Carbon Accounting System. Series 4 Publications Provide an integrated analysis of soil carbon estimation and modelling for the National Carbon Accounting System, incorporating data from paired site sampling in NSW, Qld and WA. A preliminary assessment of nitrous oxide emissions from Australian agriculture is also included (see Series 5 Publications include: 39. Continuous Improvement of the National Carbon Accounting System Land Cover Change Mapping. 4. Modelling Change in Litter and Soil Carbon Following Afforestation or Reforestation - Calibration of the FullCAM Beta Model. 41. Calibration of the FullCAM Model to Eucalyptus globulus and Pinus radiata and Uncertainty Analysis. 42. Outcomes from the Workshop: Deriving Vegetation Canopy Cover Estimates. 43. The Impact of Tillage on Changes in Soil Carbon Density with Special Emphasis on Australian Conditions. 44. Spatial Estimates of Biomass in Mature Native Vegetation.

48 The National Carbon Accounting System provides a complete accounting and forecasting capability for human-induced sources and sinks of greenhouse gas emissions from Australian land based systems. It will provide a basis for assessing Australia s progress towards meeting its international emissions commitments. technical report no. 41 Calibration of the FullCAM Model to Eucalyptus globulus and Pinus radiata and Uncertainty Analysis