Improved estimation of biomass accum. by env & mallee …€¦ · Improved estimation of biomass...

1

SUSTAINABLE AGRICULTURE FLAGSHIP

Improved estimation of biomass accumulation by

environmental plantings and mallee plantings using

FullCAM

K. Paul1, S. Roxburgh

1, J. Raison

1, J. Larmour

1, J. England

1, S. Murphy

2, J. Norris

2, P. Ritson

3, K. Brooksbank

3, T.

Hobbs4, C. Neumann

4, T. Lewis

5, Z. Read

6, D. Clifford

1, L. Kmoch

1, M. Rooney

7, D. Freudenberger

7, J. Jonson

8, A.

Peck9, R. Giles

9, J. Bartle

9, G. McAurthur

10, D. Wildy

11, A. Lindsay

5, N. Preece

12, S. Cunningham

13, T. Powe

14, J.

Carter1, R. Bennett

1, D. Mendham

1, R. Sudmeyer

5, B. Rose

15, D. Butler

16, L. Cohen

17, T. Fairman

2, R. Law

2, B.

Finn2, M. Brammar

2, G. Minchin

18, P. van Oosterzee

12 and A. Lothian

17

31st

October 2013 (Up-dated on 26th

May 2014)

Prepared for: Department of the Environment

CSIRO Sustainable Agriculture Flagship, and CSIRO Ecosystems Sciences

1CSIRO,

2Victorian Department of Environment and Primary Industries,

3Department of Agriculture and Food, WA,

4SA Department of Environment, Water and

Natural Resources, 5

Queensland Department of Agriculture, Fisheries and Forestry, 6

Australian National University, 7Greening Australia,

8Threshold

Environmental, 9WA Department of Environment and Conservation,

10AusCarbon Pty Ltd.,

11Fares Rural Pty Ltd.,

12Biocarbon Pty Ltd.,

13Monash University,

14Greenfleet Pty Ltd.,

15Carbon Neutral Pty Ltd.,

16Queensland Department of Science, Information Technology, Innovation and the Arts,

17Canopy, trading

name of Australian Carbon Biosequestration Initiative Ltd (ACBI), 18

Lachlan Catchment Management Authority.

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Citation

K. Paul, S. Roxburgh, J. Raison, J. Larmour, J. England, S. Murphy, J. Norris, P. Ritson, K. Brooksbank, T.

Hobbs, C. Neumann, T. Lewis, Z. Read, D. Clifford, L. Kmoch, M. Rooney, D. Freudenberger, J. Jonson,

A. Peck, R. Giles, J. Bartle, G. McAurthur, D. Wildy, A. Lindsay, N. Preece, S. Cunningham, T. Powe, J.

Carter, R. Bennett, D. Mendham, R. Sudmeyer, B. Rose, D. Butler, L. Cohen, T. Fairman, R. Law, B.

Finn, M. Brammar, G. Minchin, P. van Oosterzee and A. Lothian. (2013) Improved estimation of

biomass accumulation by environmental planting and mallee plantings using FullCAM. Report for The

Department of the Environment. CSIRO Sustainable Agriculture Flagship, Canberra, Australia.

Copyright and disclaimer

© 2013 CSIRO To the extent permitted by law, all rights are reserved and no part of this publication

covered by copyright may be reproduced or copied in any form or by any means except with the

written permission of CSIRO.

Important disclaimer

CSIRO advises that the information contained in this publication comprises general statements based

on scientific research. The reader is advised and needs to be aware that such information may be

incomplete or unable to be used in any specific situation. No reliance or actions must therefore be

made on that information without seeking prior expert professional, scientific and technical advice.

To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability

to any person for any consequences, including but not limited to all losses, damages, costs, expenses

and any other compensation, arising directly or indirectly from using this publication (in part or in

whole) and any information or material contained in it.

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Contents

Abbreviations used in this report ....................................................................................................................... 5

Definitions .......................................................................................................................................................... 6

1 Executive summary ............................................................................................................................. 11

2 Introduction ........................................................................................................................................ 14

3 Methodological aspects ...................................................................................................................... 15

3.1 Introduction .............................................................................................................................. 15

3.2 Sampling Error .......................................................................................................................... 15

3.3 Precision sampling: increased efficiency of measurement ...................................................... 19

3.4 Rapid measurement techniques ............................................................................................... 20

3.5 Planted area calculation ........................................................................................................... 22

3.6 Sampling error when deriving allometrics ............................................................................... 24

4 New biomass estimates ...................................................................................................................... 26

4.1 Introduction .............................................................................................................................. 26

4.2 Methodology ............................................................................................................................ 26

4.3 Allometrics for the estimation of above-ground biomass........................................................ 27

4.4 Site average root-to-shoot ratios ............................................................................................. 27

4.5 Testing of allometrics ............................................................................................................... 29

4.6 Estimates of mean annual biomass increment ........................................................................ 30

4.7 Conclusions ............................................................................................................................... 31

5 Database analysis ................................................................................................................................ 32

5.1 Introduction .............................................................................................................................. 32

5.2 Methodology ............................................................................................................................ 32

5.3 Allometrics ................................................................................................................................ 38

5.4 Uncertainty in above-ground biomass estimates..................................................................... 42

5.5 Analysis of factors influencing biomass .................................................................................... 44

5.6 Conclusions ............................................................................................................................... 53

6 Calibration of FullCAM ........................................................................................................................ 54

6.1 Introduction .............................................................................................................................. 54

6.2 Methodology ............................................................................................................................ 55

6.3 Calibration of the Tree Yield Formula ....................................................................................... 57

6.4 Implementation considerations ............................................................................................... 65

6.5 Conclusions ............................................................................................................................... 71

7 Conclusions ......................................................................................................................................... 73

8 References .......................................................................................................................................... 75

9 Appendix ............................................................................................................................................. 81

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Acknowledgments

The project was funded largely by The Department of the Environment. Additional financial support

for this project was provided by Vic DEPI, while significant in-kind support in the form of assistance

with field work was provided by Vic DEPI (10 sites), DAFWA (7 sites), SA DEWNR (3 sites), Qld DAFF (3

sites), LCMA (2 sites), ANU (1 site), Qld DSITIA (1 site) and Threshold Environmental (1 site).

Technical support was provided by Alex Drew, Gordon McLachlan, Craig Baillie, Paul Warburton, Gary

Bastin (all CSIRO), Byron Yeo, Alex Winter, Mike Cully, Len Norris and Bob Hingston (all DAFWA),

Katelyn Ryan and Mervyn Tucker (both SA DEWNR), Scott Swift (Qld DAFF) and Dailiang Peng

(Chinese Academy of Science). ANUCLIM applications were undertaken by Jenny Kesteven (ANU).

For providing broad guidance with FullCAM growth curve calibrations and issues associated with

implementation of these calibrations, we thank: Matt Searson, Rob Sturgis, Brendan Pippen, Rochelle

Christian and particularly Rob de Ligt (The Department of the Environment). For providing broad

guidance with this work, including site selection and facilitating the collation of data, we thank: Craig

Barton and Fabiano Ximenes (NSW DPI), Gavin Kay (Terrain NRM), Keith Smith (Qld DSITIA), John

McGrath, Paul Turnbull (FFI CRC), Simon Dawkins (OMA), John Field (ANU), Angela Higgins (Lachlan

CMA), Gavin Kay (Terrain NRM), Tom Baker and Lauren Bennett (The University of Melbourne), Jason

Cummings (Greening Australia), Ray Wilson and Mariana Brekalo (Carbon Neutral), Kent Broad

(AusCarbon Pty Ltd.), Harry Roberts (SA Water), Ben Keogh (Australian Carbon Traders Pty Ltd.),

Richard Smith (previously Landcare Australia), Matthew O'Connor and Helen Burnie (Regenesis),

Richard Harper and Stan Sochacki (Murdoch University), Euan Beaumont (Carbon Diversity), Brendan

Vollemaere (Citola Pty Ltd.) and Peter Milthorpe.

We are also indebted to the landowners who gave us permission to harvest trees on their properties,

including Ingrid Davies, Greg Moir, David and Michael McFall, Philip Henseleit, Alan Piggott, John

Pepal, Audrey Bird, Norm Quicke, James Williams, Leo Rijs, Robert Temby, Joe Angel, Mitch Kemp,

Len Storey, Greg Carmody, Rob Batters, Dennis Watts, Trevor Campbell, Leo Tellefson, Rodney

Milthorpe, Chris Jones, Rob Rich, Bendigo City Council, John Toll, Elders Forestry, Trevor and Muriel

Muirhead, Gladstone Area Water Board, Tony and Trudy Woodall, Graeme Fitzgerald, Ross Battern,

David Sutton and Tony and Trudy Woodall.

Drs Mike Battaglia, Phil Polglase and Kelvin Montagu are thanked for their thorough review of this

report.

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Abbreviations used in this report

ANU Australian National University

BA Basal area of a single tree (m2) or group of trees (m2 ha-1)

CF Correction factor (Snowdon 1991) used to correct for bias in back

transformations

%CV Coefficient of variation (standard deviation divided by the mean x100)

Class 1 New above-ground biomass measures or estimates obtained from this study

CVI Canopy Volume Index, calculated as Ht x CW1 x CW2 (m3)

CW Canopy width (m)

DAFF Department of Agriculture, Fisheries and Forestry

DBH Diameter of the stem measured at breast height (or 130 cm) (cm)

D10, D30 etc. Diameter of the stem measured at 10 cm, 30 cm, etc. height (cm)

DAFWA Department of Agriculture and Food WA

EF Model efficiency, increasing performance as values approach 1.0 (or 100%)

FullCAM Full Carbon Accounting Model

FFI CRC Future Farm Industries Cooperative Research Centre

GA Greening Australia

GRTS Generalised Random Tesselation Stratified sampling

Ht Tree or shrub height (m)

IBRA Interim Biogeographic Regionalisation of Australia

LCMA Lachlan Catchment Management Authority

Lox Eucalyptus loxophleba subsp. lissophloia

M Forest biomass at maturity as defined by Richards and Brack (2004a)

MAR Mean annual rainfall (mm) over the period of growth

N Number of observations within the dataset

NIS National Inventory System

NSW DPI NSW Department of Primary Industries

OMA Oil Mallee Association

Pavg Forest productivity index as defined by Kesteven et al. (2004)

Poly Eucalyptus polybractea

PropTree Proportion of total individuals within a planting which are eucalypt trees

Qld DSITIA Queensland Department of Science, Information Technology, Innovation and

the Arts

Qld DAFF Queensland Department of Agriculture, Fisheries and Forestry

UWA University of Western Australia

R:S Root-to-shoot ratio, with the boundary being defined as ground level

SA DEWNR Department of Environment, Water and Natural Resources SA

Stdev. Standard deviation of the mean

sph Stems per hectare

Vic DEPI Department of Environment and Primary Industries Victoria

WA DEC WA Department of Environment and Conservation

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Definitions

ANUCLIM

A bioclimatic analysis and prediction model. It enables users to obtain estimates, in point and grid

form, of monthly, seasonal and annual mean climate variables from supplied climate surfaces.

(http://fennerschool.anu.edu.au/research/products/anuclim)

Belt planting

Plantings that are established in a linear configuration. Belt plantings can follow contours or be

arranged in straight lines and can have geometry that is either ‘narrow linear’ or ‘wide linear’ with

the spacing between the Belts as defined below.

Block planting

Plantings that are established in a Block configuration. That is, the planting configuration:

a. Does not conform to either ‘narrow linear geometry’ or ‘wide linear geometry’(as

defined below)

b. Is not comprised of a single row,

c. Is consistent with the definition of a forest as defined in the CFI Regulations.

The treatment of spacing between blocks is consistent with the CFI Mapping Guidelines. These

Guidelines provide that Exclusion Areas are defined for: (i) features greater than 5 m in width, or (ii)

areas less than 5 m in width that total more than 5 per cent of the Project Area.

CFI Mapping Guidelines

Guidelines of that name, as published from time to time on the Department of the Environments

website.

Domain

The set of permissible values (either numerated or descriptive) of an attribute for which a function is

defined and for which valid inferences may be made. These include;

a. Age domain. It is recommended that this range only between a stand age of zero to 15

years.

b. Spatial domain. The geographic area over which the new growth curve calibrations are

applicable. ANUCLIM was used to fit continuous mathematical surfaces to measured

meteorological data and predict the full climatic extent where the new growth curve

calibrations can be applied based on the location and climate profiles of the sites from

which the new growth curve calibrations were derived. Each new growth curve

calibration will only be available within its Spatial Domain.

c. Regime domain.

Defined in accordance with the species, planting geometry, stocking density and for

Mixed-species Environmental Planting, also the tree proportion.

i. Species. The taxa that contribute to defining the applicability of a new

growth curve calibration, and includes:

- Mixed species environmental plantings – Temperate

- Mixed species environmental plantings – Tropical

- Mallee Planting – Eucalyptus loxophleba ssp. lissophloia

- Mallee Planting – E. polybractea

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-Mallee Planting – ‘Other’ Mallee including species of E. kochii, E. kochii ssp.

borealis, and E. kochii ssp. plenissima

ii. Planting geometry. The planting configuration, and includes:

- Narrow linear planting

- Wide linear planting

- Block planting

iii. Stocking density. See definition below.

iv. Tree proportion. The proportion of individual live trees relative to the total of

individual live trees and shrubs in a mixed-species environmental planting.

Carbon Estimation Area (CEA)

A sub-set of the wider Project Area that has a planting which is homogenous for the purpose of

abatement calculations (species composition, planting geometry, stand density and for

environmental plantings, tree proportion) and with consistent site characteristics (i.e. soil type,

aspect, position on slope), as well as the same management regime, and which has been established

within a 180 day period.

Densely stocked planting

Plantings where, after the first 3 years post-establishment, the number of individuals per hectare

remains relatively high at; (a) >1,500 individuals per hectare, in mixed-species environmental

plantings; or (b) >2,300 individuals per hectare, in mallee eucalypt plantings.

Mallee

Any of various Australian species of Eucalyptus that generally have multiple stems arising from a

large underground lignotuber. Individuals usually have a flattened crown that rarely exceeds 6 m in

height.

Mallee eucalypt planting

A planting, on ex-agricultural land (i.e. land cleared of forest and used primarily for agriculture for at

least five years prior to planting being established), of a single Australian species of mallee eucalypt.

Mallee eucalypt plantings have the potential to attain a crown cover of at least 20 per cent and a

height of 2 metres in the place where they are established. Establishment may be undertaken using a

range of management practices such as weed spraying and soil preparation. Included under this

definition of plantings are the species:

a. Eucalyptus loxophleba ssp. lissophloia L.A.S. Johnson & K.D. Hill (smooth bark york gum),

b. E. polybractea R.T. Baker (blue mallee) and

c. ‘Other’ mallee including E. kochii and sub-species comprising:

i. E. kochii, Maiden & Blakely, and/or

ii. E. kochii ssp. borealis C.A. Gardner, and/or

iii. E. kochii ssp. plenissima C.A. Gardner

All other mallee species known to be planted that are not applicable to the findings of this report

include:

a. plantings that have a mix of two or more of the mallee eucalypt species included under this

definition (E. loxophleba ssp. lissophloia, E. polybractea, E. kochii, E. kochii ssp. borealis and

E. kochii ssp. plenissima; and

b. plantings of one or more of mallee species such as, but not limited to: E. horistes, E.

calycogona, E. cneorifolia [Kangaroo Island CS20275], E. cyanophylla [Loxton cult.], E.

dumosa, E. gracilis [Loxton cult.], E. incrassata, E. leptophylla, E. oleosa, E. plenissima, E.

porosa, and E. socialis

Mixed-species environmental planting

A planting that consists of a mixture of tree and shrub species that:

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a. include species which are native to the local area of the planting; and

b. include species which are sourced from seeds:

i. from within the natural distribution of the species; and

ii. that are appropriate to the biophysical characteristics of the area of the planting;

and

c. may be a mix of trees, shrubs, and understorey species where the mix reflects the structure

and composition of the local native vegetation community, and

d. are established through planting (e.g. tube stock, direct seeding or broadcast seeding).It does

not include mixed-species regenerated naturally without planting seeds or seedlings (i.e.

natural regeneration or regrowth).

Narrow linear planting geometry

Where the spatial configuration of a planting is:

a. For mixed-species environmental plantings established in either rows (using tube-stock or

direct-seeding) or random (using tube-stock and/or broadcast-seeding) where:

i. the distance between stems of the outermost trees or shrubs (random plantings) or

rows of the planting is greater than zero but 20 m or less across; and

ii. the distance between the stems of trees or shrubs at the outermost edge of the

plantings is at least 40 m from the stems of any adjacent planting; and

iii. where there is no impact from adjacent trees (defined as a tree growing within the

area 20 m perpendicular to the long axis of the planting (measured from the outer

stems) and which has a potential to develop a crown that extends >5 m across at its

widest point).

b. For mallee eucalypt plantings:

i. a Belt planting of two rows of trees (from tube-stock or direct-seeding); and

ii. where the distance between the stems of the outermost rows of trees in a Belt is at

least 40 m from the stems of any adjacent planting; and

iii. where there is no impact from adjacent trees (defined as a tree growing within the



widest point).

Planted area

The spatial area defining the planting that, as per a Carbon Estimation Area, is homogenous for the

purpose of abatement calculations and has consistent physical characteristics and is established and

managed in a consistent way. In this project, the planted area of each FullCAM calibration site was

used to estimate carbon abatement where:

a. For blocks or belts in which plants are established in rows:

i. the location of the outside edge of the long axis of the rows is a distance

from the outer row of stems one half of the average spacing between trees

within rows within the planted area;

ii. the location of the outside edge perpendicular to rows is a distance from the

outer row of stems one half of the average spacing between trees within the

planted area;

iii. the location of an edge internal to the planting perimeter bordering on an

exclusion area is a distance of one half of the average width of the rows

within the planted area from the outermost stem; and

iv. requirements for the minimum area of the planting and exclusion areas are

set out in the CFI Mapping Guidelines; and

b. For blocks or belts in which plants are established randomly (i.e. not in rows):

i. the location of any outside edge from the outer stems is equal to zero

meters from the outer stems;

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ii. the location of an edge internal to the planting perimeter bordering on an

exclusion area is equal to zero meters from the outermost stem; and

iii. requirements for the minimum area of the planting and exclusion areas are

set out in the CFI Mapping Guidelines.

Project area

A spatial area of land on which the set of activities is carried out. Subsets of a Project Area include

CEAs and Exclusion Areas (including the space between adjacent plantings).

Shrub

A perennial plant that has primary supporting structures consisting of secondary xylem. For the

purposes of establishing the tree Proportion, a shrub does not have (or does not have the potential

to attain) a stem diameter measurement at breast height (130 cm height).

Sparsely stocked planting


remains relatively low at; (a) 500-1,500 individuals per hectare, in mixed-species environmental

plantings; or (b) <2,300 individuals per hectare, in mallee eucalypt plantings.

Stand density or stocking density

The number of live trees and shrubs per hectare. Excludes non-woody plants, and plants with other

life forms (i.e. ground-covers and grasses). Stand or stocking density is taken to be equivalent to

stems per hectare and for multi-stemmed individuals a single stem is counted.

Stems or multi-stems

A stem is the main woody structural component of the above-ground portion of a tree or shrub.

Although it may branch into multiple stems at heights between 10 and 130 cm from the ground

where stem diameters were measured, in this report these multi-stemmed trees or shrubs are

assigned a single stem equivalent size. The numerous stem-branches of small diameters were

converted to an equivalent single stem value.

Tree

A perennial plant that has primary supporting structures consisting of secondary xylem. For the

purposes of establishing the tree proportion, a tree has (or has the potential to attain) a stem

diameter measurement at breast height (130 cm height).

Tree-dominant planting

Mixed-species environmental plantings that have at least 75% of live individuals of tree growth-habit.

That is, the proportion of trees in the planting is ≥0.75. The definition of a tree was made in relation

to species. A species was classified as having a tree growth habit if it was (or has the potential to

attain) a stem diameter measurement at breast height (130 cm height).

Tropical planting

A planting that consists of a mixture of tree and shrub species that:

b. are native to the local area of the planting; and

c. are sourced from seeds:

i. from within the natural distribution of the species; and

ii. that are appropriate to the biophysical characteristics of the area of the

planting; and

d. may be a mix of trees, shrubs, and understorey species where the mix reflects the

structure and composition of the local native vegetation community,

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e. are established through tube stock, direct seeding or broadcast seeding. That is it

does not include mixed-species regenerated naturally without planting seeds or

seedlings (i.e. natural regeneration or regrowth), and

f. are in tropical regions of Australia classified as having hot (or warm) humid summers

as per the temperature/humidity zones of climate classification of BOM (2006).

Very sparsely stocked planting


remains very low at <500 individuals per hectare in mixed-species environmental plantings.

Wide linear planting geometry

Where the spatial configuration of a planting is:

a. For mixed-species environmental plantings established in either rows (from tube-stock or

direct-seeding) or randomly (from tube-stock and/or broadcast-seeding) where:

i. the distance between the stems of the outermost trees or shrubs (random plantings)

or rows of the planting, in the narrowest dimension, is greater than 20 m across, but

less than 40 m; and

ii. the distance between the stems of trees or shrubs at the outermost edge of the

plantings is at least 40 m from the stems of any adjacent planting; and

iii. there is no impact from adjacent trees (defined as a tree growing within the area 20

m perpendicular to the long axis of the planting (measured from the outer stems)

and which has a potential to develop a crown that extends >5 m across at its widest

point).

b. For mallee eucalypt plantings:

i. A Belt planting of three to eight rows of trees (from tube-stock or direct-seeding);

and

ii. where the distance between the outermost rows of trees in a Belt is at least 40 m

from the stems of any adjacent planting; and

iii. where the average distance between rows within the planting is 4 m or less across;

and

iv. where there is no impact from adjacent trees (defined as a tree growing within the



widest point).

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1 Executive summary

• Overview: During the last two years, a major nationally-collaborative research program has

been lead by CSIRO to improve the estimation of biomass accumulation by mixed-species

environmental plantings and mallee eucalypt plantings. It has involved evaluation of the

uncertainties associated with using alternative approaches to biomass estimation, and the

collation and refinement of new and existing field inventories and biomass estimates for

these plantings, growing in various configurations throughout the non-arid (>300 mm mean

annual rainfall) regions of Australia. A large database on growth and biomass accumulation

across a wide range of planting types has been developed, comprising 1,480 site-based

observations, or 884 site-based observations not including repeated measures at the one

site, 183,675 stem diameter measures (36% from new work in this project) and 8,288

measures of tree or shrub above- and below-ground biomass (40% from new work in this

project). These data have been analysed to identify the key factors affecting the growth of

plantings, resulting in 26 statistically-different categories of plantings. Modifiers that account

for large variations in growth of these categories of plantings have been developed for use in

FullCAM (which underpins the Reforestation Modelling Tool, RMT).

• Sampling error: Sampling error was found to be the main factor affecting the accuracy of

biomass estimates. Unless a sufficient number of trees/shrubs are sampled in a manner that

is representative of the planting, biomass estimates can have high coefficients of variation of

>50%. Even within reasonably homogeneous plantings, a large number of trees needed to be

sampled to obtain biomass estimates with a 90% chance of being within ±10% of the true

mean. For block planting geometries, the number of trees required to be measured based on

simple random sampling was 700-1,600 and 130-280 for direct seeded and tubestock

plantings, respectively. In linear plantings, the number of trees required was 540-1,030 and

116-180, respectively. Guidance is also provided on sampling strategies to decrease sample

error by providing representative plots, and correct definition of the extent (area) of the

planting for consistency in estimates of biomass when comparisons between linear and block

plantings are required.

• Verification of allometrics: Direct field measures of above- and below-ground biomass

(through whole-of-plot harvesting) were used to test the reliability of a range of allometric

equations. It was shown that uncertainties resulting from the application of allometric

equations to estimate above-ground biomass are very low (generally <10% difference

between measured and estimated biomass) when using site-based allometrics, or moderate

(generally <16% difference between measured and estimated biomass) when generalised

non-site allometrics are used. This report contains a comprehensive set of new allometric

equations that can be used to estimate biomass of mixed-species environmental and mallee

eucalypt plantings.

• Root to shoot ratios: Root to shoot ratios can be high in young plantings grown in water and

nutrient-limited environments. They ranged between 0.28 and 0.81 across 13 sites studied.

Ratios were higher in tree-dominated plantings, where the ratio tended to decline as

productivity increased. A set of new root allometric equations were verified with direct

measurement from whole plot excavation.

• Uncertainty in biomass estimates: Several sources of uncertainty contribute to the challenge

of reliably estimating biomass accumulation, with estimates being highly variable within a

planting, across the broader landscape, and over time. Substantial errors can result from

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sampling the tree/shrub population, in measuring trees/shrubs during field inventories, and

in applying allometric equations to convert field inventories of measurements of stem

diameter into biomass. The magnitude and relative importance of these sources of error was

quantified using extensive field measurements combined with simulation modelling.

• Key factors influencing growth: Detailed statistical analysis of biomass measurements

contained in the database has enabled plantings to be categorised into three planting types:

mixed-species environmental plantings in temperate regions, mixed-species environmental

plantings in tropical regions, and mallee eucalypts. These three planting types were then

further categorised according to; (i) planting geometry (blocks, or linear plantings of varying

widths), (ii) stand density, and (iii) species in the case of mallees (E. polybractea, E.

loxophleba ssp. lissophloia, or ‘other’ species), or species-mix in the case of environmental

plantings (shrub-dominant or tree-dominant). These categories provided the basis for

calibration (estimation of appropriate modifiers) of the Tree Yield Formula in FullCAM.

• FullCAM yield curve calibrations: The un-calibrated yield curve for environmental plantings in

FullCAM generally lead to underestimation of biomass. To account for differences in growth

rates between the 26 categories of plantings, new modifiers have been provided for the Tree

Yield Formula within FullCAM. With these new modifiers, the overall model efficiency was

only 46 and 63% for mixed-species and mallee eucalypt plantings, respectively. However,

there was no apparent bias in model predictions and the model is satisfactory for most

individual planting categories or types. Therefore, modelled estimates of biomass

accumulation will be reliable on average, but estimates at any particular location will be

uncertain, with either significant under- or over-prediction possible. Results indicate that

when compared to the un-calibrated yield curves, early growth was likely to be more rapid,

and total above-ground biomass may be higher for many plantings at maturity.

• Recommendations for application. Some recommendations are provided for how FullCAM

might be applied so as to increase its utility without compromising confidence in predictions.

Firstly, there are 24 new growth curve calibrations which had sufficient replication of study

sites for providing confidence in their application. These new growth curve calibrations are

for particular combinations of five species compositions, three planting geometries, stocking

density and, for mixed-species environmental plantings, tree proportion. Collectively these

attributes are referred to as the regime, and each calibration can be applied only to plantings

that fall within the relevant Regime Domain. It is also recommended that the application of

each new growth curve calibration is also restricted to plantings that fall within the relevant

spatial and age domain. It is recommended that for application of these new yield curve

calibrations, FullCAM uses the predictive spatial bioclimatic data layers to restrict the

availability of each new yield curve calibration to the appropriate spatial domain. Given the

95th percentile of stand age ranged between only 10 to 33 years, and to be conservative, the

recommended age domain of the calibrations is only 15 years, which is the first CFI crediting

period.

• Significant challenges remaining: There are three key challenges remaining:

o Most field observations were for young stands, and temporal change in longer-term

stand dynamics in above- and below-ground biomass remains poorly understood,

especially for mixed-species stands where tree/shrub dominance is likely to change

with stand age and/or disturbance (e.g. by fire or drought). Repeated temporal

measurement of growth in contrasting types of plantings will be extremely valuable

for model evaluation of both above- and below-ground biomass, and for further

refinement of the calibrations of FullCAM’s yield curves in the future.

o To increase confidence in spatial application of the calibrations, additional estimates

of biomass carbon are required from a range of climatic regions for 3 of the 26

13

planting categories (E. polybractea in dense or sparse narrow linear plantings, and

wide linear environmental plantings where stocking and fraction of trees is high).

o There are some factors which are known to influence growth but which are yet to be

accounted for in calibration of the yield curves within FullCAM. These include access

to a watertable (including establishment in riparian areas), whether a planting is

growing in saline surface soil, or whether the planting has been coppiced. Further

work is required to assess the impacts of these factors for the different types of

plantings over the longer-term.

o Current plantings may not be representative of the potential land base for new

plantings, which may change over time with changing government policies. Filling of

gaps in estimates of biomass from poorly-represented regions will be critical.

14

2 Introduction

Most new plantings established for carbon sequestration in Australia are in the 300-600 mm rainfall

zone, where relatively low land values make such revegetation more viable (Polglase et al. 2011; Paul

et al. 2013a). Many of these are either mixed-species environmental plantings or mallee eucalypt

plantings (e.g. at least 78% of the current 65,128 ha of ‘carbon forests’ estimated by Mitchell et al.

2012). These plantings also have a role in providing other environmental benefits and public good

outcomes over and above carbon mitigation. They can be integrated into existing agricultural

landscapes such that they have no negative, and possibly a beneficial, impact on agricultural

production (e.g. GHD Hassall 2010; Paul et al. 2013a).

Investment in establishing and maintaining such plantings relies heavily on accurate estimates of

biomass production and rates of carbon sequestration. In low rainfall (and high evaporation)

environments water supply will be the major constraint to biomass production. However, this may

be partially negated by planting <10% of the land area in the form of linear (or belt) plantings, or

small blocks, potentially allowing the capture of some water from adjacent land. Previous work in

southern NSW has shown that the rates of sequestration of carbon in linear plantings were higher

than that of plantings established in block planting geometries (Paul et al. 2010). Stocking densities

and species/species-mix are also likely to influence rates of carbon sequestration (e.g. Polglase et al.

2008; Paul et al. 2008; Preece et al. 2012). Moreover, access to ground- and stored-water, salinity

and coppicing also affects biomass production and rates of carbon sequestration (Carter et al. 2008;

Peck et al. 2012; Bartle et al. 2012).

One way to account for such factors is to apply growth modifiers to the yield curves commonly used

in carbon accounting models such as FullCAM (Brack and Richards 2002; Richards and Brack 2004a,b;

Brack et al. 2006; Waterworth et al. 2007; Waterworth and Richards 2008). The FullCAM model is

used in Australia’s National Inventory System (NIS, DIICCSRTE 2011) to estimate rates of carbon

sequestration through land use change spatially across Australia, including reforestation with

environmental plantings. The FullCAM model forms the basis of the Reforestation Modelling Tool

(RMT), an approved methodology for estimating project-level carbon sequestered by such plantings

under the Carbon Farming Initiatives (CFI) (DOIC, 2011). Although reliable growth modifiers of

FullCAM’s yield curves have already been developed for many traditional plantation species

(Waterworth et al. 2007), this is not so for mixed-species environmental or mallee eucalypt plantings.

The main aim of this project was to develop FullCAM calibrations for estimation of the pattern of

biomass accumulation by mixed-species environmental and mallee eucalypt plantings, which

currently do not exist in NIS. The work thereby supported a revision of the RMT. The main results

from these calibrations are reported in Section 6, and include quantification of modifiers that

account for large variations in growth of different categories of plantings. To achieve this, we

undertook method testing to ensure accurate estimates of biomass (Section 3), collection of new

above- and below-ground biomass data through direct measurement and indirect estimates (Section

4), and analysis of extensive datasets to assess key factors influencing biomass accumulation (Section

5).

15

3 Methodological aspects

3.1 Introduction

To achieve the project objective of calibration of FullCAM (see Section 6), estimates of biomass were

collated and assessed to determine key factors influencing growth (see Section 5). Many of these

datasets were obtained from project collaborators. However, there were 50 new plantings studied as

part of this project, with 30 of these being ‘direct measurement’ plantings where whole plots were

harvested for biomass to obtain ‘true’ measures of above-ground biomass, and in the case of 13

sites, below-ground biomass as well (see Section 4). The overall methodology for the project is

summarised in Figure 3.01.

During the course of this new field work, a number of methodological issues relating to field-based

biomass estimation were identified. These include the problem of quantifying and accounting for

sampling error (Sections 3.2, 3.3), the development of rapid field-inventory methods to increase the

efficiency of data collection (Section 3.4), and the issue of identifying the correct planting extent

when expressing biomass on a per-area basis such that inventory datasets collated from project

collaborators are calculated in a consistent manner (Section 3.5). Another important methodological

issue relating to field-based biomass estimation was quantifying and accounting for sampling error

when selecting trees or shrubs to develop representative relationships between stem diameter and

biomass (allometrics, Section 3.6) given that, apart from the 30 sites where we had direct measures

of biomass through whole-plot harvesting, allometrics were used to obtain indirect estimates of

biomass.

3.2 Sampling Error

Data from eight sites (Strathearn, Moir, Gumbinnen, Pepal, Bird, Quicke, Moorland 1 and Moorland

2, Appendix A9.2.1) were used to quantify sampling errors associated with estimating biomass in

environmental plantings. This assessment was made using measurements of basal area (BA) given

previous work (Paul et al. 2011) showed BA is a reliable proxy for biomass. Sampling error is defined

as the difference between the true (but usually unknown) site biomass, and the estimate of the same

value from a (usually small) number of sub-samples. The analysis described in this section was made

possible because for each of the eight sites the BA of every individual within the planting was

measured, and hence the ‘true’ site value could be calculated. To further facilitate analysis,

information on the spatial planting geometries was also collected at the same time (i.e. distances

between rows, distances between trees along rows, and the variability of these distances).

These data were embedded within a computer sampling program that created pseudo-sites of

differing extents (ha) and shapes (block vs. linear planting), and populated with ‘trees’ that had the

same statistical properties for BA and spatial planting arrangement as those observed in the field

(Figure 3.02). These pseudo-sites were then computer-sampled using plots of various dimensions and

at a range of intensities by varying the total number of sample plots. These simulations were based

on simple random sampling, and assumed homogeneity of biomass across the site – i.e. there was

variation in tree size, but no site-level patterns such as gradients or bare patches. The full results,

including description of the computational methods and a discussion of the implications of these

simplifying assumptions, were presented in an interim report (Roxburgh et al. 2011). Only a summary

of that work is provided here.

16

Figure 3.01. Overview of the methodology used in this project, and the sections of the report in which these

various aspects are discussed in more detail. *Value in parenthesis indicates that only 13 of the total 30 ‘direct’

sites had below-ground biomass harvested as well as above-ground biomass. The remaining 17 ‘direct’ sites

were mallee eucalypt plantings harvested using an operational harvester.

The coefficient of variation (CV, standard deviation divided by the mean) of 500 replicate sets of

samples for each sampling design were calculated. CV is a useful index of sampling error because it

can be used to compare sites that differ in total site biomass, with smaller values indicating higher

precision. If the CV is multiplied by 100, it conveniently represents a percentage of the mean value.

For all sampling designs, as the number of replicates increased the mean sampled BA converged

towards the ‘true’ value as calculated from all the measured trees, and hence in all cases the

sampling was unbiased (Paul et al. 2011).

Collation of above- and

below-ground biomass

datasets from collaborators

Collation of inventory (growth)

datasets from collaborators

20 planting with ‘in-direct’

estimates of above-ground

biomass

Analysis of key factors

Influencing growth & R:S ratios

Calibration of FullCAM

Testing and development of

allometrics for above- and

below-ground biomass

Assessment of methodological

aspects (e.g. Precision sampling,

new rapid techniques, plot area)Section 3

Section 6

Section 5

Section 4

Consistent calculation of plot area

Application of

verified

allometrics

Understanding of

heterogeneity

Efficient field

estimates of

biomass

‘True’ measures of

biomass to compare

estimates against

30 (13)* plantings with ‘direct’

measurement of above- (and

below) -ground biomass

17

Figure 3.02. Example random sites based on the statistical properties of each of the eight plantings derived

from field measurement. Trees are shown with symbol diameters proportional to tree diameter. ‘DS’= direct

seeded, and ‘TS’= tubestock. The full details for each site are given in Tables A9.2.1-2, including the size of each

of these plantings.

Figure 3.03 shows the relationship between CV and sampling intensity (expressed as the total

number of trees, and/or shrubs, sampled) for the five tube-stock plantings and three direct-seeded

plantings. The proportion of trees sampled ranged from 1–30% of the total site. The difference

between the two planting methods on this figure is apparent, with tube-stock plantings yielding

more precise estimates (i.e. smaller CV) for any given level of sampling intensity. This is because the

trees in tube-stock plantings tend to be more even in size, and more regularly spaced than those

established by direct-seeding, with the result that fewer trees need to be sampled in order to attain

the same level of precision.

Figure 3.03. ‘Sampling precision’ curves illustrating how the coefficient of variation (CV) of sampled basal area

varies with sampling intensity, expressed as the total number of trees (or shrubs) included in 20 m by 20 m

sample plots in plantings established by tube-stock or direct-seeding.

The above example is only for a 20 m by 20 m plot size. However, Roxburgh et al. (2011) found that

decreasing the size of the plots only slightly decreased CV. This effect was most pronounced (causing

y = 1.1346x-0.48

-100 100 300 500 700 900 1100 1300 1500

CV

of B

A e

stim

ate

# Trees sampled

Tube stock

Direct seeded

Curve Y

Series4

Curve Y

Power (Tube stock)

y = 1.135x-0.48

y = 2.265x-0.50

0.0

0.5

1.0

1.5

2.0

2.5

0 20 40 60 80 100

CV

of B

A e

stim

ate

18

a decrease in CV by up about 0.02 units), when simple random sampling was used in sites with a

gradient in variability (i.e. un-homogenous). If restricted random sampling was used, plot size only

decreased CV by <0.01 units in un-homogenous sites. Regardless of sampling regime used, plot size

had no effect on estimates of CV in sites which were assumed to be homogenous.

In addition to CV, sampling error can also be assessed by calculating the probability that a given

sampling design yields a biomass estimate that is within ±X% of the true site value, where X can be

varied depending on a pre-defined limit for precision. This probability can be calculated directly from

values of CV in Figure 3.02.

Figure 3.04 shows the minimum number of trees for each site that need to be sampled in order to

obtain an estimate that has a 90% probability of being within ±10% of the true site value. Simulations

with three example site configurations are shown. For block plantings, approximately 700-1,600

trees (mean ± stdev. for lowest to highest sites: 696 ± 77 to 1,619 ± 131) need to be sampled in

order to have a 90% probability that the site BA estimate is within ±10% of the true site value when

direct-seeded, and 127 ± 10 to 277 ± 27 when tube-stock established. Higher precision is achieved

for the same sampling effort when trees are arranged in narrow linear plantings, particularly for

direct-seeded plantings. This is because sample plots can be positioned to straddle the entire width

of the planting, thereby capturing relatively more of the variability than an equivalent planting type

established in block geometry. For linear plantings, the approximate number of trees needed for

sampling at this specified level of precision were 535 ± 54 to 1,025 ± 160 trees for direct seeded, and

116 ± 6 to 180 ± 19 trees for tube-stock established.

Figure 3.04. Sampling precision results for randomly constructed plantings for each of the eight measured sites,

for each of a 10 ha block, a 2 ha 2-row linear plantings, and a 2 ha 4-row linear plantings. The y-axis is the

number of individual tree measurements required to attain a 90% probability of obtaining an estimate within

±10% of the true value. Error bars are standard deviation across 500 replicate sampling events.

These results, based on statistically ‘generalised’ sites, confirm previous analyses using the actual site

planting configurations and tree locations (Paul et al. 2011), and show sampling error is a significant

source of uncertainty when estimating biomass. The important implication is that when sampling

intensity is too low (i.e. too few sample plots and/or plot size too small) then the resulting field-

measured biomass estimate will have a high level of uncertainty, and may appreciably over- or

under-estimate the actual site value.

0

500

1,000

1,500

2,000

Nu

mb

er

of

tre

es

10 ha block

2 ha 4-row belt

2 ha 2-row belt

19

As summarised by Stockdale and Wright (1996), others have also studied relationships between

sample number (or plot size) and coefficient of variation, with the relationship varying depending on

the ‘clumpiness’ of the vegetation. Most recently, Jazbec et al. (2011) also used computer

simulations to assess errors associated with inventories of mixed-species plantings. They also found

significant exponential decline in errors in precision of estimates of biomass with increased number

of trees sampled.

Although Figures 3.03 and 3.04 can be used as an approximate guide for defining the required

sampling effort to achieve a pre-defined level of precision, there are some important caveats. The

analyses are based on the assumption that the sites are internally homogenous in that there are no

gradients, patches of high or low growth, or areas of mortality or canopy gaps. The analysis therefore

corresponds to sampling within an area that has been stratified into homogeneous subunits or

strata. However, in reality, variation occurs at all scales, and as site extent increases it is almost

certain that there will be site-level spatial heterogeneity that will impact on sampling precision.

Additional simulation results have shown that the sampling effort required to satisfy a given level of

precision is sensitive to the assumptions surrounding this spatial variability, and to the method used

to locate sample plots in space. More precision is attained with designs that ensure a greater spatial

spread of sample locations than the simple random sampling reported here, such as Systematic

Sampling, Stratified Random Sampling (SRS), or newer methods such as Generalised Random

Tesselation Stratified sampling (GRTS; Stevens and Olson 2003) (Roxburgh et al. 2011).

3.3 Precision sampling: increased efficiency of measurement

One of the key activities of this project was to destructively harvest a number of sites, across a

representative range of environmental and management conditions, and then use these data for

FullCAM calibration (Section 6). It was therefore important to minimise sampling errors for each

planting studied. To achieve this, a two-stage ‘Precision Sampling’ procedure was developed. This

new approach was necessary due to practical constraints that limited the number of plots that were

able to be destructively harvested, and which would otherwise lead to large sampling errors (Section

3.2).

The first stage of the Precision Sampling procedure is a broad, non-destructive inventory of all plants

(trees and shrubs) across the site. This provides a database of individual diameters that represents,

as much as practically possible, a comprehensive estimate of variation in biomass across the site. For

some sites that were relatively small in extent, all individuals were measured. For larger sites (>5 ha)

where a full survey of all individuals was not possible, up to 60 sample plots were established to

capture the variation. For large linear plantings, a Stratified Random Sampling approach (as per

Greig-Smith 1983) was adopted, whereby the entire planting was split into a number of equal sized

segments, with plots placed at random within each segment, thus ensuring a good spatial coverage

of the entire planting. For block plantings, plot locations were selected using the GRTS method

(Stevens and Olson 2003), again to ensure adequate spatial coverage. The total number of plots to be

located varied from site to site (see Section 4), and depended on such factors as the planting density

and the resources available to undertake the survey (Table A9.2.2). From this survey an estimate of

the basal area of the full planting was calculated. As noted above, BA is used here as a proxy for

biomass (Paul et al. 2011).

The second stage of the process selects a smaller sub-set of inventory plots for harvesting (typically

six plots) in such a way that the BA within those inventory plots matches as closely as possible the

whole-site BA as calculated above. The selection of inventory plots is achieved via software custom

written for this study. The overall precision sampling procedure is summarised in Figure 3.05, and the

key steps are described below using the Moir environmental planting site as an example:

20

1. The basal area for the site (BASITE) was calculated from the initial non-destructive inventory.

For the Moir site a total of 13,187 stems were measured, yielding a site basal area of BASITE =

10.93 m2 ha-1.

2. The size-class distribution of stems for the site was calculated. The distribution of stem BA

for the Moir site shows a predominance of individuals in the smallest BA size class (<25 cm2)

(Figure 3.06, black bars).

3. The stand density (stems per hectare) for the site was calculated. For Moir this was 2,611

stems per ha.

4. The sampling software is then used to ‘optimise’ the locations of the required number of

sampling plots (for Moir, 12 plots each 20 m x 20 m) in such a way that the sub-sample of

trees within the plots yields a per-ha BA that is close (within ±5%) to that observed for the

whole planting (10.93 m2 ha-1), and that additionally has a similar stand density (2,611 stems

ha1) and size class distribution. The selection procedure occurs ‘blindly’, in that random sub-

sets of plots are repeatedly tested until a sampling plan is found where the statistics are in

agreement. To check on the validity of the overall procedure, several sets of equivalently

fitting solutions can be generated, and checked to ensure spatial representativeness.

5. The final step is to select one of the ‘best-fit’ solutions at random for destructive harvest. The

final solution for the Moir site shows a good agreement of the solution with respect to the

frequency distribution of individual tree sizes, with a corresponding sampled basal area of

10.90 m2 (c.f. 10.93 m2 for the full site), and a stand density of 2,665 stems ha-1 (c.f. 2,611

stems ha-1 for the full site).

The main advantage of the Precision Sampling approach is that it provides some confidence that

the estimated biomass obtained by the direct harvesting of trees within a relatively small

number of plots is representative of the actual total site biomass, i.e. the method directly

reduces the sampling error. The main disadvantage is that, to implement, it requires a non-

destructive pre-survey of the site to provide the broad-scale baseline data on spatial variability of

biomass across the site. To reduce the costs associated with this double-sampling approach a

rapid field survey technique was developed and tested (Section 3.4).

3.4 Rapid measurement techniques

The basis of non-destructive field biomass estimation is the measurement of stem diameters. For

plantings where the species involved are often multi-stemmed and/or branch close to the ground,

individual measurement of each stem using traditional diameter tapes can be prohibitively time

consuming. A consequence is that a great deal of time can be spent measuring a relatively small

number of trees. Experience through measuring 65,888 stems during the course of this project led to

the development of a rapid method for estimating stem diameters, based on a calibrated caliper

(Figure 3.07). To use this device, each stem is estimated to the nearest 0.5 cm, where estimations

can be done at ‘arms-length’, avoiding the necessity of crawling through shrubs etc. For larger stems

(<10 cm) a standard diameter tape can be used if desired.

Use of the caliper greatly increases the number of stems able to be measured in a given time, and

because of this larger sample size, the potential for large sampling errors is reduced (Section 3.2).

A limitation of the method is a loss of precision for the measurement of each individual, which is an

issue if individual stem measurements are to be used for the development of allometrics (Section 4).

To test this, 266 stems at the Lynvale and McFall plantings (Table A9.2.1) were repeat-measured

using both a diameter tape and the caliper, by two separate measurement teams. The diameter

measurements were then converted to per-tree BA for analysis. On a per-tree basis the absolute

percentage difference between the tape and the caliper averaged 13%, reflecting the per-

21

measurement 0.5 cm rounding error. However, for total BA summed across all trees (on which the

precision sampling procedure is based) the difference reduced to less than 1.5%, indicating the

individual per-tree error measurements effectively cancel each other out, with the rapid

measurement caliper providing an unbiased estimate of whole-site BA. The results for both

measurement teams were very similar (difference between tape and caliper varied by <0.2%

between teams).

Figure 3.05. Flow diagram of Precision Sampling methodology. Numbers in parentheses refer to steps 1-5

described above.

Can all stems in the

planting be measured?

Site level basalarea (BASITE)

calculated fromfull site survey

Site level basal area(BASITE) calculated from an

array of plots distributedover the whole site

From survey data calculatestatistics summarising treeattributes across the site:

(a) BASITE

(b) Stems per ha(c) Tree-size histogram

Define the attributes of theharvest plots (number of

sample plots (n) & plot size)

Select n plots at randomand compare tree statisticscalculated from this sample

with those from the initialsurvey

Are all test statisticscalculated from this sampleof n plots within +/- 5% of the

whole site statisticscalculated from the

pre-survey?

Save the solution

Yes No

Enough solutionsfound?

Select one of the savedsolutions at random, and

harvest plots

Yes

No

No

Yes

Non-destructiveinitial pre-harvestsurvey

Selection of plotsfor harvest

Destructiveharvest

(1)

(2, 3)

(4)

(5)

Figure 3.06. Precision sampling solution for the Moir site, showing (a) the location of the

by 20 m), and (b) a comparison of the individual tree size distribution for the full site (black;

individuals) and for the sample of 12 plots (red

Figure 3.07. Use of the calibrated caliper

3.5 Planted area calculation

The comparison of plantings of different species, planting geometries, management treatments, etc.

requires expressing standing biomass on a per

called biomass density. When trees are established in rows, as they often are, then calculation of

appropriate areal extents for calculating biomass density becomes problematic.

with narrow linear (<20 m wide, or in the case of mallee eu

wide, or the case of mallee eucalypts, 3

planting there are a number of possible alternatives for defining the planted area (Figure 3.08). Paul

et al. (2011) showed, for linear plantings, the variation in estimated biomass density difference

between these various methods can be substantial. Depending on the spacing between rows, and

the number of rows in a linear planting, conversion factors to obtain B

biomass from those estimated using C

m, Table A9.1.2) vary between 0.83

. Precision sampling solution for the Moir site, showing (a) the location of the 12

, and (b) a comparison of the individual tree size distribution for the full site (black;

individuals) and for the sample of 12 plots (red; N = 1,279 individuals).

caliper for estimating stem diameter (at 50 cm height in this case)

area calculation

different species, planting geometries, management treatments, etc.

requires expressing standing biomass on a per-area basis (e.g. t ha-1); a quantity which is sometimes

led biomass density. When trees are established in rows, as they often are, then calculation of

appropriate areal extents for calculating biomass density becomes problematic. The main issues arise

with narrow linear (<20 m wide, or in the case of mallee eucalypts, 2-row), and wide linear (20

wide, or the case of mallee eucalypts, 3- to 8-row), plantings. This is because at the outer edge of the


(2011) showed, for linear plantings, the variation in estimated biomass density difference


the number of rows in a linear planting, conversion factors to obtain B-B equivalent estimates of

biomass from those estimated using C-C (at 2 m; Table A9.1.1) or D-D (assuming canopy width of 3

−2.00.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 -

25

25 -

50

50 -

75

75 -

100

100

- 12

5

125

- 15

0

150

- 17

5

175

- 20

0

200

- 22

5

225

- 25

0

250

- 27

5

275

- 30

0

300

- 32

5

325

- 35

0

350

- 37

5

Individual tree basal area category (cm

Rel

ativ

e F

req

uen

cy

Relative frequency acrosswhole site (n=13187)

Relative frequency across the12 sample plots (n=1279)

22

sample plots (20 m

, and (b) a comparison of the individual tree size distribution for the full site (black; N = 13,187

cm height in this case).

different species, planting geometries, management treatments, etc.

); a quantity which is sometimes

led biomass density. When trees are established in rows, as they often are, then calculation of

The main issues arise

row), and wide linear (20-40 m

This is because at the outer edge of the


(2011) showed, for linear plantings, the variation in estimated biomass density difference


B equivalent estimates of

D (assuming canopy width of 3

350

- 37

5

375

- 40

0

Individual tree basal area category (cm2)

Relative frequency across

Relative frequency across the12 sample plots (n=1279)

23

Figure 3.08. Cross-sectional representation of a 4-row linear mallee planting with sufficient maturity for there

to be a strong edge effect, i.e. greater growth in the outer rows. Four different methods for delineating

planting area are shown. A-A is the distance between the outside planting lines; B-B adds half a planting row

width; C-C adds a constant distance (e.g. 2 m), and in D-D the planting area is defined by the width of the

crowns.

A ‘null-model’ approach can be used to derive a solution to this problem. The basis of the approach is

that, for two plantings that have the same tree stand density and same mean tree mass, but differ

only in area and shape, then by definition they have the same biomass density. Therefore, the

boundary to be drawn around each of these plantings must be done in such a way that, when the

total biomass of each planting is divided by the area within this boundary, the same biomass density

is calculated. This is illustrated in Figure 3.09, where Figure 3.09a depicts a large block planting that

for simplicity has trees arranged on a regular grid and with all trees the same size (mass). In this

hypothetical case the biomass density is uniform across the block. Figure 3.09b shows the situation

where some of the trees have been removed, to yield two smaller blocks. The question can then be

asked, what is the appropriate areal extent that can be drawn around each of these smaller blocks

that still yields the same biomass density as the large block from which they were derived? The

answer is shown by the dotted line in Figure 3.09b, and is equal to adding an equivalent of ½ a row

width top and bottom, and ½ a tree spacing left and right.

Figure 3.09. Illustration of the ‘null model’ approach to calculating the planting area for estimation of biomass

density where rows of trees are planted north-south. (a) A homogeneous planting that has the same biomass

density across its whole extent. (b) The same planting with a number of trees removed, leaving two smaller

‘remnant’ blocks of unequal size. The dotted line around each area corresponds to adding the equivalent of

half a row width top and bottom, and half a tree spacing left and right; this is the only definition of planting

area that guarantees equivalent estimates of biomass density between the two remnant areas.

A detailed explanation of the above result shows that adopting any other definition of planting area,

such as canopy extent, will yield a (spurious) biomass density difference between the two areas

(Appendix 9.1). Importantly, the same adjustment is required even when there is variability in the

row spacing and/or variability in the tree spacing. In this more general case the appropriate

A A

C C

D D

B B

N

24

corrections are based on the average row and tree distances. Appendix 9.1 also explains how this

general result can be applied to plantings that have trees randomly seeded along rows rather than

regularly spaced, and for plantings that are established by broadcast seeding.

These adjustments are critically important for linear plantings (Paul et al. 2011). For plantings that

are arranged in blocks, with a low ratio of edge to interior, then the error associated with ignoring

these adjustments may be deemed acceptably low. However calculating this error requires

knowledge of the total areal extent of the planting, its shape, and the planting geometry (i.e. the

mean distance between rows, and the mean distance between trees along rows) (Appendix 9.1).

An example of the practical importance of these results is when comparing tree growth between a

block planting and a linear planting of the same type. Because edge trees in linear plantings tend to

have access to more resources (light and water), overall growth tends to be higher, which is

expressed as a higher per-area growth increment in the linear planting compared with the block. If

an incorrect planting extent is used for this comparison, then the estimated difference in

performance due to the edge effect (i.e. the quantity that we are trying to measure) will be either

greater than or less than the true growth effect, depending on the (arbitrary) choice of planting

extent; if the planting extent is too large, the growth effect will be under-estimated, and if too small

it will be over-estimated.

Care will be needed to ensure consistency in the estimates of area of land use change under linear

plantings accounted for in the NIS, and that used in calibrations of FullCAM’s yield curves. For

project-level accounting, project area can be reported directly.

3.6 Sampling error when deriving allometrics

In most studies of biomass, estimates of biomass are derived indirectly by the application of

allometrics (empirical relationships between stem diameter and biomass) to inventories of stem

diameters. However, as with the assessment of sampling error for estimates of a plantings BA (or

biomass), the sampling error may be significant when a selection of trees (or shrubs) are harvested

to develop allometrics. Data from 23 species were used to quantify sampling errors associated with

the development of allometrics. Sampling error here was defined as the difference between the

biomass estimated by the true allometric, and that estimated from allometrics based on a smaller

number of sub-samples. Assumed ‘true’ allometrics were derived for these 23 species given the

exceptionally-large sample size (averaging 189 individuals; Table A9.1.3) used to develop these

relationships. Various estimates of these allometrics were obtained using a computer program. For

each species, this program sampled between 4 and 30 individuals from the total population to

construct 100 different simulated allometric equations.

When developing a new allometric, a key part of the protocol is to ensure that the individuals

selected for measurement and harvesting cover the full range of diameters within the wider

population of interest. The distribution of observed diameters differed among 23 species studied

here, but could be divided into either 4 or 5 size class categories, with a 4 or 5 cm stem diameter

increment between these categories. The computer program enabled the 100 iterations of sampling

to be done randomly within these 4 or 5 categories such that each simulated allometric covered the

full range of diameters. This was done firstly with only 1 individual being sampled per category, and

then increasing to up to 6 individuals being sampled per category. The resulting allometrics were

therefore developed with as little as only 4 individuals (4 categories, from which 1 individual is

randomly sampled), and up as many as 30 individuals (5 categories, from which 6 individuals are

randomly sampled).

The absolute errors in the estimates of biomass at each 1 cm increment from 1 to 30 cm (the range

of stem diameters common in these plantings, see Figures 4.01, 5.02 and 5.03) were calculated and

then averaged across each of the 100 iterations. For each species, this average percentage error in

25

estimated allometrics decreased exponentially as the number of individuals sampled increased. This

relationship was generalised by averaging across the 23 species as shown in Figure 3.10.

Figure 3.10. Relationship between sample number and average percentage error in allometrics (absolute

difference in biomass derived from the estimated allometric when compare to the 'true' allometric at each 1

cm increment from 1 to 30 cm) across 23 tree species. Error bars show the stdev. of the mean.

In a global review of allometrics of biomass (based on stem diameter) of different tree species,

Zapata-Cuartas et al. (2012) also recently found that there was an exponential decline in the

precision in predictions of tree biomass with increasing sample size. Interestingly, for a given sample

size, these workers found that the precision can be reduced when using a Bayesian approach as

opposed to the classical statistical approach of least-square regression used here. They suggest it is

possible to obtain similar significant values in the estimation of allometric parameters using a sample

size of 6 trees rather than 40–60 trees in the classical approach. Further work is required to verify

this using the datasets collated here.

y = 41.735x-0.473

R² = 0.99

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26

4 New biomass estimates

4.1 Introduction

As part of this study, inventory data of stem diameter were collated from 183,675 stems, while

biomass measures were obtained from 8,288 individual trees or shrubs. For both sets of data,

approximately 40% of observations were derived from field work conducted as part of this project.

There were four key objectives to the utilisation of these new high-quality data; (i) improved

understanding of factors driving allocation of biomass, and thus above-ground allometrics, (ii) use of

directly-measured above- and below-ground biomass to better understand the factors driving root to

shoot ratios, (iii) use of directly-measured biomass for verification of allometrics to improve the

accuracy and efficiency of indirect estimates of biomass, and (iv) application of best-available

methodology to maximise the confidence in estimates of biomass carbon in a range of different

planting types for the purpose of calibration of yield curves used within FullCAM. Here we report on

results relating to each of these four objectives.

4.2 Methodology

We obtained 50 new site estimates of biomass carbon at a wide range of plantings (Table A9.2.1).

Sites were selected across contrasting climatic regions and planting geometries. We targeted

plantings aged 5 to 25 years which had ‘typical’ (for the region) management regimes and were

successfully established. As noted in Table A9.2.1, 13 of these sites were ‘direct measurement’ sites,

where all individual trees (and shrubs) within sample plots were harvested and roots were excavated

in sub-plots. Another 17 sites were also ‘direct measurement’ sites, but where only above-ground

biomass was measured, with harvesting being done using an operational harvester. At the other 20

sites, biomass estimates were obtained indirectly through the development of site- and species-

specific allometrics from selective harvesting of trees and shrubs. Detailed methods for direct and

indirect sampling of biomass, and the development of site-specific allometrics, are described in Paul

et al. (2011). A detailed summary of the methodology used is provided in Appendix 9.2.

To gain an understanding of the accuracy of allometrics, we compared biomass estimated from site-

specific allometrics from trees and shrubs harvested in ‘direct’ plantings to the whole-plot biomass

actually measured. We also assessed the difference between measured biomass and that estimated

using the generic allometrics described in Section 5. As described in Section 5, previous work has

shown that the performance of such generic multi-site and multi-species allometrics may perform

well.

We derived several characteristics of each planting as possible descriptors of growth. Stocking

(number of stems per hectare, sph) was calculated as the number of trees or shrubs in each plot

divided by the plot area. The proportion of trees (PropTree) was calculated as the proportion of

stems in each plot that were trees. For the purposes of this study, only eucalypts and Corymbia (and

in tropical and sub-tropical regions, also other genera such as Alstonia, Araucaria, Blepharocarya,

Elaeocarpus, Flindersia, Melicope, Xanthostemon) were considered to be trees. All other genera were

considered shrubs or small trees (termed here ‘shrubs’, e.g. allocasuarina, casuarina, melaleuca and

acacia shrub species such as those listed in Table A9.3.6 with D10 measurements). Root-to-shoot

(R:S) ratios were calculated at each of the 13 ‘direct measurement’ plantings, and given that these

plantings differ in their composition and structure (namely species mix and planting geometry) and

represent contrasting climatic regions (Table A9.2.1), the impacts of species mix and productivity on

27

R:S ratios were explored. Multiple regression analyses were used to determine whether the PropTree

and productivity (measured as increments in total biomass per hectare) were significantly influencing

R:S ratios across the 41 sample plots of the 13 ‘direct measurement’ plantings.

4.3 Allometrics for the estimation of above-ground biomass

The effectiveness of allometric equations for predicting biomass was quantified by calculating the

‘model efficiency’ (Soares et al. 1995), where efficiencies of between 70−100% are regarded reliable

predictors of biomass. Efficiencies with which allometric equations predicted variations in measured

above-ground biomass ranged between 68 and 99%, but were mostly >95% (Table A9.2.3). Thus, a

reasonable fit to the data was obtained in most cases.

There were, however, some variations in allometric relationships between sites, and to a lesser

extent, between species of a given life-form, or growth-habit, within sites (Figure 4.01). For example

for ‘tree’ life-form, the %CV of predictions of above-ground biomass at stem diameters of 5, 10, 15,

20 and 25 cm averaged 26.6%. The %CV decreased by 4.4% when these predictions of above-ground

biomass were averaged between sites, and then only by another 2.0% when averaged between

species within a site. These results suggest that in terms of developing cost effective protocols for

indirect estimates of biomass, site-based allometrics may be life-form based as opposed to species

based. This could greatly decrease the costs of indirect estimates of biomass at new plantings.

Results also suggest that precise across-site generalised allometrics may be difficult to develop. For

example, there is no clear indication of an influence of mean annual rainfall on allometrics. However,

there are indications when compared to higher rainfall sites, eucalypt trees growing in lower rainfall

environments had more biomass for a given diameter, while the reverse was true for acacia trees

(Figure 4.01). This may be attributable to differences in wood density or in plant structure (e.g.

height to diameter ratios) in these different rainfall zones.

Generic allometrics are further discussed in Section 5.3 where analyses were undertaken using a

much wider dataset.

4.4 Site average root-to-shoot ratios

Stocking was not a significant factor influencing R:S ratio of a stand. However, variations in R:S ratios

were broadly, but significantly, influenced by both site productivity and PropTree. We developed a

significant Multiple Regression for R:S ratio which included productivity and PropTree (R2=0.56,

P<0.001, N=41). However, further analysis of the data showed an interaction between these factors

in their influence on R:S ratios. Only in tree-dominated plantings (i.e. PropTree ≥0.75) was

productivity a significant factor influencing R:S ratio (R2=0.43, P=0.007, N=25). In these plantings,

PropTree was not a significant factor. In contrast, in plantings with PropTree <0.75, PropTree alone

largely explained variations in R:S ratio (R2=0.40, P<0.001, N=16). These different relationships

explaining R:S ratios in tree--dominated plantings and plantings with PropTree <0.75 are shown in

Figure 4.02. Figure 4.03 provides estimates of the site average R:S ratio observed. This shows that

average R:S ratio can vary from 0.28 in more shrub-dominant plantings (PropTree <0.1) to 0.81 in low

productivity, tree-dominant plantings.

There were insufficient data to determine the impact of planting width on R:S ratios. Results for

linear plantings were confounded by PropTree and site productivity. Further work is also required to

assess the impacts of other factors, such as key species, on R:S ratios.

A more detailed discussion of such key factors influencing R:S ratios provided in Section 5.5.5, where

analyses were undertaken using a much wider dataset.

28

Figure 4.01. Above-ground biomass allometrics derived for species harvested from the 50 plantings studied for:

(a) acacia shrubs, (b) acacia trees in temperate regions, (c) acacia trees in tropical regions, (d) eucalypt and

corymbia trees in temperate regions, and (e) tropical trees (i.e. Alstonia, Araucaria, Blepharocarya,

Elaeocarpus, Flindersia, Melicope and Xanthostemon). Red and blue lines represent allometrics derived from

sites where mean annual rainfall was <500 mm and >500 mm, respectively. Green lines represent allometrics

derived from sites in tropical regions*. To demonstrate within-site and between-site variability, the same line

formatting was used for allometrics of a specific life-form obtained from a specific site.

*Note that unlike other species represented in these plots, allometrics developed for tropical mixed-species environmental plantings were

not listed in Table A9.2.3 (but are given in Table A9.3.6) as there were no new inventory data obtained from sites in the tropics in this

project. Rather these allometrics were only applied to collaborator (Qld DAFF and Biocarbon Pty Ltd) inventory datasets.

Figure 4.02. Relationship between (a) proportion of trees (PropTree) and root-to-shoot ratio (R:S) across the

plots in plantings where PropTree was less than 0.75 (shown in blue; N=16), and (b) total biomass productivity

and R:S ratio across the plots in plantings where PropTree was greater than 0.75 (shown in red; N=25).

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y = -0.03x + 0.76

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29

Figure 4.03. Planting average R:S ratio under different proportion of trees (PropTree) at sites where PropTree

was less than 0.75 (in blue, and ages of the stands were 10-20 years), or under different productivities (defined

here as total above- and below-ground biomass per hectare per year) where PropTree was greater than 0.75

(in red, and ages of the stands were 11-22 years). Bars are the average of mean site R:S ratio of two plantings

within the same category, with the exception of the lowest productivity category, which represented the

Moorland 1 site only. Error bars are the stdev. of average R:S ratios.

4.5 Testing of allometrics

Comparison of biomass estimated using allometrics versus direct harvest measures (Figure 4.04)

showed that overall, the allometrics performed very well, with errors averaging 15%. There did not

appear to be any systematic bias with biomass predictions using allometrics.

Site-specific allometrics provided a more accurate estimate of biomass, on average, than those

associated with the generic life-form allometrics. Errors in estimates of above-ground biomass made

using site-specific allometrics averaged only 3.3 t DM ha-1 (or 9.8% of measured above-ground

biomass), while differences in estimates made using generic allometrics averaged 8.1 t DM ha-1 (or

16.3% of measured above-ground biomass).

For below-ground biomass, differences resulting from the use of generic life-form allometrics

averaged 3.5 t DM ha-1 (or 19.5% of measured below-ground biomass). At Gumbinnen and Moorland

1, where below-ground biomass was very small, differences were 38-41% (Figure 4.04). Further

measurement of root biomass is required from a wider range of sites to allow the further refinement

of below-ground allometrics for high and low rainfall regions.

0.51

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30

Figure 4.04. Comparisons of biomass estimates from allometrics with direct harvest measures for: (a) live

above-ground, and (b) below-ground biomass, and how these compare with estimates generated by the

original (un-calibrated) FullCAM predictions. Note that generic allometrics have been refined to account for

effects of life-form, species and rainfall (Section 4.3). Error bars are standard deviations of the mean across the

sample plots.

There are two other important precautionary points to make with respect to these specific results.

Firstly, the fact that site-and-species specific allometrics performed very well across these 13

plantings was probably largely because these allometrics were based on a large number of

individuals of key species (see N reported in Table A9.2.3). As shown in Figure 3.10, we would

anticipate errors resulting from the application of site-specific allometrics to be even greater where

fewer trees were harvested to develop these relationships. Secondly, the fact that generic

allometrics for above-ground biomass performed reasonably well in this study may be due to the

collation and analysis of the extensive biomass database (Section 5) that enabled these generic

allometrics to be refined (categorised) based on life-form, species and rainfall. For mixed-species

environmental plantings, we know that, from testing of earlier versions of generic allometrics (Paul

et al. 2011), their accuracy was significantly improved when; (i) significantly more datasets became

available, and (ii) the generic allometric for acacia trees, and particularly eucalypt trees, were split

into high and low rainfall zone allometrics, with different species generally being represented in the

different rainfall zones. Further discussion on the accuracy of generic allometrics is provided Section

5.3.

4.6 Estimates of mean annual biomass increment

Biomass increases with time, therefore to allow comparison of plantings of different ages, we

compared the mean annual biomass increments, defined as the biomass divided by the age of the

stand (t ha-1 yr-1) among the 50 plantings studied in this project. Figure 4.05 shows the measured (for

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31

‘direct’ plantings) and estimated (for ‘indirect’ plantings) increments and compares those to

predicted using the un-calibrated yield curves in FullCAM. The results show that the un-calibrated

yield curve was not accounting for site or management factors that yield higher increments in the

more productive stands. Discussion on the general under-prediction of above-ground biomass using

the un-calibrated yield curves in FullCAM is provided in Section 6.1.

Figure 4.05. Measured (for ‘direct’ planting) and estimated (for ‘indirect’ plantings) mean annual biomass

increments across the 50 plantings studied in this project (dark and light purple bars). Black and grey bars

indicate the corresponding increment predicted using the un-calibrated FullCAM yield curves. Details of these

plantings are given in Appendix 9.2, Tables A9.2.1 and A9.2.2. Error bars are stdev. of the mean across sample

plots.

4.7 Conclusions

There are indications that within a given life-form, variations in allometrics for above-ground biomass

were greater between-sites than within-sites (i.e. relatively little difference between species of a

given life-form within a site). Rainfall at least partly contributed to between-site variations in some

life-forms. The measurement of R:S ratios in 13 contrasting ‘direct measurement’ plantings have

provided an understanding that R:S ratios are lowest in plantings with low proportions of trees, and

highest in plantings of relatively low productivity. Plantings where ‘direct measurements’ of biomass

were made have also been invaluable for testing of site-and-species specific and generic life-form

allometrics which, having been verified (i.e. average differences between measured and estimated

biomass, 15%), could be applied to provide estimates of biomass based on inventory data obtained

from a much wider range of plantings. The studies summarised above have also provided a basis for

the creation of a reliable new database on above- and below-ground biomass by plantings growing in

contrasting environments (Appendix 9.3). They also provide the foundation for evaluation of

extensive data on growth and biomass provided by collaborators (Section 5), and of highly precise

measurements of biomass for calibration of the yield curves in FullCAM (Section 6).

0

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Measured or estimated above ground biomass

Predicted total biomass using uncalibrated FullCAM yield curves

Predicted above-ground biomass using uncalibrated FullCAM yield curves

32

5 Database analysis

5.1 Introduction

This section aims to develop a reliable extensive database which can be used to statistically assess

the key factors influencing growth, and thereby ensure the calibration of FullCAM’s yield curves are

‘robust’ in terms of being widely applicable. To achieve this, four key steps were followed; (i)

collation of biomass datasets and development of further allometrics, (ii) collation of inventory

datasets and application of allometrics to obtain estimates of biomass, (iii) uncertainty analysis of

collated datasets, and (iv) statistical analysis of collated datasets to determine key factors influencing

growth.

The review of the magnitude and longevity of growth response to various site and management

factors was previous conducted as part of this project, and was summarised by Carter et al. (2011).

This work highlighted a number of key factors which would require accounting for when developing

modifiers of growth. These are in addition to the climatic and soil depth and texture parameters that

are already part of the NIS. Carter et al. (2011) found that additional (interacting) factors which

require consideration include:

• Position in the landscape,

• Stocking density,

• Planting design and geometry (influencing ‘edge effects’),

• Access to additional water derived either from irrigation, or ground- and stored soil water,

• Salinity, and its interactions with species and stocking density,

• Nutrient availability, and its interactions with water availability, and

• For harvested mallee plantings, harvest frequency and timing.

5.2 Methodology

5.2.1 The database

A database was developed from plantings shown in Figure 5.01, and comprised biomass (Tables

A9.3.1 and A9.3.2), inventory (Table A9.3.3) and auxiliary (Table A9.3.4) data. Datasets were sourced

from project collaborators as indicated. Some of the datasets are confidential and were provided to

CSIRO solely for the purpose of calibration of yield curves used within FullCAM. All other datasets are

now publicly-available (Paul et al. 2013b-g). Importantly, for all plantings, planting area (or plot area)

was calculated as per Section 3.5 so that estimates of biomass density were not confounded by

planting geometry.

5.2.2 Development and application of allometrics

All above- and below-ground allometric equations were developed as described in Appendix 9.2.

Using the collated datasets (Table A9.3.1), three types of allometrics were developed; (i) site-and-

species specific allometrics which were applied only to inventory data from the planting from which

33

the biomass harvesting was done to develop the allometric, (ii) generic species-specific allometrics,

and (iii) generic life-form allometrics. The sample sizes available for generic life-form allometrics

were relatively large, and therefore we used Multiple Regression analyses to investigate whether

these could be further segregated based on climate. In particular, we tested whether these generic

allometrics significantly differed between regions of relatively high and low rainfall. To do this, the

datasets were divided into two approximately equal halves, based on the median observed mean

annual rainfall (MAR). For mallee eucalypts, this was 400 mm MAR while for mixed-species

environmental plantings this was 500 mm MAR. Given that the datasets obtained from tropical north

Queensland were considered highly likely to differ from those attained from temperate Australia due

to their lower wood densities and different growth forms (e.g. Keith et al. 2009; Stegen et al. 2011),

these were also segregated to form generic tropical allometrics for mixed-species environmental

plantings. Here we defined ‘tropical’ as the hot (or warm) humid summer regions using the

temperature/humidity zones of climate classification (BOM 2006).

For each inventory dataset, the most relevant allometric was applied to every individual measured

such that an estimated total biomass for each sample plot within each planting could be calculated.

For sites and/or species where site-and-species specific allometrics were not available (i.e. sites not

included in Table A9.3.1), generic allometrics were applied. Ideally these were generic species-

specific allometrics, but where these were not available, generic life-form allometrics were applied.

With the exception of the 13 direct harvest plantings, at all plantings generic below-ground

allometrics were applied. The total biomass was then divided by the plot area (Section 3.5) to

calculate the biomass per unit area in t DM ha-1 in both above- and below-ground components. Plot

data were then used to calculate biomass for the planting. Where individual trees or shrubs had

multiple stems, an ‘equivalent diameter’ was calculated (as per Appendix 9.2) before applying the

allometric.

Figure 5.01. Location of the planting sites from which data on growth of environmental plantings were collated.

These localities are overlayed upon spatial outputs of potential site productivity (Pavg, unitless), and an outline

of IBRA regions.

Mixed-species environmental plantings Mallee eucalypt plantings

34

5.2.3 Uncertainty in above-ground estimates of biomass

The Palisade @Risk for Excel program was used for uncertainty analyses of the estimates of above-

ground biomass (t DM ha-1) for each planting. Triangular probability distribution functions (or, where

we had calculated standard deviation of the error, a normal distribution function) were assigned to

each key input used in the calculation of biomass. Although Table A9.3.5 summarises these functions,

below we describe in detail the basis for each of these probability distribution functions. The @Risk

program used Excel to calculate biomass from 1,000 random values sampled from these probability

distribution functions. Rank-order correlations between the value of each parameter and estimated

biomass were then calculated. Using this information, Tornado graphs were constructed to rank the

relative sensitivity of the biomass estimates to the assumptions made. Results from the uncertainty

analysis were also used to calculate the standard deviation and coefficient of variation in estimates

of biomass.

Measurement errors

Estimates of measurement errors in stem diameter, canopy volume index and height were obtained

from repeated measurements of diameters (at D10, D30, D50 and DBH) and heights on the same set

of 226 trees at the Lynvale and McFall sites. Repeatability in these measurements was similar

regardless of whether the re-measurement was done by the same or a different technician. There

tended to be a slight increase in measurement error in stem diameters taken from lower down on

the tree; being 3.0, 3.3, 3.8, and 5.8% at DBH, D50, D30 and D10, respectively. These errors for DBH

were in agreement with those noted by Gregoire et al. (1989). However, we also noted that there

was a greater measurement error for diameters of multi-stemmed trees (6.9%) than single-stemmed

trees (5.6%). To be conservative, these average errors in measurement of stem diameters were used

in the uncertainty analysis.

As indicated in Table A9.3.5, repeatability in measurement of tree heights indicated similar

measurement errors, being 5.5% on average. Similarly, Brown et al. (1995) also found measurement

errors in tree heights were relatively high, being up to 10-15% in mature stands. We assumed the

errors in measurement of canopy width to have similar errors to that estimated for tree height, and

given CVI is the product of stem height and canopy widths (= Ht x CW1 x CW2), we estimated

measurement errors in CVI to be the sum of these errors (17.4%).

No covariance (or correlation) between measurement errors was required given above-ground

biomass estimates for a given individual were predicted using allometrics where only one of the

many possible explanatory variables were used. Therefore, although the explanatory variable used in

allometrics to estimate above-ground biomass varied from planting-to-planting depending on what

was measured (i.e. CVI, D10, DBH, etc.), there was no use of multiple explanatory variables to

estimate biomass of a given individual.

We also estimated measurement error in plot areas. For all but seven plantings in the database, trees

were planted in rows and so calculations of plot widths were straightforward. However, with direct

seeded plantings in particular, the distances between trees within the rows was highly variable, and

so there was some uncertainty in measurement of plot lengths. Measurement errors in plot area

decrease with increasing plot size, with slightly less error in circular rather than rectangular plots

(e.g. FAO 1981; IPCC 2006). However in the datasets collated (N=1,480), 98% of the measured plots

were square or rectangular, and most had a plot area similar to the average size observed, which was

0.040 ha (standard deviation 0.047 ha). Using the mean plot area recorded for tube-stock and direct-

seeded plantings in the database, and assuming there is ±0.5 m uncertainty in distances between

rows, and an additional ±0.25 m uncertainty in distances between trees within rows in direct-seeded

plantings, errors averaged 3.3% and 4.4% for tube-stock and direct seeded plantings, respectively

(Table A9.3.5). Nevertheless, these are generalised errors, and further work will be required to

35

calculate errors in plot area for each specific site based on the actual plot area and the shape of the

plot used.

Errors in assumptions made during calculations

There may be additional errors in the calculation of biomass (t DM ha-1) through the application of

allometrics. For example, errors from generic life-form allometrics applied to species which were not

well represented by the particular equation (i.e. life-form was recorded as ‘unknown’, but the

generic eucalypt tree allometric was applied as a ‘default’). Here we assumed this error to be 20%

(Table A9.3.5).

There will also be errors in the application of generic life-form allometrics. Here we assumed these to

be the 16.3% average difference between measured biomass and that estimated using generic life-

form allometrics as noted in Section 4.5. Site-and-species specific and species-specific generic

allometrics (which are generally based on a much smaller N than generic life-form allometrics) will

have errors associated with their application, and we have assumed that these increase

exponentially with a decreased sample size used to derive the equation as per Figure 3.10 (Section

3.3). Although our results showed that all allometrics also had small errors (generally <3%) associated

with their fit to the observed data (i.e. model efficiency (EF)), it was assumed that these errors were

already incorporated in the above-mentioned allometric errors.

Sampling errors

As shown in Section 3.2 (Figure 3.03), the probability of measuring the true planting BA increases

with the number of trees measured, and this relationship differs for tubestock (CV<150%, as

consequence of establishment method), as compared to the more heterogeneous direct seeded

plantings (CV >150%, as consequence of establishment method). For each inventory plot for each

planting in the database, the total number of trees measured and the heterogeneity (i.e. CV in BA

measured within these plots) was known. Therefore for each planting, the equations given in Figure

3.03 were used to calculate the CV and standard deviation of both BA and above-ground biomass,

given they are linearly related (Paul et al. 2011). Unlike the measured variations in biomass between

plots within a site, these variations represented the likely variability around the true mean as

determined by the sampling design (namely, the number of trees actually measured). Using this

standard deviation in above-ground biomass, a planting-specific normal distribution of errors was

generated around the estimated planting average above-ground biomass, which thus reflected both

the sampling design (number of trees sampled) and actual heterogeneity (measured coefficient of

variation in BA). Using this defined normal distribution, Monte Carlo simulations were used to obtain

iterative estimates of above-ground biomass (Table A9.3.5).

5.2.4 Data analysis: Factors influencing above- and below-ground biomass

Estimates of above-ground biomass and R:S ratios (obtained by application of above- and below-

ground allometrics) were analysed separately. However for both datasets, to identify the key factors

influencing amounts of above-ground biomass or R:S ratios, Multiple Regression modelling were

used to determine whether any of the auxiliary variables listed in Table A9.3.4 were statistically

important.

In a preliminary analysis, it was found that across the entire datasets (N=1,480), age alone explained

55% of the variability in above-ground biomass, with the relationship differing significantly (P<0.001)

for; (i) temperate mixed-species environmental plantings, (ii) tropical mixed-species environmental

plantings, and (iii) mallee eucalypt plantings. Therefore, a detailed Multiple Regression analysis was

undertaken on these three key planting types separately.

For each of the three datasets, Box-Cox transformations (Box and Cox 1964) were used to find the

appropriate transformation of biomass response so that assumptions about normality of residuals

36

were upheld after correcting for covariates. Fourth-root transformations were used for above-

ground biomass while natural log transformations were used for R:S ratios. The final set of covariates

was found using a stepwise model selection process based on the Akaike Information Criteria

(Venables and Ripley 2002). Where appropriate, site random effects were introduced to reflect the

fact that above-ground biomass values at the same site are correlated. For such models the

restricted (or residual, or reduced) maximum likelihood (REML) approach was used to estimate

model coefficients (Harville 1977). The set of covariates was reduced further if required (i.e. if non-

significant, P>0.05), with the final set of covariates and interactions indicating which management or

site factors best explained the variation in above-ground biomass. Because analyses were conducted

on a transformed scale, bias correction factors were applied when back-transforming model

predictions to the original scale.

Although the Tree Yield Formula in FullCAM already accounts for the impacts of age and site

productivity (Pavg) on biomass, here we assessed empirically the impact of these factors on biomass.

Given Pavg was highly correlated with mean annual rainfall, rainfall per se was not considered here

as a factor influencing growth. However, we did undertake some comparisons between Pavg and

rainfall and/or temperature in terms of correlations with estimates of above-ground biomass.

For ease of implementation, calibrations for the Tree Yield Formula should be based on categories of

plantings, as opposed to altering the formula construct (i.e. by adding numerical relationships

describing changes to growth increments with planting width, or stand density etc.). Care was

therefore taken to develop categories of different plantings for the two planting types with relatively

high N’s; mixed-species environmental plantings established in temperate regions, and mallee

eucalypt plantings. See Definition section of report on page 6 and Table 5.01.

There was a trade-off between increasing the number of categories and decreasing the number of

replicates observed within each of these categories. To ensure that there were at least 8 site

replicates within each category (with average of 68 and standard deviation of 80), we used the below

approach to categorisation.

• Planting geometries: In contrast to mixed-species environmental plantings, mallee eucalypt

plantings often have trees established in clearly-defined rows, with low variation in inter-row

distances. Also, many linear mallee eucalypt plantings in the database had sample plots

established such that there was deliberately one outer row and one inner row represented.

For example, in a six-row linear planting of mallee eucalypts, sample plots may have had 50%

of the trees as edge trees while the actual planting had only 33.3% edge trees. Therefore,

although planting geometry for row-planted mallee eucalypts could be defined based on the

percentage of trees in the sample plots which were edge trees, for mixed-species, planting

geometry was defined in accordance with the width of the planting. Therefore a block

planting geometry was defined as a planting with 0% edge trees (Note that this is 0% edge

trees in the sample plots, with the actual planting having a very low fraction of edge trees),

or for mixed-species plantings, with a width of >40 m. A wide linear planting was defined as

plantings with 25-50% edge trees, or for mixed-species, plantings with a width of 20-40 m. A

narrow linear planting was defined as a planting with 100% edge trees (i.e. 2-row linear

plantings), or for mixed-species, plantings with a width of <20 m.

• Stand density, or stems per hectare (sph): For mixed-species plantings, the stand density

categories were either ‘low’ (<1,500 sph) or ‘high’ (>1,500 sph). For mallee eucalypts the

‘low’ and ‘high’ stand density categories were <2,300 and >2,300 sph. Again, the cut-off sph

used in these categories were selected to effectively divide the datasets into two equal

halves. The one exception. This was for block environmental plantings established in

temperate regions. For these particular plantings, a relatively high N supported the addition

of a third category for stocking; <500 sph, or very sparse stand density (Table 5.01).

37

• Species/species-mix: Species-mix in environmental plantings, being either tree dominant

(PropTree ≥0.75) or having a more even mix of trees and shrubs (PropTree <0.75). The

PropTree value of 0.75 was used given this effectively split the datasets into about two equal

halves. For mallee eucalypts, there were three key species; ‘Lox’ E. loxophleba subsp.

lissophloia; ‘Poly’ E. polybractea; and ‘Other’, namely E. kochii subsp. borealis or subsp.

plenissima.

In addition to species/species mix, stocking and planting geometry, a binary categorisation was also

required to indicate whether or not: (i) a planting was regrowth post-coppice (only relevant to mallee

eucalypts, N=349), (ii) the surface soil was saline (defined here as >200 mS m-1, N=45), and (iii) the

planting was likely to have access to a water table (defined here as a measured water table depth of

<5 m (N=357), or the planting was clearly riparian (N=12)). For the latter two categorisations, if no

salinity or water table access was noted in the site records collated, they were assumed to be non-

saline and having no access to water tables. Additional categorisation was based on landscape

position (upper, mid, lower, gully), method of establishment (direct-seeded, tube stock or

broadcast), previous land use, soil clay content and available water content (Table A9.3.4).

Multiple Regression models which best explained the variations in above-ground biomass or R:S

ratios were used to generate plots showing average differences between different combinations of

these categories of plantings in terms of their above-ground biomass or R:S ratios.

Table 5.01. Outline of the categorisation of plantings based on type of planting, planting geometry, stand

density and the proportion of trees in the stand.

Type of planting Planting geometry Stand density (sph)

(or trees ha-1

) PropTree

Mixed-species; temperate Narrow linear, Sparse, <1,500 <0.75

<20 m wide ≥0.75

Dense, >1,500 <0.75

≥0.75

Wide linear, Sparse, <1,500 <0.75

20-40 m wide ≥0.75

Dense, >1,500 <0.75

≥0.75

Block, Very sparse, <500 <0.75

>40 m wide ≥0.75

Sparse, 500-1,500 <0.75

≥0.75

Dense, >1,500 <0.75

≥0.75

Mixed-species; tropical Block ~ ~

‘Other’ mallee eucalypts Block, Sparse, <2,300 1.00

<25% edge trees Dense, >2,300 1.00

Wide linear, Sparse, <2,300 1.00

25-50% edge trees Dense, >2,300 1.00

Narrow linear, Sparse, <2,300 1.00

100% edge trees Dense, >2,300 1.00

‘Lox’ mallee eucalypts Block, Sparse, <2,300 1.00



25-50% edge trees Dense, >2,300 1.00



‘Poly’ mallee eucalypts Block, Sparse, <2,300 1.00



25-50% edge trees Dense, >2,300 1.00



38

5.3 Allometrics

There was a reasonable fit to the data in most cases, with allometric equations for above-ground

biomass having model efficiencies of between 68% and 99% (average 93%) (Table A9.3.6). Due to a

much smaller sample size for below-ground allometrics, efficiencies were less, being as low as 60%,

and averaging only 81%. Generic allometrics were developed based on a number of different

diameters (e.g. D10, D30, D50 and DBH). The allometric equations shown in Table A9.3.6 were those

most commonly used. Others are not shown but are available within the database. Similarly, only a

selection of key generic allometrics is shown in Figures 5.02 and 5.03.

All of these allometrics are based only on stem diameter. The fact that model efficiencies for these

allometrics were so high supports the findings from numerous researchers who have shown that

stem diameter is an adequate biomass predictor at local or regional scales, with height, or wood

density, providing little or no improvements in the efficiency of allometric predictions of above-

ground or below-ground biomass (Brown et al., 1989; Ketterings et al., 2001; Jenkins et al. 2003;

Ritson and Sochacki 2003; Zianis and Mencuccini, 2004; Lambert et al. 2005; Montagu et al. 2005;

Williams et al. 2005; Chave et al., 2005; Peichl and Arain 2007; Pajtı´k et al. 2008; Ouimet et al. 2008;

Basuki et al., 2009; Xiang et al. 2011; Jonson and Freudenberger 2011; Rance et al. 2012; Kuyah et al.

2012a,b). In addition to providing verification that allometrics based on stem diameter alone are

adequate, our high model efficiencies also provide further evidence that the simple power-law

model (Equation A9.2.1) is universal across a wide range of woody species, given they have their

origins in common geometric and hydrodynamic principles that govern the transport of essential

materials to support cellular metabolism (Enquist et al. 1998; West et al. 1999; Enquist and Niklas

2001). However, the fact that the simple power-law model worked so well was probably also partly

because the majority of the trees and shrubs measured were not mature. There is some evidence

that these power-law models fail for very large trees, with over-estimates of biomass being common

when DBH>50 cm (Niklas 1995, Chambers et al. 2001; Chave et al. 2005; Fatemi et al. 2011) due to

accelerated damage and senescence as the trees mature.

Figures 5.02 and 5.03 show the generalised life-form allometrics obtained in this study. As expected,

there are clear differences in the allometrics between various life-forms for mixed-species

environmental plantings, and between various species of mallee eucalypts. This may be attributable

to differences in both stem wood density and the partitioning of biomass between stem, bark,

branches and foliage. For mallee eucalypts, although species category was a statistically significant

(P<0.001) factor influencing the allometric, the improvement in explained variation (R2) of the

generic mallee eucalypt allometric was only 0.01% when this species factor was added. This suggests

that species only explains marginally more of the variability in biomass of mallee eucalypts than stem

diameter alone.

These results were consistent with those of Jonson and Freudenberger (2011), who also studied

generalised allometrics from mixed-species environmental plantings in south-west of Western

Australia. This work also demonstrated a difference between life-forms (i.e. A. saligna had a

significantly different relationship to the eucalypts), but no statistical differences among the different

species of eucalypt. Others (Keith et al. 2000; Bi et al. 2004) have also found that allometrics for

acacia species and other shrubs differ to those for many eucalypt trees in Australia. This has been

attributed to differences in stem geometry between these life-forms resulting from differences in

their life-span, wood density and typical environmental conditions in the crown region which

determine the proportion of branches (i.e. whether under- or over-storey species) (e.g. Keith et al.

2000).

39

Figure 5.02. Allometrics for above- and below-ground biomass of the key life forms found in mixed-species

environmental plantings, including; (a) trees, namely eucalypts in temperate regions, but also other tree-form

genera from tropical regions, (b) acacia trees, (c) various types of shrubs, or small trees, and (d) below-ground

biomass for a range of life-forms. Refer to Table A9.3.6 for a list of key species represented in each of these

graphs. Here low and high rainfall are defined as sites having <500 or >500 mm MAR, respectively. Note that a

majority of inventory datasets collated from existing plantings were within the <25 cm diameter range.

In addition to species and life-form, climate was also an important factor influencing allometrics.

There were statistically-significant (P<0.001) differences in allometrics between: (i) high rainfall

temperate, low rainfall temperate, and tropical eucalypts in mixed-species environmental plantings,

0

100

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500

600

700

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900

1,000

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bo

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(k

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Stem diameter (DBH, cm)

High rainfall

Low rainfall

Tropical

High rainfall allometric

Low rainfall allometric

Tropical allometric

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Low rainfall

Tropical



Tropical allometric

(b) Acacia trees

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Ab

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Stem diameter (D10, cm)

Acaica shrubs

Casuriana spp.

Melaluca spp.

Other shrubs

Acacia shrubs allometric

Casuriana spp. allometric

Melaluca spp. allometric

Other shrubs allometrics

0

25

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Acaica shrubs

Casuriana spp.

Melaluca spp.

Other shrubs

Acacia shrubs allometric

Casuriana spp. allometric

Melaluca spp. allometric

Other shrubs allometrics

(c) Various types of shrubs

0

50

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150

200

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350

400

450

500

0 10 20 30 40 50 60

Be

low

-gro

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(k

g t

ree

-1)

Stem diameter (DBH or D10, cm)

Eucalypt tree roots (DBH)

Acacia tree roots (DBH)

Shrub roots (D10)

Eucalypt tree roots allometric

Acacia tree root allometric

Shrub roots allometric

0

25

50

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125

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0 5 10 15 20 25

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low

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(k

g t

ree

-1)

Stem diameter (DBH or D10, cm)

Eucalypt tree roots (DBH)

Acacia tree roots (DBH)

Shrub roots (D10)

Eucalypt tree roots allometric

Acacia tree root allometric

Shrub roots allometric

(d) Below-ground

0

2,000

4,000

6,000

8,000

10,000

12,000

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High rainfall

Low rainfall

Tropical



Tropical allometric

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)


High rainfall

Low rainfall

Tropical



Tropical allometric

(a) Eucalypt trees

40

and (ii) high rainfall temperate, low rainfall temperate, and tropical acacia trees in mixed-species

environmental plantings (iii) high and low rainfall E. loxophleba subsp. lissophloia, and (iv) high and

low rainfall E. polybractea (Figures 5.02 and 5.03). Differences between species/life-forms and

interactions with climate were most pronounced in smaller, generally younger trees/shrubs of

mixed-species environmental plantings, where more data were available and variability was less.

However, although this climate factor was always highly significant, improvements in R2 of the

allometric equations when these climatic factors were added were always <1%. As was noted in

Section 4.3, this suggests that climate only explains marginally more of the variability in biomass than

stem diameter alone. Nevertheless, these results are consistent with the findings of others (e.g.

Brown et al. 1989; Sternberg and Shoshany 2001; Drake et al. 2003; Chave et al. 2005; De Walt and

Chave 2006) who showed differences in allometrics under regions of different mean annual rainfall.

As shown in Figure 5.03c, allometrics for mallee eucalypts which are regrowth post coppice are

based on CVI as they have relatively high number of stems and tend to be ‘bushy’ in structure.

Nevertheless, even if equivalent stem diameters could be accurately measured in these coppiced

stands, they are unlikely to have similar allometric relationships to that of uncut trees due to their

altered allocation of biomass (Droppelman and Berlier 2000; Kuyah et al. 2012a). Therefore, life-

form/species, climate and harvesting were the three key factors governing generic allometrics in this

study.

Figure 5.03. Biomass data and allometrics for above- and below-ground biomass of the three key species of

mallee eucalypts (E. loxophleba subsp. lissophloia; E. polybractea; and E. kochii subsp. borealis or subsp.

plenissima) when uncut (a and b) and after coppice harvesting (c and d). There were sufficient data available

for above-ground allometrics for uncut E. loxophleba subsp. lissophloia and E. polybractea to be segregated

into low-rainfall (LR, <400 MAR) and high-rainfall regions (HR, >400 mm MAR).

Several other authors have proposed such generalised allometric equations for large-scale

applicability over a range of tree or shrub species across regional scales (e.g. Pastor et al. 1984

(north-east USA); Zianis and Mencuccini 2003 (northern Greece); Jenkins et al. 2003 (USA); Williams

et al. 2005 (northern Australia); Montagu et al. 2005 (eastern Australia); Muukkonen 2007 (Europe);

Dietze et al. 2008 (south-eastern USA); Xiang et al. 2011 (China); Vieilledent et al. 2012

(Madagascar); Kuyah et al. 2012a (Kenya)). For example, studying woodlands in Australia, Williams et

al. (2005) showed that although site-species differences were significant, the amount of variation

accounted for by these site-species factors was <0.5%, thereby supporting the use of generalised

allometrics which had slightly less accuracy, but much greater certainty. Similar results were found

by Montague et al. (2005) studying eucalypt plantations in eastern Australia. We have also shown

that site-species factors added <0.5% to the model performance (Figure 5.04). On average, model

0

50

100

150

0 20 40 60 80 100

Ab

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kg

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)

Canopy Volume Index (CVI, cm3)

(c) Coppiced mallees, above-ground

0

50

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0 5 10 15 20 25 30 35

Be

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(b) Uncut mallees, below-ground

All species

Allometric

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(d) Coppiced mallees, below-ground

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(a) Uncut mallees, above-groudE. loxophleba LR

E. loxophleba HR

E. polybractea LR

E. polybractea HR

E. kochii

E. loxophleba LR allometric

E. loxophleba HR allometric

E. polybractea LR allometric

E. polybractea HR allometric

E. kochii allometric

41

efficiency was only 0.2% lower when using generic life-form as opposed to site-and-species specific

allometrics. Moreover, when estimating individual tree biomass, standard deviation of the absolute

value of residuals averaged only 9.6 kg tree-1 higher when using generic life-form as opposed to site-

and-species specific allometrics. These results are also in agreement with those presented in Section

4.5, which show that when tested against direct measures of above-ground biomass, generic life-

form allometrics were proven to be only marginally less accurate than site-and-species specific

allometrics.

Figure 5.04. Increased standard deviation of residuals, and decreased allometric model efficiency, when using

generalised opposed to site-and-species specific allometrics. In plots (a) and (b) generic life-form allometrics

tested are those shown in Figures 5.02 (excluding datasets of species-by-site combinations with N<6), including

acacia shrubs (N=449), acacia trees from tropical regions (N=31), acacia trees from high rainfall temperate

regions (N=233), acacia trees from low rainfall temperate regions (N=183), other trees from tropical regions

(N=258), other trees (mostly eucalypts) from high rainfall temperate regions (N=1,120), other trees (mostly

eucalypts) from low rainfall temperate regions (N=905), casuarinas (N=80), melaleucas (N=154) and other

shrubs (N=183). Plots (c) and (d) show differences between generic and site-and-species specific allometrics

for; (i) uncut E. kochii (N=374), (ii) uncut E. loxophleba ssp. lissophloia in low rainfall regions (898), (iii) uncut

E. loxophleba ssp. lissophloia in high rainfall regions (N=220), (iv) uncut E. polybractea in low rainfall regions

(N=504), (v) uncut E. polybractea in high rainfall regions (N=379), (vi) coppiced E. kochii (N=196), (vii) coppiced

E. loxophleba ssp. lissophloia (N=377), and (viii) coppiced E. polybractea (N=325).

Although these results support the use of generalised life-form allometrics, care is required to ensure

they are not applied outside their domain region given that significant variations in factors such as

topography, hydrology and soil nutrient availability may result in systematic biases (Clark and Clark

0.84 0.86 0.88 0.90 0.92 0.94 0.96 0.98 1.00

Model efficiency (EF)

(b)

020406080100120140160

Acacia shrubs

Acacia trees (Tropical)

Acacia trees (Temperate, high rainfall)

Acacia trees (Temperate, low rainfall)

Other trees (Tropical)

Other trees (Temperate, high rainfall)

Other trees (Temperate, low rainfall)

Casuarinas

Melaleucas

Other shrubs

Stdev. in absolute value of residuals (kg tree-1)

(a)

Specific

Generalised

0.84 0.86 0.88 0.90 0.92 0.94 0.96 0.98 1.00

Model efficiency (EF)

(d)

0246810121416

Uncut E. kochii

Uncut E. loxophleba subsp liss. (Low rainfall)

Uncut E. loxophleba subsp liss. (High rainfall)

Uncut E. polybractea (Low rainfall)

Uncut E. polybractea (High rainfall)

Cut E. kochii

Cut E. loxophleba subsp liss.

Cut E. polybractea

Stdev. in absolute value of residuals (kg tree-1)

(c)

Specific

Generalised

42

2000; Clark 2005). For this reason, generalised allometircs which have entailed the use of larger pan-

continental datasets (Cannell 1984; Brown et al. 1989; Brown 1997; Chave et al. 2005; Zapata-

Cuartas et al. 2012) need to be applied with caution. Verification of these pan-continental

generalised allometrics have often failed (e.g. Basuki et al. 2009; Vieilledent et al. 2012). For

example, Madgwick et al. (1991) found that for the eucalypt genera, allometrics developed in one

country may not be accurate for the same life-forms growing in other countries.

As in this study, most previous work on development of allometrics for below-ground biomass has

entailed development of generic rather than site-and-species specific relationships due to the limited

amount of data on root biomass available (Barton and Montagu 2006; Ouimet et al. 2008; and Peichl

and Arain 2007; Xiang et al. 2011). Further work is therefore required to verify these generic below-

ground allometrics, across many of the environmental factors of topography, hydrology and soil

nutrient availability. Additionally, further work is required to utilise Bayesian hierarchical models

given they provide an intermediate between the two extremes (species-and-site specific versus

generic life-form allometrics) that acknowledges that growth forms of trees are generally similar

across species (Dietze et al. 2008).

5.4 Uncertainty in above-ground biomass estimates

The largest source of uncertainty in estimating above-ground biomass is from sampling design

(Figure 5.05). However, this can be markedly decreased by measuring more trees and/or by changing

the sampling design to more representatively capture the variation across the site (Section 3.2).

There is relatively little that can be done about measurement errors, and unfortunately these are

also important, particularly errors resulting from the measurement of heights and canopy width to

estimate CVI in coppiced stands. Errors resulting from the application and fitting of allometric

equations were relatively minor contributors to uncertainty in estimates of above-ground biomass.

Figure 5.05. Tornado graph showing the absolute value of the average correlation between the uncertainty

distribution of the estimated above-ground biomass (t DM ha-1

) and the errors associated with the various

assumptions made in this calculation across the 747 mixed-species environmental plantings datasets, and in

the 744 mallee eucalypts datasets. A correlation of 1.0 indicates that a 10% increase in the value of the error of

a given assumption results in a 10% greater estimation of biomass. Error bars indicate standard deviation of the

means. Estimates of CVI were only made (based on measurement of tree height and canopy width) for very

small uncut mallee eucalypts, or for coppiced mallee eucalypts. In mixed-species environmental plantings,

where the life-form of an individual was unknown (often only when the individual was dead), it was assumed to

be a eucalypt, and an error, called ‘Inappropriate allometric?’ was assigned to this individuals estimate of

biomass.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Sample design

Measurement error in CVI

Measurement error in stem diam.s

Generic allometric errors

Sampling errors in allometrics

Measurement error in plot area

Inappropraite allometric?

Absolute correlation coefficient

Mixed species

Mallee eucalypts

43

For the collated datasets, a range of different measurement-based methodologies were used to

obtain estimates of biomass. For each site, the uncertainty analysis generated standard deviation of

above-ground biomass estimates, and this was used to calculate CV. Figure 5.06 shows that on

average, the CV was highest where site inventories were based on relatively few (N<100) trees, and

where non-site-and non-species based allometrics were generated, thereby resulting in the

application of generic life-form allometrics to a relatively high proportion of individuals within the

planting. The CV was least when precision sampling, combined with direct whole-plot harvesting,

was used given that there were no errors resulting from either sampling design, or the application of

allometrics.

Although applying generic life-form allometrics to obtain estimates of biomass for a greater

proportion of individual trees (or shrubs) generally increased the errors in the estimates (i.e. Figure

5.06, differences between 'some' and 'no' site-based allometrics), these differences were relatively

small when compared to the differences in errors resulting from improved sampling designs (i.e.

Figure 5.06, differences between N<100 and N>200). We conclude that application of allometric

equations was a minor contributor to uncertainty in above-ground biomass estimates. There are,

however, several possible limitations to this analysis which are currently being tested, and which

may impact on this conclusion. Firstly, further work is being undertaken to better statistically

determine rules to guide the assessment of the ‘appropriateness’ of generic life-form and species-

and-site specific allometrics when applied to new sites. Secondly, additional uncertainty from

allometrics may be derived when moisture content corrections were based on a limited number of

replicates.

Figure 5.06. Impacts of different measurement-based methodologies on key errors contributing to coefficients

of variation in estimates of above-ground biomass for mixed-species environmental plantings and mallee

eucalypt plantings. N refers to the number of trees (or shrubs) that were measured in the site inventory. Value

in parenthesis indicates the number of observations (i.e. sites) in the calculation of the mean percentage

coefficient of variation. Error bars indicate standard deviation of the mean.

Coefficients of variation in above-ground biomass derived from uncertainty analysis were used to

rank inventory datasets into different data-quality classes, with lower values for %CV corresponding

to higher-quality data. Most of the inventory datasets had values of CV between about 13 and 23%

(Figure 5.07). There were some exceptional inventory datasets with even lower values for %CV. Only

about 8-10% of plantings had CV values >30%. 'Class 1' data were those collected from this study,

generally using precision sampling (Figure 5.06). In Section 6, we used these more precise Class 1

0 5 10 15 20 25 30 35

N<100; No site-and-species based allometrics (346)

N<100; Some site-and-species based allometrics (777)

N=100-200; No site-and-species based allometrics (190)

N=100-200; Some site-and-species based allometrics (73)

N>200; No site-and-species based allometrics (28)

N>200; Some site-and-species based allometrics (37)

Precision sampled; Full site-based allometrics (8) (Class 1)

Precision sampled; Direct whole-plot harvesting (13) (Class 1)

%Coefficient of variation of above-ground biomass

All errors (Allometrics, sample design & measurement errors)

Sample design & measurement errors

Measurement errors (plot & stem diam.)

44

datasets to check that, when the yield curves used within FullCAM were calibrated, FullCAM

predictions of above-ground biomass for these particular plantings was unbiased.

Because only above-ground biomass is used to calibrate the Tree Yield Formula within FullCAM, the

above uncertainty analysis was based on above-ground biomass only, and did not include below-

ground biomass. But as discussed in Section 4.4, uncertainty in knowing the correct R:S ratio, or

below-ground allometrics, to apply to an individual planting can lead to significant variations in total

stand-scale biomass estimates.

Figure 5.07. Frequency histograms of the distribution of percentage coefficient of variation in estimates of

above-ground biomass across the inventory datasets collated in Table A9.3.3. Data from the current study

(Table 9.2.2) were not included given most were either ‘direct’ sites or had been Precision Sampled, thus

having negligible CV (Figure 5.06).

5.5 Analysis of factors influencing biomass

5.5.1 Boundaries of the analysis

Age

Most plantings were relatively young (Figure 5.08), with mixed-species plantings being about 5-20

years old, most uncut mallee eucalypts being 5-15 years old, and the coppiced harvested stands

generally very young at 1-5 years of age since harvesting. Unfortunately there were very few

plantings older than 25 years and so analyses of factors influencing these older stands, and

calibration of the yield curves for maximum biomass, will need to be done as existing stands mature.

Data from remnant vegetation were not included in the analysis due to uncertainties in their age and

disturbance history, and most importantly, whether they are representative of the biomass carrying

capacity due to possible differences in soil types (and soil improvements or degradation), landscape

positions and planting geometries. Remnant vegetation is often found where clearing was not

economical (e.g. on ridge tops, erodible soils etc.) or on stock routes. They are also generally in

blocks, or if linear, they are often along roadsides where there may be access to additional water and

nutrient runoff.

0

50

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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58

Nu

mb

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of

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tory

stu

die

s

% Coeficient of variation in estimated above-ground biomass

Mixed species

Mallee eucalypts

45

Figure 5.08. Frequency histograms of the age distribution of plantings in the inventory datasets for mixed-

species environmental plantings established in temperate and tropical regions, and 'uncut' or 'coppiced

harvested' mallee eucalypts.

Figure 5.09. Frequency histograms of the site productivity potential (Pavg, or forest productivity index during

the actual period of stand growth) across the datasets within the five types of plantings; (a) mixed-species

environmental plantings from temperate regions (N=573), (b) mixed-species environmental plantings from

0

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Mixed-species temperate

Mixed-species tropical

Uncut mallee eucalypts

Coppiced mallee eucalypts

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(c) 'Other' mallee eucalypts

Ave: 3.50

Stdev: 0.65

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(a) Mixed species plantings; Temperate

Ave: 6.21

Stdev: 2.05

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Site potential (Pavg)

(d) 'Lox liss' linear

mallee eucalypts

Ave: 3.94

Stdev: 0.72

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(b) Mixed species plantings; Tropical

Ave: 16.42

Stdev: 6.40

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(e) 'Poly' mallee eucalypts

Ave: 5.20

Stdev: 0.97

46

tropical regions (N=164), (c) ‘Other’ mallee eucalypt species (N=169), (d) ‘Lox’ mallee eucalypt species (N=363),

and (e) 'Poly' mallee eucalypt species (N=211).

Site productivity

Comparison of productivity among planting types requires an understanding of the typical site

potential (Pavg) within which these plantings are generally established (Figure 5.09). The collated

data suggest a relatively wide distribution of potential site productivities for mixed-species plantings

and a narrower range of site potentials for plantings of mallee eucalypts. For mallee eucalypt

plantings there was also a clear difference between species and Pavg, with ‘Poly’ being established

on sites of highest potential, and ‘Other’ species being established on sites of lowest potential.

5.5.2 Key factors influencing rates of above-ground biomass accumulation

In terms of assessment of key factors influencing growth determined by standard linear regression

fitted by maximising the likelihood function, results differed between mixed-species environmental

plantings established in temperate and tropical regions. In tropical regions, mixed-species

environmental plantings were largely (97%) block plantings with PropTree ≥0.75 and <1,500 sph.

Therefore for these relatively non-diverse plantings, only stand age was found to significantly

influence above-ground biomass. This factor alone explained 95% of the variation in above-ground

biomass (Table A9.4.2; P<0.001; N=164). In contrast, for such plantings established in temperate

regions, 46% of the variation in above-ground biomass was explained largely by three statistically

significant factors; stocking (2 categories; <1,500 sph or >1,500 sph), planting geometry (3 categories;

narrow linear plantings, wide linear plantings and block plantings) and PropTree (2 categories; <0.75

and ≥0.75) (Table A9.4.1; P<0.001; N=583).

Similarly, 95% of the variation in above-ground biomass of mallee eucalypt plantings was explained

by stocking (2 categories; <2,300 sph or >2,300 sph), planting geometry (3 categories; narrow,

moderate, wide) and species (3 categories; Other, Lox and Poly). For mallee eucalypts, additional

explanatory variables included stand age, regrowth after coppice harvest, and saline surface soils

(surface soil >200 mS m-1) (Table A9.4.3; P<0.001; N=744; Note, P-values not reported as a mixed

effects model was used for mallee eucalypts and these are fitted by maximising the Restricted

Maximum Likelihood (REML) function). However, generalisations about the impact of coppice

harvest and salinity on biomass are difficult to make given only younger stands (most <5 years) were

coppice harvested, and only moderately young stands from low productivity sites (<15 years; Pavg 2-

4) were salt affected.

In contrast, datasets were adequate to develop generalisations about the influences of the three key

factors influencing above-ground biomass in both temperate mixed-species environmental plantings,

and mallee eucalypt plantings; (i) planting geometry, (ii) stocking, and (iii) species/species mix. These

factors are discussed below in Section 5.5.4, as they provide the basis for generating categories of

different types of plantings. Categorical, rather than continuous variables, were used given that the

purpose of the analysis was to inform calibrations of the Tree Yield Formula used in FullCAM, which

is designed to be calibrated for different categories of plantings.

In terms of their influence on biomass, the explanatory variables of stand age and Pavg are unique in

that they are already incorporated into the Tree Yield Formula, and so development of categories of

plantings based on these factors was not required. However for mixed-species environmental

plantings growing in temperate regions, there was an interaction between age and Pavg, with the

enhancement of biomass accumulation with increased Pavg being more pronounced in younger than

older stands. The sensitivity of biomass accumulation to Pavg may be relatively pronounced in

younger stands given other factors such as planting geometry and species mix may be less important

during the early ages of growth prior to significant competition (for light, nutrients and water)

47

between individual trees within a given planting. There was no such interaction between the

influence of age and Pavg on above-ground biomass of mallees, with Pavg not being a significant

factor influencing growth of mallee eucalypts. This may be due to the relatively; (i) young nature of

the mallee eucalypts in the dataset (Figure 5.08), and (ii) lower distribution of Pavg observed across

the regions in which the mallee eucalypt datasets were derived (Figure 5.09).

It is also possible that the lack of significance of Pavg on above-ground biomass of many plantings

was due in part to Pavg being a relatively poor index of fine scale site productivity. The Pavg index is

derived from the forest growth model 3-PG (Kesteven et al. 2004), which in turn, is known to have

limited potential at estimating biomass of low rainfall plantations which have access to ground or

surface water, or for nutrient-limited plantings where inputs on soil fertility are inaccurate at the fine

site-level scale required (Almeida et al. 2007; Paul et al. 2007).

There was no evidence in this study that Pavg explained more of the variation in biomass than mean

average rainfall or temperature during the years of growth. Across all of the categories of plantings

studied, the average (and stdev.) correlation between mean annual increment in above-ground

biomass and Pavg was only 0.48 (0.24), 0.25 (NA) and 0.07 (0.27) for temperate mixed-species

environmental plantings, tropical mixed-species environmental plantings and mallee eucalypt

plantings, respectively. Similar correlations were found based on mean annual rainfall alone, being

0.41 (0.24), 0.32 (NA) and 0.07 (0.38) for these plantings types, respectively. Correlations between

mean annual biomass increment and average annual temperature tended to be even weaker. Due to

there being a strong negative correlation between rainfall and temperature (-0.55) in the temperate

sites included in our dataset, there was a slight negative correlation between mean annual increment

and temperature (ranging from -0.16 for mallee plantings to -0.32 for mixed-species environmental

plantings). In contrast for tropical regions, there was a slight positive correlation of 0.10 between

mean annual increment and temperature.

Clearly in different climatic regions, factors influencing growth are likely to be different. Having a

simple universal index of productivity (i.e. Pavg) across all regions provides a challenge. Further work

to calibrate the Pavg at a fine site-level scale is required, and this may entail some improvements to

the simple bucket water-balance model used in the calculation of this index. To achieve this, further

work is also required to improve inputs to the calculation of Pavg, including the quality and

resolution of soil fertility rating inputs, and providing options to add key water-balance inputs such as

access to surface- and ground-water. Such work is vital given there is an overwhelming amount of

evidence that biomass accumulation is primarily governed by key abiotic site factors of rainfall,

temperature and soil nutrient availability, which the Pavg index aims to quantify (e.g. Brown and

Lugo 1982; Knapp and Smith 2001; Chave et al. 2001; Sankaran et al., 2005; Hui and Jackson 2006;

Raich et al. 2006; Keeling and Phillips 2007; Keith et al., 2009; Watt et al. 2010; Stegen et al. 2011;

Hui et al. 2012; Fensham et al. 2012; Bartle et al. 2012b).

Other factors were not significant and so were excluded from subsequent regression analyses. These

factors included whether or not the planting had access to a water table, landscape position (upper,

mid, lower, gully and riparian), method of establishment (direct-seeded, tube stock or broadcast),

previous land use, soil clay content and the soils potential available water content. In some cases, a

limited dataset (e.g. on access to a water table) may be the cause of the statistical non-significance.

Nevertheless, currently there is no evidence to support these additional factors being considered in

the calibration process described in Section 6.

5.5.3 Types of plantings; environmental and mallee plantings

During the first 20 years of growth, relative differences between the three key planting types (mixed-

species temperate, mixed-species tropical, and mallee eucalypt) were reasonably consistent. At an

arbitrary stand age of 10 years, the Multiple Regression model predicted that biomass of mixed-

species environmental plantings averaged 61% lower (range 43 to 79%) when compared to linear

48

mallee eucalypts growing at sites of similar productivity (Figure 5.10). The reasons linear mallee

plantings, particularly Poly, have relatively high biomass for a given potential site productivity may be

that: (i) these plantings are generally established for commercial purposes, and so tend to be more

intensively-managed than mixed-species plantings which tend to be established for

environmental/land restoration only, (ii) there are genetic improvements in many mallee eucalypt

species in terms of survival and increased growth rates, (ii) in some cases mallees have been planted

in areas with high water tables to help combat salinity, and (iii) linear plantings are inherently more

productive, due to the edge growth effect.

These results are consistent with those of Preece et al. (2012), who also found that mixed-species

plantings in northern Queensland accumulate significantly less biomass than stands with a higher

proportion of eucalypt trees. They found that above-ground biomass of the larger (>20 cm) stems in

mixed-species plantings were only about half that of eucalypt plantings by age 16 years. Jacob et al.

(2010) also reported that above-ground biomass and wood production decreased with increasing

tree species diversity. However, other studies have provided mixed results on the impact of species

diversity on forest productivity (e.g. Loreau et al. 2001; Mittelbach et al. 2001; Vilà et al. 2003;

Swenson and Waring 2006; Kirsch et al. 2012). For example, in contrast to our results, in the humid

tropics of Australia, Erskine et al. (2006) found that diverse plantations can achieve greater

productivity than monocultures. It seems likely that whether or not mixed-species have more

biomass at a given age than monocultures is dependent on the systems being compared, and

whether or not; (i) species-rich plantings are able to more efficiently access and utilise limiting

resources because they contain species with a diverse array of ecological attributes, and perhaps one

or two relatively high-yielding species (Kelty 1992; Loreau et al. 2001; Erskine et al. 2006; Kelty 2006),

(ii) monocultures are N-limited whereas mixed-species plantings have improved N availability due to

the presence of N-fixing trees or shrubs (e.g. Binkley et al. 2003; Forrester et al. 2005; Forrester et

al., 2006a,b; Hunt et al. 2006; Nouvellon et al. 2012), and (iii) there are morphological changes at

tree level (i.e. height to basal area ratio of acacias) when species are planted together rather than

separately (Nouvellon et al. 2012).

Predicted differences in biomass between temperate and tropical mixed-species environmental

plantings were less pronounced (generally <25%), and were dependent on site productivity (Figure

5.10). This was because the sensitivity of biomass accumulation to Pavg was greater in temperate

than in tropical regions, perhaps being partly attributable to the fact that in tropical regions (which

are generally not water-limited), variations in Pavg is largely dominated by soil fertility- an input to

the calculation of Pavg which has much less spatial variation than climatic factors such as rainfall.

There was higher accumulation of biomass in temperate regions only under conditions of relatively

high site productivity. These results were consistent with the findings of others (e.g. Keith et al. 2009;

Stegen et al. 2011) that above-ground biomass accumulation in moist/wet tropical forests is lower

than that of productive moist temperate forests, but higher than that of less productive dry

temperate forests.

5.5.4 Categories of plantings; geometry, stocking and species/species mix

Based on results from Section 5.5.2 above, it was clear that within these three key planting types,

further segregation into categories of plantings was possible, with each category having similar rates

of above-ground biomass accumulation. These categories may be defined based on their planting

geometry (narrow linear, wide linear or block), stocking (low or high sph) and species/species mix.

The highest rates of above-ground biomass accumulation were found in densely-stocked narrow

linear plantings which are tree dominant in mixed-species plantings, or which are stocked with Poly,

or to a lesser extent Lox, in mallee eucalypt plantings.

In temperate regions, mixed-species environmental plantings that were tree-dominant, highly

stocked, or planted in narrow linear configurations, averaged 21, 41 and 44% greater biomass

49

accumulation after 10 years compared with shrub-dominant, lower stocked, or block plantings,

respectively (Figure 5.10). When compared to ‘Other’ species, mallee eucalypt species of ‘Lox’ and

‘Poly’ exhibit a 32 and 95% increase in average above-ground biomass accumulation after 10 years of

growth, respectively. As for environmental plantings, mallee eucalypts showed a modest increase in

biomass accumulation (45%) with high versus low stocking. However, the impact of planting

geometry was much more pronounced in mallees, with a >2-fold increase in above-ground biomass

accumulation after 10 years for narrow linear plantings as opposed to block plantings. When

compared to mixed-species environmental plantings, mallee eucalypts may have a higher sensitivity

of productivity to planting geometry because they were generally more heavily stocked, and

therefore more responsive to reduced competition along edge rows. They also tend to be grown in

Mediterranean-type climates where there is severe growth stress in summer.

Our finding that biomass accumulation in southern temperate Australia increases when plantings are

established in linear rather than block configurations was consistent with previous work. Compared

with planting in blocks, farm forestry and environmental plantings in narrow belts of 3-4 rows has

been found to increase stem volume or biomass by 20-39% due to decreased intra-specific

competition for light, water and nutrients (Henskens et al. 2001, 2008; Paul et al. 2009). In a review

of six different plantations in medium- to low-rainfall southern Australia, Carter et al. (2011) found

that trees in the outer rows of linear plantings grew 2- to 5-times faster than those in the inner rows.

At a site near Wickepin in south west WA, Noorduijn et al. (2009) estimated the above-ground

biomass of 12-year-old E. vegrandis belts of different widths from stem diameter measurements,

finding that biomass in 2-row belts was 38 kg m2, compared with 7.5 kg m2 in 5 row belts, and

decreasing to less than 1 kg m2 in a block planting.

Our finding that, for relatively young stands (generally <25 years old), biomass accumulation

increases with increased stocking was also consistent with previous work. Most spacing studies have

concluded that total production of wood increases as stand density increases, despite the fact that

production from individual stems tends to decrease with increasing stocking (Schonau and Coetzee

1989; Malimbwi et al. 1992; Niemistö 1995a; Bernardo et al. 1998; Neilsen and Gerrand 1999;

Pinkard and Neilson 2003; Barton and Montagu 2006; Chaturvedi et al. 2008; Xue et al. 2011; Hui et

al. 2012). However, at very high stand densities, biomass and wood production has been found to

decrease (Smith et al. 1997). Also, it may be that influences of stocking on biomass will only be

evident prior to attaining the site carrying capacity.

To some extent, the higher biomass in plantings with higher PropTree could be attributable to the

same factors which were governing the greater observed biomass in mallee eucalypts compared to

mixed-species environmental plantings (see Section 5.5.3). However, when compared to shrubs,

trees are generally able to access nutrients and water to a greater depth of soil than shrubs, thereby

facilitating higher productivities (e.g. Jackson et al. 2000). Furthermore, many of the shrub and small

tree species in mixed-species plantings are known to have high growth rates, short life spans and

earlier succession status. These characteristics are generally associated with low stem wood densities

(Martinez-Cabrera et al. 2011), and this would further explain why biomass accumulation tends to

decline with decreased PropTree.

50

Figure 5.10. Empirical model (Multiple-Regression) predictions of above-ground biomass after 10 years of

growth in different types of mixed-species environmental plantings at site productivities which were; (a) low

(Pavg 4), (b) medium (Pavg 6), (c) high (Pavg 8), and (d) very high (Pavg 16), and in different types of (e) mallee

eucalypt plantings where the mean site productivity was observed to vary within a narrow range, but average

Pavg 4. For mallee eucalypt plantings, positive error bars indicate the predicted increase in above-ground

biomass when regrowth following coppice harvesting, while the negative error bars indicate the decrease in

above-ground biomass when established on saline surface soils. See Figures A9.3.1 and A9.3.2 for examples of

these types of plantings.

0

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51

Figure 5.11. Empirical model (Multiple-Regression) predictions of accumulation of above-ground biomass by

contrasting types of mixed-species environmental plantings (at low, high and very high site productivities

where Pavg is 4, 8 and 16, respectively) and mallee eucalypt plantings. No data were available to base

predictions of above-ground biomass on for stands >10 years in mallee plantings, and for stands >20 years in

tropical mixed-species environmental plantings.

5.5.5 Key factors influencing root-to-shoot ratios

Multiple regression analyses of allocations to below-ground biomass (estimated from application of

the verified generic life-form allometrics for below-ground biomass) showed that much of the

variation in R:S ratio could be explained by PropTree, age, Pavg, species and planting geometry

(R2=0.69, P<0.001, N=1,480). However, there was an interaction between many of these factors and

PropTree.

For plantings with a relatively low proportion of trees (PropTree <0.75), the only factor significantly

influencing R:S was the actual PropTree, with PropTree being used as a continuous variable

(P<0.001). This explained 36% of the variation in R:S ratios of shrub-dominant plantings (Figure 5.12a,

Table A9.4.4). This finding was consistent with the observation made in the 13 direct harvested

environmental plantings (Section 4.4, Figure 4.03). Hence regardless of stand age, the R:S ratio of

these relatively shrub-dominant plantings increases from about 0.28 to 0.52 when PropTree

increases from 0.1 to 0.7 (Figure 5.12a). The lower the PropTree, the more shrubs (typically acacia

shrubs) are represented in the species mix. Previous work (e.g. Deans et al. 1999) has also shown

that R:S ratios of acacia shrubs are relatively low. Others have also found that R:S ratios vary

between vegetation types (e.g. Jackson et al. 2000; Luo et al., 2005; Kuyah et al. 2012b).

For plantings with a relatively high proportion of trees (PropTree ≥0.75) however, there were three

key factors significantly (P<0.001) influencing R:S ratios; (i) age, (ii) Pavg, and (iii) species (but with

only Poly being significantly different to the other categories of species). These factors explained 72%

of the variation in R:S ratios in tree-dominant plantings (Table A9.4.5). Based on these results we can

conclude that, on average, R:S ratios were highest in young sites with a low site productivity (Pavg)

(Figure 5.12b). Lowest R:S ratios were, on average, in Poly mallee eucalypts, especially when

established in 2-row narrow linear plantings.

The decrease in R:S ratio with increased stand age is well established (e.g. Werner and Murphy 2001;

Ritson and Sochacki 2003; IPCC 2006; Mokany et al. 2006), as is the decrease in R:S ratio with

0

50

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250

0 5 10 15 20 25 30

Pre

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ab

ov

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rou

nd

bio

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t D

M h

a-1

)

Stand age (years)

Mixed-species; Block-sparse-<0.75 tree; Pavg 4

Mixed-species; Block-sparse-<0.75 tree; Pavg 8

Mixed-species; Narrow-dense->0.75 tree; Pavg 4


Mixed-species; Tropical; Block-sparse->0.75 tree; Pavg 8

Mixed-species; Tropical; Block-sparse->0.75 tree; Pavg16

Mallee eucalypt; Other; Block-sparse

Mallee eucalypt; Poly; Narrow-dense

52

increased site productivity (Gower et al. 1992; Laclau 2003; Hiratsuka et al. 2005; Mokany et al.

2006; Barton and Montagu 2006; Pretzsch et al. 2012; Morote et al. 2012). For example in a review,

Mokany et al. (2006) also found R:S ratios of temperate eucalypts varied between 0.11 and 0.81

(average 0.34 across 37 studies), largely due to variations in stand age and mean annual rainfall.

Studying mallee eucalypt plantings in WA, Brooksbank and Goodwin (2011) found that productivity

was the key factor influencing R:S ratios. They found that mean R:S ratio was 0.37 (±0.04) across all

high productivity treatments and 0.47 (±0.08) across all low productivity treatments.

Consistent with a study on 10-year old eucalypt plantations (Barton and Montagu 2006), we found

no significant differences in the relationships between R:S ratio and stocking. However, these results

contrast to those of others who have noted that there is an increase in R:S ratios with decreased

stocking (Eastherm and Rose 1990; Puri et al. 1994; Ritson and Sochacki 2003; Mokany et al. 2006).

This might be attributed to increased sun exposure, and greater wind sway, in the more open stands.

It is possible that stocking effects on R:S ratios are to some extent dependent on the site and

planting geometry. Further work is required to test this assumption.

The estimated R:S ratios of plantings with PropTree<0.75 were reasonably similar to the original

FullCAM defaults for R:S ratios in environmental plantings (Figure 5.13). However our results suggest

that during the first 15 years of growth, for low productivity and tree-dominant plantings, much

more biomass is below-ground than assumed based on current FullCAM defaults for environmental

plantings, which start at 0.50 and decrease to 0.30 after 15 years. Our predictions of R:S ratios for

stands older than 15 years is highly uncertain given the boundaries of our analysis described above

(Figure 5.08).

We emphasise that these findings must be regarded as preliminary, given the limited data on which

the R:S analyses are based. For example, although there was no relationship between R:S ratio of the

more shrub-dominant plantings and stand age, shrub-dominant stands averaged 12 years old,

whereas tree-dominant plantings averaged only 8 years old. This may partly explain the lower R:S

ratios in shrub-dominant than tree-dominant plantings given R:S ratios tend to decrease with age.

Further work should therefore be undertaken to fill gaps in estimates of R:S ratios, particularly in

older stands with differing PropTree, productivities, stand density and planting geometries.

Figure 5.12. Empirical model (Multiple-Regression) predictions of R:S ratios in; (a) mixed-species plantings

which have relatively low proportions of trees (PropTree <0.75), and represented here with PropTree values of

0.7, 0.5, 0.3 and 0.1, (b) tree-dominant plantings of narrow 2-row linear plantings of Poly, or other mallee

eucalypt and mixed-species tree-dominant plantings, at varying site productivities, represented here as; low

(Pavg 4), medium (Pavg 6 or 8), and very high (Pavg 16).

Pavg 4

Pavg 4

Pavg 6

Pavg 6

Pavg 8

Pavg 16

0

0.1

0.2

0.3

0.4

0.5

0.6

All others Poly narrow

R:S

ra

tio

Tree-dominant plantings

(b) Pavg; @ age 10 years

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7 0.5 0.3 0.1

R:S

ra

tio

Shrub-dominant plantings

(a) PropTree

53

Figure 5.13. Empirical model (Multiple-Regression) predictions of R:S ratios by contrasting types of plantings;

Tree-dominant plantings (at low, moderate and very high site productivities where Pavg is 4, 6 and 16,

respectively), and shrub-dominant plantings (at low and high PropTree). Predictions for Poly mallee eucalypts

are only shown up to 10 years of age given the boundaries of the dataset collated (Figure 5.07).

5.6 Conclusions

1. A large database describing the characteristics and growth rates of a wide range of mixed-

species environmental and mallee eucalypt plantings was developed. Where required,

allometric equations were applied to inventory data to estimate rates of biomass production.

2. The components of uncertainties associated with field estimation of biomass have been

identified. Sampling design, measurement errors, and R:S ratios are dominant factors, while

uncertainties associated with the application of allometrics are of lesser importance.

3. At low site productivities, for a given age, mallee eucalypts generally had higher

accumulation of biomass than mixed-species environmental plantings, particularly Lox or

Poly species, and particularly when established in wide or narrow linear geometries.

Differences in biomass accumulation between temperate and tropical mixed-species

environmental plantings were less pronounced, and were dependent on site productivity.

4. Although biomass accumulation was weakly correlated to climatic factors such as rainfall and

temperature, the index of site productivity (Pavg) did not consistently explain any further

variations in above-ground biomass than these factors alone. Further work is required to

improve the Pavg index for quantifying site productivity at the fine scales required for site-

based predictions of biomass.

5. Distinct categories within each type of plantings were identified, each having significantly

different rates of biomass accumulation, with the higher rates found in planting categories of

linear planting geometries, dense stand densities, and high PropTree.

6. A preliminary analysis of the factors affecting R:S ratios for different categories of plantings,

and how they vary with age, has been undertaken. Plantings with a relatively low proportion

of trees have a R:S ratio which decreases with decreasing PropTree, while tree-dominant

plantings have a R:S ratio which is age-dependent and differs with planting geometry and

species. Higher R:S ratios were observed in younger plantings which were tree dominant and

had relatively low site productivities, particularly for plantings which were not narrow linear

belts of Poly.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 5 10 15 20 25 30

R:S

ra

tio

Age of planting (yrs)

All other; Pavg 4

All other; Pavg 6

All other; Pavg 16

Poly narrow linear; Pavg 4

Poly narrow linear; Pavg 6

PropTree 0.1

PropTree 0.5

Un-calibrated FullCAM model

Tree dom.

Shrub dom.

54

6 Calibration of FullCAM

6.1 Introduction

The aspect of FullCAM which we aimed to calibrate was the Tree Yield Formula, which predicts

changes in above-ground biomass over time. Details of the Tree Yield Formula used in FullCAM are

described in detail by Kesteven et al. (2004), and are only briefly discussed here. This formula (shown

below) is used to predict growth increments, and has Type 1 (T1) and Type 2 (T2) modifiers of growth

(Snowdon 2001). The T1 modifiers adjust the rate of growth, while the T2 modifiers adjust the

maximum above-ground biomass (M) that can be accumulated.

Δ = r x M x [ y x exp(–k / a2) - y x exp(–k / a1) ] x (P / Pavg) (Equation 1)

where,

Δ = Current annual increment in above-ground biomass (t DM ha-1

yr-1

)

r = non-endemic species-multiplier of the maximum aboveground biomass.

M = maximum aboveground biomass in ‘undisturbed’ native forests (t DM ha-1

)

y = value of the T2 multiplier

a1, a2 = adjusted age of the stand in year 1 and 2, respectively (years)

= actual age (A) + sum over each T1 treatment of

0, if A <= W

v x (A – W) / U, if A >= W and A <= W + U

v, if A > W + U

and where,

v = age advance due to the treatment, either positive or negative (years)

U = advancement period (years)

W = age (of same type as A) at which the treatment was applied (years)

k = 2 x G – 1.25, where G = tree age of maximum growth rate (years)

P = actual FPI over the period a1 to a2

Pavg = average annual forest productivity index over the life of the stand

In the existing species calibrations, the default value for G is 10 years, with v and U both set equal to

1 at planting in a T1 treatment. There were no default T2 modifiers of growth. The observation that,

in some locations, the current species calibrations available for environmental plantings or native

forests can under-estimate the above-ground biomass (Montagu et al. 2003; Wood et al. 2008;

Lowson 2008; Paul et al. 2010; Keith et al. 2010; Fensham et al. 2012; Preece et al. 2012) is

supported by the datasets collated here (Figures 4.04, 4.05 and 6.01). Figure 6.01 shows that mallee

eucalypts in particular are not well represented by the current species calibrations available, with

significant and consistent under-predictions of above-ground biomass. The ultimate objective of this

project was to utilise the empirical observations collated and the analysis of key factors influencing

growth (Section 5.5.2 and 5.5.3) to calibrate the yield curves for use in FullCAM.

Although the main objective was to calibrate the parameter ‘G’ (or effectively ‘k’) within the Tree

Yield Formula to achieve the best possible calibrations, the T2 modifier (i.e. an adjustment of M, or

predictions of maximum biomass accumulated) was also calibrated, taking account of the statistical

assessment of key factors influencing growth. Hence, the parameters G and y were calibrated (shown

in ‘red’ in Equation 1). Indeed, previous work on calibration of the Tree Yield Formula to industrial

plantation species entailed modification of both adjustments- calibration of G, and also maximum

biomass accumulated (Waterworth et al. 2007). Similarly, in a study which produced a large database

(6,153 records) of forest productivity and biomass in China, Hui et al. (2012) also found that the

scaling exponent and slope of the relationship between tree productivity and biomass varied with

biotic (i.e., tree age, size, and density) and abiotic (i.e. longitude and elevation) factors.

55

It was beyond the scope of this project to calibrate the allocation of biomass to each tree

component, including below-ground biomass components. In FullCAM, biomass allocation is

predicted based on default tables estimating the allocation of biomass to component (stem, bark,

foliage, coarse and fine roots) relative to that of the stem, or above-ground components. Currently,

in the NIS database which provides FullCAM inputs, for environmental plantings R:S ratios commence

at 0.55 at planting, and decrease exponentially to 0.40 at age 8 years, before stabilising at 0.30 after

about 50 years. Based on the data analyses in Sections 5.5.4, our results suggest that improvements

could be made to these current defaults.

Figure 6.01. Relationship between ‘observed’ (from direct or indirect field measures) estimates of above-

ground biomass for each of the five types of plantings and those ‘predicted’ when using the un-calibrated yield

curves applied by FullCAM.

6.2 Methodology

Using Equation 1 above, Δ was calculated for each year of growth for each of the inventory datasets

shown in Tables A9.3.3. These growth increments were summed to obtain a predicted above-ground

biomass at the stand age at which field estimates were made. The inputs of M, P and Pavg were

provided from the Department of Environments National Inventory System for each dataset.

The following steps were taken for calibration;

1. Given it was clear from Figure 6.01 that FullCAM performance differed greatly between

mixed-species environmental plantings and mallee eucalypt plantings, we undertook the

calibrations of these separately. However, the steps to the calibration process were the same

for both. For each, we first set G=10 and r=1 and turned off all modifiers by setting y=1 and

v=0 as per the original current species calibrations used in FullCAM for environmental

plantings.

2. After preliminary exploration of a range of y values which gave reasonable predictions of

above-ground biomass, we then commenced the calibration process by setting y values for

0

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Observed aboveground biomass (t DM ha-1)

Mixed-Narrow-sparse-<0.75 trees

Mixed-Narrow-sparse->0.75 trees

Mixed-Narrow-dense-<0.75 trees

Mixed-Narrow-dense->0.75 trees

Mixed-Wide-sparse-<0.75 trees

Mixed-Wide-sparse->0.75 trees

Mixed-Wide-dense-<0.75 trees

Mixed-Wide-dense->0.75 trees

Mixed-Block-very sparse-<0.75 trees

Mixed-Block-Very sparse->0.75 trees

Mixed-Block-Sparse-<0.75 trees

Mixed-Block-Sparse->0.75 trees

Mixed-Block-Dense

Mixed-Tropical

Other-Narrow-sparse

Other-Narrow-dense

Other-wide

Other-Block

Lox-Narrow-sparse

Lox-Narrow-dense

Lox-Wide

Lox-Block

Poly-Narrow-sparse

Poly-Narrow-dense

Poly-Wide

Poly-Block

1:1 line

56

each of the 18 categories of mixed-species plantings (3 planting geometries categories × 3

stocking categories × 2 species-mix categories, plus a tropical planting category), and 18

categories of mallee eucalypts (3 planting geometries categories × 2 stocking categories × 3

species categories), which were; (i) consistent with the relative order of estimated

productivity of these categories as per the empirical multiple-regression model shown in

Figures 5.09 and 5.10, and (ii) were as low as possible so as to ensure that predictions of

biomass in older plantings were constrained to <300 t DM ha-1 when using the average Pavg

for each of the five types of plantings shown in Figure 5.08. This was required in the absence

of any biomass data for older plantings (>30 years) to constrain an upper limit to biomass

accumulation.

3. The performance of the model was tested by calculation of model efficiency (Soares et al.

1995). Observed versus predicted above-ground biomass was plotted, with the 1:1 line used

to indicate the distribution of residuals, and display any bias. Fourth-root transformed data

were also plotted as this allowed for an improved assessment of residuals for plantings with

relatively low biomass. Although all datasets were included in the analysis, Class 1 datasets

(described in Section 5.4) were highlighted as these were considered most reliable.

4. A computer program was also written to constrain calibrations according to user-defined

weighting given to; (i) minimise the overall sum squared of residuals (using the fourth-root

transformation), (ii) constraining the relative predictions of average productivity across each

planting type such that they are the same as that predicted by the multiple regression, and

(iii) minimise the overall model bias, or differences in ‘observed’ and predicted mass balance.

Various iterations of these weightings were used, although a weighting of 90% and 10% to (i)

and (ii), respectively, was found to give the best model performance overall. Model fitting

was by genetic algorithm, minimising the weighted sum of the two fit statistics (i) and (ii).

5. For mixed-species environmental plantings in tropical regions, there were 3, 16 and 119 sites

which were measured at 3, 4 and 5 different points in time, respectively. Within the mallee

eucalypt database, there were 10, 34, 24, 476 and 71 sites which were measured at 2, 3, 4, 5

or 6 different points in time, respectively. Given that these repeated-measures were not

independent, we decreased their weightings by multiplying the sum square (and mean

square) of residuals of the repeated measures such that all the multiple estimates from a

given site added up to a total weighting of 1 (e.g. if an estimate from a site was one of six

estimates, it would be given a weighting of 1/6). Therefore every site within the database

was given an equal weighting in the calibration process. An alternative ‘data-thinning’

method for dealing with the temporal non-independence of measurements was also tested,

whereby only one observation per time series was retained (selected at random). Both

procedures produced similar results.

6. Despite Class 1 data having a higher degree of precision (i.e. relatively low %CV of estimates

of biomass, see Section 5.4), we did not give a positive weighting to these in the calibrations.

This was because these plantings may not necessarily be representative of the true ‘average’

within their respective category of planting.

7. Using the algorithm described above, we calibrated G for each planting type. Given the

changed G value, we refined y further for each planting type. These iterative steps were

repeated until the highest possible model performance was attained. However, during this

process two key checks were made; (i) y values were constrained to ensure maximum

biomass under average Pavg was <300 t DM ha-1, and (ii) there was no significant correlation

between residuals and Pavg.

8. Where calibrated values of y and G were similar between two different planting categories,

data from each of these planting categories was pooled into a new larger combined dataset,

thereby effectively decreasing the total number of planting types. Then, the calibration

process described above was repeated using a smaller number of planting types. This

57

‘bulking-up’ of planting types was only maintained if the overall model efficiency did not

significantly decline as a result. Conversely for block environmental planting, including one

additional category for stand density was found to improve the model efficiency; including a

very sparse category of <500 stems per hectare. Project collaborators were also very

supportive of including this additional category given it provided greater utility of resulting

calibrations, particularly in regions of very low rainfall where environmental plantings tend to

have very sparse stand densities when established in blocks.

9. Steps 1−7 were repeated to explore the impact of using the additional T1 or T2 modifiers for

coppice harvesting, saline surface soils and access to a water table. These additional

modifiers were only applied to plantings within the calibration dataset where auxiliary data

provided from collaborators resulted in either positive categorisation of coppice harvesting

(N=355), surface soils being >200 mS m-1 (N=40), or where the depth to the water table was

either <5 m and where Pavg was <3.25 (N=142), or where the planting was confirmed as

having a riparian landscape position (N=13). Commentary on the impact of utilising these

additional modifiers is provided in Appendix 9.5. An assessment was then made of

improvements in overall model performance with inclusion of these modifiers.

Although data from Section 5.5.4 was assessed for use in calibration of the defaults for R:S ratios

used in FullCAM, revised default R:S ratios were not recommended as; (a) testing of below-ground

biomass allometrics was limited, (b) there was a high degree of variability and uncertainty in the

relationships between R:S ratio and stand age in the datasets collated, (c) our results (Section 5.5.5)

confirm recent evidence (e.g. Mokany et al. 2006) suggesting that variation in R:S ratios can be better

explained by stand productivity or above-ground biomass as opposed to stand age, thereby

indicating that generating R:S defaults based on age in FullCAM’s default allocation tables would not

be a preferred approach, and finally (d) the calibration of allocation tables in FullCAM was beyond

the original scope of this project.

6.3 Calibration of the Tree Yield Formula

Apart from calibration of G and y for each planting type, we found that no other additional T1 or T2

modifiers were required to improve model performance. Justification for not incorporating three

possible additional modifiers; coppicing, salinity and access to water table, is provided in Appendix

9.4.

6.3.1 Model performance across different categories of plantings

The new recommended default parameters for the different types of plantings are provided in Table

6.01. We found that there were effectively 14 and 12 different types of mixed-species and mallee

eucalypt plantings, respectively. Using these categories and parameters, the overall efficiency of

estimation of above-ground biomass of mixed-species and mallee eucalypt plantings was 45.7% and

63.0%, respectively. This efficiency was consistent with the R2 of the empirical multiple regression

models, which suggests that our categorisation and parameterisation to the Tree Yield Formula

accounts for the impact of specie/species-mix, planting geometry and stocking as effectively as the

derived empirical model. Performance of these models were considered reasonable given the

uncertainties in site-based estimates of biomass (see Section 5.4), particularly with respect to the

uncertainties associated with sampling design used in many of these sites. Because of these

uncertainties, accuracy of prediction of site biomass is likely to be modest at best regardless of the

model used.

Figure 6.02 shows that, like the dataset as a whole, predictions of above-ground biomass of Class 1

datasets were unbiased, although still had a great deal of variation in the residuals between

observed and predicted biomass. This is to be expected given that, although these classes of datasets

58

are more precise, they may not necessarily be more accurate. Nevertheless, the ability of the model

to predict the above-ground biomass of these Class 1 plantings provides further confidence in the

calibrations.

Figure 6.02. Relationship between 'predicted' (from the calibrated Tree Yield Formula) and ‘observed’ (from

direct or indirect field measures) estimates of above-ground biomass of mixed-species environmental plantings

(plots a,b,c) and mallee eucalypts (plots d,e,f). The same sites are represented in each of the three sets of plots

above where; (a,d) ‘predicted’ values were obtained using the original species defaults for environmental

plantings, (b,e) ‘predicted’ values were obtained using the new calibrations, and (c,f) ‘predicted’ values were

obtained using the new calibrations, but with observed and predicted data being expressed on a fourth-root

scale.

Model performance varied across the types of plantings (Figures 6.03-6.06), although for all types of

plantings, residuals were distributed evenly around the 1:1 line. The efficiencies are relatively low for

the mixed-species. This may be partly because, unlike the linear mallee eucalypt plantings, these

plantings were established on sites covering a greater range in Pavg (Figure 5.08).

Consistent with previous work (Waterworth et al. 2007), we noted that calibrations for G differed

between species, decreased with increased stocking, and increased with increased site productivity.

Furthermore, the associated calibration of y to modify predictions of maximum biomass (M) was

similarly required for each industrial plantation species within each National Plantation Inventory

region of Australia (Waterworth et al. 2007). But given that they tend to be harvested monocultures

that are non-indigenous, r rather than y was the most appropriate modifier of M, with r being related

to growth increments, rotation length, stem wood density and a biomass expansion factor (Equation

1). Consistent with this previous work on industrial plantations, we found that multipliers of M were

required for monoculture plantation species, particularly Poly and Lox, and that there were some

regional differences (i.e. temperate versus tropical mixed-species environmental plantings).

However, this is the first study to calibrate both G and y based on planting geometry and the

0

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(t D

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)

Observed above-ground biomass

(t DM ha-1)

All datasets

Class 02

1:1 line

(a) Mixed-species; Uncalibrated

0 50 100 150 200 250 300


(t DM ha-1)

All datasets

Class 0

1:1 line

(b) Mixed-species; Calibrated,

untransformed

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0 1 2 3 4

(Pre

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.25

(t D

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a-1

)

(Observed above-ground biomass)0.25

(t DM ha-1)

All

datasets

Class 0

(c) Mixed-species; Calibrated,

transformed

0

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100

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200

250

0 50 100 150 200 250

Pre

dic

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ab

ove

-gro

un

d b

iom

ass

(t D

M h

a-1

)


(t DM ha-1)

All datasets

Class 1

1:1 line

(d) Mallee eucalypts; Uncalibrated

0 50 100 150 200 250


(t DM ha-1)

All datasets

Class 0

1:1 line

(e) Mallee eucalypts; Calibrated,

untransformed

0

1

2

3

4

0 1 2 3 4

(Pre

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.25

(t D

M h

a-1

)

(Observed above-ground biomass)0.25

(t DM ha-1)

All datasets

Class 1

1:1 line

(f) Mallee eucalypts; Calibrated,

transformed

59

proportion of trees. Our results suggest that the existing calibrations of the Tree Yield Formula for

plantation species may be refined to also include modifiers based on planting geometry, particularly

if integrated farm forestry (namely shelter belts and wind breaks) becomes more prominent with the

emerging carbon market and due to requirements to adaptation to climate change within

agricultural enterprises.


direct or indirect field measures) estimates of above-ground biomass of the different categories of mixed-

species environmental plantings established in narrow linear geometry. Data are expressed on a fourth-root

scale. Size of the symbols represents the weighting placed on the estimate, with smaller symbol sizes reflecting

a repeated measure at a given site. Each site in the database was thereby given an equal weighting.

0

1

2

3

4

0 1 2 3 4

Narrow-sparse->0.75 tree

0

1

2

3

4

0 1 2 3 4

Narrow-sparse-<0.75 tree

(Pre

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ab

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bio

ma

ss)0

.25

(t D

M h

a-1

)

0

1

2

3

4

0 1 2 3 4

Narrow-dense->0.75 tree

0

1

2

3

4

0 1 2 3 4

Narrow-dense-<0.75 tree

(Observed above-ground biomass)0.25 (t DM ha-1)

60



species environmental plantings established in wide linear geometry. Data are expressed on a fourth-root

scale. Size of the symbols represents the weighting placed on the estimate, with smaller symbol sizes reflecting

a repeated measure at a given site. Each site in the database was thereby given an equal weighting.


0

1

2

3

4

0 1 2 3 4

Wide-sparse->0.75 tree

0

1

2

3

4

0 1 2 3 4

Wide-sparse-<0.75 tree

0

1

2

3

4

0 1 2 3 4

Wide-dense->0.75 tree

0

1

2

3

4

0 1 2 3 4

Wide-dense-<0.75 tree

(Pre

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.25

(t D

M h

a-1

)

61



species environmental plantings established in block geometry. Data are expressed on a fourth-root scale. Size

of the symbols represents the weighting placed on the estimate, with smaller symbol sizes reflecting a

repeated measure at a given site. Each site in the database was thereby given an equal weighting.

0

1

2

3

4

0 1 2 3 4

Block-Very sparse-<0.75 tree

0

1

2

3

4

0 1 2 3 4

Block-Very sparse->0.75 tree

0

1

2

3

4

0 1 2 3 4

Block-Sparse-<0.75 tree

0

1

2

3

4

0 1 2 3 4

Block-Sparse->0.75 tree

0

1

2

3

4

0 1 2 3 4

Tropical; Block-low-tree dom.

0

1

2

3

4

0 1 2 3 4

Block-Dense


(Pre

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.25

(t D

M h

a-1

)

62


direct or indirect field measures) estimates of above-ground biomass of the different categories of mallee

eucalypt plantings. Data are expressed on a fourth-root scale. Size of the symbols represents the weighting

placed on the estimate, with smaller symbol sizes reflecting a repeated measure at a given site. Each site in the

database was thereby given an equal weighting.

0

1

2

3

4

0 1 2 3 4

Poly; Narrow-low

0

1

2

3

4

0 1 2 3 4

Lox; Narrow-low

0

1

2

3

4

0 1 2 3 4

Other; Narrow-low

0

1

2

3

4

0 1 2 3 4

Poly; Narrow-high

0

1

2

3

4

0 1 2 3 4

Lox; Narrow-high

0

1

2

3

4

0 1 2 3 4

Other; Narrow-high

0

1

2

3

4

0 1 2 3 4

Poly;Wide (low & high)

0

1

2

3

4

0 1 2 3 4

Lox; Wide (low & high)

0

1

2

3

4

0 1 2 3 4

Other; Wide (low & high)

0

1

2

3

4

0 1 2 3 4

Poly; Block (low & high)

0

1

2

3

4

0 1 2 3 4

Lox; Block (low & high)

0

1

2

3

4

0 1 2 3 4

Other; Block (low & high)

(Pre

dic

ted

ab

ov

e-g

rou

nd

bio

ma

ss)0

.25

(t D

M h

a-1

)


63

Table 6.01. New recommended default parameters (G and y) for various categories of plantings. N, indicates the number of sites, while N’ indicates the number of sites when

repeated measures at the same site are not included. The maximum (Age Max) and 95th

percentile of stand ages (years) is also provided for each categories dataset.

Type of planting Planting geometry#

Stand density (sph)

(or trees ha-1

) PropTree G y N N’ Age Max

Age 95th

Percentile

Ave. 95th

Percentile

Mixed-species; temperate Narrow linear <1,500 <0.75 5.5040 1.4000 55 55 29 20

≥0.75 3.6270 1.5000 27 27 30 24

>1,500 <0.75 3.3800 1.4000 53 53 18 17

≥0.75 2.6670 1.5000 14 14 24 24

Wide linear <1,500 <0.75 6.0630 1.2000 33 33 22 17

≥0.75 3.8930 1.3000 33 33 29 29

>1,500 <0.75 4.6330 1.2000 18 18 17 16

≥0.75 2.7460 1.3000 8 8* 19 19

Block <500 <0.75 8.5339 1.2000 49 49 33 28

≥0.75 7.3646 1.3000 85 85 37 33

500-1,500 <0.75 5.4595 1.2000 77 77 46 25

≥0.75 4.8280 1.3000 49 49 22 21

>1,500 ~ 5.1870 1.3000 75 75 33 20 24

Mixed-species; tropical Block ~ ~ 8.4892 0.9000 164 29 20 19 19

‘Other’ mallee eucalypts Block ~ 1.00 4.8495 1.2000 25 25 50 27

Wide linear ~ 1.00 5.4140 1.9000 19 19 15 15

Narrow linear <2,300 1.00 4.1340 2.4000 73 17 29 13

>2,300 1.00 2.2115 2.4000 52 11 16 14

‘Lox’ mallee eucalypts Block ~ 1.00 6.1450 1.0000 19 18 37 17

Wide linear ~ 1.00 2.9735 1.4000 188 56 15 10

Narrow linear <2,300 1.00 3.3095 2.5000 85 26 15 12

>2,300 1.00 3.0055 2.5000 72 16 13 10

‘Poly’ mallee eucalypts Block ~ 1.00 5.9650 0.9000 13 13 15 15

Wide linear ~ 1.00 2.9725 1.2000 157 45 15 10

Narrow linear <2,300 1.00 3.8200 3.6000 18 5* 15 11

>2,300 1.00 2.6115 3.6000 23 6* 14 14 14

~ indicates where categorisation based on stocking was not required; #Planting geometry was defined as narrow and wide linear plantings in mixed species if the width of the planting was <20 m and 20-40 m, respectively. For commercial linear plantings of mallee eucalypts, they were

defined as narrow and wide if they had 100% (or 2-rows) and 15-70% (generally 3-4 rows) of edge trees, respectively.

*Number of independent sites (N’) is <10

64

6.3.2 Comparison between plantings in predicted above-ground biomass

Figure 6.07 provides example outputs from the calibrated Tree Yield Formula for assumed Pavg

values of 4, 6, 8 or 16, with the illustrative planting types being simulated at one or two Pavg values

within the range commonly observed. As per Figure 5.11, only the extreme upper and lower

productivity planting types are shown to illustrate the possible range in predictions across planting

types in mixed-species and mallee plantings. These FullCAM simulations show the predictions of

above-ground biomass using the pre-existing default values (see the solid black line). With the

exception of mixed-species in tropical regions, for all cases the original model predictions are

substantially lower than those based on the improved calibrations, illustrating an under-prediction of

above-ground biomass by the ‘un-calibrated’ model as noted in Section 6.1.

Within mixed-species environmental plantings, Figure 6.07 clearly demonstrates that highly-stocked,

narrow linear plantings offer the highest rates of growth, and therefore sequestration of carbon, of

all categories of planting. Unfortunately for biodiversity co-benefits, the poorest rates of

sequestration were found to be in larger block plantings, particularly where the proportion of trees

was relatively low. These findings are invaluable for the implementation of the Biodiversity Fund in

that they could be used to estimate the extra funding that would be required to make establishment

of blocks of mixed species with high shrub contents competitive, in economic terms, with equivalent

plantings in linear geometries or with higher proportions of trees.

Similarly, for the relatively young mallee eucalypts studies, highly-stocked narrow linear plantings

also offer the highest rates of growth, and therefore sequestration of carbon. These results suggest

that significant rates of sequestration of carbon are possible through integrated farm forestry.

Widespread block plantings of mallee eucalypts clearly have much lower potentials for mitigation on

a per hectare basis of planted area.

Figure 6.07. Example outputs from the Tree Yield Formula calibrated for different categories of plantings. No

data were available to base predictions of above-ground biomass on for stands >10 years in mallee plantings,

and for stands >20 years in tropical mixed-species environmental plantings.

0

50

100

150

200

250

0 5 10 15 20 25 30

Pre

dic

ted

ab

ov

e-g

rou

nd

bio

ma

ss (

t D

M h

a-1

)

Stand age (years)

Mixed-species; Block-Very sparse-<0.75 tree; Pavg 4

Mixed-species; Block-dense->0.75 tree; Pavg 8



Mixed-species; Tropical; Pavg 8

Mixed-species; Tropical; Pavg16

Mallee eucalypt; Other-Block; Pavg 4

Mallee eucalypt; Poly-Narrow-dense; Pavg 4

Mallee eucalypt; Poly-Narrow-dense; Pavg 6

Original, uncalibrated species defaults; Pavg 4



65

6.4 Implementation considerations

The new calibrations for the 26 planting categories (Table 6.01) provide greatly improved predictions

of biomass accumulation. However the datasets upon which the calibrations were developed

represent only a subset of the potential national total growing area. This raises a number of issues

regarding implementation for national greenhouse gas reporting requirements, and also for local-

scale biomass assessments under the CFI when using FullCAM.

6.4.1 Number of replicates within each category of planting

Some categories of plantings had relatively low numbers of replicates within them, particularly when

we account for the fact that at some sites, biomass estimates were not truly independent as they

were from a repeated measure in subsequent years at the same site (see N and N’, Table 6.01). The

two categories of narrow linear Poly had N’ of only 5 or 6. However, despite the relatively low N’ in

these two categories, calibrations are still valid. This is because of the way calibrations were

undertaken. Confidence in calibrations for categories of plantings with relatively low N’ values (i.e.

<10) is much higher than can be judged based on its number of replicates alone. Calibrations were

not based on best-fit to data on a category-by-category basis, but by ensuring best-fit to all biomass

data overall, while at the same time maintaining constrains on parameters such that the relative

predictions of average productivity across each planting category was the same as that predicted by

the multiple regression (Step 4, Section 6.2). In other words, parameterisation of categories with

higher N’ provided guidance in the parameterisation of categories with lower N’.

Regardless of whether or not the calibration for the two narrow linear Poly categories of plantings

with low N’ are still valid, there remains questions over the implementation of these two planting

categories. This is because with such low N’, we have relatively little confidence that the calibrations

obtained are also appropriate for regions outside the very narrow range of site qualities from which

the 5 or 6 measurement sites were located. We therefore do not recommend that these three

categories of plantings be implemented until they can be further verified. For these two categories,

calibrations for a similar category with a slower growth rate could be applied. Hence it is

recommended that for Poly in narrow linear plantings, calibrations for Poly in wide linear plantings

be applied.

Another area of application which is of concern due to insufficient replications is that of the ‘Other’

mallee eucalypts planting category. This category of planting should only be applied to E. kochii sub-

species which are well represented (82% of the dataset) in this category. Due to insufficient

replication, the ‘Other’ mallee eucalypt calibrations should not be applied for plantings of mallee

species such as E. horistes, E. calycogona, E. cneorifolia [Kangaroo Island CS20275], E. cyanophylla

[Loxton cult.], E. dumosa, E. gracilis [Loxton cult.], E. incrassata, E. leptophylla, E. oleosa, E.

plenissima, E. porosa, and E. socialis. None of the mallee eucalypt calibrations are applicable for

plantings which include mixtures of one or more of the Poly, Lox or Other categories.

6.4.2 Issues of defining and justifying the implication of planting categories

It is reasonable to assume that basic information is available about the planting such planting

geometry and the species planted in mallee eucalypt planting, or for mixed-species plantings,

whether the planting is temperate or tropical. In contrast, stand density and the proportion of trees

may require at least some field-based assessment and monitoring, which unfortunately may add to

costs of implementation of these calibrations. As indicated in Table 6.02, many groups of planting

type-geometry combinations of ‘Domain Groups’ have more than one calibration available that are

dependent on stand density and, for plantings with a mix of tree and shrubs, tree proportion.

Therefore, it is recommended that, in the absence of any field-based assessment or monitoring, the

66

known planting type and geometry results in the implementation of the most conservative (i.e.

lowest available stand density and proportion of trees) calibration available for a Domain Grouping.

To use a FullCAM calibration that estimates a higher biomass accumulation than that of the default

Domain Grouping, stand density and tree proportion needs to be measured to be able to justify the

implementation of the higher yielding FullCAM calibration.

Table 6.02. Domain groupings, the calibrations available under each of these groups, and whether some

measurement of stand density and portion of trees (recommended to measure at a stand age of between 2

and 5 years) is required to justify the application of these calibrations.

Planting type Domain Grouping Calibrations

available

Sampling may

allow for higher

C estimates?

Mixed-species Environmental Plantings – Temperate

Mixed temp - Narrow linear 4 Yes

Mixed temp - Wide linear 4 Yes

Mixed temp - Block 5 Yes

Mixed-species Environmental Plantings – Tropical Mixed trop - Block 1 No

Mallee Planting – ‘Other’ Mallee

Koch - Block 1 No

Koch - Wide linear 1 No

Koch - Narrow linear 2 Yes

Mallee Planting – E. loxophleba ssp. lissophloia

Lox - Block 1 No

Lox - Wide linear 1 No

Lox - Narrow linear 2 Yes

Mallee Planting – E. polybractea Poly - Block 1 No

Poly - Wide linear 1 No

There are a number of other issues regarding implementation of these new calibrations which relate

to clarifying the definitions of the types of plantings to which these new calibrations apply. These are

listed below and are of particular importance in relation to project-scale implementation under the

CFI;

• Fertiliser use and weed control. Unlike previous calibrations of FullCAM’s yield curves by

Waterworth et al. (2007), these new calibrations incorporate, or subsume the effects of

fertiliser and weed control. It is therefore important to ensure that, when applying these

new calibrations, double accounting of the positive influences of fertiliser and weed control

is avoided by not allowing these activities to be entered as Type 1 or 2 modifiers applied

through user-input FullCAM management events.

• Grazing. There was insufficient evidence to indicate the impact of grazing intensity,

frequency and timing on the yield curves in these planting types. Nonetheless, it is

recommended that in situations where grazing does impact on stand density or the

proportion of trees there may be a requirement for either; (i) generation of exclusion areas

within the project area, or (ii) the re-drawing of CEA boundaries.

• Harvesting and thinning. There was also insufficient evidence of the impact of harvesting of

wood products or biomass on yield curves in this study (Appendix 9.5). Therefore, these new

calibrations are only recommended for use on permanent plantings and not plantings which

have been thinned or harvested.

• Minimum required distance between two adjacent linear plantings. Although when

undertaking the calibrations a linear planting was defined as having a minimum distance

between adjacent belts of ≥12 m, most had much larger distances, and the plantings that

were only 12 m apart were too young (<5 years old) for trees from adjacent planting to have

any negative impact on the edge effect of the linear planting. In regard to implementation of

the new calibrations for linear plantings which often require permanence obligations, we

recommend a more conservative distance between adjacent linear plantings of ≥40 m to

67

ensure there is no competition of resources between adjacent linear plantings by the time

they mature. The 40 m is based on findings (Peck et. al. 2012; Brooksbank et. al. 2012) that

the zone of hydrological influence of roots from linear plantings of mallee eucalypts extends

up to 20 m from the edge row of belts.

• Influence of adjacent trees in linear plantings. Another issue which may impact negatively on

the edge effects of linear plantings is the remnant isolated paddock trees, planted trees or

other regrowth which may be in the 40 m of required planting ‘exclusion area’ adjacent to

the edge row of these belts. For the linear planting calibrations to be validly applied, it is

therefore important that such trees within the adjoining area do not impact edge effects. To

ensure this is not the case, we recommend that the definition of a linear planting is that for

every 150 m (or 75 m) on either side of a narrow (or wide) linear plantings, there is <1 tree in

the 40 m of ‘exclusion area’ on both sides adjacent to the edge rows of these belts. An

exception to this threshold is where the adjacent trees are clumped in such a way that it is

unlikely that all trees will impact on the belt. It is recommended therefore that both un-

clumped and clumped trees are assumed to have an impact on the edge of the belt for a

length of 40 m along the belts edge. We can determine the total length of the linear planting

(accounting for both edges along the belts length) and ensure that the total length of impact

of trees in the adjoining area is <20 % of the length of the planting (e.g. if narrow linear

planting is 1 km long, the net length of the adjacent tree impact needs to be <200 m).

• Stand density categories. One possible perverse outcome of having categories of stand

density in the new calibrations (Table 6.01) is that this may encourage the establishment of

plantings at the lowest possible stand densities within each category to maximise returns on

investment under the CFI. To avoid this, we recommend that when plants are established in

rows the outside edge of the CEA adjacent to the long axis of the rows is a distance from the

outer row of stems of 1 m. For narrow (or wide) linear environmental and mallee plantings,

the maximum allowable width of the CEA is 22 m (or 42 m) or 4 m (or 16 m), respectively.

CEA width should be determined by assuming a maximum average row width of 2 m. Hence,

for wide linear mallee plantings, the maximum allowable width for CEAs for plantings with 3-

7 rows can then be calculated by determining the number of rows in the planting,

subtracting 1 (to give the number of gaps between rows), then multiplying by 2 m, and then

adding 2 m (1 m for each side). Also, the outside edge of the CEA perpendicular to rows

should be assumed to be only 1m from the outer stems. Similarly, a CEA edge internal to the

planting perimeter (i.e. an Exclusion Area) could be assumed to have a distance of 1 m from

the stems bordering the internal edge. For plants are established randomly (i.e. not in rows),

the perimeter of the CEA is defined the location of any outside edge of the CEA from the

outer stems may be assumed to be 0 m.

6.4.3 Issues of temporal application

It can be seen from Figure 5.08 that there were very few datasets from stands older than 30 years.

Calibrations are therefore only based on this initial period of growth. Indeed Table 6.01 shows that

the maximum age of stands in the datasets collated for the various planting categories varied

between 13 and 50 years, while the 95th percentile of stand age varied between 10 and 33 years.

One conservative approach could be to recommend that calibrations be applied to the average 95th

percentile age of 24, 19 and 14 years for temperate environmental plantings, tropical environmental

plantings, and mallee plantings, respectively (Table 6.01). Grouping of age domains at the planting

categories would be appropriate because the growth curve parameters were not calibrated on a

category-by-category basis. Rather, growth curve parameters were constrained such that the relative

predictions of average productivity across each planting categories within these groups were the

same as that predicted by the multiple regression (Step 4, Section 6.2). In other words,

68

parameterisation of categories with older stands provided guidance in the parameterisation of

categories with younger stands. However, because under the CFI the first commitment period is 15

years, for administrative simplicity it is recommended that 15 years be specified in any CFI

Methodology utilising these calibrations.

Further work is required to monitor biomass accumulation as existing plantings mature. This will be

best achieved through the use of permanent sample plots. As discussed in point 2 of Section 6.2, in

the absence of biomass data from older plantings, the y growth curve parameter was highly

uncertain, with its value being largely based on the assumption that long-term biomass predictions

do not exceed 300 t DM ha-1.

6.4.4 Spatial deployment and relative proportions of planting categories

When undertaking the spatially-explicit NIS, remote sensing information obtained on land use

change cannot determine the categories of plantings. Therefore, implementation of a national-scale

carbon accounting of these 26 plantings categories requires knowledge, on a regional basis, of (i) the

total area planted within the region, and (ii) the relative proportions of each of the categories

represented within that region. The database collected as part of this project cannot provide a basis

for making useful assessments of these, because the range of sites sampled is not spatially

representative (rather, sites were chosen to be representative of the overall range of planting

categories and practices). Therefore, this information does not currently exist, but insights could be

gained from a systematic and comprehensive survey of the major growers. In the future, changes in

these areas can be estimated from the data reported as part of implementing CFI. This would not,

however, capture plantings established for voluntary trading schemes or other purposes.

Determining where the calibrations can be applied

The spatial extent over which the calibrations reported in this project are valid needs to be

established and decisions need to be made over how to handle planting locations that fall outside of

the spatial domain within which the model calibrations were developed. This issue has relevance to

both the NIS and project-based level carbon accounting.

We applied recommended spatial constraints to the application of the growth curve calibrations

based on a bioclimatic analysis. This was done using ANCLIM with the climate variables of maximum

temperature, minimum temperature, rainfall, solar radiation, and evaporation (Xu et al. 2009).

ANUCLIM therefore provided a robust method for determining where the calibrations can be applied

as it was used to determine the full climatic extent based upon the calibration sites.

Figure 6.08 presents the regions of Australia where the bioclimatic conditions are representative of

the sites specific to each planting type. These regions are the result of consolidating the ANUCLIM

regions by applying the ArcGIS Expand (by two pixels) and Boundaryclean functions. This provided a

buffer of the extent of the regions and reduced small gaps and discontinuities in their boundaries.

There is however still a concern that the combinations of climatic variation included in the database

are not fully representative. As a result, regions of recommendation shown in Figure 6.08 may be

conservative. With future work, it may be possible to combine this bioclimatic approach with expert

knowledge to provide demarcation into further eligible areas for model application. For example,

one clear area of south-western Western Australia which is not well represented by the datasets

collated here are the narrow belts of ‘Other’ and ‘Lox’ in the higher Pavg regions of between 5−6,

and for the lower Pavg region of 2 (D. Wildy, Fares Rural Pty Ltd, pers. com., 2012).

69

Figure 6.08. Recommended regions of application of calibrations for (a) mixed-species environmental plantings in temperate regions, (b) mixed-species environmental

plantings in tropical regions, (c) ‘Other’ mallee eucalypt plantings, (d) ‘Lox mallee eucalypt plantings, and (e) ‘Poly’ mallee eucalypt plantings, based on ANUCLIM bioclimatic

zones, and expansion of this region to reduced clumping.

(a) Mixed-species; temperate (b) Mixed-species; tropical

(c) ‘Other’ mallee eucalypts (d) ‘Lox’ mallee eucalypts (e) ‘Poly’ mallee eucalypts

70

Issues of determining the relative proportions of planting categories

The issue of estimating the relative proportions of planting categories in each region is relevant to

the FullCAM database on management options and thus, implementation of the NIS. Although the

relative proportions of each of the 26 planting categories represented within a given region cannot

be ascertained from the existing database, insights that could be gained from a systematic and

comprehensive survey of the major growers, and interrogation of existing databases (i.e. OMA and

SA DEWNR databases). A survey of collaborators is required.

6.4.5 Issues for project-scale application

Having various categories of environmental and mallee plantings should improve the accuracy of

FullCAM predictions for biomass carbon at the project-level. Project proponents could select the

category which best represents the types of plantings they are establishing.

Only the specific planted area is used to estimate carbon abatement. The definition of a project area

is the spatial area defining the plantings managed under the project including exclusions and spaces

between adjacent linear plantings. Therefore, linear plantings generate more carbon per hectares

planted than block plantings. However, project proponents should be careful not to misinterpret

relatively high FullCAM outputs (expressed in terms of carbon per hectares planted) implying they

are maximalising potential carbon credits for a given property. In general, this will not be the case

because linear plantings require at least 12 m of un-planted land between these plantings.

An a simple example, assume a property area of 1 ha of land currently under pasture where the

project proponent is considering two options for a reforestation project; (i) planting a block of Lox

(Option A), or (ii) planting a narrow linear Lox (Option B). Suppose we wanted to compare above-

ground biomass produced by these two options 30 years after reforestation. Let’s assume that in

both Option A and B trees will be planted in rows (2 m apart) at a stand density of 2,500 stems per

hectare. So there will be 2,500 trees in Option A. In Option B there will only be enough land available

for 500 trees. That is, 5 narrow linear plantings (2-row belts) each taking up a width of 4 m, with 16

m between them (Figure 6.09). As a result, although there is over 200% more biomass produced

from Option A when expressed per hectare planted; per property area the situation is reversed. At

the property-area scale there is almost 40% less biomass produced in Option B when compared to

Option A. This is simply because the high growth rates achievable in narrow plantings require wide

spaces between the planted rows for the growth enhancement to be expressed, which means within

any given property less total land area is able to be planted.

We conclude that, at the property-scale, the growth curve calibrations undertaken here do not

directly provide a ranking of the carbon sequestration potential of the 26 various planting type

categories. This is because to apply the calibrations from this report, the planted area within the

project used to estimate carbon abatement must be defined in accordance with the definition of

planted area (Page 8). This fact needs to be made clear to potential project proponents. That is,

despite the fact that there may be more carbon credited per hectares planted with linear plantings

when compared to block plantings, the carbon credit potential may actually be less with linear

plantings at the project (or farm) level.

It should be noted that the ‘open’ paddock area adjacent to a linear plantings is required to support

the enhancement of growth by edge trees less affected by competition for resources of light,

nutrients and water. The recommendation of a distance of at least 12 m between two adjacent linear

plantings is based on the premise that lateral roots have been found to encompass a radius of at

least 5 m from the base of the stem (Wildy and Pate 2002), with the zone of influence of roots on

uptake of water (and thus presumably many nutrients), being at least 5 m or more from the base of

the stem (Robinson et al. 2006). Nevertheless, it should be noted that specific definition of a paddock

width between adjacent linear plantings of ≥12 m may require refinement as more data becomes

71

available on interactions between planting geometries and growth rates in stands under differing

climates and site qualities, and under stands of various ages.

6.4.6 Issues of model re-calibration

Given the ongoing development of the NIS, an important consideration is what impact future

changes to other components of the FullCAM model might have on the calibrations reported here,

and on the resulting predictions of carbon sequestration. The major modification that is currently

being considered, and that would have most impact, is improvement to the maximum biomass (M)

spatial layer, and thus the Pavg index upon which the estimates of M are derived. As noted in Section

5.5.2, further improvements in Pavg are required for improved accuracy, at the site-level, in

predictions of biomass. At any location, an increase (or decrease) in M would imply a corresponding

increase (or decrease) in sequestration over the long term. The database assembled in this project,

and the associated calibration procedure, was developed to ensure that future recalibrations due to

changes in other parts of the model can be easily implemented, and be made in such a way that

minimal changes occur to estimated biomass accumulation during the early (<35 years) growth

stages. This allows improvements to be made to the model, with minimal changes to current

estimates of reported carbon sequestration.

6.5 Conclusions

The Tree Yield Formula has been calibrated based on 1,480 (or 884 not including repeat measures at

the one site) estimates of biomass accumulation by a wide range of mixed-species environmental

and mallee eucalypt plantings. The overall model efficiency was only 43 and 63% for mixed-species

and mallee eucalypt plantings, respectively. However, there was no apparent bias in model

predictions and the model is satisfactory for most individual planting categories. New calibrations to

describe temporal change in root-to-shoot ratios for six planting categories have also been derived.

However, care is needed so that the model is not applied to growing environments not represented

in the calibration database. There are many priority areas for further work to refine these

calibrations (see Section 7).

72

Figure 6.09. Comparison of above-ground biomass predicted to be generated from two alternative reforestation activities in a hypothetical project area of 1 ha: Option A, block

planting of Lox; Option B, narrow linear dense planting of Lox.

73

7 Conclusions

The collection of new accurate measures and estimates of biomass (Section 4), collation of a large

database on estimates of biomass across a wider range of mixed-species environmental and mallee

eucalypt plantings (Section 5), and analyses of these data, has facilitated the calibration of FullCAM

(Section 6). Calibrations were derived for 26 categories of plantings, each with different growth

modifiers based on planting geometry, stocking and species/species mix. It is important to highlight

that the calibrations presented here are preliminary. Further work is required in the following areas,

ordered in terms of priority;

Calculation of Pavg and M within FullCAM: As already highlighted through Department of

Environment instigating a multi-organisational advisory group on FullCAM biomass estimates, the

Tree Yield Formula, and the calculation of M empirically from Pavg, requires further refinement

given; (a) there are now a greater number of, and more nationally representative, datasets available

which could result in the refinement of the empirical relationship between M from Pavg, (b) the

forest growth model (3-PG) upon which the calculation of Pavg is based has had significant

improvements in the water balance component (Almeida et al. 2007; Landsberg and Sands 2010)

which could be incorporated into FullCAM’s calculation of Pavg, and (c) there have been

improvements, via ASRIS (http://www.asris.csiro.au/index_ie.html#), for nation-wide estimates of

soil fertility, soil texture and depth, which could also be incorporated into the calculation of Pavg.

Improved estimates of the potential soil water availability across sites within Australia would be

particularly useful as many of these plantings are established in regions of low to medium rainfall.

Many plantings, particularly the mixed-species types, also tend to be established in regions of

relatively low rainfall, on poor and unproductive soils, and often in positions in the landscape that

are unproductive for agriculture (e.g. eroded gullies, ridge tops, waterlogged and/or saline

depressions, etc.). It is therefore likely that, with improvements in FullCAM’s calculations of Pavg and

M through incorporation of improved soil water balance and site fertility components, unexplained

variability in predictions of biomass in environmental plantings will be lower than the 41% obtained

here.

Longer-term studies: FullCAM Tree Yield Formula calibrations were based on relatively young

(generally <20 year old) stands. We currently have little understanding of patterns of accumulation of

biomass, or changes in R:S ratios, over the longer-term. Therefore, for each of the different types of

plantings there is a clear need for continued measurement of rates of biomass accumulation

(through long-term monitoring of permanent sample plots) and of R:S ratios as they age. To

capitalise on the investment made in this project, a number of the sites measured as part of this

study could form the basis of a future monitoring program.

Dynamics of stocking and proportion of trees: Given that stocking and proportion of trees were found

to be two of the key determinants of biomass accumulation in plantings, it will be important to

monitor, across the multiple categories of plantings, the dynamics of these as the stands age (e.g.

Coomes et al. 2012). For example, we conclude for the relatively young stands studied here, the

highest rates of biomass accumulation are, on average, in the most densely-stocked plantings.

However, this may not be the case over the long-term as site resources are fully utilized and

mortality (particularly of some of the acacia species) begins to affect stocking and species

composition within a planting.

Additional factors influencing productivity: As outlined in Section 5, and in more detail in Appendix

9.5, there are some factors which we know will influence growth but which are yet to be accounted

for in FullCAM calibrations. These include whether the planting is coppiced following harvesting,

74

growing in saline surface soil, or has access to a watertable (including establishment in riparian

areas). Unlike the coppicing and watertable access modifiers, the surface soil salinity modifier would

decrease FullCAM predictions of biomass accumulation. The lack of a verified and robust modifier for

surface soil salinity is of major concern given it will lead to an over-estimation of carbon

sequestration. Further work is required to assess the impacts of these factors, particularly surface

soil salinity, for the different types of plantings over the longer-term. This will require additional

measurement of biomass in plantings influenced by these factors. As a further challenge to this work,

the tolerance of different species to saline surface soils will need to be addressed. Similarly, acid soils

may impact on growth of these plantings, again with different species having different tolerances.

With regard to access to ground water, or stored soil water, a further challenge will be accounting for

the potential decline in stored soil water with time since afforestation.

New data from currently unrepresented regions and revegetation types: New estimates of biomass

productivity of environmental plantings in northern New South Wales and south-east Queensland

are required given there are currently relatively few data from these regions. In these particular

regions, regrowth will also be an important revegetation type to consider. Additional work will also

be required to include FullCAM calibrations for revegetation activities established through natural

regeneration, or through enhancement of remnants. Furthermore, current environmental plantings

may not be representative of the potential land base for new plantings, which may be driven more

by the economics of carbon farming and likely be in areas of low productivity where opportunity

costs of land are low. Therefore, further work may also be required to obtain estimates of biomass

productivity from these regions.

Further verification of generic allometrics. Further verification of generic below-ground allometrics is

required given that roots have been excavated at only 13 plantings, and these do not provide full

representation of all categories of plantings identified as having unique trends in the decline of R:S

ratios with age, or with decreased proportion of trees (Figure 5.13). Additional measurements of root

biomass are required such that generic allometrics for below-ground biomass can be developed for

low and high rainfall regions. Indeed for the data-rich above-ground biomass database, we found

significant decreases in the error resulting from the application of generic life-form allometrics when

these were segregated based on rainfall zones. Additionally, all of the verification of generic

allometrics undertaken thus far has been in temperate systems, and verification of allometrics for

sub-tropical and tropical regions of Australia is required.

Improved efficiency of sampling: Results in Section 3 clearly demonstrate the importance of sampling

strategy, and sampling an appropriate number of trees (which is often much higher than that

traditionally measured by the revegetation industry) to obtain precise estimates of biomass carbon.

We showed that traditional estimation of stem diameter using diameter tapes is slow when

compared to using the calibrated caliper (Figure 3.07), and further work is required to assess

whether this improved efficiency results in any significant loss of accuracy across a wider range of

planting types. Further work is also required to assess whether, for estimating biomass across a

range of planting types, there is a potential for measurement of fewer trees to obtain the same level

of accuracy with the implementation of new plot design methods (i.e. GRTS sampling design).

Furthermore, an improvement in sampling to attain improved accuracy of indirect estimates of

biomass will result in a reduction in unexplained variance of biomass predictions. As per an improved

method for estimation of Pavg, improved estimates of biomass will result in improved model

performance (or model efficiency) on re-calibration.

75

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81

9 Appendix

9.1 Details of testing methodologies for measurement-based estimates of biomass

Conversion tables for estimates of biomass using different methods of plot area calculation

Table A9.1.1: Multiplier required for field based estimates of biomass (t ha-1

) calculated based on the assumption that plot area of a linear

planting is defined using a width calculated by 2 m out from each edge. ‘Corrected’ values are then comparable to those estimated using

FullCAM where, for mathematical simplicity, calibrations have been done using a width calculated by ½ row out from each edge.

Number of rows in the planting

Distance between rows 2-row 3-row 4-row 5-row 6-row

2 m 1.50 1.33 1.25 1.20 1.17 3 m 1.17 1.11 1.08 1.07 1.06 4 m 1.00 1.00 1.00 1.00 1.00 5 m 0.90 0.93 0.95 0.96 0.97 6 m 0.83 0.89 0.92 0.93 0.94

Table A9.1.2: Multiplier required for field based estimates of biomass (t ha-1

) calculated based on the assumption that plot area of a linear

planting is defined using a width calculated by 3 m out from each edge. ‘Corrected’ values are then comparable to those estimated using

FullCAM where, for mathematical simplicity, calibrations have been done using a width calculated by ½ row out from each edge.

Number of rows in the planting

Distance between rows 2-row 3-row 4-row 5-row 6-row

2 m 2.00 1.67 1.50 1.40 1.33 3 m 1.50 1.33 1.25 1.20 1.17 4 m 1.25 1.17 1.13 1.10 1.08 5 m 1.10 1.07 1.05 1.04 1.03 6 m 1.00 1.00 1.00 1.00 1.00

Summary of derivation of t ha-1

for arbitrary-sized plantings

If trees are planted on a regular grid, with even spacing between rows, and even spacing between trees along rows, then the appropriate

area for calculating biomass density (with reference to the diagram below) is equal to ( ) ( )yyxx ′+×′+ 22 , where x and y define the outer

planting extent of the trees, and x′2 is equal to the spacing between adjacent trees along rows, and y′2 is equal to the row spacing. If, as

is usually the case in the real world, there is variability in the row and tree spacing, then the appropriate area is ( ) ( )yyxx ′+×′+ 22 , where

x′ and y′are the mean row and tree spacings respectively.

If trees are planted in rows, but are distributed randomly along each row (as is the case with many direct-seeded plantings) then the

appropriate area for calculating biomass density is equal to ( )yyx ′+× 2 , where x and y define the outer extent of the trees, and y′2 is

equal to the row spacing. If, as is usually the case, there is variability in the row spacing, then the appropriate area is ( )yyx ′+× 2 , where

y ′ is the mean row spacing.

If trees are broadcast seeded within an area, then no edge adjustment is required (with seeds assuming to have equal probability of

germinating anyway in the boundary of broadcasting), and the appropriate area is simply yx× .

82

Single row plantings, by definition, have no area, and therefore biomass density is undefined. In practice, biomass densities will want to be

estimated for such plantings. Using the average row spacing from a ‘typical’ multi-row planting of the same species would seem to be an

appropriate solution.

Plot area calculations for estimating biomass density

Comparison of plantings of different species, planting geometries, management treatments etc. requires expressing standing biomass on a

per-area basis (e.g. t ha-1

); a quantity which is sometimes called biomass density. When trees are established in rows, as they often are,

then calculation of appropriate areal extents for calculating biomass density becomes problematic. This Appendix outlines the derivation of

the geometrically correct way to calculate these areal extents. The derivations below apply to any size or shape rectangular or square area,

with rows planted parallel to one of the planting area axes. Here we assume rows are parallel to the x-axis.

Primary quantities

TreesD Mean distance, in m×10-2

, between adjacent trees within a row1. Mean inter-tree distance is specified as distances between

adjacent trees can, in reality, be variable.

RowsD Mean distance, in m×10-2

, between adjacent rows in the planting. Mean inter-row distance is specified as distances between

adjacent rows can, in reality, be variable.

Lx Length of the planted area in the x-axis, in m×10-2

. The Lx axis is assumed parallel to the planting rows

Ly Length of the planted area in the y-axis, in m×10-2

.

TreeM Mean individual tree mass, in t. The average per-tree mass over all trees in the planting1.

Derived quantities

( )areainrowsE __ The expected number of rows in a planted area with a y-axis length of Ly. The statistical notation ‘E’ is used because

the actual distance between individual rows can be variable, as noted above.

( )rowpertreesE __ The expected number of trees planted in each row of planted length Lx. The statistical notation ‘E’ is used because

the actual distance between individual trees can be variable, as noted above.

( )hapertreesE __ Stand density; stems/ha

1Distance units are in m×10

-2 and tree mass in t so the derivations can be expressed as t/ha, which is a unit we are more familiar with

(rather than e.g. kg m-2

).

1. Derivation of t/ha for the general case

Biomass per unit area (t/ha) is defined (in English) as:

t ha-1

= (mean per-tree mass in tonnes) × (total number of trees in planting / planting area in ha)

= (mean per-tree mass in tonnes) × (tree stocking rate per ha)

To calculate the total number of trees in a planted area, you need to know the number of rows in the planting, and the number of trees per

row, both of which can be calculated from first principles.

(a) The expected number of trees per row, in a row of arbitrary x-axis length Lx

( )Trees

x

D

LrowpertreesE =__

(b) The expected number of rows in a planting, of arbitrary y-axis length Ly.

( )Rows

y

D

LareainrowsE =__

To interpret expression (a), TreesD/1 is equal to the mean number of trees per m×10

-2, which is then multiplied by a total row length of Lx

m×10-2

, yielding the total number of trees in a row. Expression (b) can be interpreted in the same way.

The expected total number of trees in the planting is therefore E(rows_in_area) × E(trees_per_row).

The expression for t/ha is therefore:

83

( ) ( )

RowsTreesTree

TreesRowsTree

yx

TreesxRowsyTree

yx

Trees

x

Rows

yTree

yx

Tree

DDM

DDM

LL

DLDLM

LL

D

L

D

LM

LL

rowpertreesEareainrowsEMhat

××=

××=

×××××

=

×

××=

×××=

−−

−−

1

____

11

11

where the stocking rate of trees E(trees_per_ha) is equal to

RowsTrees DD ×1

2. Alternative representation of planted area

An alternative representation of the planting area is shown in Figure Figure A9.1.1.01, with Ly equal to the sum of the row-to-row planting

extent (LP), and an extra area added to that extent (2Cy); I.e. Ly = Lp + 2Cy (where Lp = (NRows -1) × RowsD ). There has been some debate over

what value C should be. Figure A9.1.1.01 illustrates these various distances, in this case for a planting with NRows = 4, and with trees

randomly position along rows;

Recall the biomass density in t/ha is given by

t ha-1

= (mean per-tree mass in tonnes) × (total number of trees in planting / planting area in ha)

which based on the above diagram is:

( )( )( )yRowsRowsx

RowsTrees

x

Tree CDNL

ND

L

Mhat21

/+×−×

××=

From the general derivation in Section 1 it was shown that stocking rate is equal to

RowsTrees DD ×1 ; therefore

( )( )( )yRowsRowsx

RowsTrees

x

RowsTrees CDNL

ND

L

DD 21

1

+×−×

×=

× (1)

We are now in a position to solve for the ‘unknown’ parameter Cy:

( )( )( )

2

2

20

2

2

2

2

2

1

21

1

1

1

1

1

Rowsy

yRows

yRows

yRowsRowsRowsRowsRows

yRowsRowsRowsRowsTreesRowsTrees

RowsTrees

yRowsRowsRowsRowsTrees

RowsTreesx

xyRowsxRowsRowsxRowsTrees

xyRowsxRowsRowsx

RowsTreesx

RowsTrees

yRowsRowsx

RowsTrees

x

RowsTrees

DC

CD

CD

CDNDND

CDNDNDDD

ND

CDNDDD

NDL

LCDLNDLDD

LCDLNDL

NDL

DD

CDNL

ND

L

DD

=

=

+−=

+−×=×

+−×=×××

×+−×

=×

×××+×−××

=×

×+×−××××=

×

+×−×

×=

×

−

−

−

−

84

3. Explanation of why this is important

The implications of the above are illustrated in Figure A9.1.1.01a. Consider a homogenous ‘block’ of trees planted in rows (the left hand

side) and then imagine a subset of that block is copied into e.g. an adjacent paddock (the right hand side). All else being equal (and ignoring

edge effects for now) this copied block will also have the same properties, i.e. t/ha biomass and stocking rate, as the original. The

derivation above states that the only way the two areas can be sampled to yield the same value of t ha-1

(or stocking rate) is by adding an

equivalent of ½ row top and bottom; adding distances other than ½ row top and bottom will yield a non-zero difference between the two

areas, i.e. a calculation artefact.

The example shown in Figure A9.1.1.01a can be seen as a kind of ‘null model’ test of the method – the example is defined to ensure the

properties between the two areas are the same, and therefore any method for estimating the sampling extent (the dotted line) must

deliver the same value for both areas.

In reality most linear plantings will exhibit an ‘edge-effect’ growth advantage. This is shown in Figure A9.1.1.01b below - the result of the

edge effect is to generate a higher t/ha. If a sampling extent other than adding equivalent to ½ row top and bottom is used, then the

measured difference in tree mass between the two linear plantings confounds two sources. One is the actual difference in tree mass (due

to the edge effect) we are trying to detect; the other is a component due solely to the arbitrary choice of sampling distance, which is

unrelated to actual sequestration, and which is an artefact.

Figure A9.1.1a

Figure A9.1.1b

4. When trees are not randomly distributed along rows

Section 2 derived the required sampling area for a planting in which trees are randomly located along the entire row length. However, in

many plantings trees are spaced at more-or-less regular intervals; the consequences of this for calculating the sampling area are analogous

to the calculations for the arrangement of trees into rows. The layout is illustrated in Figure A9.1.1.02.

Figure A9.1.2

85

Lyp is the row-to-row planting extent, and Lyx the tree-to-tree planting extent along rows. To allow generalisation we can re-cast the

distances Cx and Cy as proportions of the respective mean inter-row and inter-tree distances;

Rowsy

Treesx

DyC

DxC

=

=

In an expression analogous to Equation (1) above it is then possible to rearrange to express Cx in terms of Cy (or vice versa). First, as defined

in Section 2, the planting extent in the y axis (Lpy) is given by:

( ) RowsRows DN ×− 1

and therefore the total site height is

( ) RowsRowsRows DyDN ×+×− 21 .

In the same way the planting extent along the x axis, when trees are regularly spaced, is:

TreesTrees

x DD

L ×

−1

And therefore the total site width is

TreesTreesTrees

x DxDD

L ×+×

− 21 ,

Where

Trees

x

D

L is the number of trees per row.

Placing these quantities in Equation (1) and solving for TreesDx gives:

( )( )( )

( ) ( )

( ) ( )

( ) ( )

( ) ( )

( )

TreesxRows

RowsxTrees

TreesTreesxRows

Rowsx

TreesTreesxRowsRows

RowsxRows

RowsRowsTreesTreesxRowsTreesxRowsTrees

RowsTreesx

RowsRowsTreesTreesxRowsTrees

RowsRowsTreesTreesx

RowsTreesx

RowsTrees

RowsRowsRowsRowsTreesTreesx

RowsTreesx

RowsTrees

RowsRowsRowsTreesTreesTrees

x

RowsTrees

x

RowsTrees

DLyN

NLDx

DxDLyN

NL

DxDLyND

NLD

yNDDxDLNDLDD

NDL

yNDDxDLDD

yNDDxDL

NDL

DD

DyDDNDxDL

NDL

DD

DyDNDxDD

L

ND

L

DD

+−+−

×=

+−=+−

×

+−=+−×

××

+−××+−=××××

××+−××+−=×

+−××+−××=

×

+−××+−××=

×

+×−×

+

×

−

×=

×

−

−

−

−

212

221

221

212

212

212

1

22

1

2121

1

1

1

1

1

This result is general but not particularly useful, as the required increment to be added to the row extents (TreesDx ) is itself a function of the

quantity we are ultimately trying to estimate (Lx). However by setting the row multiplier (y) to 0.5 (meaning we add the equivalent to ½ half

a row width to planting top and bottom) the final expression reduces to:

TreesTrees DDx =2

or in other words x=0.5 when y=0.5; and therefore when a ½ row width is added to planting top and bottom, an equivalent to a ½ tree

distance must be added to the left and right hand sides of the planting area; i.e. in Figure A9.1.1.03 this corresponds to

2Rows

y

DC = , and

2Trees

x

DC = .

Because linear plantings tend to be long and skinny, the error associated with ignoring the Cx adjustment, and just applying Cy = ½, may be

negligible for many practical cases. Figure A9.1.1.03 shows that error for 4-row linear plantings of different planting extent width:height

86

ratios, and for differing row spacing (with trees spaced 2m apart). For this configuration of planting Figure A9.1.1.04 shows that linear

plantings more than about six times wider than high have an error associated with ignoring the Cx adjustment that is typically less than 5%.

Figure A9.1.3. Estimates of sampling error resulting from not applying the Cx row adjustment, for linear plantings with different planting-

extent height:width ratios. The planting configuration is for four-row linear plantings, with trees spaced 2 m apart.

5. Using canopy cover to define sampling area

An alternative to defining the sampling area relative to the spatial extent within which the trees have been planted, is to define it relative

to projected or canopy cover (which will change over time). The simple example below demonstrates how such an approach will also lead

to spurious differences in growth, unrelated to actual sequestration.

Figure A9.1.4

In this example the total planting tree mass has doubled from time 1 to time 2, so any estimate of t/ha must also double between the two

times. Basing the area calculation on canopy cover, which increases over time, yields an estimate of change in t/ha that is less than double

– i.e. the estimate of sequestration is less than what actually occurred (20/1.3 – 10/1 = 5.4 t/ha sequestered over the period). Only by using

a fixed area for assessment can the correct increment be calculated ((20/1 – 10/1 = 10 t ha-1

sequestered over the period). What that fixed

area should be is defined in Sections 2 and 4.

Concluding comments

For any planting of arbitrary dimensions Lx × Ly, and where trees are planted in rows, the biomass density is given by:

RowsTreesTree DD

M×

× 1

where TreeM is the average individual tree mass within the planting, and

RowsTrees DD ×1 is the stem stocking rate (with

TreesD being the

average distance between adjacent trees in the row, and RowsD the average inter-row distance).

For the representation of the planting in the form where trees are randomly planted along rows, the only value of Cy that yields the correct

biomass density is equal to half the mean row spacing; any other arbitrary value yields either an over- or under-estimate of the true t/ha

value.

If trees are also planted with a regular spacing along rows, then an additional extent along the x axis (Cx) must also be added to yield the

correct biomass density. Figure A9.1.1.04 shows for long skinny linear plantings the magnitude of this additional adjustment may

negligible.

Note that if an area has trees that are not planted in rows, but are rather randomly distributed, then this means, in the y-axis, there are no

limitations to where a tree can be positioned. This occurs when NRows � ∞, and therefore RowsD � 0, and therefore C � 0. I.e. in a

broadcast seeding, the planting extent Lp is the appropriate y-axis dimension for calculating biomass density.

Single row plantings, by definition, have no area, and therefore biomass density is undefined. In practice, biomass densities will want to be

estimated for such plantings. Using the average row spacing from a ‘typical’ multi-row planting of the same species would seem to be an

appropriate solution. However, given the answer is actually undefined, this calculation is somewhat arbitrary, and a case might also be

made for making the ‘width’ of a single row planting wider than the average, to provide a more conservative estimate of biomass density.

0

10

20

30

40

0 2 4 6 8 10 12 14 16 18 20

% e

rro

r

Ratio of planted area width:height

Error in estimating t ha-1 biomass through ignoring tree spacing adjustment Cx

Row spacing 2m

Row spacing 3m

Row spacing 4m

87

Sample errors in allometrics

Table A9.1.3. Species of Eucalyptus or Acacia trees used to assess the sampling error of allometrics. LR = low rainfall, HR = high rainfall.

Species Data source Number of

plantings

Number of

categories used N

A. calamifolia This project 2 4 128

A. hakeoides This project, CSIRO1 3 4 113

A. mearnsii This project, CSIRO2, CSIRO

3, UniMelb

4 6 4 128

A. melanoxylon This project, CSIRO2 3 4 51

A. saligna This project, GA5, CSIRO

3, DAFWA, CarbonNeutral 6 4 50

A. pyncantha This project, CSIRO2, SA DEWNR 8 5 102

E. blakelyi This project, CSIRO1 6 5 47

E. camaldulensis This project, CSIRO2, NSW DPI

6 8 4 89

E. kochii This project, DAFWA9,11,12

, CSIRO3,10

, Fares Rural 16 5 374

E. largiflorens This project, DAFWA9,11,12

, CSIRO10,13


E. loxophleba This project, AusCarbon 8 4 104

E. loxophleba ssp. lissophloia LR This project, DAFWA9,11

, CSIRO10


E. loxophleba ssp. lissophloia HR This project, DAFWA9,11,12


E. melliodora This project, CSIRO1 8 5 169

E. occidentalis This project, 6 5 118

E. polyanthemos This project, CSIRO2 5 4 51

E. polybractea LR This project, DAFWA9,11,12

, CSIRO10,13


E. polybractea HR This project, DAFWA9,11,12,14


E. spathulata This project 1 5 206

E. tereticornis This project 3 4 71

E. tricarpa-sideroxylon This project, CSIRO2, NSW DPI

6, CSIRO

7 6 5 54

E. viminalis This project 3 5 365

E. wandoo This project, CarbonNeutral 3 4 59 1Paul et al. (2008);

2England et al. (2006);

3Hawkins et al. (2010);

4Forrester et al. (2005);

5Jonson & Freudenberger (2011);

6Barton & Parekh (2006);

7CSIRO (unpublished);

9Peck et al. (2011);

10Grove et al. (2007),

11Ritson, P (pers. com. 2012);

12Brooksbank (2010);

13Bennett, R (pers. com. 2012);

14Sudmeyer, R (pers. com. 2012).

88

9.2 Methodologies: measurement and estimation of biomass

Characterisation of the new planting sites (as reported in Section 4)

Table A9.2.1. Summary of the main characteristics of the new sites studied in this project, including their locations (latitude, longitude, in decimal degrees), mean annual rainfall (MAR), planting type (where Mix and Mal,

represent mixed-species or mallee plantings, respectively and -B and -L represent block and linear plantings, respectively), planting method, year of establishment, age of planting at time of measurement, and the main

species which were present at the time of measurement.

Site Location MAR

(mm)

Planting

type

Planting

method

Year

planted

Age

(yrs)

Dominant species present

‘Direct measurement’ plantings

Strathearn -35.0485, 149.2325 637 Mix-B Direct seeded 1995 15 E. viminalis, E. melliodora, E. blakelyi, E. polyanthemos, E. stellulata, A. baileyana

Moir -34.2809, 118.1820 439 Mix -B Direct seeded 1990 20 A. acuminata, A. micobotrya, E. loxophleba, E. occidentalis, E. spathulata

Jenharwill -36.3958, 144.4304 406 Mix -L Tubestock 1999 12 A. calamifolia, A. hakeoides, A. pycnantha, E. leucoxylon

Gumbinnen -36.2447, 141.8148 347 Mix -B Tube & seeded 2001 10 A. pycnantha, A. trineura, E. largiflorens, Melaleuca sp.

Moorland 1 -35.3377, 139.6317 370 Mix -B Tubestock 1991 20 E. calycogona, E. Incrassata, E. leptophyll, E. phenax, E. socialis

Moorland 2 -35.3332, 139.6351 370 Mix -B Tubestock 1996 15 A. calamifolia, E. leucoxylon, E. porosa, Allocas vertical, Melaleuca sp.

McFall 1990 -33.7290, 117.3217 438 Mix-L Broadcast 1990 22 A accum., A saligna, A. huegeliana, E. gardener, E. kochii, E. wandoo, M. uncinata

Leos -37.8381, 147.7582 626 Mix-L Tubestock 1996 16 A penninervis, C. cunninghamian, E. kitsoniana , E melliodora, M. armillaris

Pepal -33.4865, 117 .7912 406 Mal-L Tubestock 2000 11 E. loxophleba ssp. lissophloia

Bird -32.8515, 117 .5892 376 Mal -L Tubestock 2000 11 E. loxophleba ssp. lissophloia

Quicke -32.6736, 118 .2361 339 Mal -L Tubestock 1997 14 E. loxophleba ssp. lissophloia

Temby -33.1457, 117.7187 353 Mal -L Tubestock 1996 16 E. loxophleba ssp. lissophloia

Angle -30.1970, 117.1160 297 Mal -L Tubestock 1996 16 E. loxophleba ssp. lissophloia

Wycheproof poly* -36.1760, 143.3803 365 Mal -B Tubestock 2004 7 E. polybractea

Wycheproof lox* -36.1760, 143.3803 365 Mal -B Tubestock 2004 7 E. loxophleba ssp. lissophloia

Carmody* -36.1602, 143.4044 366 Mal -B Tubestock 2004 7 E. polybractea

Batters poly* -36.5019, 143.2934 415 Mal -L Tubestock 2004 7 E. polybractea

Batters lox* -36.5019, 143.2934 415 Mal -L Tubestock 2004 7 E. loxophleba ssp. lissophloia

SW Watts ploy* -36.3996, 143.3102 379 Mal -B Tubestock 2003 8 E. polybractea

SW Watts lox* -36.3996, 143.3102 379 Mal -B Tubestock 2003 8 E. loxophleba ssp. lissophloia

N Watts lox* -36.3996, 143.3102 379 Mal -B Tubestock 2003 8 E. polybractea

Campbell poly* -36.2668, 143.1047 373 Mal -L Tubestock 2004 7 E. polybractea

Campbell lox* -36.2668, 143.1047 373 Mal -L Tubestock 2004 7 E. loxophleba ssp. lissophloia

Weenya sp3* -33.3422, 145.8037 366 Mal -L Tubestock 2004 7 E. polybractea

Weenya sp4* -33.3422, 145.8037 366 Mal -L Tubestock 2004 7 E. loxophleba ssp. lissophloia

Weenya sp5* -33.3422, 145.8037 366 Mal -L Tubestock 2004 7 E horistes

Brotherony 1* -33.1368, 146.6380 378 Mal -B Tubestock 2004 7 E. polybractea

Brotherony 2* -33.1368, 146.6380 378 Mal -L Tubestock 2004 7 E. loxophleba ssp. lissophloia

Brotherony 3* -33.1368, 146.6380 378 Mal -L Tubestock 2004 7 E. polybractea

Kalawa * -30.8848, 148.6090 573 Mix -B Tubestock 2001 10 E. polybractea

‘Indirect estimates’ plantings

Gunbower 1 -35.9800, 144.3847 345 Mix -B Tubestock 2002 9 A. salicina, A. stenophylla, E. occidentalis, E. camaldulensis, E. largiflorens

Gunbower 2 -35.9828, 144.3833 367 Mix -B Tubestock 2003 8 A. salicina, A. stenophylla, E. occidentalis, E. camaldulensis, E. largiflorens

89

Lynvale -37.8987, 141.6380 678 Mix -B Direct seeded 2003 8 A. mearnsii, A. melanoxylon, A. pycnantha, E. viminalis

Palomar shrubs -33.8032, 145.7451 362 Mix -B Direct seeded 1998 12 Dodonaea angustissima, A. rigens, A. hakeoides, A. pendula

Palomar trees -33.7972, 145.7389 363 Mix -L Tubestock 1993 19 E. camaldulensis, E. sideroxlon, E. leucoxylon, E. occidentalis

Netherleigh -24.2124, 151.2977 884 Mix -B Tubestock 2002 9 E. tereticornis, A. disparrrima, C. tesselaris,E. melanophloia, E. moluccana

Mooreland belt -35.3292, 139.6333 372 Mix-L Tubestock 1996 15 E. porosa, E. leucoxylon, M. lanceolata

McFall 1988 -33.7290, 117.3217 438 Mix-L Broadcast 1988 24 A accum., A saligna, A. huegeliana, E. gardener, E. kochii, E. wandoo, M. uncinata

McFall 1997 -33.7290, 117.3217 438 Mix-L Broadcast 1997 15 A. accum., A. saligna, A. huegeliana, E. gardener, E. kochii, E. wandoo, M. uncinata

Batterns 1 -38.6674, 145.9886 869 Mix-L Broadcast 2001 11 A. melanoxylon, E kitsoniana, E. ovate, E. viminalis, M. ericifolia



Batterns 4 -38.6674, 145.9886 864 Mix-L Broadcast 2001 11 M. ericifolia

Suttons -38.3998, 145.8996 1050 Mix-B Tubestock 2004 8 E. globulus, E. regnans, Olearia argophylla, Pomaderris aspera

Bendigo trial -36.7859, 144.6408 514 Mal-B Tubestock 2003 8 E. polybractea

W Tellefson -36.3294, 142.8974 370 Mal -B Tubestock 2004 7 Mix of E. polybractea and E. loxophleba ssp. lissophloia

E Tellefson -36.3294, 142.8974 370 Mal -B Tubestock 2004 7 Mix of E. polybractea and E. loxophleba ssp. lissophloia

Weenya sp1 -33.3422, 145.8037 366 Mal -L Tubestock 2004 7 Unknown mallee eucalypt species

Weenya sp2 -33.3422, 145.8037 366 Mal -L Tubestock 2004 7 E. plenissima

Brotherony 4 -33.1368, 146.6380 378 Mal -L Tubestock 2004 7 E. oleosa/E. plenissima

*Above-ground biomass harvested with operational harvester developed by Biosystem Engineering and the CRC FFI

Table A9.2.2. Summary of the plots studied within each of the new sites, including method of plot selection (E=Entire area sampled such that all trees within the planting were measured (no plots required); F-PS=Full site

survey followed by precision sampling; G-PS= GRTS to select a large number of plots from which precision sampling was based; SYS=Systematic sampling, SRS=Simple random sampling); approximate size of the planting

(ha), number of trees measured for stem diameters in the inventory (and number of plots across which these trees were measured), number of trees harvested (and the number of plots from which these trees were

harvested), height at which stem diameters were measured, area of the plot (and the total area across all plots), stocking (stems per hectares), PropTrees, Basal area (BA) and coefficient of variation of the BA.

Site Plot

selec-

tion

Size

(ha)

#Trees in

inventory

(plots)

#Trees

harvested

(plots)

Ht of diam.

measure

(cm)

Plot area

(total)

(ha)

Stocking

(sph)

Prop.

of

dead

PropTree BA

(m2 ha

-1)

CV BA

(%)

‘Direct measurement’ plantings

Strathearn F-PS 4.30 9,499 (0) 1,357 (12) 130,10 0.040 (0.48) 2,827 0.02 0.83 11.37 148

Moir F-PS 5.05 13,175 (0) 1,300 (12) 130,10 0.040 (0.48) 2,708 0.04 0.46 4.72 178

Jenharwill F-PS 1.52 3108 (62) 344 (6) 130,10 0.009 (0.05) 6,456 0.04 0.04 16.92 141

Gumbinnen G-PS 18.4 3,034 (38) 504 (6) 130,10 0.040 (0.22) 2,282 0.00 0.10 4.38 172

Moorland 1 F-PS 2.76 542 (30) 50 (4) 50 0.090 (0.36) 139 0.00 1.00 2.52 74.2

Moorland 2 F-PS 1.99 581 (22) 88 (4) 50, 10 0.090 (0.36) 244 0.52 0.52 2.88 73.4

McFall 1990 G-PS 1.46 1,145 (35) 115 (3) 130, 10 0.011 (0.36) 2,440 0.02 0.90 30.50 127

Leos SYS 1.67 470 (51) 96 (10) 130 0.011 (0.11) 845 0.03 0.40 26.61 104

Pepal F-PS 2.39 4,636 (24) 77 (3) 10 0.014 (0.04) 1,863 0.00 1.00 8.71 73.3

Bird F-PS 0.57 790 (9) 41 (3) 10 0.010 (0.03) 1,356 0.00 1.00 11.92 67.7

Quicke F-PS 0.57 2,098 (4) 29 (3) 10 0.005 (0.02) 1,894 0.00 1.00 25.55 69.9

Temby SYS 5.17 1,020 (39) 44 (3) 50 0.010 (0.03) 1,433 0.00 1.00 6.92 44.8

Angle SYS 4.67 1,030 (36) 34 (3) 50 0.010 (0.03) 1,100 0.00 1.00 3.45 58.9

Wycheproof poly* SRS 2.30 275 (4) NA 10 0.073 (0.29) 943 0.00 1.00 3.7 59

Wycheproof lox* SRS 2.10 267 (4) NA 10 0.071 (0.28) 946 0.00 1.00 4.76 54

90

Carmody* SRS 2.20 297 (10) NA 10 0.030 (0.30) 1,283 0.00 1.00 4.52 58

Batters poly* SRS 3.60 392 (4) NA 10 0.051 (0.21) 1,949 0.00 1.00 11.79 49

Batters lox* SRS 3.60 479 (6) NA 10 0.042 (0.26) 1,912 0.00 1.00 5.96 58

SW Watts ploy* SRS 1.00 204 (4) NA 10 0.034 (0.14) 1,511 0.00 1.00 6.62 54

SW Watts lox* SRS 1.00 340 (4) NA 10 0.056 (0.23) 1,511 0.00 1.00 7.01 52

N Watts lox* SRS 1.90 573 (3) NA 10 0.090 (0.27) 2,122 0.00 1.00 10.23 57

Campbell poly* SRS 0.80 71 (2) NA 10 0.010 (0.03) 1,533 0.00 1.00 13.71 40

Campbell lox* SRS 0.80 27 (1) NA 10 0.003 (0.01) 900 0.00 1.00 9.66 16

Weenya sp3* SRS 2.30 257 (4) NA 10 0.044 (0.18) 1,684 0.00 1.00 5.39 62

Weenya sp4* SRS 2.30 142 (3) NA 10 0.035 (0.11) 1,351 0.00 1.00 4.38 56

Weenya sp5* SRS 2.30 38 (2) NA 10 0.018 (0.04) 1,085 0.00 1.00 2.58 55

Brotherony 1* SRS 4.20 257 (6) 108 10 0.035 (0.21) 1,233 0.00 1.00 4.92 61

Brotherony 2* SRS 4.20 525 (6) NA 10 0.063 (0.38) 1,388 0.00 1.00 8.54 44

Brotherony 3* SRS 4.20 462 (5) NA 10 0.069 (0.35) 1,344 0.00 1.00 6.1 69

Kalawa* SRS 30.0 2,169 (6) NA 10 0.126 (0.76) 2,872 0.00 1.00 13.79 83

‘Indirect estimates’ plantings

Gunbower 1 E 2.45 2,538 (0) 97 50, 10 NA (2.45) 1,036 0.00 0.34 5.71 151

Gunbower 2 E 3.20 2,079 (0) 97 50, 10 NA (3.20) 650 0.00 0.33 3.43 108

Lynvale G-PS 3.31 1,604 (20) 80 130 0.037 (0.74) 869 0.01 0.17 14.38 168

Palomar shrubs SRS 2.40 626 (9) 94 10 0.080 (0.72) 350 0.01 0.00 1.27 99.4

Palomar trees E 0.84 294 (6) 6 130 0.140 (0.84) 2,184 0.01 1.00 11.96 82

Netherleigh E 8.60 2,987 (6) 191 130 1.433 (8.60) 325 0.01 0.95 3.97 87.2

Moorelands belt E 0.60 187 NA 50 NA (0.10) 330 0.00 0.21 10.99 85.3

McFall 1988 G-PS 0.22 314 (5) 20 130 0.015 (0.38) 5,688 0.01 0.12 26.20 160

McFall 1997 G-PS 1.96 2133 (25) 178 130 0.025 (0.13) 2,512 0.03 0.91 36.23 111

Batterns 1 SRS 0.32 378 (8) 109 130 0.010 (0.08) 4,638 0.00 0.82 35.89 123

Batterns 2 SRS 0.43 512 (7) 21 130 0.020 (0.14) 3,652 0.01 0.21 27.11 128

Batterns 3 SRS 0.60 1.253 (7) 24 130 0.018 (0.13) 7,009 0.03 0.81 38.11 126

Batterns 4 SRS 0.10 251 (1) NA 130 0.018 (0.02) 13,971 0.00 0.00 9.99 226

Suttons E 0.75 1,043 88 130 NA (0.67) 1,428 0.04 0.27 17.86 148

Bendigo trial SRS 0.16 133 (10) NA 10 0.008 (0.08) 2,075 0.00 1.00 14.66 46

W Tellefson SRS 1.00 216 (4) NA 10 0.030 (0.12) 1,800 0.00 1.00 5.22 56

E Tellefson SRS 1.90 281 (3) NA 10 0.105 (0.32) 892 0.00 1.00 2.09 72

Weenya sp1 SRS 2.30 33 (2) NA 10 0.011 (0.02) 1,528 0.00 1.00 5.78 56

Weenya sp2 SRS 2.30 107 (2) NA 10 0.038 (0.08) 1,402 0.00 1.00 4.65 52

Brotherony 4 SRS 4.20 115 (3) NA 10 0.036 (0.11) 1,181 0.00 1.00 4.36 58

*Above-ground biomass harvested with operational harvester developed by Biosystem Engineering and the CRC FFI

91

Table A9.2.3. Summary of site-based above-ground allometrics. Here CF refers to the Snowdon (1991) correction factor, and EF refers to

model efficiency (Soares et al. 1995).

Site Species Diam a b CF EF N

Strathearn E. blakelyi DBH -1.83 2.15 1.14 0.966 34

E. camaldulensis DBH -1.89 2.22 1.00 0.964 21

E. cinerea DBH -1.21 1.83 1.15 0.868 27

E. crenulata DBH -1.97 2.35 1.08 0.952 10

E. macarthurii DBH -2.08 2.26 1.02 0.937 23

E. mannifera DBH -2.46 2.45 1.04 0.968 19

E. melliodora DBH -1.49 1.96 1.07 0.964 145

E. polyanthemos DBH -1.45 2.05 1.10 0.964 39

E. stellulata DBH -1.63 2.04 1.02 0.967 37

E. viminalis DBH -2.14 2.27 1.08 0.948 321

A. baileyana DBH -1.27 2.22 1.07 0.710 37

A. decurrens DBH -1.72 2.43 1.07 0.967 10

A. cardiophylla D10 -2.11 2.26 1.15 0.823 11

A. rubida D10 -3.36 2.79 1.04 0.960 10

Moir E. leucoxylon DBH -1.24 2.03 1.07 0.985 9

E. loxophleba DBH -1.66 2.19 1.08 0.915 41

E. occidentalis DBH -1.84 2.27 1.04 0.947 83

E. phaenophylla DBH 0.32 1.38 1.01 0.985 7

E. platypus DBH -0.01 1.57 0.89 0.982 111

E. pluricaulis DBH -1.24 2.24 1.03 0.937 109

E. spathulata DBH -1.30 2.22 1.00 0.896 206

E. sporadica DBH -1.19 2.10 0.97 0.918 13

E. utilis DBH -0.49 1.85 1.14 0.679 18

A. acuminata DBH -1.26 1.83 1.02 0.981 11

A. micobotrya DBH -1.55 2.13 1.07 0.971 33

A. cyclops D10 -1.44 2.01 1.06 0.851 8

Jenharwill E. leucoxylon DBH -1.70 2.20 1.01 0.991 14

A. decurrens DBH -2.01 2.43 1.02 0.964 14

A. brachybotrya D10 -2.19 2.27 1.03 0.962 8

A. calamifolia D10 -2.29 2.45 1.02 0.947 122

A. hakeoides D10 -2.05 2.05 1.11 0.946 90

A. pycnantha D10 -1.96 2.04 1.12 0.893 38

Gumbinnen E. fasiculosa DBH -2.00 2.37 1.00 0.999 5

E. largiflorens DBH -1.30 2.05 1.01 0.926 38

A. pycnantha D10 -1.48 1.72 1.15 0.722 48

A. trineura D10 -2.78 2.55 0.94 0.970 46

Moorland 1&2 E. calycogona D50 -1.55 2.18 1.02 0.989 7

E. incrassata D50 -2.37 2.44 1.06 0.921 10

E. leptophyll D50 -2.68 2.49 1.04 0.900 8

E. phenax D50 -3.36 2.77 1.01 0.974 7

E. porosa D50 -2.62 2.42 1.04 0.982 31

E. socialis D50 -3.65 2.79 1.02 0.985 8

A. calamifolia D10 -2.02 2.20 1.15 0.881 6

Melaleuca sp D10 -4.54 2.85 1.04 0.983 13

Casurina sp D50 -3.69 2.82 1.07 0.961 7

McFall (all) E. albida DBH -1.10 2.15 1.02 0.964 18

E. argyphea DBH -1.23 2.17 1.00 0.992 7

E astringens DBH -1.51 2.22 1.00 0.986 9

E. gardineri DBH -1.79 2.39 1.00 0.986 24

E. kochii DBH -1.86 2.31 1.03 0.976 17

E. sargentii DBH -1.66 2.27 1.01 0.957 25

E. wandoo DBH -1.69 2.16 1.15 0.951 56

A. acuminata DBH -1.75 2.32 0.96 0.953 24

A.saligna DBH -1.52 2.15 0.98 0.965 25

Melaleuca sp. D10 -3.03 2.36 1.04 0.922 20

Casurina sp. D10 -1.16 2.03 1.09 0.977 42

Shrub sp. D10 -2.87 2.24 1.02 0.974 35

Leos E. golbulus DBH -1.70 2.15 1.04 0.990 8

E. kitsoniana DBH -1.48 2.07 1.07 0.977 8

E. melliodora DBH -3.28 2.66 1.00 0.993 10

E. talyuberlup DBH -1.36 2.17 1.00 0.999 6

E. tereticornis DBH -0.04 1.65 1.09 0.986 5

A. baileyana DBH -0.81 1.89 0.93 0.981 7

A. penninervis DBH -1.00 2.02 0.95 0.952 22

Melaleuca sp. DBH -2.53 2.38 1.07 0.952 8

Pepal E. lox. ssp. lissophloia D10 -3.21 2.71 0.99 0.935 74

Bird E. lox. ssp. lissophloia D10 -3.05 2.66 1.00 0.940 38

92

Quicke E. lox. ssp. lissophloia D10 -2.73 2.47 1.00 0.980 29

Temby E. lox. ssp. lissophloia D50 -2.02 2.32 1.01 0.951 43

Angle E. lox. ssp. lissophloia D50 -1.72 2.13 1.02 0.926 33

Gunbower 1&2 E. camaldulensis D50 -3.07 2.49 1.01 0.934 16

E. largiflorens D50 -1.98 2.14 1.01 0.972 19

E. occidentalis D50 -2.56 2.51 1.02 0.995 19

A. salicina D50 -2.37 2.24 1.00 0.922 13

A. stenophylla D50 -2.49 2.42 0.98 0.942 16

Melaleuca sp D10 -2.80 2.41 1.06 0.952 14

Lynvale E. viminalis DBH -2.21 2.34 1.04 0.981 16

A. mearnsii DBH -1.47 2.24 1.01 0.996 20

A. melanoxylon DBH -1.60 2.16 1.00 0.987 27

A. pycnantha DBH -1.90 2.33 0.99 0.973 17

Palomar E. camal. & E. side DBH -1.90 2.37 1.00 0.969 6

A. deanei D10 -2.01 2.20 0.80 0.888 11

A. hakeoides D10 -2.20 2.18 1.00 0.982 17

A. pendula D10 -2.33 2.26 1.02 0.986 7

A. rigens D10 -2.76 2.46 1.02 0.984 21

Shrubs D10 -2.48 2.16 1.09 0.915 23

Netherleigh E. crebra DBH -2.03 2.27 1.03 0.946 15

C. intermedia DBH -2.14 2.19 1.03 0.966 13

E. melanophloia DBH -2.83 2.50 1.07 0.929 14

E. moluccana DBH -1.68 2.32 1.01 0.967 26

L. sauveolens DBH -1.78 2.04 1.02 0.951 14

C. tesselaris DBH -2.40 2.35 1.03 0.956 22

E. tereticornis DBH -2.37 2.44 1.05 0.966 65

A. disparrrima DBH -1.99 2.30 1.03 0.974 22

Batterns 1-4 E. kitsoniana DBH -1.58 2.14 0.97 0.978 26

E. obliqua DBH -2.16 2.23 1.00 0.954 14

E. ovata DBH -2.14 2.34 0.98 0.991 21

E. viminalis DBH -2.26 2.37 1.01 0.975 20

A. melanoxylon DBH -2.08 2.26 1.04 0.984 22

Melaleuca sp DBH -1.91 2.19 0.96 0.978 33

Shrubs D10 -2.59 2.15 1.08 0.792 13

Suttons E. golbulus DBH -2.37 2.46 0.98 0.995 8

E. viminalis DBH -1.01 2.05 1.00 0.999 7

A. dealbata DBH -1.98 2.37 0.99 0.998 8

Shrubs D10 -2.81 2.30 1.02 0.893 61

Brotherony E. polybractea D10 -1.75 2.12 1.01 0.909 108

Jullatern/Shananvale Alstonia scholaris DBH -1.29 2.11 1.00 0.955 37

Araucaria cunninghamii DBH -1.75 2.29 1.00 0.998 6

Ble. involucrigera DBH -1.50 2.15 1.01 0.996 14

Elae. angustifolius DBH -2.17 2.37 1.03 0.989 24

E. cloeziana DBH -4.26 3.06 1.00 0.998 10

Flindersia brayleyana DBH -1.75 2.29 1.00 0.995 19

Melicope elleryana DBH -0.81 1.82 1.01 0.979 5

Xan. chrysanthus DBH -3.15 2.83 0.99 0.995 5

Steps used to obtain biomass estimates of plantings

Stratification and definition of ‘the planting’

At some larger (>10 ha) plantings such as Gumbinnen and Netherleigh, the sites were stratified based on species mix, planting year and

management regime. All areas within the sites which were not actually planted (i.e. avoided by the direct seeder due to the presence of

rocks or remnant paddock trees etc.) were excluded from the definition of the planting.

Sampling design: establishment of sample plots

As shown in Table A9.2.2, depending on the number of trees within the planting and the type of planting, five alternative approaches were

used to establish plots; (i) for relatively small plantings with biomass to be estimated by ‘indirect’ allometrics, inventory of all individual

trees and shrubs within the entire (E) planting so no plots were required, (ii) where direct biomass harvest was undertaken, full inventory

of all individual trees and shrubs within the entire planting followed by precision sampling (F-PS) to select representative plots for

harvesting, (iii) use of GRTS (Section 3.1) to establish plots within which the survey was done to inform the precision sampling and selection

of representative plots from within the larger wider number (G-PS), (iv) Systematic sampling (SYS) where plots were placed strategically

across the planting, and (v) where measurements were taken more opportunistically (i.e. generally taken as extra time was available on-

site) and time was not available for precision sampling, plots were established using simple random sampling (SRS).

93

Inventory

Within each selected plot, stem diameters of all individuals were measured. Height at which diameters were measured varied between

plantings based on the heights of the trees and the average height at which they branched into multiple stems (Table A9.2.2). As a general

rule, the diameters were measured as high as possible (up to 130 cm height), but below the height at which the stem became multi-

stemmed. This was to decrease measurement errors. Generally for all shrub species, diameters were measured at 10 cm height. Species, or

at least life form, was also recorded with each diameter measurement. For analysis, an ‘equivalent diameter’ was calculated for multi-

stemmed individuals (=sqrt[d12+d2

2+d3

2+....dn

2], where d1, d2 etc. are the diameter measures of each individual stem).

Harvesting of above-ground biomass

Two different approaches were used depending on whether the site was a direct harvest site or not (i.e. an indirect site):

‘Direct measurements’. Within each plot, all individual trees were weighed separately using a suspended load cell. All dead trees and/or

shrubs were weighed in bulk, as were the ‘smaller shrubs’, which could contain a mixture of species. However, if there were significant

quantities of specific shrub species such as melaleucas, these were weighed separately, again in bulk.

‘Indirect estimates’. For each of the key species at each site (Table A9.2.1), individuals of a range of stem sizes were randomly selected for

harvesting. This was to provide data for the generation of site-specific allometric equations. Although at least 20 individuals were targeted

for each allometric, at some sites we were unable to achieve this because of either (i) landowners wishes that we minimise destructive

harvesting, or (ii) there were insufficient numbers of an individual species present.

Moisture content sub-samples of the canopy and bole

As reported by Paul et al. (2011), moisture contents do not significantly vary within the 3-5 days generally required for harvesting a site.

The only exception would be if rain occurred during this time. Generally for each key species, at least three representative individuals were

selected for obtaining moisture content sub-samples. Each selected tree was divided into crown (all foliage and twigs less than about 5 mm

diameter) and the remaining bole (stem and branches). The fresh weights of these two components were measured in the field, and then

sub-samples (at least three of about 2-3 kg) were taken of each component, weighed and transported back to the laboratory and dried (at

70oC) until the dry weights stabilised. For the bole samples, this could take several weeks. Using the average moisture content of sub-

samples of each component, a weighted average whole-tree moisture content was determined based on the relative contribution to total

fresh weight of the individual. Note that for shrubs with no pronounced stem, separate bole components were not required. Once the

percentage moisture in fresh (or green) material was determined, the dry weight equivalents of material weighed in the field was then

calculated.

Harvesting of below-ground biomass

At direct harvested sites, harvested plots were also used for excavation of roots.

Area of excavation. As described above, sub-plots for root excavation were also selected using precision sampling. They were generally 10

m x 10 m. For block plantings, roots were excavated within plot boundaries while for linear plantings, roots were also excavated 8-10 m out

from the planting edge into the paddock (i.e. as far out as required to recover most of the root biomass). The only exceptions were at

plantings where stocking was too low (<500 sph, such at Moorland) to make whole plot root excavation efficient. At these sites, roots were

excavated around individual trees or shrubs. The area of excavation was halfway to the nearest neighbour on each of the four sides, as it

was assumed that roots growing out of these areas were approximately equal to roots growing into these areas. Given the relatively low

stocking, and the fact that root biomass reduces sharply with increasing distance from the tree stump (Green et al. 2007), these areas of

excavation tended to be quite large and there was little evidence of roots going into or out of them. In linear plantings where the plot

includes trees along edge rows, all coarse roots (>2 mm) extending into the adjacent paddock were harvested.

Depth of excavation. Excavation depths did not exceed 2 m depth. However, there was either no evidence of tap roots extending deeper

than this depth, or where there was (i.e. Gumbinnen, Moorelands), the tap root diameters at this depth were generally small (<100 mm)

and so did not appear to represent much of the total root biomass. The unaccounted for below-ground biomass in these deeper (>2 m) tap

roots was probably much great than the finer sized roots (<2 mm) which were also not captured (see below). Indeed, Chenk and Jackson

(2002) noted that globally 50% of all roots are within the upper 0.3 m while 95% of all roots are within the upper 2 m of the soil profile.

Size of roots excavated. Only coarse roots (>2 mm) were recovered, and to assist in extracting these from the soil, an excavator and/or

back-hoe was used. Coarse roots were manually ‘picked’ from the excavated soil, either directly from the soil as it was deposited by the

excavator at a working point (Moorland, Gumbinnen and Leos sites), or from sieving tables (Strathern, Moir, Jenharwill, McFall, Pepal, Bird,

Quicke, Angel and Tempy) which comprise about 200 mm mesh size. Although not all roots were recovered, as reported by Paul et al.

(2011), on-going studies at Strathearn (Vuillot 2011, unpublished, ANU) suggest fine roots (<2 mm) were observed to represent <4% of the

total biomass. In order to assess the mass of any fine roots falling through the sifting table, samples of the sifted soil at this planting were

collected on a per soil horizon basis. These soils are sieved back in the laboratory to determine the mass of roots ‘missed’ per volume of

soil excavated for each soil horizon.

Soil contamination. Where root samples which were taken from particularly clayey soils (i.e. Strathearn and Jenharwill), root samples were

excavated, transported and stored in a dry environment (i.e. glass house or shed), shaken, and then a high pressure hose used to remove

remaining soil before being weighed.

94

Moisture content sub-samples of roots

All excavated roots (in the case of Strathearn, Moir and Jenharwill), or sub-samples of roots (for other sites), were oven-dried at 70oC dry

until weights stabilised to determine the percentage moisture contents such that root dry weights could be calculated from field measures

of root mass.

Calculation of above-ground allometrics

At all sites, allometrics were derived for each key species shown in Table A9.2.1. Allometric power function equations were used to predict

biomass from independent variables;

y = a’xb, and their linear equivalents, Ln(y) = a + b × Ln(x) Equation A9.2.1

where

y is the dependent variable (biomass, kg DW tree-1

),

x is the independent variable (stem diameter, cm), and

a the intercept coefficient, and a’=exp(a) or a=Ln(a’)

b the scaling exponent.

Parameters a and b were estimated using a least squares approach. Natural logarithm transformations were used when fitting parameters

given biomass data often exhibited heteroscedasticity (i.e. variance was not constant across all observations).

As logarithmic regressions produce inherently biased estimates of biomass, bias corrections were calculated using the ratio of arithmetic

sample mean and mean of the back-transformed predicted values from the regression as described by Snowdon (1991);

y = Exp[ a + b × Ln(x) ] x CF Equation A9.2.2

where

CF= Correction factor as described by Snowdon (1991),

See Equation A9.2.1 for definition of other parameters.

However, various alternative methods have been proposed for correcting this bias when back-transforming data (Baskerville 1972; Ung

and Végiard 1988). Lambert (2005) reported that the Baskerville estimator may be biased for small sample sizes (Flewelling and Pienaar

1981), and it tends to overestimate the true bias (Hepp and Brister 1982). More recently, other workers (Parresol 2001; Lambert 2005)

have found that modeling the error structure on the original data scale gives results as good as or even better than applying a

transformation. Weighted non-linear models have also been used where it may not be necessary to correct for transformational bias

(Brown et al. 1989; Parresol 1999; Ritson and Sochacki 2003; Morote et al. 2012). A generalized linear model with gamma distribution and

log link function may also be used to avoid the problem of back transformation (Ketterings et al., 2001; Kuyah et al. (2012a). Others have

used a weighted combined model for estimating biomass or stem volume (e.g. Bi and Hamilton 1998; Bi et al. 2004). Further work is

currently underway to test the accuracy of these various approaches to overcoming traditional problems associated with back-

transformation of allometric relationships.

To evaluate model efficiency of allometric equations, statistics used were based on those recommended in a review by Parresol (1999), the

most important being the ‘fit index’, otherwise known as model efficiency (EF, Soares et al. 1995). For other species, and for below-ground

biomass estimations at ‘indirect’ sites, generic allometrics were used as described in Section 5.

In order to develop generic allometrics based on DBH or D10, relationships between stem diameters measured at different heights from

the same trees or shrubs were required. These are given in Figures A9.2.1 and A9.2.2.

Estimation of biomass and root-to-shoot ratios

At each site biomass in above- and below-ground components was estimated and root-to-shoot ratios calculated. This was done by

applying the appropriate allometric to the inventory of stem diameters. However, for the 13 ‘direct’ sites, direct whole plot measures of

above- and below-ground biomass were obtained and provided a test of the estimates of R:S ratios based on application of allometrics.

At these ‘direct’ sites, we calculated site average R:S ratio by taking the most accurate site estimates of roots (t DM ha-1

, determined from

direct measures on root sub-plots) and dividing this by the most accurate site estimates of shoots (t DM ha-1

, determined from direct

measures across all plots), rather than simply calculating an average R:S ratio from each sub-plot separately. This helps to overcome the

problem that, in the relatively small root plots harvested, it is hard to accurately estimate the actual above-ground biomass contributing to

measured roots, due to root contributions from trees growing outside the plot boundary. The estimates based on larger plots minimise this

source of uncertainty, and conversely, these R:S ratios should never be applied to single trees or shrubs.

95

Figure A9.2.1. Relationship between stem diameters measured at breast height for eucalypts and other trees (a-d), acacia trees (e-h), and

casuarina spp. (i-l) than that measured at 10, 20, 30 and 50 cm from the ground. Also, the relationship between stem diameter measured

at 10 cm from the ground for acacia shrubs (m-p) and other shrub species (q, r) and that measured at 0, 20, 30 and 50 cm from the ground.

Here, D0, D10, D20, D30, D50 and DBH refer to stem diameter measured at 0, 10, 20, 30, 50 and 130 cm from the ground, respectively.

(d) Eucalypts & other trees

y = 0.9097x - 0.5061

R² = 0.9677; N=1,772

0

10

20

30

40

50

60

0 10 20 30 40 50 60

DB

H (

cm)

D50 (cm)

(c) Eucalypts & other trees

y = 0.8501x - 0.501

R² = 0.9413; N=1,320

0

10

20

30

40

50

60

0 10 20 30 40 50 60

DB

H (

cm)

D30 (cm)

(b) Eucalypts & other trees

y = 0.8273x - 0.6168

R² = 0.922; N=1,292

0

10

20

30

40

50

60

0 10 20 30 40 50 60

DB

H (

cm)

D20 (cm)

(a) Eucalypts & other trees

y = 0.8181x - 0.7631

R² = 0.9317; N=1,723

0

10

20

30

40

50

60

0 10 20 30 40 50 60

DB

H (

cm)

D10 (cm)

(h) Acacia trees

y = 0.9304x - 0.4196

R² = 0.9696; N=358

0

10

20

30

40

0 10 20 30 40

DB

H (

cm)

D50 (cm)

(g) Acacia trees

y = 0.8905x - 0.3296

R² = 0.9576; N=215

0

10

20

30

40

0 10 20 30 40D

BH

(cm

)

D30 (cm)

(f) Acacia trees

y = 0.8336x - 0.1427

R² = 0.9373; N=146

0

10

20

30

40

0 10 20 30 40

DB

H (

cm)

D20 (cm)

(e) Acacia trees

y = 0.8247x - 0.2391

R² = 0.9506; N=336

0

10

20

30

40

0 10 20 30 40

DB

H (

cm)

D10 (cm)

(l) Casuarina spp.

y = 0.9175x - 0.3496

R² = 0.9139

0

10

20

30

40

0 10 20 30 40

DB

H (

cm)

D50 (cm)

(k) Casuarina spp.

y = 0.8593x - 0.4909

R² = 0.9547; N=27

0

10

20

30

40

0 10 20 30 40

DB

H (

cm)

D30 (cm)

(j) Casuarina spp.

y = 0.7814x - 0.2073

R² = 0.9603; N=27

0

10

20

30

40

0 10 20 30 40

DB

H (

cm)

D20 (cm)

(i) Casuarina spp.

y = 0.7704x - 0.0981

R² = 0.9171; N=736

0

10

20

30

40

0 10 20 30 40

DB

H (

cm)

D10 (cm)

(p) Acacia shrubs

y = 1.0753x + 1.3627

R² = 0.7059; N=17

0

10

20

30

0 10 20 30

DB

H (

cm)

D50 (cm)

(o) Acacia shrubs

y = 1.0431x + 0.2078

R² = 0.9657; N=273

0

10

20

30

0 10 20 30

DB

H (

cm)

D30 (cm)

(n) Acacia shrubs

y = 1.0301x + 0.0463

R² = 0.9918; N=273

0

10

20

30

0 10 20 30

DB

H (

cm)

D20 (cm)

(m) Acacia shrubs

y = 0.9577x - 0.3258

R² = 0.9667; N=13

0

10

20

30

0 10 20 30

D1

0 (

cm)

D0 (cm)

(r) Melaleuca

y = 1.2737x + 0.8876

R² = 0.9286; N=50

0

10

20

30

0 10 20 30

DB

H (

cm)

DBH (cm)

(q) Melaleuca

y = 1.1036x + 0.4546

R² = 0.9813; N=76

0

10

20

30

0 10 20 30

D1

0 (

cm)

D50 (cm)

96

Figure A9.2.2. Relationship between stem diameters measured at 10 cm from the ground (D10) for 300 mallee eucalypts and that

measured at 50 cm from the ground (D50). Datasets were obtained from Table 1 for all available sites where both D10 and D50 were

measured on the same tree.

9.3 Database

A9.3.1. Datasets collated on individual tree above-ground biomass from environmental and mallee plantings. Data from mixed-species

environmental plantings were collated for nine different life forms; acacia shrubs, dead acacia shrubs, acacia trees, dead acacia trees,

eucalypts (and some other large tree species), dead eucalypts, casuarina, melaleuca and other shrubs. Data from mallee eucalypts were

collated for three different species (Poly, E. polybractea; Lox, E. loxophleba subsp lissophloia; and Kochii, E. kochii subsp. borealis or subsp

plenissima). To download the non-confidential datasets, see CSIRO Data Access Portals at the links provided with Paul et al. (2013b-e)

Source Lead

organisation

N Published

Acacia shrubs

This project CSIRO 469 This publication

J. England CSIRO 41 Paul et al. (2007)

J. England CSIRO 28 England et al. (2006)

J. Carter CSIRO 17 Hawkins et al. (2010)

T. Hobbs SA DEWNR 17 Hobbs et al. (2010)

Dead acacia shrubs

J. England CSIRO 41* Paul et al. (2007)

J. England CSIRO 28* England et al. (2006)


Acacia trees




G. McAurthur AusCarbon 25 CONFIDENTIAL

R. Sudmeyer DAFWA 25 pers. com. (2011)

D. Forrester Uni Melb 12 Forrester et al. (2005)


B. Rose CN 1 CONFIDENTIAL

J. Jonson Threshold Env. 7 Jonson and Freudenberger (2011)

Dead acacia trees




Eucalypts, or other key tree genera

This project CSIRO 1,965 This publication

C. Barton NSW DPI 145 Barton and Parekh (2006)





S. Hamilton CSIRO 35 Hamilton et al. (2005)

K. Paul CSIRO 24 Paul et al. (2008)

Tivi Theiveyanathan CSIRO 19 pers. com. (2010)



Dead eucalypts



K. Paul CSIRO 24* Paul et al. (2008)


Tivi Theiveyanathan CSIRO 19* pers. com. (2010)

S. Hamilton CSIRO 18* Hamilton et al. (2005)

Casuarinas

y = 0.838x + 0.179

R² = 0.94; N=300

0

10

20

30

0 10 20 30

D1

0 (

cm)

D50 (cm)

97






Melaleuca







Other shrubs



Other mallee eucalypt species

A. Peck WA DEC 157 Peck et al. (2012)

D. Wildy Fares Rural 108 pers com. (2012)

D. Wildy UWA 93 Wildy and Pate (2002); Wildy (2003)

P. Ritson DAFWA 47 pers com. (2012)

D. Mendham CSIRO 35 Grove et al. (2007)

J. Carter CSIRO 18 Carter et al. (2008)

K. Brooksbank DAFWA 9 Brooksbank & Bevan (2010); Brooks. & Goodwin (2012)

Lox mallee eucalypt species





P. Ritson DAFWA 51 NA


Poly mallee eucalypt species




J. Bartle WA DEC 124 Bartle et al. (2012)


P. Ritson DAFWA 40 pers com. (2012)


R. Bennett CSIRO 25 Mendham (2011)



R. Sudmeyer DAFWA 11 Sudmeyer and Daniels (2010)

*Biomass data of dead individual trees or shrubs were estimated from data on the biomass of tree or shrub components by assuming that

dead individuals would only have woody components (i.e. no foliage, bark and small twigs), and for eucalypts, would have diameters

equivalent to the diameter under bark (Paul et al. 2010).

Table A9.3.2. Datasets collated on individual tree below-ground biomass. Data from mixed-species environmental plantings were collated

for nine different life forms; acacia shrubs, dead acacia shrubs, acacia trees, dead acacia trees, eucalypts, dead eucalypts, casuarina,

melaleuca and other shrubs. Data from mallee eucalypts were collated for three different species (Poly, E. polybractea; Lox, E. loxophleba

subsp. lissophloia; and Kochii, E. kochii subsp. borealis or subsp plenissima. To download the non-confidential datasets, see CSIRO Data

Access Portals at the links provided with Paul et al. (2013b-e)

Source Lead

organisation Species N Published

J. Jonson Threshold Env. Acacia tree roots 7 Jonson and Freudenberger (2011)

R. Sudmeyer DAFWA Acacia tree roots 19 Sudmeyer and Daniels (2010)

K. Paul CSIRO Eucalypt roots 21 Paul et al. (2008)

C. Barton NSW DPI Eucalypt roots 16 Barton and Montague (2006)

Tivi Theiveyanathan CSIRO Eucalypt roots 17 pers. com. (2010)

J. Jonson Threshold Env. Eucalypt roots 44 Jonson and Freudenberger (2011)

This project CSIRO Eucalypt roots 32 This publication

J. Carter CSIRO Shrub roots 10 Hawkins et al. (2010)

This project CSIRO Shrub roots 13 This publication

K. Brooksbank DAFWA Lox & Poly 251 Brooksbank & Bevan (2010); Brooks. & Goodwin (2012)

R. Sudmeyer DAFWA Poly 25 Sudmeyer and Daniels (2010)

D. Wildy Fares Rural Lox, Poly & Kochii 20 pers com. (2012)

D. Wildy UWA Kochii 93 Wildy and Pate (2002); Wildy (2003)

98

Table A9.3.3. Datasets collated containing inventory data, including number of sites, whether or not species-and-site specific allometrics were developed, age of the sand, PropTrees or mallee species studies, stocking

rates, planting geometry, whether or not the stand was regrowth post coppice harvesting, and the site productivity (Pavg). To download the non-confidential datasets, see CSIRO Data Access Portals at the links provided

with Paul et al. (2013f,g).

Source Lead

organisation N

1 Specific

2

Age

(yrs)

PropTree3 or

Species4

Sph4 Geometry

5 Coppice

6 Pavg

7 Published

A. Peck WA DEC 535* Y 1-16 Poly, Lox, Kochii 920-3920 Linear Y, N 1.29-6.97 Peck et al. (2012)

M. Rooney GA 218 N 4-30 0.00-1.00 82-6110 Block, Linear N 2.89-13.85 CONFIDENTIAL

T. Lewis Qld DAFF 138* N 2-19 1.00 133-926 Block, Linear N 8.36-24.11 CONFIDENTIAL

J. England CSIRO 79 N 3-29 0.02-1.08 128-10104 Block, Linear N 3.14-8.03 Paul et al. (2007)

T. Hobbs SA DEWNR 67 N 10-36 0.12-1.00 180-7010 Block, Linear N 3.51-7.39 Hobbs et al. (2010)

P. Ritson DAFWA 45 Y,N 5-15 Poly, Lox, Kochii 292-3759 Block, Linear N 2.89-5.96 CONFIDENTIAL

J. Bartle WA DEC 42 Y 2-8 Poly 2840-3200 Linear N 5.65-5.82 Bartle et al. (2012)

C. Lowson ANU 38 N 12-18 0.00-0.96 1330-22250 Block, Linear N 5.53-9.07 Lowson (2008)

M. Searson Hassall & Ass. 37 N 2-24 0.00-1.00 30-14140 Block, Linear N 3.45-7.49 Kesteven et al. (2004)

S. Cunningham Monash 36 N 5-46 0.33-1.00 240-1130 Block N 4.86-9.31 CONFIDENTIAL

J. Carter CSIRO 30 Y 3-7 Kochii 1600-2500 Linear N 3.35 Carter et al. (2008)

This project CSIRO 28 Y 7-14 Poly, Lox, Kochii, Other 940-2870 Block, Linear Y, N 3.24-5.80 This publication

N. Preece Biocarbon 24 N 5-20 1.00 390-2570 Block N 13.13-20.66 Preece et al. (2012)

T. Hobbs SA DEWNR 23 N 11 Poly, Other 93-2220 Block N 2.95-5.88 Hobbs et al. (2010)

This project CSIRO 22 Y 8-22 0.00-1.00 140-13791 Block, Linear N 3.58-15.18 This publication

D. Wildy Fares Rural 21 Y 4-15 Poly, Lox, Kochii 830-3020 Block, Linear N 2.41-5.76 CONFIDENTIAL

Z. Read ANU 20 N 1-19 0.00-0.96 1438-20768 Block, Linear N 5.57-7.26 pers. com. (2012)

T. Powe GreenFleet 17 N 4-10 0.00-1.00 270-2370 Block N 4.16-12.74 CONFIDENTIAL

G. McArthur AusCarbon 16 Y 4-17 0.00-1.00 96-2400 Block, Linear N 2.31-3.00 CONFIDENTIAL

J. England CSIRO 16 N 5-18 0.07-1.00 270-4870 Block, Linear N 4.86-7.13 England et al. (2006)

R. Bennett CSIRO 10 Y 5-6 Poly 1420-1490 Linear N 5.93 Mendham (2011)

B. Rose Carbon Neutral 9 N 10-37 0.00-1.00 108-1250 Block N 2.82-3.56 CONFIDENTIAL

J. Bartle WA DEC 9 Y 9-14 Poly 2100-2630 Linear N 2.82-2.87 Bartle et al. (2012)

G. McArthur AusCarbon 6 Y 12-20 Lox 180-590 Block, Linear N 2.81 CONFIDENTIAL

D. Mendham CSIRO 4 Y 3-5 Poly, Lox, Kochii 2430-5040 Block, Linear N 2.31-4.60 Grove et al. (2007)

J. Carter CSIRO 2 Y 11-12 Poly 4790-5000 Linear N 4.04-4.06 pers. com. (2012) 1N represents the number of plantings from this source which were used in the calibration of Tree Yield Formula (*Not all independent sites, and some were repeated measures at the same site);

2Refers to whether or not a site-specific above-ground allometric was applied (Y) or not (N);

3Represents the proportion of individuals within the plantings which were trees;

4Data from mallee eucalypt plantings were collated from four different species; Poly, E. polybractea; Lox, E. loxophleba subsp lissophloia; and Other (namely E. kochii subsp. borealis or subsp plenissima), which could be a

raft of different mallee eucalypt species; 4Stems per hectare, with multi-stemmed trees having an equivalent stem diameter calculated (=sqrt[d1

2+d2

2+d3

2+....dn

2], where d1, d2 etc. are the diameter measures of each individual stem)

5Planting geometry was either linear or block. For mixed species plantings; narrow linear (<20 m width), wide linear (20-40 m width), blocks (>40 m width). For mallee eucalypts plantings; narrow linear (100% edge trees,

or 2-row linear plantings), wide linear (25-50% edge trees), blocks (0% edge trees); 6Indicated whether the planting was coppice harvested; (Y) or not (N); and

7Pavg represents the plantings Forest Productivity Index at the planting site.

99

Table A9.3.4. Auxiliary data collated for each planting in the inventory databases obtained from collaborators (see Table A9.3.3), Department of

Environment’s NIS, or obtained from calculations of collated data.

Source of data Data collected

Collaborators Location (latitude and longitude)

Date of inventory

Date of planting

Landscape position (Upper slope, mid-slope, lower-slope, gully or riparian)

Previous land use

Soil textural class

Poor drainage

Watered during establishment, or irrigated

Grazing history, if any

Species planted for mallee eucalypts

Surface soil salinity (mS m-1

)

Depth to water table (m)

Whether the planting was coppiced

Number of rows planted (i.e. planting geometry)*

Percentage of trees that were measured which were edge trees*

Planting width (m)

NIS Pavg (annually during actual period of growth)

Maximum above-ground biomass anticipated (t DM ha-1

)

Average temperatures (annually during period of growth)

Average rainfall (annually during period of growth)

Soil clay content

Soil nutrient index

Soil available water holding capacity

Calculated from Stocking (sph)

inventory data Number of individuals measured

Proportion of individuals measured which where trees (PropTrees)

Proportion of biomass which was dead

Average basal area (m2 ha

-1)

Percentage coefficient of variation in basal area (%)

*Planting geometry was treated differently in mallee eucalypts than in mixed species environmental plantings. For mallees, there were generally 2

m between rows, and so the number of rows reflected whether the planting was linear or a block while for environmental plantings, inter-row

distances varied substantially, and so planting width was either recorded by the collaborator or estimated using Google Earth. Note that for mallee

eucalypts, measurement plots were often placed such that there was a greater percentage of edge trees in these plots than there were in the actual

planting (i.e. plot might include one inner row and one outer row in a 4 row linear planting).

Table A9.3.5. Inputs to the uncertainty analysis. Approximate error were generally implemented as modifiers of specific inputs in the calculations of

t DM ha-1

, and as such had a most likely value of 1, but varied between a minimum and maximum value that was based on the assumed percentage

error on these specific inputs.

Errors Input modified Triangular distribution of

modifier used Min Ave. Max

Measurement errors

Stem diameters when single-stemmed tree Stem diam. 0.944 1.00 1.056

Stem diameters when multi-stemmed tree Stem diam. 0.931 1.00 1.069

Canopy volume index (CVI=htxCW1xCW2) CVI 0.826 1.00 1.174

Tree height Ht 0.945 1.00 1.055

Plot area when clear between-tree distances Plot area 0.967 1.00 1.033

Plot area when variable between-tree distances Plot area 0.956 1.00 1.044

Errors due to assumptions made in calculations (application of allometrics)

Application of allometrics that may not be appropriate+ kg tree

-1 0.800 1.00 1.200

Application of generic allometrics+ kg tree

-1 0.892 1.00 1.108

Sample number used in allometric kg tree-1

Exponential decline with N^

Sampling design errors

Errors from sampling design (i.e. not enough trees measured) t DM ha-1

Normal distribution* +Refer to text for a description of these systematic errors.

*This normal distribution was a normalised distribution, with the standard deviation determined from Section 3.1 in accordance with the number of

trees measured, and degree of heterogeneity of the planting.

^Refer to Figure 3.10, Section 3.3.

100

Table A9.3.6. Above- and below-ground allometrics. Here CF refers to the Snowdon (1991) correction factor, and EF refers to model efficiency

(Soares et al. 1995). The maximum stem diameter for which the below equations apply are specified with the species description. Low rainfall and

high rainfall are defined as <500 and >500 mm for mixed-species environmental plantings, and as <400 and >400 mm mean annual rainfall for

mallee eucalypts.

Site Species Explanatory

variable

a b CF EF N

‘Other’ trees, mostly eucalypts

Above-ground Generic high rainfall eucalypt tree (<100 cm) DBH -2.21 2.40 1.10 0.942 1121

Generic low rainfall eucalypt tree (<80 cm) DBH -1.13 2.10 1.10 0.849 995

Generic high rainfall eucalypt tree (<110 cm) D50 -3.10 2.57 1.10 0.943 1121

Generic low rainfall eucalypt tree (<85 cm) D50 -2.06 2.33 1.10 0.924 995





Generic Tropics (<40 cm) DBH -2.71 2.58 1.04 0.959 272

Alstonia scholaris (<12 cm) DBH -1.29 2.11 1.00 0.916 6

Araucaria cunninghamii (<25 cm) DBH -1.75 2.29 1.00 0.979 6

Blepharocarya involucrigera (<40 cm) DBH -1.50 2.16 1.01 0.964 14

C. intermedia (<17 cm) DBH -2.14 2.19 1.03 0.966 13

C. maculata (<60 cm) DBH -1.79 2.33 1.07 0.990 37

C. tesselaris (<13 cm) DBH -2.40 2.35 1.03 0.956 22

E astringens (<30 cm) DBH -1.62 2.26 0.97 0.954 16

E. albida (<15 cm) DBH -1.10 2.15 1.02 0.964 18

E. annulata (<30 cm) DBH -1.76 2.36 0.96 0.984 10

E. argophloia (<15 cm) DBH -1.11 2.03 1.01 0.906 25

E. argyphea (<28 cm) DBH -1.23 2.17 1.00 0.992 7

E. blakelyi (<20 cm) DBH -1.83 2.15 1.08 0.966 47

E. bridgesiana (<20 cm) DBH -1.35 1.93 1.06 0.953 17

E. calycogona (<25 cm) DBH -1.11 2.12 1.02 0.845 7

E. camaldulensis (<70 cm) DBH -1.88 2.32 1.15 0.952 89

E. captiosa (<20 cm) DBH -0.97 2.06 1.05 0.974 7

E. cinerea (<30 cm) DBH -1.21 1.83 1.15 0.868 27

E. cladocalyx (<55 cm) DBH -1.36 2.30 1.13 0.978 37

E. cloeziana (<40 cm) DBH -4.26 3.06 0.13 0.990 10

E. crebra (<15 cm) DBH -2.03 2.27 1.03 0.946 15

E. crenulata (<20 cm) DBH -1.97 2.35 1.08 0.952 10

E. falcata (<35 cm) DBH -1.19 2.15 1.02 0.985 16

E. fasiculosa (<16 cm) DBH -1.41 1.99 1.08 0.834 8

E. flocktoniae (<30 cm) DBH -1.56 2.30 1.00 0.982 6

E. gardineri (<32 cm) DBH -1.79 2.39 1.00 0.986 24

E. globulus (<39 cm) DBH -1.66 2.19 1.01 0.963 17

E. incrassata (<20 cm) DBH -1.72 2.31 1.06 0.825 10

E. kitsoniana (<31 cm) DBH -1.54 2.11 1.00 0.977 34

E. kochii (<16 cm) DBH -1.86 2.31 1.03 0.976 17

E. largiflorens (<20 cm) DBH -1.23 2.01 1.01 0.943 57

E. leptophyll (<20 cm) DBH -2.01 2.37 1.03 0.899 11

E. leucoxylon (<25 cm) DBH -1.37 2.07 1.04 0.979 28

E. loxophleba (<30 cm) DBH -0.78 1.82 1.11 0.930 104

E. macarthurii (<30 cm) DBH -2.08 2.26 1.02 0.937 23

E. mannifera (<25 cm) DBH -2.46 2.45 1.04 0.968 19

E. melanophloia (<13 cm) DBH -2.83 2.50 1.07 0.929 14

E. melliodora (<39 cm) DBH -1.75 2.14 1.15 0.939 169

E. microcarpa (<110 cm) DBH -3.46 2.73 1.15 0.823 30

E. moluccana (<27 cm) DBH -1.68 2.32 1.01 0.967 26

E. obliqua (<28 cm) DBH -2.16 2.23 1.00 0.954 14

E. occidentalis (<80 cm) DBH -2.14 2.44 1.15 0.979 118

E. ovata (<30 cm) DBH -2.16 2.35 0.99 0.988 24

E. phaenophylla (<15 cm) DBH 0.32 1.38 1.01 0.974 7

E. phenax (<15 cm) DBH -2.63 2.65 1.01 0.966 7

E. platypus (<30 cm) DBH -0.74 1.98 1.06 0.935 39

E. pluricaulis (<10 cm) DBH -1.24 2.24 1.03 0.937 9

E. polyanthemos (<25 cm) DBH -1.46 2.05 1.07 0.961 51

E. porosa (<30 cm) DBH -1.95 2.30 1.06 0.963 33

E. sargentii (<40 cm) DBH -1.66 2.27 1.01 0.951 25

E. socialis (<30 cm) DBH -1.73 2.28 1.03 0.867 10

E. spathulata (<45 cm) DBH -1.30 2.22 1.00 0.954 206

E. sporadica (<30 cm) DBH -1.19 2.10 0.97 0.918 11

E. stellulata (<15 cm) DBH -1.63 2.04 1.02 0.951 37

E. talyuberlup (<35 cm) DBH -1.36 2.17 1.00 0.999 6

E. tereticornis (<50 cm) DBH -2.15 2.34 0.97 0.959 71

E. tri-sideroxylon (<60 cm) DBH -2.39 2.40 1.10 0.954 54

101

E. utilis (<15 cm) DBH -0.49 1.85 1.14 0.679 18

E. viminalis (<30 cm) DBH -2.19 2.30 1.05 0.954 365

E. wandoo (<22 cm) DBH -1.71 2.20 1.15 0.925 58

Elaeocarpus angustifolius (<35 cm) DBH -2.17 2.37 1.03 0.958 24

Flindersia brayleyana (<35 cm) DBH -1.75 2.29 1.00 0.956 19

L. sauveolens (<17 cm) DBH -1.78 2.04 1.02 0.951 14

Melicope elleryana (<16 cm) DBH -0.81 1.82 1.00 0.977 5

Xanthostemon chrysanthus (<25 cm) DBH -3.15 2.82 0.99 0.973 5

Acacia trees

Generic high rainfall acacia tree (<35 cm) DBH -1.49 2.20 1.00 1.005 241

Generic low rainfall acacia tree (<40 cm) DBH -2.32 2.49 0.89 0.886 217

Generic high rainfall acacia tree (<40 cm) D50 -2.21 2.38 0.99 0.948 241

Generic low rainfall acacia tree (<45 cm) D50 -2.67 2.50 0.90 0.950 217

Generic high rainfall acacia tree (<40 cm) D30 -2.44 2.44 0.96 0.947 241

Generic low rainfall acacia tree (<45 cm) D30 -2.93 2.56 0.92 0.958 217

Generic Trop acacia tree (<35 cm) DBH -2.16 2.37 1.06 0.980 31

A. acuminata (<30 cm) DBH -2.20 2.41 0.84 0.925 58

A. baileyana (<28 cm) DBH -0.98 2.05 0.97 0.893 44

A. dealbata (<31 cm) DBH -1.21 2.11 1.06 0.968 17

A. decurrens (<25 cm) DBH -1.94 2.43 1.04 0.945 36

A. disparrrima (<25 cm) DBH -1.99 2.30 1.03 0.974 22

A. implexa (<15 cm) DBH -1.53 2.13 1.03 0.990 5

A. mangium (<34 cm) DBH -2.28 2.44 1.06 0.989 9

A. mearnsii (<25 cm) DBH -2.02 2.46 0.95 0.967 48

A. melanoxylon (<25 cm) DBH -1.70 2.15 1.02 0.977 51

A. micobotrya (<25 cm) DBH -1.55 2.13 1.07 0.910 33

A. murrayana (<20 cm) DBH -1.38 2.10 1.00 0.961 15

A. penninervis (<35 cm) DBH -1.00 2.02 0.95 0.952 22

A. pycnantha (<15 cm) DBH -1.90 2.33 0.99 0.974 33

A. salicina (<15 cm) DBH -1.78 2.16 1.01 0.959 13

A. saligna (<45 cm) DBH -2.23 2.42 0.94 0.954 50

A. stenophylla (<15 cm) DBH -2.22 2.47 0.99 0.936 16

Acacia shrubs

Generic acacia shrub (<30 cm) D10 -2.27 2.28 1.07 0.911 560

Generic acacia shrub (<25 cm) D50 -3.65 2.98 1.15 0.823 560

A. brachybotrya (>15 cm) D10 -2.19 2.27 1.03 0.962 8

A. calamifolia (<20 cm) D10 -2.23 2.41 1.02 0.939 128

A. cardiophylla (<15 cm) D10 -2.11 2.26 1.15 0.773 11

A. cyclops (< 20 cm) D10 -1.44 2.01 1.06 0.851 8

A. deanei (<35 cm) D10 -2.04 2.20 0.97 0.914 26

A. hakeoides (<25 cm) D10 -2.10 2.10 1.10 0.946 113

A. pendula (<30 cm) D10 -2.91 2.58 1.06 0.985 18

A. pendula (<25 cm) D10 -4.01 3.16 0.85 0.931 18

A. pycnantha (<25 cm) D10 -2.37 2.36 1.05 0.927 102

A. rigens (<15 cm) D10 -2.68 2.41 1.02 0.973 22

A. rubida (<25 cm) D10 -2.01 1.95 1.11 0.812 18

A. trineura (<15 cm) D10 -1.74 1.84 1.15 0.699 48

A. verniciflua (<15 cm) D10 -2.66 2.48 1.07 0.941 12

Melaleucas, casuarinas and other shrubs or small trees

Melaleuca sp. (<30 cm) DBH -0.03 1.40 1.15 0.678 161

Melaleuca sp. (<35 cm) D50 -1.70 1.91 1.15 0.887 161

Melaleuca sp. (<35 cm) D10 -2.59 2.19 1.07 0.909 161

Casuarina sp. (<30 cm) DBH -2.24 2.45 1.01 0.899 97

Casuarina sp. (<40 cm) D50 -2.87 2.60 1.00 0.931 97

Casuarina sp. (<45 cm) D10 -2.92 2.47 1.01 0.943 97

Shrub sp. (<25 cm) D10 -2.61 2.17 1.04 0.903 184

Dead life-forms

Dead eucalypt tree (<90 cm) DBH -2.33 2.51 1.15 0.943 156

Dead eucalypt tree (<100 cm) D50 -3.51 2.76 1.15 0.965 156



Dead acacia tree (<20 cm) DBH -1.64 2.31 1.05 0.929 43

Dead acacia tree (<20 cm) D50 -2.57 2.56 1.04 0.931 43

Dead acacia shrub (<25 cm) D50 -3.55 3.02 1.04 0.812 86

Dead acacia shrub (<30 cm) D10 -2.91 2.52 1.06 0.868 86

Mallee eucalypt species

Generic uncut mallee (<35 cm) D10 -2.73 2.50 0.99 0.940 1753

E. loxophleba liss., low rainfall, uncut (<25 cm) D10 -2.88 2.61 0.99 0.943 868

E. loxophleba liss., high rainfall, uncut (<20 cm) D10 -2.41 2.34 1.04 0.895 220

E. polybractea, low rainfall, uncut (<30 cm) cm) D10 -2.62 2.46 1.01 0.945 504

E. polybractea, high rainfall, uncut (<35 cm) D10 -2.99 2.55 1.05 0.908 379

E. kochii, uncut (<30 cm) D10 -2.95 2.48 1.03 0.956 374

102

Generic coppiced mallee (<95 m3) CVI -0.12 0.95 1.06 0.786 969

E. loxophleba ssp. lissophloia, coppiced (<60 m3) CVI -0.70 1.09 1.02 0.705 377

E. polybractea, coppiced (<95 m3) CVI -0.44 1.08 1.02 0.850 325

E. kochii, coppiced (<55 m3) CVI 0.13 0.99 1.01 0.881 196

Below-ground Generic eucalypt (<55 cm) DBH -1.09 1.77 1.06 0.883 130

Generic acacia tree (<40 cm) DBH -2.28 1.81 0.99 0.937 26

Generic shrub (<25 cm) D10 -5.54 2.94 1.00 0.662 23

Uncut generic mallee euc. (<35 cm) D10 -1.69 1.76 1.13 0.983 229

Coppiced generic mallee euc. (<10 m) Ht 1.56 0.75 1.14 0.603 160

9.4 Multiple Regression

Note that for the below Multiple Regression outputs; (i) All coefficients are statistically significant with the exception of those marked ‘ns’. These

insignificant coefficients were left in the model given they were significant in terms of an interaction; (ii) When back-transforming; Above-ground

biomass = prediction4 + 6*prediction

2 * variance + 3*variance

2; (3) When the site random effect was applied, the log(Pavg) coefficient became

insignificant given the site random effect is capturing the information in Pavg.

Table A9.4.1 Multiple Regression for above-ground biomass of mixed-species (temperate).

(Above-ground biomass)^0.25 = Intercept + log(Age) + log(Pavg) + Stocking + Width + PropTree + log(Age):log(Pavg)

Estimate Coefficients: Std. Error P

Intercept 1.838 0.484 <0.001

Stocking* 0.212 0.044 <0.001

Width2^ -0.199 0.061 <0.001

Width3^ -0.246 0.046 <0.001

log(Age) -0.102 ‘ns’ 0.208 0.6237

log(Pavg) -0.501 ‘ns‘ 0.273 0.0662

PropTree2 0.119 0.041 0.0034

log(Age):log(Pavg) 0.439 0.118 <0.001

*Stocking was a binary variable here of either < or >1,500 sph; ^Width2 (and Width3) was also binary variables, being either wide linear planting

geometry, or not (and a block planting geometry, or not). The between-site variance is 0.13. sigma^2 is 0.201. The model explains 46% (i.e. R2) of

the variation (P<0.001, N=583).

Table A9.4.2 Multiple Regression for above-ground biomass of mixed-species (tropical).

(Above-ground biomass)^0.25 = Intercept + log(Age)

Estimate Coefficients: Std. Error P

Intercept 0.990 0.076 <0.001

Log(age) 0.789 0.025 <0.001

The within-site variance is 0.025 and the between-site variance is 0.13. Therefore, when converting a prediction for a new site use the variance

estimate 0.025 + 0.13 = 0.155. Between-site variance is higher for the tropical plantings than for the other mixed-species environmental plantings.

The model explains 95% (i.e. R2) of the variation (P<0.001, N=164).

Table A9.4.3 Multiple Regression for above-ground biomass of mixed mallee eucalypts.

(Above-ground biomass)^0.25 = Intercept + log(Age) + Stocking + Width + Species + Coppicing + Salinity + Coppicing:Species + log(Age):Coppicing

Estimate Coefficients: Std. Error

Intercept -0.053 0.083

log(Age) 0.825 0.029

Width2^ 0.450 0.047

Width3^ 0.739 0.049

Species2* 0.127 0.043

Species3* 0.340 0.048

Stocking# 0.234 0.028

Coppiced~ 0.287 0.077

Saline~ -0.123 0.073

Species2:Coppiced 0.078 0.047

Species3:Coppiced 0.188 0.055

log(Age):Coppiced -0.117 0.034

^Width2 (and Width3) was binary variables, being either wide linear planting geometry, or not (and a narrow linear planting geometry, or not).

*Species2 (and Species3) was also binary variables, being either Lox, or not (and Poly, or not). #Stocking was a binary variable, being either >2,300

sph, or not. ~Coppiced and Saline were also binary variables, being either coppice harvested or not, or having of surface soil >200 mS m-1

, or not.

The within-site variance is 0.025 and the between-site variance is 0.05. Therefore, when converting a prediction for a new site use the variance

estimate 0.025 + 0.05 = 0.075. The model explains 95% (i.e. R2) of the variation (P<0.001, N=744).

103

Table A9.4.4 Multiple Regression for R:S ratios of PropTree<0.75 mixed-species environmental plantings.

Ln(R:S) = Intercept + PropTree


Intercept -1.373 0.034

PropTree* 1.017 0.072

*PropTree here was a continuous variable (i.e. not the categories of < or >0.75). The standard error was 0.30. The model explains 36% (i.e. R2) of the

variation (P<0.001, N=358).

Table A9.4.5 Multiple Regression for R:S ratios of PropTree≥0.75 mixed-species environmental and mallee eucalypt plantings.

Ln(R:S) = Intercept + Ln(Pavg) + Ln(Age) + Species


Intercept 1.667 0.050

Ln(Pavg) -0.170 0.027

Ln(Age) -0.844 0.167

Species* -0.363 0.077

*Species here had a binary definition of either Poly, or not Poly. The standard error was 0.48. The model explains 72% (i.e. R2) of the variation

(P<0.001, N=1,133).

104

9.5 Evaluation of the need for additional growth modifiers

We found that apart from calibration of G and y for each of the 22 planting categories, no other T1 or T2 modifiers were justified in terms of

improving FullCAM performance. Below we outline the analyses which lead us to disregard the incorporation of three additional modifiers to

account for; coppicing, salinity and access to a water table.

Figure A9.5.1 demonstrates that although there were plantings within the datasets used for calibration which had been coppiced, were saline, or

which had access to a watertable, these plantings did not appear to add any biases to the model as there was no clustering of these sites above or

below the 1:1 line.

Figure A9.5.1. Relationship between predicted (from the calibrated Tree Yield Formula) and ‘observed’ (from direct or indirect field measures)

fourth-root estimates of above-ground biomass across the entire environmental plantings database with sites highlighted, (a) in dark red to indicate

plantings which had been coppice harvested, (b) light red to indicate sites that had surface soil salinity (>200 mS m-1

), and (c) blue to indicate

plantings which had access (i.e. <5 m depth) to a watertable and where Pavg<3.5.

Coppice harvesting

We know from analysis of the collated datasets (Section 5) that within the first 5 years, coppicing can significantly accelerate growth rates of above-

ground biomass by an average of 27% compared to on-going growth of uncut stands. However, during the calibration process it was evident that no

T1 modifier for coppice harvesting was required to improve the model efficiency of any of the mallee eucalypt plantings. Increases in model

efficiency when a T1 was applied to all 355 coppice harvested plantings was about zero given the best fit for the v parameter of this T1 modifier was

also about zero.

These findings were further supported by testing this theory against the data obtained from Peck et al. (2012) where above-ground biomass

estimates were available for 17 contrasting sites over a 6 year period with and without coppice harvesting. When the calibrated Tree Yield Formula

was applied to each of these sites using their M and Pavg, we tested whether a T1 modifier was required for coppiced treatments to enhance the

rate of growth. As was done for the wider datasets, we minimised the sum squared of the residuals between observed and predicted above-ground

biomass, by solving for the parameter of v and U that would be required for a generic (i.e. across the 17 sites) coppicing T1 modifier. As the best fit

value for v was approximately zero, this again suggested that there was no T1 effect of coppicing (Figure A9.5.2).

Figure A9.5.2. Calibrated Tree Yield Formula for coppiced (red) and uncut (blue) linear mallee eucalypts across 17 different long-term experimental

sites studied by Peck et al. (2012) from across south-west Western Australia. The optimal parameter for v in the T1 coppice modifier was near zero,

indicating no modification was required for coppice harvesting.

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Saline surface soils

We know that on average, productivity of above-ground biomass of young (<14 years) narrow linear plantings was significantly decreased by 46%

when established on saline (>200 mS m-1

) compared to non-saline surface soils. Consistent with these findings, Bartle et al. (2012) observed a

significant (P<0.02) decrease in productivity of above-ground biomass between saline and non-saline surface soils under a 2-row linear planting of E.

polybractea. When the calibrated Tree Yield Formula was applied to this site of Bartle et al. (2012) using its M and Pavg, we tested whether a T2

modifier was required for coppiced treatments to enhance the rate of growth (Figure A9.5.3a). Given the variability in the data, we found this

modifier could be anywhere between 0.40 and 0.70. Therefore, we applied T2 modifiers with a y value of between 0.4 and 0.70 across our datasets,

applying this modifier only to plantings where there were data available to indicate the surface soils were >200 mS m-1

(N=40). With this limited

dataset of saline surface soils, we found an improvement in model efficiency of only about 1% when this additional T2 modifier was applied. This

was not considered significant enough to warrant the required complexities which would be associated with the application of the model had a T2

modifier for salinity (i.e. requirements for input data on surface soil salinity). Furthermore, generalisations about the influence of salinity on growth

would be difficult to justify in these calibrations until additional work to collate data on the long-term impacts of salinity on growth have been

obtained for the range of planting types and categories.

Nevertheless, as stated in Section 7, it is important to note that although the impacts of including a salinity T2 modifier were marginal given the

current database was deficient in the representation of plantings growing in saline surface soils, the absence of such a modifier is of concern, given

this does not satisfy the conservative approach required under the NIS and CFI. Landowners may well target saline surface soils to establish their

plantings (due to the low opportunity costs), and thereby over-predict carbon offsets.

Access to watertable

In sites of Pavg<3.5, access to a water table increased productivity of above-ground biomass by an average of 46%. These findings are consistent

with results from a replicated long-term study of Carter et al. (2008) where there was a significant (P<0.001, N=30) increase in productivity of

above-ground biomass with decreased depth to the watertable at a non-saline site planted to E. kochii (Figure A9.5.3b). An average T2 modifier of

about 2.00 accounted for the differences in above-ground biomass between the water table treatment plots. However, using a ‘best-fit’ y value of

2.09, the incorporation of a T2 modifier to plantings with water table access (and where Pavg<3.5) increased the efficiency of prediction of above-

ground biomass by only 4%. Note that here riparian plantings were also assumed to have access to ground water (e.g. Schultz et al. 1995).

Final calibrations were undertaken without water table modifiers because; (i) the potential long-term accumulation of biomass was effectively

doubled, and this led to serious concerns over the application of this T2 modifier given there were no longer-term datasets with which to verify such

long-term impacts; (ii) the influence of access to a water table will vary markedly across the landscape, and effects may be ephemeral, (iii) there is

also concern that the modifier calibrated was not robust, given that many of the datasets collated had no auxiliary data available on depth to water

table, and (iv) as with surface soil salinity modifiers, there are difficulties associated with the application of a water table modifier in the NIS given

that reliable spatial data on access to a water table are not available.

Figure A9.5.3. Measured above-ground biomass in uncut 2-row linear plantings of (a) E. polybractea at a site (Pavg=2.87, and MAR 330 mm) where

Bartle et al. (2012) monitored soil salinity treatments, and (b) E. kochii at a poor productivity site (Pavg=3.25, and MAR 334 mm) with and without

access to a water table (Carter et al. 2008). Error bars are ±40% of the fitted values, which represent the variation between observed treatments.

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106

CONTACT US

t 1300 363 400

+61 3 9545 2176

e [email protected]

w www.csiro.au

YOUR CSIRO

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FOR FURTHER INFORMATION

CSIRO Ecosystem Sciences

Keryn Paul

t +61 2 6246 4227

e [email protected]

w www.csiro.au

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