developing a national forest productivity model - ncas technical

tech

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The National Carbon Accounting System provides a complete

accounting and forecasting capability for human-induced sources and

sinks of greenhouse gas emissions from Australian land based

systems. It will provide a basis for assessing Australia’s progress

towards meeting its international emissions commitments.

http://www.greenhouse.gov.au

technical report no. 23

Developing a NationalForest Productivity Model

Jenny Kesteven,Joe Landsberg andURS Australia

Developing a National Forest Productivity M

odel

national carbonaccounting system

The National Carbon Accounting System:• Supports Australia's position in the international development of policyand guidelines on sinks activity and greenhouse gas emissionsmitigation from land based systems.

• Reduces the scientific uncertainties that surround estimates of landbased greenhouse gas emissions and sequestration in the Australian context.

• Provides monitoring capabilities for existing land based emissions andsinks, and scenario development and modelling capabilities thatsupport greenhouse gas mitigation and the sinks development agendathrough to 2012 and beyond.

• Provides the scientific and technical basis for internationalnegotiations and promotes Australia's national interests ininternational fora.

http://www.greenhouse.gov.au/ncas

For additional copies of this report phone 1300 130 606

Series 1 Publications

1. Setting the Frame

2. Estimation of Changes in Soil Carbon Due to Changes in Land Use

3. Woody Biomass: Methods for Estimating Change

4. Land Clearing 1970-1990: A Social History

5a. Review of Allometric Relationships for Estimating Woody Biomass for Queensland, the NorthernTerritory and Western Australia

5b. Review of Allometric Relationships for Estimating Woody Biomass for New South Wales, theAustralian Capital Territory, Victoria, Tasmania and South Australia

6. The Decay of Coarse Woody Debris

7. Carbon Content of Woody Roots: Revised Analysis and a Comparison with Woody ShootComponents (Revision 1)

8. Usage and Lifecycle of Wood Products

9. Land Cover Change: Specification for Remote Sensing Analysis

10. National Carbon Accounting System: Phase 1 Implementation Plan for the 1990 Baseline

11. International Review of the Implementation Plan for the 1990 Baseline (13-15 December 1999)


12. Estimation of Pre-Clearing Soil Carbon Conditions

13. Agricultural Land Use and Management Information

14. Sampling, Measurement and Analytical Protocols for Carbon Estimation in Soil, Litter and CoarseWoody Debris

15. Carbon Conversion Factors for Historical Soil Carbon Data

16. Remote Sensing Analysis Of Land Cover Change - Pilot Testing of Techniques

17. Synthesis of Allometrics, Review of Root Biomass and Design of Future Woody BiomassSampling Strategies

18. Wood Density Phase 1 - State of Knowledge

19. Wood Density Phase 2 - Additional Sampling

20. Change in Soil Carbon Following Afforestation or Reforestation

21. System Design

22. Carbon Contents of Above-Ground Tissues of Forest and Woodland Trees

23. Developing a National Forest Productivity Model

24. Analysis of Wood Product Accounting Options for the National Carbon Accounting System

25. Review of Unpublished Biomass-Related Information: Western Australia, South Australia, NewSouth Wales and Queensland

26. CAMFor User Manual

DEVELOPING A NATIONAL FORESTPRODUCTIVITY MODEL

Jenny Kesteven+

, Joe Landsberg#

and URS Australia°

+

Centre for Resource and Environmental Studies, Australian National University, Canberra

#

Landsberg Consulting Pty Ltd°

URS Australia

National Carbon Accounting System Technical Report No. 23

May 2004

Australian Greenhouse Officeii

Printed in Australia for the Australian Greenhouse Office

© Commonwealth of Australia 2004

This work is copyright. It may be reproduced in whole or part for study or training purposessubject to the inclusion of an acknowledgement of the source and no commercial usage orsale results. Reproduction for purposes other than those listed above requires the writtenpermission of the Communications Team, Australian Greenhouse Office. Requests andenquiries concerning reproduction and rights should be addressed to the CommunicationsTeam, Australian Greenhouse Office, GPO Box 621, CANBERRA ACT 2601.

For additional copies of this document please contact the Australian Greenhouse OfficePublications Hotline on 1300 130 606.

For further information please contact the National Carbon Accounting System athttp://www.greenhouse.gov.au/ncas/

Neither the Australian Government nor the Consultants responsible for undertaking thisproject accepts liability for the accuracy of or inferences from the material contained in thispublication, or for any action as a result of any person’s or group’s interpretations,deductions, conclusions or actions in reliance on this material.

May 2004

Environment Australia Cataloguing-in-Publication

Kesteven, Jennifer L.

Developing a national forest productivity model / Jenny Kesteven, Joe Landsberg and URSAustralia.

p. cm.

(National Carbon Accounting System technical report; No. 23)

Bibliography

ISSN: 1442 6838

1. Forest productivity-Australia-Simulation methods. 2. Forest productivity-Australia-Forecasting. I. Landsberg, J.J. (Joseph John), 1938- . II. URS Australia. III. AustralianGreenhouse Office.IV. Series

333.75'0994'011-dc22

National Carbon Accounting System Technical Report iii

SUMMARYKnowledge of the spatial and temporal patterns offorest growth is fundamental to estimating thecarbon stocks (and biomass) of mature forests andrates of carbon accumulation in any forest regrowth.

As part of the National Carbon Accounting Systemdeveloped by the Australian Greenhouse Office,indices of forest growth were developed and usedto predict potential biomass at maturity (forestproductivity) and rates for biomass incrementacross the Australian continent over time.

This report documents the application of theseforest growth indices in developing a NationalForest Productivity Model.

Australian Greenhouse Officeiv

National Carbon Accounting System Technical Report v

TABLE OF CONTENTS

Page No.

Summary iii

List of Symbols and Acronyms vii

1. Introduction 1

2. Input Data 2

2.1 Soils 2

2.1.1 Soil Water Holding Capacity (SC) 2

2.1.2 Soil Nutrient Status (SN) 4

2.2 Climate 5

2.2.1 Fitting the Climate Surfaces 5

2.2.1.1 Errors in the Model 7

2.2.1.2 From Surface Coefficient Files to Maps 8

2.2.2 Digital Elevation Model (DEM) 8

2.2.3 Station Dictionary 10

2.2.4 Long-Term Average Climate Variables 11

2.2.4.1 Average Temperature 12

2.2.4.2 Number of Frost Days 12

2.2.4.3 Radiation 12

2.2.4.4 Vapour Pressure Deficit (VPD) 15

2.2.5 Monthly Average Climate Variables 15

2.2.5.1 Average Temperature 16

2.2.5.2 Maximum Temperature 17

2.2.5.3 Minimum Temperature 19

2.2.5.4 Rainfall 20

2.2.5.5 Frost Days 23

2.2.5.6 Radiation 24

2.3 Normalised Difference Vegetation Index (NDVI) 24

3. Calculation of Plant Productivity 26

3.1 Temperature Modifier 26

3.2 Frost Modifier 27

3.3 Vapour Pressure Deficit (VPD) Modifier 27

3.4 Soil Water Content (SW) 28

3.5 Absorbed Photosynthetically Active Radiation (APAR) 32

3.6 Plant Productivity Index 33

4. Select References 36

Appendix 1 – Preliminary Investigations: Spatial Estimation of Plant Productivity and Classification of Non-Commercial Vegetation Types 39

Australian Greenhouse Officevi

LIST OF FIGURESPage No.

Figure 1. Potential available soil water holding capacity (SC). 3

Figure 2. Soil nutrient status (SN). 4

Figure 3. The Australian DEM. 9

Figure 4. A screen capture for station 061121 – Lostock Post Office. 11

Figure 5. Long-term monthly average temperature. 13

Figure 6. Long-term average number of frost days per month. 14

Figure 7. Annual average temperature derived from monthly data grids for 1968-2002 and examples of low (1976), average (1982) and high (1996) temperature years. 16

Figure 8. Australian average maximum temperature (Tmax) and the 12 month running mean. 17

Figure 9. Maximum temperature grids for high and low continental average values for July and December. 18

Figure 10. Australian average minimum temperature (Tmin) and the 12 month running mean. 19

Figure 11. Minimum temperature grids for high and low continental average values for July and February. 20

Figure 12. Australian continental average monthly rainfall. 21

Figure 13. Annual rainfall 1970–2002. 22

Figure 14. Australian continental average annual number of frost days. 23

Figure 15. Example grids of low (1991, average = 6.1 days), medium (1987, average =14.0 days) and high (1982, average = 21.1 days) number of days frost. 24

Figure 16. Temperature modifier spatial equations (from Equation 11) for January and July long-term averages. 26

Figure 17. The frost modifier equation for the July long-term average. 27

Figure 18. VPD modifier spatial equations for January and July long-term averages. 28

Figure 19. Spatial equation for equilibrium evaporation, January 1995. 29

Figure 20. Spatial equation for transpiration, January 1995. 29

Figure 21. Spatial equation for interception, January long term average. 29

Figure 22. Spatial equation for water balance, January 1995. 30

Figure 23. Spatial equation for soil water content, January 1995. 30

Figure 24. Spatial equation for moisture ratio, January 1995. 31

Figure 25. Spatial equation for soil water modifier, January 1995. 32

Figure 26. Long-term average APAR for January and July. 33

Figure 27. Spatial equations for productivity indices, January and July long-term averages. 33

Figure 28. Derived long-term average plant productivity index for Australia. 34

Figure 29. Derived annual continental productivity indices 1970-2002. 35

National Carbon Accounting System Technical Report vii

LIST OF TABLESPage No.

Table 1 ANUCLIM variables used in the plant productivity modelling. 12

Table 2. ANUSPLIN variables created for the long-term plant productivity modelling. 12

Table 3. Monthly climate variables. 16

Australian Greenhouse Officeviii

P Rainfall (mm)

RA Adjusted direct radiation

RC Slope and aspect modified radiation

RD Direct radiation

RF Diffuse radiation

RG Global radiation

RN Net radiation (MJ m-2 day-1)

RP Radiation with rainfall

SN Soil nutrient status

SC Soil water holding capacity

SW Soil water

SWmod Soil water modifier

T Temperature (oC)

Tavg Average monthly temperature/meanair temperature (oC)

Tdew9, Tdew3 Dew point temperature (9 am and 3 pm)

Tdry9, Tdry3 Dry bulb temperature (9 am and 3 pm)

Thigh Monthly average temperature abovewhich plant growth stops

Tlow Monthly average temperature belowwhich plant growth stops

Tmax Maximum air temperature (oC)

Tmin Minimum air temperature (oC)

Tmod Temperature modifier

Topt Optimum temperature for growth

Trans Transpiration

VPD Vapour Pressure Deficit

VPDmod Vapour Pressure Deficit modifier

W Water balance

LIST OF SYMBOLS AND ACRONYMS

AGO Australian Greenhouse Office

APAR Absorbed PhotosyntheticallyActive Radiation

BoM Bureau of Meteorology (Australia)

CRES Centre for Resource andEnvironmental Studies

CSIRO Commonwealth Scientific andIndustrial Research Organization

DEM Digital Elevation Map

e°(T) Saturation vapour pressure at airtemperature T (kPa)

Eeq Equilibrium evaporation

exp(x) 2.7183 (base of natural logarithm)raised to the power x

F Frost (days per month)

Fmod Frost modifier

fPAR Fraction of Photosynthetically ActiveRadiation absorbed

GCV Generalised Cross Validation

GPP Gross Primary Productivity

I Interception intercept

J Month

LAI Leaf Area Index (m2 (leaf area) m-2

(soil surface))

M Moisture ratio

NDVI Normalised Difference Vegetation Index

λI Near infra-red electromagneticwavelengths

λR Low micron range electromagneticwavelengths

NPP Net Primary Production

National Carbon Accounting System Technical Report 1

1. INTRODUCTION

Net primary productivity (NPP) is the rate at whichchemical or solar energy is converted to biomass.The main primary producers are the green plants,which convert solar energy, carbon dioxide andwater to glucose, and eventually to plant tissue.

Estimation of productivity can be obtained throughdifferent methods. Direct measurement methodsinclude destructive sampling of the above-groundand below-ground plant biomass, and the recordingof carbon dioxide fluxes at the vegetation-atmosphere interface.

Available direct measurements of NPP have somelimitations for mapping productivity across theAustralian continent. The reliability of available datais variable – the data is limited in number and notevenly distributed among the various types ofecosystems. To overcome these deficiencies inmeasured data several model types have beendeveloped. These are either physiological modelssimulating ecosystem fluxes from environmentalvariables, remote sensing-based models interpretingthe light spectrum reflected by the land surface, orinverse models deducing fluxes from time and spacevariations in atmospheric CO2 and 13C data. Processmodels with physiological functions (i.e. those thatacknowledge temporal variability due to changingbiophysical conditions such as rainfall andtemperature) offer the opportunity to simulate pastand future changes in NPP according toenvironmental changes.

A truncated spatial version of the 3-PG (process)model (after Landsberg and Waring, 1997) wasdeveloped as part of the National CarbonAccounting System (NCAS) to predict forestproductivity across the Australian continent. Theresultant ‘productivity index’ model as documentedhere retains the essential features of biomass NPPestimation, without the biomass fixation (GrossPrimary Productivity (GPP) minus NPP) and carbonpartitioning procedures.

A relative index of plant productivity was mappedfor Australia using the ‘productivity index’ model –based on the relationship between the amount ofphotosynthetically active radiation absorbed byplant canopies (APAR) and the various productivitymodifiers that affect plant growth (e.g. temperature,soil water content, frost). The model used a monthlytime step to derive both a long-term averagemonthly productivity index (~250 m resolution) anda monthly productivity index for 1970 to 2002 (1 kmresolution).

Factors converting APAR to productivity indiceswere reduced from presumed optimum values bymodifiers dependent on soil fertility, atmosphericvapour pressure deficits, soil water content andtemperature. The ANUCLIM and ANUSPLINprograms were used to generate climate surfaces forthe continent. Soil fertility and water holdingcapacity values were obtained from the CSIROusing the digital Soil Atlas of Australia. Leaf AreaIndex, essential for the calculation of APAR, wasestimated from 10-year mean values of NormalisedDifference Vegetation Indices (NDVI), for 1 kmpixels, for the entire country. Incoming short-waveradiation – and hence APAR – was corrected forslope and aspect using a Digital Elevation Map(DEM) for the long-term average, but with 1 kmpixels being used in the monthly productivity mapsderived as this made no significant difference to theresults. Analyses were, therefore, carried out onestimates based on the assumption of flat terrain.

The resultant maps (as digital grids) of plantproductivity index, for each month for Australia,provide relative indices of plant productivity(reflecting the spatial and temporal patterns of plantgrowth) in any region. The data generated isintegral to the estimation of carbon stocks under theNCAS and provides a framework for assessingcarbon accumulation by various vegetation types.

Australian Greenhouse Office2

2. INPUT DATA

The production of mapped productivity indices forAustralia required integration and analysis of anumber of spatially represented environmentalvariables. These included: soil water holdingcapacity (to estimate water balance), soil nutrientstatus, Advanced Very High Resolution Radiometer(AVHRR) data (to estimate the NormalisedDifference Vegetative Index (NDVI)), and variousclimate variables (temperature, rainfall and numberof frost days).

2.1 SOILSThe Atlas of Australian Soils (Northcote et. al.

1960–1968) was integrated with a set of interpretedsoil variables from CSIRO (McKenzie and Hook1992) to produce continental maps of potentialavailable water holding capacity (PAWHC) andnutrient status of soils.

The Atlas of Australian Soils completed in 1968(Northcote et. al. 1960-1968) and made available indigital form in 1990 provides the only consistentsource of spatial information for the continent.While large areas have been surveyed in more detailduring the last 30 years, those surveys have not beencompiled to produce an updated continental map.

McKenzie et. al. 2000 provided a set of interpretedsoil variables (from McKenzie and Hook 1992) thatcan be used with the Digital Atlas. Theinterpretations for each soil type were based,wherever possible, on data held within the CSIRONational Soil Database. Summaries from thedatabase were used with other sources ofinformation to assign an interpreted value foreach variable.

Soil fertility and water holding capacity (SC) wereobtained by merging the Atlas of Australian soils(Northcote et. al. 1960-1968) and data provided byMcKenzie et. al. 2000. The data provided includedsoil fertility and water holding capacity for both the

dominant soil type (by area) and the average of thetop five dominant soils. McKenzie (pers. comm.)advised using the properties of the dominant soiltype (by area) to characterise the region. Caveats onthe use of the Digital Atlas of Australian Soils arepresented in McKenzie et. al. (2000).

The resultant data had limitations, as a largeproportion of soil variation within a region occurredover short distances which could not be resolved atthe mapping scale of the Atlas. The qualitativenature of the Digital Atlas and restrictions associatedwith the classification scheme and structure of thesoil-landscape model imposed further constraints.

The Atlas of Australian Soils uses 725 soil profileclasses, normally at the level of Principle ProfileForm (PPF) (e.g. Ug5.15). The legend of the Atlasdefines 3,060 map unit types. The map unit typeshave various combinations of the 725 soil profileclasses. The map unit type descriptions identifydominant and subdominant soil profile classes.Many of the map unit types occur more than onceand the Digital Atlas has 22,560 mapped polygons.

McKenzie and Hook (1992) prepared interpretationsof the 725 soil profile classes. The dominant soil ineach map unit type was identified and interpretedvalues for each soil profile class were ascribed toeach map unit type. Lookup tables were createdfrom the accompanying spreadsheets. Some soilpolygons had PPFs containing ‘Undefined’ or ‘NS’ -their soil attributes were coded so as to representno data.

2.1.1 Soil Water Holding Capacity (SC).

Available water capacity is presented on a per unitdepth basis, as a total for each horizon, and as atotal for the solum. The total solum wasrecommended (McKenzie pers. comm.) for use.The available water capacities were calculated as thedifference in volumetric water content at matricpotentials of –0.1 bar and –15 bar for a specifieddepth increment.


It should be noted that the total available watercapacity for the solum is constrained by limitationsassociated with the estimate of solum thicknessof the actual plant available water capacity (seeHillel 1980). Despite these limitations, McKenzieet. al. 2000 suggest that it provides a reasonablefirst approximation of the water storage capacityof a soil.

In the resultant SC maps derived, PPFs with an SC

equal to 0 were assigned the value of the A HorizonSC, except for PPF Dy5.91 (comprising 2 spatial unitsin Kangaroo Island SA) which was assigned thePPF2 SC as there was no PPF1 A Horizon value.

Mapped values (with a 250 m resolution) rangedfrom 21 to 270 mm with a mean value of104 mm (see Figure 1).

Initial soil water content was set to 75% of thepotential available water holding capacity. As this isnot a true representation of the January soil watercontent for Australia, the water balance routine wasrun for two years to derive a reliable estimation ofthe actual soil water content to inititialise the modelfor the long-term average. The water balanceroutine was run for the years 1968 and 1969 toestimate initial soil water holding capacity for the1970 to 2002 monthly productivity model.

Figure 1. Potential available soil water holding capacity (SC).

Soil water holding capacity, SC (mm)

0 100 200 300


2.1.2 Soil Nutrient Status (SN)

Because of natural variation and the considerableuncertainty surrounding soil fertility values, onlythree levels of fertility were used:

• high (effective modifier = 1),

• medium (effective modifier = 0.8) and

• low (effective modifier = 0.6)

giving SN values of 1.25, 1 and 0.75, respectively.These were applied to each polygon representing asoil type in the Atlas of Australian Soils (Figure 2).

The rating system for gross nutrient status preparedby McKenzie and Hook (1992) relates to thebehaviour of profiles under agriculturaldevelopment. Profiles with a low status (class 1)

exhibited major responses to nitrogen, phosphorusand potassium along with most micronutrients.Profiles with a moderate nutrient status (class 2)responded to nitrogen and phosphorus withoccasional responses to some micronutrients. It wasuncommon for profiles with a high nutrient status(class 3) to respond to nitrogen and phosphorusexcept after intensive farming. The main sources ofinformation for the assessment of nutrient statuswere Stace et. al. (1968) and Northcote et. al. (1975).

In the resultant map derived for application in theproductivity model, soil polygons with no nutrientvalue were assigned the lowest class i.e. SN = 0.75.The average value for Australia was 0.81 (seeFigure 2).

Figure 2. Soil nutrient status (SN).

Soil nutrient status, SN

0.75 1.00 1.25


2.2 CLIMATE Climate data was needed to produce the long-termaverage productivity maps and the 1968 to 2002monthly productivity maps. Spatial coverage oflong-term average climate surfaces for severalclimate variables were available from theANUCLIM package. ANUCLIM (incorporatingESOCLIM, BIOCLIM and GROCLIM) is used tostore climate surfaces derived by ANUSPLIN and toenable systematic interrogation of these surfaces, inpoint and grid form, for biophysical applications.Most surfaces need a Digital Elevation Model(DEM) to provide climate values in grid form(see http://cres.anu.edu.au). These climatevariables include:

• Long-term average rainfall (P)

• Long-term average radiation (Rad)

• Long-term average minimum temperature (Tmin)

• Long-term average maximum temperature (Tmax)

• Long-term average 9am and 3pm dry bulbtemperature(Tdry)

• Long-term average 9am and 3pm dew pointtemperature(Tdew)

Data layers that were required as inputs for theproductivity model but not available in theANUCLIM package included:

• Long-term average temperature (Tavg)

• January 1968 to December 2002 monthlyminimum temperature (Tmin)

• January 1968 to December 2002 monthlymaximum temperature (Tmax)

• January 1968 to December 2002 monthlyaverage temperature (Tavg)

• January 1968 to December 2002 monthlyrainfall (P)

• Long-term average number of days withfrost (F)

• January 1968 to December 2002 monthlynumber of days with frost (F).

2.2.1 Fitting The Climate Surfaces

Climate surfaces for long-term averagetemperature and frosts and for all the monthlyclimate variables from 1968 to 2002 were createdusing the ANUSPLIN software package(see http://cres.anu.edu.au). The ANUSPLINpackage provides a facility for transparent analysisand interpolation of noisy multi-variate data usingthin plate smoothing splines. The package supportsthis aim by providing comprehensive statisticalanalyses, data diagnostics and spatially distributedstandard errors. It also supports flexible data inputand surface interrogation procedures. The climatesurfaces available in the ANUCLIM package werecreated using the ANUSPLIN package.

A brief description of the surface fitting techniquesis given below but for a more comprehensivediscussion see Kesteven (1998), Huchinson (1991a,1991b), Kesteven and Hutchinson (1996)and Wahba (1990).

The original thin plate (formerly Laplacian) surfacefitting technique was described by Wahba (1979),with modifications for larger data sets due to Batesand Wahba (1982), Elden (1984) and Hutchinson(1984). Thin plate smoothing spline interpolationis a global method as it uses all the given datafor prediction at each point (Laslett et. al. 1987). Themethod is also non-parametric and so is relativelyinsensitive to the distribution of the parentpopulation.

Thin plate smoothing splines can be viewed as ageneralisation of standard multi-variate linearregression, in which the parametric model isreplaced by a suitably smooth non-parametricfunction. The degree of smoothness, or inversely thedegree of complexity, of the fitted function is usuallydetermined automatically from the data byminimising a measure of predictive error of thefitted surface given by the generalised crossvalidation (GCV).

The main idea is to consider a spatial variable as arealisation of a spatially autocorrelated randomfunction that accounts for the complicatedbehaviour of natural spatial data:

(1)

where f is a function to be estimated from theobservations zi , which include a zero mean, usuallyspatially discontinuous, error term εi. The xi arecommonly assumed to represent coordinates in two- or three-dimensional Euclidean space.

The value of the variable over a region isinterpolated by estimating the true underlyingfunction. This function is estimated to be as smoothas possible without significantly distorting the givendata values (Hutchinson 1991a). The observationalmodel for a thin plate spline with three independentspline variables is that the observed monthly meanzi at the position xi, yi , zi of the ith station is givenby Equation 1, where f is an unknown smoothfunction and the εi are independent random errorswith zero mean and variance ds2, where di is the(local) relative variance and s2 is the common(normally unknown) variance.

The unknown smooth function is estimatedby finding the function f (a thin plate spline)which minimises

(2)


where is Jm(f) a measure of the roughness of fin terms of the mth order derivatives of f(usually second order), and λ is a positivesmoothing parameter.

In meteorological and climatological applications theweights, di, are used when the data do not haveequal length records. The smoothing parameter λdetermines a trade-off between data fidelity andsurface roughness. It is usually calculated byminimising the generalised cross validation (GCV).This is a measure of the predictive error of the fittedsurface which is calculated by removing each datapoint in turn and summing, with appropriateweighting, the square of the discrepancy of eachomitted data point from a surface fitted to all theother points. The value of λ is chosen to minimisethis sum, that is, to minimise the predictive error asascertained by the performance of the fitted functionin predicting omitted data points.

It is possible to calculate the GCV implicitly, andhence efficiently, because the fitted values dependlinearly on the data. Intuitively the GCV is a goodmeasure of the predictive power of the fittedsurface, as has been verified both theoretically andin applications to real and simulated data (Wahba 1990 and Hutchinson 1991a).

Statistical interpretation proceeds by way ofanalogy with least squares regression analysis.Thin plate splines can be viewed as a non-parametric generalisation of linear regressionanalysis. A comprehensive introduction to thetechnique of thin plate smoothing splines, withvarious extensions, is given in Wahba (1990).A summary of the basic methodology, with climateinterpolation principally in mind, can be found inHutchinson (1991a). More comprehensive discussionof the algorithms and the associated statisticalanalyses are given in Hutchinson and Gessler (1994)and Hutchinson (1993, 1995). Comparisons withgeostatistical (kriging) methods have been presentedby Hutchinson (1993), Hutchinson and Gessler(1994) and Laslett (1994).

Σn

i=l[

zi - f(xi,yi,hi)di

]2+ λ J

m( f )

zi = f(xi) + εi (i = l,...,n)


The surface fitting procedure was primarilydeveloped for the task of fitting climate data so thatthere are normally at least two independent splinevariables, longitude and latitude in units of decimaldegrees. A third independent variable, elevationabove sea-level, is often appropriate when fittingsurfaces to temperature or precipitation. This isnormally included as a third independent splinevariable and was scaled to be in units of kilometres.Minor improvements can sometimes be made byslightly altering this scaling of elevation.This scaling was originally determined byHutchinson and Bischof (1983) and has beenverified by Hutchinson (1995, 1996). Extensionto real time simulation and surface fitting isdiscussed by Hutchinson (1995) and Kestevenand Hutchinson (1996).

Over restricted areas, superior performance cansometimes be achieved by including elevation, notas an independent spline variable but as anindependent covariate. Thus, in the case of fitting atemperature surface, the coefficient of an elevationcovariate would be an empirically determinedtemperature lapse rate (Kesteven 1998, Hutchinson,1991a). Other factors which influence the climatevariable may be included as additional covariates ifappropriate parameterisations can be determinedand the relevant data are available. These mightinclude, for example, topographic effects other thanelevation above sea-level such as slope and aspect.Other applications to climate interpolation havebeen described by Hutchinson et. al. (1984) andHutchinson (1989, 1991a, 1991b). Applications offitted spline climate surfaces to global agroclimaticclassifications and to the assessment of biodiversityare described by Hutchinson et. al. (1992).

To fit multi-variate climate surfaces, the values ofthe independent variables need to be known at thedata points. Thus meteorological stations should beaccurately located in position and elevation. Errorsin these locations are often indicated by large valuesin the output ranked residual list. A number ofapplications have examined the utility of using

elevation and slope and aspect obtained from digitalelevation models of various horizontal resolutions(Hutchinson 1995, 1998b).

2.2.1.1 Errors in the Model

In applying data smoothing to climate means, twocomponents of the covariance structure of the εi inthe model given by Equation 1 should berecognised. The first component allows fordeficiencies in the model being fitted. These areessentially due to local effects, includingmeasurement error, which are below the spatialresolution of the observed point data network. Themagnitude of these effects depends on the adequacyof the model represented by the function zi and onthe spatial density of the data. These effects can bereasonably assumed to be independent betweendifferent locations.

The second component arises when using seriallyincomplete data to interpolate monthly means for astandard period. This component can be ignored forsome variables such as temperature. Monthly meansof temperature stabilise after a few years. However,this component is significant for rainfall whichnormally exhibits large variation from year to year.This variability gives rise to strong correlationsbetween the error components of rainfall means atstations with common periods of record.

Hutchinson (1995) shows how the resultingcorrelated error structure can be incorporated intothe interpolation process. The model permits therigorous estimation of this smooth function by athin plate smoothing spline in two ways. Thestatistical error structure of the data can either beaccommodated directly, by using the correspondingnon-diagonal error covariance matrix, or observedmeans can first be standardised to long-termestimates using linear regression. Both methods givesimilar interpolation accuracy and error estimates ofthe fitted surfaces were in good agreement withresiduals from withheld data (Hutchinson 1995).


There are several useful output statistics in thediagnostics file generated by the SPLINA program,which can be used to check for data homogeneity.The square root of the generalised cross validation(RTGCV) is a good measure of the predictive powerof the fitted surface, especially when comparing thevalues from the same climate variable fitted fordifferent times. In combination with the rankedresiduals listing, which allows for each station to beassessed in relation to its deviation from thecalculated surface, the RTGCV provides a powerfultool for investigation of station homogeneity. Thisprocess of assessing each station in relation to itsdeviation from the calculated surface and correctingerrors may be repeated many times, steadilyreducing overall surface error. Once the statisticsreach a level where there is very little change withthe removal of additional stations and residuals arewithin expected ranges, the fitted surfacecoefficients are used to create the monthly surfaces.Coefficients of the surfaces fitted to the monthlyvalues were stored by month. Values for any monthat any site or for grids can be calculated using theLAPPNT or LAPGRD programs in the ANUSPLINpackage. A visual representation for each month wascreated using ARCInfo although they could bedisplayed by any of a number of commonlyavailable computer programs which can displayraster images.

2.2.1.2 From Surface Coefficient Files to Maps

The LAPGRD program can be used to calculateregular grids of fitted climate values and theirstandard errors, for mapping and other purposes,provided a regular grid of values of eachindependent variable, additional to longitudeand latitude, is supplied. This usually means thata regular grid digital elevation model (DEM)is required.

Coefficients of the surfaces fitted to the monthlyvalues are stored by year. They can be used tocalculate values of the climate variable for anymonth at any site using the LAPPNT program in the

ANUSPLIN package. Alternatively, regular gridsacross Australia of monthly climate variables for anymonth can be calculated using the LAPGRDprogram in ANUSPLIN and displayed or used byany of a number of commonly available computerprograms which can display raster images. Thesystem can be updated to incorporate later years asnew data become available.

2.2.2 Digital Elevation Model (DEM)

A Digital Elevation Model (DEM) is a representationof the terrain using point elevation informationwhere each grid point represents the approximateelevation of the centre of the corresponding grid cell.A DEM was required for the production of theclimate surfaces and for the estimation of slope andaspect corrected radiation grids.

The GEODATA 9 Second DEM Version 2 is a grid ofelevation points covering the whole of Australiawith a grid spacing of 9 seconds in longitude andlatitude (~250 m resolution) in the GDA94coordinate system. Version 2 is based on theANUDEM 5.0 elevation gridding programdeveloped by the Centre for Resource andEnvironmental Studies (CRES) at the AustralianNational University.

The ANUDEM program has been designed toproduce accurate digital elevation models withsensible drainage properties from point elevations,stream lines, contour lines and cliff lines(Hutchinson 2000). The major data sources for theproduct were revised national spot height elevationdata taken from 1:100,000 scale topographicmapping and revised river information from1:250,000 scale topographic mapping. Thesedatasets are components of AUSLIG’s GEODATATOPO–250K digital map product (AUSLIG 1994).All revisions to the source data were made by CRES.The data was augmented by national trigonometricdata supplied by AUSLIG from the NationalGeodetic Data Base (NGDB).


The source elevation data has a standard deviationof around 7.5 m. Errors in the DEM are closelyrelated to terrain complexity. Theoretical estimatesand tests of the DEM against trigonometric datadistributed evenly across the continent indicate thatthe standard elevation error of the DEM variesbetween about 7.5 m and 20 m for most of thecontinent. Standard errors are larger in highlandareas with steep and complex terrain where thelargest errors can exceed 200 metres (Hutchinsonet. al. 2000).

The density of source data points used to createthe grid and the horizontal resolution of the gridwarrant that the final grid be considered as havinga scale of approximately 1:250,000. This makes theDEM useful for national, statewide and regionalapplications.

A bilinear interpolation technique was used toresample the 250 m elevation grid to produce dataat a 1 km resolution to derive subsequent monthlyproductivity index map grids.

Figure 3. The Australian DEM.

Elevation (m)

0 500 1000 1500 2000


2.2.3 Station Dictionary

The task of spatial interpolation is made difficult bya number of factors. Climate data are often sparse,measured for a limited duration and of varyingquality. More significantly, the density of the datamay be much less than the resolution of the requiredinterpolated grid. In spite of this, the size of theavailable data set may be sufficient to causecomputational difficulties, particularly when dealingwith continental or global data sets.

The iterative process of ‘fitting’ the statistical surfacedemonstrates a vitally important characteristic of thespline technique. Verification of climate data posesmany challenges and one tool to assist in thatverification is placing a climate station in its spatialcontext. The results of the spline interpolationprovided feedback about the meteorological stationdatabase by providing summary statistics and thedeviation for each station relative to the fittedsurface. Examination of data residuals is a powerfulaid for detecting and correcting the data errors.

To fit multi-variate climate surfaces, the values ofthe independent variables need to be known at thedata points. Thus meteorological stations shouldbe accurately located in position and elevation.The Bureau of Meteorology (BoM) provided abase station dictionary (23/05/01) with the data.The dictionary provided basic station informationincluding:

• BoM site number – Australian mainlandstations range from 001000 to 099999where the first three digits represent themeteorological region.

• Latitude and longitude – in decimal degrees

• Elevation – in metres (both station andbarometer heights may be given – stationheight is used for all climate variables asbarometer heights are only used forpressure data)

• Station name – descriptive

• Start and finish year – gives years ofoperation of the station.

Climate data from 1968 to 2002 was required toproduce the 1970 to 2002 plant productivity mapgrids for the NCAS, stations with no climate datafor this period were removed from the data base.This version of the BoM dictionary was comparedwith both the CRES (1998) and AGO (2001)dictionary. Any station with a difference greaterthan 0.1o in position or 50 m in elevation waschecked. The BoM base dictionary is updatedmonthly and comparisons were done in May 2001,September 2001, January 2002 and August 2002.

Stations which showed up as high residuals in theproduction of the climate surface were checked forcorrect locations. The final station dictionary whichwas used consisted of 11,714 stations, 2,511 of whichwere checked/geo-coded using the digital maps ofAustralia and the 9 second DEM. The digital mapsprovided a complete coverage of Australia at the1:100,000 scale and at 1:250,000. The 1:100,000 scalewas used where possible but in areas where onlydyelines were available the 1:250,000 maps were alsoused. The 9 second DEM was used as a generalcheck on elevation. A screen capture of each stationchecked was also taken (see Figure 4).


Figure 4. A screen capture for station 061121 – Lostock Post Office.

2.2.4 Long-Term Average Climate Variables

The production of a productivity index map forAustralia required a number of climate variables.Long-term average climate surfaces from theANUCLIM software package were used for theclimate variables available (Table 1). The ANUSPLINsoftware package and methods described in theprevious section were used to produce a gridded setof long-term means for average temperature andnumber of frost days (Table 2).

The concept of a ‘long-term average’ climate surfacehas problems associated with the assumption ofstationarity of the time series. The earth’s climatewill probably never settle reliably into anequilibrium with average long-term behaviour.Climatic averages are best given for either the fulllength of the record or for a standard period (forexample a set thirty year period). The long-termaverages used to produce productivity index mapsused the full length of the record although recordsprior to 1920 were not used.

2.2.4.1 Average Temperature

ANUSPLIN (SPLINA) was fitted to data createdfrom averaging the BoM monthly minimum andmaximum temperature data and then calculatinglong-term averages. Both a spline and a partialspline function were fitted to these long-termaverage data (1,126 stations). A second orderspline function was fitted to this data using threeindependent variables (longitude, latitude andelevation). The iterative process meant that fivesurfaces were produced and the final surfacecoefficient file was used in the productivitymodelling. Errors associated with these surfaceswere approximately ± 0.5oC. Figure 5 shows thefitted monthly long-term continental averagetemperature (Tavg).

A second order partial spline function fitted to twoindependent variables (longitude and latitude) andone independent variable (elevation) was used tocheck lapse rate values. The lapse rates for averagetemperature for the Australian continent rangedfrom 4.9oC in June to 6.0oC in December.

2.2.4.2 Number of Frost Days

SPLINA was used to fit the average of monthly frostdays from data provided by the BoM. A secondorder spline function using three independentvariables (longitude, latitude and elevation) wasused to create the frost surfaces. Values on theoutput grids which were greater than the number ofdays in the month were reduced to the number ofdays in the month and any values less than 0 wereconverted to 0. Errors associated with the surfacesranged from ± 0.2 days in summer to ± 2 days inwinter. Figure 5 shows the fitted continental long-term average number of frost days per month (F).

2.2.4.3 Radiation

Long-term radiation surfaces (with rainfall as acovariate, RP) from the ANUCLIM package wereused for both the long-term and monthlyproductivity indices.

For the slope and aspect corrected solar radiationthe ratio of diffuse (RF) to global radiation (RG) wasderived for each station from the original BoM data.For each of the 32 BoM stations which had monthlymeasurements of direct and global radiation a long-term average was determined from the monthly


ANUSPLIN Climate Variable Data points (knots) Fitted variables Order of spline Transformation

Average temperature 1126 3 independent (long,lat,elev) 2 -

Frost days (number) 700 3 independent (long,lat,elev) 2 Square root

Table 2. ANUSPLIN variables created for the long-term plant productivity modelling.

Table 1. ANUCLIM variables used in the plant productivity modelling.

ANUCLIM Climate Variable Data points (knots) Fitted variables Order of spline Transformation

Maximum temperature 1200 3 independent (long,lat,elev) 2 none

Minimum temperature 1200 3 independent (long,lat,elev) 2 none

Rainfall 13000 (3500) 3 independent (long,lat,elev) 2 Square root

9am Dry bulb temperature 185 3 independent (long,lat,elev) 2 none

9am Dew point temperature 176 3 independent (long,lat,elev) 2 none

3pm Dry bulb temperature 190 3 independent (long,lat,elev) 2 none

3pm Dew point temperature 183 3 independent (long,lat,elev) 2 none

Radiation with rainfall 45 3 independent (long,lat,elev) 2 none1 dependent (rainfall)


Figure 5. Long-term monthly average temperature.

Average Temperature, Tavg (oC)

10 20 30

November DecemberOctober

August SeptemberJuly

May JuneApril

February MarchJanuary

0


Figure 6. Long-term average number of frost days per month.

Number of Frost days, F

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August SeptemberJuly

May JuneApril

February MarchJanuary

6 8 10 15 20 31


data to calculate the ratio of diffuse to globalradiation. A 2-dimensional spline (using longitudeand latitude) was fitted to the data to give a spatialcoverage of the ratio expressed as a percentage foreach month. This ratio surface was then applied tothe ANUCLIM long-term radiation (RP) surface toproduce coverages of direct and diffuse radiation.By definition the effects of slope and aspect apply todirect solar radiation only.

RD = RP – (RP * RF/RG) (3)

The radiation calculator is a computer programwritten by ScienceSpeak in C+ and is based on aspreadsheet by J.B. Moncrieff of EdinburghUniversity. The radiation calculator estimates thecorrection to direct radiation from location (latitude)and slope and aspect (calculated from the 9 secondDEM). The calculator only uses the zenith-anglecorrection factor from the Moncrieff spreadsheet astransmissivity effects are assumed to be mainlyincorporated in the measured flat-earthmeasurements. Output is a grid of percentagechange to flat earth which is used to adjust thedirect radiation grid. This adjusted direct radiation(RA) grid is then added to the diffuse radiation gridto give the corrected global radiation (RC) grids.

RC = RA + RF (4)

Where RC is the corrected global radiation, RA is theslope and aspect adjusted direct radiation and RF isthe diffuse radiation. Grids of net radiation werederived where:

RN = (RC * 0.8) - 4) if RC > 5

RN = 0 if RC <= 5 (5)

2.2.4.4 Vapour Pressure Deficit (VPD)

The air absorbs water vapour and as water vapouris a gas it exerts a pressure in addition to that of theair. The higher the air temperature the more watervapour can be absorbed. Saturation vapour pressure(e°) specifies the maximum amount of water vapourwhich air, at a given temperature, can hold. Thesaturation vapour pressure increases with increasing

air temperature. The air inside a green leaf, near thesurfaces of the cells where water is evaporating, isvery nearly always saturated.

Vapour pressure deficit (VPD) is the differencebetween the actual vapour pressure and thesaturation vapour pressure. For water vapour, VPD

is normally in the range 0.1 kPa (very humid) to3 kPa (very dry air), or 1 to 30 mbar. A low VPD

means a high air humidity, and vice-versa. VPD

indicates the ‘drying effect’ of the air, the higher theVPD the stronger the drying effect, so the strongerthe driving force on transpiration.

The ANUCLIM program has long-term averagecoverages for 9am and 3pm dry bulb temperature(the ambient air temperature) and the 9am and 3pmdew point temperature (the temperature to whichair must be cooled if the moisture present is toreach saturation). VPD was calculated byaveraging the 9am and 3pm difference betweenthe saturation vapour pressure for the dry-bulbtemperature and the saturation vapour pressurefor the dew point temperature.

VPD = ((e°( Tdry9) - e°( Tdew9)) + (e°( Tdry3)- e°( Tdew3)) / 2) (6)

2.2.5 Monthly Average Climate Variables

The production of monthly and annual productivityindices for Australia from 1970 to 2002 requiredmonthly climate surfaces from 1968 to 2002. Climatesurfaces for all variables were created using theANUSPLIN software package and data from theBoM (see Table 3). Long-term radiation data wasused as data was not available for monthlyradiation for the early part of the time period anddata numbers were considered insufficient for thelatter part of the time period.

The methods described in the previous sectionwere used to produce a gridded set of mapsfor monthly and annual means for temperature(minimum, maximum and average), rainfalland number of frost days, from January 1968to December 2002 (Table 3).


2.2.5.1 Average Temperature

Monthly average temperature (Tavg) data wascalculated by averaging the BoM monthly minimumand maximum temperature data at each station.Any month which had less than 15 days recordedmeasurements was removed from the data set andall years which did not have 12 months of data were

also removed. The average number of stationnumbers used for all years was 489.5 ranging from346 stations in 1986 to 741 stations in 2002.

SPLINA was then fitted to the average temperaturedata set. Both a spline and a partial spline functionwas fitted to the data. A second order splinefunction using three independent variables

1976 1982 1996

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Temperature, Tavg (oC)

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ees

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Average Temperature

Figure 7. Annual average temperature derived from monthly data grids for 1968-2002 and examples oflow (1976), average (1982) and high (1996) temperature years.

ANUSPLIN Climate Variable Data points (knots) Fitted variables Order of spline Transformation

Average temperature 346-741 3 independent (long,lat,elev) 2 none

Maximum temperature 346-741 3 independent (long,lat,elev) 2 none

Minimum temperature 363-733 3 independent (long,lat,elev) 2 none

Rainfall 4719-7080 (2000) 3 independent (long,lat,elev) 2 Square root

Frost days 350-739 3 independent (long,lat,elev) 2 none

Table 3. Monthly climate variables.


(longitude, latitude and elevation) was used tocreate the average temperature surfaces. Theaverage error associated with the grids wasapproximately ± 0.5oC.

The continental annual average temperature rangedfrom 20.75oC in 1976 to 22.37oC in 1998 (see graph,Figure 7). The mean continental averagetemperature for the period 1968 to 2002 was 21.64oC.The graph in Figure 7 shows a rising trend of 0.45oCbut most of the rise in temperature is due to therelatively cooler years of 1974 to 1978 with only a0.2oC rising trend evident in the last twenty years.

2.2.5.2 Maximum Temperature

Monthly maximum temperature (Tmax) was fittedfrom data from the BoM monthly maximumtemperature data set. Any month which had lessthan 15 days recorded measurements was removedfrom the data set and all years which did not have12 months of data were also removed. The averagenumber of station numbers used for all years was489.5, ranging from 346 stations in 1986 to 741stations in 2002.

SPLINA was then fitted to the average temperaturedata set. Both a spline and a partial spline functionwas fitted to the data. A second order splinefunction using three independent variables(longitude, latitude and elevation) was used tocreate the average temperature surfaces. Theaverage error associated with the data grids rangedfrom 0.2 to 0.75oC.

Figure 8. Australian average maximum temperature (Tmax) and the 12 month running mean.

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Tmax12 per. Mov. Avg. (Tmax)


Figure 8 shows the variation in the Australianaverage maximum temperature and the 12 monthrunning mean shows an overall increase (~0.4oC)over the period January 1968 to December 2002.Continental monthly maximum temperatures hada mean value of 28.53oC and ranged from a low of

18.9oC in July 1968 to a high of 36.7oC inDecember 1972. Figure 9 shows example mapsfor the high (July 2002 and December 1972) andlow (July 1968 and December 1975) continentalaverage values.

Figure 9. Maximum temperature grids for high and low continental average values for July and December.

July 1968Grid Average = 18.9 0C


December 1972Grid Average = 36.7 0C

December 1975 Grid Average = 18.9 0C

5 10 15 20-5 0 30 35 4025

Temperature Tmax (oC)


2.2.5.3 Minimum Temperature

Monthly maximum temperature (Tmin) was fittedfrom data from the BoM monthly minimumtemperature data set. Any month which had lessthan 15 days recorded measurements was removedfrom the data set and all years which did not have12 months of data were also removed. The averagenumber of station numbers used for all years was516, ranging from 363 stations in 1986 to 733stations in 2002.

Both spline and partial spline functions were fittedto the BoM data. A second order spline functionusing three independent variables (longitude,latitude and elevation) was used to create theminimum temperature surfaces. The average errorassociated with the maps ranged from 0.6 to 1.0oC.

Figure 10 shows the variation in the Australianaverage minimum temperature and the 12 monthrunning mean shows an overall increase (~0.5oC)over the period January 1968 to December 2002.Figure 11 shows the continental monthly minimumtemperatures have a mean value of 14.8oC andranged from a low of 5.5oC in July 1982 to a high of22.3oC in February 1983. Figure 11 shows examplemaps for the high (February 1983 and July 1973)and low (February 1972 and July 1982) continentalaverage values.

Figure 10. Australian average minimum temperature (Tmin) and the 12 month running mean.

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Tmin12 per. Mov. Avg. (Tmin)


2.2.5.4 Rainfall

Rainfall (P) was fitted to the data from the BoMmonthly rainfall data set. Any month in which hadless than 20 days recorded measurements wasremoved from the data set and all years which didnot have 12 months of data were also removed fromthe data set. The average number of station numbersused for all years was 5,972 ranging from 4,719stations in 1994 to 7,080 stations in 1970.

A second order spline function using threeindependent variables (longitude, latitude andelevation) and a square root transformation wasused to create the rainfall surfaces. The average

grid values ranged from 15.5 mm in November1982 to 219 mm in January 1974. The errorassociated with the grids ranged from 14% to 30%,with the higher errors resulting from the lower thanaverage value grids.

Figure 12 shows the Australian continental averagemonthly and annual rainfall. The mean value forthe continental monthly average is 40 mm andranges from a low of 7 mm in October 2002 to ahigh of 235 mm in January 1974. The mean annualrainfall was 485 mm and ranged from 327 mm in2002 to 770 mm in 1974. Figure 13 shows annualrainfall maps for 1970 to 2002.



February 1983Grid Average = 22.3 0C

February 1972 Grid Average = 20.10C

5 10 15 20-5 0 30 35 4025

Temperature, Tmin (oC)

Figure 11. Minimum temperature grids for high and low continental average values for July and February.


Figure 12. Australian continental average monthly rainfall.

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Figure 13. Annual rainfall 1970–2002.

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1980 1981 1982 1983 1984

1985 1986 1987 1988 1989

1990 1991 1992 1993 1994

1995 1996 1997 1998 1999

2000 2001 2002

Rainfall, P (mm)

0 100 200 300 400 500 1000 2000


2.2.5.5 Frost Days

The number of frost days per month (F) was fitted tothe data from the BoM monthly data set. Any monthwhich had less than 20 days recorded measurementswas removed from the data set and all years whichdid not have 12 months of data were also removed.The average number of station numbers used for allyears was 548, ranging from 350 stations in 2002 to739 stations in 1973.

A second order spline function using threeindependent variables (longitude, latitude andelevation) was used to create the frost surfaces.Values on the output grids which were greater thanthe number of days in the month were reduced tothe number of days in the month and any valuesless than 0 were converted to 0. The continentalaverage annual number of frost days ranged from21.1 days in 1982 to 6.1 days in 1991(Figures 14and 15) with a sharp decline in the latter half ofthe period. The error associated with the datagrids ranged from 0.01 days in the summermonths to 3 days in the winter months, with thehigher errors resulting from the higher thanaverage value grids.

Figure 14. Australian continental average annual number of frost days.

Annual number of frost days

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2.2.5.6 Radiation

As the BoM monthly data would not support thederivation of monthly surfaces, long-term averageswere used. For monthly productivity it was decidedthat ‘flat earth’ estimates of global radiation wouldbe used as slope and aspect correction at the 1 kmscale was thought to be inappropriate. The radiationwith rainfall surfaces from ANUCLIM were used.Grids of net radiation were derived where:

RN = (RP * 0.8) - 4) if RP > 5

RN = 0 if RP <= 5 (7)

2.3 NORMALISED DIFFERENCEVEGETATION INDEX (NDVI)

The NDVI provides a crude estimate of vegetationhealth and a means of monitoring changes invegetation over time and is one of the most commonvegetation indices derived from remotely senseddata. NDVI is related to vegetation as healthyvegetation reflects very well in the near infrared partof the spectrum. Green leaves have a reflectance of20 percent or less in the 0.5 to 0.7 micron range(green to red, λR) and about 60 percent in the 0.7 to1.3 micron range (near infra-red, λI). The value isthen normalised to the range –1 <= NDVI <= 1 to

partially account for differences in illumination andsurface slope but is undefined when λR and λI arezero. The NDVI is computed by the product of theratio of the two electromagnetic wavelengths.

NDVI = (λI -λR)/( λI +λR) (8)

The typical range is between about -0.1 (λI less thanλR for a not very green area) to 0.6 (for a very greenarea). NDVI values vary with absorption of red lightby plant chlorophyll and the reflection of infraredradiation by water-filled leaf cells. Becausephotosynthesis occurs in the green parts of plantmaterial the NDVI is normally used to estimategreen vegetation.

The Advanced Very High Resolution Radiometer(AVHRR) sensor collects imagery at a 1 kmresolution that is then used to generate a NDVIproduct for Australia. The Environmental ResourcesInformation Network (ERIN) of the AustralianGovernment Department of Environment andHeritage .01 degree NDVI archive contains datafrom the National Oceanic and AtmosphericAdministration (NOAA) Satellite 9, 11, 14 and 16.The NOAA 14 data is calibrated using the equationsin ‘Revised post-launch calibration of channels 1 and2 of the AVHRR on board the NOAA-14 spacecraft’by Rao and Chen on the NOAA web site at

Figure 15. Example grids of low (1991, average = 6.1 days), medium (1987, average =14.0 days) and high(1982, average = 21.1 days) number of days frost.

1991 1987 1982

Annual Frost days, F

0 10 30 455 2015 3525 5040


http://orbit-net.nesdis.noaa.gov/. Data fromNOAAs 9, 11 and 16 are cross-calculated to NOAA14 by assuming that the reflectance of the 10,000pixels with the lowest standard deviation in theNOAA 14 dataset from each of 5 of AustralianDeserts are stable.

The data for April to September 1994 obtained fromNOAA 11 was very poor due to very low sunangles, thus large angular corrections. For thisperiod, NOAA 9 NDVI images were calibrated andthen composited into the NOAA 11 image data.

The images were cloud screened by masking outpixels that did not appear to be biologicallyconsistent with the pixels in the images takenimmediately before and after. The first stage of thisprocess was performed by a filter that masks outpixels that show a large decrease followed by a largeincrease. This procedure was also designed toremove ‘dropout’ values and abnormally highvalues probably associated with low sun angles.This was followed by a manual (subjective) stagewhere suspect areas were compared on screen withadjacent images. The cloud masks were thenadjusted as required. It should be noted that thescreening procedure contained a degree ofsubjectivity and was not exhaustive and the cloudmasks were expected to change slightly with furtherwork (N. Fitzgerald pers. comm.).

NDVI values were available for the ten year period,1991 to 2000. Long-term averages were derived fromthe data and used in both the long-term andmonthly productivity indices to provide acomprehensive cloud free data set. Monthly andannual data were not usable because of the extent ofcloud cover obscuring data signals over the shortertime spans.

The 28 day average NDVI (in BIL file format)(NDVIb) files supplied by ERIN had values whichranged from 1 to 256. NDVI values less than 50 wereconsidered to be cloud and given a no data value of-99. For each month NDVI was then rescaled:

NDVI = (NDVIb - 50)/200 if NDVIb > 50

NDVI = -99 if NDVIb <= 50 (9)

The satellite-derived NDVI data represents thephotosynthetic capacity of all vegetation within a 1km pixel for a given month, correlated with thefraction of photosynthetically active radiation (PAR)absorbed (fPAR). Sellers (1985, 1987) derived animportant relationship between leaf area index(LAI), absorbed photosynthetically active radiation(APAR) and NDVI. This research found that underspecified canopy properties APAR was linearlyrelated to NDVI and curvilinear related to LAI. LAIis usually positively corelated to an increase inthe difference between λI and λR radiation. Thisrelationship has been shown to hold generallyover a number of different biomes. LAI can becalculated from:

LAI = ln ( 1 - (NDVI * 1.0611) + 0.3431) / - 0.5 (10)

year and Topt as equal to the average of Tlow andThigh. Tavg was the average temperature described inSections 2.2.4.1 and 2.2.5.1. The equation describingthe effects of temperature is:

(11)

Tlow represents the monthly average temperaturebelow which plant growth stops; Thigh is the monthlyaverage temperature above which plant growthstops and Topt is the optimum temperature forgrowth. Minimum and maximum temperature datawere derived from the ESOCLIM and ANUSPLINpackages and Tavg Thigh, Tlow and Topt were derivedfrom these values. The temperature modifier (Tmod)was 1 when Tavg = Topt. Equation 10 gives ahyperbolic response curve, with Tmod = 0 when Tavg = Tlow or Thigh. Consequently Tmod generally hadrelatively small effects on the calculation of plantproductivity. Figure 16 shows the spatialtemperature modifier equations for Januaryand July long-term averages.


Tmod =Tavg - Tlow

Topt - Tlow

Thigh - Tavg

Thigh - Topt

Figure 16. Temperature modifier spatial equations (from Equation 11) for January and July long-term averages.

January

Tmod

Tavg Tlow

Topt Tlow

Thigh Tavg

Thigh Topt

=

July

Tmod

= Tavg Tlow

Topt Tlow

Thigh Tavg

Thigh Topt

Temperature modifier, Tmod Temperature (oC)

100 20 300 0.5 1

3. CALCULATION OF PLANT PRODUCTIVITY

Monthly values of environmental constraints onproductivity indices were expressed by modifierscalculated from the temperature, vapour pressuredeficit (VPD) of the atmosphere, soil water deficitand number of frost days.

3.1 TEMPERATURE MODIFIERThe growth of any plant species is limited bytemperatures outside the optimum range for thatspecies. A general assumption is made that, in anyparticular region, the plants are well-adapted to theexisting temperature range. Tlow was set to 1⁄2 theminimum temperature of the coldest month if theminimum temperature of the coldest month wasgreater than or equal to 0. If the minimumtemperature of the coldest month was less than 0,Tlow was set to 1⁄2 the minimum temperature of thecoldest month plus the minimum temperature of thecoldest month. Thigh was set to 5oC above themaximum temperature of the hottest month of the

( )

Number of Frost days per month, F

= 1 –

Frost modifier, Fmod

0 2 10 306

: 31

0 0.5 1 15

July


3.2 FROST MODIFIERA frost modifier (Fmod) was applied, using the simpleassumption that frost temporarily inactivates thephotosynthetic mechanism in foliage, so there isno growth on a frost day. The modifier is simplyone minus of number of frost days per month (F)divided by the number of days in the month(Figure 17).

Fmod = (1 - F / Days in month) (12)

3.3 VAPOUR PRESSURE DEFICIT (VPD)MODIFIER

The VPD modifier is important because plantsrespond to the VPD rather than relative humidityand the VPD provides the driving force forevaporation of water from the leaves. There is agradient of water vapour from the leaves to the airbecause the air in the leaf is saturated whereas theair outside is drier. The larger the VPD the steeperthe gradient. The VPD acting on stomatal, andhence canopy, conductance. The equation used is:

VPDmod = Exp(-0.05 * VPD) (13)

Figure 18 shows the VPD modifier spatial equationsfor the January and July long-term averages. Thismodifier essentially acts as a control on the rate ofwater loss; it is conditional upon soil water balance(see over page).

Figure 17. The frost modifier equation for the July long-term average.


3.4 SOIL WATER CONTENT (SW)Soil water content (SW) is derived from waterbalance calculations, which take into account themaximum soil water holding capacity (SC) in theroot zone of plants. Plant water use, transpiration(Trans), is calculated from the equation forequilibrium evaporation (Eeq, see Landsberg andGower 1997; p 79), modified by feed-back fromcurrent soil water content, and a conventionalwater balance equation:

Eeq = ((0.67 * RN * (1 - 0.05)) / 2.47) * days in month (14)

Where Eeq is the equilibrium evaporation and RN isthe net radiation (see Equation 7)

Monthly transpiration (Trans) was calculated as:

Trans(j) = Eeq(j) * SWmod(j-1) (15)

Where Trans(j) is the transpiration for the month (j),Eeq(j) is the equilibrium evaporation for the monthand SWmod(j-1) is the soil water modifier (seeEquation 20) for the previous month (Figure 20).

Rainfall interception (I) is derived from the leaf areaindex (LAI) and is defined as:

I = 0.05 + (0.02 * LAI) (16)

Australian continental rainfall interception (I)values were derived for long-term averages onlyas monthly data was not available for the period1970 to 2002. Figure 21 shows the long-term averageI for January.

Figure 18. VPD modifier spatial equations for January and July long-term averages.

0 2010 30

= exp – 0.05 *

January

0 0.5 1

= exp – 0.05 *

VPD

July

VPDmod

VPDmod VPD

Vapour Pressure Deficit,VPDVPDmod


January 1995

= * (1 – 0.05)

* 312.47

0

Equilibrium evaporation, Eeq (mm)

100 200 0

Net radiation, RN (MJm-2day-1)

10 20 30 40

0.67 *

0

Equilibrium evaporation, Eeq (mm)

100 200

= *

Transpiration (mm)

0 200

Soil water modifier, SWmod (mm)

0.5 10

December 1994January 1995

Figure 19. Spatial equation for equilibrium evaporation, January 1995.

Figure 20. Spatial equation for transpiration, January 1995.

January

0

Intercept

0.5 0

Leaf Area Index, LAI

5 10

= 0.05 + 0.02 *

Figure 21. Spatial equation for interception, January long term average.


January 1995

=

0

Soil water, SW (mm)

100 200 300 400 500 0

Soil water, SW (mm)

100 200 300 400 500

+

Water balance (mm)

-200 6000 200 400 800

December 1994 January 1995

Figure 23. Spatial equation for soil water content, January 1995.

The conventional water balance (W) equation isderived from the rainfall (P), the rainfall interception(I) and transpiration (Trans) and is given by:

W = (P * (1 - I)) – Trans (17)

Figure 22. Spatial equation for water balance, January 1995.

January 1995

0

Rainfall, P (mm)

1000

Transpiration (mm)

* 1 –= –

0 0.5

InterceptWater balance, W (mm)

0 200200 400 800 1700-200 6000 200 400 800

The soil water content (SW) is derived by adding thewater balance (change in soil water content over themonth) (W) to the previous months soil watercontent (Equation 18, Figure 23). The soil watercontent is reduced to the soil water holding capacityif it is greater than soil holding water capacity.Negative soil water content values are set to zero.

SW(j) = SW(j-1) + W (18)


Initial (first month) SW was taken as 0.75 of the soilwater holding capacity (SC) and carries over fromone time step to the next. The soil moisturecalculation was sequenced so that initial SW was setby running the water balance model for two yearsprior to initiating the productivity model. For theJanuary 1970 to December 2002 monthly analysis,the SW calculation sequence was initialised using the1968 and 1969 climate data.

The soil water modifier (SWmod) (Equation 20) wascalculated from the moisture ratio (M), which is theSW divided by SC. The equation describes thevariable effect of M across the range from wet soil(M ≈ 1) to dry soil (M ≈ 0).

M = SW / SC (19)

0

Moisture ratio, M

0.5 1

=

Soil water, SW (mm)

0 250 500

Soil water holding capacity, SC (mm)

0 100 200 300

January 1995

Figure 24. Spatial equation for moisture ratio, January 1995.

The soil water and VPD modifiers are notmultiplicative; the lowest one applies. The argumentis that if plant growth (conversion of radiant energyinto biomass) is limited more by VPD than soilwater (i.e. if VPDmod < SWmod) then soil water is not alimiting factor, even if soil water content is relativelylow. The converse applies; i.e. if SWmod < VPDmod, soilwater is the limiting factor.

SWmod = 1 / (1 + ((1 - M) / 0.6)7 ) if 1 /(1 + ((1 - M) / 0.6)7 ) < VPDmod

SWmod = VPDmod if 1 / (1 + ((1 - M) / 0.6)7 ) > VPDmod (20)


not used to infer an absolute mass increase. Usedin this way the conversion factor was not critical asit had only a linear (direct multiplicative) effect onthe index.

Absorbed photosynthetically active radiation(APAR) was estimated from global solar radiation,interpolated from station data provided by the BoM,and from a linear relation with the satellite-derivedNDVI which represents the photosynthetic capacityof all vegetation within each 1 km pixel and iscorrelated with the fraction of PAR absorbed (fPAR).

APAR is calculated as half the amount of short-wave(global) incoming radiation (RP) absorbed by plantcanopies:

APAR = RP * 0.5 * (1 - Exp(-0.5 * LAI)) x days in month (21)

= lesser of 1 + 1 -

January 1995

0

Soil water modifier, SWmod

0.5 1 0

Moisture ratio,M

0.5 1

0

Vapour Pressure Deficit modifier, VPDmod

0.5 1

0.6

1

7

or

Figure 25. Spatial equation for soil water modifier, January 1995.

3.5 ABSORBED PHOTOSYNTHETICALLYACTIVE RADIATION (APAR)

In the original version of the 3-PG model (followingLandsberg and Waring 1997), APAR is multipliedby a factor that converts it to a biomassaccumulation equivalent. This, in effect,amalgamates two physiological processes: theconversion of absorbed CO2 into initial carbonproducts (gross primary production) and the loss ofa proportion of those products by respiration to givenet primary productivity. The value of theconversion factor (ε, gm C MJ-1 APAR) used wasobtained from the literature (Potter et. al. 1993,Ruimey et. al. 1994, Landsberg et. al. 1997). There issignificant variation in ε values, but no clear patternin relation to plant type, so a ‘best estimate’ value of1.25 gm C MJ-1 APAR was used. In the productivitymodel the net primary productivity equivalent wastaken as an index of ‘plant productivity’ and was


Where LAI is the Leaf Area Index and the coefficient0.5 is a general value for the extinction coefficient.In a slope and aspect corrected form of theproductivity model radiation with rainfall (RP) isreplaced by slope and aspect corrected radiation(RC). APAR was calculated for long-term meansas the available radiation and LAI data were long-term averages.

3.6 PLANT PRODUCTIVITY INDEXProductivity is reduced by modifiers reflecting non-optimal nutrition, soil water status, temperatureand atmospheric vapour pressure deficits. Modifiersare dimensionless factors with values between0 (complete restriction of growth) and1 (no limitation). Modifiers used in this way arediscussed by Landsberg (1986), McMurtrie et. al.

(1994) and Landsberg and Waring (1997).

0 5 10

1 – exp – 0.5 *

January

0 250 500

July

Leaf Area Index, LAIAbsorbed Photosynthetically

Active Radiation, APAR

= * 31* 0.5 *

Radiation (MJ m-2 day-1)

400 10 20 30

* 31* 0.5 *= 1 – exp – 0.5 *

Figure 26. Long-term average APAR for January and July.

January

=

July

Plant Productivity Index

* 0.01* * * *

Temperature modifier, Tmod Soil water modifer, SWmodSoil nutrient status, SN Frost modifier, Fmod

0 250 5000 1 5 0 0.5 1 0.75 1.0 1.25 0 0.5 1 0 0.5 1

= * 0.01* * * *

Absorbed Photosynthetically Active Radiation, APAR

Figure 27. Spatial equations for productivity indices, January and July long-term averages.



0 1 2 3 5 10 15 25

Monthly values of plant productivity index(Figure 27) are expressed by APAR multiplied bythe modifiers calculated from soil waterbalance, temperature, number of frost days andsoil nutrient values.

Productivity Index = APAR * Tmod * SN *SWmod * 0.01 * Fmod (22)

Figure 28. Derived long-term average plant productivity index for Australia.

Productivity Indexann=Σj=12

j=1Productivity Indexj

Australia’s annual plant productivity is the sum ofthe 12 monthly values for the year of interest

(23)

where j is the month. Figure 28 shows the long-termannual average productivity index for Australia andFigure 29 shows the annual productivity indices for1970 to 2002.

A comparison of the annual productivity indexmaps in Figure 29 and the annual rainfall maps(Figure 13) shows the dominance of rainfall in theestimation of plant productivity.


1970 1971 1972 1973 1974

1975 1976 1977 1978 1979

1980 1981 1982 1983 1984

1985 1986 1987 1988 1989

1995 1996 1997 1998 1999


2000 2001 2002

1990 1991 1993 19941992

0 1 2 3 5 10 15 25

Figure 29. Derived annual continental productivity indices 1970-2002.

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APPENDIX 1

Preliminary Investigations: Spatial Estimation of Plant Productivity and Classification of Non-Commercial Vegetation Types

URS Australia

Joe Landsberg

Jenny Kesteven

July 2001


TABLE OF CONTENTSPage No.

A1. Overview 43

A2. Collation and Analysis of Growth Data – URS Australia 44

A2.1 Assembly of Datasets 44

A2.2 Coding 45

A2.3 Selection of Vegetation Types 45

A2.4 Preparation of Growth Tables 45

A2.5 Findings 45

A2.5.1 Review of Publications 45

A2.5.2 Comparability Issues and Adjustment Factors 47

A2.5.3 Forest Growth Data 47

A2.6 Discussion 48

A2.7 References 48

A3. Spatial Estimation of Plant Productivity – Joe Landsberg & Jenny Kesteven 50

A3.1 Introduction 50

A3.2 Background 50

A3.3 Planning and Procedure 50

A3.4 The Plant Productivity Model 52

A3.5 Analysis of Plant Productivity by IBRA Regions 52

A3.6 Plant Productivity Values by IBRA Regions 53

A3.7 Spatial Variation in Plant Productivity within Regions 55

A3.8 Analysis of Plant Productivity by Carnahan Vegetation Classes 62

A3.9 Conclusions 63

A3.10 Follow Up Research 64

A3.11 Relevant Citations 64

A4. Forest Growth Data Tables – URS Australia 65

LIST OF TABLESPage No.

Table 1a. Growth data for sites in South Australia, Hassall & Associates (1999a). 66

Table 1b. Growth data for sites in Victoria, Hassall & Associates (1998). 74

Table 1c. Growth data for sites in Western Australia, Hassall & Associates (1999b). 81

Table 2a. Growth data for sites in South Australia, Wong et. al. (2000). 88

Table 2b. Growth data for sites in Victoria, Wong et. al. (2000). 92


LIST OF FIGURESPage No.

Figure 1. Derived long-term average plant productivity index for Australia. 52

Figure 2. Annual average Plant Productivity by IBRA regions. 53

Figure 3. Monthly plant productivity index in four IBRA regions: South East Highlands (SEH); Jarrah Forest (JF); Darling River Plains (DRP); and Central Kimberly (CK). 54

Figure 4. Monthly water balance in the four IBRA regions for which plant productivity data are shown in Figure 3. Negative values indicate the dryness of the area but note that in thecalculation of water balances for the model soil water content cannot go below zero. 55

Figure 5a. Change in the standard error of the mean productivity value with various numbersof sample points, up to 100, in the Wet Topics region. There was no indication that a minimum value had been reached. b. Spatial distribution of plant productivity across the Wet Tropics region. The data exhibit significant variation. 56

Figure 6a. Change in the standard error of the mean productivity value with various numbersof sample points, up to 100, in the Tanami Desert region. There was no indication thata minimum value had been reached. b. Spatial distribution of mean annual plantproductivity across the Tanami Desert. The data exhibit significant variation, with a marked peak in the low productivity range. 57

Figure 7a. Change in the standard error of the mean productivity value with various numbers of sample points, up to 300, in the Jarrah Forest region. The curve indicates that 2-300 samplepoints would be required to give an accurate value of the mean. b. Spatial distributionof mean annual plant productivity across the Jarrah Forest region. The curve is skewedtowards lower values. 58

Figure 8a. Change in the standard error of the mean productivity value with various numbers of sample points, up to 300, in the South Eastern Highlands. The curve indicates that 2-300 sample points would be required to give an accurate value of the mean. b. Spatial distribution of mean annual plant productivity across the South Eastern Highlands region. The distribution is more uniform than in most other regions. 59

Figure 9a. Change in the standard error of the mean productivity value with various numbersof sample points, up to 300, in the Darling River Plains. The curve indicates that at least 300 sample points would be required to give an accurate value of the mean. b. Spatial distribution of mean annual plant productivity across the Darling River Plains. The distribution is reasonably uniform, slightly skewed to the higher values. 60

Figure 10a. Change in the standard error of the mean productivity value with varying numbers ofsample points, up to 300, in the Central Kimberley region. The curve has not reached stablevalues; at least 300 sample points would be required to give an accurate value of the mean. b. Spatial distribution of mean annual plant productivity across the Central Kimberleyregion. The distribution is approximately normal. 61

Figure 11. The distribution of open woodland shaded by the plant productivityindex value. 62

Figure 12. Range of plant productivity values for the forest classes of present Carnahanvegetation classes. 63


A1. OVERVIEW

The work reported here represents preliminaryinvestigations toward developing the capability ofthe National Carbon Accounting System (NCAS) toestimate carbon sequestration in ‘non-commercial’vegetation types, largely environmental plantingsand native regrowth. This preliminary workprovided guidance on the preferred framework andevolution of methods presented in the main body ofthis report.

Most of the collection of forest growth data andmodelling activity has historically been focussed oncommercial plantings which have higherproductivity. Non-commercial plantings areimportant because of their associated benefits e.g.windbreaks, salinity control, erosion control, habitatand biodiversity, visual amenity. With the drive tomore sustainable land management, there is a needfor more of such plantings integrated with wholefarm and whole region planning. Likewise, nativeregrowth following disturbance, e.g. fire and landclearing, is a common feature of the Australianlandscape, and is a desirable form of revegetation.Carbon sequestration can likewise facilitate soundland management.

A relatively small, yet important, part of Australia’sNational Greenhouse Gas Inventory is the carbonstored in environmental plantings and nativeregrowth; however there are few data available onwhich to develop Growth Tables, whereby empiricalestimates may be made for specific projects or forregions. The NCAS is working to fill this gap, andthis project is a means to that end. The NCAS haschosen the second of two broad options to do so:

1. Sample stands of environmentalplantings and native regrowthsystematically across Australia, alongwith associated environmental data, anddevelop the relationships needed to developGrowth Tables; or

2. Stratify the Australian landmass on thebasis of plant productivity and use this toundertake a more-efficient stratifiedsampling and modelling of the growthpotential within these strata.

This project comprises two steps:

1. Collate and analyse available data for themain types of environmental plantings andregrowth vegetation relevant to formingGrowth Tables (as reported in SectionA2 and A4); and, concurrently

2. Develop the possible methodologies forproductivity stratification and vegetationclassification; and apply the stratificationmethodology to produce the documentedGIS layer over Australia (as reported inSection A3).

The original intent of this work was to develop aclassification method for non-commercial plantingsaround potential carbon accumulation and todevelop Growth Tables for regions of varyingproductivity throughout Australia. The aim was toclassify vegetation into a manageable number oftypes of similar carbon accumulation patterns. Thiswould provide default estimates of the carbonaccumulation for a particular vegetation class in aparticular region/location.

This was planned as a desktop study with 3 steps:

1. Draft classification of environmentalplantings and native regrowth.

a) Define classes (classification)

b) Define productivity regions (stratification);

2. Consult with experts to refine theclassification; and

3. Refine the classification.


The initial intention was to produce a series of ‘look-up’ Growth Tables for each of the types ofnon-commercial plantings, so that they could beapplied in the relevant regions as defined in thefirst step of this project.

However, it was soon recognised that there werefew data available concerning carbon accumulationpatterns i.e. ‘plant growth rates’ of these vegetationtypes. Thus the classification would be extremelycoarse and, on its own, would add little value. Thisconclusion led to the development of methodsexplored in Richards (2002) and described indetail in Richards (2001).

In light of this, an alternative approach ofdeveloping Growth Tables for typical vegetationtypes was pursued and is reported in the followingSection A2 and data is tabulated in Section A4.

To guide data collection a list of about 40 vegetationtypes was compiled, but feedback was that thisneeded to be greatly expanded by incorporatingheight classes as well. Height class is generally usedas a site index, that is, as a measure of siteproductivity. There are few experts in this field, andthose who provided information or adviceconfirmed the paucity of this data and that it wouldbe difficult to provide the necessary Growth Tablesfor each vegetation type.

Feedback received confirmed that the best way toproceed was to collate as much of the availablegrowth information for native regrowth andenvironmental plantings that would allow potentialdevelopment of growth models and likely carbonaccumulation for particular regions. This sparse datacould then be used as a basis for interpolation,guided by relative site productivity assessments, tofill gaps. The paucity of purely ‘non-commercial’data led to the inclusion of data from somecommercial plantings data.

A2. COLLATION AND ANALYSIS OF GROWTH DATA

URS Australia

A2.1 ASSEMBLY OF DATASETSDatasets were sourced widely and because the datawere sparse, all possibilities were considered:Fortech (now URS Forestry) files, CSIRO, GreeningAustralia, State agencies, Bureau of Rural Sciences(BRS), and relevant Universities.

Data sources included:

• The Australian GovernmentBushcare/Natural Heritage Trust (NHT)funded work undertaken by Hassalls‘Measurements of Plantations of Trees in[region] to Improve the Estimates of CarbonSequestration in Low Rainfall Areas’, for thefollowing regions: South Australia, Victoriaand WA;

• The publication commissioned by the AGO(NCAS Technical Report No. 25) by Grierson,Williams and Adams (2000) which listedsources of unpublished data; and

• The ABARE Trees on Farms survey, forleads as to what might be available.(Wilson et al. 1995)

A number of other published and unpublishedreports and databases were reviewed. In mostcases, these resources did not incorporate measuredvolumetric growth data and/or were not madeavailable to URS Australia.

The surprising finding was just how little relevant

data were available, even from native forestryagencies. This was after extensive consultations.The paucity of data led to the inclusion of somecommercial plantings data. Because of this,careful consideration of the comparability andcompatibility of data was undertaken beforecalculating Growth Tables.


It was clear that a large volume of related dataexists, but there would be a need to establish itsutility for this particular purpose. It is likely thatfurther data will be identified and its utilityshould also be examined. Synthesis of such datahas considerable cost benefit advantages overfield sampling.

A2.2 CODINGInformation was gathered under data fields thatwould be useful for developing Growth Tables andinterpreting the variation, including:

• Site information (ID #, name/location/coordinates, year established, area planted,species list);

• Biomass estimate (value, age of trees at timeof measurement, type of estimate);

• Mean Annual Increment (MAI);

• Planting density (trees per hectare);

• Wood density (kg/m3);

• Site factors, e.g. rainfall, soil type/quality,topography, access to water, salinity;

• Husbandry, e.g. tubestock/direct seed, weedand pest control, fire, irrigation; and

• Source of information.

A2.3 SELECTION OF VEGETATION TYPESThe selection of vegetation types for analysisof growth parameters was based on twoconsiderations:

• The datasets that were made available –the best represented types can supportthe most robust Growth Tables and modelcalibrations; and

• Having a wide range of vegetation types,to give most scope for estimating theperformance of other vegetation typesby ‘interpolation’.

A2.4 PREPARATION OF GROWTH TABLESThe revised plan was that the data for each specieswould be summarised statistically to understandthe causes of variation. These have been presentedin Section A4, Tables 1 and 2.

Having compiled the growth tables and examinedthe data, it became apparent that the paucity of dataand the different methodologies used to arrive atthis data, meant that comparisons of growth and theestimation of growth tables was inappropriate.Adjustments and assumptions could be made tolessen the impacts of these incompatibilities.Undertaking these adjustments was outside thescope of this project.

A2.5 FINDINGS

A2.5.1 Review of Publications

Grierson et. al. (2000) Review of UnpublishedBiomass-Related Information: Western Australia,South Australia, New South Wales and Queensland

This presents data for mature vegetation or at leastsnap shots in time, e.g. Table 2. There are referencesto publications and datasets that may be able tosupply growth rate data. The publication representsthe first stage (identification of potentially usabledatasets) of a two stage process. The second stagewill provide comprehensive assembling andsynthesis of this data.

Particular issues that were noted within Griersonet. al. (2000) include:

• Mallee-type species will likely needdifferent estimates for single and multi-stemmed species;

• p. 7-8: Forestry SA, see Kiddle et. al. (1987),Boardman et. al. (1992) for growth curves ofnative species growth in plantations;

• p. 9: see reviews by Eamus et. al. (2000),Snowden et. al. (2000), Keith et. al. (2000),Burrows et. al. (2001) for mulga andother species;


• Root:shoot ratios - most decline with age, afew increase and a few have no clear trend(Table 4, p. 12). There is a wide range of meanvalues, even within the same genus. There isclear evidence of an environmental gradientand further work on the dominant factors ofchange, e.g. soil depth texture etc., nutrientand water availability that may permitmodelled estimates;

• p. 21: see Todd (2000) for jarrah regeneration;

• p. 23: WA oil mallee growth data. Not reallyrelevant as these are dense plantings. Mightilluminate the shape of the growth curve;

• p. 24: see chenopod growth (Koonamore data,see Noble (1977) for recovery after over-grazing), also Sinclair (Botany Department,Adelaide University);

• p. 26: cypress pine growth rates quoted;

• MAI quoted for tropical dry forestand woodland;

• p. 29: wide variation in (fine) root biomass,similar to variation in leaf biomass; and

• p. 30: mountain ash data – summary values,see Grierson et. al. (1992).

Hassall & Associates (1998, 1999a 1999b)Measurements of Plantations of Trees in [region] toImprove the Estimates of Carbon Sequestration inLow Rainfall Areas

This set of three reports funded by Bushcare and theNHT aims to improve the knowledge on carbonsequestration estimates from tree planting in threeStates, Victoria, South Australia and WesternAustralia. Approximately a dozen sites weresampled in each state, incorporating a large range ofclimatic and physical variation and species mix.

Very diverse growth rates were observedthroughout the regions studied. The diversity of thespecies mix, site characteristics and differinghusbandry made comparisons between different

sites and regions difficult and few conclusions couldbe reached regarding the effect of differentenvironmental conditions on biomass accumulation.

The approach used a constant wood density valuefor all tree ages in their estimation of volume, plustheir use of total stem volume including bark as thebase for estimating volume, make comparisonsdifficult with other major sources of data. Also, thesurveying of multiple species plots necessitates theuse of mean estimates for many of the numericalresults, leading to further incompatabilities withother datasets.

Summaries of growth data for each site in the threeStates are provided in Section A4, Tables 1a-1c.

Eamus et. al. (2000) Review of AllometricRelationships for Estimating Woody Biomass:Queensland, the Northern Territory and WesternAustralia

This report entailed the collection of primaryvolumetric data from a number of sites inQueensland and the Northern Territory. The reportitself concentrates on constructing allometricequations linking this data with growth parameterssuch as height (DBH) and growth rate. Thefundamental datasets from this work were notavailable for this study.

Wong et. al. (2000) Forecasting Growth of KeyAgroforestry Species in South Eastern Australia

This report, prepared for the Agroforestry JointVenture Program, aimed to improve the reliability offorecasting tree growth and yield of keyagroforestry, i.e. commercial species on cleared landacross South Australia and Victoria. As part of this,primary data including volumetric information, wascollected from long-established trials for six majorEucalypt species.

Despite focussing on commercial plantings, theWong report was included in this study given thepaucity of purely non-commercial data.

Summaries of growth data for each site in each Stateare provided in Section A4, Tables 2a-2b.


Miscellaneous hardcopy and online databasesand reports

A number of other published and unpublishedreports and databases were reviewed. In most cases,these resources did not incorporate measuredvolumetric growth data and/or were not madeavailable for this study. These included:

• TREEDAT – Stores the results of field trials ofspecies, provenances and other sub-speciestaxa so they can be selectively retrieved toassist people to choose plants for nominatedend uses and sites.

• MPTDAT – Provides information on thegrowth of Australian trees in variouslocations and assists staff of the AustralianTree Seed Centre.

• Subtropical Tree Site Management (STSM) –Provides a recording, analysis and deliverystructure for data on tree growth,management techniques, and species selectionin the subtropical region of Australia.

• Optimising the Growth of Trees Planted on Farms

– This report summarises farm tree and shrubplantings on the Northwest slopes and Plainsand Northern Tablelands of New South Wales.

The records available for E. camaldulensis in Hassall& Associates (1998, 1999a and 1999b) were one-offsnap-shots only, hence no growth estimates for theone plot were able to be estimated. Further recordsfor E. camaldulensis and other species are thought tobe available in other inaccessible (and/or costly toobtain) databases and in the records of individualsand other operators in the industry.

A2.5.2 Comparability Issues andAdjustment Factors

For the data that was collected, there were severalcomparability and compatibility issues whichprevented the generation and comparison ofbiomass growth curves. These are discussed below.

Wood Density. Biomass growth data can beestimated in Wong et. al. (2000) by applyingestimates of wood density at various ages to thesupplied volumetric growth data. This differs fromHassall & Associates (1998, 1999a and 1999b) whereconstant wood density values are assumed in itsbiomass projections. URS Australia initiallyattempted to estimate biomass values for Wongusing wood density estimates provided from URSForestry consultant staff, but subsequentlyabandoned this given the constant densityassumptions used in Hassall & Associates and otherbiasing factors described below.

Basis for Estimating Volume. Hassall & Associates(1998, 1999a and 1999b) used total stem volumeoverbark as the basis for estimating volume. Wong’sSouth Australian data was for total stem volumeunder bark, and the Victorian data was stemvolume to 2 cm under bark. Even with similar wooddensity assumptions, the difference in base volumeestimates prevents direct comparability of anyresultant biomass estimates.

Unclear Volume Parameters. The height at whichdiameter measurements are made for estimatingvolume directly influences all subsequentcalculations. The same applies for the shape of thetree bole. Hassall & Associates (1998, 1999a and1999b) assumes a conical shape, while the Wongreporting is unclear about this assumption. Wongpoints out that the methodologies are not consistentby saying “The growth data has been collected by

numerous forestry organisations over the past 10 to 15

years. Tree measurements followed the standard

measurational procedures for each State organisation…”

The inconsistency in the current data collectionshighlights the merit of the NCAS in developingstandard protocols for new data collection, makingthe protocols widely available, and requiring themto be used for all new data collection.

A2.5.3 Forest Growth Data

All data compiled in this study is summarised in theGrowth Tables presented in Section A4.


A2.6 DISCUSSIONThe above is a modest harvest for a reasonablesearch effort. It is however a fair reflection of howsparse forest growth data are, and how difficult theyare to access. There is doubtless more data around,effectively ‘lost’ in old files, and it would take moreresources than were available (including financialpurchase) to access them.

Adding to the problem of accessing data, is thecurrent lack of suitable data. There is little data thatincludes biomass estimates. The focus on heightparameters for the majority of vegetation andrevegetation databases, rather than biomass, is oneof the factors that limited the development of theGrowth Tables. The incompatabilities of the datathat do exist and are available is another majorfactor, as discussed above.

It would be possible to estimate biomassaccumulation using allometric equations of heightgrowth data, but this was outside the scope of thestudy and is in itself potentially subject toconsiderable error – see Eamus et. al. (2000).

In summary, the growth data contained in SectionA4, Tables 1 and 2 provides a good representationof biomass accumulation levels for some majornative species in differing husbandry, environmentaland climatic conditions across southern Australia.It could be argued that with appropriateadjustments and suitably broad assumptions,growth tables could be estimated and comparedover different datasets, as has been discussed inSection 3.2, albeit with certain caveats forcompatibility and comparability.

A2.7 REFERENCESBoardman, R., Thomas, M., Wood, R.R. & Ferguson,

T. 1992. Selected Plantations of Native Trees Growing

in the Semi-Arid Zone of South Australia and Their

Capacity to Sequester Atmospheric Carbon Dixoide.

Forest Research Branch, Central and NorthernRegion, Forestry SA, Adelaide.

Burrows, W.H., Hoffmann, M.B., Compton, J.F., &Back, P.V. 2001. Allometric Relationships and

Community Biomass Stocks in White Cypress Pine

(Callitris glaucophylla) and Associated Eucalypts of

the Carnarvon Area - South Central Queensland (with

additional data for Scrub Leopardwood - Flindersia

dissosperma). National Carbon Accounting SystemTechnical Report No. 33 Australian GreenhouseOffice, Canberra, 16pp.

Eamus, D., McGuinness, K. & Burrows, W. 2000.Review of Allometric Relationships for Estimating

Woody Biomass: Queensland, the Northern Territory

and Western Australia. National CarbonAccounting System Technical Report No. 5a,Australian Greenhouse Office, Canberra, pp56.

Grierson, P.F., Adams, M.A. & Attiwill, P.M. 1992.Estimates of Carbon Storage in the Above-ground

Biomass of Victoria’s Forests. Aust. J. Botany:631-640.

Grierson, P., Williams, K. & Adams, M. 2000. Review

of Unpublished Biomass-Related Information: Western

Australia, South Australia, New South Wales and

Queensland. National Carbon Accounting SystemTechnical Report No. 25, Australian GreenhouseOffice, Canberra, pp86.

Hassall & Associates 1998. Carbon sequestration in

low rainfall areas: the measurement of plantations

of trees in Victoria. Environment Australia,Canberra, pp47.

Hassall & Associates 1999a. The measurement of

plantations of trees in South Australia to improve the

estimates of carbon sequestration in low rainfall areas.

Environment Australia, Canberra, pp96.


Hassall & Associates 1999b. The measurement of

plantations of trees in Western Australia to improve

the estimates of carbon sequestration in low rainfall

areas. Environment Australia, Canberra, p56.

Keith, H., Barrett, D. & Keenan, R. 2000. Review of

Allometric Relationships for Estimating Woody

Biomass: New South Wales, the Australian Capital

Territory, Victoria, Tasmania and South Australia.National Carbon Accounting System TechnicalReport No. 5b, Australian Greenhouse Office,Canberra, pp111.

Kiddle, G. Boardman, R. & Van Der Sommen, F.1987. A Study of Growth and Characteristics of

Woodlot and Amenity Tree Planting in Semi-Arid

Rural South Australia. Final Report, Forestry SA,Adelaide.

Noble, I.R. 1977. Long-term biomass dynamics in an

arid chenopod shrub community at Koonamore, South

Australia. Australian Journal of Botany. 25:639-653.

Richards, G.P. 2001. The FullCAM Carbon Accounting

Model: Development, Calibration and Implementation

for the National Carbon Accounting System. NationalCarbon Accounting System Technical Report No.28, Australian Greenhouse Office, Canberra, pp49.

Richards, G.P. 2002. Biomass Estimation: Approaches

for Assessment of Stocks and Stock Change.

National Carbon Accounting System TechnicalReport No. 27, Australian Greenhouse Office,Canberra, pp140.

Snowdon, P., Eamus, D., Gibbons, P., Khanna, P.,Keith, H., Raison, J. & Kirschbaum, M. 2000.Synthesis of Allometrics, Review of Root Biomass and

Design of Futrue Woody Biomass Sampling Strategies.

National Carbon Accounting System TechnicalReport No. 17, Australian Greenhouse Office,Canberra, pp114.

Todd, M.C. 2000. The Role of Nutrient Cycling in the

Sustainability of Young Plant Communities on Mined

Sites. Ph. D. Thesis, University of WesternAustralia, Perth.

Wilson, S., Whitham, J., Bhati, U.N. & Tran, Y. 1995.Trees on Farms. Survey of trees on Australian

Farms: 1993-94. ABARE Research Report 95.7,ABARE, Canberra.

Wong, J., Baker, T., Duncan, M., McGuire, D. &Bulman, P. 2000. Forecasting growth of key

agroforestry species in south-eastern Australia. RuralInductries Research and DevelopmentCorporation, Canberra.


A3. SPATIAL ESTIMATION OFPLANT PRODUCTIVITY

Joe Landsberg & Jenny Kesteven

A3.1 INTRODUCTIONPreliminary plant productivity indices werecalculated for the 80 IBRA regions of Australia(following Thackway and Cresswell, 1995) using avariant of the 3-PG (spatial) model based on therelationship between the photosynthetically activeradiation absorbed by plant canopies (APAR) andthe biomass productivity of those canopies. Themodel used a monthly time step. The factorconverting APAR to biomass was reduced from theselected optimum value by modifiers dependent onsoil fertility, atmospheric vapour pressure deficits,soil water content and temperature. The ESOCLIMpackage was used to generate climate surfaces forthe country. Soil fertility and water holding capacityvalues were obtained from the Digital Soil Atlas ofAustralia. Leaf Area Index, essential for thecalculation of APAR, was estimated from 10-yearmean values of Normalised Difference VegetationIndices, for 1 km pixels, for the whole country.Incoming short-wave radiation – and hence APAR –was corrected for slope and aspect using a DigitalElevation Map (DEM), but with 1 km pixels (whichmade no significant difference to the results). Allanalyses were carried out on estimates based on theassumption of flat terrain.

One hundred point plant productivity values,evenly spaced, were calculated for every IBRAregion. Statistical analysis indicated that, asexpected, there were significant differences inproductivity between regions and, moreimportantly, that there was significant within-regionvariation. IBRA regions were not homogenous forbiomass production. Part of the reason for this lay inthe fact that most of them crossed climatic zones,and contained a variety of soils.

The result of this preliminary exercise was a map(and digital values) of plant productivity index, bymonths, for Australia. This provided generalestimates of plant productivity in any region, andhence a basis for estimates of carbon sequestrationand a framework for the analysis of carbonaccumulation by various vegetation types. Theproducts of the project could be tested empiricallyby statistical methods applied to all the measuredbiomass data available, or by modellingproductivity in detail, for selected locations forwhich the appropriate soil and climatic data wereprovided as inputs.

A3.2 BACKGROUNDURS Australia were contracted by the AustralianGreenhouse Office (AGO) to produce a map ofAustralia showing the regional distribution ofestimated plant productivity potential, in terms ofcarbon sequestration.

The task was to develop an information systemwhich could be used to obtain reasonable estimatesof probable carbon accumulation rates in anyregion of the country. These regional estimateswould also provide the framework for moredetailed research and analysis of carbon flows andcarbon storage capacities.

It was recognised that a simple modelling approachwould have to be adopted and Dr. Joe Landsbergwas sub-contracted to do the work.

A3.3 PLANNING AND PROCEDURESeveral meetings were held to determine the scopeof the project, and the best approach to take. It wasagreed that the project would be based around asimplified version of the 3-PG model (Landsbergand Waring, 1997), which has been extensivelytested as a tool for the spatial estimation of plantproductivity (see Coops et. al., 2001). This isdescribed in the following section.

It was agreed that the project would be basedaround an analysis of plant productivity potentialby IBRA regions1. These provided defined areas,


classified in terms of clearly stated characteristics,that are well recognised and quite widely used. Itwas assumed that the IBRA regions were based ondata likely to correlate with plant productivity.

The work was to be GIS-based, using a rastersystem, with sampling on the basis of pixels, orsample points, rather than polygons whosedefinitions involved assumptions that may not bejustified. Initial discussions proposed the use of~250 m pixels, since this was the scale of the DigitalElevation Map (DEM) available and it was proposedthat the short-wave radiation incident on landsurfaces should be corrected for slope and aspect.However, this involved an enormous amount ofcomputing time so the calculations were initiallydone using 1 km pixels, compatible with the scale atwhich estimates of Leaf Area Index (LAI), derivedfrom satellite data, were available for the country asa whole. Estimates of plant productivity usingradiation not corrected for slope and aspect werereferred to as the ‘flat earth’ estimates. Water bodieswere screened out of the Normalised DifferenceVegetation Index (NDVI) data.

The basic procedure was to sample 100 pixels perIBRA region, for each of which plant productivitywould be calculated. This allowed statisticalevaluation of the variation in plant productivityacross a region – information that cannot beobtained from analysis of polygons. To determinewhether 100 samples was enough, 300 samples fromeach of 4 regions were to be analysed to determinethe number needed to reduce the standard error ofthe mean to the lowest possible (stable) level. Notethat taking 100 samples per region, across regionsthat vary in size from 2,400 to 42,400 km2, means

that the sampling density will vary enormously. Thiswas considered reasonable on the grounds that thelarger regions were, virtually by definition, morehomogeneous2.

It was agreed that the AGO would make availableall the data bases it had, or had access to, and that itwould be actively involved in the project through itsScientific Adviser, Dr Jenny Kesteven. That is, theproject would be, in effect, collaborative with theAGO. In fact the AGO did most of the computingwork involving data bases and GIS analysis, JoeLandsberg produced the productivity model andthey worked together on de-bugging and analyses.The data bases required, and their sources, aredescribed below.

The project took a great deal longer to complete thanwas initially planned. This was largely a result ofdelays in obtaining data; Australia-wide digital soilmaps, including values for fertility and waterholding capacity in each pixel, had to be obtainedfrom ERIN (Environmental Resources InformationNetwork, The Australian Government Departmentof Environment and Heritage) and interpretationprovide by Dr Neil McKenzie (CSIRO Division ofLand and Water; see McKenzie et. al. 2000). Climaticdata for each month were produced using theANUCLIM package; the number of frost days had tobe calculated for each month for the whole country.The greatest delays were caused by the requirementfor estimates of Leaf Area Index (LAI) for the wholecountry. These were initially provided by DrNicholas Coops (CSIRO Division of Forestry andForest Products), using NDVI3 data for 1995.However, the data had gaps and it was eventuallydecided that using data from a single year, even one

1 The IBRA (Interim Biogeographic Regionalisation for Australia) regions were developed to provide a framework for establishingpriorities for delivering the National Reserves System Cooperative Program (NRSCP) (Thackway and Creswell, 1995). They werederived by compiling the best available data and information about each State and Territory including specialist field knowledge,published resource and environmental reports and are based on climate, lithology/geology, landform, vegetation, flora and fauna andland use. There are 80 IBRA regions within Australia.

2 In fact there was no reason to assume that plant productivity would be homogenous across IBRA regions (Richard Thackway;pers. comm.)

3 Normalised Digital Vegetation Index, obtained from satellite data. This is a ratio of the reflectances in the infra-red and near infraredwavebands. It actually provides a good measure of the amount of chlorophyll in the vegetation, but this generally correlates well withLAI. Empirical equations are used to calculate LAI from NDVI.


that conformed, in many respects, to averageclimatic conditions, was a dangerous procedure.There is always significant seasonal variation inweather conditions across Australia, and a year thatis ‘average’ in one region may be far from average inothers. LAI was therefore estimated from 10-yearmeans of NDVI (from Landsat) for the wholecountry, provided by ERIN. Joe Landsberg producedthe code (in Visual Basic) for a simplified, spatialversion of the 3-PG model. This was implemented inthe ARCINFO system by the AGO.

A3.4 THE PLANT PRODUCTIVITY MODELThe long-term average plant productivity mapsused in this preliminary study (Figure 1) weregenerated using the methods described in the mainbody of this report (Section 3.6, see page 33).

A3.5 ANALYSIS OF PLANT PRODUCTIVITYBY IBRA REGIONS

There were no significant differences between the‘flat earth’ and slope and azimuth-correctedestimates of APAR when the analysis was based on1 km pixels. All the analyses presented in thissection are based on the ‘flat earth’ analysis.


0 1 2 3 5 10 15 25

Figure 1. Derived long-term average plant productivity index for Australia.


Two approaches were used to characterise plantproductivity in the IBRA regions. As noted inSection A3.1, 100 points uniformly spaced, weresampled in each region. This provided the basis forstatistical analysis of the variation in primaryproductivity across these areas, allowing assessmentof whether they were homogeneous for thisproperty. The characteristics used to calculate thesepoint estimates of plant productivity were those ofthe pixels on which each sampling point fell.Subsequently the model was applied to the wholesurface of Australia to give a complete map of plantproductivity (Figure 1).

A3.6 PLANT PRODUCTIVITY VALUES BYIBRA REGIONS

Average annual plant productivity values for each ofthe 80 IBRA regions are given in Figure 2. As wewould expect, the highest productivity is in the WetTropics (13.4), followed by the New South WalesNorth Coast (11.1), while the lowest productivityareas are the dry desert areas (Little Sandy andSimpson-Strezlecki deserts, each with 1.1).

Standard analysis of variance shows that, betweenIBRA regions, many of the productivity values weresignificantly different, an unsurprising result of littleinterest in itself. For comparisons between particularvalues the critical value for F-tests with 95%confidence (that differences are real) was 1.28.

Figure 2. Annual average Plant Productivity by IBRA regions.


0 1 2 3 5 10 15 25


The widest range of calculated productivity valueswas in the Wet Tropics (between 5.8 and 22.5;Standard Deviation 4.22), followed by the CentralMackay Coast (between 6.2 and 18.2; StandardDeviation 2.98). However, taking StandardDeviation as a ratio of the mean regional value, thelargest variation was in the Victorian Volcanic Plain(mean productivity index = 5.9, ratio = 56 %),followed by the Tanami Desert (mean productivityindex = 2.3, ratio = 47 %) and the Little Sandy Desert(mean productivity index = 1.1, ratio = 44 %).

Figure 3 shows the variation in average monthlyplant productivity across four IBRA regions withmarkedly different characteristics and more thanthree-fold difference in mean annual productivity4.

The regions were (mean productivity index inparenthesis): Jarrah Forest (4.7); South EastHighlands (7.5), Darling River Plains (5.3); andCentral Kimberly (2.1). Figure 4 shows the monthlywater balance for the same regions. Clearly, waterbalance, while it explains some of the monthlyvariation, does not account for all of it, particularlyin the case of the South East Highlands, wheremaximum productivity was in the spring eventhough the water balance for plant growth in thatperiod was decreasing. Productivity in earlysummer was reduced by the lack of water in thesoil. Similarly, in the Jarrah Forest region, maximumproductivity was in the late winter/spring period,based on water stored from the winter rainfall. Heretoo, productivity declines in the summer.

Figure 3. Monthly plant productivity index in four IBRA regions: South East Highlands (SEH); Jarrah Forest(JF); Darling River Plains (DRP); and Central Kimberly (CK).

DRP

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4 Maps of monthly plant productivity, water balance and the effects of temperature (temperature modifier) can be produced.


A3.7 SPATIAL VARIATION IN PLANTPRODUCTIVITY WITHIN REGIONS

To assess the spatial variation of plant productivityacross IBRA regions, we plotted the standard errors(SEs) of the mean values obtained with varyingnumbers of samples for the two regions (Wet Tropicsand the Tanami) with the widest range in pointvalues. In both cases (Figs. 5a, 6a) the SE decreasedwith increasing numbers of sample points, butshowed little sign of reaching a steady value. Thespatial distribution of plant productivity values inthe two regions was quite different. In the WetTropics (Figure 5b) there was a fairly uniformdistribution of values, with a peak towards thehigher productivity end of the spectrum, while inthe Tanami (Figure 6b) the distribution was skewedstrongly left (towards the low productivity end of

the spectrum). There was little apparentrelationship between the spatial distribution of plantproductivity and the SE of the sample mean.

To evaluate the number of samples needed tominimise the SE of the mean, we took 300 samplepoints from each of the four regions (Jarrah Forest,South East Highlands, Darling River Plains, CentralKimberly) analysed earlier. The results are shown inFigures 7a, 8a, 9a and 10a, where the SEs are plottedagainst number of samples. The spatial distributionsof plant productivity are presented in Figures 7b,8b, 9b and 10b. The data indicate that, in mostcases, at least 200 sample points would need tobe analysed to minimise the SE of the mean value.Again, the spatial distribution of plant productivityvalues provides little guidance to the number ofsamples needed.

0

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Figure 4. Monthly water balance in the four IBRA regions for which plant productivity data are shown inFigure 3. Negative values indicate the dryness of the area but note that in the calculation of water balancesfor the model soil water content cannot go below zero.


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Figure 5a. (top) Change in the standard error of the mean productivity value with various numbers ofsample points, up to 100, in the Wet Topics region. There was no indication that a minimum value had beenreached. b. (bottom) Spatial distribution of plant productivity across the Wet Tropics region. The dataexhibit significant variation.

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Figure 6a. (top) Change in the standard error of the mean productivity value with various numbers ofsample points, up to 100, in the Tanami Desert region. There was no indication that a minimum value hadbeen reached. b. (bottom) Spatial distribution of mean annual plant productivity across the Tanami Desert.The data exhibit significant variation, with a marked peak in the low productivity range.

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Figure 7a. (top) Change in the standard error of the mean productivity value with various numbers ofsample points, up to 300, in the Jarrah Forest region. The curve indicates that 2-300 sample points wouldbe required to give an accurate value of the mean. b. (bottom) Spatial distribution of mean annual plantproductivity across the Jarrah Forest region. The curve is skewed towards lower values.

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Figure 8a. (top) Change in the standard error of the mean productivity value with various numbers ofsample points, up to 300, in the South Eastern Highlands. The curve indicates that 2-300 sample pointswould be required to give an accurate value of the mean. b. (bottom) Spatial distribution of mean annualplant productivity across the South Eastern Highlands region. The distribution is more uniform than inmost other regions.


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36

<= 1 (1,2) (2,3) (3,4) (4,5) (5,6) (6,7) (7,8) > 8

Figure 9a. (top) Change in the standard error of the mean productivity value with various numbers ofsample points, up to 300, in the Darling River Plains. The curve indicates that at least 300 sample pointswould be required to give an accurate value of the mean. b. (bottom) Spatial distribution of mean annualplant productivity across the Darling River Plains. The distribution is reasonably uniform, slightly skewedto the higher values.


Number of samples

SE o

f mea

n an

n. p

lant

pro

duct

ivity

Central Kimberley

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 50 100 150 200 250 300

Num

ber o

f sam

ples


0

3

6

9

12

15

18

21

24

27

30

33

36

<= 0.5 (0.5,1) (1,1.5) (1.5,2) (2,2.5) (2.5,3) (3,3.5) (3.5,4) > 4

Figure 10a. (top) Change in the standard error of the mean productivity value with varying numbersof sample points, up to 300, in the Central Kimberley region. The curve has not reached stable values;at least 300 sample points would be required to give an accurate value of the mean. b. (bottom) Spatialdistribution of mean annual plant productivity across the Central Kimberley region. The distribution isapproximately normal.


A3.8 ANALYSIS OF PLANT PRODUCTIVITY BYCARNAHAN VEGETATION CLASSES

An analysis of productivity by Carnahan vegetation(present) classes was done by overlaying theCarnahan vegetation on the productivity surface.While the distribution of productivity values formost of the Carnahan vegetation classes shows that

there was a preferred value or niche, the range foreach vegetation class could be large. Figure 11shows the distribution of open woodland as shadedby the plant productivity index value. Due to thewide distribution of the open woodland vegetationclass it is not surprising that the productivity indexvalues have a range from 1 to 25.


0 1 2 3 5 10 15 25 No Data

Figure 11. The distribution of open woodland shaded by the plant productivity index value.


Figure 12 clearly shows the wide range of plantproductivity values for each vegetation class(e.g. open woodland ranges from a productivityindex of 1 to 20, open forest ranges from 2 to 20).It also clearly shows that the overlap between thevegetation classes is large and would not allow fordiscrimination of classes by plant productivity. Thiswill have implications for any stratification based onvegetation classes.

A3.9 CONCLUSIONSThe mean productivity values obtained for IBRAregions appear to be ‘reasonable’, insofar as theycorrespond well to vegetation and climatic regions.The method of calculation used here is based on thepremise that there is a linear relationship betweenthe PAR absorbed by vegetation and production.This is not linear over short periods (e.g. hours-days), and the use of the simple Beer’s Lawequation to calculate energy absorption by plantcanopies could lead to significant errors over suchperiods. However when we consider time periods aslong as a month, the assumption that biomassproduction is linearly related to biomass productionis justified (Landsberg et. al., 1997) and Beer’s Lawprovides good estimates of the energy absorbed.

0

20

40

60

80

100

1-1.5

1.5-2

2-2.5

2.5-3

3-4

4-5

5-7.5

7.5-10

10-12.5

12.5-15

15-20

20-25

closed open forest woodland open woodland scrub and heath

Perc

ent

Figure 12. Range of plant productivity values for the forest classes of present Carnahanvegetation classes.


The full version of 3-PG (spatial) model has beenshown to provide good estimates of the productivityof forest types ranging from large, long-livedconifers (Coops et. al. 2001) to native forests in NewSouth Wales (Tickle et. al. 2001) and fast-growingeucalypt plantations in Brazil (A.C. de Almeida andJ.J.Landsberg; unpublished data). The model hasalso been tested against biomass measurementsmade in New Zealand re-growth scrub (White et. al.,2000; Neal Scott, Landcare NZ; pers. comm.) andwas found to perform well. These examples showthat, given appropriate inputs, the basic modelprovides good estimates of productivity for specificlocations, allowing confidence in the model as anestimator of wide-scale productivity values.

The spatial variation in plant productivity across theIBRA regions indicates that, whatever their value interms of ecological regions and guides for theestablishment of reserves, these regions are nothomogeneous in terms of productivity. It is clearthat the IBRA regions not only contain a variety ofsoil types – which would have been impossible toavoid – but, in most cases, they lay across theboundaries of several quite different climaticregions. This would explain some of the variancethat needs to be able to be taken into account whenmaking calculations for specific locations.

A3.10 FOLLOW UP RESEARCHUltimately, a variant of the 3-PG model (as usedhere in these preliminary investigations, followingLandsberg et. al. 1997) was used as the productivitymodel, in addition to the long-term average plantproduction index and top soil moisture deficits forthe NCAS (as fully described in the main body ofthis report). Annual plant productivity indices werealso subsequently used in development of the fullcarbon accounting model for the NCAS (seeRichards 2001).

A3.11 RELEVANT CITATIONS Coops, N.C., Waring, R.H., Brown, S.R. & Running,

S.W. 2001. Comparisons of Predictions of Net Primary

Production and Seasonal Patterns in Water Use

Derived with Two Forest Growth Models in

Southwestern Oregon. Ecological Modelling.142:61-81.

Landsberg, J.J. 1986. Coupling of carbon, water and

nutrient interactions in woody plant soil systems.

Tree Physiology 2.

Landsberg, J.J. & Gower, S.T. 1997. Applications of

Physiological Ecology to Forest Management.

Academic Press: San Diego Press. 354pp.

Landsberg, J.J. & Waring, R.H. 1997. A generalised

model of forest productivity using simplified concepts

of radiation-use efficiency, carbon balance and

partitioning. Forest Ecology and Management95:209–228.

Landsberg, J.J., Prince, S.D., Jarvis, P.G., McMurtrie,R.E., Luxmoore, R. & Medlyn, B.E. 1997. Energy

conversion and use in forests: An analysis of forest

production in terms of radiation utilisation efficiency.

In The Use of Remote Sensing in the Modeling ofForest Productivity at Scales from the Stand to theGlobe. Eds: Gholz, H.L., Nakane, K., & Shimoda,H. Kluwer Academic Publishers: Dordrecht.

McKenzie, N.J., Jacquier, D.W., Ashton, L.J. &Cresswell, H.P. 2000. Estimation of Soil Properties

Using the Atlas of Australian Soils. CSIRO Landand Water Technical Report 11/00, February 2000.

McMurtrie, R.E., Gholz, H.L., Linder, S. & Gower,S.T. 1994. Climatic factors controlling the productivity

of pine stands: A model-based analysis. EcologicalBulletins 43:173–188.

Potter, C.S., Randerson, J.T., Field, C.B., Matson,P.A., Vitousek, P.M., Mooney, H.A. & Klooster,S.A. 1993. Terrestrial ecosystem production: A process

model based on global satellite and surface data.

Global Biogeochemical Cycles 7:811–841.


Richards, G.P. 2001. The FullCAM Carbon Accounting

Model: Development, Calibration and Implementation

for the National Carbon Accounting System. NationalCarbon Accounting System Technical ReportNo. 28, Australian Greenhouse Office,Canberra, pp49.

Ruimey, A., Saugier, B. & Dedieu, G. 1994.Methodology for the estimation of terrestrial net

primary production from remotely sensed data.

J. Geophys. Res. 99:5263–5283.

Thackway, R. & Cresswell, I.D. (Eds). 1995. An

Interim Biogeographic Regionalisation for Australia:

A Framework for Establishing the National System of

Reserves, Version 4.0. Australian NatureConservation Agency, Canberra.http://www.environment.gov.au/bg/nrs/ibraimcr/ibra_95/

Tickle, P.K., Coops, N.C. & Hafner, S.D. 2001.Assessing Forest Productivity at Local Scales Across a

Native Eucalypt Forest Using a Process Model, 3PG-

SPATIAL. Forest Ecology and Management.152:275-291.

White, J.D., Coops, N.C. & Scott, N.A. 2000Predicting broad-scale forest and scrub biomass for

New Zealand: Investigating the application of a

physiologically-based model. Ecological Modeling131: 175–190.

A4. FOREST GROWTH DATA

URS Australia

Compiled forest growth data tables arepresented overleaf.


Site

Info

rmat

ion

(Sou

th A

ustra

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Biom

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334

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Visu

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Site

Info

rmat

ion

(Sou

th A

ustra

lia, H

assa

ll &

Ass

ocia

tes,

199

9a)

Biom

ass

MAI

Plan

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Volu

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Site

Fac

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Esta

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Habi

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Site

Info

rmat

ion

(Sou

th A

ustra

lia, H

assa

ll &

Ass

ocia

tes,

199

9a)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Esta

blis

hmen

t & S

tand

Man

agem

ent

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

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List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

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oil t

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qual

ity,

e.g.

site

pre

para

tion

and

prio

r lan

duse

, tub

esto

ck/

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

dire

ct s

eed,

wee

ds/d

isea

se/p

ests

, fire

, irr

igat

ion

Rain

fall:

Av

427

mm

Soil:

Cla

y lo

am, p

H ap

prox

. 8.0

Topo

grap

hy: F

lat p

lain

Wat

er: R

ainf

ed, d

epth

togr

ound

wat

er10

-15m

.

Rain

fall:

Av

495

mm

Soil:

San

dy lo

am, p

H ap

prox

. 6.5

Topo

grap

hy: G

ently

slo

ping

low

hill

s

Wat

er: R

ainf

ed, d

epth

togr

ound

wat

er10

-15m

Aspe

ct: S

.

Rain

fall:

Av

424

mm

Soil:

San

dy lo

am o

ver c

lay,

pH

unkn

own

Topo

grap

hy: G

ently

slo

ping

low

hill

s

Wat

er: R

ainf

ed, d

epth

togr

ound

wat

er5m

Aspe

ct: S

.

Rain

fall:

Av

420

mm

Soil:

San

d ov

er li

mes

tone

, pH

7.5

atsu

rfac

e to

8.5

-9.0

at 3

0-40

cm

Topo

grap

hy: F

lat p

lain

- ge

ntle

slo

pe

Wat

er: R

ainf

ed, d

epth

togr

ound

wat

er3m

Aspe

ct: S

W.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

as

ara

ilway

cor

ridor

.

Dire

ct s

eedi

ng.

No T

hinn

ings

.

Sign

ifica

nt w

eed

infe

stat

ion

requ

iring

trea

tmen

t.No

oth

er p

robl

ems.

Rain

fed

only.

Site

pre

para

tion

unkn

own.

Pre

viou

s us

e un

know

n.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts,d

roug

ht o

r fire

.

Rain

fed

only.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

for

mix

ed fa

rmin

g.

Tube

stoc

k.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ith w

eeds

/dis

ease

/pes

ts,

drou

ght o

r fire

.

Rain

fed

only.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

for

graz

ing.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ith w

eeds

/dis

ease

/pes

ts,

drou

ght o

r fire

.

Rain

fed

only.

A4

- Ta

ble

1a.

Gro

wth

Dat

a fo

r S

ites

in S

out

h A

ustr

alia

, Has

sall

& A

sso

ciat

es (

1999

a) c

ont

inue

d.


13Ja

ckso

n19

90-

E. g

raci

lis,

133.

371

816

.67

-N/

A(d

irect

see

d)E.

leuc

oxyl

on,

100.

032

Uppe

r Sou

th E

ast

E. le

ptop

hylla

Lat/l

ong:

362

251,

140

1934

Purp

ose

not s

peci

fied

14Ja

ckso

n19

880.

6 ha

E. g

raci

lis,

15.2

1110

1.52

-N/

A(tu

best

ock)

E.

fasc

icul

osa,

11.4

02

Uppe

r Sou

th E

ast

E. c

amal

dule

nsis

Lat/l

ong:

362

248,

140

1934

Purp

ose

not s

peci

fied

15M

enin

gie-

Coon

alpy

n Ro

ad,

1993

-E.

leuc

oxyl

on,

10.7

715

2.15

-N/

AUp

per S

outh

Eas

tAl

loca

suar

ina

8.08

2

verti

cilla

ta,

Lat/l

ong:

354

132,

139

2646

Allo

casu

arin

am

uelle

riana

Road

side

she

lterb

elt,

reve

geta

tion

trial

16De

l Fab

bro,

19

410

ha

Mel

aleu

ca

6.89

14

1.72

-N/

AUp

per S

outh

Eas

tbr

evifo

lia5.

162

Mel

aleu

ca

Lat/l

ong:

363

921,

140

1708

halm

atur

orum

E. le

ucox

ylon

Grou

ndw

ater

rech

arge

/sa

linity

con

trol

Rain

fall:

Av

560

mm

Soil:

San

d ov

er s

hallo

w c

lay,

pH

6.0

atsu

rfac

e to

7.0

at 5

0 cm

Topo

grap

hy: R

ollin

g pl

ains

Wat

er: R

ainf

ed, d

epth

togr

ound

wat

erun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

s us

e un

know

n.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts,d

roug

ht o

r fire

.

Rain

fed

only.

Site

Info

rmat

ion

(Sou

th A

ustra

lia, H

assa

ll &

Ass

ocia

tes,

199

9a)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Esta

blis

hmen

t & S

tand

Man

agem

ent

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

site

pre

para

tion

and

prio

r lan

duse

, tub

esto

ck/

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

dire

ct s

eed,

wee

ds/d

isea

se/p

ests

, fire

, irr

igat

ion

Rain

fall:

Av

560

mm

Soil:

San

d, lo

w s

alin

ity, p

H 6.

0-6.

5 at

surf

ace

to 7

.0-8

.5 a

t 50

cm

Topo

grap

hy: R

ollin

g pl

ains

Wat

er: R

ainf

ed, d

epth

togr

ound

wat

erun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

s us

e un

know

n.

Tube

stoc

k.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts,d

roug

ht o

r fire

.

Rain

fed

only.

Rain

fall:

Av

468

mm

Soil:

Non

-wet

ting

sand

, low

sal

inity

,pH

8.0

at s

urfa

ce to

9.0

at 5

0 cm

Topo

grap

hy: F

lat p

lain

Wat

er: R

ainf

ed, d

epth

togr

ound

wat

erun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

s us

e un

know

n.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ith w

eeds

/dis

ease

/pes

ts,

drou

ght o

r fire

.

Rain

fed

only.

Rain

fall:

Av

521

mm

Soil:

Poo

r san

ds o

ver c

lay,

evi

denc

e of

salin

ity, p

H 6.

0

Topo

grap

hy: R

ollin

g pl

ain

Wat

er: R

ainf

ed, p

lus

shal

low

dep

th to

wat

erta

ble,

par

ticul

arly

in w

inte

r mon

ths.

Site

fenc

ed o

ff an

d sp

raye

d fo

r wee

ds p

rior t

oso

win

g. P

revi

ousl

y us

ed a

s sh

eep

graz

ing.

Dire

ct s

eedi

ng.

No th

inni

ngs.

No p

robl

ems

asso

ciat

ed w

ith w

eeds

/dis

ease

/pes

ts,

drou

ght o

r fire

.

Rain

fed,

plu

s ac

cess

to s

hallo

w g

roun

dwat

er.


17La

ngho

rne

Cree

k Ce

met

ery,

1992

-E.

leuc

oxyl

on,

10.3

616

1.73

-N/

AM

urra

ylan

dsAc

acia

pyc

nant

ha,

7.77

2

Acac

iaLa

t/lon

g: 3

5174

9, 1

3903

12br

achy

botr

ya

Seed

col

lect

ion

18Ha

rtley

Dum

p,19

92-

E. le

ucox

ylon

,5.

261

60.

88-

N/A

Mur

rayl

ands

E. p

oros

a,3.

942

Acac

ia c

alam

ifolia

Lat/l

ong:

351

111,

139

0205

Site

reha

bilit

atio

n

19Sw

anpo

rt,19

91-

E. le

ucox

ylon

spp

.24

.171

73.

45-

N/A

Mur

rayl

ands

leuc

oxyl

on,

18.1

32

E. le

ucox

ylon

La

t/lon

g: 3

5085

5, 1

3918

42sp

p. s

teph

enae

,E.

por

osa

Purp

ose

not s

peci

fied

20Hu

gh L

ongb

otto

m,

1992

0.9

haE.

por

osa,

11

.511

61.

92-

N/A

York

e Pe

nins

ula

Acac

ia p

ycna

ntha

,8.

632

Acac

ia n

otab

ilis

Lat/l

ong:

342

137,

137

3235

Shel

terb

elt

Rain

fall:

Av

392

mm

Soil:

Cla

y w

ith v

aryi

ng le

vels

of

limes

tone

, pH

8.0

at s

urfa

ce to

9.0

at30

cm

Topo

grap

hy: G

ently

rolli

ng h

ills

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Site

pre

para

tion

unkn

own.

Adj

oini

ng ru

bbis

hdu

mp/

quar

ry.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts, d

roug

ht o

r fire

.

Rain

fed

only.

Site

Info

rmat

ion

(Sou

th A

ustra

lia, H

assa

ll &

Ass

ocia

tes,

199

9a)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Esta

blis

hmen

t & S

tand

Man

agem

ent

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

site

pre

para

tion

and

prio

r lan

duse

, tub

esto

ck/

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

dire

ct s

eed,

wee

ds/d

isea

se/p

ests

, fire

, irr

igat

ion

Rain

fall:

Av

392

mm

Soil:

Cla

y w

ith in

crea

sing

leve

ls o

flim

esto

ne a

t 50

cm, p

H 6.

0-6.

5 at

surf

ace

to 7

.5-8

.5 a

t 50

cm

Topo

grap

hy: L

ow g

ently

slo

ping

hill

side

Wat

er: R

ainf

ed, d

epth

to w

ater

tabl

eap

prox

. 3m

.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

for

dairy

farm

ing

and

crop

ping

.

Dire

ct s

eedi

ng.

Thin

ning

s of

low

er li

mbs

and

infe

rior s

aplin

gs.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts, d

roug

ht o

r fire

.

Rain

fed,

plu

s po

ssib

le a

cces

s to

gro

undw

ater

.

Rain

fall:

Av

346

mm

Soil:

San

d, lo

w s

alin

ity, p

H 7.

0 at

sur

face

to 8

.0-8

.5 a

t 50

cm

Topo

grap

hy: G

ently

und

ulat

ing

Wat

er: R

ainf

ed, d

epth

to

wat

erta

ble

7-10

m

Aspe

ct: W

.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

fora

gric

ultu

ral p

urpo

ses.

Tube

stoc

k.

Thin

ning

s hi

stor

y un

know

n.

Som

e pr

oble

ms

asso

ciat

ed w

ith w

eeds

and

rabb

itsw

hich

may

be

affe

ctin

g gr

owth

. No

othe

rpro

blem

s.

Rain

fed

only.

Rain

fall:

Av

504

mm

Soil:

Cla

y-lo

am, p

H un

know

n

Topo

grap

hy: G

ently

rolli

ng p

lain

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

for

crop

ping

.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ith w

eeds

/dis

ease

/pes

ts,

drou

ght o

r fire

.

Rain

fed

only.

A4

- Ta

ble

1a.

Gro

wth

Dat

a fo

r S

ites

in S

out

h A

ustr

alia

, Has

sall

& A

sso

ciat

es (

1999

a) c

ont

inue

d.


Site

Info

rmat

ion

(Sou

th A

ustra

lia, H

assa

ll &

Ass

ocia

tes,

199

9a)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Esta

blis

hmen

t & S

tand

Man

agem

ent

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

site

pre

para

tion

and

prio

r lan

duse

, tub

esto

ck/

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

dire

ct s

eed,

wee

ds/d

isea

se/p

ests

, fire

, irr

igat

ion

21M

aitla

nd-M

inla

ton

Road

,19

940.

6 ha

E. p

oros

a,2.

101

40.

52-

N/A

York

e Pe

nins

ula

Acac

ia s

pine

scen

s,1.

572

Dode

naea

vis

cosa

Lat/l

ong:

342

637,

137

3920

Road

side

re

vege

tatio

n tri

al

22Kl

opp,

19

900.

27 h

aE.

por

osa,

22

.561

82.

82-

N/A

York

e Pe

nins

ula

E. s

ocia

lis,

16.9

22

E. c

alyc

ogon

aLa

t/lon

g: 3

4285

5, 1

3745

20Ro

adsi

de p

lant

ing

23M

ount

Bar

ker T

anne

ry,

1992

12 h

aE.

vim

inal

is,

30.3

016

5.05

-N/

AM

ount

Lof

ty R

ange

sE.

leuc

oxyl

on,

22.7

22

E. fa

scic

ulos

a

Lat/l

ong:

350

332,

138

5151

Site

reha

bilit

atio

n,

amen

ity

24Fl

axle

y Ho

listic

Tra

ding

, 19

915

haE.

fasc

icul

osa,

97.5

517

13.9

4-

N/A

Mou

nt L

ofty

Ran

ges

E. o

bliq

ua,

73.1

72

Acac

ia

Lat/l

ong:

350

820,

138

4950

mel

anox

ylon

Reve

geta

tion

Rain

fall:

Av

504

mm

Soil:

Cla

y-lo

am, p

H un

know

n

Topo

grap

hy: G

ently

rolli

ng p

lain

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

s us

eno

tspe

cifie

d.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts,d

roug

ht o

r fire

.

Rain

fed

only.

Rain

fall:

Av

414

mm

Soil:

San

dy lo

am, p

H 8.

5 at

sur

face

to9.

0 at

50

cm

Topo

grap

hy: R

ollin

g pl

ains

Wat

er: R

ainf

ed, d

epth

to w

ater

tabl

eun

know

n bu

t bel

ieve

d to

sea

sona

lly b

ew

ithin

root

zon

e

Aspe

ct: N

.

Site

pre

para

tion

unkn

own.

Pre

viou

s us

eno

tspe

cifie

d.

Tube

stoc

k.

Thin

ning

s hi

stor

y un

know

n.

Som

e w

eeds

pre

sent

but

not

affe

ctin

g gr

owth

. No

othe

r pro

blem

s as

soci

ated

with

dis

ease

, pes

ts,

drou

ght o

r fire

.

Rain

fed

but w

ith p

oten

tial a

cces

s to

gro

undw

ater

.

Rain

fall:

Av

769

mm

Soil:

San

dy lo

am o

ver c

lay,

pH

8.5

to 9

.0

Topo

grap

hy: U

ndul

atin

g hi

lls

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Fres

h so

ils s

prea

d ov

er c

onta

min

ated

site

.Pr

evio

usly

use

d as

a ta

nner

y an

d la

ter o

n as

past

ure

for g

razin

g.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts,d

roug

ht o

r fire

.

Rain

fed

only.

Rain

fall:

Av

740

mm

Soil:

San

dy lo

am, p

H 6.

0 to

5.5

Topo

grap

hy: U

nkno

wn

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

s us

e un

know

n.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts,d

roug

ht o

r fire

.

Rain

fed

only.


25Fl

axle

y Lo

dge

1990

0.3

haE.

glo

bulu

s46

.371

85.

80-

N/A

(tube

stoc

k)34

.782

Mou

nt L

ofty

Ran

ges

Shel

terb

elt,

farm

fore

stry

Lat/l

ong:

350

812,

138

4950

26Di

ngo

Plai

ns

1991

0.9

haAc

acia

3.

491

70.

50-

N/A

(dire

ct s

eed)

argy

roph

ylla

2.61

2

Mal

lee

Acac

ia c

alam

ifolia

,Ac

acia

mic

roca

rpa

Lat/l

ong:

352

042,

140

2337

Purp

ose

not s

peci

fied

27La

mer

oo19

901.

1 ha

E. le

ucox

ylon

14.3

518

1.78

-N/

A(tu

best

ock)

E. o

ccid

enta

lis10

.762

Mal

lee

Farm

fore

stry

tria

lLa

t/lon

g: 3

5201

2, 1

4025

39

28W

ilkaw

att

1992

0.6

haDo

dona

ea v

isco

sa,

24.9

916

4.16

-N/

A(d

irect

see

d)E.

por

osa,

18

.742

Mal

lee

Mel

aleu

ca

acum

inat

aLa

t/lon

g: 3

5254

1, 1

4024

51Ro

adsi

desh

elte

rbel

t

Rain

fall:

Av

740

mm

Soil:

San

dy lo

am, p

H 5.

5 to

6.0

Topo

grap

hy: F

lat

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

fors

heep

gra

zing

Tube

stoc

k.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts,d

roug

ht o

r fire

.

Rain

fed

only.

Site

Info

rmat

ion

(Sou

th A

ustra

lia, H

assa

ll &

Ass

ocia

tes,

199

9a)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Esta

blis

hmen

t & S

tand

Man

agem

ent

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

site

pre

para

tion

and

prio

r lan

duse

, tub

esto

ck/

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

dire

ct s

eed,

wee

ds/d

isea

se/p

ests

, fire

, irr

igat

ion

Rain

fall:

Av

368

mm

Soil:

Cla

y lo

am w

ith s

igni

fican

t cal

cium

carb

onat

e de

posi

ts, p

H 7.

5 at

sur

face

to8.

0 at

30

cm

Topo

grap

hy: R

ollin

g pl

ain

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

s us

e un

know

n.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts,d

roug

ht o

r fire

.

Rain

fed

only.

Rain

fall:

Av

368

mm

Soil:

San

d ov

er c

lay

loam

, pH

7.0

atsu

rfac

e to

8.0

at 3

0 cm

Topo

grap

hy: R

ollin

g pl

ain

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

s us

e un

know

n.

Tube

stoc

k.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts,d

roug

ht o

r fire

.

Rain

fed

only.

Rain

fall:

Av

368

mm

Soil:

Red

bro

wn

loam

ove

r bro

wn

clay

,pH

7.0

at s

urfa

ce to

8.5

at 4

0 cm

Topo

grap

hy: F

lat p

lain

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

for

mix

ed c

ropp

ing.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts,d

roug

ht o

r fire

.

Rain

fed

only.

A4

- Ta

ble

1a.

Gro

wth

Dat

a fo

r S

ites

in S

out

h A

ustr

alia

, Has

sall

& A

sso

ciat

es (

1999

a) c

ont

inue

d.


1m

easu

red

usin

g bo

le a

nd b

ranc

hes

2m

easu

red

usin

g bo

le o

nly

29Ka

lisch

, 19

954

haAc

acia

osw

aldi

i, 0.

001

30.

00-

N/A

Rive

rland

Acac

ia li

gula

ta,

0.00

2

Acac

ia c

yano

phyl

laLa

t/lon

g: 3

4124

5, 1

4004

23Pu

rpos

e no

t spe

cifie

d

30Ka

relia

,19

940.

7 ha

Acac

ia li

gula

ta,

0.07

14

0.02

-N/

ARi

verla

ndAc

acia

osw

aldi

i,0.

052

Acac

ia s

p.

Lat/l

ong:

140

1751

, 341

156

(juve

nile

)

Dryl

and

reve

geta

tion

dem

onst

ratio

n

31Or

land

o-W

yndh

am,

1994

0.9

haCa

suar

ina

obes

a,

31.9

714

7.99

-N/

ARi

verla

ndE.

cam

aldu

lens

is,

23.9

82

E. la

rgifl

oren

s

Lat/l

ong:

340

849,

139

5434

Grou

ndw

ater

rech

arge

con

trol

32Li

ttle

Dese

rt Lo

dge,

Allo

casu

arin

a3.

001

60.

5-

N/A

Vict

oria

verti

cilla

ta2.

252

E. fa

scic

ulos

a,

Lat/l

ong:

362

725,

141

3937

E. le

ucox

ylon

Soil

stab

ilisa

tion,

habi

tat

Site

Info

rmat

ion

(Sou

th A

ustra

lia, H

assa

ll &

Ass

ocia

tes,

199

9a)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Esta

blis

hmen

t & S

tand

Man

agem

ent

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

site

pre

para

tion

and

prio

r lan

duse

, tub

esto

ck/

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

dire

ct s

eed,

wee

ds/d

isea

se/p

ests

, fire

, irr

igat

ion

Rain

fall:

Av

266

mm

Soil:

San

d, p

H 6.

5 at

sur

face

to 7

.5at

50cm

Topo

grap

hy: F

lat p

lain

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

forg

razin

g.

Dire

ct s

eedi

ng.

No th

inni

ngs.

No p

robl

ems

asso

ciat

ed w

ith w

eeds

/dis

ease

/pes

ts,

drou

ght o

r fire

.

Rain

fed

only.

Rain

fall:

Av

241

mm

Soil:

San

dy lo

am o

ver l

imes

tone

with

sign

ifica

nt c

alci

um c

arbo

nate

thro

ugho

ut, p

H 9.

0

Topo

grap

hy: R

ollin

g pl

ain

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

s us

e un

know

n.

Dire

ct s

eedi

ng.

No th

inni

ngs.

No p

robl

ems

asso

ciat

ed w

ithw

eeds

/dis

ease

/pes

ts,d

roug

ht o

r fire

.

Rain

fed

only.

Rain

fall:

Av

235

mm

Soil:

San

dy lo

am, p

H 7.

0-7.

5

Topo

grap

hy: R

iver

floo

dpla

in

Wat

er: R

ainf

ed p

lus

acce

ss ir

rigat

ion

from

nei

ghbo

ring

vine

yard

s, d

epth

tow

ater

tabl

e un

know

n bu

t exp

ecte

d to

be

with

in ro

ot z

one.

Site

pre

para

tion

unkn

own.

Pre

viou

s us

e un

know

n.

Tube

stoc

k.

Thin

ning

s hi

stor

y un

know

n.

Som

e w

eeds

pre

sent

but

no

thre

at. N

o ot

her

prob

lem

s as

soci

ated

with

dis

ease

, pes

ts, d

roug

htor

fire

.

Rain

fed

plus

irrig

ated

.

Rain

fall:

Av

418

mm

Soil:

San

d, p

H 7.

0 at

sur

face

to 7

.5at

50cm

Topo

grap

hy: L

ow u

ndul

atin

g pl

ain

Wat

er: R

ainf

ed, d

epth

tow

ater

tabl

eun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

for

cattl

e gr

azin

g.

Dire

ct s

eedi

ng.

Thin

ning

s hi

stor

y un

know

n.

No p

robl

ems

asso

ciat

ed w

ith w

eeds

, dis

ease

,pe

sts,

dro

ught

or f

ire.

Rain

fed

only.


1W

ail,

1973

Appr

ox

Suga

r Gum

81

123

3.5

N/A

N/A

near

Hor

sham

, Vic

130

ha(E

. cla

doca

lyx)

612

Vic

Road

s m

ap re

f: 26

C10

Woo

d pr

oduc

ts

2W

ail,

1975

Appr

oxSe

e ab

ove

1211

215.

8N/

AN/

Ane

ar H

orsh

am, V

ic.

130

ha90

2

Vic

Road

s m

ap re

f: 26

C10

3Ba

rret

t Res

erve

, 19

78Ap

prox

Shin

ing

Gum

771

174.

5N/

AN/

Ane

ar H

orsh

am, V

ic.

32 h

a(E

. sie

beri)

582

Suga

r Gum

Vic

Road

s m

ap re

f: 26

F8

(E. c

lado

caly

x)

Woo

d Pr

oduc

ts

4Ba

rret

t Res

erve

, 19

86Ap

prox

See

abov

e16

19

1.8

N/A

N/A

near

Hor

sham

, Vic

.32

ha

122

Vic

Road

s m

ap re

f: 26

F8

5Ba

rret

t Res

erve

,19

73Ap

prox

See

abov

e72

122

3.3

N/A

N/A

near

Hor

sham

, Vic

.32

ha

542

Vic

Road

s m

ap re

f: 26

F8

6Ba

rret

t Res

erve

,19

71Ap

prox

Se

e ab

ove

781

243.

3N/

AN/

Ane

ar H

orsh

am, V

ic.

32 h

a59

2

Vic

Road

s m

ap re

f: 26

F8

Site

Info

rmat

ion

(Vic

toria

, Has

sall

& A

ssoc

iate

s, 1

998)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Husb

andr

yID

Na

me,

Loc

atio

n,

Est.

Area

Sp

ecie

s Li

st &

t/ha

Age

t/ha/

yrDe

nsity

m

3 /ha

e.g.

rain

fall,

soi

l typ

e/qu

ality

,e.

g. tu

best

ock/

dire

ct s

eed,

wee

d &

pes

t con

trol,

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

fire,

irrig

atio

n

Rain

fall:

Av

420

mm

Soil:

Cla

y lo

am o

verly

ing

impe

ding

laye

rof

cla

y, p

H 6.

5-7.

5

Topo

grap

hy: F

lat

Wat

er: N

o ru

non/

runo

ff, d

epth

togr

ound

wat

er u

nkno

wn.

Seed

lings

pla

nted

in b

lock

form

atio

n, th

inni

ngs

hist

ory

unkn

own.

Land

pre

viou

sly

was

nat

ive

bush

, cle

ared

,cu

ltiva

ted

and

rippe

d be

fore

pla

ntin

g.

Wee

ds/d

isea

se/p

ests

hav

e no

t bee

n a

prob

lem

.

Unbu

rnt.

Rain

fed

only,

som

e pe

riods

of d

roug

ht.

See

abov

eSe

e ab

ove

See

abov

e

See

abov

e

See

abov

e

See

abov

e

See

abov

e

Rain

fall:

Av

454

mm

Soil:

Bla

ck w

ith h

igh

clay

con

tent

,pH

7.0-

7.5

Topo

grap

hy: F

lat

Wat

er: N

o ru

non/

runo

ff, d

epth

togr

ound

wat

er u

nkno

wn.

Seed

lings

pla

nted

in b

lock

form

atio

n, s

elec

tive

thin

ning

s in

par

ts o

f the

pla

ntat

ion.

Land

pre

para

tion

unkn

own,

pre

viou

sly

used

forg

razin

g.

Wee

ds/d

isea

se/p

ests

hav

e no

t bee

n a

prob

lem

.

Unbu

rnt.

Rain

fed

only,

som

e pe

riods

of d

roug

ht w

hich

may

have

affe

cted

gro

wth

.

See

abov

e

A4

- Ta

ble

1b

. Gro

wth

Dat

a fo

r S

ites

in V

icto

ria,

Has

sall

& A

sso

ciat

es (

1998

)


7Ba

rret

t Res

erve

,19

75Ap

prox

Su

gar G

um64

120

3.2

N/A

N/A

near

Hor

sham

, Vic

.32

ha

(E. c

lado

caly

x)48

2

Shin

ing

Gum

Vi

c Ro

ads

map

ref:

26 F

8(E

. sie

beri)

Woo

d Pr

oduc

ts

8Yo

u Ya

ngs,

19

66Ap

prox

Su

gar G

um14

5130

4.8

N/A

N/A

near

Gee

long

, Vic

.45

0 ha

(E. c

lado

caly

x)10

82

Vic

Road

s m

ap re

f: 77

H9

Woo

d Pr

oduc

ts

9Yo

u Ya

ngs,

1965

Appr

ox

See

abov

e17

5131

5.6

N/A

N/A

near

Gee

long

, Vic

.45

0 ha

1312

Vic

Road

s m

ap re

f: 77

H9

10Yo

u Ya

ngs,

1958

Appr

ox

Brow

n M

alle

tt17

5139

4.5

N/A

N/A

near

Gee

long

, Vic

.45

0 ha

(E. a

strin

gens

)13

12

Vic

Road

s m

ap re

f: 77

H9

Woo

d Pr

oduc

ts

11Yo

u Ya

ngs,

Appr

ox

Brow

n M

alle

tt11

5138

3.0

N/A

N/A

near

Gee

long

, Vic

.45

0 ha

(E. a

strin

gens

)86

2

Vic

Road

s m

ap re

f: 77

H9

Woo

d Pr

oduc

ts

12‘T

itang

a’,

1994

Appr

ox

Sout

hern

7.

112

3.6

N/A

N/A

near

Lis

mor

e, V

ic.

8 ha

Blue

Gum

5.32

(E. g

lobu

lus

ssp.

glo

bulu

s)

Woo

d Pr

oduc

ts

See

abov

e

Rain

fall:

Av

491

mm

Soil:

Gra

nitic

Topo

grap

hy: F

lat

Wat

er: N

o ru

non/

runo

ff, d

epth

togr

ound

wat

er u

nkno

wn.

See

abov

e

Tree

s pr

obab

ly p

lant

ed a

s se

edlin

gs,

bloc

kfo

rmat

ions

. No

thin

ning

s.

Land

pre

para

tion

unkn

own.

No s

igni

fican

t pro

blem

s w

ith w

eeds

,di

seas

eor

pest

s.

Fire

has

occ

urre

d bu

t doe

s no

t app

ear t

oha

veaf

fect

ed th

e tre

es s

igni

fican

tly.

Rain

fed

only,

som

e pe

riods

of d

roug

ht w

hich

may

have

affe

cted

the

trees

.

See

abov

eSe

e ab

ove

See

abov

eSe

e ab

ove

Site

Info

rmat

ion

(Vic

toria

, Has

sall

& A

ssoc

iate

s, 1

998)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Husb

andr

yID

Na

me,

Loc

atio

n,

Est.

Area

Sp

ecie

s Li

st &

t/ha

Age

t/ha/

yrDe

nsity

m

3 /ha

e.g.

rain

fall,

soi

l typ

e/qu

ality

,e.

g. tu

best

ock/

dire

ct s

eed,

wee

d &

pes

t con

trol,

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

fire,

irrig

atio

n

See

abov

eSe

e ab

ove

Rain

fall:

Av

630

mm

Soil:

Gra

nitic

san

d an

d re

dla

terit

icgr

avel

, pH

unkn

own

Topo

grap

hy: G

entle

slo

pe fr

omel

evat

ion

30m

to 2

00m

, NW

asp

ect

Wat

er: S

urfa

ce ru

noff

dow

nslo

pe,

dept

hto

gro

undw

ater

unk

now

n.

Seed

lings

use

d in

row

s. N

o th

inni

ngs.

Land

ripp

ed, f

orm

ed a

nd s

pray

ed a

long

row

sbe

fore

pla

ntin

g, p

revi

ousl

y us

ed fo

r gra

zing.

Wee

ds a

pro

blem

dur

ing

esta

blis

hmen

t, m

ore

rece

ntly

atta

cks

from

inse

cts.

No

treat

men

tun

derta

ken.

No

othe

r pro

blem

s.

Rain

fed

only,

som

e pe

riods

of d

roug

ht th

atdo

nota

ppea

r to

have

affe

cted

gro

wth

.


13‘L

inga

long

a’, M

itiam

o,19

84Ap

prox

Ri

ver R

ed G

um66

112

5.5

N/A

N/A

NW o

f Ben

digo

, Vic

40 h

a(E

. cam

aldu

lens

is)

502

Vic

Road

s m

ap re

f: 30

B4

Amen

ity

14Co

huna

, 19

79Ap

prox

Ri

ver R

ed G

um28

6117

16.8

N/A

N/A

N of

Ben

digo

, Vic

2.5

ha(E

. cam

aldu

lens

is)

2142

Vic

Road

s m

ap re

f: 21

E6

Shel

terb

elts

, pe

st c

ontro

l, am

enity

15St

anho

pe,

1955

N/A

Rive

r Red

Gum

9021

3724

.4N/

AN/

AW

of S

hepp

arto

n, V

ic(E

. cam

aldu

lens

is)

6772

Vic

Road

s m

ap re

f: 31

H9

Shel

terb

elts

Rain

fall:

Av

399

mm

Soil:

Gre

y cr

acki

ng c

lay,

pH

neut

ral

Topo

grap

hy: F

lat

Wat

er: S

ome

surf

ace

runo

n du

ring

wet

mon

ths;

dep

th to

gro

undw

ater

1m

.

Grow

n fro

m s

eed

colle

cted

on

prop

erty

in to

2ro

ws

alon

g ro

ads

and

prop

erty

bou

ndar

ies.

No

thin

ning

s, b

ut s

ome

trees

blo

wn

over

or d

ead.

Lim

ited

prep

arat

ion

prio

r to

plan

ting,

dee

p rip

ped

but n

o w

eed

cont

rol,

prev

ious

ly u

sed

for i

rrig

ated

dairy

pas

ture

s.

Initi

al w

eed

prob

lem

s, re

cent

ly ra

bbits

/har

es in

plag

ue n

umbe

rs. N

o tre

atm

ents

und

erta

ken.

Unbu

rnt.

Rain

fed

mai

nly,

but

clo

se to

irrig

ated

pas

ture

s.

Rain

fall:

Av

450

mm

Soil:

Cla

y lo

ams,

pH

6.0-

6.5,

sal

ine

inar

eas

Topo

grap

hy: F

lat

Wat

er: S

ome

surf

ace

runo

n fro

mirr

igat

ion;

dep

th to

gro

undw

ater

>1m

.

Seed

lings

pla

nted

in b

lock

form

atio

ns. N

oth

inni

ngs

sinc

e.

Land

dee

p rip

ped

and

form

ed p

rior t

o pl

antin

g,pr

evio

usly

use

d fo

r irr

igat

ed a

nnua

l and

pere

nnia

lpas

ture

s.

Som

e di

seas

e an

d pe

sts

pres

ent d

urin

ges

tabl

ishm

ent,

but n

o tre

atm

ents

use

d.

Unbu

rnt.

Rain

fed,

plu

s so

me

area

s flo

oded

occ

asio

nally

from

adj

acen

t irr

igat

ed p

astu

res.

Site

Info

rmat

ion

(Vic

toria

, Has

sall

& A

ssoc

iate

s, 1

998)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Husb

andr

yID

Na

me,

Loc

atio

n,

Est.

Area

Sp

ecie

s Li

st &

t/ha

Age

t/ha/

yrDe

nsity

m

3 /ha

e.g.

rain

fall,

soi

l typ

e/qu

ality

,e.

g. tu

best

ock/

dire

ct s

eed,

wee

d &

pes

t con

trol,

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

fire,

irrig

atio

n

Rain

fall:

Av

500

mm

Soil:

Lem

nos

loam

, pH

4.5,

no

salin

ity,

high

ferti

lity

from

a lo

ng h

isto

ry o

fda

iryfa

rmin

g

Topo

grap

hy: F

lat

Wat

er: R

egul

arly

irrig

ated

plu

s ra

infe

d,de

pth

to g

roun

dwat

er 1

-2m

.

Seed

lings

pla

nted

mai

nly

as s

ingl

e ro

ws

alon

gfe

nce

lines

. No

thin

ning

s si

nce.

Land

not

pre

pare

d be

fore

pla

ntin

g.

Hare

s a

prob

lem

dur

ing

esta

blis

hmen

t. A

once

-off

outb

reak

of S

awfli

es a

ffect

ed y

oung

tree

s an

dgr

owth

, tre

ated

with

‘Cur

acin

e’.

Unbu

rnt.

Regu

larly

irrig

ated

(app

rox

once

per

fortn

ight

) via

adja

cent

irrig

ated

dai

ry p

astu

res.

A4

- Ta

ble

1b

. Gro

wth

Dat

a fo

r S

ites

in V

icto

ria,

Has

sall

& A

sso

ciat

es (

1998

) co

ntin

ued

.


16St

anho

pe,

1958

N/A

Rive

r Red

Gum

2,88

9142

16.8

N/A

N/A

W o

f She

ppar

ton,

Vic

(E. c

amal

dule

nsis

)2,

1672

Vic

Road

s m

ap re

f: 31

H9

Shel

terb

elts

17St

anho

pe,

1960

N/A

Rive

r Red

Gum

4131

3910

.6N/

AN/

AW

of S

hepp

arto

n, V

ic(E

. cam

aldu

lens

is)

3102

Vic

Road

s m

ap re

f: 31

H9

Shel

terb

elts

18St

anho

pe,

1965

N/A

Rive

r Red

Gum

1,62

0132

50.6

N/A

N/A

W o

f She

ppar

ton,

Vic

(E. c

amal

dule

nsis

)1,

2152

Vic

Road

s m

ap re

f: 31

H9

Shel

terb

elts

19Ka

tam

atite

,19

65N/

ARi

ver R

ed G

um1,

2711

2160

.5N/

AN/

ANE

of S

hepp

arto

n, V

ic(E

. cam

aldu

lens

is)

9532

Vic

Road

s m

ap re

f: 33

C3

Shel

terb

elts

,am

enity

20Le

icha

rdt,

1987

10 h

aRi

ver R

ed G

um5.

119

0.6

N/A

N/A

near

Ben

digo

, Vic

.(E

. cam

aldu

lens

is)

3.82

Vic

Road

s m

ap re

f: 44

B5

Droo

ping

She

oak

(Allo

casu

arin

a ve

rtici

llata

)

Vario

us M

alle

e sp

ecie

s

Eros

ion

cont

rol,

wat

erta

ble/

dry

land

sa

linity

con

trol

See

abov

eSe

e ab

ove

See

abov

eSe

e ab

ove

Rain

fall:

Av

500

mm

Soil:

Moi

ra lo

am, p

H 5.

5, n

o sa

linity

,hi

gh fe

rtilit

y fro

m a

his

tory

of

dairy

farm

ing

Topo

grap

hy: F

lat

Wat

er: R

egul

arly

irrig

ated

plu

s ra

infe

d,de

pth

to g

roun

dwat

er v

arie

s be

twee

n 3m

and

9m d

epen

ding

on

seas

on.

Seed

lings

pla

nted

as

a si

ngle

row

alo

ng p

rope

rtybo

unda

ry. N

o th

inni

ngs

sinc

e.

Land

ripp

ed a

nd fo

rmed

bef

ore

plan

ting.

Hare

s a

prob

lem

dur

ing

esta

blis

hmen

t. No

oth

erpr

oble

ms

sinc

e.

Regu

larly

irrig

ated

(app

rox

once

per

fortn

ight

) via

adja

cent

wat

er c

hann

el fo

r dai

ry p

astu

re.

Site

Info

rmat

ion

(Vic

toria

, Has

sall

& A

ssoc

iate

s, 1

998)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Husb

andr

yID

Na

me,

Loc

atio

n,

Est.

Area

Sp

ecie

s Li

st &

t/ha

Age

t/ha/

yrDe

nsity

m

3 /ha

e.g.

rain

fall,

soi

l typ

e/qu

ality

,e.

g. tu

best

ock/

dire

ct s

eed,

wee

d &

pes

t con

trol,

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

fire,

irrig

atio

n

Rain

fall:

Av

373

mm

Soil:

Red

loam

, slig

htly

aci

dic,

rock

yin

parts

Topo

grap

hy: G

entle

und

ulat

ing

Wat

er: R

ainf

ed o

nly,

dep

th to

grou

ndw

ater

var

ies

betw

een

1m a

nd 2

m.

Seed

lings

pla

nted

in w

idel

y sp

aced

she

lterb

elt

form

atio

ns. N

o th

inni

ngs.

Land

pre

viou

sly

used

for g

razin

g, ri

pped

prio

rto

plan

ting.

Rabb

its in

itial

ly a

pro

blem

but

less

so

now

due

toon

goin

g co

ntro

l. No

oth

er p

robl

ems.

Rain

fed

only.

See

abov

eSe

e ab

ove


21Be

ar’s

Lago

on,

1972

N/A

Rive

r Red

Gum

1901

247.

9N/

AN/

ANE

of B

endi

go, V

ic.

(E. c

amal

dule

nsis

)14

32

Vic

Road

s m

ap re

f: 29

H7

Blac

k Bo

x (E

. lar

giflo

rens

)

Man

na G

um(E

. vim

inal

is)

Shel

terb

elts

22Bu

rke’s

Fla

t, 19

835-

10 h

aRe

d Iro

nbar

k 72

113

5.6

600-

300

N/A

W o

f Ben

digo

, Vic

.(E

ucal

yptu

s 54

2

side

roxy

lon)

Vic

Road

s m

ap re

f: 43

A4

Yello

w G

um(E

ucal

yptu

s le

ucox

ylon

)

Eros

ion

cont

rol,

wat

erta

ble/

dry

land

sa

linity

con

trol

23Co

lbin

abbi

n Ra

nge

1982

5-10

ha

Grey

Box

25

114

1.8

N/A

N/A

(Too

lleen

),(E

ucal

yptu

s 19

2

W o

f She

ppar

ton,

Vic

.m

icro

carp

a)

Vic

Road

s m

ap re

f: 45

D5

Droo

ping

She

oak

(Allo

casu

arin

a ve

rtici

llata

)

Wat

erta

ble/

dry

land

salin

ity c

ontro

l, am

enity

Rain

fall:

Av

458

mm

Soil:

Hea

vily

leac

hed,

gra

datio

nal,

pH4.

0-4.

5

Topo

grap

hy: G

ently

und

ulat

ing

Wat

er: R

ainf

ed o

nly,

dep

th to

grou

ndw

ater

<10

m.

Tree

s pl

ante

d as

tube

stoc

k. N

o th

inni

ngs

but s

ome

natu

ral d

eath

s an

d re

gene

ratio

n.

Land

ripp

ed p

rior t

o pl

antin

g. P

revi

ousl

y us

edfo

rgra

zing.

Som

e in

sect

dam

age

from

198

9-20

01 d

urin

g th

ew

inte

r mon

ths.

No

othe

r pro

blem

s fro

m in

sect

s,di

seas

e, p

ests

, dro

ught

or f

ire.

Rain

fed

only.

Rain

fall:

Av

491

mm

Soil:

Gra

datio

nal r

ed b

row

n, m

ild a

cidi

ty,

not s

alin

e

Topo

grap

hy: 1

0% s

lope

on

hillt

op

Wat

er: R

ainf

ed o

nly,

dep

th to

grou

ndw

ater

<10

m.

Seed

lings

pla

nted

in ro

ws

on h

illto

p. N

o th

inni

ngs,

but s

igni

fican

t nat

ural

attr

ition

.

Land

ripp

ed p

rior t

o pl

antin

g. P

revi

ousl

y us

edfo

rgra

zing.

No k

now

n pr

oble

ms

from

inse

cts,

dis

ease

, pes

tsor

fire

. Sev

ere

drou

ght i

n 19

82/8

3 ki

lled

appr

ox55

% o

f plo

t.

Rain

fed

only.

Site

Info

rmat

ion

(Vic

toria

, Has

sall

& A

ssoc

iate

s, 1

998)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Husb

andr

yID

Na

me,

Loc

atio

n,

Est.

Area

Sp

ecie

s Li

st &

t/ha

Age

t/ha/

yrDe

nsity

m

3 /ha

e.g.

rain

fall,

soi

l typ

e/qu

ality

,e.

g. tu

best

ock/

dire

ct s

eed,

wee

d &

pes

t con

trol,

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

fire,

irrig

atio

n

A4

- Ta

ble

1b

. Gro

wth

Dat

a fo

r S

ites

in V

icto

ria,

Has

sall

& A

sso

ciat

es (

1998

) co

ntin

ued

.

Rain

fall:

Av

450

mm

Soil:

Hea

vy c

lay

loam

, pH

5.5

Topo

grap

hy: F

lat

Wat

er: R

ainf

ed w

ith o

ccas

iona

lflo

odin

g,de

pth

to g

roun

dwat

er 7

m.

Seed

lings

pla

nted

in a

sin

gle

row

alo

ng fe

nce

lines

. No

thin

ning

s si

nce.

Land

pre

para

tion

unkn

own.

Pre

viou

sly

used

form

ixed

farm

ing.

No k

now

n pr

oble

ms

from

inse

cts,

dis

ease

,pe

sts,

drou

ght o

r fire

.

Rain

fed,

with

occ

asio

nal f

lood

ing.


Site

Info

rmat

ion

(Vic

toria

, Has

sall

& A

ssoc

iate

s, 1

998)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Husb

andr

yID

Na

me,

Loc

atio

n,

Est.

Area

Sp

ecie

s Li

st &

t/ha

Age

t/ha/

yrDe

nsity

m

3 /ha

e.g.

rain

fall,

soi

l typ

e/qu

ality

,e.

g. tu

best

ock/

dire

ct s

eed,

wee

d &

pes

t con

trol,

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

fire,

irrig

atio

n

24Py

alon

g,19

910.

25 h

aRe

d St

ringy

bark

615

1.2

N/A

N/A

SW o

f She

ppar

ton,

Vic

.(E

. mac

rorh

ynch

a)4.

52

Vic

Road

s m

ap re

f: 60

F3

Blac

k W

attle

(Aca

cia

mea

rnsi

i)

Wat

erta

ble/

dry

land

sa

linity

con

trol,

expe

rimen

tal

25Pu

ckap

unya

l Ran

ge,

1980

40 h

aRe

d Iro

nbar

k 65

9115

43.9

N/A

N/A

SW o

f She

ppar

ton,

Vic

(Euc

alyp

tus

4942

side

roxy

lon)

Vic

Road

s m

ap re

f: 61

B1

Yello

w G

um

(E.le

ucox

ylon

)

Grey

Box

(E

. mic

roca

rpa)

Wat

erta

ble/

dryl

and

salin

ity c

ontro

l, ec

olog

y &

bi

odiv

ersi

ty

26To

olle

en,

1987

N/A

Rive

r Red

Gum

10

419

11.6

N/A

N/A

E of

Ben

digo

, Vic

.(E

ucal

yptu

s 78

2

cam

aldu

lens

is)

Vic

Road

s m

ap re

f: 45

D5

Yate

(E

. acc

iden

talis

)

Wat

erta

ble/

dr

ylan

d sa

linity

co

ntro

l, sh

elte

rbel

ts

Rain

fall:

Av

450

mm

Soil:

Red

vol

cani

c lo

am, w

ith h

eavy

cla

ylo

wer

hor

izon,

pH

appr

ox 5

.9

Topo

grap

hy: U

nkno

wn

Wat

er: R

ainf

ed o

nly,

dep

th to

grou

ndw

ater

var

ies

arou

nd 1

mde

pend

ing

on s

easo

n.

Seed

lings

pla

nted

as

a do

uble

row

win

dbre

akal

ong

fenc

e lin

es. N

o th

inni

ngs.

Site

was

dee

p rip

ped

(up

to 4

0 cm

) and

spr

ayed

.Pr

evio

usly

use

d fo

r mix

ed fa

rmin

g.

Outb

reak

of s

pitfi

res

treat

ed w

ith M

alat

hion

. Som

etre

es h

ave

died

and

bee

n re

plan

ted

beca

use

ofdr

ough

t in

1982

/83.

No

othe

r pro

blem

s fro

min

sect

s, d

isea

se, p

ests

or f

ire.

Rain

fed,

with

occ

asio

nal f

lood

ing.

Flo

odin

gfre

quen

cy u

nkno

wn.

Rain

fall:

Av

598

mm

Soil:

Hea

vy c

lay

(buc

ksho

t gra

vel),

pH5.

5-6.

0

Topo

grap

hy: G

entle

slo

pe o

r fla

t

Wat

er: R

ainf

ed p

lus

grou

ndw

ater

disc

harg

e in

par

ts.

Tree

s pl

ante

d as

see

dlin

gs o

n co

ntou

r lin

es a

roun

dhi

lltop

. No

thin

ning

s.

Befo

re p

lant

ing,

the

site

was

con

tour

ripp

ed a

ndsp

raye

d. P

revi

ousl

y us

ed fo

r gra

zing

and

arm

ytra

inin

g.

Atta

cks

by in

sect

s ha

ve b

een

assu

med

alth

ough

not r

ecor

ded

as th

e ca

use

of fo

liage

dec

line

over

the

year

s. N

o tre

atm

ent u

nder

take

n. N

o ot

her

prob

lem

s w

ith d

isea

se, f

ire o

r dro

ught

.

Rain

fed,

alth

ough

pro

xim

ity to

gro

undw

ater

disc

harg

e is

hav

ing

an im

pact

on

grow

th ra

tes.

Rain

fall:

Av

631

mm

Soil:

Iron

ston

e of

sed

imen

tary

orig

in,

pHap

prox

4.8

Topo

grap

hy: 2

0% h

illsi

de

Wat

er: R

ainf

ed o

nly,

dep

th to

grou

ndw

ater

unk

now

n.

Tree

s pl

ante

d vi

a di

rect

see

ding

. Som

e re

mov

al o

fun

wan

ted

spec

ies

sinc

e pl

antin

g, b

ut o

ther

wis

eno

thin

ning

s.

Som

e rip

ping

to p

repa

re th

e la

nd p

rior t

ose

edin

g,bu

t not

ove

r the

ent

ire p

lot.

Prev

ious

lyus

ed fo

rgra

zing.

No p

robl

ems

from

inse

cts,

dis

ease

, pes

ts,

drou

ghto

r fire

.

Rain

fed

only.


27Ar

dmon

a,19

78N/

AVi

ctor

ia E

urab

bie

9301

1811

.6N/

AN/

Ane

ar S

hepp

arto

n, V

ic.

(E. g

lobu

lus

ssp.

69

72

pseu

dogl

obul

us)

Vic

Road

s m

ap re

f: 32

E8

Sout

hern

Mah

ogan

y(E

. bot

ryoi

des)

Sydn

ey B

lue

Gum

(E. s

alig

na)

Yello

w G

um

(E. l

euco

xylo

n)

Spot

ted

Gum

(C

orym

bia

mac

ulat

a)

Shel

terb

elts

Rain

fall:

Av

550

mm

Soil:

Fin

e sa

ndy

loam

, pH

appr

ox 7

.2

Topo

grap

hy: F

lat

Wat

er: R

ainf

ed, w

ith o

ccas

iona

l flo

odin

gfro

m a

djac

ent d

airy

farm

. Dep

th to

grou

ndw

ater

>1m

.

Seed

lings

pla

nted

in a

sin

gle

row

alo

ngre

side

ntia

l/dai

ry fa

rm b

ound

ary.

No

thin

ning

s.

Site

was

ripp

ed b

efor

e pl

antin

g. P

revi

ousl

y us

edas

a tr

ack

and

then

as

a fru

it or

char

d.

Som

e pr

oble

ms

asso

ciat

ed w

ith w

eeds

, har

es,

frost

and

stro

ng w

ind.

Tre

es re

plan

ted

as re

quire

d.

No o

ther

pro

blem

s fro

m in

sect

s, d

isea

se, p

ests

orfir

e.

Rain

fed,

with

occ

asio

nal f

lood

ing

from

adj

acen

tda

iry fa

rm. F

lood

ing

frequ

ency

unk

now

n.

Site

Info

rmat

ion

(Vic

toria

, Has

sall

& A

ssoc

iate

s, 1

998)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Husb

andr

yID

Na

me,

Loc

atio

n,

Est.

Area

Sp

ecie

s Li

st &

t/ha

Age

t/ha/

yrDe

nsity

m

3 /ha

e.g.

rain

fall,

soi

l typ

e/qu

ality

,e.

g. tu

best

ock/

dire

ct s

eed,

wee

d &

pes

t con

trol,

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

fire,

irrig

atio

n

3m

easu

red

usin

g bo

le a

nd b

ranc

hes

4m

easu

red

usin

g bo

le o

nly

A4

- Ta

ble

1b

. Gro

wth

Dat

a fo

r S

ites

in V

icto

ria,

Has

sall

& A

sso

ciat

es (

1998

) co

ntin

ued

.


1W

ongo

n Hi

lls19

653.

5 ha

Dwar

f Sug

ar G

um76

132

.52.

415

0N/

A(E

.cla

doca

lyx

572

var.

nana

)

Amen

ity

2W

ongo

n Hi

lls19

653.

5 ha

Suga

r Gum

16

0132

.54.

911

0N/

A(E

. cla

doca

lyx)

1202

Amen

ity

3W

ongo

n Hi

lls19

518

haSa

lt Ri

ver G

um73

146

.51.

620

0N/

A(E

. sar

gent

ii)55

2

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l

4Ej

andi

ng W

est

1984

20 h

aDw

arf S

ugar

Gum

681

13.5

285

N/A

(E. c

lado

caly

x 51

2

var.

nana

)

Coas

tal M

ort

(E. p

laty

pus

var.

hete

roph

ylla

)

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l, w

indb

reak

Site

Info

rmat

ion

(Wes

tern

Aus

tralia

, Has

sall

& A

ssoc

iate

s, 1

999b

)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

A4

- Ta

ble

1c.

Gro

wth

Dat

a fo

r S

ites

in W

este

rn A

ustr

alia

, Has

sall

& A

sso

ciat

es (

1999

b)

Rain

fall:

Av

392

mm

Soil:

Yel

low

/bro

wn

sand

ove

r dee

p lo

amy

sand

, pH

appr

ox 4

.8-5

.2 (C

aCl2

)

Topo

grap

hy: G

ently

und

ulat

ing

Wat

er: R

ainf

ed o

nly,

dep

th to

grou

ndw

ater

>1m

Aspe

ct: N

E.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

crop

ping

/gra

zing.

Tube

stoc

k pl

ante

d in

row

s

No th

inni

ngs

and

no re

plan

ting.

Som

e sp

ecie

s ha

ve s

uffe

red

from

com

petit

ion

from

mor

e vi

goro

us s

peci

es. N

o ot

her p

robl

ems

asso

ciat

ed w

ith w

eeds

/dis

ease

/pes

ts, d

roug

htor

fire.

Rain

fed

only.

Rain

fall:

Av

392

mm

Soil:

Bla

ck c

laye

y sa

nd o

ver b

row

n sa

ndy

loam

, pH

appr

ox 5

.0 (C

aCl2

)

Topo

grap

hy: F

lat

Wat

er: R

ainf

ed p

lus

poss

ible

acc

ess

togr

ound

wat

er, d

epth

to g

roun

dwat

erap

prox

20

cm

Aspe

ct: N

/A.

Site

pre

para

tion

unkn

own.

Prio

r lan

dus

eun

know

n.

Tube

stoc

k pl

ante

d in

row

s al

ong

cont

ours

.

No th

inni

ngs,

but

site

repl

ante

d to

repl

ace

deat

hsan

d in

crea

se d

ensi

ty.

Inci

denc

e of

wee

ds/d

isea

se/p

ests

, dro

ught

or

fire

unkn

own.

Rain

fed

plus

acc

ess

to s

hallo

w g

roun

dwat

er.

Rain

fall:

Av

311

mm

Soil:

San

d ov

er y

ello

w b

row

n cl

ayey

sand

, non

-sal

ine,

pH

appr

ox 4

.5

Topo

grap

hy: G

entle

slo

pe

Wat

er: R

ainf

ed o

nly,

dep

th to

grou

ndw

ater

app

rox

10-1

2m

Aspe

ct: W

.

Site

ripp

ed p

rior t

o pl

antin

g. P

revi

ousl

y us

ed fo

rcr

oppi

ng/g

razin

g.

Tube

stoc

k pl

ante

d in

row

s ac

ross

con

tour

s.

No th

inni

ngs.

Som

e in

itial

loss

of t

rees

due

todr

ough

t and

win

d da

mag

e, b

ut n

one

sinc

e.

Inci

denc

e of

wee

ds/d

isea

se/p

ests

, dro

ught

or

fire

unkn

own.

Rain

fed

only.

See

abov

eSe

e ab

ove


Site

Info

rmat

ion

(Wes

tern

Aus

tralia

, Has

sall

& A

ssoc

iate

s, 1

999b

)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

5Ej

andi

ng19

852

haSa

lt Ri

ver G

um10

112

.550

0N/

A(E

. sar

gent

ii)72

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l

6Tr

ayni

ng19

871

haRi

ver R

ed G

um45

110

.545

0-50

0N/

A(E

. cam

aldu

lens

is)

342

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l

7Tr

ayni

ng19

871

haSe

e ab

ove

7.31

10.5

450-

500

N/A

5.42

8Tr

ayni

ng19

871

haSa

lt Ri

ver G

um28

110

.545

0-50

0N/

A(E

. sar

gent

ii)21

2

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l

9Tr

ayni

ng19

871

haSe

e ab

ove

3.91

10.5

450-

500

N/A

3.02

Rain

fall:

Av

311

mm

Soil:

yel

low

bro

wn

sand

ove

r lig

ht g

rey

sand

y cl

ay lo

am, m

oder

ate

salin

ity, p

Hap

prox

5.0

Topo

grap

hy: u

nkno

wn

Wat

er: R

ainf

ed p

lus

poss

ible

acc

ess

togr

ound

wat

er, d

epth

to g

roun

dwat

erap

prox

20

cm

Aspe

ct: N

/A.

Site

ripp

ed a

nd s

pray

ed p

rior t

o pl

antin

g.Pr

evio

usly

use

d fo

r cro

ppin

g/gr

azin

g.

Seed

lings

pla

nted

in ro

ws

para

llel t

o a

bulld

ozed

drai

n lin

e us

ing

a tre

e pl

ante

r.

No th

inni

ngs.

Som

e re

cent

dea

ths

due

to d

roug

ht.

No re

plan

ting.

No d

amag

e fro

m w

eeds

/dis

ease

/pes

ts o

r fire

.

Rain

fed

only,

plu

s ac

cess

to s

hallo

w g

roun

dwat

er.

Rain

fall:

Av

323

mm

Soil:

Yel

low

bro

wn

sand

ove

r brig

htye

llow

bro

wn

sand

y lo

am, p

H 6.

1

Topo

grap

hy: M

idsl

ope,

ver

y ge

ntle

slo

pe

Wat

er: R

ainf

ed o

nly,

dep

th to

grou

ndw

ater

app

rox

30 c

m

Aspe

ct: N

NE.

Site

ripp

ed a

nd s

pray

ed p

rior t

o pl

antin

g.Pr

evio

usly

use

d fo

r cro

ppin

g.

Seed

lings

pla

nted

in c

onto

ured

row

s us

ing

atre

epl

ante

r.

No th

inni

ngs.

Som

e re

cent

dea

ths

due

to d

roug

ht.

No re

plan

ting.

No d

amag

e fro

m w

eeds

/dis

ease

/pes

ts o

r fire

.

Rain

fed

only.

See

abov

e ex

cept

:

Wat

er: R

ainf

ed o

nly,

dep

th to

grou

ndw

ater

>1.

5m.

See

abov

e

See

abov

e ex

cept

:

pH 5

.1

Aspe

ct: S

SW.

See

abov

e ex

cept

:

Som

e de

aths

due

to b

orer

s in

the

drie

r are

as.

See

abov

eSe

e ab

ove

A4

- Ta

ble

1c.

Gro

wth

Dat

a fo

r S

ites

in W

este

rn A

ustr

alia

, Has

sall

& A

sso

ciat

es (

1999

b)

cont

inue

d.


Site

Info

rmat

ion

(Wes

tern

Aus

tralia

, Has

sall

& A

ssoc

iate

s, 1

999b

)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

10Be

ncub

bin

1982

N/A

Rive

r Red

Gum

161

15.5

500

N/A

(E. c

amal

dule

nsis

)12

2

Win

dbre

ak

11Be

acon

1986

0.25

ha

Rive

r Red

Gum

111

11.5

200

N/A

(E. c

amal

dule

nsis

)82

Shad

e an

d sh

elte

r

12M

urki

nbud

in19

861.

5 ha

York

Gum

(I)

181

11.5

250

N/A

132

Stric

klan

d’s

Gum

(E. s

trick

land

ii)

Shea

th’s

Gum

(E. s

heat

hian

a)

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l

Rain

fall:

Av

322

mm

Soil:

Dee

p ye

llow

wod

jil s

ands

,pH

4.5

(H20

)

Topo

grap

hy: H

igh

up o

n a

gent

lysl

opin

ghi

llsid

e

Wat

er: R

ainf

ed o

nly,

dep

th to

grou

ndw

ater

unk

now

n bu

t dee

p

Aspe

ct: W

.

Site

ripp

ed a

nd s

pray

ed p

rior t

o pl

antin

g.Pr

evio

usly

use

d fo

r cro

ppin

g/gr

azin

g.

Seed

lings

pla

nted

in ro

ws.

Thi

nnin

gshi

stor

yun

know

n.

Initi

al d

eath

s du

e to

rabb

its. T

reat

ed b

y ba

iting

and

prot

ectiv

e ne

tting

. See

dlin

gs re

plan

ted.

Som

ere

cent

dea

ths

due

to d

roug

ht a

nd a

ssoc

iate

d bo

rer

activ

ity. N

o tre

atm

ent.

Rain

fed

only.

Rain

fall:

Av

314

mm

Soil:

Red

dish

bro

wn

sand

y lo

am, l

owsa

linity

, pH

7.6-

7.8

(H20

)

Topo

grap

hy: F

lat

Wat

er: R

ainf

ed p

lus

acce

ss to

grou

ndw

ater

. Dep

th to

gro

undw

ater

appr

ox 1

.5m

, sal

inity

600

0 m

S/m

Aspe

ct: N

/A.

Site

pre

para

tion

hist

ory

unkn

own.

Pre

viou

sly

used

for c

ropp

ing/

graz

ing.

Seed

lings

pla

nted

in ro

ws

by h

and.

Thi

nnin

gshi

stor

y un

know

n.

The

only

site

diff

icul

ty a

ppea

rs to

be

reas

onab

lysh

allo

w a

nd s

alin

e gr

ound

wat

er.

Rain

fed

plus

acc

ess

to g

roun

dwat

er.

Rain

fall:

Av

285

mm

Soil:

Bro

wn

sand

y lo

am, o

ver b

edro

ck,

salt

scal

ds in

par

ts, m

oder

atel

y ac

id.

Topo

grap

hy: V

ery

gent

le m

idsl

ope

Wat

er: R

ainf

ed p

lus

pote

ntia

l acc

ess

togr

ound

wat

er s

eep.

Dep

th to

grou

ndw

ater

unkn

own.

Aspe

ct: S

.

Site

ripp

ed b

ut n

ot s

pray

ed. P

revi

ousl

y us

ed fo

rcr

oppi

ng/g

razin

g.

Seed

lings

pla

nted

in 5

con

tour

ed ro

ws

adja

cent

salt

scal

d us

ing

a tre

e pl

ante

r. Th

inni

ngs

hist

ory

unkn

own.

No re

porte

d pr

oble

ms

with

pes

ts/w

eeds

/dis

ease

,fir

e or

dro

ught

.

Rain

fed,

plu

s po

tent

ial a

cces

s to

gro

undw

ater


13Be

lka

East

1988

2 ha

Rive

r Red

Gum

561

9.5

N/A

N/A

(E. c

amal

dule

nsis

)42

2

Red

Ironb

ark

(E. s

ider

oxyl

on)

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l,am

enity

14Be

lka

East

1988

2 ha

See

abov

e,

301

9.5

N/A

N/A

exce

pt o

nly

222

Red

Ironb

ark

(E. s

ider

oxyl

on)

15Ba

kers

Hill

1978

N/A

Rive

r Red

Gum

381

19.5

Orig

inal

lyN/

A(la

rger

(E. c

amal

dule

nsis

)28

255

0, n

ow

than

muc

h th

ose

Eros

ion

cont

rol,

low

erbe

low

)w

ater

tabl

e/dr

ylan

dsa

linity

con

trol,

expe

rimen

t

16Ba

kers

Hill

1977

1 ha

See

abov

e46

120

.565

0N/

A35

2

17Ba

kers

Hill

1984

1.5

haSe

e ab

ove

301

13.5

260

N/A

222

Site

Info

rmat

ion

(Wes

tern

Aus

tralia

, Has

sall

& A

ssoc

iate

s, 1

999b

)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

Rain

fall:

Av

332

mm

Soil:

San

d ov

er b

right

yel

low

ish

brow

nsa

ndy

loam

, pH

4.2

Topo

grap

hy: M

idsl

ope

Wat

er: R

ainf

ed p

lus

pote

ntia

l acc

ess

togr

ound

wat

er s

eep,

dep

th to

grou

ndw

ater

>5m

Aspe

ct: N

E.

Site

ripp

ed a

nd s

pray

ed th

e ye

ar b

efor

e.Pr

evio

usly

use

d fo

r cro

ppin

g/gr

azin

g.

Seed

lings

pla

nted

in c

onto

ured

row

s pe

rpen

dicu

lar

from

see

p us

ing

a tre

e pl

ante

r. No

thin

ning

s.

No re

porte

d pr

oble

ms

with

pes

ts/w

eeds

/dis

ease

,fir

e or

dro

ught

.

Rain

fed,

plu

s po

tent

ial a

cces

s to

gro

undw

ater

.

See

abov

e ex

cept

:

No a

cces

s to

gro

undw

ater

see

p,gr

eate

rdep

th.

See

abov

e ex

cept

:

Low

er g

row

th ra

tes

asso

ciat

ed w

ith d

roug

htin

g.

Rain

fall:

Av

593

mm

Soil:

Bro

wni

sh b

lack

gra

velly

san

d ov

ergr

avel

ly y

ello

w o

rang

e sa

nd,

pHun

know

n.

Topo

grap

hy: V

ery

gent

ly s

lopi

nglo

whi

llsid

e.

Wat

er: R

ainf

ed, d

epth

togr

ound

wat

er>2

m

Aspe

ct: U

nkno

wn.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

forg

razin

g.

Tree

s pl

ante

d in

row

s. T

hinn

ings

occ

urre

d se

vera

lye

ars

ago.

Hist

ory

of p

robl

ems

with

pes

ts/w

eeds

/dis

ease

, fire

or d

roug

ht u

nkno

wn.

Rain

fed

only.

See

abov

e ex

cept

:

Soil:

Dar

k br

own

grav

elly

loam

y sa

ndov

er a

gra

velly

cla

yey

sand

.

See

abov

e, e

xcep

t:

No th

inni

ngs.

See

abov

e ex

cept

:

Soil:

Dar

k br

own

loam

y sa

nd o

ver

yello

wis

h br

own

light

cla

y w

ith m

ottle

s.Ad

jace

nt s

alin

e ar

eas.

Wat

er: R

ainf

ed, d

epth

togr

ound

wat

er<2

m.

See

abov

e

A4

- Ta

ble

1c.

Gro

wth

Dat

a fo

r S

ites

in W

este

rn A

ustr

alia

, Has

sall

& A

sso

ciat

es (

1999

b)

cont

inue

d.


18No

rth B

anni

ster

1976

0.75

ha

Rive

r Red

Gum

1971

21.5

815

N/A

(E. c

amal

dule

nsis

)14

82

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l, ex

perim

ent

19No

rth B

anni

ster

1976

0.75

ha

Blue

Gum

5311

21.5

815

N/A

(E. g

lobu

lus)

3982

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l

20No

rth B

anni

ster

1976

1.5

haRi

ver R

ed G

um14

6121

.581

5N/

A(E

. cam

aldu

lens

is)

1102

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l

21Dr

yand

ra19

761.

5 ha

Rive

r Red

Gum

431

21.5

815

N/A

(E. c

amal

dule

nsis

)32

2

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l,ex

perim

ent

22Dr

yand

ra19

761.

5 ha

See

abov

e80

121

.581

5N/

A60

2

Rain

fall:

Av

760

mm

Soil:

Dup

lex

with

san

dy g

rave

l ove

rsa

ndy

clay

, inp

enet

rabl

e la

yers

in p

arts

.pH

unk

now

n.

Topo

grap

hy: G

entle

mid

slop

e.

Wat

er: R

ainf

ed. D

epth

togr

ound

wat

er>2

.8m

Aspe

ct: W

.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

forg

razin

g.

Tree

s pl

ante

d in

rand

omis

ed b

lock

s. N

o th

inni

ngs.

No re

porte

d pr

oble

ms

with

pes

ts/w

eeds

/dis

ease

,fir

e or

dro

ught

.

Rain

fed,

plu

s po

tent

ial a

cces

s to

gro

undw

ater

.

See

abov

eSe

e ab

ove

Site

Info

rmat

ion

(Wes

tern

Aus

tralia

, Has

sall

& A

ssoc

iate

s, 1

999b

)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

See

abov

eSe

e ab

ove

exce

pt:

Surv

ival

rate

poo

r (66

%),

appa

rent

ly d

ue to

impe

netra

ble

laye

r in

soil

horiz

on a

nd d

roug

htin

g.

Rain

fall:

Av

530

mm

Soil:

San

dy lo

am w

ith g

rave

l ove

r lig

ht to

dens

e br

own

clay

, pH

unkn

own.

Topo

grap

hy: G

entle

slo

pe.

Wat

er: R

ainf

ed. D

epth

togr

ound

wat

er>1

.6m

.

Aspe

ct: N

.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

forg

razin

g.

Tree

s pl

ante

d in

rand

omis

ed b

lock

s. N

o th

inni

ngs.

No re

porte

d pr

oble

ms

with

pes

ts/w

eeds

/dis

ease

,fir

e or

dro

ught

.

Rain

fed,

plu

s po

tent

ial a

cces

s to

gro

undw

ater

.

See

abov

eSe

e ab

ove


23Bi

ngha

m R

iver

1976

N/A

Blue

Gum

831

20N/

AN/

A(E

. glo

bulu

s)62

2

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l

24Bi

ngha

m R

iver

1978

N/A

See

abov

e69

118

N/A

N/A

522

25Bo

wel

ling

1981

N/A

Rive

r Red

Gum

1131

1460

0N/

A(E

. cam

aldu

lens

is)

852

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l,w

ood

prod

ucts

26Co

rder

ing

1984

N/A

Blue

Gum

1211

1237

5N/

A(E

. glo

bulu

s)91

2

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l,w

ood

prod

ucts

27Co

rder

ing

1984

N/A

Rive

r Red

Gum

361

1232

5N/

A(E

. cam

aldu

lens

is)

272

Rain

fall:

Av

713

mm

Soil:

San

d ov

er s

andy

cla

y, p

Hun

know

n.

Topo

grap

hy: G

entle

slo

pe.

Wat

er: R

ainf

ed. D

epth

togr

ound

wat

erun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

for

mix

ed fa

rmin

g.

Esta

blis

hmen

t and

man

agem

ent r

egim

e un

know

n.

See

abov

eSe

e ab

ove

Rain

fall:

Av

692

mm

Soil:

Hea

vy c

lay,

mod

erat

ely

salin

e(E

C14

0m S

/m),

pH u

nkno

wn.

Topo

grap

hy: F

lat.

Wat

er: R

ainf

ed, p

lus

poss

ible

acc

ess

togr

ound

wat

er. D

epth

to g

roun

dwat

er<1

.5m

. Wat

er ta

ble

salin

ity 2

000

mS/

m.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

forg

razin

g.

Tree

s pl

ante

d in

mou

nded

row

s. T

hinn

ings

hist

ory

unkn

own.

No re

porte

d pr

oble

ms

with

pes

ts/w

eeds

/dis

ease

,fir

e or

dro

ught

.

Rain

fed,

plu

s po

tent

ial a

cces

s to

gro

undw

ater

.

Rain

fall:

Av

621

mm

Soil:

N/A

Topo

grap

hy: V

ery

gent

le s

lope

to fl

at.

Wat

er: R

ainf

ed. D

epth

togr

ound

wat

erun

know

n.

Site

pre

para

tion

unkn

own.

Pre

viou

sly

used

forg

razin

g.

Tree

s pl

ante

d in

row

s. T

hinn

ings

his

tory

unk

now

n.

No re

porte

d pr

oble

ms

with

pes

ts/w

eeds

/dis

ease

,fir

e or

dro

ught

.

Rain

fed.

See

abov

eSe

e ab

ove

exce

pt:

Tree

s pl

ante

d on

a s

alt s

cald

and

bar

ley

gras

s si

te.

Site

Info

rmat

ion

(Wes

tern

Aus

tralia

, Has

sall

& A

ssoc

iate

s, 1

999b

)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

A4

- Ta

ble

1c.

Gro

wth

Dat

a fo

r S

ites

in W

este

rn A

ustr

alia

, Has

sall

& A

sso

ciat

es (

1999

b)

cont

inue

d.


28Di

nnin

up19

83<

1 ha

Blue

Gum

831

14.5

1,10

0N/

A(E

. glo

bulu

s)62

2

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l,w

ind

brea

k

29Di

nnin

up19

84<

1 ha

Rive

r Red

Gum

771

13.5

1,10

0N/

A(E

. cam

aldu

lens

is)

582

30Di

nnin

up19

83<

1 ha

Blue

Gum

1461

14.5

900

N/A

(E. g

lobu

lus)

1092

31M

ayan

up19

875

haRi

ver R

ed G

um76

18.

550

0N/

A(E

. cam

aldu

lens

is)

572

Eros

ion

cont

rol,

wat

erta

ble/

dryl

and

salin

ity c

ontro

l,am

enity

See

abov

eSe

e ab

ove

exce

pt:

No d

eath

s as

soci

ated

with

dro

ught

ing.

See

abov

e ex

cept

:

Soil:

Gre

y sa

nd o

ver y

ello

w b

row

nlo

amy

sand

.

See

abov

e

Rain

fall:

Av

601

mm

Soil:

Bla

ck lo

am o

ver s

andy

cla

y lo

am,

pH u

nkno

wn.

Topo

grap

hy: G

entle

mid

slop

e.

Wat

er: R

ainf

ed. D

epth

to g

roun

dwat

er>2

m a

lthou

gh it

is w

et in

win

ter.

Aspe

ct: E

.

Site

ripp

ed a

nd s

pray

ed b

ut n

ot m

ound

ed p

rior t

opl

antin

g. P

revi

ousl

y us

ed fo

r gra

zing.

Seed

lings

han

d pl

ante

d in

irre

glua

rly s

pace

d ro

ws.

Thin

ning

s hi

stor

y un

know

n.

No re

porte

d pr

oble

ms

with

wee

ds/d

isea

se o

r fire

.So

me

initi

al d

eath

s fro

m k

anga

roos

and

som

ere

cent

ext

ensi

ve d

eath

s du

e to

dro

ught

ing.

Rain

fed.

Site

Info

rmat

ion

(Wes

tern

Aus

tralia

, Has

sall

& A

ssoc

iate

s, 1

999b

)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

Rain

fall:

Av

668

mm

Soil:

Sha

llow

san

d ov

er c

lay,

mod

erat

ely

salin

e (E

C 50

-140

mS/

m),

pH u

nkno

wn.

Topo

grap

hy: G

entle

slo

pe to

flat

.

Wat

er: R

ainf

ed. S

hallo

w, d

epth

unk

now

n,sa

line

(2,2

00m

S/m

)

Aspe

ct: N

.

Site

ripp

ed a

nd m

ound

ed p

rior t

o pl

antin

g.Pr

evio

usly

use

unk

now

n.

Thin

ning

s hi

stor

y un

know

n.

No re

porte

d pr

oble

ms

with

pes

ts/w

eeds

/dis

ease

,dr

ough

t or f

ire.

Rain

fed,

plu

s ac

cess

to g

roud

wat

er.

5m

easu

red

usin

g bo

le a

nd b

ranc

hes

6m

easu

red

usin

g bo

le o

nly


1RT

123C

, Mt.

Lofty

Ran

ges

1988

N/A

E. g

lobu

lus

3N/

AN/

A4

Rain

fall:

Av

926

mm

Prev

ious

ly p

lant

ed w

ith P

inus

radi

ata.

Sout

hern

Blu

e Gu

mLa

t/lon

g: 3

453,

138

49So

il: L

oam

ove

r med

ium

cla

y, d

epth

to

clay

30

cm

Topo

grap

hy: 2

0% s

lope

.

See

abov

e5

42

See

abov

e7

108

See

abov

e9

200

See

abov

e11

32

See

abov

eN/

AE.

nite

ns3

N/A

N/A

5Sh

inin

g Gu

m

See

abov

e5

65

See

abov

e7

151

See

abov

e9

268

See

abov

e11

390

See

abov

eN/

AE.

vim

inal

isM

anna

Gum

3N/

AN/

A1

See

abov

e5

32

See

abov

e7

89

See

abov

e9

173

See

abov

e11

288

See

abov

eN/

AE.

bot

ryoi

des

3N/

AN/

A4

Sout

hern

Mah

ogan

y

See

abov

e5

37

See

abov

e7

91

A4

- Ta

ble

2a.

Gro

wth

Dat

a fo

r S

ites

in S

out

h A

ustr

alia

, Wo

ng e

t al

.(20

00)

Site

info

rmat

ion

(Sou

th A

ustra

lia, W

ong

et. a

l., 2

000)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Husb

andr

yID

Na

me,

Loc

atio

n,

Est.

Area

Sp

ecie

s Li

st &

t/ha

Age

t/ha/

yrDe

nsity

m

3 /ha

e.g.

rain

fall,

soi

l typ

e/qu

ality

,e.

g. tu

best

ock/

dire

ct s

eed,

wee

d &

pes

t con

trol,

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

fire,

irrig

atio

n


Site

info

rmat

ion

(Sou

th A

ustra

lia, W

ong

et. a

l., 2

000)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Husb

andr

yID

Na

me,

Loc

atio

n,

Est.

Area

Sp

ecie

s Li

st &

t/ha

Age

t/ha/

yrDe

nsity

m

3 /ha

e.g.

rain

fall,

soi

l typ

e/qu

ality

,e.

g. tu

best

ock/

dire

ct s

eed,

wee

d &

pes

t con

trol,

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

fire,

irrig

atio

n

RT12

3C, M

t. Lo

fty R

ange

s9

164

See

abov

eN/

AE.

gra

ndis

3N/

AN/

A2

Floo

ded

Gum

See

abov

e5

37

See

abov

e7

88

See

abov

e9

150

See

abov

e11

232

See

abov

eN/

AE.

sal

igna

3N/

AN/

A2

Sydn

ey B

lue

Gum

See

abov

e5

35

See

abov

e7

85

See

abov

e9

162

See

abov

e11

240

2EP

205,

NW

of M

t. Ga

mbi

er19

88N/

AE.

glo

bulu

s2

N/A

N/A

11Ra

infa

ll: A

v 76

6 m

mPr

evio

usly

use

d as

pas

ture

for g

razin

g.So

uthe

rn B

lue

Gum

Lat/l

ong:

374

3, 1

4037

Soil:

San

d ov

er m

ediu

m c

lay,

dep

th to

clay

70

cm

Topo

grap

hy: F

lat.

See

abov

e3

37

See

abov

e4

60

See

abov

e6

130

See

abov

e9

196

See

abov

e11

279

See

abov

eN/

AE.

nite

ns2

N/A

N/A

5Sh

inin

g Gu

m

See

abov

e3

20

See

abov

e4

39


See

abov

e6

93

See

abov

e9

147

See

abov

eN/

AE.

vim

inal

is2

N/A

N/A

4M

anna

Gum

See

abov

e3

20

See

abov

e4

37

See

abov

e6

80

See

abov

e9

123

See

abov

eN/

AE.

bot

ryoi

des

2N/

AN/

A5

Sout

hern

Mah

ogan

y

See

abov

e3

20

See

abov

e4

35

See

abov

e6

92

See

abov

e9

147

3EM

133,

N o

f Mt.

Gam

bier

1991

N/A

E. g

lobu

lus

2N/

AN/

A3

Rain

fall:

Av

745

mm

Prev

ious

ly u

sed

as p

astu

re fo

r gra

zing.

Sout

hern

Blu

e Gu

mLa

t/lon

g: 3

745,

140

47So

il: U

nifo

rm s

andy

pro

file,

dep

th to

clay

200

cm

+

Topo

grap

hy: F

lat.

See

abov

e3

21

See

abov

e4

48

See

abov

e5

69

See

abov

eN/

AE.

sal

igna

Sydn

ey B

lue

Gum

3N/

AN/

A4

See

abov

e4

15

See

abov

e5

24

Site

info

rmat

ion

(Sou

th A

ustra

lia, W

ong

et. a

l., 2

000)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Husb

andr

yID

Na

me,

Loc

atio

n,

Est.

Area

Sp

ecie

s Li

st &

t/ha

Age

t/ha/

yrDe

nsity

m

3 /ha

e.g.

rain

fall,

soi

l typ

e/qu

ality

,e.

g. tu

best

ock/

dire

ct s

eed,

wee

d &

pes

t con

trol,

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

fire,

irrig

atio

n

A4

- Ta

ble

2a.

Gro

wth

Dat

a fo

r S

ites

in S

out

h A

ustr

alia

, Wo

ng e

t al

.(20

00)

cont

inue

d.


See

abov

eN/

AE.

gra

ndis

Floo

ded

Gum

2N/

AN/

A1

EM13

3, N

of M

t. Ga

mbi

er3

13

See

abov

e4

37

See

abov

e5

51

Site

info

rmat

ion

(Sou

th A

ustra

lia, W

ong

et. a

l., 2

000)

Biom

ass

MAI

Plan

ting

Volu

me

Site

Fac

tors

Husb

andr

yID

Na

me,

Loc

atio

n,

Est.

Area

Sp

ecie

s Li

st &

t/ha

Age

t/ha/

yrDe

nsity

m

3 /ha

e.g.

rain

fall,

soi

l typ

e/qu

ality

,e.

g. tu

best

ock/

dire

ct s

eed,

wee

d &

pes

t con

trol,

Coor

dina

tes

Plan

ted

Purp

ose

(tree

s/ha

)to

pogr

aphy

, acc

ess

to w

ater

, sal

inity

fire,

irrig

atio

n


Site

info

rmat

ion

(Vic

toria

, Won

g et

. al.,

200

0)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

1To

star

ee,

1988

N/A

E. g

lobu

lus

2N/

A95

113

near

Lak

es E

ntra

nce,

Vic

.So

uthe

rn B

lue

Gum

Lat/l

ong:

374

7S, 1

4811

E

See

abov

e4

942

74

See

abov

e6

924

157

See

abov

e8

884

262

See

abov

e10

840

334

2To

star

eeE.

nite

ns2

N/A

920

13Sh

inin

g Gu

m

See

abov

e4

916

76

See

abov

e6

900

147

See

abov

e8

860

237

See

abov

e10

832

287

3To

star

eeE.

vim

inal

is2

N/A

980

12M

anna

Gum

See

abov

e4

904

81

See

abov

e6

892

166

See

abov

e8

876

262

See

abov

e10

872

319

4To

star

eeE.

bot

ryoi

des

2N/

A96

48

Sout

hern

Mah

ogan

y

See

abov

e4

960

49

See

abov

e6

952

103

See

abov

e8

936

171

See

abov

e10

888

216

A4

- Ta

ble

2b

. Gro

wth

Dat

a fo

r S

ites

in V

icto

ria,

Wo

ng e

t al

.(20

00)

Rain

fall:

Av

819

mm

Soil:

Loa

my

sand

ove

r lig

ht m

ediu

m c

lay

Topo

grap

hy: 3

-6%

slo

pe.

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

ly u

sed

for g

razin

g on

impr

oved

pas

ture

with

a h

isto

ry o

f rel

ativ

ely

high

rate

s of

ferti

liser

appl

icat

ion.

Dire

ct s

eedi

ng.


5To

star

eeE.

gra

ndis

2N/

A98

08

Floo

ded

Gum

See

abov

e4

972

45

See

abov

e6

972

93

See

abov

e8

960

161

See

abov

e10

940

215

6To

star

eeE.

sal

igna

2N/

A96

08

Sydn

ey B

lue

Gum

See

abov

e4

948

46

See

abov

e6

948

95

See

abov

e8

928

154

See

abov

e10

N/A

908

195

7W

ayga

ra,

1989

N/A

E. g

lobu

lus

2N/

A93

810

near

Orb

ost,

Vic.

Sout

hern

Blu

e Gu

m

Lat/l

ong:

374

1S, 1

4819

E

See

abov

e4

938

65

See

abov

e6

917

115

See

abov

e8

892

159

See

abov

e10

858

181

8W

ayga

raE.

nite

ns2

695

5Sh

inin

g Gu

m

See

abov

e4

685

41

See

abov

e6

662

70

See

abov

e8

643

87

See

abov

e10

656

114

Site

info

rmat

ion

(Vic

toria

, Won

g et

. al.,

200

0)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

Rain

fall:

Av

870

mm

Soil:

Loa

m o

ver l

ight

med

ium

cla

y

Topo

grap

hy: F

lat.

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

land

use

deg

rade

d na

tive

fore

st s

ever

ely

affe

cted

by

fung

al p

atho

gen

Phyt

opht

hora

cinn

amom

i. Cl

eare

d be

fore

dire

ct s

eedi

ng.


9W

ayga

raE.

vim

inal

is2

713

5M

anna

Gum

See

abov

e4

696

38

See

abov

e6

688

74

See

abov

e8

683

103

See

abov

e10

679

109

10W

ayga

raE.

bot

ryoi

des

296

35

Sout

hern

Mah

ogan

y

See

abov

e4

963

24

See

abov

e6

958

64

See

abov

e8

938

86

See

abov

e10

938

102

11W

ayga

raE.

gra

ndis

291

15

Floo

ded

Gum

See

abov

e4

911

30

See

abov

e6

911

68

See

abov

e8

894

92

See

abov

e10

886

95

12W

ayga

raE.

sal

igna

295

46

Sydn

ey B

lue

Gum

See

abov

e4

946

30

See

abov

e6

921

69

See

abov

e8

904

92

See

abov

e10

896

105

Site

info

rmat

ion

(Vic

toria

, Won

g et

. al.,

200

0)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

A4

- Ta

ble

2b

. Gro

wth

Dat

a fo

r S

ites

in V

icto

ria,

Wo

ng e

t al

.(20

00)

cont

inue

d.


13M

t Wor

th W

est,

1986

N/A

E. g

lobu

lus

3.5

N/A

800

31ne

ar W

arra

gul,

Vic.

Sout

hern

Blu

e Gu

m

Lat/l

ong:

381

8S, 1

4559

E

See

abov

e6

831

135

Mt W

orth

Wes

t8.

580

022

7

See

abov

e12

631

401

See

abov

eE.

nite

ns3.

597

048

Shin

ing

Gum

See

abov

e6

954

206

See

abov

e8.

597

035

6

See

abov

e12

818

558

14Na

rrac

an E

ast,

1986

N/A

E. g

lobu

lus

3.5

N/A

927

33ne

ar M

orw

ell,

Vic.

Sout

hern

Blu

e Gu

m

Lat/l

ong:

381

7S, 1

4616

E

See

abov

e6

889

94

See

abov

e12

800

365

See

abov

eE.

nite

ns3.

591

229

Shin

ing

Gum

See

abov

e6

902

85

See

abov

e12

776

305

15Yi

nnar

,19

86N/

AE.

glo

bulu

s3.

5N/

A97

08

near

Mor

wel

l, Vi

c.So

uthe

rn B

lue

Gum

Lat/l

ong:

381

8S, 1

4618

E

See

abov

e6

929

38

See

abov

e12

866

152

Site

info

rmat

ion

(Vic

toria

, Won

g et

. al.,

200

0)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

Rain

fall:

Av

963

mm

Soil:

Loa

m o

ver m

ediu

m c

lay

Topo

grap

hy: 2

-3%

slo

pe.

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

land

use

Pinu

s ra

diat

a.

Clea

red

befo

re d

irect

see

ding

.

Rain

fall:

Av

930

mm

Soil:

Sha

llow

san

dy lo

am o

ver m

ediu

mhe

avy

clay

Topo

grap

hy: 0

-5%

slo

pe.

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

land

use

Pinu

s ra

diat

a.

Low

gro

wth

rate

s ex

perie

nced

at t

his

site

due

tosh

allo

w s

urfa

ce s

oils

.

Clea

red

befo

re d

irect

see

ding

.

Rain

fall:

Av

1212

mm

Soil:

Cla

y lo

am o

ver l

ight

med

ium

cla

y

Topo

grap

hy: 2

4-28

% s

lope

.

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

ly u

sed

for g

razin

g on

impr

oved

pas

ture

with

a h

isto

ry o

f rel

ativ

ely

high

rate

s of

ferti

liser

appl

icat

ion.

Cle

ared

bef

ore

dire

ct s

eedi

ng.


See

abov

eE.

nite

ns3.

580

06

Shin

ing

Gum

See

abov

e6

803

28

See

abov

e12

707

81

16M

aryv

ale,

1986

N/A

E. g

lobu

lus

3.5

N/A

932

20N

of M

orw

ell,

Vic.

Sout

hern

Blu

e Gu

m

Lat/l

ong:

381

2S, 1

4628

E

See

abov

e6

888

65

See

abov

e12

883

214

See

abov

eE.

nite

ns3.

596

613

Shin

ing

Gum

See

abov

e6

966

41

See

abov

e12

638

87

17Go

rmon

dale

,19

86N/

AE.

glo

bulu

s3.

5N/

A99

231

betw

een

Sale

and

Tra

ralg

on,

Sout

hern

Blu

e Gu

mVi

c.

Lat/l

ong:

381

6S, 1

4642

E

See

abov

e6

992

86

See

abov

e12

868

216

See

abov

eE.

nite

ns3.

597

920

Shin

ing

Gum

See

abov

e6

966

58

See

abov

e12

916

135

Site

info

rmat

ion

(Vic

toria

, Won

g et

. al.,

200

0)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

Rain

fall:

Av

768

mm

Soil:

San

dy lo

am o

ver m

ediu

m c

lay

Topo

grap

hy: F

lat.

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

land

use

Pinu

s ra

diat

a.

Clea

red

befo

re d

irect

see

ding

.

Rain

fall:

Av

830

mm

Soil:

San

d ov

er lo

amy

sand

Topo

grap

hy: 0

-5%

slo

pe.

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

land

use

Pinu

s ra

diat

a.

Clea

red

befo

re d

irect

see

ding

.

A4

- Ta

ble

2b

. Gro

wth

Dat

a fo

r S

ites

in V

icto

ria,

Wo

ng e

t al

.(20

00)

cont

inue

d.


18M

t Wor

th E

ast,

1987

N/A

E. g

lobu

lus

3.5

N/A

884

27ne

ar W

arra

gul,

Vic.

Sout

hern

Blu

e Gu

m

Lat/l

ong:

381

9S, 1

4559

E

Mt W

orth

Eas

t6

876

114

See

abov

e11

828

340

See

abov

eE.

vim

inal

is3.

596

032

Man

na G

um

See

abov

e6

961

138

See

abov

e11

938

435

See

abov

eE.

bot

ryoi

des

3.5

884

15So

uthe

rn M

ahog

any

See

abov

e6

857

77

See

abov

e11

764

212

See

abov

eE.

gra

ndis

3.5

846

11Fl

oode

d Gu

m

See

abov

e6

856

44

See

abov

e11

720

120

See

abov

eE.

sal

igna

3.5

918

23Sy

dney

Blu

e Gu

m

See

abov

e6

901

101

See

abov

e11

838

281

Site

info

rmat

ion

(Vic

toria

, Won

g et

. al.,

200

0)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

Rain

fall:

Av

1224

mm

Soil:

Cla

y lo

am o

ver l

ight

to m

ediu

m c

lay

Topo

grap

hy: 5

-28%

slo

pe.

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

ly u

sed

for g

razin

g on

impr

oved

pas

ture

with

a h

isto

ry o

f rel

ativ

ely

high

rate

s of

ferti

liser

appl

icat

ion.

Unr

ealis

tical

ly h

igh

prod

uctiv

ityex

perie

nced

at t

his

site

. Not

con

side

red

repr

esen

tativ

e of

bro

ad-s

cale

pla

ntat

ions

.

Clea

red

befo

re d

irect

see

ding

.


19De

lbur

n,19

87N/

AE.

glo

bulu

s3.

5N/

A93

515

near

Leo

ngat

ha, V

ic.

Sout

hern

Blu

e Gu

m

Lat/l

ong:

382

1S, 1

4614

E

See

abov

e6

935

84

See

abov

e11

917

316

See

abov

eE.

nite

ns3.

581

711

Shin

ing

Gum

See

abov

e6

817

70

See

abov

e11

817

259

See

abov

eE.

vim

inal

isM

anna

Gum

3.5

876

11

See

abov

e6

850

63

See

abov

e11

850

224

See

abov

eE.

bot

ryoi

des

3.5

972

8So

uthe

rn M

ahog

any

See

abov

e6

943

57

See

abov

e11

926

211

See

abov

eE.

gra

ndis

3.5

951

9Fl

oode

d Gu

m

See

abov

e6

935

54

See

abov

e11

892

183

See

abov

eE.

sal

igna

3.5

978

10Sy

dney

Blu

e Gu

m

See

abov

e6

962

59

See

abov

e11

935

215

Site

info

rmat

ion

(Vic

toria

, Won

g et

. al.,

200

0)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

A4

- Ta

ble

2b

. Gro

wth

Dat

a fo

r S

ites

in V

icto

ria,

Wo

ng e

t al

.(20

00)

cont

inue

d.

Rain

fall:

Av

1002

mm

Soil:

Loa

m o

ver m

ediu

m c

lay

Topo

grap

hy: 0

-8%

slo

pe.

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

land

use

Pinu

s ra

diat

a.

Clea

red

befo

re d

irect

see

ding

.


20Fl

ynns

Cre

ek,

1987

N/A

E. g

lobu

lus

3.5

N/A

919

13be

twee

n Sa

le a

nd

Sout

hern

Blu

e Gu

mTr

aral

gon,

Vic

.

Lat/l

ong:

381

6S, 1

4636

E

See

abov

e6

910

52

See

abov

e11

884

179

See

abov

eE.

nite

ns3.

574

410

Shin

ing

Gum

See

abov

e6

695

44

See

abov

e11

678

139

See

abov

eE.

vim

inal

is3.

586

09

Man

na G

um

Flyn

ns C

reek

686

037

See

abov

e11

835

105

See

abov

eE.

bot

ryoi

des

3.5

910

9So

uthe

rn M

ahog

any

See

abov

e6

902

40

See

abov

e11

885

136

See

abov

eE.

gra

ndis

3.5

886

8Fl

oode

d Gu

m

See

abov

e6

877

34

See

abov

e11

860

98

See

abov

eE.

sal

igna

3.5

851

9Sy

dney

Blu

e Gu

m

See

abov

e6

842

38

See

abov

e11

786

113

Site

info

rmat

ion

(Vic

toria

, Won

g et

. al.,

200

0)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

Rain

fall:

Av

1173

mm

Soil:

Loa

my

sand

ove

r lig

ht to

med

ium

clay

Topo

grap

hy: F

lat.

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

land

use

Pinu

s ra

diat

a.

Clea

red

befo

re d

irect

see

ding

.


21St

radb

roke

,19

87N/

AE.

glo

bulu

s3.

5N/

A94

318

near

Sal

e, V

ic.

Sout

hern

Blu

e Gu

m

Lat/l

ong:

381

6S, 1

4703

E

See

abov

e6

919

54

See

abov

e11

888

106

See

abov

eE.

nite

ns3.

593

416

Shin

ing

Gum

See

abov

e6

927

49

See

abov

e11

876

86

See

abov

eE.

vim

inal

is3.

595

120

Man

na G

um

See

abov

e6

943

59

See

abov

e11

898

107

See

abov

eE.

bot

ryoi

des

3.5

959

30So

uthe

rn M

ahog

any

See

abov

e6

959

97

See

abov

e11

947

257

See

abov

eE.

gra

ndis

Floo

ded

Gum

3.5

934

23

See

abov

e6

927

74

See

abov

e11

921

172

See

abov

eE.

sal

igna

Sydn

ey B

lue

Gum

3.5

935

28

See

abov

e6

927

78

See

abov

e11

924

183

Site

info

rmat

ion

(Vic

toria

, Won

g et

. al.,

200

0)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

A4

- Ta

ble

2b

. Gro

wth

Dat

a fo

r S

ites

in V

icto

ria,

Wo

ng e

t al

.(20

00)

cont

inue

d.

Rain

fall:

Av

598

mm

Soil:

San

d

Topo

grap

hy: 2

-7%

slo

pe.

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

land

use

Pinu

s ra

diat

a.

Clea

red

befo

re d

irect

see

ding

.


22St

ockd

ale,

1987

N/A

E. g

lobu

lus

3.5

N/A

919

19N

of S

ale,

Vic

.So

uthe

rn B

lue

Gum

Lat/l

ong:

375

1S, 1

4711

E

See

abov

e6

902

61

See

abov

e11

877

141

See

abov

eE.

nite

ns3.

591

017

Shin

ing

Gum

See

abov

e6

893

52

See

abov

e11

679

84

See

abov

eE.

vim

inal

is3.

595

918

Man

na G

um

See

abov

e6

934

59

See

abov

e11

883

117

See

abov

eE.

bot

ryoi

des

3.5

919

15So

uthe

rn M

ahog

any

See

abov

e6

919

49

Stoc

kdal

e11

900

102

See

abov

eE.

gra

ndis

3.5

901

12Fl

oode

d Gu

m

See

abov

e6

901

43

See

abov

e11

876

89

See

abov

eE.

sal

igna

3.5

911

15Sy

dney

Blu

e Gu

m

See

abov

e6

893

48

See

abov

e11

829

98

Site

info

rmat

ion

(Vic

toria

, Won

g et

. al.,

200

0)Bi

omas

sM

AIPl

antin

gVo

lum

eSi

te F

acto

rsHu

sban

dry

ID

Nam

e, L

ocat

ion,

Es

t.Ar

ea

Spec

ies

List

&t/h

aAg

et/h

a/yr

Dens

ity

m3 /h

ae.

g. ra

infa

ll, s

oil t

ype/

qual

ity,

e.g.

tube

stoc

k/di

rect

see

d, w

eed

& p

est c

ontro

l,Co

ordi

nate

sPl

ante

dPu

rpos

e(tr

ees/

ha)

topo

grap

hy, a

cces

s to

wat

er, s

alin

ityfir

e, ir

rigat

ion

Rain

fall:

Av

1182

mm

Soil:

Loa

my

sand

ove

r med

ium

heav

ycl

ay

Topo

grap

hy: F

lat

Smal

l sca

le p

lot (

<1 h

a).

Prev

ious

land

use

Pinu

s ra

diat

a.

Clea

red

befo

re d

irect

see

ding

.

The National Carbon Accounting System:• Supports Australia's position in the international development of policyand guidelines on sinks activity and greenhouse gas emissionsmitigation from land based systems.

• Reduces the scientific uncertainties that surround estimates of landbased greenhouse gas emissions and sequestration in the Australian context.

• Provides monitoring capabilities for existing land based emissions andsinks, and scenario development and modelling capabilities thatsupport greenhouse gas mitigation and the sinks development agendathrough to 2012 and beyond.

• Provides the scientific and technical basis for internationalnegotiations and promotes Australia's national interests ininternational fora.

http://www.greenhouse.gov.au/ncas

For additional copies of this report phone 1300 130 606


1. Setting the Frame

2. Estimation of Changes in Soil Carbon Due to Changes in Land Use

3. Woody Biomass: Methods for Estimating Change

4. Land Clearing 1970-1990: A Social History

5a. Review of Allometric Relationships for Estimating Woody Biomass for Queensland, the NorthernTerritory and Western Australia

5b. Review of Allometric Relationships for Estimating Woody Biomass for New South Wales, theAustralian Capital Territory, Victoria, Tasmania and South Australia

6. The Decay of Coarse Woody Debris

7. Carbon Content of Woody Roots: Revised Analysis and a Comparison with Woody ShootComponents (Revision 1)

8. Usage and Lifecycle of Wood Products

9. Land Cover Change: Specification for Remote Sensing Analysis

10. National Carbon Accounting System: Phase 1 Implementation Plan for the 1990 Baseline

11. International Review of the Implementation Plan for the 1990 Baseline (13-15 December 1999)


12. Estimation of Pre-Clearing Soil Carbon Conditions

13. Agricultural Land Use and Management Information

14. Sampling, Measurement and Analytical Protocols for Carbon Estimation in Soil, Litter and CoarseWoody Debris

15. Carbon Conversion Factors for Historical Soil Carbon Data

16. Remote Sensing Analysis Of Land Cover Change - Pilot Testing of Techniques

17. Synthesis of Allometrics, Review of Root Biomass and Design of Future Woody BiomassSampling Strategies

18. Wood Density Phase 1 - State of Knowledge

19. Wood Density Phase 2 - Additional Sampling

20. Change in Soil Carbon Following Afforestation or Reforestation

21. System Design

22. Carbon Contents of Above-Ground Tissues of Forest and Woodland Trees

23. Developing a National Forest Productivity Model

24. Analysis of Wood Product Accounting Options for the National Carbon Accounting System

25. Review of Unpublished Biomass-Related Information: Western Australia, South Australia, NewSouth Wales and Queensland

26. CAMFor User Manual

national carbonaccounting system

tech

nica

l rep

ort n

o. 2

3

The National Carbon Accounting System provides a complete

accounting and forecasting capability for human-induced sources and

sinks of greenhouse gas emissions from Australian land based

systems. It will provide a basis for assessing Australia’s progress

towards meeting its international emissions commitments.

http://www.greenhouse.gov.au

technical report no. 23

Developing a NationalForest Productivity Model

Jenny Kesteven,Joe Landsberg andURS Australia

Developing a National Forest Productivity M

odel

developing a national forest productivity model - ncas technical

Documents