project number: pnc068-0708 october 2009 climate change ... · figure 4. cabala validation using...

Climate Change and Australia’s plantation estate: Analysis of vulnerability and preliminary investigation of adaptation options

PROJECT NUMBER: PNC068-0708 October 2009

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Climate Change and Australia’s plantation estate: Analysis of vulnerability

and preliminary investigation of adaptation options

Prepared for

Forest & Wood Products Australia

By

M. Battaglia, J. Bruce, C. Brack and T. Baker

Publication: Climate Change and Australia’s plantation estate: analysis of vulnerability and preliminary investigation of adaptation options Project No: PNC068-0708 © 2009 Forest & Wood Products Australia Limited. All rights reserved. Forest & Wood Products Australia Limited (FWPA) makes no warranties or assurances with respect to this publication including merchantability, fitness for purpose or otherwise. FWPA and all persons associated with it exclude all liability (including liability for negligence) in relation to any opinion, advice or information contained in this publication or for any consequences arising from the use of such opinion, advice or information. This work is copyright and protected under the Copyright Act 1968 (Cth). All material except the FWPA logo may be reproduced in whole or in part, provided that it is not sold or used for commercial benefit and its source (Forest & Wood Products Australia Limited) is acknowledged. Reproduction or copying for other purposes, which is strictly reserved only for the owner or licensee of copyright under the Copyright Act, is prohibited without the prior written consent of Forest & Wood Products Australia Limited. ISBN: 978-1-920883-77-5 Researcher: M. Battaglia, J. Bruce, C. Brack and T. Baker CSIRO College Road, Sandy Bay TAS 7005 Final report received by FWPA in October, 2009

Forest & Wood Products Australia Limited Level 4, 10-16 Queen St, Melbourne, Victoria, 3000 T +61 3 9614 7544 F +61 3 9614 6822 E [email protected] W www.fwpa.com.au

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EXECUTIVE SUMMARY

Project objective

This project was established to provide estimates of the potential of climate change to affect wood supply from Australia’s plantation estate and indicate the extent to which management and estate design strategies can be used to reduce these effects.

It was rapidly recognised that it was not possible to do this accurately given the high levels of uncertainty associated with predicted impact. Instead the scenario analysis was used to highlight critical processes and assumptions affecting outcomes and to indicate those portions of Australia’s plantation estate most likely to benefit or be disadvantaged by climate change.

Review of climate change effects

Consensus is that Australia’s climate is changing under the influence of anthropogenic greenhouse gas emissions and that wood production in Australia may be susceptible to the impacts of climate change.

The direct effects of climate change on plantation production and risk are through changes in temperature, soil water and elevated CO2 are reviewed. Principal conclusions are:

There is a high degree of interaction between factors and responses to climate changes will vary depending on the particulars of local site and climate.

While we can draw on historical precedence and adaptive experience for changes in temperature and rainfall, our knowledge of the effects of elevated CO2 on our plantation species is poor.

In addition to the direct effects of climate change on plantation production and risk, indirect effects through changes in pest distribution and host-pest dynamics, and changes in fire frequency and severity may be important.

Model development and verification

The process-based model CABALA was parameterised and verified for the principal plantation species in Australia. Performance over the existing range of plantation conditions was adequate though in some cases limited data meant that only a superficial verification (stand volume growth only) could be carried out.

The model CABALA was modified to allow for the effects of elevated CO2 on production to be simulated. Recognising that individual plant species responses are still unclear, the model was changed in a way that allowed for the testing of three hypotheses of plant response: i) no increase in photosynthesis; ii) a rise in photosynthesis consistent with the average

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observed in all trees species from free to air CO2 experiments; iii) that no down-regulation of photosynthesis occurs and the short-term and long-term effects of elevated CO2 exposure on photosynthesis are the same.

Climate change predictions and future risk to Australia’s plantation estate

Predictions of future growth and drought risk were developed for ‘bellwether’ sites across Australia using different Global Climate Models (GCM), different IPCC special report emission scenarios, (SRES 2000) and the different assumptions of plant response to elevated CO2. The following conclusions were reached:

Plantation production changes at particular sites will depend upon the relationship between the current site climate and species optima and other site attributes such as fertility that will determine the ability of species to benefit from changing conditions.

Without a significant benefit to production from elevated levels of atmospheric CO2

and unless adaptation options are explored, production in some regions will decrease, potentially markedly if the predicted increases in number of hot-dry days either directly through damage or death, or indirectly through pest attack further decrease production.

If plantation species are able to maintain increased net photosynthetic rates under elevated CO2 levels productivity in many regions is forecast to increase. Increases in cool wet locations are forecast to be marked.

Recognising the need to present spatial maps of plantation change and the uncertainty associated with these change estimates based on model assumptions and the risk that drought death may restrict realisation of putative gains, over 1,000,000 simulations were run for the plantation land-base of Australia. Maps of change and uncertainty are provided. Conclusions reached are as follows:

The following plantation species and region combinations are predicted to increase in production with little increase in risk or uncertainty based on model assumptions:

– Eucalyptus globulus, Eucalyptus nitens and Pinus radiata in Tasmania

– the mid to lower northern regions of the hybrid pine estate

– P. radiata and E. globulus plantations in East Gippsland and higher altitude parts of central and north-east Victoria.

The following plantation species and region combinations are predicted to increase in production with an increase in risk or predictions are associated with high uncertainty based on model assumptions:

– that part of the Western Australian E. globulus and P. radiata estate in the high rainfall zone (>1000mm) where soils are fertile and deep

– plantations of radiata pine in northern and central New South Wales/ACT

– E. nitens, P. radiata and E. globulus plantations in Victoria and the Green Triangle.

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The following plantation species and region combinations are predicted to decrease in production (unless significant adaptation occurs), will have an increase in risk or predictions are associated with high uncertainty based on model assumptions: – P. radiata plantations in southern NSW, and possibly at the western edge of the

southern and central estates

– the eastern and northern extents of the Western Australian E. globulus andP. radiata estates.

Adaptation options

Adaptation was briefly investigated at two scales: the estate design scale and at the operational management scale.

At the estate scale the effects of uncertainty on optimal strategic decisions at the plantation estate level were investigated. It is clear from the scenario analysis that higher uncertainty will surround future management decisions for many plantation areas. This study found that many silvicultural decisions are robust and remain as optimal decisions under a wide range of variation scenarios. The case-study in Mt Gambier showed that there are isolated decisions (around 10% of each age class) that may need to be changed to lower risk management strategies to reach an optimal outcome. It was however found that ignoring variability in future yields and not allowing for temporal changes in yields with climate change could lead to suboptimal harvest planning.

A review at the operational management scale has revealed that in some cases species substitutes exist and that in some areas they are already being utilised as managers adapt to drying conditions.

Experience with balancing the trade-off between drought risk and production in Western Australia, and analysis of volume response to stocking and the matching water stress and harvesting costs show that operational adaptation may be cost-effective and advantageous irrespective of climate outcomes. Fertiliser management, planting density and thinning remain powerful silvicultural tools to reduce drought risk in plantations.

Emerging research on second and subsequent rotation soil water stores and recharge in the inter rotation period under seedling and coppice regeneration will important for future management under potentially more drought-prone conditions.

Table of Contents

EXECUTIVE SUMMARY ................................................................................................ i

1. Introduction ......................................................................................................... 81.1. Australia’s climate is changing..................................................................................8

1.2. Australia’s forests will be susceptible to climate change effects ..............................9

1.3. Review of main climate change impacts on forest production .................................91.3.1. Temperature changes ......................................................................................... 101.3.2. Soil water changes .............................................................................................. 111.3.3. Changes in atmospheric CO2 concentration........................................................ 131.3.4. Pests and climate change ................................................................................... 16

1.4. Focus of this study ..................................................................................................17

2. Model parameterisation, Development and validation .................................. 222.1. The CABALA model................................................................................................22

2.2. Model verification ....................................................................................................23

2.3. Plot data ..................................................................................................................242.3.1. Verification .......................................................................................................... 25

3. Climate surfaces ............................................................................................... 293.1. Global Climate Models and SRES scenarios .........................................................29

3.2. Downscaling climate change projections................................................................31

4. Predicting future climates For bellwether sites ............................................. 354.1. Introduction .............................................................................................................35

4.2. Modelling the effects of elevated CO2 ....................................................................35

4.3. Defining scenarios ..................................................................................................39

4.4. Results ....................................................................................................................43

4.5. Discussion...............................................................................................................64

4.6. Conclusions ............................................................................................................66

5. CLimate change surfaces and uncertainty..................................................... 675.1. Introduction .............................................................................................................67

5.2. Methods ..................................................................................................................67

5.3. Results ....................................................................................................................685.3.1. Australia .............................................................................................................. 695.3.2. Victoria ................................................................................................................ 745.3.3. Tasmania ............................................................................................................ 805.3.4. Northern NSW..................................................................................................... 865.3.5. Western Australia................................................................................................ 885.3.6. Queensland......................................................................................................... 92

5.4. Discussion...............................................................................................................94

5.5. In Summary.............................................................................................................94

6. Strategic Planning and Risk Mitigation at Estate Level ................................ 96

6.1. Introduction .............................................................................................................96

6.2. Purpose...................................................................................................................97

6.3. Data and Methods...................................................................................................98

6.4. Results ..................................................................................................................1016.4.1. Variation in the objective function...................................................................... 1016.4.2. Variation in yield flow......................................................................................... 1036.4.3. Variation in allocation of silvicultural regimes .................................................... 104

6.5. Discussion.............................................................................................................104

6.6. Conclusions ..........................................................................................................106

7. Operational plantation management adaptation options............................ 1087.1. Introduction ...........................................................................................................108

7.2. Species .................................................................................................................109

7.3. Sites ......................................................................................................................111

7.4. Silviculture.............................................................................................................112

7.5. Conclusions ..........................................................................................................114

8. References....................................................................................................... 116

9. Acknowledgements ........................................................................................ 124

10. Researcher’s Disclaimer ................................................................................ 125

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List of Figures

Figure 1. An example of process for determining climate change vulnerability. Adapted from D. Schroter and the ATEAM consortium, Postdam Institute for Climate Impact Research 2004, Global change vulnerability –assessing the human-environment system. The intensity of colour indicates the focus of this project being largely around determining the sensitivity and likely impact of climate change on forest production. 18

Figure 2. Pathway for adaptation engagement (Gardner et al. 2009) 18

Figure 3. Bigeographic domains within which commercial and agro-forestry plantations exist defined by annual precipitation and average mean annual temperature for current conditions, 2030 and 2070 (from CSIRO Mk3 model A2 scenario) for A. E. globulus, B. P.radiata. 23

Figure 4. CABALA validation using data from 110 E. globulus plots from Tasmania, Victoria, South Australia and Western Australia. Stands are at time of measurement were between 6 and 14 years of age and cover a range of silvicultural treatments including thinning and fertilisation. 25

Figure 5. CABALA validation using data from 31 E. nitens plots in Victoria and Tasmania. Stands at time of measurement were between 8 and 19 years old. 26

Figure 6. CABALA validation using data from Tasmania, South Australia, New Zealand (North and South Island) and Chile. The data is from a range of ages and regimes. 27

Figure 7. Observed vs predicted volumes for 15 hybrid pine and Caribbean pine 28

Figure 8. Projected climate surface warming (GCM model average) under different SRES scenarios. Figures taken from IPCC (2007) Summary for Policymakers, In: ‘Climate Change 2007: The Physical Science Basis. Contribution of Working Group 1 to the Fourth IPCC Report 30

Figure 9. Observed and predicted historical rainfall distributions and intensity data made using the CSIRO Mk3 A2 model downscaled to 20 km (CSIRO Technical Report 2007 http://www.csiro.au/resources/ps3j6.html) 30

Figure 10. Annual average temperature surfaces from the Hadley mk2 A1Fi future climate projection 32

Figure 11. Change in annual rainfall from current climate: Hadley mk2 A1Fi climate projection33

Figure 12. Annual average temperature surfaces from the CSIRO Mk3 A2 future climate projection 34

Figure 13. Change in annual rainfall from current climate: CSIRO Mk3 A2 climate projection34

Figure 14. Comparison of the different photosynthetic acclimation assumptions on photosynthetic rate and stand volume for a deep-soil and fertile non-water limited site in Western Australian. 36

Figure 15. Comparison of the different photosynthetic acclimation assumptions on photosynthetic rate and stand volume for a fertile and water-limited site in Western Australian environment. 36

Figure 16. National maps showing locations of bellwether plots. 39

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Figure 17. Location of bellwether plots in the Green Triangle 39

Figure 18. Location of bellwether plots in Victoria and southern NSW 40

Figure 19. Location of bellwether plots in Queensland 40

Figure 20. Location of bellwether plots in south west Western Australia 41

Figure 21. Location of bellwether plots in Tasmania 41

Figure 22. Example of inter-rotation variation captured using 20 weather sequences. End of rotation variation is often between 10 and 20% of the mean value (shown with the thicker line). 43

Figure 23. Percentage change in yield of bellwether plots 1980-2030 using the CSIRO Mk3 a2 model and scenario and assuming no photosynthetic up-regulation 62

Figure 24. Percentage change in yield of bellwether plots 1980-2070 using the CSIRO Mk3 a2 model and scenario and assuming no photosynthetic up-regulation. 62

Figure 25. Percentage change in yield of bellwether plots 1980-2030 using the CSIRO Mk3 a2 model and scenario and assuming photosynthetic increase 63

Figure 26. Percentage change in yield of bellwether plots 1980-2070 using the CSIRO Mk3 a2 model and scenario and assuming photosynthetic increase 63

Figure 27. Change the cumulative number of high stress days (>35oC and soil water potential <3.25 MPa) for a pine site (GT_3) average of 20 simulated rotations of bluegum site (GT_16) under CSIRO Mk3 a2 climate scenario for current and modelled 2030 climates.

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Figure 28. Model Plantation Estate used for study. 98

Figure 29.Total volume from P. radiata standard (2 commercial thinning) rotations under 3 climate scenarios. 100

Figure 30. Total volume from Eucalyptus standard short rotation with no thinning under 3 climate scenarios. 100

Figure 31. Stochastic variation total volume (by period) 101

Figure 32. Percentage frequency of stochastic LP objective function solutions as a percentage of the deterministic solution 102

Figure 33. Comparison of yield flows for 20 stochastic runs with the deterministic run. 103

Figure 34. Relationship between planting density, final production and the number of hot dry days (days > 35oC and with soil water potential <-3.2 MPa) for the GT-16 site. Production under 1980 conditions was 191 m3 ha-1 with no hot dry days. 113

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List of Tables

Table 1. Range of conditions contained within the 110 plots used in E. globulus verification. 25

Table 2. Range of conditions contained within the 31 plots used in E. nitens verification. 26

Table 3. Range of conditions contained within the 28 plots used in P. radiata verification. 27

Table 4. Combination of GCMs and SRES scenarios used in this study 31

Table 5. Comparison of predicted and observed changes from FACE experiments (Ainsworth and Long 2004) in key physiological parameters to 200 ppm CO2 enrichment. 38

Table 6. Summary of regional outcomes from climate change simulations for bellwether sites for the CSIRO Mk3 a2 model. 44

Table 7. Changes in Eucalyptus globulus plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the CSIRO Mk 3 a2 GCM 45

Table 8. Changes in Eucalyptus globulus plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 a1fi GCM. 46

Table 9. Changes in Eucalyptus globulus plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 b1 GCM. 47

Table 10. Changes in Eucalyptus nitens plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the CSIRO Mk 3 a2 GCM. 48

Table 11. Changes in Eucalyptus nitens plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 a1fi GCM. 49

Table 12. Changes in Eucalyptus nitens plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 b1 GCM. 50

Table 13. Changes in Pinus radiata plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the CSIRO Mk 3 a2 GCM. 51

Table 14. Changes in Pch and hybrid pine (HYB) plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 a1fi GCM. Note – the site QLD7 was not used in the Pch analysis; it was unsuitable due to water logging. 52

Table 15. Changes in Pch and hybrid pine (HYB) plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 b1 GCM. note – the site QLD7 was not used in the PCH analysis; it was unsuitable due to water logging. 53

Table 16. Changes in PCH and hybrid pine (HYB) plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the CSIRO Mk 3 a2 GCM. Note – the site QLD7 was not used in the PCH analysis; it was unsuitable due to water logging. 54

Table 17. Changes in the number of average annual hot, dry days (sufficient to threaten drought death) for the Pch and hybrid pine (HYB) plantation plantations using the Hadley Mk 2 a1fi GCM. Note – the site QLD7 was not used in the Pch analysis; it was unsuitable due to water logging. 55

Table 18. Changes in the number of average annual hot, dry days (sufficient to threaten drought death) for the PCH and hybrid pine (HYB) plantation plantations using the Hadley Mk 2 b1 GCM. Note – the site QLD7 was not used in the PCH analysis; it was unsuitable due to water logging. 56

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Table 19. Changes in the number of average annual hot, dry days (sufficient to threaten drought death) for the PCH and hybrid pine (HYB) plantation plantations using the CSIRO Mk 3 a2 GCM. Note – the site QLD7 was not used in the Pch analysis; it was unsuitable due to water logging. 58

Table 20. Drought-risk indicators (over 20 rotations) for E. globulus plantations under the three GCM by SRES scenario combinations for the partial acclimation model of photosynthetic acclimation (model 2). Plant water stress below -3.2MPa when combined with temperatures in excess of 35oC is an indicator of the possibility of drought death in trees, especially when young. H-A is Hadley A1FI scenario, H-B is Hadley B1 scenario and C-A is CSIRO A2 scenario. 59

Table 21. Drought-risk indicators (over 20 rotations) for E. nitens plantations under the three GCM by SRES scenario combinations for the partial acclimation model of photosynthetic acclimation (model 2). Plant water stress below -3.2MPa when combined with temperatures in excess of 35oC is an indicator of the possibility of drought death in trees, especially when young. 60

Table 22. Drought-risk indicators (over 20 rotations) for P. radiata plantations climate scenarios generated with the CSIRO Mk3 models for the partial acclimation model of photosynthetic acclimation (model 2). Plant water stress below -3.2MPa when combined with temperatures in excess of 35oC is an indicator of the possibility of drought death in trees, especially when young 61

Table 23. Standard soil description used in each region. 68

Table 24. Operational management considerations/options for Australia’s plantations for adaptation to climate change, including climate variability/extremes 115

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1. INTRODUCTION

1.1.Australia’s climate is changing

The overwhelming scientific consensus, embodied in the IPPC Fourth Assessment Report (IPCC 2007), is that the world’s climate is changing under the influence of anthropogenic greenhouse gas emissions. The implications of this for Australia have been explored (Whetton et al. 2005) and remain an unfolding area of scientific inquiry.

Predicting changes in climate requires:

a prediction of global greenhouse gas and aerosol emissions for the next century

a global carbon cycle model to convert these emissions into changes in carbon dioxide concentrations and similar models for other greenhouse gases

a coupled atmosphere-ocean global circulation model (AOGCM) that uses the atmospheric greenhouse gas and aerosol concentration information to predict climate variations

downscaling of the AOGCM results that accounts for the influence of topography on local climate.

Uncertainty surrounds each of these steps, and consequently forecasts of future climates vary depending on the assumptions and models used. The forecasts vary more for some climate attributes than others and become increasingly uncertain the further into the future we forecast. This is in part because longer term projections are dependent on the way the global community responds to the threat of climate change. However, for the near future (up to 2030) the radiative forcing from greenhouse gases already accumulated in the atmosphere will contribute significantly to warming. Consequently forecasts vary little for this timeframe and there is general consensus among the models used in forecasting. Forecasts for 2070 and beyond are less certain and demonstrate more divergence (Whetton et al.2005). Similarly forecasts are made for some regions with more certainty than for others. These uncertainties will be discussed more fully, but it is noted here that this uncertainty frames the approach taken in this report where the focus is not on definitive changes in forest production. In this report we focus on how this uncertainty affects confidence in future yields and defines plantation regions where production is unlikely to be adversely affected, where the impact on production is least uncertain and where it is most likely to be adversely affected by climate change.

Despite the uncertainty about climate change some generalisations are widely held.Applying a range of climate change models to Australia for the range of global emissions scenarios generated by the Intergovernmental Panel on Climate Change, (CSIRO Technical Report 2007) identified a number of possible outcomes for Australia:

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an increase in annual average temperatures for Australia of between 0.4-2 oCby 2030 and between 1 and 6 oC by 2070 – with significantly larger changes in some regions

more heatwaves and fewer frosts

possibly more frequent El Nino Southern Oscillation events resulting in a more pronounced cycles of prolonged drought and heavy rains

possible reductions in average rainfall and run-off in southern and much of eastern Australia with rainfall increases across much of the Tropical North – as much as a further 20% reduction in rainfall in southwest Australia, and up to a 20% reduction in run-off in the Murray Darling Basin by 2030

an increase in severe weather events – more severe wind speeds in cyclones, associated with storm surges being progressively amplified by rising sea levels and high bushfire danger days.

1.2.Australia’s forests will be susceptible to climate change effects

Hennessy et al. (2007) in reviewing climate change effects on forests, concluded that ‘productivity of exotic softwoods and native hardwood plantations is likely to be increased by CO2 fertilisation effects, although the amount of increase will be limited by projected increases in temperature, changes in rainfall and by feedbacks such as changes to nutrient cycling’. How these positive and negative effects are manifest will depend on the combination of individual site factors, coupled with management inputs. The benefits of elevated CO2 for productivity may occur on wet and fertile sites but Hennessy et al. (2007) also warned that increased pest, disease and fire damage may negate some gains such that productivity declines are also a possibility. It is generally recognised that in Australia’s dry environment, water limitations, and the possibility of increasing water shortages, are the greatest concern for plantations in the future (Pittock et al. 2001). Any reduction in rainfall, as seen in various climate change scenarios, coupled with increased water requirements in a warmer climate and associated increases in respiratory costs (Kirschbaum 2000), are likely to lead to increased tree mortality which constitutes a major concern for plantation managers in Australia. These changes have been assessed as making timber production in Australia particularly susceptible to the impacts of climate change (Seppälä et al.2009).

1.3.Review of main climate change impacts on forest production

Three aspects of climate are of particular importance to forestry: rises in temperature, changes in rainfall distribution manifesting as changes in soil water, and changes in atmospheric CO2 concentration. A short summary of how their effects on forest production may change in the future is provided below. While we discuss these separately, it is important to note that they interact strongly. Different production

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changes will result from increases in temperature and CO2 concentration under differing conditions of water stress and fertility (Kirschbaum 2004). A brief synopsis of pest effects under climate change is given because of the potentially major impact on forest performance and vulnerability.

1.3.1. Temperature changes

Nearly all biological and chemical processes are affected by temperature. The effects of temperature on photosynthesis and transpiration have been well reviewed (Kirschbaum 2004). In general photosynthesis is strongly affected by temperature (e.g. Slatyer 1977a) and plants acclimate to the ambient temperature environment (Slatyer 1977b, 1978; Battaglia et al. 1996). While net photosynthetic production (photosynthesis minus respiration) has an optimum temperature above which it is reduced, the process of acclimation and the broad temperature response curves for most of the plantation species (particularly radiata pine and bluegum that collectively provide over 70% of Australia’s plantation area) means that average temperature increases of 1 to 2 oC are unlikely to markedly affect productivity. High-end temperature forecasts for the end of the century in the range of 5 oC of rise may have a significant impact. Consensus is that warmer temperatures in higher latitudes, where not constrained by nutrition or water, will likely enhance photosynthesis and growth through increases in production in the warmer months and by increasing the seasonal duration of high photosynthetic production (Norby et al. 2003). Similar effects will occur where temperature and growing season are limited by altitude.

Respiration rate of plants also increase with short-term increases in temperature.However, respiration may acclimate to growing temperature. An important aspect of temperature change and the production of forests is variation in the ratio of photosynthesis to respiration. Observation is that changes in this ratio are slight (Gifford 1995, 2001, Turnbull et al. 2004) leading to the conclusion that increases in temperatures are not likely to lead to significantly increased carbon losses in respiration (Gifford 1995, 2001).

Increased temperature may also affect production indirectly through changes in nutrient cycling processes in the soil. For example, in a modelling analysis Kirschbaum (1999b) predicted that in nutrient limited regions with high rainfall, increasing temperature increased plantation yield by increasing nitrogen mineralisation.

High temperature, especially in association with drought conditions, has been implicated in reduced photosynthesis and even death in young bluegum plantations (Ögren and Evans 1992). It is proposed that under very dry conditions trees may be unable to use evapotranspiration to cool leaves and that leaf temperatures between 50 and 60 oC may eventuate during extreme heat waves and denature leaf proteins.If, as is likely, climate change manifests as more extreme temperature days then the incidence of this type of tree-killing event may increase.

Frost curtails the planting extent of some Australian plantation species such as E. globulus (Turnbull et al. 1993). There may be the potential for the planting range of economically preferred species such as E. globulus to be extended as minimum

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temperatures rise. Data from the BOM (http://www.bom.gov.au/cgi-bin/climate/change/) suggests rising average annual minima but not a rise in winter minima. Consequently, while frost frequency may decrease and early or late season frosts may be less common, severe frost events may still prevent the successful expansion of plantings of vulnerable species to colder regions. Also, there is evidence that elevated CO2 may increase frost sensitivity (Barker et al. 2005) so that despite reducing minima, other factors may make plants more vulnerable to damage.

1.3.2. Soil water changes

It is generally recognised that in Australia’s dry environment, increasing water limitation to productivity is the greatest concern for plantations in the future (Pittock et al. 2001). This review briefly summarises what we know of the relationship between rainfall and drought effects on plantation productivity.

Numerous studies from many different ecosystems ( Myers et al. 1996, Hingston and Galbraith 1998; Calder 1998; Dye 2000; Landsberg 1999a; Esprey et al. 2004;Almeida et al. 2004) have demonstrated that total precipitation and its distribution during the year have a strong influence on eucalypt plantation growth. These and other studies have shown water to be arguably the factor most limiting forest production, though site factors and management will modify the relationship between rainfall and wood production.

Maintenance of high rates of tree growth in the main production areas of southern Australia may be hindered by the erratic nature of rainfall. E. globulus trees for example are well adapted to short periods of water stress punctuated by rainfall events, but are vulnerable to prolonged periods of water stress (White et al. 2000). Water stress influences the development of E. globulus plantations in a number of ways. At low to moderate levels it impedes the development of leaf area (Battaglia et al. 1997) and reduces stomatal conductance (White 1996) and will change patterns of biomass allocation (White et al. 1998) leading in the end to reduced stem wood production (Mendham et al. 2007). These effects may continue for a period of time after water stress is removed (e.g. White et al. 1999). Xylem embolism occurs as the soil further dries, reducing the capacity for water transport to leaves. Under severe levels of water stress leaf shedding may be induced, and finally drought death may occur (Dutkowski 1995, Mendham et al. 2007).

The relationship between rainfall and growth varies from site to site but time-averaged vapour pressure deficit is a key determinant of this relationship. The amount of evapotranspiration required to produce a cubic metre of wood varies with region. For E. globulus at best 0.15 ML of water is required to produce a cubic metre of wood in low vapour pressure deficit environments with intensive management, and in higher vapour pressure deficit regions this climbs to close to 1 ML m-3 (Don White, CSIRO pers comm.).

One potential impact of climate change is that rising temperatures may increase vapour pressure deficit, leading to increased evapotranspiration (Jarvis and McNaughton 1986). If average vapour pressures deficits do increase the effect may partially offset decrease transpiration rates resulting from increased CO2

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concentration (see discussion of this later). A compounding factor will be if (as expected) night time minima rise more than daytime maxima, reducing diurnal temperature variation (Easterling et al.1997), a factor that may also potentially increase nocturnal transpiration resulting in decreased water use efficiency of wood production. Decreased diurnal temperature ranges may result in a lesser vapour pressure deficit increase than might otherwise be expected for a given average temperature rise. ‘Global dimming’ or recent reports on ‘global brightening’ similarly need to be taken into account when forecasting longer term trends in evapotranspiration (Roderick 2006). There is evidence that declines in global solar irradiance up until 1990 were matched by reduced pan evaporation (Roderick and Farquhar 2002). There is no analysis as yet to see if the brightening reported since 1990 is associated with increased pan evaporation.

It is of course impossible to give a definitive statement of the effects of drought on production; the outcome of drought will be weather sequence and site specific. In the worst of cases, plantations may die as was the case in the Western Australian bluegum estate in the early 1990s (Dutkowski 1995). In less extreme cases, production is lost. In Western Australia, for example, (Mendham et al. 2007) found that a drought in 2003 reduced current annual increment of E. globulus at Avery’s from 25 m-3ha-1yr-1 in 2002 to 4 m-3ha-1yr-1, before higher rainfall in 2004 restored growth to 20 m-3ha-1yr. Similarly in Brazil, Almeida et al. 2004, showed that a drought in 1999 reduced Eucalyptus grandis x urophylla production from 40 m-3ha-1yr-1 to 20 m-3ha-1yr-1, and that release from drought restored production to pre-drought levels but the volume production lost during the drought years was never recovered.

Stand management can also have an effect on the degree to which production is reduced by drought. Where growth is already limited by nutrition, drought is less likely to lead to a marked reduction in volume increment. Similarly where stand leaf-area index has been reduced by thinning or by low initial planting density the effect of drought may be less severe. This is because the water use within the drought year is reduced and rates of soil water depletion decrease over the rotation as a whole.

The age at which a plantation is affected by drought can also be significant with some data suggesting that droughts later in the plantation rotation have a more marked effect. Almeida and Soares (1997) for example, note that the effect on current annual increment was around 40% greater on plantations of E. grandis x urophylla at age 6 years than it was at age 3 years (a 50% decrease in CAI at age 6 years compared with 30% for a 600 mm rainfall deficit, and 17% compared with 8% decrease for a 200 mm rainfall deficit). However, some care needs to be taken with this data as at all ages the absolute decrease in current annual increment is nearly equivalent and the relative increase is due to the lower current annual increment of the older plantations.

Changes in patterns of rainfall also affect tree physiology and change pest risk. It has been demonstrated that for a number of species moderate water deficits increase secondary metabolites in the foliage, whereas extreme deficits decrease secondary metabolites (Ayres and Lombardero 2000) and change the C:N ratio (Harrington etal. 2001). Such responses are likely to influence susceptibility to defoliation. Drought stress also has been linked to increased susceptibility in the host to attack by stem borers and some root and stem pathogens (Old and Stone 2005).

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In a study comparing irrigated and non-irrigated tees at a low rainfall site in Tasmania (MAR~550mm/yr) it was found that when irrigation was applied to ensure that water was not a factor limiting growth throughout the rotation, fibre length increased in E.nitens whilst fibre coarseness decreased in E. globulus; pulp yield, and tear and tensile indexes were increased in both species; conversely wood basic density was decreased compared to that from stands that had received incident rainfall only (Beadle et al. 2000). The importance of levels of available water to the development of wood properties was confirmed in a later study by Raymond (unpublished) who observed a strong and single negative linear relationship between basic density and incident rainfall for E. globulus and E. nitens. Raymond and Muneri (2000) also found evidence of an interaction between the effects of rainfall and fertiliser on wood properties where the effects of fertiliser were largely negative (reducing pulp yield) on dry sites. However, this observation was not consistent for all sites. Similar observations on the importance of rainfall on wood density are evident from the drought risk series of E. globulus trials in Western Australia (Mendham et al. 2007). Basic density was 20% higher at the low rainfall Boyup Brook site (MAR=600mm) compared with the high rainfall site at Scott River (MAR=1000mm). This effect acts as an offset to the water-use efficiency figures in Table 2 which are reported on a volumetric basis. Converting the two Western Australia figures to a mass basis with the figures above reduces the difference (the figures become something like 0.66 ML/t dry stem wood for the northern part of the plantation estate and 0.50-0.60 ML/t wood for the southern part of the estate).

At the Tasmanian irrigation study site described above, Downes et al. (2006) considered the growth history of trees harvested when eight-years-old. These trees had been monitored for the three previous years using point dendrometers. Annual diameter increments differed between trees across treatments, but similar patterns of growth within treatments indicated that the same proportions of cells in each treatment contributed to differing increments. Thus, although cellulose content varied markedly between spring (~39%) and autumn (~46%) (Downes et al. 1999), mean pulp yield would not be expected to differ between trees within treatments. As the irrigated trees produced more high cellulose wood in the late summer and autumn period, they had a higher pulp yield than rain-fed trees. Two conclusions flow from this work. First, the same genetic material grown at sites with contrasting growth patterns is likely to produce trees with quite different pulp yields. Second, a growth pattern that encourages latewood production will also tend to benefit pulp yield: benefits might also flow to other wood properties, for example basic density. These results suggest that there might be some basis for using site characteristics to predict the expression of particular wood properties of interest.

The influence of reduced rainfall and increased dry period length may be manifest as much through fire losses as in reduced growth rates. This review does not consider the incidence of fire but notes that because of the potentially major impact it should be considered in future studies.

1.3.3. Changes in atmospheric CO2 concentration

Of the many aspects of climate change we are most certain that atmospheric CO2

levels will be higher. Atmospheric CO2 partial pressures have increased from pre-

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industrial of levels of ~28Pa to a current level of 37Pa and are continuing to rise at around 0.2-0.4Pa yr-1 (IPCC 2007). Future levels will depend upon the rates of emission, however IPCC SRES scenarios currently place it at between 40 and 45 Pa at the year 2030 and between 48 and 60 Pa by the year 2070. Despite the relatively certainty surrounding these predictions, our understanding of plant response to these higher CO2 partial pressures is limited. The following section reviews what is known and where significant uncertainty remains.

At current levels of atmospheric CO2, Rubisco (Ribulose-1,5 bisphosphate) is not saturated with CO2. As a result, it has been observed in many experiments that photosynthesis of C3 species (all forest trees have C3 photosynthesis) increased with increasing concentration of CO2. Typically the increase in short-term experiments is 25-75% for a doubling of atmospheric concentrations (Kimball 1983; Eamus and Jarvis 1989; Luxmoore et al. 1993; Drake et al. 1997). There is a strong temperature interaction with the extent of CO2 up-regulation and models of photosynthesis (e.g. Kirschbaum and Farquhar 1984 suggest that effects will be more marked at high rather than low temperatures (Kirshcbaum 2004)) and that on low temperature sites (5-10 oC) little increase in photosynthetic rates are anticipated by 2100 at forecast rates of CO2 increase (as per IPCC 2007). This increase in photosynthesis is generally seen in short term experiments (glasshouse or chamber experiments) but it is not expected to be sustained in the long term. The average increase in productivity from Free Air CO2 Enrichment (FACE) experiments of 29% is lower than observed in chamber experiments (Ainsworth and Long 2005). The extent of this down-regulation is highly variable and poorly understood (Medlyn et al. 1999, Ainsworth and Long 2005, Buckley 2007). There are examples of species of from the same genera (Evans et al. 2000) and even clones of the same species differing markedly in their response to elevated CO2. The lower increase in photosynthesis in long compared to short term CO2 enrichment studies is accompanied by an increase in leaf carbohydrate level (Ainsworth and Long 2005) which is known to inhibit photosynthesis (Long and Drake 1991). The most likely explanation seems to be that enhanced photosynthetic rates are maintained while sink strength is high enough to use the increased products of photosynthesis (Greenep et al. 2003), when sink strength is limited, photosynthetic rates will drop.

Of Australian plantation species, Pinus radiata has been the subject of experimental exposure to prolonged periods of elevated CO2 (Turnbull et al. 1998; Griffin et al.2000; Tissue et al. 2001; Greenep et al. 2003). These studies suggest that doubling atmospheric CO2 may result in 30-50% gains in photosynthetic rates in situations where water and nutrients do not limit growth; however, the studies indicate that the response of photosynthesis to elevated CO2 may be a function of tree size. Experiments on the elevated CO2 on eucalypts to date have only involved short term exposure on small plants (e.g. Roden et al. 1999, Atwell et al. 2007), however existing experiments in NSW investigating the effect of sustained levels of CO2 ontree functioning are underway (the Hawkesbury open-topped chamber experimentand the proposed University of Western Sydney FACE site). Up-regulation of photosynthesis by temperate Australia plantation eucalypt species, E. globulus and E. nitens following partial defoliation (Pinkard et al. 1998, 2006) suggests that these trees are often sink rather than source limited and that as with the P. radiata case(after Greenep et al. 2003) enhanced photosynthetic rates may not be as high for mature trees as in seedlings.

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Much of the research on the effects of elevated CO2 has focussed on young trees.Given the often-observed, and largely unexplained, decline in productivity with age in forests there is doubt about the applicability of such results in older stands (Ryan etal. 1997). It is possible that if maturity-related decreases in stand-level productivity are a result of tree size or stand heterogeneity arising from size related competitive process (e.g. Binkley et al. 2004) and not to age per se then forests might simply end up at the same production level but reach this level more quickly (which may be an economic advantage for production forests). Results from experimentation are equivocal with some indicating on-going growth enhancement (e.g. Delucia et al. 2005), some with continued photosynthetic stimulation but with increased allocation going below-ground (Norby et al. 2004) and others indicating declining benefits (Asshoff et al. 2006).

Even without any direct effect of CO2 on photosynthesis there will almost certainly be a benefit to forest growth under water limited conditions because of improved water-use efficiency arising from steeper gradients between inter- and intra-cellular CO2

concentration. For trees a meta-analysis of FACE experiments has shown on average a 20% reduction in stomatal conductance, which when coupled with increased photosynthetic performance has resulted in an instantaneous transpiration efficiency gain of 60-90%. In factorial CO2 × water limitation experiments the growth response is increased under water-stress conditions when expressed on a percentage basis (Arp et al. 1998, Centritto et al. 1999).

It has been observed that the sensitivity of C3 photosynthesis to CO2 concentration increases with increasing temperature (Kirschbaum, 1994), and hence the stimulation of plant growth by increasing CO2 concentration is likely to be larger at higher temperatures, with little stimulation and sometimes even inhibition at low temperatures (Kimball, 1983). However, Norby and Liu (2004) warn that it will be difficult to make conclusions about how this experimental observation will affect production because of the many other factors influenced by temperature, including the optimum temperature for a particular species growth. As evidence they cite the Oak Ridge CO2 by temperature FACE experiment where no interaction between temperature and CO2 was observed, and the principal conclusion was that when temperatures are close to optimal for a species the relative biomass increases caused by CO2 enrichment is greater than when temperatures are sub- or supra-optimal. The result was not related directly to the CO2 by temperature interaction on photosynthesis but other temperature related effects around frost damage (Norby et al. 2003) and the negative effect on raising temperatures above the species optimum for net photosynthesis. The simple conclusion is that the direct interaction of CO2 by temperature on photosynthesis may be slight compared to the other effects of CO2 as a substrate for photosynthesis and the other effects of temperature on production.

Growth enhancements by CO2 are also evident under nutrient-limited conditions (e.g., Idso and Idso, 1994) but these tend to be less than under conditions where nutrition is adequate (Drake et al. 1997, Stitt and Krapp 1999). It has been suggested that nutrient deficiency restricts the development of new foliage increasing the potential for source-sink imbalance. On low fertility sites there may be a decrease in allocation of nitrogen to photosynthetic components in response to elevated CO2

(Crous, Walters and Ellsworth 2008), however even under such conditions net

16

photosynthesis is observed to be higher under elevated than ambient CO2. Nitrogen limitation have been found to reduce the above-ground biomass growth from between +20-30 to +9-15% in response to doubling of CO2 concentration (Curtis and Wang 1998; de Graaff et al. 2006).

Another consideration is the changes in plant allocation that occur in response to elevated CO2. While it is noted in a meta-analysis that tree total biomass generally responds positively to a doubling of ambient CO2 concentration (average of around 21% increase), the above-ground biomass has only a non-significant 14% increase (Ainsworth and Long 2005). This is because in some experiments most of the extra carbon sequestered is allocated to production of leaves and fine roots (Norby et al.2002, Hyvonen et al. 2007).

Elevated CO2 concentration can directly affect plant respiration rates, however observation in longer-term experiments show variable results (Gonzalez-Meler et al.1997; Drake et al. 1998, 1999). Gifford (1985, 1995, 2001) concluded from a series of experiments under ambient and elevated CO2, for assessing the impacts of changing CO2 on respiration, it is be best to assume that the ratio of respiration rate to photosynthetic rate remained unchanged.

.

1.3.4. Pests and climate change

While not explicitly the focus of this report we touch on pests because their activity will determine if any benefits of climate change on forest production are realised.

There is general consensus that climate change is likely to increase the baseline activity of many pest species, and the potential for epidemics (Whittaker 2001, Burdon et al. 2006). Booth et al. (2000a, b) described the climatic requirements of pathogens that either are already present in Australia or might pose a potential threat to trees in Australia.

A recent report (Pinkard et al. 2009) details the potential for particular pests to impact on forest production, in a case study of wet sclerophyll forests in Tasmania. When changes in both insect and disease populations are modelled under future climate scenarios a few common trends are evident:

distributions and habitat suitability shift southward and coastward as average temperatures rise

higher altitude environments become more favourable for pests and diseases

tropical and sub-tropical pests shift into the present warm temperate zone.

Forecasting what and when things might happen, however, is difficult. The vulnerability of plantations to insects under climate change will depend upon tree vigour, insect activity (both duration of activity and number of lifecycles per year), the interaction between trees and insects (how attractive trees are and how they recover) and what climate change does to natural enemies and biological control agents.

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The activity of insects is very strongly dependent of temperature. Insect numbers and survival are controlled in part by low and high temperatures. Severe insect outbreaks such as the mountain pine beetle in western USA and Canada have occurred because temperatures in winter have not been low enough to exert any control on population build up. For insects with short reproductive cycles such as aphids, temperature rises of 1-2 oC (such as forecast by the IPCC over the next 50-70 years) can lead to up to five extra generations a year.

Such simple analysis may, however, overlook complex interactions with other aspects of climate change. In Australia, fire regimes may alter with changing climate. Pests and diseases may thrive better on the attractive green coppice leaves provided by more frequent fires.

Pinkard et al. (2009) conclude that defoliation, nutrition and water are likely to be highly interactive in their effect on forest productivity. These complexities arise because defoliation interacts with plant nitrogen (N) status (through consumption of plant N stores which need to be replenished), a situation that can be exacerbated if high rainfall rates lead to increased nitrogen leaching under reduced canopies. Reductions in foliar N concentration, and an increase in the C:N ratio are commonly reported responses to elevated CO2 (Ayres and Lombardero 2000, Hunter 2001, Johnson et al. 2006) that are likely to affect the palatability of foliage to pests (Harrington et al. 2001). Such changes may result in pest species undergoing compensatory feeding for adequate dietary N intake, and may affect rates of larval development. Carbon-rich secondary metabolites also may increase under elevated CO2 (Ayres and Lombardero 2000, Hunter 2001), which may reduce the palatability of foliage. Trees under stress, particularly drought stress, are much more vulnerable to pest attack. It is likely that many of the places where trees are grown will become drier and warmer potentially making trees more susceptible to pest attack.

The study by Pinkard et al. (2009) indicated that the impact of defoliation on production might increase under future climates at some E. globulus and P. radiatasites, and that there would be considerably more variation in responses than is observed under current conditions. The maximum impact of defoliation on production (comparing average production at sites modelled with and without pest effects over many rotations) under future climates was predicted rise up to 25% reduction for E. globulus and up to 15% for P. radiata.

1.4.Focus of this study

Vulnerability to climate change results from exposure to climate change and the sensitivity of the system to the impacts. We can reduce vulnerability through adaptation. This is shown schematically in Figure 1. [Note: this is one conceptualisation of vulnerability (outcome vulnerability) and does not consider the unfolding and contextual nature of vulnerability that identifies it in relation to societal and institutional resilience at particular times]. The intensity of the colours in the figure indicates the relative focus of this project.

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Exposure Sensitivity

Impact

Vulnerability

communities

policy integration markets

resources

Adaptive capacity

Figure 1. An example of process for determining climate change vulnerability. Adapted from D. Schroter and the ATEAM consortium, Postdam Institute for Climate Impact Research 2004, Global change vulnerability –assessing the human-environment system. The intensity of colour indicates the focus of this project being largely around determining the sensitivity and likely impact of climate change on forest production.

Figure 2. Pathway for adaptation engagement (Gardner et al. 2009)

The vulnerability of forests and forest industries to climate change clearly varies between regions and is a function of the rate, magnitude and variation to which the system is exposed, and its sensitivity to change (i.e. the degree to which the system

19

will respond to external change). Adaptive capacity is the ability to adjust or take opportunities in response to these changes.

Forestry is somewhat different to many agricultural enterprises because of the long rotation length of the forest crop. P. radiata plantations being planted now will not be harvested until after 2030 by which time significant changes in climate are forecast to have occurred. Thus the possibility of climate change and system vulnerability must be considered in current decision-making processes. Prioritising adaptation actions requires identification of vulnerable forest systems and regions, the potential for disruption or loss if these fail, the scope to reduce this risk and the ability to capture any potential benefits. A priori those forest types and regions or activities most at risk (adapted from Allen Consulting Group (2005)) will be:

those already stressed or at the edge of their climate tolerance

those where large and long-lived investments are being made – such as with longer rotation crops or significant processing infrastructure (e.g. pulpmills) are planned.

Three points of contribution for specialised knowledge and outside assistance in adaptation planning are suggested (Figure 2). The first is providing information about climate change, the second identifying of potential local impacts and the third working with stakeholders to develop options and capacity for strategic planning. Participating in these processes is the role of the project described in this report: first by generating spatial information about potential impacts and the uncertainty (and causes of this uncertainty) and engaging in workshops and in-house seminars with stakeholders to explore this information and adaptation options.

The confidence with which predictions of the impact of climate change on forest production can be made are limited by the uncertainty associated with climate and forest models. Adaptive strategies need to be developed in the face of this uncertainty. Lindner et al. (2002) suggest among other things that key ways of handing uncertainty include the use of scenario analysis to show how systems further downstream respond to critical factors. It is suggested that a variety of regional climate inputs, for example, could be used to assess the system’s sensitivity to climate variable. The climate impact risk assessment proposed by Jones (2001) has a similar element to this and has seven steps:

1. Identify the key climatic variables affecting the exposure units being assessed.

2. Create scenarios and/or projected ranges for these key climatic variables.

3. Carry out sensitivity analysis to assess the relationship between climate change and impacts.

4. Identify the impact thresholds to be analysed for risk with stakeholders.

5. Carry out risk analysis.

6. Evaluate risk and identify feedbacks likely to result in autonomous adaptations.

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7. Consult with stakeholders, analyse proposed adaptations and recommend planned adaptation options.

This report works through steps 1-3 of this process in detail, looks at an example of risk at both the plot and estate level and develops strategies (4-5). The latter steps are dealt with by way of an example of estate level modelling and the various workshops that will flow from this project.

The actual steps undertaken in this process are described in subsequent chapters and are also given here in overview:

1. Climate change projections are downscaled to regional estimates for key variables that determine forest production for 2030 and 2070. Rotation length weather sequences are generated (20 separate simulations for each climate by location scenario used in this report) recognising that production generated under average conditions does not reflect what occurs under real weather sequences due to the non-linear response and temporal interdependencies of production to temperature and soil water (Almeida et al. 2004).

2. Models of principal plantation species are validated against historic inventory data with plots selected from as broad a cross-section of nutrient × water × temperature combinations as available data allowed. The premise here is that many future conditions, for at least till 2030, will be encompassed within historical experience and that if we can simulate this then we have some confidence in our predictions into the future. Because the effects of prolonged exposure to elevated CO2 on Australia’s plantation species remain untested, we developed predictions of production and drought risk for 2030 and 2070 assuming either: 1) no effect of CO2 increase on photosynthesis, 2) a level of photosynthetic increase that matches the average from the meta-analysis of Ainsworth and Long (2006), 3) an unconstrained increase in which the observed short term increase in potential photosynthetic rate is maintained. Predictions are made for each of the validation points described above.

3. We develop spatial maps of Australia’s plantation estate giving productivity under different scenarios of plant response to elevated CO2, soil fertility and climate change period. We collate these and produce an average (similar to the IPCC ensemble means) change surface and a variance surface.

4. At the estate level we investigate how climate variability may affect yield forecasts and selection of more or less conservative silvicultural regimes over varying portions of the estate.

5. For several plantation areas identified as ‘at risk’ or vulnerable to climate change we explore how silviculture can balance productivity maximisation objectives with risk minimisation goals.

In using this report the warnings of Constable and Friend (2000) on the limitations of process-based models of tree growth for addressing tree response to climate change should be remembered. These are:

models are imperfect simulations of complex physiological process

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they have an inherent emphasis on well understood processes, not necessarily those that will be important in determining climate change effects

they have a simplification of anatomical structure

generally they have poor simulation of interactions between different physiological processes

they have limited ability to address the effects of interactions between neighbouring vegetation

they reflect our current understanding of tree physiology, not necessarily the reality of how trees will respond.

We have attempted to guard against these dangers in the following ways. Firstly, we have sought to validate the process-based models across the full current bio-geographic range of plantations to gain confidence that they capture salient physiological processes and interactions under known conditions and climates which will form a large subset of future conditions (other than CO2). For elevated CO2

where we are less certain we have used a range of scenarios to examine how critical the assumptions might be in the results. CABALA itself has a high degree of integration between carbon, water and nitrogen cycles and an allocation model that is responsive to tree state, so despite being ‘an imperfect simulation of the complex processes’ it still allows for rich interactions.

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2. MODEL PARAMETERISATION, DEVELOPMENT AND VALIDATION

2.1.The CABALA model

The forest-growth model used in this work is the CABALA model (Battaglia et al. 2004). It was selected for the following reasons:

It has a photosynthetic model that, while simple, is amenable to inclusion of CO2 effects and will capture the interactions between VPD, CO2

concentration, water stress and nutrient supply. Daily gross primary production is calculated by coupling uptake of carbon and transpiration of water through stomatal aperture via the Ball-Berry equation (Ball et al. 1987). Daily gross photosynthetic production and canopy conductance are then summed from calculations made at half daily intervals using average morning and afternoon temperature, incident radiation, partial pressure of CO2 and vapour pressure deficit (after Sands, 1995). These daily values are used in the daily time-step component of the rest of the model.

Both temperature and respiration respond and acclimate to temperature changes. Both of these traits will be important for modelling plants in changing environments; failure to include acclimation may over-estimate respiration as climates warm and lead to under-estimation of photosynthetic production as temperatures move away from temperature optima (Battaglia etal. 1997). For models calibrated to specific climate conditions it may be possible to develop lumped parameters that implicitly incorporate these processes. The validity of these parameterisations when applied in new environmental combinations is uncertain.

It offers the potential for allocation to respond to changes in supply of resources for growth. Changes in allocation have been observed in FACE experiments to increase below-ground allocation such that proportional increases in tree volume production are less than proportional changes in NPP, particularly when fertility is low (e.g. Norby et al. 2003).

In addition to the effects of resource supply (light, water, nutrients) on growth the model captures the effects of some stress factors such as frost, photoinhibition and loss of hydraulic conductivity associated with drought. Current limits to plantation development and the selection of species are controlled by survival and risk of damage as well as average production. These limits will change under climate change and need to be explored as part of the impact assessment.

The model allows for silvicultural management scenarios around spacement at planting, fertilisation and thinning to be developed and the implications of these to be explored. Realistic inclusion of these aspects is vital if management based adaptation options are to be explored. Evidence from the ‘drought risk trials’ in Western Australia (Mendham et al. 2007) suggest that

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those that control the expression of leaf area will be important in the balancing of risk and productivity under water-limited conditions.

The model runs on a daily time step. This allows exploration of drought risk, changes in maximum and minimum temperature shifts to be explored and shifts in seasonal rainfall patterns.

The model has been well verified for E. globulus (Mendham et al. 2007; Miele et al. 2009). As part of this project parameter sets were verified for P. radiata and E. nitens and a preliminary parameter sets were developed for P. caribaea var. hondurensis(PCH) and P. elliottii var. elliottii × PCH (hybrid pine). These validations are provided in a subsequent section. Because data used was often commercial-in-confidence site details are not identified against plots.

2.2.Model verification

A. E globulus

B. P. radiata

Figure 3. Bigeographic domains within which commercial and agro-forestry plantations exist defined by annual precipitation and average mean annual temperature for current conditions, 2030 and 2070 (from CSIRO Mk3 model A2 scenario) for A. E. globulus, B. P. radiata.

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We presume that for the medium term (at least to 2030) the range of conditions of temperature and rainfall are likely to be encompassed within the historical data record. If plantation performance can be reliably simulated for historical situations across the environmental domain in which the species are planted we can be more confident of future predictions (not withstanding the interacting effects of elevated atmospheric CO2 concentrations). If we map the current biogeographic domain as defined by mean annual temperature and mean annual precipitation for the two principal plantation species in Australia, E. globulus and P. radiata we can see that this is generally true (Figure 3).

2.3.Plot data

Growth measurements and model input data (climate, soil and management information) were available for 184 permanent sample plots (PSPs) located in plantations (11 in hybrid pine, 4 PCH, 28 radiata pine, 31 E. nitens, and 110 E. globulus. For each species they covered the range of fertility, rainfall and temperature ranges within the estates as far as possible.

Soils data taken from soil pit descriptions at or near the plot location were provided by data providers or where these were not provided by matching to soil descriptions for the same land-systems and soil types from published sources. Silvicultural treatments applied at each plot were provided by data providers.

Daily rainfall and air temperature data for all PSPs were taken from the interpolated climate surfaces of SILO Data Drill, supplied by the Queensland Government, Department of Natural Resources and Mines. The climate surfaces were created by interpolating daily historic climate observations to a resolution of 0.05 degrees (approximately 500 ha cell size) (Jeffrey et al., 2001).

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2.3.1. Verification

E. globulus

y = 1.04x - 1.54R2 = 0.91

0

100

200

300

400

500

0 100 200 300 400 500

Observed entire stem volume (m3 ha-1)

Pred

icte

d en

tire

stem

vol

ume

(m3 h

a-1)

Figure 4. CABALA validation using data from 110 E. globulus plots from Tasmania, Victoria, South Australia and Western Australia. Stands are at time of measurement were between 6 and 14 years of age and cover a range of silvicultural treatments including thinning and fertilisation.

Abundant data spanning a wide range of conditions is available for testing CABALA (Table 1). Although some plots are poorly predicted there is no bias against the measures of fertility, rainfall or temperature indicating that predicative capacity will be adequate for simulations in this study (Figure 4). Consistently poor predictions (under-estimates) are made on inland Victorian sites where frost limitations are over-predicted. Sites where mortality has been high are consistently over-predicted. The reasons for unexpected tree mortality are often not evident in the available data and consequently difficult to represent in model inputs. Events such as insect pest infestation or waterlogging are rarely documented.

Table 1. Range of conditions contained within the 110 plots used in E. globulus verification.

Min. Max.

Average max. air temperature (°C) 13.7 - 23.1

Average min. air temperature (°C) 5.5 - 11.6

Precipitation (mm year-1) 593 - 1456

Initial stocking (trees ha-1) 720 - 1330

Total N fertiliser (kg N ha-1) 0 - 3000

Thinning (% stems removed) 0 - 75

Mean annual increment (m3 ha-1 yr-1) 5 - 46

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E. nitens

Figure 5. CABALA validation using data from 31 E. nitens plots in Victoria and Tasmania. Stands at time of measurement were between 8 and 19 years old.

Parameterisation developed for E. nitens appears adequate for project purposes (Figure 5). Validation is not as rigorous as for E. globulus and has only compared observed and predicted volume without the many other state variable comparisons conducted for E. globulus (as per Battaglia et al.2004). In addition fewer silvicultural options were investigated (Table 2).

Table 2. Range of conditions contained within the 31 plots used in E. nitens verification.

Min. Max.








y = 0.90x + 20.29R2 = 0.90

0

200

400

600

800

0 200 400 600 800

Observed entire stem volume (m3/ha)

Pred

icte

d en

tire

stem

vol

ume

(m3 /h

a)

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P. radiata

y = 0.8493x + 83.788R2 = 0.9206

100

200

300

400

500

600

700

800

100 200 300 400 500 600 700 800

observed standing volume (m3/ha)

pred

icte

d st

andi

ng v

olum

e (m

3/ha

)

TAS

SA

NZ

Chile

Figure 6. CABALA validation using data from Tasmania, South Australia, New Zealand (North and South Island) and Chile. The data is from a range of ages and regimes.

Table 3. Range of conditions contained within the 28 plots used in P. radiata verification.

Min. Max.








CABALA was verified with an international set of data for P. radiata. Sites were sourced from Tasmania, South Australia, New Zealand (North and South Island) and Chile were used (Figure 6). This data used for validation came from a wide variety of climates and silvicultural regimes. Predictive accuracy is sufficient for project purposes.

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P. caribaea var. hondurensis and P. elliottii var. elliottii x PCH

0

100

200

300

400

500

600

0 100 200 300 400 500 600

Measured volume (m3/ha)

Pred

icte

d vo

lum

e (m

3 /ha)

Hybrid pinePinus carribea hondurensis

Figure 7. Observed vs predicted volumes for 15 hybrid pine and Caribbean pine

This parameterisation of CABALA was carried out specifically for this project. The principal physiological data in the parameterisation was drawn from Teskey et al.(1994). Relationship for photosynthetic response to VPD, temperature and light were gathered from this source. The best evidence of likely effect of elevated CO2 onphotosynthesis comes from the Duke FACE experiment using Pinus taeda (Rogers and Ellsworth 2002; Crous et al. 2008) and shows marked acclimation of photosynthesis to elevated CO2 though after nine years of exposure to elevated CO2

Despite this, one year old needles still showed stimulation to elevated CO2whencompared with trees at ambient CO2 especially when nitrogen limitations were avoided (where fertiliser was not applied increases were less or not sustained). For the same level of leaf nitrogen concentration photosynthetic rates of one year old foliage under elevated CO2 averaged 40% greater and for current year foliage up to 68% greater than those under ambient CO2 levels. If this translates to the hybrid pines then with good nutrition the simulations assuming sustained up-regulation are indicated.

The relationship between observed and predicted is y=0.89x+63, r2=0.82 (Figure 7).While the parameter set appears adequate it is based on limited physiological data. Only scant tree biomass and nutrient distribution within plant biomass data was available. CABALA only allows for nitrogen as a soil limiting nutrient so sites where nutrients other than nitrogen are known to be limiting, growth will be over-predicted. Phosphorous deficiency is common on most coastal lowland pine plantations in Queensland, as are K and Cu deficiencies on podzol soils.

For the validation of the current parameter sets only stand volume was available. A more reliable parameterisation of hybrid pine would require detailed information on stand volume, soil water, plant water-stress and stand leaf area.

29

3. CLIMATE SURFACES

3.1.Global Climate Models and SRES scenarios

There are a number of global climate models and scenarios that describe the range of potential futures. Changes in predicted temperature and precipitation can vary substantially, particularly when projected over long time frames. Most global climate models show little difference in climate predictions for 2030 because the forcing components that will contribute to climate change up to that period are already in the atmosphere. Beyond this point, future climate will be markedly influenced by emissions over the next 50 years.

To account for this uncertainty in human response, a range of differing scenarios based on emission reductions and population growth were developed by the Intergovernmental Panel on Climate Change (IPCC 2007). These are known as Special Report Emission Scenarios (SRES). Each scenario represents a storyline future based on demographic, economic and technological futures. Three SRES futures were chosen A1fi, A2 and B2, to broadly cover the range proposed by IPCC. The A1fi is based on rapid economic and population growth (declining in the second half of the century) with a focus on fossil fuels and is a high emission scenario. This scenario assumes there is little effort to reduce or mitigate emissions. The A2 scenario describes a heterogeneous world with a focus on regional economies, a continuously increasing population with fragmented technological change with some reductions/mitigation of emissions. The B2 scenario describes a more sustainable world, with a focus on the local solutions to economic, social and environmental sustainability with a low emission future (Figure 8). The B series scenarios are becoming increasing unlikely given current emission levels.

There are a number of GCMs that can be used to develop projections of future climates for Australia. It is possible to use an average of all available models but the combining of models requires the addition of the errors associated with each model, leading to very high levels of uncertainty. A more appropriate solution may be to include weightings for each model to reduce the effect of those considered less suitable or more unlikely for Australia, producing a more reasonable average but still result in high levels of uncertainty (CSIRO Technical Report 2007). This approach was beyond the scope of the project. For this study, two global climate models were chosen, the Hadleys Mk2 and the CSIRO Mk3 model. The Hadleys Mk 2 and CSIRO Mk3 models were selected to allow us to consider an extreme future climate (Hadleys – a hot dry future) case and a more moderate future (CSIRO – a moderate, less drying future). The M-skill score (a measure of climate model performance) for the HADCM2 and CSIRO Mk3 models demonstrates both models are suitable predictors of Australia’s climate (CSIRO Technical Report 2007). Initial work had included the Echam3 GCM but this was discarded after further investigation revealed it produced unrealistically high levels of summer rain.

30

Figure 8. Projected climate surface warming (GCM model average) under different SRES scenarios.Figures taken from IPCC (2007) Summary for Policymakers, In: ‘Climate Change 2007: The Physical Science Basis. Contribution of Working Group 1 to the Fourth IPCC Report

Figure 9. Observed and predicted historical rainfall distributions and intensity data made using the CSIRO Mk3 A2 model downscaled to 20 km (CSIRO Technical Report 2007 http://www.csiro.au/resources/ps3j6.html)

The Hadleys model was obtained from the TYN SC 2.0 dataset from the University of East Anglia Climate Research Unit (CRU) website. The model was downscaled to 0.5 x 0.5 degree regular grids of precipitation and temperature using linear regression techniques. Grids were monthly data based on 30 year averages around the time frames 2030 and 2070.

31

The CSIRO model was obtained from CSIRO Marine and Atmospheric Research. The model was downscaled using CCAM (Cubic Conformal Atmospheric Model 805) to 0.5 degrees. Initial datasets were 10 year averages for 2020 through to 2070.Comparison of observed and model data from downscaling are shown in Figure 9.

Table 4 lists the combinations of models and scenarios used in this study. This combination allowed us to explore a reasonable range of the SRES scenarios using GCMs that have a demonstrated ability to predict Australia’s historical climate, thus giving us confidence in future projections. Both models were also readily available on a moderately fine scale.

Table 4. Combination of GCMs and SRES scenarios used in this study

CGM SRES scenario

Time Frames

CSIRO Mk3 A2 2030 2070 Hadleys Mk2 A1fi 2030 2070 Hadleys Mk2 B2 2030 2070

3.2.Downscaling climate change projections

Historical climate data for each forest site was obtained from the Bureau of Meteorology's Data Drill (http://www.longpaddock.qld.gov.au/silo/). The data in the Data Drill is synthetic, consisting of interpolated grids splined using data from meteorological station records but has the benefit of being available for all locations in Australia on a scale of 0.05 degree. Individual data points were used for each validation plot and a regular grid of points was obtained for the regional surfaces. Blocks of 30 years of historical data were used for the base data. This allowed us to preserve the variability inherent in the historical weather sequences. The base data used for the Hadleys M2 model ranged from 1961 to 1991, and 1975 to 2005 for the CSIRO MK3 model.

A relatively simple approach was used to modify the historical weather. The temperature and rainfall was modified using monthly averages from the three potential future climates. Radiation was not adjusted as it is expected there will be only small changes of between -1 to + 2% (CSIRO – Climate Change in Australia. Technical Report 2007). The monthly changes in temperature for the two time periods were added to the historical data. Rainfall was modified using proportional change (a simple additive approach is not appropriate given the variation in absolute rainfall across a single cell in the GCM grids). Where rotation lengths were greater than 30 years, the data was looped for the required time frame.

There are limitations to this approach. Most importantly, there is the assumption there is no change in the number of rain days in future scenarios compared to historical climate. Where there is an overall drying trend, this can result in an increased number of days with very small rainfall events. It is more likely rainfall will be concentrated into fewer rain days with more intense precipitation events. Nor does it capture the predicted increase in extreme weather events.

32

The spatial scale of the data can cause problems for both the individual plots and also at the regional level. In areas of high relief, the spatial scale of the data drill can be too coarse; the matching data drill for plots in the foothills may be too cold, for example. Where required, adjacent data points were used. At the regional level, individual data points were used to model areas up to 60 square km. Any future work will need to address the issue of downscaling at both the plot and regional level.

Examples of change surfaces for temperature and rainfall from the GCMs are given in Figures 10-13.

Figure 10. Annual average temperature surfaces from the Hadley mk2 A1Fi future climate projection

33

Figure 11. Change in annual rainfall from current climate: Hadley mk2 A1Fi climate projection

34

Figure 12. Annual average temperature surfaces from the CSIRO Mk3 A2 future climate projection

Figure 13. Change in annual rainfall from current climate: CSIRO Mk3 A2 climate projection

35

4. PREDICTING FUTURE CLIMATES FOR BELLWETHER SITES

4.1.Introduction

In this chapter we take a well distributed sub-set of the verification plots from Chapter 2 and examine likely production and risk levels at these sites under the various weather scenarios developed Chapter 3. These will be termed ‘bell-wether’ sites – or sites that can be used as indicators of likely vulnerability of production. At any particular point (although the point is chosen to be typical of plantations in that region) the effects of changing temperature, rainfall and atmospheric CO2 on production and risk will be determined by the soil conditions, the silvicultural regime and existing climate.

An initial requirement for this is modelling the effects of elevated CO2 on production.The method of dealing with this is described below.

4.2.Modelling the effects of elevated CO2

Given the uncertainty surrounding photosynthetic enhancement under elevated CO2

we utilise three potential plant responses in our modelling.

1. No increase in photosynthesis: In the first case we assume no photosynthetic enhancement. In this case Cs in the calculation of net photosynthesis Anet

(equation A.20 in Battaglia et al. 2004) is assumed to be 350 ppm and this is used in the solution of the Ball-Berry equation (Appendix B in Battaglia et al. 2004). This then is considered the potential upper limit of Anet and is set for a subsequent recalculation solution to the coupled Ball-Berry equations. This allows for an increase in water use efficiency but not an increase in photosynthetic rate driven by elevated CO2.

2. Acclimation of photosynthesis: In the second case we assume that the maximum potential photosynthetic rate in CABALA (comparable to Vcmax) is partially increased by elevated CO2 according to:

A*=Ab (1-FCO2(ca-caref)/caref),

where Ab is the base level of maximum photosynthetic rate at saturating CO2

without acclimation, ca is the atmospheric carbon dioxide concentration ( molmol-1), FCO2 is the rate of increase of A* with CO2 and caref is the base line CO2

partial pressure from which increases are indexed. For this study FCO2 was set to 0.2 and caref to 350 ppm to give an increase of 20% for a doubling of CO2

observed consistent with FACE experiments.

3. No down-regulation of photosynthesis: In the third case we assume that no down-regulation occurs and that short-term and long-term effects of CO2 are

36

the same and all equations are as per Battaglia et al. (2004) and potential photosynthesis follows the carboxylation curve.

The effect of these differing assumptions on photosynthetic rate and stand volume for a fertile, deep soil western Australian bluegum plantation is given in Figure 14 in which soil water does not limit production. In Figure 15, on a water limited site in western Australia, the benefits of increased water-use efficiency resulting from elevated CO2 in the absence of any photosynthetic increase as a direct result of increased atmospheric CO2 can be seen.

Figure 14. Comparison of the different photosynthetic acclimation assumptions on photosynthetic rate and stand volume for a deep-soil and fertile non-water limited site in Western Australian.

Figure 15. Comparison of the different photosynthetic acclimation assumptions on photosynthetic rate and stand volume for a fertile and water-limited site in Western Australian environment.

0

100

200

300

400

500

600

700

800

300 500 700 900 1100

Amospheric CO2 concentration (ppm)

Sta

nd v

olum

e ag

e 10

yea

rs (m

3 /ha)

No increase in photosynthesis

Acclimation of photosynthesis

No dow n-regulation of photosynthesis

0

5

10

15

20

25

300 400 500 600 700 800 900 1000 1100

Amospheric CO2 concentration (ppm)

Net

pho

tosy

nthe

tic ra

te (

mol

/m2 /s

)


Acclimation of photosynthesis

No dow n-regulation of photosynthesis

0

100

200

300

400

500

600

300 500 700 900

Atmospheric CO2 concentration (ppm)

Sta

nd v

olum

e ag

e 10

yea

rs (m

3 /ha)


Acclimation photosynthesis

No down-regulation of photosynthesis

37

A comparison of the effect of these three models on a range of plant processes and state variables is given in Table 5 and compared with observations from various meta-studies. Three contrasting sites varying in temperature, droughtiness and nutrition were used. The first observation is that across the range of variables for which data are collated CABALA does a reasonable job at predicting the changed responses in net primary production, dry matter change, assimilation, stomatal conductance, changed leaf specific area and changed stand leaf area index for the sites illustrated. For these sites allowing for photosynthetic acclimation clearly had an effect over the base model assumption of no effect, and somewhat surprisingly, for many variables the difference between the acclimation model and the non-down-regulated model were slight. It is apparent that the particulars of each site will be influential in the outcome, with NPP change varying from an increase of 15 to 29% depending on the site.

38

Table 5. Comparison of predicted and observed changes from FACE experiments (Ainsworth and Long 2004) in key physiological parameters to 200 ppm CO2 enrichment.

Acclimation of photosynthesis model (model 2)

Response to CO2 enrichment (200pm)%

Site T (oC)

Nitrogen Mineralisation

(kg/ha/yr)

Annual rain / Annual

PET gs Ax LAI NPP ci/ca NF

treeheight

dry matter

(age 10)

dry matter

(age 50)volume (age 10)

volume (age 50)

Sth Tas 9.7 84 1.4 -43 10 18 14 1 -10 -10 3 7 6 6 7WA 16.6 64 0.8 -28 23 15 26 -4 -11 -11 9 17 22 22 17NE Tas 12.7 187 1.0 -22 24 13 23 -1 -10 -10 12 18 31 34 18

Average Predicted -31 19 15 21 -1 -10 -10 8 14 20 21 14Average Observed -16 29 21 24 -2 -13 -9 6 28 - -

No down-regulation photosynthesis model (model 3)

Response to CO2 enrichment (200pm)%

Site T (oC)

Nitrogen Mineralisation

(kg/ha/yr)

Annual rain / Annual

PET gs Ax LAI NPP ci/ca NF

treeheight

dry matter

(age 10)

dry matter

(age 50)volume (age 10)

volume (age 50)

Sth Tas 9.7 85 1.4 -36 15 13 15 1 -7 -7 6 9 10 11 9WA 16.6 64 0.8 -20 28 8 29 -4 -6 -6 13 20 23 26 20NE Tas 12.7 187 1.0 -18 28 13 26 -3 -11 -11 15 22 34 37 21

Average Predicted -25 24 12 23 -2 -8 -8 11 17 23 25 17Average Observed -16 29 21 24 -2 -13 -9 6 28 - -

39

4.3.Defining scenarios

The location of bellwether sites is provided in Figures 16-21

Figure 16. National maps showing locations of bellwether plots.

Figure 17. Location of bellwether plots in the Green Triangle

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Figure 18. Location of bellwether plots in Victoria and southern NSW

Figure 19. Location of bellwether plots in Queensland

41

Figure 20. Location of bellwether plots in south west Western Australia

Figure 21. Location of bellwether plots in Tasmania

42

For all 2030 and 2070 simulations, CO2 levels were increased from a base level for that time frame and SRES scenario at a rate of 4ppm per year after planting for all scenarios from a base level for that period and climate scenario. For the 1980 period simulations CO2 levels were kept at 350ppm. The CO2 levels used for the two models vary according the emission scenario and are listed below:

Hadleys A1FI (HA) 2030 – 450 ppm 2070 – 600 ppm

CSIRO Mk3 A2b (CA) 2030 – 430 ppm 2070 – 550 ppm

Hadleys B1 (HB) 2030 – 410 ppm 2070 – 480 ppm

The silvicultural regime used for the E. globulus and E. nitens was a 10 year rotation planted at 1000 s h-1 (stems per hectare). For P. radiata, the regime used was more complex: planting is at 1000 s h-1, with a rotation length of 25 years, with two commercial thinning events. The first thinning is at age 13 to 750 s h-1 and the second at age 18 to 400 s h-1. For the hybrid pine and PCH, a 28 year rotation was used, with planting stockings at 833 s h-1, a waste thinning at age three to simulate average mortality (to 700 s h-1) and a commercial thinning at age 18. All volumes reported include removed trees.

The three photosynthetic models described in section 4.2 were used in each simulation.

For each combination of species by photosynthetic model by climate scenario by time period, 20 separate rotations were simulated by running the model with 20 different planting dates over a 30 year block of weather data. Where rotation lengths were longer than 10 years, the 30 year block of weather data was looped. In total over 50,000 simulations were carried out reflecting site by species by time period by climate model by physiological model combinations.

In summary the combinations are:

3 climate models x 3 time periods x 3 plant photosynthetic responses x 134 bellwether sites (spp x site combinations) x 20 planting dates.

For our analysis we define two change measures: the percentage rotation length wood volume change from the ‘current’ baseline; the change in the average number of days per year during the rotation which are hot (>35

oC) and coincident with days on which

forests are simulated to be water stressed (<-3.2 MPa of pre-dawn leaf water potential).The actual level of water stress is somewhat notional and may not reflect the level of water stress actually experienced by stands but reflects the lower bounds of plant available soil water in current parameterisations. The volume change we will subsequently refer to as ‘change in plantation yield’ and the change in coincident hot, dry days we will refer to as ‘hot, dry days’.

It should be noted that the base-line years for the Hadley GCM and the CSIRO Mk3 models are different. The CSIRO model uses 1975 – 2005 climate as the base data and the Hadley model used 1961 – 1991 climate. As a consequence more of the directional change (arguably step-change) in climate in the 1990s and early 2000s observed in south-western Western Australia, southern Australia and south-eastern Queensland is captured in the CSIRO baseline or current weather compared to the Hadley representation. The effect of this can be seen, for example, in the average prediction for

43

the hybrid pine estate in SE Queensland being lower under ‘current’ conditions when predictions are made with the CSIRO model than with the Hadley model.

4.4.Results

Averages of percentage volume change and increase in hot, dry days for the 20 weather sequences are provided in the following tables. An example of the inter-rotation variation is given in Figure 10. A summary of the results is provided in Table 5 with full data in subsequent tables. Results for both the Hadley and CSIRO models are included for the hybrid pines in Queensland because of the marked differences in predicted future climates for the region by the two GCMs.

Figure 22. Example of inter-rotation variation captured using 20 weather sequences. End of rotation variation is often between 10 and 20% of the mean value (shown with the thicker line).

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Table 6. Summary of regional outcomes from climate change simulations for bellwether sites for the CSIRO Mk3 a2 model.

Specie Plantation estate Assumption203 207

P. radiata Green TrianglePn increase (C-Mk3 a2) - 18No pn increase (C-Mk3 a2) - 7

New South WalesPn increase (C-Mk3 a2) 16 27No pn increase (C-Mk3 a2) 8 13

Central, NE Victoria and GippslandPn increase (C-Mk3 a2) 12 19No pn increase (C-Mk3 a2) 3 5

QueenslandPn increase (C-Mk3 a2) 26 26No pn increase (C-Mk3 a2) 19 18

TasmaniPn increase (C-Mk3 a2) 19 29No pn increase (C-Mk3 a2) 10 14

E. globulus TasmaniPn increase (C-Mk3 a2) 45 81No pn increase (C-Mk3 a2) 22 33

Central, NE Victoria and GippslandPn increase (C-Mk3 a2) 35 81No pn increase (C-Mk3 a2) 11 30

Western AustralianPn increase (C-Mk3 a2) 17 35No pn increase (C-Mk3 a2) 1 4

Green TrianglePn increase (C-Mk3 a2) 20 51No pn increase (C-Mk3 a2) 2 17

E. nitens VictoriPn increase (C-Mk3 a2) 12 23No pn increase (C-Mk3 a2) 4 9

TasmaniPn increase (C-Mk3 a2) 15 23No pn increase (C-Mk3 a2) 8 12

Hybrid Pine QueenslandPn increase (C-Mk3 a2) 30 48No pn increase (C-Mk3 a2) 22 34Pn increase (HAD a1fi) 7 -No pn increase (HAD a1fi) - -

% volume change from 1980

45

Table 7. Changes in Eucalyptus globulus plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the CSIRO Mk 3 a2 GCM

Model 1 Model 2 Model 3No increase in photosynthesis Acclimation of photosynthesis No down regulation of photosynthesis

Site Region 1980 2030 2070 Site Region 1980 2030 2070 Site Region 1980 2030 2070GT_8 GT 202 210 234 GT_8 GT 203 232 264 GT_8 GT 202 235 271GT_9 GT 161 166 194 GT_9 GT 160 189 240 GT_9 GT 161 192 247GT_10 GT 131 143 164 GT_10 GT 131 172 224 GT_10 GT 131 177 237GT_11 GT 328 320 362 GT_11 GT 326 356 429 GT_11 GT 328 364 448GT_12 GT 118 121 148 GT_12 GT 118 151 204 GT_12 GT 118 154 208GT_13 GT 175 179 214 GT_13 GT 174 208 277 GT_13 GT 175 213 293GT_14 GT 159 169 208 GT_14 GT 159 204 261 GT_14 GT 159 205 269GT_15 GT 302 301 343 GT_15 GT 301 341 418 GT_15 GT 302 351 440GT_16 GT 178 186 214 GT_16 GT 178 208 250 GT_16 GT 178 215 260GT_17 GT 228 224 253 GT_17 GT 227 252 308 GT_17 GT 228 256 317GT_18 GT 188 188 220 GT_18 GT 188 210 264 GT_18 GT 188 215 272GT_19 GT 213 220 229 GT_19 GT 212 237 260 GT_19 GT 213 246 269GT_20 GT 59 76 71 GT_20 GT 60 100 104 GT_20 GT 59 101 112GT_21 GT 89 92 109 GT_21 GT 89 120 161 GT_21 GT 89 123 167GT_22 GT 177 174 197 GT_22 GT 176 206 263 GT_22 GT 177 211 277Average 181 185 211 Average 180 212 262 Average 181 217 273Region Change 2% 17% Region Change 18% 45% Region Change 20% 51%

TAS_10 TAS 235 276 296 TAS_10 TAS 234 316 375 TAS_10 TAS 235 326 397TAS_11 TAS 70 83 96 TAS_11 TAS 70 109 145 TAS_11 TAS 70 113 161TAS_12 TAS 135 176 177 TAS_12 TAS 135 201 222 TAS_12 TAS 135 203 232TAS_13 TAS 72 87 89 TAS_13 TAS 72 108 134 TAS_13 TAS 72 113 140TAS_14 TAS 72 84 98 TAS_14 TAS 71 99 133 TAS_14 TAS 72 102 139TAS_15 TAS 170 205 230 TAS_15 TAS 169 241 299 TAS_15 TAS 170 249 320TAS_16 TAS 134 165 173 TAS_16 TAS 134 193 231 TAS_16 TAS 134 198 242TAS_17 TAS 259 298 325 TAS_17 TAS 258 337 398 TAS_17 TAS 259 349 430Average 143 172 185 Average 143 200 242 Average 143 207 258Region Change 20% 29% Region Change 40% 69% Region Change 44% 80%

VIC_6 VIC 194 191 250 VIC_6 VIC 193 224 325 VIC_6 VIC 194 230 342VIC_7 VIC 237 249 285 VIC_7 VIC 236 285 355 VIC_7 VIC 237 294 382VIC_8 VIC 131 135 182 VIC_8 VIC 130 171 256 VIC_8 VIC 131 175 271VIC_9 VIC 191 185 223 VIC_9 VIC 190 220 302 VIC_9 VIC 191 225 319VIC_10 VIC 201 231 241 VIC_10 VIC 199 251 281 VIC_10 VIC 201 258 291VIC_11 VIC 118 144 159 VIC_11 VIC 117 173 214 VIC_11 VIC 118 177 220VIC_12 VIC 107 137 153 VIC_12 VIC 107 162 208 VIC_12 VIC 107 167 219VIC_13 VIC 150 168 195 VIC_13 VIC 148 205 271 VIC_13 VIC 150 210 286VIC_14 VIC 130 160 195 VIC_14 VIC 130 189 261 VIC_14 VIC 130 193 275VIC_15 VIC 179 183 215 VIC_15 VIC 178 210 262 VIC_15 VIC 179 214 273VIC_16 VIC 103 125 146 VIC_16 VIC 102 152 199 VIC_16 VIC 103 155 210VIC_17 VIC 154 179 198 VIC_17 VIC 154 213 260 VIC_17 VIC 154 220 277VIC_18 VIC 114 138 164 VIC_18 VIC 113 169 229 VIC_18 VIC 114 174 244VIC_19 VIC 149 146 179 VIC_19 VIC 148 177 249 VIC_19 VIC 149 182 261VIC_20 VIC 63 81 100 VIC_20 VIC 62 114 168 VIC_20 VIC 63 118 176VIC_21 VIC 172 176 217 VIC_21 VIC 172 212 294 VIC_21 VIC 172 216 310VIC_22 VIC 173 180 206 VIC_22 VIC 173 209 270 VIC_22 VIC 173 214 279VIC_23 VIC 155 183 219 VIC_23 VIC 154 216 288 VIC_23 VIC 155 221 304VIC_24 VIC 129 153 182 VIC_24 VIC 128 187 248 VIC_24 VIC 129 194 265VIC_25 VIC 263 299 324 VIC_25 VIC 261 341 406 VIC_25 VIC 263 355 441VIC_26 VIC 256 293 317 VIC_26 VIC 254 334 399 VIC_26 VIC 256 348 433VIC_27 VIC 120 105 166 VIC_27 VIC 120 134 237 VIC_27 VIC 120 136 248VIC_28 VIC 105 130 149 VIC_28 VIC 102 160 210 VIC_28 VIC 105 163 217VIC_29 VIC 142 168 197 VIC_29 VIC 142 203 274 VIC_29 VIC 142 207 287VIC_30 VIC 186 221 252 VIC_30 VIC 185 264 345 VIC_30 VIC 186 270 360VIC_31 VIC 144 164 176 VIC_31 VIC 144 186 206 VIC_31 VIC 144 191 199VIC_32 VIC 135 156 166 VIC_32 VIC 135 177 200 VIC_32 VIC 135 180 209Average 156 173 202 Average 155 205 267 Average 156 211 281Region Change 11% 30% Region Change 33% 73% Region Change 35% 81%

WA_1 WA 262 244 254 WA_1 WA 261 270 299 WA_1 WA 262 274 307WA_2 WA 102 109 112 WA_2 WA 101 134 166 WA_2 WA 102 135 170WA_3 WA 190 211 216 WA_3 WA 190 239 264 WA_3 WA 190 241 267WA_4 WA 99 117 83 WA_4 WA 101 152 139 WA_4 WA 99 154 145WA_6 WA 201 199 217 WA_6 WA 200 239 297 WA_6 WA 201 247 315WA_7 WA 406 400 416 WA_7 WA 405 442 489 WA_7 WA 406 451 509WA_8 WA 307 297 326 WA_8 WA 304 331 386 WA_8 WA 307 339 402Average 224 225 232 Average 223 258 292 Average 224 263 302Region Change 1 4 Region Change 16 31 Region Change 17 35

Average 176 189 208 Average 175 219 266 Average 176 224 278Change 7% 18% Change 25% 52% Change 28% 58%

46

Table 8. Changes in Eucalyptus globulus plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 a1fi GCM.


Site Region 1980 2030 2070 Site Region 1980 2030 2070 Site Region 1980 2030 2070GT_8 GT 202 217 219 GT_8 GT 203 237 259 GT_8 GT 202 246 267GT_9 GT 165 183 188 GT_9 GT 165 211 246 GT_9 GT 165 214 256GT_10 GT 134 154 166 GT_10 GT 134 191 241 GT_10 GT 134 197 256GT_11 GT 308 329 329 GT_11 GT 308 373 409 GT_11 GT 308 386 431GT_12 GT 116 135 138 GT_12 GT 115 166 206 GT_12 GT 116 168 214GT_13 GT 178 195 193 GT_13 GT 179 232 272 GT_13 GT 178 238 288GT_14 GT 161 190 208 GT_14 GT 159 219 285 GT_14 GT 161 223 294GT_15 GT 300 322 324 GT_15 GT 298 370 414 GT_15 GT 300 384 438GT_16 GT 183 200 205 GT_16 GT 183 223 253 GT_16 GT 183 229 263GT_17 GT 240 254 241 GT_17 GT 239 289 308 GT_17 GT 240 294 318GT_18 GT 172 193 196 GT_18 GT 170 217 251 GT_18 GT 172 218 259GT_19 GT 200 214 227 GT_19 GT 198 236 265 GT_19 GT 200 239 278GT_20 GT 41 55 73 GT_20 GT 38 73 118 GT_20 GT 41 74 128GT_21 GT 90 103 110 GT_21 GT 89 134 176 GT_21 GT 90 138 183GT_22 GT 169 183 179 GT_22 GT 169 221 257 GT_22 GT 169 227 269Region Average 177 195 200 Region Average 176 226 264 Region Average 177 232 276Region Change 10% 13% Region Change 28% 50% Region Change 31% 56%

TAS_10 TAS 233 274 311 TAS_10 TAS 233 319 405 TAS_10 TAS 233 330 435TAS_11 TAS 52 71 94 TAS_11 TAS 53 102 153 TAS_11 TAS 52 97 166TAS_13 TAS 67 81 100 TAS_13 TAS 67 105 156 TAS_13 TAS 67 109 162TAS_14 TAS 57 75 107 TAS_14 TAS 56 91 149 TAS_14 TAS 57 94 159TAS_15 TAS 158 193 245 TAS_15 TAS 158 233 327 TAS_15 TAS 158 243 354TAS_16 TAS 127 154 180 TAS_16 TAS 126 188 253 TAS_16 TAS 127 193 267TAS_17 TAS 252 297 339 TAS_17 TAS 250 340 426 TAS_17 TAS 252 354 467Region Average 135 164 196 Region Average 135 197 267 Region Average 135 203 287Region Change 21% 45% Region Change 46% 98% Region Change 50% 113%

VIC_6 VIC 184 192 176 VIC_6 VIC 183 229 254 VIC_6 VIC 184 238 272VIC_7 VIC 223 246 261 VIC_7 VIC 221 285 334 VIC_7 VIC 223 298 362VIC_8 VIC 124 133 124 VIC_8 VIC 124 173 206 VIC_8 VIC 124 178 218VIC_9 VIC 177 185 176 VIC_9 VIC 176 226 260 VIC_9 VIC 177 233 274VIC_10 VIC 190 217 230 VIC_10 VIC 190 245 289 VIC_10 VIC 190 250 307VIC_11 VIC 127 144 142 VIC_11 VIC 127 180 215 VIC_11 VIC 127 183 224VIC_12 VIC 104 120 129 VIC_12 VIC 103 151 200 VIC_12 VIC 104 155 210VIC_13 VIC 140 151 152 VIC_13 VIC 140 192 238 VIC_13 VIC 140 199 251VIC_14 VIC 131 143 171 VIC_14 VIC 132 175 247 VIC_14 VIC 131 177 259VIC_15 VIC 155 173 183 VIC_15 VIC 156 204 248 VIC_15 VIC 155 210 258VIC_16 VIC 104 121 139 VIC_16 VIC 104 152 204 VIC_16 VIC 104 158 217VIC_17 VIC 140 152 153 VIC_17 VIC 139 189 225 VIC_17 VIC 140 195 238VIC_19 VIC 141 149 139 VIC_19 VIC 141 187 214 VIC_19 VIC 141 193 226VIC_20 VIC 62 68 70 VIC_20 VIC 61 104 152 VIC_20 VIC 62 108 161VIC_21 VIC 180 182 157 VIC_21 VIC 179 223 239 VIC_21 VIC 180 229 250VIC_22 VIC 177 193 176 VIC_22 VIC 177 226 249 VIC_22 VIC 177 232 259VIC_23 VIC 143 158 159 VIC_23 VIC 142 198 240 VIC_23 VIC 143 204 253VIC_24 VIC 130 150 165 VIC_24 VIC 129 188 249 VIC_24 VIC 130 195 266VIC_25 VIC 246 294 315 VIC_25 VIC 245 341 411 VIC_25 VIC 246 358 449VIC_26 VIC 240 287 308 VIC_26 VIC 238 334 404 VIC_26 VIC 240 349 442VIC_27 VIC 121 117 91 VIC_27 VIC 120 153 170 VIC_27 VIC 121 157 175VIC_28 VIC 102 115 109 VIC_28 VIC 101 144 178 VIC_28 VIC 102 148 186VIC_33 VIC 106 113 104 VIC_33 VIC 106 152 182 VIC_33 VIC 106 157 192VIC_31 VIC 128 148 156 VIC_31 VIC 127 174 207 VIC_31 VIC 128 179 211VIC_32 VIC 135 155 154 VIC_32 VIC 134 179 206 VIC_32 VIC 135 185 215Region Average 148 164 166 Region Average 148 200 241 Region Average 148 207 255Region Change 11% 12% Region Change 36% 63% Region Change 39% 72%

WA_1 WA 243 241 242 WA_1 WA 243 268 289 WA_1 WA 243 274 298WA_2 WA 96 107 111 WA_2 WA 95 137 183 WA_2 WA 96 138 190WA_3 WA 187 196 190 WA_3 WA 187 222 248 WA_3 WA 187 229 251WA_4 WA 94 109 117 WA_4 WA 94 140 176 WA_4 WA 94 143 184WA_5 WA 281 304 313 WA_5 WA 278 346 392 WA_5 WA 281 357 413WA_6 WA 189 203 210 WA_6 WA 187 247 302 WA_6 WA 189 256 324WA_7 WA 404 406 395 WA_7 WA 402 474 514 WA_7 WA 404 490 550WA_8 WA 308 339 347 WA_8 WA 308 379 420 WA_8 WA 308 387 438Region Average 225 238 241 224 277 315 225 284 331Region Change 6% 7% Region Change 23% 41% Region Change 26% 47%


47

Table 9. Changes in Eucalyptus globulus plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 b1 GCM.


Site Region 1980 2030 2070 Site Region 1980 2030 2070 Site Region 1980 2030 2070GT_8 GT 202 219 222 GT_8 GT 203 240 259 GT_8 GT 202 247 267GT_9 GT 165 185 193 GT_9 GT 165 210 248 GT_9 GT 165 214 261GT_10 GT 134 153 165 GT_10 GT 134 190 242 GT_10 GT 134 197 258GT_11 GT 308 332 332 GT_11 GT 308 375 414 GT_11 GT 308 388 435GT_12 GT 116 134 146 GT_12 GT 115 167 210 GT_12 GT 116 171 218GT_13 GT 178 196 197 GT_13 GT 179 233 275 GT_13 GT 178 241 291GT_14 GT 161 191 215 GT_14 GT 159 221 286 GT_14 GT 161 223 293GT_15 GT 300 323 325 GT_15 GT 298 371 416 GT_15 GT 300 384 441GT_16 GT 183 201 209 GT_16 GT 183 225 255 GT_16 GT 183 229 264GT_17 GT 240 256 247 GT_17 GT 239 291 314 GT_17 GT 240 296 324GT_18 GT 172 194 207 GT_18 GT 170 218 257 GT_18 GT 172 222 270GT_19 GT 200 214 224 GT_19 GT 198 237 263 GT_19 GT 200 240 275GT_20 GT 41 52 70 GT_20 GT 38 71 117 GT_20 GT 41 68 120GT_21 GT 90 104 114 GT_21 GT 89 134 179 GT_21 GT 90 137 189Region Average 178 197 205 Region Average 177 227 267 Region Average 178 233 279Region Change 11% 15% Region Change 28% 51% Region Change 31% 57%

TAS_10 TAS 233 266 290 TAS_10 TAS 233 296 351 TAS_10 TAS 233 296 351TAS_11 TAS 52 72 94 TAS_11 TAS 53 93 140 TAS_11 TAS 53 93 140TAS_13 TAS 67 79 90 TAS_13 TAS 67 94 123 TAS_13 TAS 67 94 123TAS_14 TAS 57 73 98 TAS_14 TAS 56 85 128 TAS_14 TAS 56 85 128TAS_15 TAS 158 189 231 TAS_15 TAS 158 216 281 TAS_15 TAS 158 216 281TAS_16 TAS 127 148 167 TAS_16 TAS 126 172 212 TAS_16 TAS 126 172 212TAS_17 TAS 252 291 325 TAS_17 TAS 250 322 384 TAS_17 TAS 250 322 384Region Average 135 160 185 Region Average 135 183 231 Region Average 135 183 231Region Change 18% 37% Region Change 36% 72% Region Change 36% 72%

VIC_6 VIC 184 184 163 VIC_6 VIC 183 211 211 VIC_6 VIC 183 211 211VIC_7 VIC 223 239 245 VIC_7 VIC 221 267 294 VIC_7 VIC 221 267 294VIC_8 VIC 124 126 116 VIC_8 VIC 124 153 164 VIC_8 VIC 124 153 164VIC_9 VIC 177 181 167 VIC_9 VIC 176 209 217 VIC_9 VIC 176 209 217VIC_10 VIC 190 210 212 VIC_10 VIC 190 229 253 VIC_10 VIC 190 229 253VIC_11 VIC 127 139 131 VIC_11 VIC 127 163 179 VIC_11 VIC 127 163 179VIC_12 VIC 104 115 118 VIC_12 VIC 103 135 158 VIC_12 VIC 104 135 158VIC_13 VIC 140 143 137 VIC_13 VIC 140 171 187 VIC_13 VIC 140 171 187VIC_14 VIC 131 151 151 VIC_14 VIC 132 176 194 VIC_14 VIC 131 179 200VIC_15 VIC 155 168 169 VIC_15 VIC 156 192 212 VIC_15 VIC 156 192 212VIC_16 VIC 104 116 124 VIC_16 VIC 104 138 164 VIC_16 VIC 104 138 164VIC_17 VIC 140 146 140 VIC_17 VIC 139 170 184 VIC_17 VIC 139 170 184VIC_18 VIC 115 126 128 VIC_18 VIC 114 151 172 VIC_18 VIC 114 151 172VIC_19 VIC 141 146 136 VIC_19 VIC 141 171 180 VIC_19 VIC 141 171 180VIC_20 VIC 62 65 63 VIC_20 VIC 61 88 108 VIC_20 VIC 61 88 108VIC_21 VIC 180 176 151 VIC_21 VIC 179 203 199 VIC_21 VIC 179 203 199VIC_22 VIC 177 188 169 VIC_22 VIC 177 209 211 VIC_22 VIC 177 209 211VIC_23 VIC 143 150 142 VIC_23 VIC 142 178 192 VIC_23 VIC 142 178 192VIC_24 VIC 130 144 152 VIC_24 VIC 129 171 204 VIC_24 VIC 129 171 204VIC_25 VIC 246 285 290 VIC_25 VIC 245 318 350 VIC_25 VIC 246 318 350VIC_26 VIC 240 278 284 VIC_26 VIC 238 310 344 VIC_26 VIC 240 310 344VIC_27 VIC 121 116 94 VIC_27 VIC 120 140 140 VIC_27 VIC 120 140 140VIC_28 VIC 102 109 100 VIC_28 VIC 101 130 141 VIC_28 VIC 102 130 141VIC_33 VIC 106 108 97 VIC_33 VIC 106 134 143 VIC_33 VIC 106 134 143VIC_31 VIC 128 144 141 VIC_31 VIC 127 163 181 VIC_31 VIC 128 163 181VIC_32 VIC 135 147 142 VIC_32 VIC 134 167 180 VIC_32 VIC 135 167 180Region Average 147 158 152 Region Average 146 183 199 Region Average 147 183 199Region Change 7% 4% Region Change 25% 36% Region Change 25% 36%

WA_1 WA 243 241 238 WA_1 WA 243 260 270 WA_1 WA 243 260 270WA_2 WA 96 105 107 WA_2 WA 95 122 145 WA_2 WA 96 122 145WA_3 WA 187 193 186 WA_3 WA 187 211 219 WA_3 WA 187 211 219WA_4 WA 94 104 112 WA_4 WA 94 127 152 WA_4 WA 94 127 152WA_5 WA 281 296 300 WA_5 WA 278 326 351 WA_5 WA 281 326 351WA_6 WA 189 197 202 WA_6 WA 187 228 258 WA_6 WA 189 228 258WA_7 WA 404 403 395 WA_7 WA 402 453 475 WA_7 WA 404 453 475WA_8 WA 308 329 328 WA_8 WA 308 355 375 WA_8 WA 308 355 375Region Average 225 234 234 Region Average 224 260 281 Region Average 225 260 281Region Change 4% 4% Region Change 16% 25% Region Change 16% 25%


48

Table 10. Changes in Eucalyptus nitens plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the CSIRO Mk 3 a2 GCM.


Site Region 1980 2030 2070 Site Region 1980 2030 2070 Site Region 1980 2030 2070TAS_19 TAS 306 349 336 TAS_19 TAS 306 370 368 TAS_19 TAS 306 378 384TAS_20 TAS 353 363 394 TAS_20 TAS 356 380 415 TAS_20 TAS 353 389 428TAS_21 TAS 320 345 375 TAS_21 TAS 321 356 395 TAS_21 TAS 320 363 406TAS_9 TAS 373 366 368 TAS_9 TAS 340 389 394 TAS_9 TAS 373 397 410TAS_22 TAS 316 321 329 TAS_22 TAS 314 325 337 TAS_22 TAS 316 327 353TAS_27 TAS 361 361 369 TAS_27 TAS 363 377 394 TAS_27 TAS 361 378 405TAS_23 TAS 192 227 220 TAS_23 TAS 192 238 247 TAS_23 TAS 192 241 254TAS_24 TAS 392 460 465 TAS_24 TAS 393 483 497 TAS_24 TAS 392 490 515TAS_25 TAS 27 39 74 TAS_25 TAS 33 42 73 TAS_25 TAS 27 45 72TAS_26 TAS 264 311 313 TAS_26 TAS 266 330 336 TAS_26 TAS 264 336 346Average 290 314 324 Average 288 329 346 Average 290 334 357Region Change 8% 12% Region Change 14% 20% Region Change 15% 23%

VIC_34 VIC 195 192 206 VIC_34 VIC 195 201 230 VIC_34 VIC 195 206 235VIC_10 VIC 262 253 277 VIC_10 VIC 263 266 301 VIC_10 VIC 262 271 309VIC_11 VIC 193 197 218 VIC_11 VIC 190 208 242 VIC_11 VIC 193 212 246VIC_14 VIC 292 281 302 VIC_14 VIC 292 297 336 VIC_14 VIC 292 303 351VIC_37 VIC 269 259 277 VIC_37 VIC 268 272 310 VIC_37 VIC 269 275 322VIC_22 VIC 155 160 173 VIC_22 VIC 153 170 187 VIC_22 VIC 155 171 194VIC_25 VIC 496 481 498 VIC_25 VIC 493 505 538 VIC_25 VIC 496 516 562VIC_26 VIC 493 476 491 VIC_26 VIC 491 495 527 VIC_26 VIC 493 505 553VIC_37 VIC 271 263 269 VIC_37 VIC 270 280 307 VIC_37 VIC 271 285 321VIC_38 VIC 236 228 244 VIC_38 VIC 236 244 273 VIC_38 VIC 236 250 287VIC_28 VIC 166 184 207 VIC_28 VIC 165 198 236 VIC_28 VIC 166 201 244VIC_29 VIC 228 244 271 VIC_29 VIC 227 270 330 VIC_29 VIC 228 274 343VIC_39 VIC 241 227 244 VIC_39 VIC 241 245 280 VIC_39 VIC 241 250 294VIC_30 VIC 251 277 308 VIC_30 VIC 250 305 363 VIC_30 VIC 251 310 376VIC_31 VIC 164 174 190 VIC_31 VIC 165 182 204 VIC_31 VIC 164 187 209VIC_32 VIC 173 172 194 VIC_32 VIC 170 182 211 VIC_32 VIC 173 185 212Average 255 254 273 Average 254 270 305 Average 255 275 316Region Change 0 7 Region Change 6 20 Region Change 8 24


49

Table 11. Changes in Eucalyptus nitens plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 a1fi GCM.


Site Region 1980 2030 2070 Site Region 1980 2030 2070 Site Region 1980 2030 2070TAS_19 TAS 318 338 338 TAS_19 TAS 319 359 374 TAS_19 TAS 318 370 394TAS_20 TAS 318 339 350 TAS_20 TAS 319 357 374 TAS_20 TAS 318 366 386TAS_21 TAS 324 335 331 TAS_21 TAS 323 347 354 TAS_21 TAS 324 356 367TAS_9 TAS 357 359 352 TAS_9 TAS 361 378 382 TAS_9 TAS 357 385 396TAS_22 TAS 300 314 323 TAS_22 TAS 302 323 334 TAS_22 TAS 300 329 345TAS_23 TAS 206 223 225 TAS_23 TAS 207 237 252 TAS_23 TAS 206 241 262TAS_24 TAS 399 438 452 TAS_24 TAS 396 462 492 TAS_24 TAS 399 474 518TAS_25 TAS 36 64 79 TAS_25 TAS 37 55 84 TAS_25 TAS 36 57 81TAS_26 TAS 243 276 320 TAS_26 TAS 242 293 352 TAS_26 TAS 243 300 365Average 448 472 484 Average 449 484 507 Average 448 491 519Region Change 5% 8% Region Change 8% 13% Region Change 10% 16%

VIC_34 VIC 191 201 177 VIC_34 VIC 191 215 209 VIC_34 VIC 191 219 216VIC_35 VIC 274 288 251 VIC_35 VIC 274 309 290 VIC_35 VIC 274 317 305VIC_36 VIC 232 242 222 VIC_36 VIC 233 262 254 VIC_36 VIC 232 267 265VIC_10 VIC 251 259 240 VIC_10 VIC 250 274 267 VIC_10 VIC 251 277 274VIC_11 VIC 197 204 187 VIC_11 VIC 197 217 216 VIC_11 VIC 197 221 222VIC_14 VIC 286 299 267 VIC_14 VIC 285 322 311 VIC_14 VIC 286 328 323VIC_37 VIC 268 279 245 VIC_37 VIC 268 302 289 VIC_37 VIC 268 308 300VIC_22 VIC 164 171 158 VIC_22 VIC 163 181 177 VIC_22 VIC 164 184 184VIC_25 VIC 485 496 452 VIC_25 VIC 482 523 493 VIC_25 VIC 485 537 516VIC_26 VIC 476 490 442 VIC_26 VIC 475 512 476 VIC_26 VIC 476 523 500VIC_36 VIC 170 178 165 VIC_36 VIC 165 194 193 VIC_36 VIC 170 194 202VIC_37 VIC 267 273 235 VIC_37 VIC 265 296 277 VIC_37 VIC 267 302 291VIC_38 VIC 220 240 201 VIC_38 VIC 220 259 241 VIC_38 VIC 220 266 255VIC_28 VIC 170 176 147 VIC_28 VIC 169 191 173 VIC_28 VIC 170 188 185VIC_29 VIC 218 212 181 VIC_29 VIC 218 238 222 VIC_29 VIC 218 241 232VIC_39 VIC 242 246 210 VIC_39 VIC 241 268 248 VIC_39 VIC 242 274 258VIC_30 VIC 227 230 205 VIC_30 VIC 227 259 259 VIC_30 VIC 227 264 271VIC_31 VIC 156 168 168 VIC_31 VIC 155 179 186 VIC_31 VIC 156 178 192VIC_32 VIC 168 179 170 VIC_32 VIC 166 192 193 VIC_32 VIC 168 193 196Average 245 254 228 Average 244 273 262 Average 245 278 273Region Change 4% -7% Region Change 12% 7% Region Change 13% 11%


50

Table 12. Changes in Eucalyptus nitens plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 b1 GCM.


Site Region 1980 2030 2070 Site Region 1980 2030 2070 Site Region 1980 2030 2070TAS_18 TAS 189 199 190 TAS_18 TAS 188 211 212 TAS_18 TAS 189 214 218TAS_19 TAS 318 331 315 TAS_19 TAS 319 350 349 TAS_19 TAS 318 355 357TAS_20 TAS 318 336 337 TAS_20 TAS 319 350 357 TAS_20 TAS 318 355 365TAS_21 TAS 324 328 314 TAS_21 TAS 323 341 331 TAS_21 TAS 324 345 338TAS_9 TAS 357 357 354 TAS_9 TAS 361 374 376 TAS_9 TAS 357 380 383TAS_22 TAS 300 317 324 TAS_22 TAS 302 320 336 TAS_22 TAS 300 324 335TAS_23 TAS 206 216 203 TAS_23 TAS 207 225 222 TAS_23 TAS 206 230 226TAS_24 TAS 399 428 434 TAS_24 TAS 396 447 461 TAS_24 TAS 399 452 473TAS_25 TAS 36 57 69 TAS_25 TAS 37 52 74 TAS_25 TAS 36 49 71TAS_26 TAS 243 276 311 TAS_26 TAS 242 287 335 TAS_26 TAS 243 291 340Average 269 285 285 Average 269 296 305 Average 269 299 311Region Change 6% 6% Region Change 10% 13% Region Change 11% 15%

VIC_34 VIC 191 193 160 VIC_34 VIC 191 201 180 VIC_34 VIC 191 204 183VIC_35 VIC 274 279 233 VIC_35 VIC 274 292 256 VIC_35 VIC 274 294 264VIC_36 VIC 232 236 206 VIC_36 VIC 233 249 224 VIC_36 VIC 232 253 229VIC_10 VIC 251 253 219 VIC_10 VIC 250 262 238 VIC_10 VIC 251 263 240VIC_11 VIC 197 195 173 VIC_11 VIC 197 207 191 VIC_11 VIC 197 207 190VIC_14 VIC 286 289 248 VIC_14 VIC 285 305 275 VIC_14 VIC 286 310 281VIC_37 VIC 268 271 232 VIC_37 VIC 268 287 257 VIC_37 VIC 268 291 263VIC_22 VIC 164 164 148 VIC_22 VIC 163 171 161 VIC_22 VIC 164 174 166VIC_25 VIC 485 484 423 VIC_25 VIC 482 504 448 VIC_25 VIC 485 512 460VIC_26 VIC 476 476 414 VIC_26 VIC 475 493 434 VIC_26 VIC 476 500 444VIC_36 VIC 170 172 152 VIC_36 VIC 165 182 167 VIC_36 VIC 170 182 172VIC_37 VIC 267 264 218 VIC_37 VIC 265 279 242 VIC_37 VIC 267 283 249VIC_38 VIC 220 231 186 VIC_38 VIC 220 245 211 VIC_38 VIC 220 250 219VIC_28 VIC 170 170 143 VIC_28 VIC 169 180 162 VIC_28 VIC 170 177 163VIC_29 VIC 218 220 181 VIC_29 VIC 218 238 211 VIC_29 VIC 218 236 216VIC_39 VIC 242 240 199 VIC_39 VIC 241 255 227 VIC_39 VIC 242 261 233VIC_30 VIC 227 222 191 VIC_30 VIC 227 244 227 VIC_30 VIC 227 246 233VIC_31 VIC 156 162 155 VIC_31 VIC 155 167 164 VIC_31 VIC 156 169 168VIC_32 VIC 168 171 151 VIC_32 VIC 166 180 167 VIC_32 VIC 168 183 170Average 245 247 212 Average 244 260 234 Average 245 263 239change 1% -14% Region Change 6% -4% Region Change 7% -3%

Average 257 266 249 Average 257 278 270 Average 257 281 275Change 3% -3% Change 8% 5% Change 9% 7%

51

Table 13. Changes in Pinus radiata plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the CSIRO Mk 3 a2 GCM.


Site Region 1980 2030 2070 Site Region 1980 2030 2070 Site Region 1980 2030 2070GT_1 GT 578 450 535 GT_1 GT 577 462 568 GT_1 GT 578 460 554GT_2 GT 657 660 731 GT_2 GT 655 679 758 GT_2 GT 657 703 800GT_3 GT 816 656 777 GT_3 GT 813 648 806 GT_3 GT 816 648 815GT_4 GT 674 676 768 GT_4 GT 672 695 794 GT_4 GT 674 723 858GT_5 GT 547 550 633 GT_5 GT 544 566 653 GT_5 GT 547 588 703GT_6 GT 684 688 773 GT_6 GT 682 709 801 GT_6 GT 684 740 873GT_7 GT 650 648 725 GT_7 GT 646 669 753 GT_7 GT 650 699 817Region Average 658 618 706 Region Average 656 633 733 Region Average 658 652 774Region Change -6% 7% Region Change -4% 12% Region Change -1% 18%

QLD_1 SE_QLD 305 418 403 QLD_1 SE_QLD 311 414 404 QLD_1 SE_QLD 305 439 425QLD_2 SE_QLD 304 397 396 QLD_2 SE_QLD 307 418 407 QLD_2 SE_QLD 304 420 428QLD_3 SE_QLD 350 480 453 QLD_3 SE_QLD 348 492 458 QLD_3 SE_QLD 350 513 467QLD_4 SE_QLD 853 857 880 QLD_4 SE_QLD 850 883 875 QLD_4 SE_QLD 853 907 967Region Average 453 538 533 Region Average 454 552 536 Region Average 453 570 572Region Change 19% 18% Region Change 21% 18% Region Change 26% 26%

VIC_1 VIC 889 926 906 VIC_1 VIC 882 946 927 VIC_1 VIC 889 1001 1022VIC_2 VIC 947 940 990 VIC_2 VIC 944 969 1023 VIC_2 VIC 947 1018 1121VIC_3 VIC 979 988 1021 VIC_3 VIC 970 1015 1051 VIC_3 VIC 979 1071 1156VIC_4 VIC 974 1020 1049 VIC_4 VIC 972 1056 1077 VIC_4 VIC 974 1120 1205VIC_5 VIC 970 1015 1036 VIC_5 VIC 960 1043 1063 VIC_5 VIC 970 1104 1182Region Average 952 978 1001 Region Average 946 1006 1028 Region Average 952 1063 1137Region Change 3% 5% Region Change 6% 9% Region Change 12% 19%

TAS_1 TAS 832 906 949 TAS_1 TAS 829 925 969 TAS_1 TAS 832 960 1053TAS_2 TAS 652 747 788 TAS_2 TAS 648 758 810 TAS_2 TAS 652 791 872TAS_3 TAS 894 984 1031 TAS_3 TAS 888 1012 1059 TAS_3 TAS 894 1064 1166TAS_4 TAS 736 868 905 TAS_4 TAS 733 887 917 TAS_4 TAS 736 942 1027TAS_5 TAS 942 1026 1055 TAS_5 TAS 936 1052 1075 TAS_5 TAS 942 1115 1193TAS_6 TAS 881 997 1030 TAS_6 TAS 875 1021 1047 TAS_6 TAS 881 1082 1165TAS_7 TAS 933 1029 1053 TAS_7 TAS 926 1059 1076 TAS_7 TAS 933 1119 1194TAS_8 TAS 1150 1191 1208 TAS_8 TAS 1144 1229 1246 TAS_8 TAS 1150 1294 1369Region Average 878 968 1002 Region Average 872 993 1025 Region Average 878 1046 1130Region Change 10% 14% Region Change 14% 17% Region Change 19% 29%

NSW_1 NSW 842 1002 1077 NSW_1 NSW 838 1026 1105 NSW_1 NSW 842 1090 1231NSW_2 NSW 847 912 956 NSW_2 NSW 839 941 986 NSW_2 NSW 847 996 1096NSW_3 NSW 827 970 1042 NSW_3 NSW 822 1002 1064 NSW_3 NSW 827 1063 1188NSW_4 NSW 800 912 944 NSW_4 NSW 789 940 971 NSW_4 NSW 800 993 1069NSW_5 NSW 861 1021 1102 NSW_5 NSW 853 1043 1119 NSW_5 NSW 861 1107 1252Region Average 835 964 1024 Region Average 828 990 1049 Region Average 835 1050 1167Region Change 15% 23% Region Change 20% 27% Region Change 26% 40%


52

Table 14. Changes in Pch and hybrid pine (HYB) plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 a1fi GCM. Note – the site QLD7 was not used in the Pch analysis; it was unsuitable due to water logging.

Model 1No increase in photosynthesis

site species 1980 2030 2070 site species 1980 2030 2070QLD1 HYB 118 144 110 QLD1 PCH 90 114 66QLD2 HYB 324 320 240 QLD2 PCH 223 241 213QLD3 HYB 566 542 386 QLD3 PCH 519 530 388QLD4 HYB 732 709 470 QLD4 PCH 667 666 455QLD5 HYB 286 310 261 QLD5 PCH 287 312 238QLD6 HYB 263 280 236 QLD6 PCH 249 266 232QLD7 HYB 397 423 369QLD8 HYB 469 436 292 QLD8 PCH 418 398 284QLD9 HYB 481 479 360 QLD9 PCH 396 405 342QLD10 HYB 500 518 393 QLD10 PCH 501 531 405QLD11 HYB 612 558 362 QLD11 PCH 362 358 272QLD12 HYB 739 663 360 QLD12 PCH 629 569 326QLD13 HYB 673 631 429 QLD13 PCH 676 641 443average 474 463 328 average 418 419 305% change from 1980 -2 -31 % change from 1980 0 -27

Model 2 Acclimation of photosynthesis

site species 1980 2030 2070 site species 1980 2030 2070QLD1 HYB 122 153 119 QLD1 PCH 84 121 78QLD2 HYB 326 327 247 QLD2 PCH 227 244 220QLD3 HYB 563 560 400 QLD3 PCH 518 540 401QLD4 HYB 722 736 482 QLD4 PCH 669 680 467QLD5 HYB 286 320 272 QLD5 PCH 291 322 246QLD6 HYB 262 289 248 QLD6 PCH 247 276 239QLD7 HYB 391 442 388QLD8 HYB 465 453 308 QLD8 PCH 416 414 300QLD9 HYB 479 499 376 QLD9 PCH 395 413 351QLD10 HYB 501 524 405 QLD10 PCH 501 536 422QLD11 HYB 601 581 383 QLD11 PCH 359 366 279QLD12 HYB 734 702 385 QLD12 PCH 624 602 349QLD13 HYB 669 666 454 QLD13 PCH 672 671 469average 471 481 343 average 417 432 318% change from 1980 2 -27 % change from 1980 4 -24

Model 3No down regulation of photosynthesis

site species 1980 2030 2070 site species 1980 2030 2070QLD1 HYB 118 158 153 QLD1 PCH 90 131 98QLD2 HYB 324 334 258 QLD2 PCH 223 250 235QLD3 HYB 566 582 428 QLD3 PCH 519 551 429QLD4 HYB 732 788 515 QLD4 PCH 667 702 489QLD5 HYB 286 333 293 QLD5 PCH 287 333 280QLD6 HYB 263 302 266 QLD6 PCH 249 286 256QLD7 HYB 397 470 441QLD8 HYB 469 479 336 QLD8 PCH 418 435 323QLD9 HYB 481 511 403 QLD9 PCH 396 434 372QLD10 HYB 500 541 431 QLD10 PCH 501 549 450QLD11 HYB 612 627 419 QLD11 PCH 362 384 302QLD12 HYB 739 759 440 QLD12 PCH 629 636 390QLD13 HYB 673 718 508 QLD13 PCH 676 718 522average 474 508 376 average 418 451 345% change from 1980 7 -21 % change from 1980 8 -17

53

Table 15. Changes in Pch and hybrid pine (HYB) plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the Hadley Mk 2 b1 GCM. note – the site QLD7 was not used in the PCH analysis; it was unsuitable due to water logging.


site species 1980 2030 2070 site species 1980 2030 2070QLD1 HYB 118 137 145 QLD1 PCH 90 98 116QLD2 HYB 324 343 338 QLD2 PCH 223 240 250QLD3 HYB 566 595 568 QLD3 PCH 519 567 554QLD4 HYB 732 750 730 QLD4 PCH 667 713 690QLD5 HYB 286 321 324 QLD5 PCH 287 320 321QLD6 HYB 263 289 289 QLD6 PCH 249 270 274QLD7 HYB 397 421 434QLD8 HYB 469 489 458 QLD8 PCH 418 433 414QLD9 HYB 481 510 498 QLD9 PCH 396 427 417QLD10 HYB 500 552 545 QLD10 PCH 501 557 551QLD11 HYB 612 618 578 QLD11 PCH 362 376 373QLD12 HYB 739 729 680 QLD12 PCH 629 638 592QLD13 HYB 673 686 655 QLD13 PCH 676 697 665average 474 495 480 average 418 445 435% change from 1980 5 1 % change from 1980 6 4





54

Table 16. Changes in PCH and hybrid pine (HYB) plantation yields (m2 ha-1 at end of rotation) under three photosynthetic model scenarios using the CSIRO Mk 3 a2 GCM. Note – the site QLD7 was not used in the PCH analysis; it was unsuitable due to water logging.


site spp 1980 2030 2070 site spp 1980 2030 2070QLD1 HYB 135 133 129 QLD1 PCH 92 88 90QLD2 HYB 291 387 448 QLD2 PCH 217 248 288QLD3 HYB 487 658 744 QLD3 PCH 463 609 710QLD4 HYB 644 772 834 QLD4 PCH 581 731 832QLD5 HYB 307 389 443 QLD5 PCH 319 374 422QLD6 HYB 267 330 365 QLD6 PCH 263 304 326QLD7 HYB 370 413 455QLD8 HYB 446 559 628 QLD8 PCH 415 506 558QLD9 HYB 435 540 594 QLD9 PCH 393 459 508QLD10 HYB 471 618 705 QLD10 PCH 479 626 708QLD11 HYB 539 631 659 QLD11 PCH 333 397 441QLD12 HYB 608 684 730 QLD12 PCH 537 623 666QLD13 HYB 608 717 768 QLD13 PCH 647 762 823average 431 526 577 average 395 477 531% change from 1980 22 34 % change from 1980 21 34





55

Table 17. Changes in the number of average annual hot, dry days (sufficient to threaten drought death) for the Pch and hybrid pine (HYB) plantation plantations using the Hadley Mk 2 a1fi GCM. Note – the site QLD7 was not used in the Pch analysis; it was unsuitable due to water logging.


site species 1980 2030 2070 site species 1980 2030 2070QLD1 HYB 0.0 0.0 0.1 QLD1 PCH 0.0 0.0 0.1QLD2 HYB 0.3 0.9 1.4 QLD2 PCH 0.4 0.9 1.3QLD3 HYB 0.2 0.7 5.5 QLD3 PCH 0.9 1.0 8.6QLD4 HYB 0.0 0.6 3.7 QLD4 PCH 0.0 0.9 4.7QLD5 HYB 0.2 1.4 3.1 QLD5 PCH 0.2 1.3 3.8QLD6 HYB 0.0 0.1 0.2 QLD6 PCH 0.3 0.5 0.4QLD7 HYB 0.0 0.0 0.0QLD8 HYB 2.8 7.2 54.3 QLD8 PCH 3.7 7.3 69.1QLD9 HYB 0.0 1.0 11.2 QLD9 PCH 0.0 1.3 12.1QLD10 HYB 0.0 0.0 0.1 QLD10 PCH 0.3 0.0 0.1QLD11 HYB 0.0 0.3 3.7 QLD11 PCH 0.0 0.3 3.8QLD12 HYB 0.0 4.1 90.6 QLD12 PCH 0.0 7.0 110.7QLD13 HYB 0.7 3.5 28.4 QLD13 PCH 0.7 5.3 36.0average 0.3 1.5 15.5 average 0.5 2.1 20.9





56

Table 18. Changes in the number of average annual hot, dry days (sufficient to threaten drought death) for the PCH and hybrid pine (HYB) plantation plantations using the Hadley Mk 2 b1 GCM. Note – the site QLD7 was not used in the PCH analysis; it was unsuitable due to water logging.







57

Table 18. (cont.)

Temperature > 35CAge

Climate 0 1 2 3 4 5 6 7 8 9 TotalWA_4_1980_H_A 300 289 288 291 291 284 277 290 282 273 2865WA_4_2030_H_A 407 393 395 397 400 395 386 404 402 392 3971WA_4_2070_H_A 584 561 562 562 573 568 563 581 580 570 5704WA_4_1980_H_B 300 289 288 291 291 284 277 290 282 273 2865WA_4_2030_H_B 374 360 362 364 367 361 355 370 369 360 3642WA_4_2070_H_B 513 492 492 493 502 497 486 504 504 496 4979WA_4_1980_C_A 269 273 276 277 270 263 274 273 279 273 2727WA_4_2030_C_A 380 377 374 374 375 383 379 366 373 365 3746WA_4_2070_C_A 593 573 577 579 598 598 592 612 611 598 5931

VIC_22_1980_H_A 91 95 105 103 99 93 90 89 92 97 954VIC_22_2030_H_A 130 133 146 144 141 137 136 137 145 154 1403VIC_22_2070_H_A 248 257 274 273 274 268 264 268 273 286 2685VIC_22_1980_H_B 91 95 105 103 99 93 90 89 92 97 954VIC_22_2030_H_B 136 139 152 152 149 145 144 145 153 162 1477VIC_22_2070_H_B 288 295 310 308 311 307 307 312 320 331 3089VIC_22_1980_C_A 103 100 108 115 114 122 126 120 113 115 1136VIC_22_2030_C_A 140 145 150 153 152 154 158 158 173 175 1558VIC_22_2070_C_A 185 173 170 171 165 172 182 184 191 193 1786

GT_16_1980_H_A 104 113 121 124 124 120 114 105 110 111 1146GT_16_2030_H_A 124 135 144 149 149 146 145 135 143 148 1418GT_16_2070_H_A 198 211 228 231 234 234 231 222 230 237 2256GT_16_1980_H_B 104 113 121 124 124 120 114 105 110 111 1146GT_16_2030_H_B 124 136 147 151 151 148 146 136 143 147 1429GT_16_2070_H_B 205 218 235 239 243 241 239 231 240 248 2339GT_16_1980_C_A 108 104 105 106 106 114 114 108 104 106 1075GT_16_2030_C_A 134 136 147 154 156 157 161 158 163 167 1533GT_16_2070_C_A 171 159 153 156 151 156 157 160 171 173 1607

TAS_16_1980_H_A 1 2 2 2 2 2 2 2 2 17TAS_16_2030_H_A 2 3 3 3 3 3 4 4 4 29TAS_16_2070_H_A 4 8 10 10 10 10 9 9 9 9 88TAS_16_1980_H_B 1 2 2 2 2 2 2 2 2 17TAS_16_2030_H_B 2 3 3 3 3 3 4 4 4 29TAS_16_2070_H_B 1 5 7 7 7 7 6 7 7 7 61TAS_16_1980_C_A 2 2 2 2 2 2 2 1 15TAS_16_2030_C_A 2 3 3 3 3 3 3 3 23TAS_16_2070_C_A 3 3 3 3 3 3 3 3 3 3 30

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Table 19. Changes in the number of average annual hot, dry days (sufficient to threaten drought death) for the PCH and hybrid pine (HYB) plantation plantations using the CSIRO Mk 3 a2 GCM. Note – the site QLD7 was not used in the Pch analysis; it was unsuitable due to water logging.




site spp 1980 2030 2070 site spp 1980 2030 2070QLD1 HYB 0.0 0.0 0.0 QLD1 PCH 0.0 0.0 0.0QLD2 HYB 0.1 0.0 0.0 QLD2 PCH 0.6 0.1 0.4QLD3 HYB 0.1 0.0 0.0 QLD3 PCH 0.8 0.1 0.3QLD4 HYB 0.0 0.0 0.0 QLD4 PCH 0.0 0.0 0.0QLD5 HYB 4.5 5.3 6.5 QLD5 PCH 4.9 6.4 7.8QLD6 HYB 0.7 0.0 0.0 QLD6 PCH 1.6 0.1 0.8QLD7 HYB 0.0 0.0 0.0QLD8 HYB 3.0 5.4 5.0 QLD8 PCH 4.4 7.5 6.8QLD9 HYB 0.0 0.6 0.4 QLD9 PCH 0.0 0.6 0.4QLD10 HYB 0.0 0.0 0.0 QLD10 PCH 0.0 0.0 0.5QLD11 HYB 0.3 0.5 0.5 QLD11 PCH 0.0 0.1 0.2QLD12 HYB 1.4 2.9 5.0 QLD12 PCH 3.1 5.0 8.5QLD13 HYB 0.6 2.0 1.1 QLD13 PCH 0.6 2.1 1.8average 0.8 1.3 1.4 average 1.3 1.8 2.3


site spp 1980 2030 2070 site spp 1980 2030 2070QLD1 HYB 0.0 0.0 0.0 QLD1 PCH 0.0 0.0 0.0QLD2 HYB 0.1 0.0 0.0 QLD2 PCH 0.5 0.2 0.4QLD3 HYB 0.1 0.0 0.0 QLD3 PCH 0.8 0.1 0.5QLD4 HYB 0.0 0.0 0.0 QLD4 PCH 0.0 0.0 0.0QLD5 HYB 4.5 5.1 6.9 QLD5 PCH 4.9 7.0 8.0QLD6 HYB 0.8 0.0 0.0 QLD6 PCH 1.6 0.1 1.3QLD7 HYB 0.0 0.0 0.0QLD8 HYB 3.1 5.4 5.1 QLD8 PCH 4.4 7.4 7.7QLD9 HYB 0.0 0.6 0.4 QLD9 PCH 0.0 0.7 0.4QLD10 HYB 0.0 0.0 0.0 QLD10 PCH 0.0 0.0 0.5QLD11 HYB 0.3 0.5 0.6 QLD11 PCH 0.0 0.1 0.2QLD12 HYB 1.4 3.0 5.2 QLD12 PCH 3.1 5.3 8.4QLD13 HYB 0.6 2.1 1.3 QLD13 PCH 0.6 2.1 1.8average 0.8 1.3 1.5 average 1.3 1.9 2.4

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Table 20. Drought-risk indicators (over 20 rotations) for E. globulus plantations under the three GCM by SRES scenario combinations for the partial acclimation model of photosynthetic acclimation (model 2). Plant water stress below -3.2MPa when combined with temperatures in excess of 35oC is an indicator of the possibility of drought death in trees, especially when young. H-A is Hadley A1FI scenario, H-B is Hadley B1 scenario and C-A is CSIRO A2 scenario.

Pre dawn water potential < -3.2 and Temperature > 35CAge

Climate 0 1 2 3 4 5 6 7 8 9 TotalWA_4_2070_H_A 12 1 13WA_4_2070_H_B 3 3 6WA_4_2070_C_A 8 11 1 20

VIC_22_1980_H_A 7 7VIC_22_2030_H_A 10 14 1 5 2 32VIC_22_2070_H_A 29 49 33 37 24 28 25 225VIC_22_1980_H_B 7 7VIC_22_2030_H_B 9 10 7 26VIC_22_2070_H_B 34 46 33 33 17 20 17 200VIC_22_2030_C_A 5 5VIC_22_2070_C_A 2 2 6 6 16

GT_16_1980_H_A 1 1GT_16_2030_H_A 2 2GT_16_2070_H_A 15 7 6 6 34GT_16_1980_H_B 1 1GT_16_2030_H_B 2 2 3 3 10GT_16_2070_H_B 6 9 12 12 39GT_16_2030_C_A 3 3GT_16_2070_C_A 3 3 6

TAS_16_1980_H_A 1 1 1 1 1 1 1 7TAS_16_2030_H_A 1 1 1 1 1 2 2 2 11TAS_16_2070_H_A 3 5 5 5 6 6 6 36TAS_16_1980_H_B 1 1 1 1 1 1 1 7TAS_16_2030_H_B 1 1 1 1 2 2 2 10TAS_16_2070_H_B 1 3 3 3 4 4 4 22TAS_16_1980_C_A 1 1 1 1 4TAS_16_2030_C_A 1 1 1 1 1 1 1 7TAS_16_2070_C_A 1 1 1 1 1 1 1 7

Pre dawn water potential < -3.2 Age

Climate 0 1 2 3 4 5 6 7 8 9 TotalWA_4_2030_H_A 3 27 47 28 105WA_4_2070_H_A 15 135 95 72 317WA_4_2030_H_B 21 17 7 45WA_4_2070_H_B 9 44 85 20 158WA_4_2030_C_A 5 5WA_4_2070_C_A 57 176 201 130 33 2 599

VIC_22_1980_H_A 32 2 2 36VIC_22_2030_H_A 113 156 76 85 70 80 80 660VIC_22_2070_H_A 53 313 417 310 308 296 294 274 2265VIC_22_1980_H_B 32 2 2 36VIC_22_2030_H_B 119 170 84 97 71 90 84 715VIC_22_2070_H_B 33 331 472 371 371 326 283 267 2454VIC_22_1980_C_A 5 25 18 28 1 77VIC_22_2030_C_A 37 100 59 56 54 31 7 344VIC_22_2070_C_A 55 31 67 39 30 26 68 316

GT_16_1980_H_A 6 6GT_16_2030_H_A 2 15 17 11 45GT_16_2070_H_A 77 64 83 76 300GT_16_1980_H_B 6 6GT_16_2030_H_B 11 14 32 33 90GT_16_2070_H_B 26 59 98 94 277GT_16_2030_C_A 18 1 12 34 29 94GT_16_2070_C_A 5 26 24 4 59

TAS_16_1980_H_A 3 206 340 392 333 359 366 391 2390TAS_16_2030_H_A 12 175 351 395 319 349 348 375 2324TAS_16_2070_H_A 147 300 374 312 369 358 385 2245TAS_16_1980_H_B 3 206 340 392 333 359 366 391 2390TAS_16_2030_H_B 177 347 407 339 368 371 406 2415TAS_16_2070_H_B 159 307 390 349 392 422 443 2462TAS_16_1980_C_A 44 213 328 396 410 380 404 410 2585TAS_16_2030_C_A 42 130 186 265 296 293 354 300 1866TAS_16_2070_C_A 1 130 241 339 361 381 359 366 2178

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Table 21. Drought-risk indicators (over 20 rotations) for E. nitens plantations under the three GCM by SRES scenario combinations for the partial acclimation model of photosynthetic acclimation (model 2). Plant water stress below -3.2MPa when combined with temperatures in excess of 35oC is an indicator of the possibility of drought death in trees, especially when young.

Pre dawn water potential < -3.2 and Temperature > 35CAge

Climate 0 1 2 3 4 5 6 7 8 9 TotalVIC_10_1980_H_A 11 19 22 19 16 15 14 116VIC_10_2030_H_A 23 26 33 30 20 21 21 174VIC_10_2070_H_A 50 63 68 62 48 45 42 378VIC_10_1980_H_B 11 19 22 19 16 15 14 116VIC_10_2030_H_B 21 27 35 34 25 25 23 190VIC_10_2070_H_B 2 56 65 76 72 60 60 50 441VIC_10_1980_C_A 9 16 17 15 13 6 7 83VIC_10_2030_C_A 17 19 21 21 20 24 17 139VIC_10_2070_C_A 3 11 17 17 16 18 16 98

TAS_9_1980_H_A 5 6 6 6 5 5 5 5 43TAS_9_2030_H_A 5 6 6 6 4 4 4 4 39TAS_9_2070_H_A 5 7 7 7 5 6 7 7 51TAS_9_1980_H_B 5 6 6 6 5 5 5 5 43TAS_9_2030_H_B 5 6 6 6 5 5 5 5 43TAS_9_2070_H_B 5 7 7 7 5 4 5 5 45TAS_9_1980_C_A 3 3 3 3 2 1 15TAS_9_2030_C_A 3 4 4 4 4 4 4 3 30TAS_9_2070_C_A 3 3 3 3 3 3 3 2 23

Pre dawn water potential < -3.2Age

Climate 0 1 2 3 4 5 6 7 8 9 TotalVIC_10_1980_H_A 3 211 322 373 346 322 328 329 2234VIC_10_2030_H_A 238 340 395 390 352 359 344 2418VIC_10_2070_H_A 15 330 426 479 438 400 388 364 2840VIC_10_1980_H_B 3 211 322 373 346 322 328 329 2234VIC_10_2030_H_B 6 229 357 436 427 395 393 376 2619VIC_10_2070_H_B 28 415 499 597 555 543 541 515 3693VIC_10_1980_C_A 3 163 230 228 239 213 200 212 1488VIC_10_2030_C_A 173 249 274 279 277 306 257 1815VIC_10_2070_C_A 90 146 201 202 196 180 186 1201

TAS_9_1980_H_A 190 307 308 282 262 317 340 354 2360TAS_9_2030_H_A 191 321 318 275 252 326 346 368 2397TAS_9_2070_H_A 209 340 333 298 268 345 370 385 2548TAS_9_1980_H_B 190 307 308 282 262 317 340 354 2360TAS_9_2030_H_B 171 303 297 264 246 315 332 351 2279TAS_9_2070_H_B 183 299 296 268 245 310 331 341 2273TAS_9_1980_C_A 186 329 350 393 365 360 367 348 2698TAS_9_2030_C_A 152 289 299 293 270 251 295 310 2159TAS_9_2070_C_A 191 301 302 299 306 301 333 301 2334

Temperature > 35CAge

Climate 0 1 2 3 4 5 6 7 8 9 TotalVIC_10_1980_H_A 57 62 67 64 61 59 57 56 59 61 603VIC_10_2030_H_A 86 91 100 97 95 92 88 88 92 97 926VIC_10_2070_H_A 173 180 194 192 188 183 181 176 184 191 1842VIC_10_1980_H_B 57 62 67 64 61 59 57 56 59 61 603VIC_10_2030_H_B 89 94 105 102 100 97 95 96 102 107 987VIC_10_2070_H_B 193 201 214 212 209 204 202 199 208 215 2057VIC_10_1980_C_A 62 58 63 66 66 66 62 54 51 55 603VIC_10_2030_C_A 79 86 92 96 96 98 99 99 109 107 961VIC_10_2070_C_A 114 108 109 111 106 111 113 113 116 113 1114

TAS_9_1980_H_A 6 8 9 9 9 9 8 8 8 8 82TAS_9_2030_H_A 9 10 12 12 12 12 11 10 10 10 108TAS_9_2070_H_A 18 20 22 23 25 24 22 21 22 22 219TAS_9_1980_H_B 6 8 9 9 9 9 8 8 8 8 82TAS_9_2030_H_B 8 9 11 11 11 11 10 10 10 10 101TAS_9_2070_H_B 18 20 22 23 25 24 22 21 22 22 219TAS_9_1980_C_A 9 8 7 7 7 7 6 4 3 4 62TAS_9_2030_C_A 10 11 13 14 14 14 13 11 11 11 122TAS_9_2070_C_A 11 11 11 14 13 11 12 13 13 12 121

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Table 22. Drought-risk indicators (over 20 rotations) for P. radiata plantations climate scenarios generated with the CSIRO Mk3 models for the partial acclimation model of photosynthetic acclimation (model 2). Plant water stress below -3.2MPa when combined with temperatures in excess of 35oC is an indicator of the possibility of drought death in trees, especially when young

Pre dawn water potential < -3.2 and Temperature < 35CAge

Climate 0 1 2 3 4 5 6 7 8 9 10 11 12 13 TotalGT_3_1980_C_A 2 8 5 3 3 2 2 1 2 1 29GT_3_2030_C_A 12 23 27 31 29 31 32 25 18 19 7 254GT_3_2070_C_A 14 6 9 7 5 4 6 6 2 3 62

QLD_2_2070_C_A 1 1

VIC_1_1980_C_A 15 14 12 11 10 4 3 3 4 4 5 85VIC_1_2030_C_A 18 18 18 17 16 17 17 18 21 21 17 198VIC_1_2070_C_A 25 26 28 25 23 20 21 22 11 12 7 220

Pre dawn water potential < -3.2 Age

Climate 0 1 2 3 4 5 6 7 8 9 10 11 12 13 TotalGT_3_1980_C_A 13 177 96 68 54 41 39 38 38 4 7 2 577GT_3_2030_C_A 285 391 446 486 499 513 506 477 412 341 118 4474GT_3_2070_C_A 11 98 53 81 88 66 41 46 46 28 51 609

QLD_2_1980_C_A 6 33 61 71 65 103 28 367QLD_2_2030_C_A 9 18 98 119 81 205 49 579QLD_2_2070_C_A 47 34 35 78 91 11 296

VIC_1_1980_C_A 27 441 496 430 387 381 357 356 285 251 256 232 3899VIC_1_2030_C_A 96 439 375 400 411 377 425 348 341 394 434 294 4334VIC_1_2070_C_A 48 417 414 468 484 475 422 416 415 393 400 225 4577

NSW_4_1980_C_A 1 23 17 20 22 26 43 36 34 8 230NSW_4_2030_C_A 5 32 24 21 60 47 40 47 35 311NSW_4_2070_C_A 1 10 11 26 30 22 31 34 165

Temperature < 35CAge

Climate 0 1 2 3 4 5 6 7 8 9 10 11 12 13 TotalGT_3_1980_C_A 108 104 107 106 106 112 110 101 97 99 98 100 99 96 1443GT_3_2030_C_A 131 139 149 156 158 159 160 157 161 160 163 161 151 141 2146GT_3_2070_C_A 208 200 202 207 205 209 210 211 220 219 204 197 198 193 2883

QLD_2_1980_C_A 14 14 15 15 14 8 9 9 8 8 8 8 9 15 154QLD_2_2030_C_A 36 36 37 40 40 39 33 33 32 30 32 37 35 34 494QLD_2_2070_C_A 48 46 48 50 52 56 60 59 48 52 53 49 49 48 718

VIC_1_1980_C_A 46 45 48 52 52 50 43 34 29 30 30 31 34 37 561VIC_1_2030_C_A 54 62 70 76 77 79 79 79 81 76 72 77 75 75 1032VIC_1_2070_C_A 102 98 100 98 95 99 102 103 102 97 86 77 81 83 1323

NSW_4_1980_C_A 31 31 31 32 32 31 31 29 26 26 24 24 26 23 397NSW_4_2030_C_A 52 61 65 76 74 80 80 79 85 85 82 82 85 81 1067NSW_4_2070_C_A 106 100 100 96 97 94 96 92 84 80 73 64 64 64 1210

62

Figure 23. Percentage change in yield of bellwether plots 1980-2030 using the CSIRO Mk3 a2 model and scenario and assuming no photosynthetic up-regulation

Figure 24. Percentage change in yield of bellwether plots 1980-2070 using the CSIRO Mk3 a2 model and scenario and assuming no photosynthetic up-regulation.

63

Figure 25. Percentage change in yield of bellwether plots 1980-2030 using the CSIRO Mk3 a2 model and scenario and assuming photosynthetic increase

Figure 26. Percentage change in yield of bellwether plots 1980-2070 using the CSIRO Mk3 a2 model and scenario and assuming photosynthetic increase

64

4.5.Discussion

A vast amount of data has been generated and, as shown in Figures 22-26, the results are highly dependent on site attributes and management. Proximal sites differ significantly in prediction of climate change impact. While a broad trend is evident with climate model and region, individual plantation vulnerability is dependent on local factors. Some general observations are applicable.

The effect of the climate models and scenarios is highly significant. The Hadley projections under both a1fi and b1 scenarios showed greater impact of climate change on production than the CSIRO Mk3 projection under the a2 scenario. This is particularly evident in SE Queensland, a region where the models differ. The best estimate (50th

percentiles) of percentage change in annual precipitation for SE Queensland (CSIRO Technical Report. 2007) suggests a +2 to -5% change under most SRES scenarios by 2030 and a -2 to -10% change by 2070. For this region of the 23 models considered in producing the averages, between 30 and 50% of the models agreed on an increase in annual precipitation for the region for the 2070-2099 period (cf. 0-10% for Southern Australia excepting Tasmania). While intuitively it may seem sensible to use the average of the 23 models for our projections there are limitations to this approach. The errors associated with each of the models are additive when the projected climates are averaged. This results in such large errors associated with the averaged GCMs that the output becomes almost meaningless. The impact of climatic variables such as temperature and rainfall on production is not linear, consequently it is important to understand the impact of the individual GCMs on productivity before attempting to simplify the results. Under the Hadley A and B projections, SE Queensland (SEQ) dries markedly, whereas using the CSIRO Mk3 model this is not the case for the 2030 period though drying does occur by 2070.

Not surprisingly the extent to which photosynthetic up-regulation endures plays a critical role in plantation response to changing climates. As has been indicated previously, considerable uncertainty is associated with this physiological process. The best-case (not withstanding increases in drought risk discussed later) is for marked growth improvements in regions where temperatures are currently low, water abundant and fertility high (50-80% in some case by 2070, consistent with the high end observed in some FACE studies, Norby et al. 1999, Ainsworth and Long 2005). Without up-regulation the effects of climate change will be severe in some regions under some climate scenarios. Under the Hadley A1FI scenario drying in SE Queensland and southern Australia is forecast to have a severe effect on the hybrid pine plantations, the radiata pine estate in the Green Triangle, E. nitens estates in Victoria and to a lesser extent Tasmania, some E. globulus plantation site types in Victoria and the northern extent of the E. globulus plantation estate in Western Australia.

In the simulations of future conditions the interactive and contrary effects of a drying climate and increasing carbon dioxide concentrations emerge. In the period around 2030, for example, the simulations suggest that radiata pine production in the Green Triangle will change by between -6 and 1% when averaged across our bellwether sites rising to between an 8-21% gain by 2070 as CO2 fertilisation effects become more marked.

65

Predictions show that increased future production almost invariably comes at a higher risk of drought days (Figure 27, Tables 17-22). More very hot days, often reduced rainfall, increased seasonality of rainfall and increased evapotranspiration associated with predictions of increased leaf area and increased total gas exchange as a result of elevated CO2 (despite reduced stomatal conductance) leads soil water depletion and to more dry days coincident with hot days. Large differences in Victoria, the Green Triangle, Western Australia, New South Wales and, under the Hadley a1fi scenario, SE Queensland are predicted.

Figure 27. Change the cumulative number of high stress days (>35oC and soil water potential <3.25 MPa) for a pine site (GT_3) average of 20 simulated rotations of bluegum site (GT_16) under CSIRO Mk3 a2 climate scenario for current and modelled 2030 climates.

The increase in the number of hot-dry days will mean that projected production increases may not be realised. Firstly trees may be killed by drought, as has been observed during hot, dry periods in the past. Secondly, high stress levels may have a physiological impact on production not captured in our modelling, manifest for example through loss of leaf area or other physiological memory effects. While CABALA predicts drought to increase leaf shedding and cause temporary and slightly lagged effects on canopy conductance, recovery in modelled simulations from drought is rapid, in reality this is unlikely to occur.

In colder regions, production gains in simulations are likely to be a result of rising temperatures. Notable increases are simulated in Tasmania with only slight or no increase in drought risk. This is an example of the importance of initial conditions in predicting the impact of climate change. The radiata and bluegum estates in Australia are planted across very large environmental ranges. In parts of Tasmania both species are grown in places sub-optimal in temperature for maximum growth but under slight water stress (see environmental limits analysis in Battaglia et al. 1997). Increases in temperature and decreases in the available water lead to different outcomes compared to other warmer regions in Australia, for example, a bluegum site in the northern extent of the great southern region of Western Australia where water is very limiting and temperatures close to or exceeding optimum for the species.

Pine Caroline

0

50

100

150

200

250

300

0 2 4 6 8 10 12 14

Age

Cum

ulat

ive

high

str

ess

days current

2030

Bluegum Macarthur

0

2

4

6

8

10

12

0 2 4 6 8

Age

Cum

ulat

ive

high

str

ess

days

current2030

GT_3 site GT_16 site

66

4.6.Conclusions

In summary:

Plantation production changes at particular sites will depend upon the relationship between the current site climate and species optima and other site attributes such as fertility that will determine the ability of species to benefit from changing conditions.

Without a significant benefit to production from elevated levels of atmospheric CO2, production in some regions will decrease, potentially markedly if the predicted increases in number of hot-dry days either directly through damage or death, or indirectly through pest attack further decrease production.

If plantation species are able to maintain increased photosynthetic rates under elevated CO2 levels productivity in many regions is forecast to increase. Increases in cool wet locations are forecast to be marked.

The analysis is this chapter suggests that two types of climate impact predictions may be needed for forestry climate adaptation planning. The first is the individual plot estimates carried out in this chapter. The between plot estimate suggests that a considerably larger number of sites will be required for modelling adequate estate yield variation. Another useful form of analysis is a series of national scale change surfaces with a measure of uncertainty attached to each grid – indicating areas where gains in production (or losses) are likely under most site x plant response assumptions, and where the outcome is highly dependent on assumptions and management. This is explored in the next chapter.

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5. CLIMATE CHANGE SURFACES AND UNCERTAINTY

5.1.Introduction

The complexity of the interactions between site, local climate and species make it difficult to identify trends across the bellwether sites that so far have been the basis of this study (Chapter 4). To identify regional changes and extrapolate beyond the individual plots there is a need to simplify the outputs and remove the site variation but where comparison between surfaces can reveal the importance of model assumptions.The ability to predict the impact of climate change on plantation performance is fraught with uncertainty, as has been discussed previously. Making spatial predictions of production alone across large tracts of Australia is itself difficult because of the paucity of important spatial data layers such as plant available soil water, soil texture, soil depth, soil organic matter and so on that are requisite for production prediction. This becomes compounded, as discussed in Chapter 4, by uncertainty about plant responses to long-term exposure to elevated CO2. A useful contribution for future decision making is an indication of how important assumptions are to future predictions: this both indicates where some confidence can be attached to predictions, defines what site factors need to be carefully defined and finally guides future research investment to reduce uncertainty.

5.2.Methods

Six standard soil types were set up (low, medium and high fertility for each of a shallow and a deep soil) and an even grid used to sample across each plantation region. Fertility ranges and soil depths were selected from the bellwether sites as appropriate for each region (Table 23). A grid of sample points varying between 0.25 and 0.5 degrees was sampled across each region. One representative location in the centre of each grid was selected (there is some risk that the topography at this point may not be indicative of the grid cell as a whole, however the scale of the task prevented any spatial averaging method). At each sample point 20 rotations were simulated to resolve inter-rotation weather sequence variation as in Chapter 4. In all, approximately 1,000,000 simulations were carried out. Given the task of generating this data was substantial, only one climate projection to be used. The CSIRO Mk3 A2 climate projection was selected as it describes a closer to average predicted future, the Hadley GCM is a more extreme future.

In this section we provide a selection of outputs for discussion. A full set of grids for all combinations can be obtained from CSIRO. We concentrate on two output types presented in two forms. As in the previous chapter we report percentage change in production and average number of hot-dry days per rotation for the periods 2030 and 2080 compared to the baseline period 1975-2005. We also present a series of surfaces showing the range in percentage change between the different modelling surfaces (that is the maximum differences between the percentage change of each soil by photosynthesis combination from its base case).

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While we feel the results are intrinsically interesting in their own right, we present the results also as an exploration of how uncertainty in biophysical models can be presented for managers.

Table 23. Standard soil description used in each region.

Region

Fertile OM% top

10 cm

Medium fertile OM% top 10 cm

Infertile OM% top

10 cm

Shallow soil depth

(m)Deep soil depth (m)

Grid resolution (degrees)

Tasmania 7.0 2.5 1.3 0.8 2.0 0.25Victoria & Southern NSW 5.0 2.5 1.2 0.8 3.0 0.50Western Australia 6.5 3.0 1.5 3.0 7.0 0.50Green Triangle 4.0 2.5 1.5 - 4.1 0.25Northern NSW 5.2 2.5 1.1 0.8 2.2 0.50Queensland hybrid pine 3.0 2.0 0.9 - 2.2 0.25

5.3.Results

Results are presented in a traffic light system. Green shows a very positive result, yellow a marginally positive result, orange a marginally negative outcome and red a highly negative outcome. Because the impacts of pest and mortality are not considered, anything with less than a 10% increase in production (ie. not green) should be considered to be a neutral forecast at best and perhaps as indicative of future decrease in yields. Where the number of hot-dry days is markedly increased this too should be indicative of future uncertainty. CABALA does not have a mortality function so many of the stands indicated as surviving but with a high number of hot-dry days should be taken as an indication that catastrophic mortality is possible and certainly a level of ‘salt and pepper’ mortality with resultant yield loss (as Avery’s in drought risk trial, Mendham et al. 2007) is probable.

Individual scenario analyses are given in the following figures. A useful and powerful overview is provided in the initial national figures which indicate the likely direction of change and the uncertainty (or dependence on how we construct scenarios) associated with this estimate of change.

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5.3.1. Australia

Summary of percentage change in production for 2030 (summary of species, soil types and photosynthetic models) overlayed with the range in response. The more intense the hatching the more the nature of plant response and local conditions affect modelled outcomes. Green areas with no hatching are likely to be favourably affected in all construct scenarios. Yellow areas are likely to experience little change (given that other factors not considered may adversely affect outcomes). Yellow areas with intense hatching should be considered vulnerable to adverse impact. Orange areas are likely to be adversely affected.

Change in the average number of hot dry days per year (summary of species, soil types and photosythetic model)

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Green Triangle

P. radiata: percentage change in production, all soil surfaces averaged

E. globulus: percentage change in production, all soil surfaces averaged

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P. radiata: percentage change in production with changing fertility, 2030 only

E. globulus: percentage change in production with changing fertility, 2030 only

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P. radiata: range in percentage change in production between all combinations of soil and photosynthetic models

E. globulus: range in percentage change in production between all combinations of soil and photosynthetic models

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P. radiata: change in the average number of hot-dry days per year

E. globulus: change in the average number of hot-dry days per year

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5.3.2. Victoria

P. radiata: percentage change in production, all soil surfaces averaged

E. globulus: percentage change in production, all soil surfaces averaged

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E. nitens: percentage change in production, all soil surfaces averaged

P. radiata: percentage change in production with changing fertility, 2030 only

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E. globulus: percentage change in production with changing fertility, 2030 only

E. nitens: percentage change in production with changing fertility, 2030 only

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E. nitens: range in percentage change in production between all combinations of soil and photosynthetic models

P. radiata: change in number of hot-dry days per year

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E. globulus: change in number of hot-dry days per year

E. nitens: change in number of hot-dry days per year

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5.3.3. Tasmania

P. radiata: percentage change in production all soil surfaces averaged

E. globulus percentage change in production all soil surfaces averaged

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E. nitens percentage change in production all soil surfaces averaged

P. radiata: percentage change in production with fertility 2030 only

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E. globulus percentage change in production with fertility 2030 only

E. nitens percentage change in production with fertility 2030 only

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E. nitens: range in percentage change in production between all combinations of soil and photosynthetic models


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E. globulus change in number of hot-dry days per year

E. nitens change in number of hot-dry days per year

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5.3.4. Northern NSW



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5.3.5. Western Australia


E. globulus percentage change in production all soil surfaces averaged

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E. globulus percentage change in production with fertility 2030 only

90



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E. globulus change in number of hot-dry days per year

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5.3.6. Queensland

P. elliottii var. elliottii × PCH: percentage change in production all soil surfaces averaged

P. elliottii var. elliottii × PCH: percentage change in production with fertility 2030 only

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P. elliottii var. elliottii × PCH: range in percentage change in production between all combinations of soil and photosynthetic models

P. elliottii var. elliottii × PCH: change in number of hot-dry days per year

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5.4.Discussion

As in Chapter 4 there is much to explore in the data presented (and more available as underlying grids for different model simulations).

Climate change is predicted to have a generally positive outcome for all of E. globulus, E. nitens and P. radiata plantations in Tasmania. Significant gains are forecast for most areas and only slight increases in the number of hot-dry days are forecast. While outcomes are dependent on model assumptions, these involve the extent of gains resulting from elevated CO2 and not around soil depth or fertility assumptions.

Climate change in Victoria (outside the Green Triangle) is predicted to be positive in general for P. radiata, to lead to marginal gains for E. globulus with gains in higher elevation areas in the north-east and central areas and to lead to gains for E. nitens inthe east but decrease or marginal gains in the west and Otways area. For all species, predictions are very dependent on model assumptions, increasing in divergence by 2070. For P. radiata drought risk increase steeply for areas in southern New South Wales.

Models suggest that outside the high rainfall zone in Western Australia production may be reduced by climate change even with the inclusion of up-regulation of photosynthesis for radiata pine. With no up-regulation of photosynthesis, production of bluegum decreased in northern areas of region. Models show an increase in drought risk in the north and away from the coast by 2070. Not surprisingly the soil model assumptions made are very important to predicted changes.

Predictions for both radiata pine and bluegum for the green triangle are generally positive with the degree of photosynthetic up-regulation having a strong influence on the bluegum response in particular. Drought risk for radiata pine and bluegum is modelled as changing little. These are somewhat surprising results given the forecast 5-10% decrease in rainfall for the region and warrant further testing with a finer grid of sites and updated climate models given the economic significance of forestry in the region.

Parts of the northern hybrid pine estate may decrease in production and may become drought stressed. If nutrition and other site factors allow there is the potential for the bulk of the estate to increase in production.

5.5.In Summary

The results suggest the following conclusions.

The following plantation species and region combinations are predicted to increase in production with little change in risk or uncertainty based on model assumptions:

– E. globulus, E. nitens and P. radiata in Tasmania

– the mid to lower northern regions of the hybrid pine estate

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– P. radiata and E. globulus plantations in East Gippsland and higher altitude parts of central and north-east Victoria

– Green Triangle plantations (n.b. this is not consistent with the bellwether plot analysis and should be treated with caution).

The following plantation species and region combinations are predicted to increase in production with an increase in risk or with predictions associated with high uncertainty based on model assumptions:

– parts of the Western Australian E. globulus and P. radiata estate in the high rainfall zone (>1000mm) where soils are fertile and deep

– plantations of radiata pine in northern and central NSW/ACT

– E. nitens, P. radiata and E. globulus plantations in Victoria.

The following plantation species and region combinations are predicted to decrease in production with an increase in risk or with predictions associated with high uncertainty based on model assumptions:

– P. radiata plantations in southern NSW, and possibly at the western edge of the southern and central estates

– the eastern and northern extents of the Western Australian E. globulus and P. radiata estates.

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6. STRATEGIC PLANNING AND RISK MITIGATION AT ESTATE LEVEL

6.1.Introduction

Climate variability and future climate change projections have focused attention on the impact of uncertainty and risk on long term planning. There are a large range of factors that introduce uncertainty or risk into forest planning decisions and it is known that optimal planning decisions should change in the presence of risk. Brack and Richards (2002) for example demonstrate that uncertainty in predictions of growth due to future weather or local site characteristics will have major impacts on decisions relating to the amount of carbon sequestration that could be offered to carbon afforestationmarket from any single stand of plantation. Even more importantly, they found that decisions on when to harvest or establish plantations will dominate the uncertainty about the optimal amount of carbon that should be offered for future carbon sequestration markets.

In forest management, decisions are rarely made in an environment of certainty, but under risk (multiple outcomes with known probability), or even more commonly under uncertainty (outcome probabilities unknown) (Kao 1984). Risk is also used in the economic literature as the integration of the probability of an event and the cost of that event, so high risk under this definition may be something that is likely to happen and/or something that is very costly if it did happen.

Dixon and Howitt (1979) identify three categories of uncertainty:

1. Forest dynamics or growth

2. Forest inventory or stock levels

3. Preference function or objective.

Marshall (1987), on the other hand, classified uncertainty as coming from sources internal or external to the wood supply models:

internal sources included simplifications required by the models, inaccuracies in the database, imprecision in yield projections

external sources included the changing nature of the desired forest state, improper specifications of the returns, potential changes in political or policy decisions.

Despite this risk/uncertainty, forest managers need to plan the activities for the immediate future – what to plant next year, what to harvest, where to road, how much product to offer to short and long term markets. In all but the most special cases, optimal decisions change when risk or uncertainty is explicitly recognized in formulating a problem (e.g., Lohmander 1990, Reed and Errico 1986). Also, in all but the most special cases, the optimal management of a plantation or forest estate is not simply the sum of the optimal management for each of the individual stands that make up the estate.

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Often, a simple approach to dealing with risk is ‘discounting’ future values, either arbitrarily assuming a constant discount across the predicted outputs or using an economic discounting rate (in addition to the cost of waiting for future income). However, this approach may distort the immediate as well as the future management actions and does not adequately deal with the probability that growth or yield may improve under future climate conditions.

Since the early 1970s, Mathematical Programming and its derivatives (especially Linear Programming) have been used extensively in forest management to determine optimal approaches to the management of plantation or forest estates (e.g. Ware and Clutter, 1971; Navon, 1971). In many cases, the use has been to determine the levels of product yield that can be sustainably produced or strategic decisions on the type of silviculture (e.g. multiple commercial or non-commercial thinning of regeneration versus plantation establishment and rotation length). These programming solutions are an elegant approach to operations scheduling and strategic management planning (Brack and Marshall, 1992) and are currently used as an objective and practical tool in many forest planning systems.

A mathematical programming solution however is open loop, i.e. if data change unexpectedly there is no way to modify the decision variables to account for such changes (Casti 1983). The optimal solution is valid for the initial input data; if any part changes beyond a very restricted range, the solution must be recomputed with the new data set. Even in the intensively managed plantations internal sources of error may be significant (e.g., height and basal area inventories are known within 3% and 5% error intervals respectively (p=0.05)), and may not balance positive against negative errors (Brack and Richards, 2002). Kangas and Kangas (1999) demonstrate analytically that, for simple problems at least, optimization under uncertainty leads to a biased estimate of the objective function.

Simulation and sensitivity analyses may be used to determine the effect of uncertainty on solutions when the problem or the error structure is too complex for an analytical approach. The values for predicted yields can be systematically changed, the matrix regenerated and solved. Uncertainty from external sources has to be treated similarly. The interpretation of the mass of results from these multiple runs would be beyond the ability of most practitioners. Casti (1983) outlined a method to determine which features of a solution are important and how great a change is allowable before a full recomputation of the problem is warranted. However, this method requires a dynamic programming model which probably could not handle the large model formulations needed in operation scheduling problems.

6.2.Purpose

This chapter demonstrates a method that combines the elegance and power of a Linear Programming approach to strategic planning with a practical way for understanding the impact of variations in the inputs into the model caused by internal sources of uncertainty. The demonstration will focus on strategic decisions (i.e. determination of maximum sustainable levels of production from an extensive plantation estate) and on determining which operational decisions are most heavily influenced by variations. The

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demonstration will be restricted to internally sourced variations, in particular yield variation that are caused by uncertainty in climate of the next 100 years.

6.3.Data and Methods

A model plantation estate with an area of about 200,000 ha was generated (Figure 28). This estate has an age class distribution similar to the post-1990 plantings and proposed plantings in the Green Triangle area of Australia (Richards and Brack, 2004). This actual distribution is not important except that it is representative of the ‘non-normal’ distribution commonly found in large plantation estates.

Figure 28. Model Plantation Estate used for study.

Three climate scenarios with stochastic variation were used as input into CABALA to estimate growth and yield from a ‘standard’ silvicultural regime for long rotation softwood (P. radiata) and short rotation hardwood (Eucalyptus) plantations:

1. current climate representing normal climate variation induced growth/yield in 1980 – 2005

2. 2030 – based on Hadley model A1FI discussed earlier representing a 5-10% decrease in annual rainfall

3. 2070 – based on Hadley model A1FI discussed earlier representing a 10-15% decrease in annual rainfall

The standard regime for P. radiata included two thinnings (14 and 19 years) with a final clearing at about 25 years, while the eucalypt was only a short rotation with no thinning and final harvest at 10 years. A deterministic yield table was created as a mean of the 20 stochastic runs under the three climate scenarios (Figures 29, 30). Alternative regimes allowed for rotation lengths to increase by five years, and for the P. radiata to incorporate only one thinning and an earlier final harvest.

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Variations in total volume from the stochastic runs were used to generate ‘periodic’ variation (i.e. one five-year period may cause the plantation to grow more than expected, but the next period may be less) (Figure 30). Note that the mean variation is zero for the initial 30 years which corresponds to the ‘current climate’, but then rises to greater than zero to reflect the period where elevated CO2 gains offset modest rainfall decreases before dropping again to close to, but still above zero as the drier climate scenario takes effect.

This period variation is needed rather than an ‘age-based’ variation because all trees in the same period are likely to experience the same proportional increases or decreases in growth in any one year, whereas if age was used, the age class distribution would mean in any one year some, say, younger trees would have better growth while others would have poorer growth thus negating the climate effect.

For the long-term planning, each age class started with the option of following one of the silvicultural regimes with yields determined by the Current climate scenario. After final harvest, each area was re-established and followed the yields from standard regime for the 2030 climate scenario with the 3rd rotation under the 2070 climate scenario.

These area, silvicultural options and yield tables were included into a Linear Programming model (LP). The objective function of the LP was defined as maximising the discounted total yield flow. A discount of 5% was included to improve the impact of bringing the yields closer to the immediate future rather than allowing the yield to concentrate at the end of the planning horizon (100 years). The major LP constraint was to ensure non-declining yields for the initial 50 years, however to reduce the complexity of the model, a slight decline of less than 10% of the previous five years was allowed. The analysis also used five-year periods rather than annual periods to further reduce the size and complexity of the LP formulation.

The LP was initially run using the deterministic inputs (Figures 29, 30) and the optimal allocation of silvicultural regimes to each age class and species recorded. The value of the objective function and the flow of (non-discounted) total yield flows from the 200,000 ha estate was also recorded. The deterministic yield tables were subsequently replaced by the stochastic yield tables generated by CABALA (Figure 31) and the climate regimes and the ‘new’ optimal silvicultural regimes, objective function values and yield flows compared with the deterministic outputs.

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Figure 29.Total volume from P. radiata standard (2 commercial thinning) rotations under 3 climate scenarios.

Figure 30. Total volume from Eucalyptus standard short rotation with no thinning under 3 climate scenarios.

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Figure 31. Stochastic variation total volume (by period)

6.4.Results

The deterministic LP found a solution that generated a maximum objective function (cumulative discounted yield) of 6.1 x 107 m3 of wood. This corresponds to a total of about 44.2 x 107 m3 of wood over the 100 year planning horizon. Most of the age class areas were allocated a standard silvicultural regime, although the eucalypt plantations established in 2010 and a significant area of P.radiata plantation established in 2015 were allocated extended regimes (i.e. clear fall delayed by five years), apparently to meet the non-declining yield requirements.

6.4.1. Variation in the objective function

The objective functions for the stochastic LP solutions ranged from 50% to over 200% compared to the deterministic function (Figure 32). This distribution is skewed with significantly more than half of the runs having an objective function value less than the mean value, although both the median and mean are greater than 100% of the deterministic value.

A linear correlation between the periodic stochastic variations in yields (Figure 31) with the objective function value for each stochastic run indicates a positive correlation for all periods. That is, if the growth is above the deterministic yield table for any period, the objective function value is also increased. However, the strength of the relationship is not constant through time with the periods in decreasing order of linear correlation: 10, 15, 40, 20, 25, 70, 45, 55, 30, 50, 85, 75, 5, 80, 35, 65, 60 and finally 90. Mostly, the earlier variations have a greater impact that the later ones (e.g. the r value for the

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correlation with relative growth at 10 years is eight times greater than that with 90 years). This trend is not surprising given the objective function was discounted yield, so 1 m3 of wood at 90 is only ‘worth’ (1-0.05)90 or 0.0099 of 1 m3 in year 1 at a 5% discount rate. However, the strength of the linear correlation is not in strict chronological order (e.g. growth at 40 years appears more strongly correlated to the objective function than 20 or 25 years and much more strongly than 30 years). This variation is due to the age class distribution and restrictions to the allowable silvicultural regimes as many areas cannot be harvested at 20, 25 or 30 years so the effect of variation in those periods is less than the effect at 40 years when large areas and yields can be influenced by variation in growth.

Figure 32. Percentage frequency of stochastic LP objective function solutions as a percentage of the deterministic solution

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6.4.2. Variation in yield flow

The flow of wood yield over the 100 year planning horizon also differed significantly between the deterministic and stochastic runs, but was not consistent over the planning horizon (Figure 33).

Figure 33. Comparison of yield flows for 20 stochastic runs with the deterministic run.

All the runs (deterministic and stochastic) allow for an increasing yield over the 1st 3 – 5 periods as more area is established or becomes commercially harvestable. However, the deterministic yield flow allowed for a higher initial production of wood in these 1st periods than more than 75% of the stochastic runs. By the 11th period, 75% of the stochastic runs were allowing a higher yield than the deterministic run.

The deterministic yield flow is also sustained (neither declining nor increasing) after the initial 50 year constraint of non-declining yields. The stochastic runs tend to continue to increase in allowable yield until about the 15th period, then start to drop as the ‘harsher’ climate scenario begins to take effect. Despite this decrease, at least 75% of the stochastic runs allow for more yield than the deterministic run over the later half of the planning horizon even in the ‘harsher’ climate scenarios.

If the non-declining constraint were expanded to include the entire 100 year planning horizon, the LP was unable to find a feasible solution where the objective function was greater than 0. Much more sophisticated silvicultural options would be required to find a profitable and feasible solution given the increase in productivity in the second rotation followed by the decrease productivity in the third rotation (Figures 29, 30, 31). The added complexity was not considered relevant for this research but may be examined in future work.

Most runs had a plateau at around 5–10 period (25–50 years), but this varied substantially between stochastic runs (9-fold from min to max). Linear correlations

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between the periodic growth variation and the minimum yield between 25–50 years for each of the runs again found significant positive correlations. In decreasing order of linear correlations for each period were: 15, 10, 20, 25, 40, 70, 45, 55, 50, 5, 30, 80, 85, 75, 35, 65, 60 and finally 90. This order of correlation strength is similar to that for the objective function although the earlier periods have slightly increased their strength. The small (but still statistically significant) correlation with growth well beyond the 20–50 year period may be due to co-correlation between growth periods (e.g. a ‘good trend’ in 2030 may to related to a ‘good trend’ in 2070), or because some of that ‘future volume’ may be brought forward into earlier periods due to the objective function discounting approach.

The correlation between growth variation and the yield in the final two periods of the planning horizon were generally weaker and did not show the chronological trend seen earlier: 45, 15, 10, 40, 70, 75, 85, 55, 20, 25, 30, 80, 50, 5, 35, 65, 90 and finally 60.

6.4.3. Variation in allocation of silvicultural regimes

The allocation of silvicultural regimes differs between the deterministic and stochastic runs for some of the age classes only. For example, 75% of the stochastic runs continue to allocate the standard 10 year short rotation for eucalypt plantations planted in 1990, 1995, 2005 and 2015. For the remaining 25% of runs, these age classes are allocated to a delayed regime with clearfall scheduled at age 15 years. In about two-thirds of the stochastic runs, the delayed harvesting of the P. radiata 2015 age class was not found to be optimal. The increased allocation to delayed harvest regimes appeared to be positively correlated with the growth variation in years: 15, 45, 10 and 40 (in decreasing order). In practice, a decision on whether or not to allocate the 1990, 1995, 2005 and 2015 eucalypt age classes to standard or delayed regimes, however, needs to be made well before the actual growth in 40–45 years, so the optimal decision will be difficult. The decision on the standard or delayed P. radiata established in 2015 is not so difficult as the actual trends in growth from the 1st 40 years would be known before an irrevocable final harvest decision needed to be made.

Small areas (less than 10% of each age class) were allocated to other non-standard regimes, but these were considered to be relatively unimportant changes required to meet the strict constraints imposed by the simple LP formulation.

6.5.Discussion

Like Kangas and Kangas (1999), the introduction of variation into the internal inputs in the LP leads to the conclusion that the objective function determined under deterministic conditions is biased. Figure 33 summarises the skew of stochastic distributions with both the mean and median values of the stochastic runs greater than 100% of the deterministic value. Given that the climate scenarios 2030 and 2070 both allow for more growth on average that the ‘current’ scenario (Figure 30) it is not surprising that the objective function increases – up to 200% of the deterministic – but it is surprising that there is a large number of stochastic runs where only 25% - 85% of the deterministic value is achieved despite the average improvement in growth. So,

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even allowing that future climate conditions could, on average, lead to more yield from a plantation, there is a substantial chance that this increase would not be realised in an increased objective function.

This ‘failure’ to consistently return a highly objective function may be related to the non-declining yield constraints introduced for the 1st 50 years. The potential to ‘exploit’ an increase in expected yield due to the climate variation in an early period is negated or substantially reduced by a decrease in yield in a future period because the overall yields cannot drop through time. The effect of this inability to exploit future growth variation in the early periods is clearly demonstrated by the vast majority of stochastic runs providing for lower yields in the initial periods compared to the deterministic runs (Figure 33). Delaying the ‘extra’ yield to later in the planning horizon to avoid the non-declining constraint, may still provide for more yield overall but concentrated to later in the planning horizon and therefore not as valuable under a time discounted objective function.

The correlation statistics indicate that the relative growth in the immediate future has a significant relationship with the objective function value and the size of the initial production plateau. However the relative growth at age 40 appears to have an unexpectedly powerful effect on the objective function, plateau and selection of optimal silviculture regimes for the age classes. It is difficult to isolate the importance of the internal variation at this age from dual or shadow statistics provided by the LP from the deterministic run. The reason for the strength of the relationship at this age appears to be due to the non-normal age class distribution with large areas potentially being harvested or thinned during this period, and the range of relative growth from 50 m3 ha-1

below expectation to 200 m3 ha-1 above.

The climate scenarios and CABALA modelling present additional statistics on the number of hot and dry days that may be experienced during critical times in the growth of a plantation. The hotter climate scenarios for example may result in significantly more hot and dry days when the plantations are at critical points of growth and this may lead to wide-spread mortality and loss of yield despite the expectation that yield would, on average, increase. Such mortality and potential loss of yield has not been included in this research but it expected that it would exaggerate the effects of below expectation growth as it would result in several periods of less growth than expected or in the worse case the total loss of yield from the area while a new plantation stand was established. The importance of the timing and risk of these mortality events on the strategic decisions would again depend on the age class distribution and the amount of ‘slack’ in the non-declining or other constraints.

The LP programs were not given an option to change the original age distribution, i.e. the areas scheduled for planting in 2010 and 2015 are constrained to happen and stands will be re-established after clear fall, but no new areas will be established (Figure 28). Therefore the only strategic decisions being modelled in these runs were how much yield should be offered as a long-term non-declining amount, and what are the most likely and important silvicultural strategies that should be applied to the estate. The stochastic runs suggest that, due to the internal variation as modelled, the optimal achievable yield in the initial decade will be less that found by the deterministic run. However, by halfway through the planning horizon (the 10th period or 50th year), it is highly likely that the achievable yield will increase well above the deterministic level. A

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manager’s decision on the quantity to offer as short and long term commitments in that 1st decade will still be a function of his/her nature (i.e. risk adverse or risk neutral) but increasing the short term yield above the deterministic level based on an expectation of increased yields in the future due to climate change would require a committed risk taker and is unlikely to be an optimal decision. However planning for a long term future that supplies only the levels found by the deterministic model would be quite conservative, and any infrastructure development for the region would likely be under utilised.

Most of the silvicultural decisions are robust – the optimal regime for most stands stays the same under the various stochastic runs even though the actual yields produced (and associated income to the grower) may vary. However, some stands deviate from the deterministic run regimes to ‘fill in’ shortfalls in some periods caused by the non-declining constraint, or possibly to exploit the better than expected growth in the period following the standard rotation length. Decisions to lengthen or shorten an expected rotation cascade into operational decisions about the timing of road construction/maintenance and other infrastructure and manpower decisions, so advanced warning of these changes are required. Depending on the spatial distribution of the estate, infrastructure and manpower decisions can be made with a high degree of certainty into the future for the ‘robust’ age classes and regimes, but planning for the ‘variable’ regimes may need to be more flexible – trading off the marginal costs of building the required infrastructure in conjunction with the ‘robust’ areas if the growth experienced at the time suggests a need for early harvest versus the cost of not having the infrastructure available at an optimal time.

6.6.Conclusions

The effects of internal sources of uncertainty on optimal strategic decisions at plantation estate level are variable, but can be isolated from multiple LP runs with realistic stochastic variation in the input parameters. The specific effects will be related to the age class distribution and how different stands with the possible regimes contribute to critical production periods in the planning horizon. Multiple LP runs with realistic stochastic variation can isolate the important stands and how their management may need to change in response to variation from expected growth.

An increase in the growth of stands in a plantation estate due to a hotter and drier climate from, say, 30 years will result in an increase in the overall sustainable yield and an improvement in a discounted objective function in the majority of cases. However, the uncertainty and the non-declining constraints on yield mean that the initial volumes available will be less than estimated by a deterministic run. This is likely to be a very general finding which means that future improved growth cannot be ‘exploited’ in the immediate future if there is any sort of non-declining constraint.

Many broad silvicultural decisions are robust and remain as optimal decisions under a wide range of variation scenarios. However, the approach used here can isolate a few decisions that may need to be changed, with the optimal choice dependent on relative growth in the future – so some risk-based decisions will still need to be made.

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Further work is required to detail the effects of increased mortality when hot and dry events occur during critical phases of plantation growth. The risk introduced by this mortality may negate the possibility of relying on any increase in sustainable yield due to the otherwise expected increase in growth under future climate scenarios. Research is also required to examine the interaction of wider sources of error – external and more internal sources – to determine if some dominate strategic decisions more than others.

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7. OPERATIONAL PLANTATION MANAGEMENT ADAPTATION OPTIONS

7.1.Introduction

In this chapter we focus on operational plantation management adaptation options (Table 24), considered under species, sites and silviculture (management) after Lambert and Turner (2000), assuming that the existing plantation industry will continue to be primarily concerned with wood production for current markets, and more or less in and around existing production regions (e.g. NPI regions, Parsons et al. 2006). We acknowledge that longer-term adaptation of the industry may include changing or diversifying products (e.g. for environmental services) or expanding/moving to new regions, and these may be significant co-drivers to operational management adaptation.

A few of the adaptation options (Table 24) are illustrated or discussed in more detail below. Some of the options are competitive or mutually exclusive and therefore response criteria (e.g. yield, financial return, risk) need to be explicitly specified and commonly valued to enable objective choices. Some options can be exercised before plantation establishment, some during the rotation (and in where stand age or stage of development may be critical to the effect), and some between rotations. Some are speculative, and some are incremental improvement along business-as-usual or current trends (e.g. refinement and application of ‘site specific’ or ‘precision’ silviculture). Some options can be implemented now, others require significant research effort.

Three comments are pertinent to consideration of species – sites – silviculture choices for wood production:

1. Harvesting technology and processing technology for particular products must also be considered. For example, unit mechanized harvesting costs will generally decrease, and sawn timber recoveries will generally increase, with increasing tree/log diameter, that result from lower stockings or from thinning. However, actual optimal log sizes will vary significantly with sawing technology and pattern, and the volume of throughput (e.g. Washusen and Innes 2008).

2. Externalities to the enterprise, and equity considerations (including inter-generational), variously regards use of the atmosphere, land, water, energy, nutrients (and non-renewable resources generally) should also be accounted for.

3. Since risk is a function of probability and consequence, risk may be lowered by reducing the stake invested in an enterprise. For example, we might consider significantly re-designing silvicultural systems for lower inputs or intensity of management, say using species mixtures with a (preferably productive, but alternatively sacrificial) N-fixing species to reduce costs (investment) in fertiliser.

The Australian plantation estate of about 1.9 M ha is comprised mostly of P. radiata,P. pinaster, E. globulus and E. nitens in southern regions, and P. elliottii, P. caribaea

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(and their hybrid), Araucaria cunninghamii, E. grandis, E. pilularis, E. dunnii, Corymbiaspp. (and hybrids) and Acacia mangium in northern (subtropical to tropical) regions (Parsons et al. 2006). E. globulus and P. radiata together comprise about 70% of the total estate. The relatively rapid expansion of the estate from about 1.1 M ha in 1995 has been mostly hardwoods, including at least in mainland Australia expansion towards the lower rainfall end of the traditional 600+ mm mean annual rainfall (MAR) zones for commercial plantations. Agroforestry / farm forestry, including a diversity of species, is a minor component (< 10% of total area) of the plantation industry.

As described in Chapters 4 and 5, climate change in some regions, may indeed be beneficial (increase) to plantation productivity where to date climatic factors have been limiting tree growth directly, or indirectly (e.g. by limiting species choice). However, for that portion of the estate where growth is primarily limited by water availability, it is clear that operational adaptation options for future warmer, dryer (and more erratic rainfall) climates, must optimise (in which risk is part of the calculation) growth (actually yield or financial return) by managing (through species, sites and silviculture choices) water availability to the stand, and stand water use and water-use efficiency if productivity is not to be compromised by rising drought risk. This may be primarily through managing (where possible): site water conservation, plant available water-holding capacity in the soil rooting zone (PAWCr) and stand leaf area (through time); but more generally all factors significantly driving, mediating or mitigating evapotranspiration.

7.2.Species

In southern Australia where expected impacts of climate change (warmer, dryer) are apparent, and where coincidentally mainstream industry has been seeking to expand from a limited, competitive higher-rainfall land base, there is considerable operational experience and accompanying yield data for E. globulus and trial experience for P. radiata near their lower rainfall limits. While not mainstream, hardwood species such E. cladocalyx, Corymbia maculata, E. occidentalis, E. tricarpa, E. muelleriana have been investigated for the 400-600 mm MAR zone. Since the 1920’s, P. pinaster has been grown (and selected and bred) for the low rainfall zone in Western Australia and South Australia (Dieters 2008). Thus for softwoods, there is already an established species substitution to serve similar markets, either in response to climate change or for expansion of the estate, the latter for both production and potential environmental services. However, the low rainfall hardwood species either do not have sufficient growth rates (even on high quality sites, and where mainstream species will be more attractive anyway) or wood properties for commodity wood production. The prospect of niche wood markets and tradable environmental services (carbon biosequestration, salinity control) are held forth as an essential co-driver for investment in plantations of these species.

There is now a relatively mature southern mainland hardwood pulpwood industry, dominantly of E. globulus (about 0.5 M ha in total) in the 600-800 mm MAR zone, with about 0.38 M ha planted since 1999 and due for harvest over the next 10 years. This industry must address for a second rotation, in light of experience of first rotation yields often being significantly less than anticipated on many sites, the need for substitute species better suited to some sites per se and where water availability will be exacerbated by climate change. The more mature P. radiata industry is arguably better

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placed and informed to respond to the near-term effects of climate change. It has significant areas well into their second rotation and some in third rotation, and therefore better knowledge of site factor growth relationships, or at least the empirical growth data for site types. Moreover, the industry has not significantly expanded to lower rainfall areas from its medium to higher rainfall base.

E. smithii is grown in South Africa and China since it has some cold/frost tolerance and therefore allows expansion of the estate to new areas. It is also grown in China for oil production. Anecdotally in Western Australia it appears to have greater tolerance to drought than E. globulus, and operational plantings and some selection and breeding work have commenced. Experience from field trials in Gippsland, Victoria (Duncan etal. 2000) is that volume growth of E. smithii across a range of site productivities (MAI 10 to 40 m3 ha-1 y-1 at age 10 years) is on average about 90% that of E. globulus. On lower rainfall sites (<700 mm MAR) growth of E. smithii is equal to that of E. globulus and some of the difference on the lower rainfall sites is attributable to greater survival. However, the eastern Australian sites do not have the clear Mediterranean type distribution of rainfall found in south-western Western Australia and therefore the sites do not test the relative drought tolerance of E. smithii to the same extremes.

It must be noted that the wood properties of alternative/substitute species are critical to their consideration for existing markets, but the relative importance of wood basic density and pulp yield in pulpwood production depends on perspective. In general, pulp yield is critical to hardwood pulp mill profitability and differences of 1-2% are significant given the quantity of wood processed by large world-scale mills. Density is more important to woodchip exporters aiming to maximize wood delivery (as BDU) per shipload, although density effects (say differences of 40 kg/m3) on digestor production efficiency and papermaking properties are also significant for hardwoods. Simplistically giving equal weight to the two properties (ranked for different species by Washusen and Clark 2005), we might conclude that the relative pulpwood value of species is: E. globulus > E. nitens = E. smithii.

A series of ten reviews of achievements in forest tree genetic improvement in Australia and New Zealand have been recently published: P. radiata (Wu et al. 2007, Burdon et al. 2008), P. pinaster and P. brutia (Butcher 2007), A. cunninghamii (Dieters et al.2007), Pseudotsuga menziesii (Shelbourne et al. 2007), E. nitens (Hamilton et al.2008), E. dunnii (Smith and Henson 2007), E. pilularis (Henson and Smith 2007), Corymbia (Lee 2007), and for low rainfall farm forestry species (Harwood et al. 2007). For the dominant industry species, little is indicated on selection and breeding particularly for lower rainfall sites (although some work for P. radiata, Wu et al. 2007) or for future (dryer) climates, drought, or increased atmospheric carbon dioxide concentration. It is clear that selection for survival and growth in water limited environments has been included in species deployment strategies and this is apparent for species naturally adapted to and being targeted for low rainfall sites (P. pinaster,P. brutia, and E. cladocalyx, C. maculata, E. occidentalis, E. tricarpa and E. sideroxylon). There is an extensive body of research on E. globulus (e.g. see Potts et al. 2004) and recognition of genetic variation in drought tolerance (e.g. Dutkowski 1995). Generally we would expect that tree breeding for future climates will be useful, including that addressing tolerance/resistance to insect pests and diseases assuming that the incidence/distribution and activity of these may change with climate. We also raise the possibility that there may be genetic variation within species in the ability to

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utilise elevated CO2 conditions. There is no understanding of the degree of photosynthetic acclimation likely to be exhibited by Australian plantation species, however our earlier analysis suggests that plant response may greatly affect future plantation production. Elevated CO2 studies in other plant species have indicated potential genetic variation in photosynthetic acclimation based around changing source/sink dynamics and carbohydrate metabolism (Crous et al. 2008).

7.3.Sites

While site factors may affect wood properties, an enterprise will choose to grow a product generally suitable to market(s), and then choose appropriate species, and then sites where these species may be profitably grown. The scale of industrial plantation forestry relative to the scale of variation in topographic and edaphic factors is such that plantations will be invariably be established and to some degree uniformly managed in units having a range of soil properties (e.g. depth, texture, structure, porosity, drainage, nutrient availability, salinity) that may significantly affect growth. It is evident in the variation in individual tree growth in both seedling and clonal plantations, on apparently uniform sites and where there is uniform and high quality of site preparation and planting, that micro-scale changes in site factors indeed significantly affect growth.Such variation may not be evident when stands are well within optimal conditions for growth. If climate change shifts such stands closer to the climatic margin for species performance or survival then such variations in site conditions may influence growth more markedly and produce a more heterogeneous stand or patchiness where tree death has occurred.

Site factors (climatic, edaphic, topographic) that cannot be practicably/economically managed, ameliorated or mitigated are of primary interest in evaluating new sites for plantation establishment, or choosing to exit from sites that have proven through experience or now indicated from better knowledge on growth-site factor relationships to be unprofitable. The effect of climate variability and climate change on growth is explored in detail elsewhere in this report. That at least within zones of similar radiation and evaporative demand, rainfall and soil plant available water-holding capacity in the rooting zone (PAWCr) explains much of the variation in growth (e.g. Harper et al. 2009) has led to a focus on rooting depth, next to rainfall, in site selection, although soil texture and structure must also be considered.

Detailed soil description methodologies relevant to plantation establishment are well established (e.g. Ryan et al. 2002). Industry has operational implementations of these including interpretation of these for growth rates. While site factors have been explored in process based models following extensive physiological studies, routine site assessment for new plantations by industry is often based on rules of thumb drawn from experience. For example, a guide in Western Australia (Mediterranean climate) is that soils shallower than 2 m rooting depth should not be planted because of the probability of drought death. In south-eastern Australia where there is more uniform distribution of rainfall, or at least some summer rainfall and lower extreme evaporative; plantation survival (at least) and growth (see Wang and Baker 2007) can be adequate on soils with relative shallow heavy clay sub-soils occurring at 1 – 2 m depth, into which there is little apparent root penetration (Grant et al. 2007). Adaptation guiding principals for site selection may be required in the face of climate change: in a rapidly changing

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environment historical experience may become unreliable and will therefore need to be tempered with some process understanding.

7.4.Silviculture

Soil cultivation pattern

In addition to soil cultivation to remove/reduce impedances to root growth (conventionally by deep ripping), or to elevate young plants from seasonal soil water logging (conventionally by bedding/mounding), cultivation might be used to conserve rainwater on site by reducing run-off, particularly that arising from high-rainfall or high rainfall intensity events. Typically cultivation patterns (direction of beds relative to contour) have aimed to move water off-site to avoid water logging. The converse might be investigated, although uncertainty in the balance of benefits and risks may be too great for adoption.

Stocking, thinning and fertiliser application

Plantation stocking (planting density, espacement) is a primary silvicultural decision that must consider the intended products (e.g. yield and dimensions of log sizes over a rotation), the species growth habit (e.g. apical dominance, branching), potential site productivity, and site factors (e.g. topography and therefore options/costs of operations from planting through to harvest).

For the relatively simple silvicultural system (i.e. plant, then harvest) used for hardwood pulpwood production over much, but not all, of the Australian E. globulus estate there is some information on stand growth response to stocking. In addition to conventionally motivated studies on yield responses, drought-associated mortality in some plantations in Western Australia in the early 1990s prompted experimental work on stocking effects on growth and risk, with further impetus from apparent long-term decline in rainfall associated with climate change. However, there are few published analyses of yield responses, and financial analysis, across the production value-chain that include stocking effects on establishment and harvesting costs. The now rapidly increasing area of plantations being harvested has particularly focused attention on stocking as it affects tree size and therefore harvesting efficiency, and a revisiting of the stocking question for the next rotation.

Various unpublished data of rotation-length growth data from E. globulus stocking trials in Western Australia have shown that growth and yield will generally increase with stocking, although the sensitivity on mid to lower productivity sites is relatively flat in the range 600 – 1200 trees ha-1. During dry conditions mortality on highly stock stands can lead to drought death and reduce stand volumes (Mendham et al. 2007). Varying stand density either at planting or by thinning is one silvicultural option shown to reduce the effects of seasonal drought in on E. globulus and P. radiata plantations. Thinning or reducing planting density will increase the volume of soil available to each tree and can reduce the amount of water stress experienced by retained trees (Butcher and Havel, 1976; Donner and Running, 1986; Cregg et al., 1990; Breda et al., 1995; Aussenac and Granier, 1998). In the overwhelming majority of published studies, diameter and volume growth of individual trees is greater at lower stand-density (e.g. Ginn et al., 1991;

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Schonau and Coetzee, 1989; Goncalves et al., 2004).This has been attributed to increased radiation interception per tree and reduced water stress, resulting in a lengthened growing season (Butcher and Havel, 1976; Laurent et al., 2003). Laurent et al. (2003) argued that thinning was a mechanism for drought-proofing stands of Picea abies L. (Karst.). Many studies show, in addition to increasing tree size, that thinning or reduction in density can reduce yield at the stand scale (Schonau and Coetzee, 1989 in a review of studies in Eucalyptus plantations). Although there is a strong, positive relationship between stand density and stand volume growth in wet or summer rainfall dominated environments (Schonau and Coetzee, 1989), there is good evidence that total water supply constrains the stand-scale response to increased stand density in water-limited environments (Butcher and Havel, 1976; Will et al., 2001).White et al. (in prep) have demonstrated in a series of fertiliser and thinning experiments in the south west of Western Australia that reducing leaf area either by thinning or withholding fertiliser can greatly reduce tree water stress and prevent drought death. They found in these highly water limited sites that stands with lower leaf area index used site water resources more slowly, although by rotation end had used similar amounts to produce similar amounts of wood at stands growing with higher leaf area indices and at greater risk of drought death.

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Figure 34. Relationship between planting density, final production and the number of hot dry days (days > 35oC and with soil water potential <-3.2 MPa) for the GT-16 site. Production under 1980 conditions was 191 m3 ha-1 with no hot dry days.

These relationships are illustrated in results of a CABALA simulation for the GT_16 site from Chapter 5 (Figure 34). When simulated with 1980 climate this site at 1000 stems per hectare planting density had an average yield of 191 m3 ha-1 and a prediction of zero drought death days. Under 2030 forecast condition from the Hadley A1Fi model combination yield is forecast without photosynthetic up-regulation to be 203 m3 ha-1, but with on average seven high risk days (hot dry days) per rotation. Under the 2030 climate volume increases with stocking though increase is slight above 1000 trees per hectare (trees ha-1). Near equivalent volume production (186 m3 ha-1) can be produced under 2030 climates by reducing stocking to 800 trees ha-1. Under these conditions there is the added advantage that average tree diameter is 21 cm compared with 20 cm at 1000 trees ha-1 and 18.5 at 1200 trees ha-1.

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Coppicing

A final silvicultural consideration is whether to regenerate stands in the case of eucalypt plantations with coppice or seedlings. Coppicing of stands is the cheaper option and avoids the risk of seedlings dying from drought death during establishment. Overseas experience is that for the first coppice rotation, production is often equivalent to that for seedling established stands but falls away in subsequent rotations as an increasing number of coppice stools fail and the stand become heterogeneous. Furthermore, coppicing delays the deployment of genetically improved material. With relatively short rotations this may not be a significant issue unless improved breeding strategies and deployment, as is emerging, leads to better site by genotype matching in order to capture GxE effects of drought tolerance. An emerging issue for the bluegum plantation industry is the issue of depletion of soil water stores during the first rotation and the incomplete recharge of soil water during the inter-rotation and early establishment phase in medium rainfall areas. In high rainfall areas (>1000 mm) soils seem to be adequately recharged. This is a topic of on-going investigation, with initial evidence that seedlings planted into such sites experience significantly higher levels of water stress than coppice stands on matched sites. With much of the bluegum estate predicted to be dryer in the next 20 to 50 years this may be an issue of increased importance.

7.5.Conclusions

Operational plantation management options to adapt to climate variability/change will need to simultaneously optimise interacting species, sites and silviculture choices to produce wood for the target markets. Moreover, risk analysis must be applied to include not only the conventional commercial uncertainties associated with investment, but also explicitly include biophysical risks that may affect production costs, and the yield and therefore revenue. The analysis should extend across the entire value chain of an enterprise, that is to the point of transaction, since it is clear that plantation management decisions will also affect, for example harvesting costs.

Management, including silvicultural practice, is ultimately concerned with return on investment and it is impossible to be prescriptive here regards adaptation options because circumstances (e.g. investment model, products, location, etc.) will vary markedly across enterprises/estates. General illustrative examples are useful, but cannot substitute for specific analysis. To this end, plantation growth and yield models, responsive to site (including climate, soils), species and silviculture factors at a scale relevant to site variation and to the application of management operations must be at the heart of any decision support system.

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Table 24. Operational management considerations/options for Australia’s plantations for adaptation to climate change, including climate variability/extremes

SpeciesSpecies substitution: Use species better adapted to expected climates Species selection/breeding/hybridization/GM: Tree improvement for existing/new species for adaptation to expected climates SitesClimate: Use expected climates as a site selection criteria Rooting zone plant-available soil water storage: Use selection specifications consistent with expected climates. Planting location: Use biophysical stratification of planting sites to facilitate intra-site-specific management SilvicultureSoil cultivation: To increase soil rooting volume and thus plant-available water. Use site cultivation to reduce run-off and conserve water Weeding: Use spatial weeding patterns and/or cover species that minimize non-crop and soil evapotranspiration and/or provide other benefits (e.g. N-fixation) Watering-in, Hydrogels: Use strategically or tactically to ensure/improve survival after planting Irrigation: Only a niche application in Australia using wastewaters (see Baker et al. 2005) Stocking (planting density): Use lower stockings to reduce stand leaf area development and to increase resource availability to planted trees Thinning (waste/commercial): To (temporarily) reduce stand leaf area, to increase resource availability to retained trees, and for stand vigor/hygiene. Note that pruning solely to reduce stand leaf area is costly and not relevant to pulpwood production; and if simply reactive to drought probably would be undertaken too late to capture solid timber wood quality benefits. Nutrition and nutrient conservation, fertiliser application, biological N-fixation: Strategically manage site fertility (nutrient supply capacity/intensity) and tree nutrition consistent with crop (age-dependent) demand under expected climates. Use short term weather outlook in decisions for tactical application of N. Control leaf area by withholding fertiliser to reduce drought stress. Forest health: Timely/enhanced surveillance/monitoring for pests and diseases, and early intervention where required. Species selection and breeding (see Species). Adopt IPM principals, including biological control to provide continuous base-level control/resilience.Fire protection: Fuel reduction burning in some species/environments. Wider firebreaks and break-tree management zones. Rotation length: Shorten to decrease period of vulnerability (drought, pests and diseases, fire) and to facilitate species/germplasm changeover. Balance estate structural/age class-distribution to reduce risk. Adjust harvest schedules according to stand vulnerability. Inter-rotation period: Include fallow to allow soil water recharge, perhaps including green and/or cover crops Slash/debris management: Retain on site (chopped/mulched) to reduce evaporation from soil. Note that there will be associated nutrient conservation and weed control benefits Next rotation: See Species. Include climate change considerations in the coppice or replant decision.Species mixtures: Crop species rotation, or interplant crop species with a crop/sacrificial N-fixing species

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9. ACKNOWLEDGEMENTS

We are grateful to the project partners who provided data and forest management advice. These include Ian Last, Eric Keadie and Kerry Catchpoole FPQ; Dave Osborne QDPI; Steve Read, Matt Wood, Paul Adams Forestry Tasmania; Sandra Hetherington Norske Skog; Steven Elms HVP; Paul Fiekema University of Melbourne; Ben Bradshaw Timbercorp; Tim Murphy, Jim O’Hehir Forestry SA; Christine Stone State Forest NSW; Adrian Goodwin Bushlogic (and apologies to those forgotten). We also are grateful to the contributions of Don White, Chris Harwood and Tony O’Grady for reviewing the report and Maria Ottenschlaeger for assistance in preparation of the document. Finally we would like to thank the many people who made their computers available to perform over 1,000,000 simulations of forest production synthesised in this report.

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10. RESEARCHER’S DISCLAIMER

Enquiries should be addressed to:Dr Michael Battaglia CSIRO Sustainable Ecosystems Private Bag 12 Hobart TAS 7001

Copyright and Disclaimer © 2009 CSIRO To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO.

Important Disclaimer CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

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