Opportunities & Threats for S Australia’s Agric Landscapes from Reforestation under a Carbon...

CSIRO Sustainable Ecosystems

Opportunities and Threats for South Australia’s Agricultural Landscapes from Reforestation under a Carbon Market

Final Report – March 2010

Neville D. Crossman, David M. Summers and Brett A. Bryan

Enquiries should be addressed to:

Dr Neville Crossman CSIRO Sustainable Ecosystems

Ph: +61 8 8303 8663

[email protected]

Please Cite as:

Crossman, N.D., Summers, D.M. and Bryan, B.A. (2010). Opportunities and Threats for South Australia’s Agricultural Landscapes from Reforestation under a Carbon Market. CSIRO Client Report.

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Contents Executive Summary......................................................................................................1

1. Introduction..........................................................................................................3 1.1 Background............................................................................................................... 3 1.2 Purpose of This Study .............................................................................................. 3

2. Literature Review.................................................................................................5 2.1 Markets and Emissions Reduction ........................................................................... 5 2.2 Quantifying Biosequestration of Carbon................................................................... 5

2.2.1 Overview.................................................................................................................. 5 2.2.2 Existing Models........................................................................................................ 6

2.3 Economics of Biosequestered Carbon ..................................................................... 7 2.3.1 Quantifying Agriculture Profitability .......................................................................... 7 2.3.2 Quantifying Carbon Profitability................................................................................ 8

2.4 Spatial Targeting of Opportunity and Threat ............................................................ 8

3. Methods ..............................................................................................................10 3.1 Study Area .............................................................................................................. 10 3.2 Economics of Dryland Agriculture .......................................................................... 11

3.2.1 Modelling Yields – Grains and Legumes................................................................ 11 3.2.2 Comparison of APSIM Yields to ABS Agricultural Statistics .................................. 12 3.2.3 Modelling Yields – Grazing .................................................................................... 12 3.2.4 Quantifying Profit ................................................................................................... 12 3.2.5 Allocating Profit to Land Use.................................................................................. 14

3.3 Economics of Carbon ............................................................................................. 14 3.3.1 Modelling Biosequestration of Carbon ................................................................... 14 3.3.2 Comparisons to Other Carbon Productivity Models ............................................... 15 3.3.3 Quantifying Net Present Value of Carbon Plantings .............................................. 16

3.4 Estimating Opportunities and Threats .................................................................... 17 3.4.1 Landscape Productivity.......................................................................................... 17 3.4.2 Water Resources ................................................................................................... 17 3.4.3 Biodiversity ............................................................................................................ 19 3.4.4 Existing Dryland Agriculture................................................................................... 21 3.4.5 Summary of Threat and Opportunity...................................................................... 22

4. Results................................................................................................................23 4.1 Economics of Dryland Agriculture .......................................................................... 23 4.2 Economics of Carbon Biosequestration ................................................................. 26

4.2.1 Carbon Productivity................................................................................................ 26 4.2.2 Comparisons to Other Carbon Sequestration Models............................................ 27 4.2.3 Economic Viability of Carbon ................................................................................. 28

4.3 Threats and Opportunities ...................................................................................... 32 4.3.1 Landscape Productivity.......................................................................................... 32 4.3.2 Water Resources ................................................................................................... 35

Opportunities and Threats from Reforestation • February 2010 i

ii Opportunities and Threats from Reforestation • February 2010

4.3.3 Biodiversity............................................................................................................. 37 4.3.4 Productive Agriculture ............................................................................................ 42 4.3.5 Summary................................................................................................................ 45

5. Discussion and Conclusion............................................................................. 46 5.1.1 Opportunities.......................................................................................................... 46 5.1.2 Threats................................................................................................................... 47 5.1.3 Policy and Planning................................................................................................ 47

Acknowledgements.................................................................................................... 50

References .................................................................................................................. 51

List of Figures Figure 1. Location of the study area and the dominant dryland agricultural land uses modelled

in this study. ......................................................................................................................... 10

Figure 2. Location of water management zones occurring within higher rainfall regions of South Australia. .............................................................................................................................. 19

Figure 3. Annual yields of the major dryland agricultural commodities modelled across South Australia: a) sheep and beef; b) wool; c) wheat; d) field peas. Comparative yields drawn from the agricultural census for e) wheat, and; f) field peas................................................ 24

Figure 4. Profit at full equity of the major dryland agricultural commodities modelled across South Australia under the baseline commodity price scenario: a) sheep; b) beef; c) field peas, and; d) wheat. ............................................................................................................ 25

Figure 5. Profit at full equity for dryland agriculture in South Australia under the four commodity price scenarios: a) 50% of baseline; b) baseline; c) 150% of baseline, and; d) 200% of baseline................................................................................................................................ 26

Figure 6. Average annual carbon productivity (CO2-e) of the four tree systems modelled in the study area: a) Eucalyptus globulus; b) Eucalyptus cladocalyx and Eucalyptus camaldulensis blend; c) Eucalyptus kochii, and; d) mixed environmental plantings. .......... 27

Figure 7. Comparisons of average annual carbon productivity (CO2-e) between this study’s tree system models and the models of Hobbs (2009) and Polglase et al (2008). ...................... 28

Figure 8. Profitability of reforestation of cleared landscapes for carbon sequestration, expressed as equalised annual equivalents (EAE), under a carbon price of $20/t of CO2-e and the baseline commodity price scenario. Carbon plantings are economically viable when EAE > 0. The tree systems modelled are: a) Eucalyptus globulus; b) Eucalyptus cladocalyx and Eucalyptus camaldulensis blend; c) Eucalyptus kochii, and ; d) mixed environmental plantings............................................................................................................................... 29

Figure 9. Proportion of existing dryland agriculture (cropping, grazing) that is economically viable for reforestation for carbon sequestration (left side), and annual carbon (CO2-e) sequestered (right side), under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario. Dashed line in right side figures is the total annual CO2-e emissions for South Australia in 2005 (Government of South Australia 2007a). ................ 31

Figure 10. Individual metrics that form the landscape productivity priority surface for deep-rooted perennials. ................................................................................................................ 33

Figure 11. Landscape productivity priority for planting deep-rooted perennials: a) raw continuous surface, and; b) classified high priority locations. ............................................. 34

Figure 12. Proportion of high priority landscape productivity locations that are economically viable for reforestation for carbon sequestration under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario............................................................... 35

Figure 13. Total water evapotranspired by reforestation for carbon under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario. .................................. 36

Opportunities and Threats from Reforestation • February 2010 iii

Figure 14. Annual volume of water evapotranspired by E. globulus reforestation for carbon at $20/t of CO2-e in higher rainfall water management zones given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario. .........................................................................................37

Figure 15. Individual metrics that form the biodiversity priority surface for ecological restoration...............................................................................................................................................38

Figure 16. Biodiversity priority for reforestation of cleared dryland agricultural landscapes using mixed native species: a) raw continuous surface, and; b) classified high priority locations...............................................................................................................................................40

Figure 17. Proportion of high priority biodiversity locations that are economically viable for reforestation for carbon sequestration under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario. ..............................................................41

Figure 18. Annual profit (EAE - equalised annual equivalents) per hectare of reforestation for carbon sequestration under the four tree systems in high priority biodiversity locations only: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario....................................42

Figure 19. Annual net economic returns (EAE - equalised annual equivalents) to South Australia from adoption of reforestation for carbon sequestration under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario. ........................43

Figure 20. Proportion of potential wheat (left side) and sheep (right side) production that could be lost following reforestation for carbon sequestration under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and; d) 200% commodity price scenario...................................44

iv Opportunities and Threats from Reforestation • February 2010

List of Tables Table 1. Climate class summary statistics from BIOCLIM climate surfaces............................... 12

Table 2. Profit function parameters and associated values used to for the dominant commodities produced in the study area. ............................................................................ 13

Table 3. Description of metrics used to estimate spatially explicit priorities for biodiversity conservation......................................................................................................................... 20

Table 4. Summary of opportunities and threats from reforestation for carbon sequestration. Results are for the baseline commodity price scenario. ...................................................... 45

Opportunities and Threats from Reforestation • February 2010 v

vi Opportunities and Threats from Reforestation • February 2010

EXECUTIVE SUMMARY

There may be substantial demand for carbon dioxide equivalent (CO2-e) permits should a carbon trading market be established in Australia. Some prediction of the behaviour and impacts of the carbon market can be used to inform potential policy responses. Introduction of a carbon market could result in strong demand for widespread conversion of land in agricultural regions to tree-based production of permits if it is economically and logistically viable to do so. This could potentially provide a number of opportunities for South Australia’s natural resources, rural environments and communities. Conversely, some potential threats exist from widespread land conversion and policy needs to be designed to mitigate damage.

We have modelled the profit from traditional dryland agriculture, the potential biosequestration of carbon under permanent monoculture and mixed native tree reforestation systems, the economic viability of carbon biosequestration under various commodity price scenarios, and the spatial location and extent of management priorities for key natural resources.

The analysis demonstrates the nature of the inter-relationships but does not prescribe the behaviour of individuals regarding land use change in a formal carbon market. There are many other factors that influence landholder behaviour and land use change in addition to commercial considerations. This is especially the case when land use change involves legally binding agreements to maintain carbon-based reforestation for many decades. There are also a range of existing natural resource management policy and planning controls that influence the conversion of agricultural land to forestry purposes.

Our analysis suggests that many areas of South Australia’s dryland agricultural landscapes could potentially be converted to permanent reforestation for the generation of carbon permits. But carbon price has a large bearing on the extent of reforestation as do the logistics of planting including seed stocks, planting and silvicultural resources. Large parts of the South Australian agricultural landscape are economically viable for permanent reforestation under higher carbon prices that could realistically be expected under a carbon market. South Australia’s annual carbon emissions could be offset by reforestation under a carbon price of $20/t of CO2-e and this would require less than 20% of existing agricultural lands.

There are substantial economic benefits for regional South Australian communities of reforestation for carbon biosequestration that offset South Australia’s emissions. For example, adoption of carbon plantings and sale of permits could see a boost in income to the State’s agricultural sector in the order of 20%, or $1billion annually. This growth includes any economic losses from the conversion of traditional dryland agriculture to permanent reforestation.

There are also significant opportunities for managing soil erosion and degradation, improving water quality and enhancing the stocks of biodiversity through biosequestration. The size of the gains is dependant on carbon price, level of uptake

Opportunities and Threats from Reforestation • February 2010 1

and tree species planted. Extensive areas of land identified as high priority for ecological restoration could be economically viable for reforestation using mixed local native species. Well designed policy is needed to ensure the right trees go in the right places to provide the best opportunities for South Australia’s agricultural landscapes and environments.

Robust policy must also be designed to prevent undesired natural resource management, economic and social outcomes in South Australia resulting from the introduction of a carbon market. From our exploratory analyses, and in the absence of policy and planning constraints, undesirable outcomes from reforestation for carbon could include a significant reduction in food and fibre production; reduced recharge of groundwater and surface water reserves; and only marginal biodiversity benefits.

Some of these undesired outcomes are already recognised and work is underway on appropriate policy development to further understand and manage threats posed by land use change. For example, the National Water Initiative reforms may result in full-cost pricing of water for all land uses, including reforestation.

Policy developments will need to be spatially targeted to maximise the return on investment in reforestation in regions where ecological restoration is identified as a high priority for biodiversity conservation. Incentives may need to be offered in key locations for ecological restoration to steer reforestation plantings for carbon away from monocultures and toward local native species.

Overall, land use change will occur in response to new carbon markets. The speed at which this change will occur is unknown because there are other factors that effect uptake which are not considered here. These factors include underlying attitudes and behaviours of farmers to changing practices, availability of capital and resources for widespread reforestation, other market factors, capital and investment profiles of farmers currently practicing dryland agriculture, and perceptions of risk associated with long-term permanent land use change.

However, land use change will be critical for agricultural communities to adapt, survive and prosper under a changing climate. Traditional dryland agriculture is under threat from climate change. The widespread uptake of reforestation for carbon will build community and landscape resilience to climate change.

2 Opportunities and Threats from Reforestation • February 2010

1. INTRODUCTION

1.1 Background

The establishment of a carbon market in Australia is a top priority for the Australian Government. For example, the Australian Government’s proposed Carbon Pollution Reduction Scheme (CPRS; Australian Government, 2008) will, if legislated, establish a cap-and-trade market instrument on Australia’s carbon emissions by 2011. Under the proposed cap-and-trade instrument, the largest 1,000 CO2-e emitting companies in Australia will need to acquire a permit for each tonne of CO2-e they emit. At the end of each year liable companies will need to surrender permits for each tonne they emitted. The annual issuing of permits by the Government will be capped at a level consistent with their emissions target. Companies will therefore have to compete to purchase the permits they require. Companies for whom it is expensive to reduce emissions will buy permits while companies for whom it is cheap to reduce emissions will do so and then sell their permits on the market. A price is then established for a tonne of CO2-e.

Establishing a price for CO2-e will create an income stream for activities that permanently sequester carbon because permits will be generated for each tonne of CO2-e sequestered. One such activity is the biosequestration of carbon by trees. The CPRS deals specifically with land management activities that sequester carbon. Reforestation of cleared landscapes for the permanent biosequestration of carbon is an activity that will generate permits that can then be sold into the market.

If the market functions correctly, there will be demand for CO2-e permits with the introduction of a carbon market in Australia. Some prediction of the behaviour and impacts of the emerging carbon market can be used to inform potential policy responses. Introduction of a market for carbon could result in strong demand for widespread conversion of land in agricultural regions to tree-based production of permits if it is economically viable to do so. This poses a number of potential opportunities and threats for South Australia’s natural resources, rural environments and communities.

1.2 Purpose of This Study

This study explores the potential influences of carbon permit prices and existing landuse values as economic drivers for change from traditional agriculture to carbon market forestry in the dryland regions of South Australia. For the purpose of this study, and to simplify results presented, we have assumed price-only driven scenarios unrestrained by natural resource management policy and planning controls, investment risks or other barriers to adoption.

Spatial analyses and summaries of land potentially available to carbon forestry under a range of price scenarios gives us insight into the potential supply of land for carbon


sequestration activities using mixed species environmental planting and monocultures of carbon crops. This study also provides preliminary evaluations of regional soil, water, biodiversity and agricultural production issues that can influence the extent and placement of revegetation within regions.

This exploratory study has been conducted in collaboration with the SA Department of Water, Land and Biodiversity Conservation (DWLBC). It was prepared as a piece of informative research with intent of providing a better understanding of issues that may need to be addressed in future policies relating to carbon sequestration activities in South Australia.


2. LITERATURE REVIEW

2.1 Markets and Emissions Reduction

There are two commonly cited market instruments available for reducing the level of CO2-e emissions. One is the cap-and-trade, or quantity based instrument for controlling emissions. The other is a tax, or price based instrument for controlling emissions. Debate has continued since the early 1970s over which of the two instruments should be used to control emissions (Weitzman, 1974; Metcalf, 2008; Keohane, 2009). Proponents of cap-and-trade argue that it is a more precise abatement mechanism than a tax for meeting an environmental target, such as reducing global emissions by 20% by 2020, but its critics say it is administratively more complex to implement and is subject to greater price volatility and uncertainty. Proponents of a tax on CO2-e claim this instrument is much simpler to administer and is more mature, transparent and stable. Despite the merits of one over the other, the cap-and-trade instrument is the current instrument of choice for developed nations that have either implemented (e.g. European Union) or are considering implementing (e.g. USA, Australia, Canada) market mechanisms for controlling CO2-e emissions. On the other hand, the economic literature suggests that price-based instruments are better in terms of economic efficiency (Kolstad 1996; Hoel and Karp 2002; Pizer 2002; Newell and Pizer 2003).

2.2 Quantifying Biosequestration of Carbon

2.2.1 Overview

Biosequestration of carbon through the photosynthetic activity of living vegetation removes carbon from the atmosphere and stores it in persistent plant biomass (e.g. stemwood, branches and roots) (Dewar and Cannell, 1992). The changes in the volume of carbon stored in a location, or the carbon flux, is a function of historic and existing land management (Houghton et al., 1983; DeFries et al., 2002; Houghton, 2003; Canadell et al., 2007). For example, a land management system dominated by grass and herbaceous species will typically have a low amount of biomass and hence carbon. Afforestation or reforestation of this type of system will rapidly increase the volume of carbon sequestered until the vegetation reaches a mature state (Silver et al., 2000; Wise and Cacho, 2005). There will be minimal additional carbon sequestered at the site once the vegetation matures, but the total carbon held at the location remains relatively constant if the vegetation is left permanently. These reforestation sites where carbon is actively being sequestered through vegetation growth are called forest carbon sinks.

The increase in biomass and hence carbon sequestered at a reforestation site from time of establishment to maturity is a function of the tree species planted, the management actions and the soil, climatic and topographic characteristics at the site


(Landsberg and Waring, 1997; Silver et al., 2000; Stavins and Richards, 2005; Roxburgh et al., 2006). The carbon sequestered at the site above the known/estimated/measured baseline is the commodity that can be traded in a carbon market. As a general rule, faster growing species in warmer and wetter climates and on more fertile soils will initially sequester carbon at a faster rate, but ultimately the total storage of carbon is dependant on the total biomass accumulated over a longer time frame (Silver et al., 2000). Numerous models and methods have been developed to quantify the amount of carbon sequestered and these are reviewed in the next section.

2.2.2 Existing Models

The history of modelling vegetative biomass, and hence biosequestered carbon, is rooted in the precision forestry and silvicultural sciences. Forest biomass models tend to take one of two forms: i) the empirical forest growth and yield models that aim to predict volumes of biomass typically using simple allometric equations integrating tree height and trunk diameter (e.g. Losi et al., 2003; Redondo-Brenes and Montagnini, 2006; Roxburgh et al., 2006; Yemshanov et al., 2007; Henry et al., 2009), and; ii) the process-based, or mechanistic models that aim to simulate key growth processes and fundamental causes of tree biomass production (Landsberg and Waring, 1997; Battaglia and Sands, 1998; van Noordwijk and Lusiana, 1999; Battaglia et al., 2004). The simpler empirical models can estimate biomass in unsampled reforested sites under similar ecological conditions and species types and ages. But they don’t generally account for finer site scale spatially-explicit variations in environmental and management parameters that determine biomass production.

Process-based models can model these finer-scale determinants of productivity because they have at their core mathematical algorithms describing the physiological and ecological mechanisms of individual tree growth. Thus process-based models produce more accurate estimates of total biomass at a site because they can simulate the productivity based on the key driving parameters. These models are arguably better suited to spatially-explicit estimates of carbon biosequestration volumes. The major limitation to process-based model is their data intensiveness.

The more prominent recent examples of process-based models used to estimate total biomass includes CABALA (Battaglia et al., 2004), WaNuLCAS (van Noordwijk and Lusiana, 1999) and 3-PG (Landsberg and Waring, 1997). CABALA (CArbon BALAnce, Battaglia et al., 2004) is a dynamic forest growth model that links carbon, water and nitrogen flows through the atmosphere, trees and soil. The model is species specific and has been calibrated for the common silviculture species Eucalyptus globulus. Although CABALA requires around 100 species-specific parameters, Battaglia et al (2004) suggest that parameterisation of the model for other common forestry species would be relatively easy because of readily available data. Adaptation of CABALA to other less intensively studies species would require significant research effort.

3-PG (Physiological Principles in Predicting Growth, Landsberg and Waring, 1997) is a relatively simple stand-growth model requiring less than half the number of parameters of CABALA. 3-PG calculates total above- and below-ground carbon fixed in a stand,


corrected for the effects of soil water deficit, atmospheric vapour pressure deficits and stand age. 3-PG was originally parameterised for a generic species, but species-specific parameters have since been calibrated for many forestry trees (see Paul et al., 2007), and most recently for mixed species permanent environmental plantings (Polglase et al., 2008).

3-PG is widely used in Australia. For example, the model is integral to the Australian Government’s National Carbon Accounting Toolbox (NCAT), a derivative of the National Carbon Accounting System (NCAS), used to track emissions and changes in carbon stocks given changes in land use and management. The toolbox includes the Full Carbon Accounting Model (FullCAM, Richards and Evans, 2004) that quantifies carbon stocks of soil and biomass in forest and agricultural systems, including the amount of carbon sequestered at a site by revegetation. FullCAM includes five component models including 3-PG. Further applications of 3-PG for quantifying the amount of carbon sequestered in Australian landscapes can be found in Ward et al. (2005) and Polglase et al. (2008).

2.3 Economics of Biosequestered Carbon

The economic viability of carbon biosequestration is a function of the income available from the generation and sale of carbon permits and the cost of establishing and maintaining the reforestation system, and must include considerations of the opportunity cost associated with the previous use of the land (Stavins and Richards, 2005). The economics of two land management systems must be quantified, namely profit from traditional existing agriculture and profit from proposed reforestation for carbon capture via sequestration and trading. The spatially variable nature of yields in both economic systems requires a spatial analysis approach.

2.3.1 Quantifying Agriculture Profitability

The profitability of agricultural land is variable within a farm and few studies have attempted to map profitability at such fine scales. Agricultural and farm statistics acquired through census and survey techniques have been used for mapping agricultural profitability across wide areas but the data is coarse due to its aggregated nature. Bateman et al. (1999) provides an early attempt at modelling and mapping spatially explicit agricultural profit by combining site-specific biophysical factors that influence commodity yields with farm-scale economic data. Bateman et al (1999) estimate annual profit of the dominant agricultural sectors, dairy and sheep farming, across the entirety of Wales. Precision agriculture (Cook and Bramley, 1998; Khosla et al., 2002) is used to map variations in yield at a sub-paddock scale but has only been applied to small geographical areas.

A few studies have attempted to capture the spatial heterogeneity of profit at a fine spatial resolution and wide extent. On a global scale, Naidoo and Iwamura (2007) assessed agricultural profit by calculating gross income based on spatially varying estimates of production of selected crops and livestock. Polasky et al. (2005)


developed a landscape-scale model of agricultural opportunity costs using spatially varying yields and subsequently applied these methods in modelling the spatially explicit returns for agricultural land uses in the Willamette Basin, Oregon (Polasky et al., 2008). Bryan and Crossman (2008) calculated the spatial distribution of agricultural opportunity costs by modifying typical gross margin values by rainfall. Hajkowicz and Young (2005) mapped the spatial distribution of agricultural profits at a 1.1km resolution over the entire Australian continent for the year 1996/97. This work integrated spatial information on productivity, price, and costs from several disparate data sources including agricultural statistics, field data, and remote sensing using a profit function within a GIS. Bryan et al. (2008) extended this methodology in mapping agricultural profits in the Murray-Darling Basin, Australia for the years 1996/97 and 2000/01.

2.3.2 Quantifying Carbon Profitability

Only a few studies have attempted to quantify the spatially explicit profit of reforestation for the generation of carbon permits (Ward et al., 2005; Lawson et al., 2008; Polglase et al., 2008; Hobbs, 2009). The common message from these studies is that reforestation using permanent plantings for generation of carbon permits provides commercial opportunities for woody biomass (carbon) crops across much of Australia’s agricultural landscapes (Lawson et al., 2008; Polglase et al., 2008; Hobbs, 2009). Lawson et al. (2008) estimate that approximately 5.8 million hectares of Australia’s agricultural land is economically viable for reforestation under a carbon price path starting at $20.88/t CO2-e, half of which could be permanent plantings. The potential area increases to 26 million hectares under a higher carbon price path starting at $29.20/t CO2-e, approximately 83% of which could be permanent plantings. Polglase et al. (2008) estimate that potentially 9 million hectares of permanent reforestation plantings could be established in Australia under a carbon price of $20 CO2-e.

2.4 Spatial Targeting of Opportunity and Threat

The widespread uptake of permanent reforestation plantings for carbon sequestration provides significant opportunities for the restoration of natural capital degraded by agriculture (Tilman et al., 2001). Recent attention has focused on the potential win-win outcomes, or opportunities, from carbon-motivated reforestation and the provision of ecosystem services derived from expanding the stocks of natural capital (Bekessy and Wintle, 2008; Hunt, 2008; Nelson et al., 2008). For example, payment for the provision of ecosystem services (PES) is a policy instrument of increasing interest (Wunder et al., 2008). A landowner could earn multiple incomes on a single tract of land by reforestation that sequesters carbon, establishes wetlands and improves biodiversity and water quality in locations of greatest need (Fox, 2008). However, rarely are the threats to natural capital sufficiently accounted for in carbon-motivated and PES reforestation programs. A noticeable exception is the study by Jackson et al. (2005) who report that, globally, tree plantations for carbon sequestration have resulted in an average reduction in stream flow by 52%. There is a pressing need to quantify potential


spatially-explicit opportunities and threats to natural capital from reforestation for carbon permits. Policy can be spatially targeted to locations of threat and opportunity.

Recent developments in the spatial targeting literature (van der Horst, 2006; Gimona and van der Horst, 2007; Crossman et al., 2009) have centred on the mapping of hotspots where targeted policy can have ‘multiple win’ outcomes. Gimona and van der Horst (2007) map hotspots for targeting conservation and restoration actions that provide multiple environmental benefits. However, they do not incorporate economic impacts at the farm level of undertaking actions within their hotspots despite the fact that inclusion of ‘cost’ can lead to substantial improvements in the cost effectiveness of efforts to restore natural capital (e.g. Moore et al., 2004). In an earlier paper, van der Horst (2006) presents a framework for spatial targeting that employs estimates of cost but doesn’t include an application. Crossman and Bryan (2009) extend the hotspot literature by identifying hotspots of greatest benefit to the restoration of natural capital for the least impact on farm income. Crossman and Bryan (2009) define their hotspots as locations where farm profit is lowest and environmental benefit is greatest.


3. METHODS

3.1 Study Area

The 15.8m ha study area (Figure 1) is defined by the limit of soil mapping undertaken by the South Australian Department of Water Land and Biodiversity Conservation (DWLBC, 2007). Approximately 32.9% of native vegetation remains across the study area (Figure 1). The bulk of the remnant vegetation is located within the low rainfall northern and western parts of the study area. The total cleared area is 10.6m ha.

Recent land use mapping (DWLBC, 2008) identifies the dominant dryland agricultural land uses within the cleared parts of the study area as cropping, including wheat, barley and canola (5m ha; 47.2% of cleared area), legumes (0.3m ha; 2.8%) and livestock grazing, including sheep and beef, on modified pasture (4m ha; 37.7%). Climate is largely Mediterranean with average annual rainfall ranging from 250 mm in the dry northern parts of the study area to over 1,000 mm in the Southern Mt Lofty Ranges and lower South-east of the State.

Other

NRM Regions

Cropping (Including Cerealsand Canola)Mixed Pasture (Including Sheep,Beef and Dairy)

Remnant Native Vegetation

Legumes

0 100 200 km¯

Figure 1. Location of the study area and the dominant dryland agricultural land uses modelled in this study.


3.2 Economics of Dryland Agriculture

3.2.1 Modelling Yields – Grains and Legumes

Yields of the dominant agricultural commodities grown in the dryland agricultural districts of South Australia are spatially variable. The drivers for this variation are soils, climate and land management practices. Production of the predominant cereal cropping (wheat) and legume (field peas) land uses were modelled using the Agricultural Production systems SIMulator (APSIM; Keating et al., 2003) with historical climate data and a soils database. APSIM can estimate potential yields for the major production systems present in Australian agriculture.

While APSIM offers a wide range of modelling outputs, only wheat and field pea yield were of interest for this study. Wheat was modelled as a surrogate for all cereal cropping in South Australia’s agricultural districts because it is the most common grain by an order of magnitude (Australian Bureau of Agriculture and Resource Economics, 2008).

APSIM uses a range of climate, soil and plant parameters as input data, including:

• Daily weather data including, radiation, rainfall, pan evaporation and maximum and minimum temperatures;

• Soil hydraulic properties over the profile including saturated water content, drained upper limit (field capacity), 15 bar lower limit (wilting point), and drainage coefficient (SWCN) for each soil layer;

• Information about the crop type and variety including, growth parameters and rooting depth in different soil profiles, and;

• Crop management regimes such as sowing, fertiliser and harvest parameters.

APSIM is a one dimensional model that only makes predictions for single sites in an area of interest. Extending APSIM predictions to two dimensions requires a summary of the spatial variation in input parameters, namely soil and climate, across the study area. The spatially heterogeneous soil and climate data were therefore geographically stratified into relatively homogenous zones of climate and soil. The zones of soil and climate were related back to the key input parameters of APSIM so that yield simulations could then be assigned to the climate and soil zones to produce surfaces of potential yield predictions under long-term average climate.

The ten dominant soil groups were extracted from the DWLBC soil mapping data (DWLBC, 2007). Climate zones were defined using multivariate cluster analysis and classification. Climate surfaces of mean annual precipitation, mean annual temperature and annual mean moisture index were modelled in BIOCLIM. Clusters in the three climate surfaces were identified using an iterative k-means classification (ISOcluster) technique with a maximum likelihood classification algorithm used to assign all cells to the nearest climate zone. Eight climate zones were selected following an iterative


process of cluster analysis and inspection. The climate zones are these are described in Table 1. The climate zones were related to daily historical weather data for use in APSIM by overlaying with the location of APSIM Patch Point climate data sites. For each of the eight climate zones the Patch Point site with the longest historical record was selected as representative as an input for APSIM.

Table 1. Climate class summary statistics from BIOCLIM climate surfaces.

Class Area Mean Annual Temperature (°C)

Mean Annual Precipitation (mm)

Annual Mean Moisture Index

(km2) Mean SD Mean SD Mean SD 1 20,938 15.7 0.3 279 26 0.22 0.03 2 29,075 16.6 0.3 290 32 0.20 0.02 3 23,822 16.3 0.3 362 19 0.28 0.03 4 17,529 15.7 0.3 426 26 0.37 0.03 5 16,774 15.0 0.4 358 29 0.31 0.04 6 22,644 14.8 0.5 479 34 0.44 0.03 7 15,437 14.4 0.3 582 41 0.55 0.03 8 11,939 13.5 0.3 733 63 0.63 0.03

3.2.2 Comparison of APSIM Yields to ABS Agricultural Statistics

Agricultural census data (Australian Bureau of Statistics, 2006) contains yield estimates of wheat and field peas and total area under each crop at the scale of Statistical Local Area. These estimates were converted to unit area (t/ha) estimates for comparison to modelled APSIM outputs. Although the census data was collected in 2006, a drier than average year, the census yield estimates do provide a useful reality check for a central component of this study.

3.2.3 Modelling Yields – Grazing

The agricultural census data (Australian Bureau of Statistics, 2006) provides estimates of stocking numbers for beef and sheep and total area under grazing at the Statistical Local Area scale. These estimates were converted to unit area (DSE/ha) estimates for both sheep and beef cattle.

3.2.4 Quantifying Profit

Profit from grazing and cropping is spatially heterogeneous within the study area. The drivers for this are spatially varying differences in yields and production costs. Methods used to calculate dryland agricultural profit are based on Bryan et al. (2008) which should be consulted for detailed description of the methods. The remaining value of production was sourced from agricultural census data (Australian Bureau of Statistics, 2006), and costs of production from gross margin handbooks (Rural Solutions, 2008).


Baseline commodity price values were taken as the average over the period 2002-2007, before the commodity boom of mid-2008. Three alternative commodity price scenarios were run by adjusting the baseline values by 50%, 150% and 200%, to represent a price crash, a mild boom and a 2008 style boom, respectively. An annual Profit at Full Equity PFEcs layer was calculated for each commodity c, for c = wheat, field peas, beef and sheep, and each price scenario, s, using the following function:

PFEcs = Revenuecs – (Variable Costsc + Fixed Costsc) (1)

Where,

Revenuecs = (P1cs * Q1c * TRNc) + (P2cs * Q2c * Q1c) (2)

Variable Costsc = (QCc * Q1) + ACc (3)

Fixed Costsc = (FOCc + FDCc + FLCc) (4)

The parameter descriptions and associated values for each commodity are presented in Table 2.

Table 2. Profit function parameters and associated values used to for the dominant commodities produced in the study area.

Values Parameter Description Units

Wheat Field Peas

Beef Sheep

P1 Price of primary product $/t or $/DSE 216 274 97 25-35

P2 a Price of secondary product

$/kg n.a. n.a. n.a. 3.19-4.27

Q1 Yield of primary product t/ha or DSE/ha

0 – 3.62 0 – 4.84 0 – 9.7 0 – 9.7

Q2a Yield of secondary product

kg/DSE n.a. n.a. n.a. 5

TRN Proportion of herd sold 0 ≤ TRN ≤ 1 for livestock

n.a. n.a. 0-0.46 0-0.96

QC Quantity dependent variable costs

$/t or $/DSE 16-32 16-32 1.63-3.28

3-7

AC Area dependent variable costs

$/ha 122-311 153-197 6-13 1-13

FOC Fixed operating costs $/ha 22-32 22-32 5-21 1-46

FDC Fixed depreciation costs $/ha 10-29 10-29 1-7 1-15

FLC Fixed labour costs $/ha 22-36 22-36 1-20 1-25 aWool production.


3.2.5 Allocating Profit to Land Use

Recent land use mapping completed by DWLBC provides a snap shot of land use across South Australia based on remotely sensed image classification (DWLBC, 2008). However, agricultural systems in the State are characterised by annual rotations that are not captured by the temporally static DWLBC land use mapping. Land use that accounts for rotations was modelled by calculating the proportion of each land use for a given neighbourhood. For example, if the area mapped as cereal cropping, grazing and field peas was 50%, 30% and 20% respectively of the total area of a region, it can be assumed that 5 out of 10 years a paddock in the region is cropped, 3 out of 10 years the paddock is grazed, and 2 out of 10 years it is sown to legumes. The proportions of cereal cropping ww, field peas wfp and grazing wg were calculated for the study area using a 10 km radius moving window and profit PFE at under price scenario s was calculated as:

PFEs = wwPFEws + wfpPFEfps + wgPFEgs (5)

Where PFEws, PFEfps and PFEgs are profit from wheat, field peas and grazing, respectively. The resulting output is a single spatially heterogeneous surface of an estimate of annual profit from dryland agriculture in the study area.

A prior calculation was made to calculate profit under grazing because land use mapping does not distinguish between sheep and beef. The agriculture census data (Australian Bureau of Statistics, 2006) provides estimates of beef and sheep numbers at Statistical Local Area scale. This data was used to estimate the likelihood that a location is grazed by sheep or beef based on the proportions of sheep and beef DSE within each Statistical Local Area. The proportion of sheep wm and beef wb grazing were then used to calculate grazing profit PFEgs for each price scenario:

PFEgs = wmPFEms + wbPFEbs (6)

Where PFEm and PFEb are profit from sheep and beef, respectively.

3.3 Economics of Carbon

3.3.1 Modelling Biosequestration of Carbon

This study uses the spatial version of 3-PG to model spatially explicit estimates of tree biomass under a permanent reforestation land management scenario. Selection of the tree species modelled was constrained by the species parameter datasets already calibrated and available for 3-PG (see Polglase et al., 2008). Five native species parameter datasets were selected from the available datasets (Polglase et al., 2008) based on their suitability for growth in the various climate zones found across the agricultural districts of South Australia. The species selected were:

• Monoculture and low diversity tree systems:


1. High rainfall Tasmanian Blue Gum (Eucalyptus globulus),

2. Moderate rainfall Eucalypt blend (average of Eucalyptus cladocalyx and Eucalyptus camaldulensis),

3. Low rainfall Oil Mallee species (Eucalyptus kochii)

• Diverse tree system:

4. A ‘Mixed Environmental Plantings’ dataset representative of temperate climate species used in revegetation for conservation.

The initial planting density for each species was constant at 1,000 stems/ha.

Climate inputs into 3-PG consisted of BIOCLIM-derived (Nix, 1986; Bryan, 2003) long-term average monthly precipitation, mean temperature and solar radiation surfaces tempered according to topography. The two soil inputs into 3-PG, soil structure and soil water availability, were derived from the DWLBC Soil Landscape Units spatial dataset (DWLBC, 2007).

A rainfall mask of ≥ 350 mm/yr was applied to the E. cladocalyx and E. camaldulensis blend because these species are not generally viable at low rainfall. A rainfall mask of ≥ 550 mm/yr was also applied to E. globulus because of its low tolerance to dryer conditions. The masks reduced total dry biomass to zero for each of these species at locations receiving less than rainfall cut off. A 41 year time horizon (2009-2050) was modelled. Outputs for each species took the form of the annual dry biomass flux for each year to 2050. Dry biomass weights Bj for each of the four tree systems j where j = Eucalyptus globulus; Eucalyptus cladocalyx and Eucalyptus camaldulensis blend; Eucalyptus kochii, and; the mixed environmental plantings were converted to units of CO2-e j using the following formula:

2667.32

jj

BeCO =− (7)

Dry biomass weights Bj were divided by 2 to convert to carbon and the carbon weights were multiplied by 3.667 to convert to CO2-e.

3.3.2 Comparisons to Other Carbon Productivity Models

Comparisons of the carbon productivity estimates under the tree systems modelled in this study were made to similar systems modelled by Polglase et al. (2008) and Hobbs (2009). The comparisons provided a reality check to the central component of this study. Polglase et al. (2008) used 3-PG and similar tree parameter sets to estimate annual CO2-e sequestration across the whole of Australia at a resolution of 1km. The annual CO2-e sequestration rates from the Polglase et al (2008) ‘Hardwood Carbon Plantings’ and ‘Mallee Carbon Plantings’ models were compared, respectively, to the Eucalyptus globulus and Eucalyptus kochii tree systems modelled in this study.


Hobbs (2009) used a regression model to estimate various tree species CO2-e sequestration rates across South Australia at a 1ha resolution. The dependent variable, carbon sequestered, was collected for tree systems using destructive sampling techniques. This was regressed against climate parameters to produce a continuous surface of carbon sequestration for each tree system. The annual CO2-e sequestration rates from the Hobbs (2009) ‘Pulpwood’, ‘Oil Mallee’ and ‘Habitat’ models were compared, respectively, to the Eucalyptus globulus, Eucalyptus kochii and Mixed Environmental Planting tree systems modelled in this study.

3.3.3 Quantifying Net Present Value of Carbon Plantings

Net present value (NPV) is the present value of a time series of cash flows and is the standard approach for appraising long-term projects. The NPV of permanent carbon reforestation plantings to a time horizon of 2050 was calculated across the study area as:

jsijijs PVCPVBNPV −= (8) Where PVBij is the present value of the benefits and was calculated based on a range of carbon prices pi where i = $10/t, $15/t, $20/t, $25/t, $30/t and $45/t of CO2-e. These carbon prices were selected to reflect a range of prices realistically expected in a cap and trade carbon market (Garnaut, 2008; Lawson et al., 2008). The annual carbon sequestration potential (qtj), or carbon flux, was taken from the CO2-ej (Equation 7) values for the four tree systems j. PVBij was calculated as:

∑= +

=T

tt

tjiij r

qpPVB

0 )1( (9)

Where r is the annual discount rate of 7% and t is the time horizon of 41 years (i.e. 0 ≤ t ≤ 41). PVCsj is the present value of the costs for each commodity price scenario s and each tree system j, and was calculated as:

∑= +

++=

T

tt

sjjs r

PFEMCECPVC

0 )1( (10)

Where PFE is profit from agriculture, i.e. the opportunity cost of carbon reforestation plantings, ECj is a one off establishment cost and MC is the annual maintenance and transaction cost. Both ECtj and MC are uniform over the study area. The value of MC was $25/ha. The value of EC was $2,000/ha for the Mixed Environmental Plantings and $1,250/ha for the three monoculture tree systems. The monoculture figure is based on similar figures in Polglase et al (2008) for carbon planting monocultures, and the Mixed Environmental Planting figure is comparable to establishment costs typically seen in bids received under the South Australian Government’s River Murray Forest program (Chris Nichols, DWLBC Project Officer, pers. comm.).


A total of 96 spatial layers were created quantifying the NPV of permanent carbon reforestation plantings, one for each of the 6 carbon prices, 4 tree systems and 4 commodity price scenarios.

For ease of interpretation, NPVijs was converted to equalised annual equivalents (EAEijs) using the following function:

1)1()1(−+

+= t

t

ijsijs rrrNPVEAE (11)

Where r is the annual discount rate of 7% and t is the time horizon of 41 years. Locations that are economically viable for reforestation of cleared landscapes for carbon sequestration were identified where EAEijs > 0. In other words, growing trees for carbon is more profitable than existing dryland agriculture in these economically viable locations.

3.4 Estimating Opportunities and Threats

3.4.1 Landscape Productivity

Management priorities for soil protection, salinity mitigation and waterlogging that improve landscape productivity were modelled using existing metrics contained in the South Australian Soil Landscape Unit database (DWLBC, 2007). This soil database classifies all soil in the study area according to wind erosion risk (SWI), water erosion risk (SWA), gully erosion risk (SG), dry saline land (SS) and shallow water table risk (ST). Highest risk areas will benefit most from deep-rooted perennials and hence are highest priority for permanent carbon reforestation plantings to improve landscape productivity. Reforestation priority for soil and water was calculated using an additive function that combines the soil risk attributes. Each attribute was rescaled to the range 1 (low priority) to 5 (high priority) before input into the landscape productivity priority (LPP) function:

5151515151 −−−−− ++++= TSGWAWI SSSSSLPP (12) The output is a spatial layer that scores every cleared location in the study area according to its soil and water management priority for reforestation. A location was arbitrarily considered high priority when its value was equal to or greater than the average priority score for the whole study area, i.e. LPPLPP ≥ . Reforestation for carbon in locations of high management priority are opportunity hotspots, regardless of the tree species used.

3.4.2 Water Resources

A spatially explicit water management layer was modelled to estimate the direct threats to surface and groundwater yields from permanent carbon reforestation plantings in


water management zones occurring within higher rainfall regions of the state (Figure 2). The scarcity of water resources has motivated the South Australian State Government to manage the use of water in certain locations.

Carbon reforestation activities impact on water yield through increased interception and evapotranspiration (Zhang et al., 2001). Total evapotranspiration before and after permanent carbon reforestation plantings was calculated for each management unit k within mapped prescribed water resource areas and south-east groundwater management areas (Figure 2) using the following equation (from Zhang et al., 2001):

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

++

+−+

++

+=

110011005.01

11005.01)1(

1410141021

141021

k

k

kk

k

k

kkkk P

P

Pf

PP

PfPET (13)

Where Pk is average annual rainfall and fk is proportion of tree cover for each management unit k, with fk varying according to tree cover before and after carbon reforestation. The before tree cover is the remnant native vegetation. The after tree cover is the remnant native vegetation plus the economically viable areas of carbon reforestation. The change in evapotranspiration ∆ETk caused by carbon reforestation was than calculated as:

VkRkk ETETET −=Δ (14)

Where ETVk and ETRk are total evapotranspiration before and after reforestation, respectively. The ∆ETk was calculated for all 96 carbon reforestation viability layers. The gigalitre (GL) volumetric water Vk of each ∆ETk was calculated as:

910kk

kETxA

VΔ

= (15)

Where Ak is the area (m2) of each management unit k. The volumetric reduction of water is a threat posed by reforestation for carbon sequestration, regardless of tree species used.


0 100 200 km¯

NRM Regions

Remnant Native Vegetation

Water Management Zones

Figure 2. Location of water management zones occurring within higher rainfall regions of South Australia.

3.4.3 Biodiversity

Spatially-explicit priorities for permanent carbon reforestation plantings using diverse mix of locally indigenous species were identified using a series of spatial metrics drawn from established conservation planning (Margules and Sarkar, 2007; Moilanen at al., 2009) and landscape ecology (Turner and Gardner, 1991) principles. The logic is based on the premise that landscapes are heterogeneous and reforestation in certain locations will arguably contribute more to biodiversity conservation goals than in other locations (Malanson and Cramer, 1999). This is particularly the case in heavily fragmented and degraded landscapes such as the study area. Table 3 describes each metric modelled in this study.

Locations of high and low priority for reforestation to potentially improve biodiversity conservation were identified in a two stage process. Firstly, high priority remnant vegetation patches were identified using an additive function that combines all remnant vegetation metrics (Table 3). Each metric was rescaled to the range 1 (low priority) to 5 (high priority) before input into the vegetation priority (VP) function:

(16) 5151515151 −−−−− ++++= CZSRSA PPPPPVP


Table 3. Description of metrics used to estimate spatially explicit priorities for biodiversity conservation.

Symbol Metric Description

Remnant Vegetation

PA Patch Area Total area of contiguous patches of remnant vegetation.

PS Patch Shape An index of patch shape complexity calculated for all contiguous patches of remnant vegetation. Incorporates patch area (A) and perimeter (P). Values closer to 1 indicate lower shape complexity. Calculated as:

APPSπ2

= (13)

PR Remnant Vegetation Protection

Percentage of each remnant vegetation community formally protected under a conservation agreement which includes NPWS reserves, Heritage Agreements, RAMSAR sites. High priority is given to locations of low protection.

PS Soil Protection Percentage of each vegetated Soil Landscape Unit formally protected under a conservation agreement. High priority is given to locations of low protection.

PCZ Climate Protection

Percentage of each vegetated climate zone formally protected under a conservation agreement. Climate zones derived using methodology reported in Crossman and Bryan (2006). High priority is given to locations of low protection.

Cleared Landscapes

CV Veg. Fragmentation

Percentage remnant vegetation cover within 5km radius from every location in study area.

CDA Dispersal Distance - All

Euclidean distance (D) from all remnant vegetation rescaled using a negative exponential transformation. Locations closer to remnant vegetation have exponentially greater importance based on dispersal ecology theory (Willson, 1993). Calculated as:

DDA eC 001.0−= (14)

CDH Dispersal Distance – High Priority

Euclidean distance (D) from high priority remnant vegetation patches rescaled using a negative exponential transformation. High priority remnant vegetation defined from remnant vegetation metrics described above. Calculated using equation 14.

CS Soil Remnancy Percentage of each Soil Landscape Unit remaining under remnant vegetation. High priority is given to locations of low remnancy.

CC Climate Remnancy

Percentage of each climate zone remaining under remnant vegetation. Climate zones derived using methodology reported in Crossman and Bryan (2006). High priority is given to locations of low remnancy.

A remnant vegetation patch was considered high priority for management when its value was equal to or greater than the average priority score for the whole study area, i.e. VPVP ≥ . The high priority patches were used as input into an element of the second stage (CDH in Table 3). The second stage involved computation of another additive function that combines all cleared landscape metrics (Table 3). Each metric was rescaled to the range 1 (low priority) to 5 (high priority) before input into the biodiversity priority (BP) function:

5151515151 −−−−− ++++= CSDHDAV CCCCCBP (17)


The output is a spatial layer that scores every cleared location in the study area according to its biodiversity priority for reforestation using diverse mix of locally indigenous species. A location was arbitrarily considered high priority when its value was equal to or greater than the average priority score for the whole study area, i.e.

BPBP ≥ . Carbon motivated reforestation of high priority areas using Mixed Environmental Plantings is an opportunity for biodiversity. Conversely, carbon motivated reforestation of high priority areas using any of the other monoculture systems modelled in this study arguably poses a threat to biodiversity (Sayer et al., 2004).

3.4.4 Existing Dryland Agriculture

The impact on existing dryland agriculture from permanent carbon reforestation plantings poses both opportunities and threats.

On the opportunity side, there will be positive net economic returns if reforestation for carbon is adopted where it is economically viable across the study area. Depending on carbon permit price, there may be greater income generated on farms under a permanent carbon reforestation system than are currently available under traditional agricultural cropping and grazing systems. Net economic returns (NER) were calculated for the study area, using the following function:

∑=

=L

llNPVNER

1 (18)

Where NPVl is the net present value of permanent carbon reforestation plantings for every economically viable location l in the study area. NER was calculated for all 96 carbon reforestation viability layers. NER was converted back to equalised annual equivalents using equation 6 above, with NPV substituted for NER.

On the threat side, permanent carbon reforestation plantings will replace production of food commodities and impact on food security if widely adopted across extensive tracts of land. Changing land uses from cropping/grazing to reforestation where it is economically viable will result in lower total production of each modelled dryland agricultural commodity. Change in total potential yields ΔY of each commodity c were calculated as:

(19) ∑ ∑= =

−=ΔL

l

L

lRclclc QQY

1 1

Where Qcl is the quantity of commodity c produced at modelled location l before carbon reforestation plantings and QRcl is the quantity of commodity c produced at location l after carbon reforestation plantings.


3.4.5 Summary of Threat and Opportunity

Spatially-explicit opportunities and threats for reforestation for carbon vary according the reforestation system and the location of the biodiversity, water and soil management priorities, and productive agriculture, forestry and urban communities.

Opportunities occur when:

1. Reforestation using high diversity indigenous species occurs in high biodiversity priority locations.

2. Reforestation of any species occurs in high landscape productivity management priority locations.

3. Reforestation of any species provides a positive net economic return to a region.

Threats occur when:

1. Reforestation using low diversity species occurs in high biodiversity priority locations.

2. Reforestation of any species occurs in important water yield locations.

3. Reforestation of any species reduces agricultural production.


4. RESULTS

4.1 Economics of Dryland Agriculture

Yields for each commodity modelled across the dryland agricultural districts of South Australia are presented in Figure 3. Climate is a clear driver for all commodity yields except for wool production per head which was considered homogenous across the study area. Stocking rates for sheep and beef (Figure 3a) are less spatially heterogeneous than wheat (Figure 3c) and field pea (Figure 3d) yields because the latter are also influenced by soil types. Stocking rates for sheep and beef are greatest in the highest rainfall zones because of the increased availability of pasture, with peaks at nearly 10 dry-sheep-equivalents (DSE) per hectare. Field pea yields are greatest in the higher rainfall zones of the southern Mt Lofty Ranges, Kangaroo Island and the South-East, with potential yield peaking at just below 5 t/ha. Wheat yields are greatest in the mid-North, Yorke Peninsula and southern Eyre Peninsula where optimum rainfall occurs. Potential wheat yields peak at approximately 3.6 t/ha.

Comparison of APSIM-derived wheat yields (Figure 3c) to agricultural census (Australian Bureau of Statistics, 2006) wheat yields (Figure 3e) reveals similar distributional patterns of high and low yields. However, the APSIM-derived wheat yields are higher that the 2006 agricultural census wheat yields. The APSIM-derived field pea yields (Figure 3d) are higher than the agricultural census yields (Figure 3f) by an order of magnitude. These differences across both commodities can be explained by the relatively poor 2005/06 season (Australian Bureau of Agriculture and Resource Economics, 2008) which was the year of the census.

Potential profit at full equity for each commodity modelled across the dryland agricultural districts of South Australia is presented in Figure 4. Profits presented in Figure 4 were calculated using the baseline commodity price scenario derived from average commodity prices before the 2008 price boom. Sheep (Figure 4a) and beef (Figure 4b) are potentially profitable across much of the study area under typical, pre-2008 commodity prices, with the higher rainfall zones returning $200/ha/yr or more. Field peas are potentially more profitable than wheat across much of much of the State (Figure 4c,d). In many areas field peas can return in excess of $500/ha a year under the baseline commodity price scenario. Land use mapping identifies only relatively small portions of the study area grown to legumes (Section 3.1). Wheat returns at best between $200/ha and $500/ha a year and is only marginally profitable or not profitable at all in many locations given the baseline price scenario.

The profit at full equity layer for all dryland agriculture in South Australia under each of the commodity price scenarios is presented in Figure 5. Much of the dryland agriculture practised in the study area is not profitable under the 50% commodity price scenario (Figure 5a). Most of the study area is profitable for dryland agriculture under the baseline price scenario, with only a small number of areas loss-making on average. Locations in the mid-North and on Eyre Peninsula potentially make up to $500/ha/yr


(Figure 5b). Much of the dryland agriculture in the study area could potentially return $500/ha/yr or more under a commodity price boom comparable to that experienced in mid-2008 (Figure 5d).

t/hat/ha

DSE/ha kg/DSE

a) b)

d)c)

NRM Regions

Not Modelled

5

0 150 300 km ¯f)e)

0 - 1.92 - 3.94 - 5.96 - 7.98 - 9.9

0 - 0.91 - 1.92 - 2.93 - 3.94 - 4.9

0 - 0.91 - 1.92 - 2.93 - 3.94 - 4.9

t/ha

0 - 0.91 - 1.92 - 2.93 - 3.94 - 4.9

t/ha

0 - 0.91 - 1.92 - 2.93 - 3.94 - 4.9

Figure 3. Annual yields of the major dryland agricultural commodities modelled across South Australia: a) sheep and beef; b) wool; c) wheat; d) field peas. Comparative yields drawn from the agricultural census for e) wheat, and; f) field peas.


a) b)

c) d)

NRM Regions

Not Modelled ¯0 150 300 km

$100 - $199

$200 - $499

$500 or more

$/ha/yr

Less than $0

$0 - $99

Figure 4. Profit at full equity of the major dryland agricultural commodities modelled across South Australia under the baseline commodity price scenario: a) sheep; b) beef; c) field peas, and; d) wheat.


a) b)

c) d)

NRM Regions


$100 - $199

$200 - $499

$500 or more

$/ha/yr

Less than $0

$0 - $99

Figure 5. Profit at full equity for dryland agriculture in South Australia under the four commodity price scenarios: a) 50% of baseline; b) baseline; c) 150% of baseline, and; d) 200% of baseline.

4.2 Economics of Carbon Biosequestration

4.2.1 Carbon Productivity

Average annual carbon productivity under each the four tree systems modelled in this study is presented in Figure 6. The totals presented in Figure 6 were calculated by dividing the total CO2-e at 2050 by the total number of modelled number of years (n = 41). It is clear that E. globulus is the most productive species in the higher rainfall zones of the southern Mt Lofty Ranges, Kangaroo Island and the lower South-East (Figure 6a). Annual CO2-e sequestration rates of 16 t/ha or more can be expected under E. globulus in these locations. The abrupt drop-off from high rates to zero is an artefact of the 550 mm annual rainfall mask applied to E. globulus.

The annual carbon productivity of the mixed hardwood blend of E. cladocalyx and E. camaldulensis is low compared to all other species modelled in the study area (Figure 6b). The maximum annual rate of CO2-e sequestration that could be expected is 16-20


t/ha in a small number of locations. E. kochii is the most productive species in the lower rainfall northern parts of the study area and in the Mallee (Figure 6c). Annual CO2-e sequestration rates of 11-15 t/ha can be expected under E. kochii in these locations. Annual carbon productivity of the mixed environmental planting shows the most spatial heterogeneity of all tree systems (Figure 6d).

a) b)

c) d)

NRM Regions

Not Modelled ¯0 150 300 kmCO2-et/ha/yr

0

1- 10

11 - 15

16 - 20

21 - 25

26 - 31

Figure 6. Average annual carbon productivity (CO2-e) of the four tree systems modelled in the study area: a) Eucalyptus globulus; b) Eucalyptus cladocalyx and Eucalyptus camaldulensis blend; c) Eucalyptus kochii, and; d) mixed environmental plantings.

4.2.2 Comparisons to Other Carbon Sequestration Models

Comparisons of annual carbon productivity (CO2-e) modelled in this study with models of Hobbs (2009) and Polglase et al. (2008) show that the carbon productivity models used here align well with other recent research (Figure 7) and can be used with confidence. Our estimates for E. globulus carbon productivity sit between those of Hobbs and Polglase (Figure 7). There are similar patterns of variation in estimates between the E. kochii models. The strongest agreement is between this study’s and the Hobbs’ Mixed Environmental Plantings models (Figure 7).


NRM Regions

Not Modelled

CO2-e t/ha/yr

0

1- 10

11 - 15

16 - 20

21 - 25

26 - 31

This Study Hobbs (2009) Polglase et al (2008)

Eucalyptusglobulus

Eucalyptuskochii

MixedEnvironmental Plantings ¯0 200 400 km

Pulpwood

Oil Mallee

Habitat

HardwoodCarbon Plantings

MalleeCarbon Plantings

Figure 7. Comparisons of average annual carbon productivity (CO2-e) between this study’s tree system models and the models of Hobbs (2009) and Polglase et al (2008).

4.2.3 Economic Viability of Carbon

The price received for the generation of carbon permits, the agricultural commodity price scenarios and the carbon plantation tree systems all have a significant bearing on the profitability and therefore economic viability of carbon across the agricultural districts of South Australia. The 96 maps of profitability, reported as equalised annual equivalents, under each commodity and carbon price scenario and tree system are available as an Appendix from the authors.

Figure 8 presents an example of the expected profitability of carbon plantings for the four modelled tree systems. The opportunity cost of foregone dryland agricultural production that occurred prior to reforestation is included in profit estimates. The expected annual profit from carbon plantings varies considerably according to tree species planted and commodities currently produced. In Figure 8a, under a typical carbon price of $20/t of CO2-e and the baseline commodity price scenario, E. globulus reforestation for carbon sequestration could return up to $500 ha/yr in a number of locations. E. globulus is competitive with existing agriculture across the high rainfall zones of the study area. E. kochii carbon plantings are economically viable, and therefore an alternative to existing agriculture across much of the study area, including


the lower rainfall Mallee districts and northern Eyre Peninsula, given the baseline commodity and $20/t of CO2-e price scenarios (Figure 8c).

Potential economic gains from mixed environmental plantings under the price scenarios in Figure 8 are also spatially heterogeneous across the study area. Greatest returns from mixed environmental plantings for carbon are found in the southern Yorke and Eyre Peninsulas, the southern Mt Lofty Ranges and the upper South East (Figure 4).

a) b)

c) d)

NRM Regions


$100 - $199

$200 - $499

$500 or more

$/ha/yr

Less than $0

$0 - $99

Figure 8. Profitability of reforestation of cleared landscapes for carbon sequestration, expressed as equalised annual equivalents (EAE), under a carbon price of $20/t of CO2-e and the baseline commodity price scenario. Carbon plantings are economically viable when EAE > 0. The tree systems modelled are: a) Eucalyptus globulus; b) Eucalyptus cladocalyx and Eucalyptus camaldulensis blend; c) Eucalyptus kochii, and ; d) mixed environmental plantings.

Figure 9 summarises the proportion of dryland agriculture areas where reforestation for carbon is economically viable and the potential carbon that could be sequestered annually. There exists an interesting interplay between commodity price and carbon price. Under a low commodity price, permanent reforestation for carbon sequestration is more profitable than existing agriculture across more than 50% of the dryland agricultural districts even at a low carbon price of $10/t of CO2-e, with approximately 90% of the dryland agricultural area suited to E. kochii carbon reforestation (Figure 9a).


Total carbon sequestered annually would be well in excess of total South Australian emissions of 31 Mt of CO2-e (Government of South Australia, 2007a) if reforestation was adopted in economically viable locations. Under the baseline commodity price scenario, a carbon price of $15/t of CO2-e could potentially see reforestation for carbon offsetting South Australia’s annual emissions, which would take up less than 20% of the State’s dryland agricultural districts.

At the other end of the commodity price spectrum, the 200% price boom (Figure 9d), a carbon price of $20-$35/t of CO2-e is needed before there would be any noticeable areas where carbon is economically viable. Sequestration of South Australia’s emissions would not occur until the carbon price exceeded $20/t of CO2-e assuming complete adoption of reforestation in economically viable areas.


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Figure 9. Proportion of existing dryland agriculture (cropping, grazing) that is economically viable for reforestation for carbon sequestration (left side), and annual carbon (CO2-e) sequestered (right side), under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario. Dashed line in right side figures is the total annual CO2-e emissions for South Australia in 2005 (Government of South Australia 2007a).


4.3 Threats and Opportunities

4.3.1 Landscape Productivity

The individual metrics that form the landscape productivity priority surface are shown in Figure 10. Each metric is rated high to low risk which is used to inform management priority according to the contribution that deep-rooted perennials make to improving the underlying condition of agricultural landscapes. Soils at greatest risk from erosion and rising water tables will receive the greatest benefit from reforestation for carbon and are therefore the highest priority. Wind erosion risk tends to be highest on the sandy soils characteristic of Mallee landscapes and the upper Eyre Peninsula. The older soils in the Mallee and upper Eyre Peninsula are also at a higher risk to dryland salinity. Water erosion risk is higher in the steep terrain of the Mt Lofty and southern Flinders Ranges. Risk of gully erosion is higher in the southern Flinders Ranges due to the denuded watercourses and erosion events driven by intense but infrequent rainfall.

The overall landscape productivity priority locations for planting deep-rooted perennials is presented in Figure 11a. The 430,000 ha of high priority locations are presented in Figure 11b. It is evident from Figure 11a that no locations stand out as high priority for deep-rooted perennials that provide multiple landscape productivity benefits. Locations of high risk rarely coincide across all metrics (Figure 10). There are some locations where deep-rooted perennials may provide two benefits, such as combating shallow water tables and wind erosion in the South East (Figure 10). These locations appear as high priority in Figure 11b. Other high priority locations are found in eastern Mt Lofty Ranges and mid-North where deep-rooted perennials may combat both gully erosion and water erosion.

The proportion of high priority locations for landscape productivity that are economically viable for reforestation for carbon sequestration under the various tree systems, carbon and commodity price scenarios are plotted in Figure 12. The proportion of high priority locations that are economically viable for carbon is highly variable depending on carbon and commodity price.


Wind Erosion Risk Water Erosion Risk

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Figure 10. Individual metrics that form the landscape productivity priority surface for deep-rooted perennials.


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Figure 11. Landscape productivity priority for planting deep-rooted perennials: a) raw continuous surface, and; b) classified high priority locations.


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Figure 12. Proportion of high priority landscape productivity locations that are economically viable for reforestation for carbon sequestration under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario.

4.3.2 Water Resources

Significant threats are posed to water interception and surface and groundwater yields by reforestation for carbon. The volume of water evapotranspired by the additional trees in locations where it is economically viable to undertake reforestation is summarised in Figure 13 for all prescribed water resource and groundwater management areas. Nearly 1,600 GL of water would be intercepted and transpired annually if reforestation was adopted in locations economically viable under the lower commodity price scenarios. E. globulus would be responsible for greater volumes of interception when commodity prices are stronger and carbon prices are lower (Figure 13b,c). The species would be preferentially planted in prescribed water management areas because it is more profitable to grow in many of these areas.

The variation of evapotranspiration is also captured spatially across higher rainfall water management zones. Figure 14 provides an example of the volume of water intercepted within each management unit by a single reforestation species (E. globulus) given a carbon price of $20/t of CO2-e if adopted where economically viable. In excess of 50 GL of water would be intercepted annually in the Mt Lofty Ranges region if E. globulus is planted where it is economically viable for reforestation for carbon under the 50%, baseline and 150% commodity scenarios (Figure 14a,b,c). The


full set of 96 maps covering each species and carbon and commodity price scenario are available on request from the authors.

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Figure 13. Total water evapotranspired by reforestation for carbon under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario.


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Figure 14. Annual volume of water evapotranspired by E. globulus reforestation for carbon at $20/t of CO2-e in higher rainfall water management zones given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario.

4.3.3 Biodiversity

The individual metrics for remnant vegetation and cleared landscapes that form the biodiversity conservation priority surface are shown in Figure 15. Each metric is rated high to low conservation priority according to underlying landscape ecology and conservation planning principles. It is observable in Figure 15 that patches of remnant vegetation that are large, regularly shaped and contain vegetation communities poorly represented in the protected area network are a conservation priority. Soil and climate zones that are poorly represented in the protected area network are also a higher priority (Figure 15). In cleared landscapes, locations within relatively intact, low fragmented landscapes are a higher restoration priority, as are locations near to existing remnants. Soil classes and climate zones that have low levels of remnant vegetation cover are also a higher priority for reforestation using mixed native species.


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Figure 15. Individual metrics that form the biodiversity priority surface for ecological restoration.

The overall biodiversity priority locations for ecological restoration are presented in Figure 16a and the high priority locations are presented in Figure 16b. Approximately 2m ha of cleared land is classified as being of relatively high priority in Figure 16b. The richest locations of high priority are found in the southern Mt Lofty Ranges, Kangaroo Island and in the lower South East. At a finer scale, locations adjoining many of the larger patches of remnant vegetation across the study area are a high priority for restoration of cleared areas for biodiversity.

The magnitude and extent of threats to biodiversity from low diversity, monoculture reforestation plantings for carbon, and conversely the opportunities presented by mixed environmental plantings, are highly variable. The area that is economically viable for carbon is a function of tree species, carbon and commodity price (Figure 9). The intersection between economically viable reforestation for carbon and high priority biodiversity locations is complex but can best be summarised through plots presented in Figure 17. The plots in Figure 17 show the proportion of high priority biodiversity


areas (Figure 16b) that are economically viable for the different trees systems under the various carbon and commodity price scenarios. In many instances, regardless of carbon and commodity price, E. kochii, is economically viable across a greater proportion of high priority biodiversity locations than Mixed Environmental Plantings.

Taking another perspective, Figure 18 shows the per hectare profits of each tree system within locations of high priority for biodiversity. The figures are only considered where it is economically viable for reforestation for carbon. It is clear that E. globulus is the more profitable tree system in the high priority biodiversity areas as commodity prices increase. Under the baseline commodity price scenario and $25/t of CO2-e, E. globulus would return a profit of approximately $350/ha annually, compared to Mixed Environmental Plantings which would return approximately $225/ha annually (Figure 18b).

If there are no constraints placed on the types or locations of plantings that are eligible for sequestration benefits under a carbon market then the $125/ha gap is the annual amount that may need to be provided to landowners as an incentive to plant mixed native species that provide the biodiversity benefits in high priority locations as an alternative to a monoculture. The gap widens between biodiverse and monoculture carbon plantings as carbon price increases. For example, at $45/t of CO2-e and a baseline commodity price scenario, the gap is nearly $300/ha annually (Figure 18b). Approximately 0.5m ha of high priority biodiversity locations are modelled as suitable for E. globulus at this carbon and commodity price scenario, making the total annual incentive required for alternative mixed environmental plantings very large.


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Figure 16. Biodiversity priority for reforestation of cleared dryland agricultural landscapes using mixed native species: a) raw continuous surface, and; b) classified high priority locations.


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Figure 17. Proportion of high priority biodiversity locations that are economically viable for reforestation for carbon sequestration under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario.


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Figure 18. Annual profit (EAE - equalised annual equivalents) per hectare of reforestation for carbon sequestration under the four tree systems in high priority biodiversity locations only: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario.

4.3.4 Productive Agriculture

The opportunity presented to South Australian farming landscape by reforestation for the generation of carbon permits is significant. Figure 19 shows the annual net economic returns to South Australia if reforestation for carbon is adopted where it is economically viable. The numbers in Figure 19 account for the lost value of production after switching from cropping/grazing systems to the carbon reforestation systems. The value of agricultural production in South Australia could increase by nearly $7 billion annually, compared to existing returns, under a $45/t of CO2-e carbon price and 50% commodity price scenario (Figure 19a). More likely is the baseline commodity price scenario (Figure 19b), where the value of South Australia’s agricultural production could lift by between $0.5 - $2 billion annually if reforestation for carbon is adopted where it is economically viable at a carbon price of $20 - $25/t of CO2-e. To put these figures in perspective, the current gross value of agricultural production in South Australia is approximately $5 billion annually and the gross state product is approximately $70 billion annually (Australian Bureau of Statistics, 2009).


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Figure 19. Annual net economic returns (EAE - equalised annual equivalents) to South Australia from adoption of reforestation for carbon sequestration under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and d) 200% commodity price scenario.

Threats posed to agriculture and food security come from the loss in commodity production after widespread adoption of reforestation for carbon. Figure 20 plots the potential lost production of wheat and sheep (as a proportion of total South Australian production) if reforestation for carbon is adopted where it is economically viable. Approximately 2.3m tonnes of wheat were produced in South Australia in 2007-08 and sheep holdings in that period were approximately 10.2m head (Australian Bureau of Agriculture and Resource Economics, 2008). Reforestation for carbon has the potential to replace much of this production under low commodity prices (Figure 20a). Under the baseline commodity scenario, reductions in sheep production of 60% - 80% could be expected as the carbon price approaches $20/t of CO2-e, assuming reforestation for carbon is purely commercially driven (Figure 20b). Similarly large reductions in wheat production could be expected given the baseline commodity price scenario if carbon price is $25/t of CO2-e (Figure 20b). High commodity prices, such as those seen in the commodity price boom of 2008, would make sheep and wheat significantly more competitive against reforestation for carbon in many regions.



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Figure 20. Proportion of potential wheat (left side) and sheep (right side) production that could be lost following reforestation for carbon sequestration under the four tree systems given: a) 50% commodity price scenario; b) baseline commodity price scenario; c) 150% commodity price scenario, and; d) 200% commodity price scenario.

4.3.5 Summary

A summary of opportunities and threats is provided in Table 4.

Table 4. Summary of opportunities and threats from reforestation for carbon sequestration. Results are for the baseline commodity price scenario.

Eucalyptus globulus Eucalyptus kochii Mixed Environmental Plantings

Eucalyptus blend

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Area Economically Viable (m ha) 1.4 1.4 7.9 9.0 5.1 7.7 1.8 3.1

Carbon Sequestered (CO2-e Mt/yr) 30.2 30.2 133.2 150.6 84.0 113.4 19.0 31.1

Area of high priority for landscape productivity reforested (‘000 ha)

61.1 63.8 421.0 429.5 260.3 411.0 95.2 158.8

Area of high priority biodiversity reforested (m ha)

0.5 0.5 1.9 2.0 1.4 1.8 0.5 0.9

Increased returns to agriculture ($m/yr) 308.5 635.7 1,241.7 2,778.8 758.5 1,958.8 130.1 381.4

Reduced water yields (GL/yr) 1,190.2 1,190.2 1,574.8 1,577.4 1,483.2 1,572.3 330.2 979.4

Reduced wheat production (Mt) 0.1 0.1 3.7 4.9 1.8 3.7 0.5 0.8

Reduced sheep numbers (m) 3.2 3.2 7.1 7.3 6.0 7.0 1.8 4.2


5. DISCUSSION AND CONCLUSION

This study has demonstrated that significant areas of South Australia’s dryland agricultural landscapes could be converted to permanent reforestation for the generation of carbon permits. Carbon price has a large bearing on the extent of reforestation. Large parts of the South Australian agricultural landscape may be economically viable under the higher carbon prices that could realistically be expected if a carbon market such as that proposed under the Carbon Pollution Reduction Scheme (CPRS) is established.

Two major assumptions flow through much of the analysis: i) a cap-and-trade market instrument will be introduced in Australia and reforestation will be included as an emissions offsetting mechanism, and; ii) adoption of reforestation for carbon will predominantly occur in locations that are economically viable, i.e. that are more profitable than existing agricultural land uses. There could be significant opportunities and threats posed to natural resources and agricultural communities if both assumptions are eventually met.

Assumption (i) is currently (February 2010) the subject of extensive debate in Australian public and political circles. Australia will see the cap-and-trade CPRS implemented by 2011 if the Government’s proposed legislation is passed through Parliament. Assumption (ii) may not be met because new, more profitable practices often face significant barriers to adoption that typically centre on farmer risk and uncertainty, policy and landuse zoning requirements, social norms and traditions, preferences, complexity, relative economic advantage, lack of knowledge, skills and experience, and ineffective extension services (Feder and Umali, 1993; Guerin and Guerin, 1994; Pannell et al., 2006).

5.1.1 Opportunities

If both assumptions are met, South Australia’s annual carbon emissions, which in 2005 were 31 Mt/CO2-e (Government of South Australia, 2007a), could be offset by reforestation under a carbon price of $20/t of CO2-e and this would require less than 20% of existing agricultural lands. It would be possible to achieve the South Australian Strategic Plan target that commits the State to achieving Kyoto obligations of reducing emissions by 60% by 2050 (Government of South Australia, 2007b).

There are substantial economic benefits for regional South Australian communities of reforestation for carbon biosequestration that offset South Australia’s emissions. For example, adoption of carbon plantings and sale of permits could see a boost in income to the State’s agricultural sector in the order of 20%, or $1billion annually under a carbon price of $20/t of CO2-e. The economic benefits could be even greater at higher carbon prices.

There are also significant opportunities available for managing soil erosion and degradation, improving water quality and enhancing the stocks of biodiversity through


carbon biosequestration. Many 1,000s of hectares of degraded lands could be restored. The size of the gains is dependant on carbon price, level of uptake and tree species planted. Extensive areas of land identified as high priority for ecological restoration could be economically viable for reforestation using mixed local native species.

5.1.2 Threats

Robust policy must also be designed to prevent undesired natural resource management, economic and social outcomes in South Australia resulting from the introduction of a carbon market. For example, considerable volumes of fresh water resources could potentially be unavailable for consumptive uses due to increased evapotranspiration by reforestation. Widespread plantation of reforestation monocultures may provide only marginal biodiversity value in cleared landscapes that urgently require large-scale ecological restoration. Assuming unrestrained adoption, South Australia may see large reductions in wheat/sheep production after conversion of dryland agriculture to reforestation.

5.1.3 Policy and Planning

Some of these undesired outcomes are already recognised and work is underway on appropriate policy development. For example, the National Water Initiative recognises the impact of reforestation on water interceptions and requires that water entitlements be held for plantations in catchments that are over-allocated or are approaching over-allocation (National Water Commission, 2004). Eventually National Water Initiative reforms may result in full-cost pricing of water for all land uses, including reforestation (National Water Commission, 2004). The market price for high security water has exceeded $2,000/ML in recent trades within the South Australian Murray Darling Basin (GHD Hassall, 2009). The economic viability of reforestation for carbon would be impacted by inclusion of this cost, assuming evapotranspiration can be calculated with reasonable accuracy at a fine scale. Further research is required because the Zhang et al. (2001) model used in this study is only applicable at coarser scales of catchments and sub-catchments.

Carbon-motivated reforestation could be targeted in landscapes either by: i) avoiding stressed water catchments completely; or, ii) dispersing reforestation across landscapes so that local impacts on water and land availability are minimised. This would also help reduce the risk to the new plantings by, for example, fire. The outcome from the National Water Initiative is that large-scale forestry developments are discouraged where they would have adverse impacts on water security or that more dispersed plantings of least impact would be encouraged. The impacts on water resources indicated in this report highlight the importance of such complementary measures.

In regions where ecological restoration is identified as a high priority, policy developments will need to be spatially targeted to maximise the return on investment in reforestation for multiple benefits. For example, incentives equal to the gap between


the profit from mixed environmental plantings and the profit from E. globulus would encourage reforestation for carbon away from monocultures and toward mixed planting of locally native species. These incentives need only be offered to land owners in high priority locations for ecological restoration.

The size of such a ecological restoration incentive could be relatively small under lower carbon prices (e.g. $125/ha/yr for 41 years at $25/t of CO2-e and baseline commodity prices), but widens considerably as carbon price increases (e.g. $300/ha/yr at $45/t of CO2-e). Increased incentive payments may be required to provide a competitive advantage of ecological restoration projects over monoculture carbon plantings. Approximately 0.5m ha of high priority biodiversity locations are modelled as economically viable for E. globulus at $45/t CO2-e and the baseline commodity price scenario, making the total annual incentive required for biodiversity very large.

Alternatively maximum areas and eligible revegetation types could be zoned. For example, non-restoration activities might only become eligible for credits once representation targets are reached. This could ensure that the potential biodiversity benefits are achieved and water yield impacts are controlled at low cost.

The modelled reductions in agricultural commodities (reported for wheat and sheep) as a consequence of widespread adoption of reforestation for carbon where it is economically viable, are arguably less of a threat to productive agriculture and food security at higher commodity prices. The implementation of a carbon market may create a new economically viable land use that has the potential to compete with traditional dryland agricultural land uses and reduce the overall production of agricultural commodities. This shift of the supply curve could result in an increase in price for commodities, as seen in the 2008 commodity price boom. Traditional commodities will be more competitive under 2008-style commodity prices. How the competing land uses and trade-offs play out remains to be seen.

Overall, land use change will occur in response to new markets created under a carbon market such as the CPRS. The speed at which this change will occur is unknown because there are other factors that effect uptake which are not considered here. These factors include underlying attitudes and behaviours of farmers to changing practices, availability of capital and resources for widespread reforestation, other market factors and capital and investment profiles of farmers currently practicing dryland agriculture.

But land use change will also be critical for agricultural communities to adapt, survive and prosper under a changing climate. The widespread uptake of reforestation for carbon will build community and landscape resilience to climate change. Traditional dryland agriculture is under threat from climate change. For example, regional changes in rainfall and temperature are predicted to decrease wheat grain yield by 30% or more by 2080 (Luo et al., 2005). Such effects on yields and tree growth were not modelled in this study. However the generation and sale of carbon permits could provide significant alternative sources of income to offset cropping yield losses incurred in a hotter and drier climate.



Reforestation, especially if using mixed local native species, will further contribute to adaptation and resilience by protecting South Australian agricultural and regional landscapes against the expected declines in biodiversity (Crossman et al., 2008) and increased risk of soil erosion (Wang et al., 2007) under hotter and drier climates.

Well designed policy and strategic planning is needed to ensure the right trees go in the right places to provide the best opportunities for South Australia’s agricultural landscapes and environments and to prevent undesired outcomes.

ACKNOWLEDGEMENTS

We thank Peter Butler, Glenn Gale, Chris Nichols and Trevor Hobbs from the South Australian Department of Water, Land and Biodiversity Conservation for their valuable input and support. Phil Polglase, Charlie Hawkins, Keryn Paul and Anders Siggins, CSIRO Sustainable Ecosystems, are thanked for their supply of 3P-G and associated datasets. Brendan George (NSW Department of Primary Industries), Trevor Hobbs and the Commonwealth Department of Agriculture, Fisheries and Forestry are kindly thanked for constructive reviews of an earlier version of this report. This research was partly funded under the CSIRO Sustainable Agriculture Flagship.


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