An assessment of the economic and environmental potential of biomass production in an agricultural region

ARTICLE IN PRESS

Land Use Policy 25 (2008) 533–549 www.elsevier.com/locate/landusepol

An assessment of the economic and environmental potential of biomass production in an agricultural region Brett A. Bryana,, John Warda, Trevor Hobbsb a

Policy and Economic Research Unit, CSIRO Land and Water, PMB 2, Glen Osmond, South Australia 5064, Australia Cooperative Research Centre for Plant-based Management of Dryland Salinity, Department of Water, Land and Biodiversity Conservation, Pasadena Natural Resource Centre, 5 Fitzgerald Road, Pasadena, South Australia 5042, Australia

b

Received 15 August 2006; received in revised form 10 October 2007; accepted 11 November 2007

Abstract The establishment of deep-rooted perennial species and their processing for biomass-based products such as renewable energy can have benefits for both local and global scale environmental objectives. In this study, we assess the potential economic viability of biomass production in the South Australian River Murray Corridor and quantify the resultant benefits for local and global scale environmental objectives. We model the spatial distribution of economically viable biomass production in a Geographic Information System and quantify the model sensitivity and uncertainty using Monte Carlo analysis. The total potentially viable area for biomass production under the Most Likely Scenario is 360,728 ha (57.7% of the dryland agricultural area), producing over 3 million tonnes of green biomass per annum, with a total Net Present Value over 100 years of A$ 88 million. The salinity in the River Murray could be reduced by 2.65 EC (mS/cm) over a 100-year timeframe, and over 96,000 ha of land with high wind erosion potential could be stabilised over a much shorter period. With sufficient generating capacity, our Most Likely Scenario suggests that economically viable biomass production could reduce carbon emissions by over 1.7 million tonnes per annum through the production of renewable energy and a reduced reliance on coal-based electricity generation. Our analyses suggest that biomass production is a potentially viable alternative agricultural system that can have substantial local scale environmental benefits with complimentary global scale benefits for climate change mitigation. r 2007 Elsevier Ltd. All rights reserved. Keywords: Biomass production; Economic incentives; Climate change; Landscape scale; Natural resource management; Spatial analysis

Introduction In many human-dominated regions, development of natural resources has resulted in environmental degradation (Vitousek et al., 1997), and from an anthropocentric perspective, less productive and resilient ecosystems. Development in the South Australian River Murray Corridor (or simply the Corridor) has occurred primarily for agriculture, usually involving both the broad acre clearance of deep-rooted native vegetation and replacement with shallow-rooted, rainfed annual crops and pastures. The effects of this large-scale land clearance commonly results in the degradation of biological, land, and water resources (Williams and Saunders, 2005). Natural resource Corresponding author. Tel.: +61 8 8303 8581; fax: +61 8 8303 8582.

E-mail address: [email protected] (B.A. Bryan). 0264-8377/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.landusepol.2007.11.003

management (NRM) actions such as the establishment of deep-rooted perennials are required over broad areas to ameliorate degrading processes such as wind erosion and salinity (INRM Group, 2003). Actions involve substantial economic costs but may also provide significant economic and environmental benefits. Most land tends to be under private ownership in Australia’s agricultural regions. Hence, in order to achieve environmental objectives, NRM actions are often required by private landholders. NRM actions often involve a significant establishment cost to landholders and there may also be a long-term loss of revenue from agricultural production (opportunity cost). However, most of the benefits are realised over long time periods and there is usually some uncertainty involved. The benefits to the landholder resulting from NRM actions may be insufficient to compensate for incurred costs (Bryan et al., 2007).

ARTICLE IN PRESS 534

B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

Rather, benefits tend to accrue predominately off-farm to the wider community although the beneficiaries rarely share the costs of remedial NRM actions. Thus, private landholders are reluctant to undertake investment in NRM actions on the scale required to mitigate the processes of environmental degradation, and public agencies rarely have the funding required to offset these private costs. Market-based policy instruments such as auctions, cap and trade systems, levies, credit systems, offsets, trusts, and other instruments have been investigated for their ability to encourage on-ground environmental management works by private landholders (Sterner, 2003). In contrast to policy approaches using explicit directives, as a general rule, market-based instruments (MBI) use the price signals of markets and market-like mechanisms to influence the choices made by land managers. Rather than relying on regulations to identify the best course of action, individuals are able to select actions that best meet environmental objectives. The potential advantage of MBI approaches is that through flexible decision making, they can achieve environmental goals at lower cost. In the context of the South Australian River Murray Corridor, however, an array of factors may potentially impede market-based instruments from encouraging land management actions at a sufficient scale required to achieve environmental objectives (Connor and Bryan, 2005; Ward and Trengove, 2005). Substantial establishment costs incurred by private landholders are likely to be the major impediment (Ward et al., 2005). Market-based instruments most likely to facilitate broad-scale NRM actions are those that yield economic returns to the landholder that sufficiently compensate the establishment and opportunity costs by introducing positive income streams realised within a few years of establishment. Biomass production that integrates with existing agricultural activities offers the potential to provide significant economic returns (Bennell et al., 2004). Biomass production based on deep-rooted perennial species may also make substantial contributions to environmental objectives (Tolbert and Wright, 1998). In the Corridor study area, large-scale plantings of deeprooted species for biomass production are expected to have limited biodiversity benefits, but can mitigate processes of salinity and wind erosion. Biodiversity benefits are limited because of the lack of biological diversity typically found in the monoculture plantation and repeated disturbance caused by regular harvesting. Conversely, the deep-rooted perennials can reduce groundwater recharge and consequent saline groundwater intrusion into the River Murray, thereby reducing the potential contribution of dryland areas to river salt load. Establishment of deep-rooted perennials may also eliminate the impact of wind erosion through the soil-binding effect of the roots and the mitigation of wind speed by standing biomass (Cleugh and Bennell, 2002). In addition to local-scale environmental benefits, the production of renewable energy from biomass can have global scale impacts (Schneider and

McCarl, 2003; Van Ierland and Lansink, 2003) in the form of climate change mitigation through the reduction in carbon emissions associated with coal-based energy generation (IPCC, 1996; Sands and Leimbach, 2003). We note that other environmental impacts of coal-based electricity generation such as sulphur, nitrogen, and mercury emission can also be avoided through biomass-based generation although we have not included these additional environmental benefits in this analysis. In this paper, we jointly examine the economic viability and environmental benefits of biomass production in the Corridor study area based on deep-rooted perennial Eucalyptus species. The focus of this analysis is the commercial production of biomass for the supply of feedstock to an integrated tree processing plant and subsequent processing into renewable electricity, activated charcoal, and eucalyptus oil. We conduct a detailed, spatially explicit analysis of the economic viability of biomass production in the Corridor study area. Uncertainty is made explicit through sensitivity analyses. The local environmental benefits of biomass production are quantified in terms of wind erosion and salinity mitigation, as are the global-scale benefits for climate change through carbon emission reductions associated with the generation of renewable energy. This analysis enables the integration of both economic and environmental processes that are heterogeneous over the landscape. The high spatial resolution enables the detailed analysis of farm-scale production economics along with the analysis of landscape-scale soil and hydrological processes such as wind erosion and salinisation. In addition, we discuss the policy strategies and institutional design required to encourage the adoption of biomass production at scales that make substantial contributions towards local and global scale environmental objectives.

Assessing the economic and environmental benefits of biomass Recently, interest in the production of biomass and bioenergy has increased substantially in response to the threat of climate change and the need to reduce carbon emissions (Hoffert et al., 2002; Steininger and Voraberger, 2003; Walsh et al., 2003; Nord-Larsen and Talbot, 2004). In a global analysis of biomass energy futures, Hoogwijk et al. (2005) estimate that biomass has the technical potential to supply energy at 2050 and 2100 equivalent to several times that currently is derived from crude oil. Renewable energy can be generated from a variety of biomass feedstock. These include both residues of agricultural crops such as sugarcane, corn and wheat (Askew and Holmes, 2002; Gallagher et al., 2003), and purpose-grown tree species such as Salix spp., Populus spp., Eucalyptus spp., and Acacia spp. (Varela et al., 2001) and herbaceous species (Hallam et al., 2001) such as switchgrass (Panicum virgatum L.).

ARTICLE IN PRESS B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

The role of biomass production in environmental management has been the subject of some investigation. Biomass production has been found to have potential for enhancing post-disturbance landscapes such as contaminated lands (Goor et al., 2001; Vandenhove et al., 2002) and mining sites (Bungart et al., 2000). Biomass is also expected to have significant potential for enhancing agricultural landscapes (Updegraff et al., 2004; Ska¨rba¨ck and Becht, 2005). Specifically, the results of Bell (1999) and Lefroy and Stirzaker (1999) suggest that biomass production based on deep-rooted perennial species can have a beneficial effect in restoring soil water balance and in mitigating dryland salinisation. Hence, deep-rooted perennial biomass plantings have significant potential for enhancing agricultural landscapes in the Corridor study area. Several studies have assessed the economics of biomass production for renewable energy. Schneider and McCarl (2003) assess the role of biofuels in the context of their potential for emission reduction and conclude that biofuels could play an important role if the carbon equivalent price was above $30/tonne (all dollar amounts are in Australian dollars). Walsh et al. (2003) found that at prices of $44/dry tonne, switchgrass production for renewable energy would be more profitable than current agricultural production on over 17 million ha of agricultural land in the US and that this would supply 7.3% of US energy needs. Varela et al. (1999) and Miranda and Hale (2001) have assessed the full social costs of biomass and other energy options. Monti et al. (2007) found that switchgrass production in Italy may be viable at the high market prices that may be expected over the next few years. Biomass and the associated production of renewable energy and complimentary products (oil, activated carbon) provides an important economic incentive to offset the opportunity costs of foregone agricultural production and to encourage large-scale tree plantings (Walsh et al., 2003; Updegraff et al., 2004). There have been few spatially explicit, integrated economic and environmental assessments of biomass production as a means of achieving multiple environmental and economic objectives. Recent studies have used spatial planning to estimate the potential of biomass production in the landscape. Freppaz et al. (2003) combined Geographic Information Systems (GIS) with mathematical programming to optimise the exploitation of forests for biomass production in the context of sustainable forest management. Varela et al. (2001) assess the potential for locations and integration of a biomass energy plant in Spain using a GIS. The authors identify sites with the lowest environmental impacts and costs including production, storage, and transport costs and conclude that biomass energy production is a feasible energy generating approach in Spain. Schneider et al. (2001) use GIS to model potential biomass yields using a range of biophysical variables assessing productivity against the spatial distribution of land availability in Brazil with respect to the distribution of

535

land degradation, existing agriculture and endemic land cover types. Schneider et al. (2001) point to the potential for integrating biomass production models with other data such as transport and population to provide policy relevant advice to the development of a biomass production policy in Brazil. Nelson et al. (2006) found that switchgrass production for renewable energy can generate both economic and water quality benefits of in northeast Kansas. In a recent comprehensive regional ecological and economic assessment, Rosenberg (2007) found that the abandonment of agriculture for the uptake of large-scale biomass production in the North American Great Plains may have significant environmental, economic, and social benefits. In this study, we undertake a spatially explicit assessment of the economic viability of biomass production in the River Murray Corridor area in southern Australia, and quantify the associated benefits for multiple environmental objectives on a local and global scale. Biomass production and processing in southern Australian landscapes As assessed in this study, farm-based biomass production involves the planting of oil mallee species at a density of between 1000 and 2500 trees per hectare. There are several suitable species for integrated processing in the Corridor study area including Eucalyptus oleosa, Eucalyptus polybractea, and Eucalyptus porosa (Bennell et al., 2004; Hobbs and Bennell, 2005). Biomass production cycles are characterised by an establishment phase of up to 6 yr until first harvest and an optimal 3-yr rotation harvesting regime. At harvest, the trees are cut near ground level and subsequently coppice from rootstock. There are significant opportunities for the expansion of forest bioenergy in the low rainfall areas of southern Australia (Raison, 2006). Rozakis et al. (2001) and Ellis (2001) provide an early assessment of the potential for broad-scale Eucalyptus-based biomass production in the low rainfall agricultural areas of South Australia. Bennell et al. (2004) found that growing eucalypts for biomass may be at least comparable, if not better, in economic terms than existing agricultural production as summarised by Sadras (2004). Appraisals by Enecon (2001), Bennell et al. (2004), and Howard and Olszak (2004) indicate that the positive returns on investment in biomass production in low rainfall areas is reliant on an integrated and diversified production strategy. In this study, we focus on biomass production for supply of feedstock to an integrated tree processing plant for the generation of renewable energy, eucalyptus oil, and activated charcoal. Integrated tree processing has been subject to substantial research interest (Enecon, 2001; Howard and Olszak, 2004; Ward and Trengove, 2005) and a trial 1 MW plant has been established at Narrogin, Western Australia (WA). The demonstration plant has the capacity to process 20,000 tonnes of green biomass per year and produce 7500 MWh/yr of electricity (enough electricity to power

ARTICLE IN PRESS 536

B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

1000 homes). Production of biomass by farmers in WA has the complementary NRM benefit of mitigating dryland salinity caused by land clearance and agricultural development. The Narrogin trial has demonstrated the potential viability of the concept. The Oil Mallee Company who had a major role in supply of feedstock to the plant suggests that there is the potential for 10 integrated tree processing plants, each five times the size of the existing Narrogin demonstration plant in south-west WA. The results of Kumar et al. (2003) also indicate that larger plants may achieve economies of scale.

total world market for activated carbon is estimated to be 700,000 tonnes per annum and is increasing at a rate of approximately 4–5% per annum (Enecon, 2001). However, Bennell et al. (2004) are less optimistic about the commercial prospects for activated carbon products because of the potential for oversupply. Currently, carbon is sourced from UK, Dutch, and French carbon producers. Production and sale of activated carbon as powder, granules, or pellets is expected to contribute approximately 65% of the estimated annual revenue from an integrated tree processing plant (Enecon, 2001).

Renewable energy

Eucalyptus oil

Electricity demand predictions (ETSA, 2004a, b) indicate the annual increase of 3.4% for the River Murray region— a substantially higher rate of increased demand compared to the rest of South Australia, and Australia as a whole. Currently, there is no local electricity generation facilities in the region and power generated in distant localities suffers substantial transmission losses (ETSA, 2004a, b). Hence, there are potential efficiency gains in the location of small-scale electricity generation capacity in the Corridor study area. In Australia, electricity providers need to meet Mandatory Renewable Energy Targets (MRET). The MRET national initiative (Commonwealth of Australia, 2001), implemented in 2001, places a legally enforceable obligation on wholesale electricity retailers to provide an additional 9500 GWh (or 2% of 2001 levels) per annum of renewable energy by 2010–2020 (although recent Australian Government policy announcements suggest that this may soon be increased). The penalty for noncompliance is set at a non-tax deductible $40/MWh, redeemable if the MRET shortfalls are met by purchasing renewable energy certificates (REC) within 3 yr. In lieu of enforceable carbon emission levels, RECs represent a surrogate Australian Government initiative to promote renewable energy generation. Akmal et al. (2004) predict wind and biomass (estimated at 33% of renewable generation in 2019–2020) to be the second largest sources of renewable energy behind hydro power. Biomass generation based on woody perennial feedstock seems well placed to meet future MRET market demands in Australia.

Eucalyptus oil derived from mallee eucalypt species is used as a pharmaceutical product with potential use as an industrial solvent (Enecon, 2001). Traditional world markets for eucalyptus oil already exist in non-prescription pharmaceuticals, cleaning products, and perfumery. Currently, the world market consumes 3000–5000 tonnes/yr of eucalyptus oil (produced mainly from E. globulus), most of which is produced in China, with approximately 200 tonnes produced in Australia for specialty fragrance markets. Cineole (the active degreasing agent of eucalyptus oil) has been found to be an effective degreaser (Enecon, 2001). Large volume availability of eucalyptus oil would facilitate access to a degreasing solvent market, substituting for the petrochemical-based solvent, trichloroethane which is being phased out internationally due to its impact on the ozone layer. The industrial solvent market exceeds 1 million tonnes per annum. The Enecon (2001) assessment indicates an opportunity to develop an alternate and extensive solvent market using high grade, stable, and biodegradable eucalyptus oil.

Activated carbon Charcoal produced from the first stage of processing the woody perennial feedstock can be further activated by steam to produce high value activated carbon. By preferentially adsorbing chemicals, ions, and odours, activated carbon’s primary applications are in potable water purification and treatment, microcystin adsorption (blue green algae toxin removal), atrazine adsorption (pesticide removal), gas cleaning, and the removal of odours in the food and beverage industry, and the extraction of gold from ore slurries (Enecon, 2001). The

The commercial viability of the integrated tree processing plant Ward and Trengove (2005) analysed the economic viability of a 5 MW integrated tree processing plant in the study region, producing biomass-based electricity, activated carbon, and eucalyptus oil. On the farm-side, biomass productivity, production costs, and foregone opportunity costs were quantified for three rainfall and soil regions in the Corridor study area by Ward and Trengove (2005). Based on the revenue streams summarised below, they concluded that the plant was able to incur factory gate biomass costs of up to $47/tonne as a mean for the three regions and maintain a 15% internal rate of return (mean break even for biomass producers was estimated at $38/tonne). Net present value (NPV) over a 15-yr life of plant at a 10% discount rate was estimated at $9.5 million. Biomass processing revenues were estimated as follows (Ward and Trengove, 2005, pp. 25–26). With regard to electricity generation, a 5 MW plant has the potential to generate 40,000 MWh/yr at a net sales price of $22/MWh

ARTICLE IN PRESS B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

511,000 ha of remnant native vegetation, and 50,000 ha of high-value irrigated agriculture. An area of 193,000 ha is protected for nature conservation purposes and approximately 956,000 ha is unreserved land privately owned. The Corridor study area covers a diverse range of landscapes from the moist, cool, hilly eastern edge of the Mt. Lofty Ranges in the south-west of the study area to the warm dry plains of the Mallee in the north-east of the study area. Mean annual rainfall varies from 226 mm/yr in the north-east to 662 mm/yr in the south-west. Mean annual temperature ranges from 13.2 1C in the southwest to 16.8 1C in the north-east. Approximately 80,000 people reside in the Corridor study area, with major centres located in Fig. 1. Dryland agriculture is dominated by livestock grazing, particularly sheep grazing, and cereals such as wheat and barley, with some smaller areas of oilseeds and legumes (Bryan and Marvanek, 2004). Soils are commonly sandy and nutrient poor. Land clearance and agricultural land use has exacerbated wind erosion of soils through increased exposure due to typical farming practices, and through the modification of soil structure. Hence, the mitigation of wind erosion is a major environmental

($60/MWh production costs minus $38/MWh return from tradeable RECs). This is competitive with the average electricity pool price for 2006 in the study area of $28/ MWh. The Australian Greenhouse Office (2003) estimate for 2007 Australian pool prices is $47/MWh and $34/MWh in 2010, suggesting an ongoing competitive advantage. The 5 MW integrated tree processing plant has the capacity to produce 2729 and 1090 tonnes of granulated and pelleted activated carbon, respectively, both with an estimated market value of $3000/tonne, in addition to 294 tonnes of powdered carbon valued at $1000/tonne. A total of 1050 tonnes of eucalyptus oil can be produced with a market value of $3000/tonne. Description of the study area The River Murray Corridor study area in South Australia is defined as a 15 km buffer from the floodplain (defined as the area inundated by the 1956 1-in-100-yr flood) stretching from the Victorian/NSW border in the east to Tailem Bend in the south (Fig. 1). The study area covers an area of 1,217,000 ha including 108,000 ha of floodplain, 628,000 ha of cleared dryland agriculture, 350,000

537

400,000

450,000

500,000

6,250,000

6,250,000

Morgan

Australia

Renmark

Waikerie Kingston-On-Murray Berri Blanchetown

6,200,000

6,200,000

Loxton

Swan Reach

6,150,000

6,150,000

Mannum

Irrigated Areas Native Vegetation Murray Bridge

1956 Floodplain River Murray Dryland Corridor

Tailem Bend

6,100,000

0

10

20

6,100,000

Towns

Kilometres 350,000

400,000

450,000

Fig. 1. Location map of the South Australian River Murray Corridor.

500,000

ARTICLE IN PRESS 538

B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

objective and is formalised in the regional target (INRM Group, 2003): By 2020, reduce the area of agricultural land at risk of wind erosion during June each year by 40%. Regional groundwater systems are naturally saline and flow towards the River Murray, delivering a natural influx of salt to the river. Land clearance and irrigation development has exacerbated this process through increased groundwater recharge (Cook et al., 2004). Increasing river salinity has important implications because the city of Adelaide (approximately 1.1 million people) and many rural South Australian towns rely on the River Murray for supplying much of the fresh water. Addressing the environmental objective of river salinity mitigation is embodied in a Murray-Darling Basin wide target (MDBC, 2001): By 2020, have salinity of water in the River Murray less than 800 EC (mS/cm) for 95% of the time at Morgan to ensure drinking water standards.

Agricultural Production

Crop Establishment

First third of crop

1

Second third of crop

2

Final third of crop

3

Cash Flow

4 5 First third of crop Second third of crop

First Harvest

Final third of crop

Second Harvest

Economic viability of biomass production

6 7 8

First third of crop

9

Second third of crop

10

Final third of crop

11

First third of crop

Third Harvest

Production-side economic analysis of biomass in the Corridor study area involves calculation of the revenue from biomass and the costs of production over a specific time frame. Based on the results of Bennell et al. (2004), the optimal harvesting regime for oil mallee species in the study area is a 6-yr establishment period followed by 3-yearly harvests. Plant material is harvested by coppicing near ground level. Oil mallees can regrow from coppiced rootstock. In normal production, the integrated tree processing plant needs a constant supply of 100,000 green tonnes of biomass each year. To provide this amount, we assume the staggered planting regime of one-third of the crop planted at the beginning of year 1, one-third in year 2 and the final third in year 3. We also assume a staggered harvest after the 6-yr establishment period of one-third of the crop harvested each year and each hectare of land harvested every 3 yrs. Immediately following harvest the biomass crops require fertilisation. The crops also require minimal annual maintenance (Fig. 2). In modelling the economic potential of biomass we need to characterise returns from and costs of production. This production regime involves irregular cash flow. Revenue and costs of biomass production are calculated for each year based on this production schedule and discounted back to NPV terms. Revenue from biomass production occurs first in year 6 and then regularly each year after that as one-third of the crop is harvested each year. The costs of biomass production include establishment, maintenance, harvest, fertiliser, opportunity and transport costs. Different costs and returns occur at different times in the biomass production schedule (Fig. 2). The economic potential of biomass industries in the study area is assessed according to NPV or returns, Modified Internal Rate of Return

Year

12 continued

Opportunity Costs

Establishment Costs

Maintenance Costs

Harvest Costs Transport Costs Fertiliser Costs

Income

Fig. 2. Agricultural production schedule and cash flow for oil mallee production for integrated processing.

(MIRR) and Equal Annual Equivalent (EAE) returns which incorporate irregular cash flow and constant time preference. Maps of the spatial variation in the economic measures of biomass profitability are developed using the spatially varying parameters of biomass productivity, opportunity costs, transport costs, and scalar parameters including harvest costs, maintenance costs, and fertiliser costs. The spatial model of the profitability of biomass production is created in a GIS. A raster data structure is used based on grid cells of 254 m resolution (6.4516 ha—the resolution of existing salinity model outputs). Production-side economic assessment in this study is necessarily highly parameterised. It involves specification of a range of values for model parameters that significantly affect the results of the analysis. There is some uncertainty surrounding all of the specified parameters. The economic assessment is conducted in two phases to cope with this uncertainty. First, the Most Likely Scenario performs a single analysis of the profitability of biomass production using the most likely parameter values. Second, a sensitivity analysis quantifies the effects of parameter uncertainty on the economic potential of biomass production.

ARTICLE IN PRESS B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

Biomass productivity Economic returns to biomass production depend on site productivity and the price per tonne of biomass. The principal model input is the GIS layer of green biomass productivity of E. oleosa in m3/ha/yr. Based on extensive field trial data, Bennell et al. (2004) produced a layer capturing the spatial distribution of stemwood productivity based on experimental growth rates of E. oleosa. Originally created at a resolution of 5 km grid cells, this layer was downscaled using bilinear resampling to 254 m grid cell resolution to match the other GIS data. Revenue To calculate the revenue from biomass (rt) at harvest at year (t) for each grid cell (k), we first calculate the production (Pt) as a layer with values for green tonnes of biomass calculated for each grid cell. To do this, we multiply the stemwood volume productivity at harvest spatial layer (s) by the cell area (a) and the time in years since last harvest (mt). A coppicing productivity multiplier (jt) is then applied, which attempts to capture the increased productivity of the species after coppicing compared with establishment productivity rates. A stemwood fraction conversion factor (yt) is also applied, which converts the stemwood volumes into total tonnes of green biomass (wood, twigs, leaves inclusive). The values of jt and yt are different before and after the first harvest (i.e. tX6 and tp8) due to the differing productivity rates caused by coppiced regrowth. Values for jt and yt are derived from field-based empirical measurements. This is all divided by a harvest schedule staggering component (g). In this case, we use g ¼ 1/3 because we are harvesting a third of the area each year: ( 0 for to6; Pt ¼ s  a  mt  jt  yt  g for tX6; where Pt is production (green tonnes of biomass), s is stemwood volume productivity (m3/ha/yr), a is cell area (6.4516 ha), mt is time since last harvest (years), jt is coppicing productivity multiplier (green tonnes per m3), yt is stemwood fraction conversion factor (scalar), g is harvest schedule staggering component (g ¼ 1/3), t is year. For tX6 and tp8: jt ¼ 1, yt ¼ 1:9074, mt ¼ 6. For t48: jt ¼ 1:5,

Finally, the revenue in dollars for each cell is calculated as a layer by multiplying the production in green tonnes of biomass of each grid cell by the factory gate price per green tonne in dollars (p): rt ¼ Pt  p. Costs of production All costs are calculated as layers with values in dollars for each grid cell. Significant establishment costs are incurred from planting one-third of the biomass crop at the beginning of years 1, 2, and 3 such that ( ec  a  g for tX1; tp3; EC t ¼ 0 for t43; where ECt is total establishment costs for year t ($), ec is establishment costs ($/ha). Maintenance costs are incurred every year (but only occur for the total area of crop after year 3 due to staggered plantings) and are calculated as ( mc  a  t  g for tX1; tp3; MC t ¼ mc  a for t43; where MCt is total maintenance costs for year t ($), mc is maintenance costs ($/ha). Harvest costs occur first at year 6 and then every year after that for one-third of the total crop area harvested each year: HC t ¼ Pt  hc for all t, where HCt is total harvest costs for year t ($), hc is harvest costs ($ per green tonne of biomass). Fertiliser costs follow harvest and are calculated as ( 0 for to6; FC t ¼ fc  a  g for tX6; where FCt is total fertiliser costs for year t ($), fc is fertiliser costs ($/ha). In this analysis, opportunity costs are also incurred each year as the growing of biomass involves the conversion of all prior agricultural land uses. Opportunity costs are calculated based on the current value of agricultural production and are mapped in a sequence of steps. Initially, the spatial distribution of dryland agriculture was quantified and mapped using the catchment scale land use mapping which classifies land parcels according to the Australian Land Use and Mapping Classification standard Version 4 (ALUMC V.4). ALUMC land use classes were then generalised to five categories of dryland land use: cereals; grazing; hay and silage; legumes; and other minimal use. Each class was assigned an average gross margin (GM) in present value terms for the region based on figures from Sadras (2004) and Bryan and Marvanek (2004):

yt ¼ 1:7521, mt ¼ 3.

539

 

Cereals $80/ha, Grazing $37.50/ha,

ARTICLE IN PRESS B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

540

  

Hay and silage $150/ha, Legumes $120/ha, Other minimal use $10/ha.

Although soil quality and other factors have a strong influence, the primary driver of dryland agricultural productivity in the Corridor study area is rainfall. There exists a steep climatic gradient and similar dryland agriculture types can return very different gross margins depending on rainfall. Hence, in order to gain better representation of opportunity costs, average gross margin figures were redistributed following the spatial distribution of rainfall. Gross margin figures for each grid cell were adjusted using the ratio of the mean annual precipitation (modelled using BIOCLIM) of the cell to the geographic average mean annual precipitation (300 mm/yr). As a result, agricultural land uses in drier climates were attributed lower opportunity costs and land uses in moister climates were attributed higher opportunity costs. Annual opportunity costs of dryland agriculture as modelled in this study range from $7.83/ha to $199.00/ha with a mean of $46.53 and a standard deviation of $22.54. The total economic returns to agriculture in gross margin terms and hence, the total opportunity costs to dryland agriculture in the Corridor study area as estimated using the methods described above is $29.25 million/yr. The aggregate gross margin value is in accordance with the $21.4 million in returns to agriculture when profit was estimated at full equity (Bryan and Marvanek, 2004). Opportunity costs are calculated for each cell by multiplying the opportunity costs per hectare by the cell area: OC t ¼ oc  a for all t where OCt is total opportunity costs for year t ($) and oc is opportunity costs ($/ha). Transport costs involve the costs incurred from trucking green biomass from each grid cell to a hypothetical integrated tree processing plant located at Kingston-onMurray along the existing road system. Transport costs were calculated as a layer for the entire SA MDB INRM region using a cost distance function in a GIS after Mo¨ller and Nielsen (2007). To construct this layer a cost multiplier layer was created using a variety of data sources to characterise the relative cost of traversing cells of different surfaces. The South Australian roads database was used to identify sealed and unsealed roads. Areas of irrigated and dryland agriculture, flood plain, and remnant vegetation were also identified. Transportation cost multipliers are lowest along sealed roads (1), slightly higher along unsealed roads (1.2), and higher again over open paddocks (1.4). Transport is permitted across native vegetation and irrigated areas but the cost multiplier is high (3) and so traversal of these surfaces is not favoured in the cost distance analysis. No travel across the flood plain is permitted unless it is along a road. The cost multiplier layer is multiplied by a transport price (tp) based on current commercial rates to calculate for each grid cell the total

cost in dollars per tonne per kilometre ($/t/km) for traversing the cell and this layer is used as input into cost distance analysis. Cost distance analysis is a global function in raster GIS that is able to calculate the least expensive route from each cell to a target cell. Cost distance analysis combines the cost of traversal layer in $/t/km with distance measurement to the integrated tree processing plant at Kingston-on-Murray to calculate for each grid cell of the minimum cost of transport per green tonne of biomass to the integrated tree processing plant in dollars per tonne (tc). To calculate the total transport cost layer, we multiply this by the total production for each cell: TC t ¼ Pt  tc

for all t,

where TCt is total transport costs for year t ($), tc is transport costs ($ per green tonne of biomass). The six different types of costs involved in biomass production (establishment costs, maintenance costs, harvest costs, fertiliser costs, opportunity costs, and transport costs) can be used to calculate total biomass production costs in year t (ct): ct ¼ EC t þ MC t þ HC t þ FC t þ OC t þ TC t . Economic measures of biomass profitability Three measures are used to assess the economic potential of biomass based on the above revenue and cost layers. NPV is the total net returns to growing biomass (revenuecosts) discounted to present day dollars. The Modified Internal Rate of Return is the discount rate that results in the NPV of growing biomass equalling zero or, in other words, the percentage rate of revenue over costs. EAE is the equivalent annual payment required to return the NPV derived from growing biomass considering all of the irregular revenues and costs over time. Using these measures, we can assess the profitability of growing biomass compared to current agricultural practices. By incorporating spatially varying data on production and cost parameters we can calculate the spatial distribution of the profitability of biomass for the study area. More formally, NPV can be calculated as NPV ¼

n X ðrt  ct Þ t¼1

ð1 þ iÞt

,

where i is interest (discount) rate, rt is the revenue at year t, ct is costs at year t, n is the number of years. The Modified Internal Rate of Return assumes that all accrued revenue will be reinvested and is a function of the ratio of the present value of all costs (PVc) to the future value of all returns (FVr) from biomass. MIRR is calculated as rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n FV r MIRR ¼  1, PV c

ARTICLE IN PRESS B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

This enables the mapping of the spatial distribution of the most likely, the lowest and highest NPV returns reasonably expected from biomass production for each grid cell. Finally, the risk of biomass production is calculated for each grid cell as the proportion of the 1000 runs for which biomass production returns a NPV less than zero at a discount rate of 7%. The Monte Carlo iteration was programmed within a GIS.

where PV c ¼

n X t¼1

ðct Þ ð1 þ iÞt

and FV r ¼

n X

ðrt Þð1 þ iÞnt .

t¼1

Using the NPV of biomass production, the Equal Annual Equivalent can be calculated as EAE ¼ NPV 

541

ið1 þ iÞt ð1 þ iÞt  1

Most Likely Scenario and sensitivity analysis The Most Likely Scenario involves a single calculation of the economic measures of NPV, MIRR, and EAE over a 100-yr time frame using the parameters in Table 1. To establish a suitable time frame, we adopt the findings of Cook et al. (2004), who estimate that salinity reduction from revegetation is maximised over a 100-yr time horizon. The sensitivity analysis employs a Monte Carlo simulation of the economic measures by running the model 1000 times for each of four different discount rates. For each iteration random parameter values are taken from the ranges specified in Table 1. Economic measures are recalculated over a time span of 20 yrs during each iteration. Hence, the sensitivity analysis tests the economic potential of biomass under the full range of possible parameter values. One thousand NPV grids were calculated for the 0%, 3%, 6%, and 9% discount rates using model runs with randomised cost and revenue parameters. For each discount rate the mean and upper and lower 95% confidence intervals were calculated for each grid cell based on the 1000 iterations.

Most Likely Scenario The Most Likely Scenario provided a detailed assessment of the relative effect of different costs and prices on the viability of growing biomass in the study area compared with existing agriculture. Biomass productivity of E. oleosa after the first harvest ranges from 24 to 115 green tonnes of biomass per year per grid cell (6.4516 ha) with an average of 51 tonnes per cell. Biomass productivity is highest in the higher rainfall areas in the southern parts of the study area and in eastern Mt. Lofty Ranges (Fig. 3). The value of establishment costs involved in a planting density of 1000 plants per hectare is the same for all cells and was specified at $740/ha/yr. This equates to a present value of $4468 for each grid cell. Maintenance costs are also specified on a per hectare basis and are the same for each grid cell. Specified in the Most Likely Scenario model at a nominal $10/ha, the total present value of maintenance costs is $860. Harvest costs vary spatially with biomass productivity. Specified at $12/tonne, the total present value of harvest costs of grid cells ranges between $3400 and $16,300. Fertiliser costs are based on area and so are the same for each cell. Specified in the Most Likely Model at $40/ha this equates to a present value of $935 for each cell. Opportunity costs vary spatially according to land use and climate. The present value of opportunity costs ranges from $771 per cell in the drier northern parts of the study area to $19,600 per cell under cereal cropping the southern

Table 1 Model parameters and parameter ranges used in the Most Likely Scenario and the sensitivity analysis Model parameter

Symbol Units Most Likely Scenario values

Sensitivity analysis value range

Reasoning behind parameter choice

$/ha 740

400–1200

Based on survey of commercial rates

Years 100

20

i mc

% $/ha

0, 3, 6, 7, 9 5–15

100 years used to provide long term estimate, 20 years used in Monte Carlo simulation to reduce computational load Based on a reasonable estimate of variability Based on survey of commercial rates

hc tp

7–20 0.04–0.07

Based on Bennell et al. (2004) and commercial rates Based on survey of commercial rates

fc p Pt

$/t 12 $/t/ 0.046 km $/ha 40 $/t 35 t See Fig. 3

30–50 15–47 720%

Based on Bennell et al. (2004) and commercial rates Based on Ward and Trengove (2005) Based on a reasonable estimate of variability

OCt

$

730%

Based on a reasonable estimate of variability

Establishment ec cost Time frame n Discount rate Maintenance costs Harvest cost Transport price Fertiliser costs Biomass price Biomass productivity Opportunity costs

7 10

See Fig. 3

Economic parameters and parameter ranges were defined based on Bennell et al. (2004) and a variety of other sources. t ¼ green tonnes of biomass.

ARTICLE IN PRESS 542

B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

Green Biomass Productivity Green Tonnes Per Hectare 3.87 - 6.48 6.49 - 7.51 7.52 - 8.81 8.82 - 10.27 10.28 - 12.01 12.02 - 14.45 14.46 - 17.70

Opportunity Costs Public Land $/ha $7 - $14 $14 - $24 $24 - $38 $38 - $57 $57 - $81 $81 - $134 $134 - $199

Transport TransportCosts Costs Dollars per Green Dollars per Green Tonne of Biomass Tonne of Biomass $0.00 - $1.60 $0.00 - $2.80 $1.60 $1.60 $1.60 -- $4.10 $2.80 $2.80 $2.80 -- $5.60 $4.10 $4.10 $4.10 -- $7.10 $5.60 $5.60 $5.60 $7.10 $7.10 - $8.30 $7.10 - $8.30 $8.30 - $10.20 $8.30 - $10.20

Fig. 3. Spatial distribution of key economic model inputs including green biomass productivity from Bennell et al. (2004).

areas (Fig. 3). Transport costs also vary spatially according to production and distance to the integrated tree processing plant at Kingston-on-Murray. Present value of transport

costs ranges from $0 for cells adjacent to the proposed processing plant at Kingston-on-Murray to $12,180 for grid cells far from the plant (Fig. 3). Present value of total costs of biomass production for grid cells ranges from $11,600 to $46,200 over a time period of 100 yrs and at a discount rate of 7%. Assessment of economic measures in the Most Likely Scenario values reveals that biomass is more profitable than current agriculture in most parts of the study area. The total NPV of biomass production ranges between an economic loss of $13,400 to a benefit of $10,500 compared to returns from existing agriculture, with an average NPV benefit of $40 per cell (Fig. 4). The MIRR ranges between 6.5% and 7.4% (Fig. 4), which is acceptable, given that opportunity costs of existing agriculture are included in this analysis. EAE payments range from $146 to $113/yr/ ha. Areas with higher net economic returns to biomass are located in the Mt. Lofty Ranges to the west of Mannum, where productivity is higher relative to the opportunity cost of foregone production (Fig. 4). We can consider biomass production to be potentially viable where the NPV of production, which includes the opportunity costs of existing agriculture, is greater than zero. As a result, the total potentially viable area for biomass production under the Most Likely Scenario is 360,728 ha or 57.7% of the dryland area of the Corridor. Major foci of economically viable areas for biomass are located in the southern and north central parts of the Corridor (Fig. 4). The potential tonnage of green biomass supplied by the economically viable area (3 million tonnes) is some 30 times the required supply of 100,000 tonnes and the total NPV over 100 yrs is around $88 million. Sensitivity analysis Sensitivity analysis of the biomass modelling using Monte Carlo iteration and random perturbation of model parameters reveals that there is robust and positive economic potential for biomass production in the Corridor study area. The viability of biomass production is subject to spatial variability and the parameter values used in economic modelling. Biomass production is not viable under all parameter possibilities. It is important to understand where the most viable areas are and what parameter variation affects their viability. Fig. 5 presents the mean and the upper and lower 95% confidence intervals for the NPV of biomass production for each grid cell at 0%, 3%, 6%, and 9% discount rates, These statistical maps have been calculated over 1000 Monte Carlo iterations and demonstrate the average scenarios and the lower and higher limits between which 90% of the simulated economic returns from biomass production occur. The analysis of the statistical mean and 95% confidence intervals calculated on the NPV from 1000 iterations at different discount rates illustrate the uncertainty involved in assessment of biomass profitability. The 95% confidence interval maps show that no grid cells are profitable under all possible parameter values. The mean values

ARTICLE IN PRESS B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

Net Present Value

543

Modified Internal Rate of Return

-$13,400 - -$8,200

6.53% - 6.77%

-$8,200 - -$3,100

6.78% - 6.85%

-$3,100 - -$1,100

6.86% - 6.94%

-$1,100 - $500

6.95% - 7.02%

$500 - $1,900

7.03% - 7.10%

$1,900 - $4,100

7.11% - 7.21%

$4,100 - $10,500

7.22% - 7.37%

Equal Annual Equivalent ($/ha)

Economically Viable Areas

-$146 - -$89 -$89 - -$34

Not Viable

-$34 - -$12 -$12 - $5

Viable

$5 - $20 $20 - $45

0

20

40

Kilometres

$45 - $114

Fig. 4. Economic indicators of the Most Likely Scenario of biomass production for the Corridor.

suggest many, but not all parts of the study area are viable for biomass production. The +95% confidence interval, indicates all areas are potentially viable and some are considerably more profitable than existing agriculture (Fig. 5). Therefore, there is no guarantee of the viability of biomass under all potential economic situations. However, biomass is likely to be viable in many areas under typical cost and revenue situations. Conservatively, a reliable and consistent supply of 4100,000 tonnes of biomass per year can be expected with a factory gate price of biomass of $35 per green tonne. At this price biomass production becomes more profitable than current agriculture over an area sufficient to produce a supply of 4100,000 tonnes, the plant production threshold requirement for biomass feedstock. The internal rate of return for the integrated tree processing plant at a factory gate price of $35 per green tonne is estimated at between 15.75% and 22.23% (Ward and Trengove, 2005). Variation in cost parameters tends to have only very slight effects on the mean NPV of grid cells. Variation in

maintenance and fertiliser costs have no effect on mean NPV. Transport, establishment, harvest and opportunity cost parameter variation have a slight inverse relationship with mean NPV because the higher the costs, the lower the returns. In all cases there is significant variation about these trends. Variation in productivity of biomass within 720% of the empirical levels found by Hobbs (unpublished data 2004) did not affect the mean NPV of grid cells. However, the factory gate price of biomass has a strong influence on the mean NPV returns from biomass production. Thus, the price of biomass is the single most important factor affecting the profitability of biomass production in the Corridor study area. Taken conservatively at a 9% discount rate, there is potentially a reliable supply of 4100,000 tonnes of biomass per year when the factory gate price of biomass exceeds $35 per green tonne. Supply is guaranteed at lower prices per tonne for lower discount rates. This leaves a satisfactory level of flexibility in factory gate price between this price and the $47/tonne maximum price found by Ward and

ARTICLE IN PRESS B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

544

-95%

Mean

+95%

Confidence Interval

Confidence Interval

0%

Discount Rate

3%

6%

9%

High :$ -7,472

High :$ 14,813

High :$ 74,984

Low :$ -50,209

Low :$ -3,999

Low :$ 2,593

Native Vegetation or Irrigated Areas

Native Vegetation or Irrigated Areas

Native Vegetation or Irrigated Areas

0

40

80

Kilometres

Fig. 5. Mean, lower and upper 95% confidence intervals of Net Present Value of biomass production summarised for 1000 model runs at each discount rates of 0%, 3%, 6%, and 9%.

Trengove (2005) that still provides a 15% internal rate of return to the processing plant. Hence, the price of $35/ tonne for biomass is imputed as a starting price to allow for uncertainty and other variable effects such as imperfect information and risk aversion of landholders. We note that others (Bennell et al., 2004; Enecon, 2001) have used lower prices per green tonne of biomass in their analyses. The effect of varying people’s time preference is to moderate the extremes of economic returns. Assessment of the mean, and upper and lower 95% confidence interval grids calculated from the 1000 models runs reveals that

under a lower discount rate the high and low returns are more extreme. The other effect is that returns are generally lower at higher discount rates (Fig. 5). Risk of biomass production is the proportion of the 1000 model runs at 7% discount rate that the net returns from biomass production of each cell is greater than existing agriculture (NPV 4$0). This measure captures the probability that each grid cell will be viable (i.e. have an NPV 4$0) given the range of parameters tested in the sensitivity analysis. Risk probabilities range from a low of 30% to a high of 70% (Fig. 6). That is, even the most viable cells

ARTICLE IN PRESS B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549 350,000

400,000

450,000

545 500,000

6,250,000

6,250,000

6,200,000

6,200,000

6,150,000

6,150,000

Risk of Biomass Production 30% - 40% 40% - 50% 50% - 60% 60% - 70% 70% - 80% 0

6,100,000

10

20

1956 Flood plain

6,100,000

Kilometres 350,000

400,000

450,000

500,000

Fig. 6. Risk of non-viability of biomass production calculated as the proportion of 1000 Monte Carlo runs where biomass production is not economically viable at a discount rate of 7%.

have a negative NPV for 30% of the time given the range of cost and revenue values tested. This is a relatively high risk overall and is due to the low range of values for the factory gate price of biomass. As a result, this assessment is conservative. There is relatively low risk and positive returns associated with biomass production around the stretch of high salinity benefit areas near the River Murray between Morgan and Renmark. Additionally, Fig. 6 highlights the low risk/high return area in the eastern Mt. Lofty Ranges immediately west of Mannum, characterised by relatively low opportunity costs of grazing as the primary agricultural activity. However, there may be other unaccounted for barriers to biomass production in this area, including surface water and groundwater conservation issues and the loss of amenity value. The results suggest that biomass production may have greater potential in the higher rainfall areas of South Australia. Conversely, estimates from Bennell et al. (2004) indicate other higher value agroforestry products such as pulpwood production and fodder crops may be more profitable than biomass industries in the higher rainfall areas.

Environmental benefits of biomass production Carbon Carbon emissions are an emerging environmental priority. Significant interest exists in Australia in reducing carbon emissions through processes such as renewable electricity generation. The production of renewable electricity through the integrated processing of Eucalyptus biomass can reduce atmospheric carbon in two ways— through sequestration in biomass and through a reduction in carbon emissions from the production of renewable electricity and reduced reliance on coal-based generation. The major area of discordance is that the biomass is burned during processing for electricity and the carbon is released back into the atmosphere. Thus, apart from the subsurface lignotuber storage, the carbon sequestration involved in biomass production is a zero sum game. In terms of emissions reduction, Howard and Olszak (2004) state that on average, 1.363 tonnes of CO2 is produced per MWh of coal-based electricity production in Victoria. Typical electricity production from a 5 MW integrated tree

ARTICLE IN PRESS 546

B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

processing plant is approximately around 40,000 MWh/yr (Enecon, 2001), which would offset about 55,000 tonnes of CO2 per year. This is the equivalent of taking 10,500 cars off the road (1 car ¼ 11,560 lbs or 5254 kg CO2/yr). In the Most Likely Scenario presented above, biomass production is more economically viable than current agriculture for over 360,000 ha. This area can produce more than 3 million tonnes of biomass per annum, which can potentially supply more than 30  5 MW processing plants. This translates into a reduction in carbon dioxide emissions of around 1,650,000 tonnes per annum or the equivalent of taking 315,000 cars off the road. If the biomass production displaces sheep grazing at a stocking unit rate of 2.5/ha, another 113,400 tonnes of CO2 may be offset (or 21,649 cars). The introduction and integration of tradable carbon offsets, produced as a result of biomass electricity generation, into the biomass revenue stream could substantially enhance its economic viability. Salinity South Australia has a responsibility to meet salinity targets under the Murray-Darling Basin Salinity Management Strategy (MDBC, 2001) set to ensure drinking water quality standards. It has been documented that a substantial proportion of the salt contribution to the River Murray originates from the dryland areas of the South Australian River Murray Corridor (Barnett et al., 2002). Land clearance has increased the salt load contribution from the dryland areas of the Corridor study area. The removal of deep-rooted perennial native vegetation leads to greater leaching of salts from the soil profile, an increase in saline groundwater recharge and base flow, and an increase in the delivery of salt load to the river. Groundwater in some parts of the SA Murray-Darling Basin is saline, travels towards the River Murray as base flow, and is discharged directly into the river. This process has been well documented in the SA River Murray (Cook et al., 2004). The widespread establishment of deep-rooted perennials may be able to contribute substantially to reversing this process through groundwater recharge reduction (Bell, 1999; Lefroy and Stirzaker, 1999). We quantify potential river salinity benefits (calculated using the SIMPACT model by Bryan et al., 2005) achieved by the establishment of deep-rooted perennial species for biomass production in the dryland areas of the Corridor study area. Under the Most Likely Scenario, over 130,000 ha of land is viable for biomass production which can also contribute limited benefit to reducing the salinity of the River Murray after 100 years (Table 2). The total reduction in EC units (note 1 EC ¼ 1 micro-siemens/cm at 25 1C (mS/cm)) resulting from planting of this area with biomass species is 2.65 Ecs after 100 years. The most profitable locations for biomass production under the Most Likely Scenario were found to be interspersed within existing irrigation areas. Biomass production in these areas may also have synergistic salinity

Table 2 Cross-tabulation of areas (ha) showing relationship between the economic viability of biomass production (EAE/ha) with salinity benefits EAE/ha

p$0 $0–$30 $30–$70 4$70

Salinity (EC  106) 0

1–299

300–1447

1448–12,772

2,582,800 2,906,700 512,870 57,681

68,650 35,551 18,388 65

14,194 19,550 19,098 194

7807 14,453 22,905 65

benefits by lowering ground aquifer water tables and reducing recharge whilst concurrently increasing biomass production through soil water mining. Wind erosion Soils in the Corridor study area have varying levels of susceptibility to wind erosion according to the level of clay content in the soil profile. Sandy soils of low clay content are common and tend to have an inherently higher susceptibility to erosion by wind. Land clearance has exacerbated the problem of wind erosion on susceptible soils. Land clearance involves the removal of deep-rooted perennial native vegetation and replacement with shallowrooted annual crops and pastures. Removal of the soilbinding action and wind speed mitigation provided by deep-rooted perennials increases the risk of soil erosion. We can assume that the replanting of land with the deeprooted perennial biomass species will mitigate soil wind erosion through the permanent soil-binding action of the root systems and the improved protection of the soil from wind exposure. Hence, the large-scale planting of biomass species can help address regional wind erosion mitigation objectives in the study area. Wind erosion potential is mapped into five classes from high to low based on the clay content of the soil. There is more than 7200 ha mapped as high wind erosion potential in the study area which is considered to be unsuitable for cropping and a further 32,000 ha classed as moderately high which is only semi-arable. Over 96,000 ha of moderate, moderately high, and high wind erosion potential land are economically viable for biomass production in the Corridor study area (Table 3). This includes over 2000 ha of high wind erosion potential and nearly 6000 ha of moderately high wind erosion potential. Thus, biomass production has the potential to remediate around 53% of the land classified as having a moderate or greater potential for wind erosion in the study area. Institutional design issues Conservatively, the results of this analysis indicate that biomass production in many areas of the South Australian River Murray Corridor is likely to provide returns similar

ARTICLE IN PRESS B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549 Table 3 Cross-tabulation of areas (ha) showing relationship between the economic viability of biomass production (EAE/ha) with Wind Erosion Potential EAE/ha

p$0 $0–$30 $30–$70 4$70

Wind erosion potential High

Mod. High

Moderate

Mod. Low

Low

2568 2019 226 0

10,607 4833 961 0

71,185 77,734 11,020 13

141,660 154,110 29,086 748

41,325 58,926 16,033 5039

to or marginally more profitable than existing agriculture. This analysis was based on the assumption that returns from agriculture remain constant. This may not be the case especially in the light of estimates of increased temperatures and decreased rainfall associated with climate change (Luo et al., 2005b). Luo et al. (2005a) estimate a decrease in grain yield of wheat between 3% and 58% in the region under their most likely climate change scenario. However, several factors may combine to negate the influence of climate change. Farmers are very adaptable and will undoubtedly adapt land management practices and agricultural systems to cope with climate change. In addition, improvements in technology such as drought-resistant crop genotypes, pesticides and fertilisers may also combat the influence of climate change. Biomass production involves the plantation of locally adapted, deep-rooted tree species which are much more suited to the dryer, warmer conditions anticipated under climate change scenarios. A warmer, dryer climate will also reduce the productivity of oil mallees although probably not as much as cereal crops (Bryan et al., 2007). In addition, because oil mallees are a deep-rooted perennial species the likelihood of crop failure is less compared to cereals. As such, as an agricultural enterprise, biomass is a much more resilient agricultural system. These considerations enhance the picture of economic viability of biomass given the uncertainty of future conditions. Carbon sequestration and trading also looms as another potential economic incentive for biomass production in the Corridor study area. In the context of biomass production, there is potential to trade below ground carbon sequestration and the carbon emission reductions achieved through the production of renewable electricity in an integrated tree processing plant. The additional income generated from a potential involvement in carbon trading could increase further the profitability of biomass. However, the realisation of a viable biomass industry involves much more than demonstrating its potential viability. A viable biomass industry in the Corridor is contingent on the synchronised establishment of an Integrated Tree Processing plant and contractual biomass production arrangements finalised with landholders. A substantial industry development initiative, nominally led by the SA government or other relevant agencies such as the Regional Development Board, is likely to hasten the

547

uptake of the biomass enterprise opportunity. Co-involvement by the private sector would further speed the development of a biomass industry. In Western Australia, involvement by the state government and a private power company provided momentum in the biomass industry including the establishment of the processing plant. We have already had enquiries from renewable energy companies regarding this research so the future looks bright for the establishment of biomass industry in the SA River Murray region. Concomitantly, establishment of a biomass industry requires large-scale land use change. Demonstration of economic viability may be necessary but not sufficient to secure the uptake of biomass production by landholders at a scale to guarantee the plant supply. Land use change demands significant extension work, education and information provision to create the inertia required for largescale land use conversion. Traditional agricultural productions of cereals, wool and meat have well known production costs coupled with access to established and robust markets. There is substantial risk involved in conversion to new and untested biomass crops for farmers. One way of reducing this risk is to establish contractual arrangements. There are several possible models including private contractual arrangements between the commercial sector (e.g. energy companies) and landholders, farmer’s co-operatives, and other agroforestry models. Contractual arrangements may also remedy the delayed cash flow problem; where the first returns from biomass are only realised after first harvest at 6 yrs. Existing contractual and management models have successfully addressed the cash flow timing constraints and impediments that make perennial plantings unattractive to landholders. Contracts guaranteeing fixed annual payments may be required to encourage the conversion of land use to biomass production. Contractual arrangements may need to be established that provide a regular payment to landholders such as the Equal Annual Equivalent payment. Conclusion Biomass production was found to be potentially economically viable in the Corridor study area and the most profitable sites and under the Most Likely Scenario modelled, could return a NPV of $88 million more than existing agricultural land uses over 100 yrs of simulation. Under the Most Likely Scenario, profitable areas were found to produce many times the amount of biomass required to supply a single 5 MW integrated tree processing plant. Hence, if the market for renewable energy, oil and activated charcoal creates sufficient demand, farm production could support several 5 MW or larger plants. Additional environmental benefits would accrue from these plantings and production costs may be reduced through economies of scale. Sensitivity analysis suggests that there is some risk involved and sufficient biomass production was not

ARTICLE IN PRESS 548

B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549

guaranteed under all reasonable economic model parameter values to ensure a viable biomass industry in the region. Offsetting this, the Eucalyptus species planted for biomass production are more resilient that cereal crops and pastures to the drier, more erratic rainfall patterns expected under climate change scenarios. Biomass production has the potential to reduce river salinity and wind erosion and thereby contribute to regional environmental objectives. Whilst the river salinity benefits of economically viable areas of biomass production under the Most Likely Scenario are limited (2.65 EC), the wind erosion benefits are more substantial including the potential to stabilise over 96,000 ha of land classified as having a moderate or higher wind erosion potential. In addition, the production of renewable energy from biomass can have benefits for global scale climate change through reducing over 1.7 million tonnes of carbon dioxide emissions per annum from coal-based electricity generation. The environmental benefits of biomass production, shared publicly by the off-farm community, may be sufficient to justify the effort and expenditure required by government to facilitate the establishment an integrated tree processing plant to support a biomass industry. Careful institutional design is required however, to assuage risk to farmers through robust contractual arrangements and increase adoption through farm extension. Spatially heterogeneous economic and environmental processes have been integrated in this analysis of biomass production as a land use policy for achieving local scale environmental benefits with complementary impacts on the global scale process of climate change. The spatial analysis techniques used in this study enables analysis of production economics at the scale of the farm-based decision making unit and the quantification of the explicit areas in the landscape where biomass production is profitable. At this commensurate scale of analysis, the landscape processes of wind erosion and salinisation were able to be modelled and the local-scale environmental impacts of biomass production evaluated in detail. The spatially explicit results of this study can also be used to guide targeted information provision and extension by management agencies in those parts of the study area that are both more likely to be profitable for biomass production and produce attendant environmental benefits. Acknowledgements The authors gratefully acknowledge financial assistance for and guidance of this project by the South Australian Department of Water, Land and Biodiversity Conservation and the CSIRO Flagship ‘Water for Healthy Country’. References Akmal, M., Thorpe, S., Dickson, A., Burg G., Klijn N., 2004. Australian Energy: National and State Projections to 2019–20, ABARE eReport

04.11. Prepared for the Australian Government Department of Industry, Tourism and Resources, Canberra. Askew, M.F., Holmes, C.A., 2002. The potential for biomass and energy crops in agriculture in Europe, in land use, policy and rural economy terms. International Sugar Journal 104 (1247), 482 (Reprinted from Aspects in Applied Biology 65, 365–374, 2001). Australian Greenhouse Office (AGO), 2003. Greenhouse gas abatement program website. /http://www.greenhouse.gov.au/ggap/round3/emission-factors.htmlS (accessed 8.9.2004). Barnett, S.R., Yan, W., Watkins, N.R., Woods, J.A., Hyde, K.M., 2002. Murray Darling Basin salinity audit: groundwater modelling to predict future salt loads to the River Murray in South Australia. Department of Primary Industries and Resources Report DWR 2001/017. Bell, D.T., 1999. Australian trees for the rehabilitation of waterlogged and salinity-damaged landscapes. Australian Journal of Botany 47 (5), 697–716. Bennell, M., Hobbs, T., Ellis, M., 2004. FloraSearch species and industry evaluation: low rainfall agroforestry options for southeastern Australia. A report for the RIRDC/Land and Water Australia/FWPRDC/ MDBC Joint Venture Agroforestry Program. SA Department of Water, Land and Biodiversity Conservation. Bryan, B., Marvanek, S., 2004. Quantifying and valuing land use change for Integrated Catchment Management evaluation in the MurrayDarling Basin 1996/97–2000/01. Stage 2 Report to the Murray-Darling Basin Commission, CSIRO Land and Water. Bryan, B., Crossman, N., Shultz, T., Connor, J., Ward, J., 2005. Systematic Regional Planning for Multiple Objective Natural Resource Management. Stage 2 Client Report for the River Murray Dryland Corridor Project, CSIRO Land and Water. ISBN 0643091963, Folio S/05/225. Bryan, B.A., Crossman, N.D., King, D., McNeill, J., Wang, E., Barrett, G., Ferris, M.M., Morrison, J.B, Pettit, C., Freudenberger, D., O’Leary, G., Fawcett, J., Meyer, W., 2007. Lower Murray Landscape Futures Dryland Component: Vol. 3—Preliminary Analysis and Modelling. CSIRO Water for a Healthy Country, 378pp. Bungart, R., Bens, O., Huttl, R.F., 2000. Production of bioenergy in postmining landscapes in Lusatia—perspectives and challenges for alternative landuse systems. Ecological Engineering 16, S5–S16. Cleugh, H., Bennell, M., 2002. Using windbreaks to protect soil, crops and livestock and integrating agroforestry into low rainfall farming systems. RIRDC Project No. CSL-10A and SAR-19A. Commonwealth of Australia, 2001. Renewable Energy (Electricity) Act 2000, the Renewable Energy (Electricity) Regulations 2001. Connor, J.D., Bryan, B.A., 2005. River Murray Dryland Corridor Revegetation Policy Options. Policy and Economic Research Unit, CSIRO Land and Water. Cook, P.G., Leaney, F.W., Miles, M., 2004. Groundwater Recharge in the North-East Mallee Region, South Australia. CSIRO Land and Water Technical Report No. 25/04. Ellis, M., 2001. The potential for biomass industries in the lower rainfall agricultural areas of South Australia. A strategic approach to new large scale revegetation opportunities. Land Management and Revegetation Program, Sustainable Resources, PIRSA. Enecon Pty Ltd., 2001. Integrated Tree Processing of Mallee Eucalypts. A Report for the RIRDC/Land and Water Australia/FWPRDC Joint Venture Agroforestry Program. RIRDC Publication No. 01/160. RIRDC Project No. OIL-3A. ETSA, 2004a. ETSA utilities’ charges-effective 1 July 2004. Website /http://www.etsautilities.com.au/networktariffsv1.4S (accessed 17.8.2004). ETSA, 2004b. Murraylands regional development plan. Website: /http:// www.etsautilities.com.au/pdf/ESDPIssue1.0.pdfS (accessed 17.8.2004). Freppaz, D., Minciardi, R., Robba, M., Rovatti, M., Sacile, R., Taramasso, A., 2003. Optimizing forest biomass exploitation for energy supply at a regional level. Biomass & Bioenergy 26 (1), 15–25. Gallagher, P.W., Dikeman, M., Fritz, J., Wailes, E., Gauthier, W., Shapouri, H., 2003. Supply and social cost estimates for biomass from

ARTICLE IN PRESS B.A. Bryan et al. / Land Use Policy 25 (2008) 533–549 crop residues in the United States. Environmental and Resource Economics 24 (4), 335–358. Goor, F., Davydchuk, V., Ledent, J.F., 2001. Assessment of the potential of Willow Src plants for energy production in areas contaminated by radionuclide deposits: methodology and perspectives. Biomass & Bioenergy 21 (4), 225–235. Hallam, A., Anderson, I.C., Buxton, D.R., 2001. Comparative economic analysis of perennial, annual, and intercrops for biomass production. Biomass & Bioenergy 21 (6), 407–424. Hobbs, T., Bennell, M., 2005. Plant biometrics and biomass productivity in the River Murray Dryland Corridor. A Report for the SA Centre for Natural Resource Management. Cooperative Research Centre for Plant-based Management of Dryland Salinity. Hoffert, M.I., Caldeira, K., Benford, G., Criswell, D.R., Green, C., Herzog, H., Jain, A.K., Kheshgi, H.S., Lackner, K.S., Lewis, J.S., Lightfoot, H.D., Manheimer, W., Mankins, J.C., Mauel, M.E., Perkins, L.J., Schlesinger, M.E., Volk, T., Wigley, T.M.L., 2002. Advanced technology paths to global climate stability: energy for a greenhouse planet. Science 298 (5595), 981–987. Hoogwijk, M., Faaij, A., Eickhout, B., de Vries, B., Turkenburg, W., 2005. Potential of biomass energy out to 2100, for four IPCC SRES land-use scenarios. Biomass & Bioenergy 29 (4), 225–257. Howard, B., Olszak, C., 2004. Fuelling landscape repair: a bioenergy industry as a sustainable land-use and energy option for Australia. A Report Prepared for the Australian Conservation Foundation and Joint Venture Agroforestry Program, with the Assistance of the Myer Foundation. RIRDC Publication No. 04/098. RIRDC Project No. URS-1A. INRM Group, 2003. Integrated Natural Resource Management Plan for the South Australian Murray-Darling Basin. The Integrated Natural Resource Management Group for the South Australian MurrayDarling Basin Inc. IPCC, 1996. Climate change 95: the science of climate change. In: Houghton, J.T., et al. (Eds.), Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. Kumar, A., Cameron, J.B., Flynn, P.C., 2003. Biomass power cost and optimum plant size in Western Canada. Biomass & Bioenergy 24 (6), 445–464. Lefroy, E.C., Stirzaker, R.J., 1999. Agroforestry for water management in the cropping zone of southern Australia. Agroforestry Systems 45 (1–3), 277–302. Luo, Q., Bryan, B.A., Williams, M.A.J., Bellotti, W., 2005a. Spatial analysis of environmental change impacts on wheat production in Mid-Lower North, South Australia. Climatic Change 72 (1/2), 213–228. Luo, Q., Jones, R., Williams, M., Bryan, B.A., Bellotti, W., 2005b. Construction of probabilistic atmospheric change scenarios and their application in risk analysis. Climate Research 29 (1), 41–52. MDBC, 2001. Sasin Salinity Management Strategy 2001–2015. MurrayDarling Basin Commission. Miranda, M.L., Hale, B., 2001. Protecting the forest from the trees: the social costs of energy production in Sweden. Energy Journal 26 (9), 869–889. Mo¨ller, B., Nielsen, P.S., 2007. Analysing transport costs of Danish forest wood chip resources by means of continuous cost surfaces. Biomass & Bioenergy 31 (5), 291–298. Monti, A., Fazio, S., Lychnaras, V., Soldatos, P., Venturi, G., 2007. A full economic analysis of Switchgrass under different scenarios in Italy estimated by BEE Model. Biomass & Bioenergy 31 (4), 177–185. Nelson, R.G., Ascough II, J.C., Langemeier, M.R., 2006. Environmental and economic analysis of switchgrass production for water quality improvement in northeast Kansas. Journal of Environmental Management 79 (4), 336–347. Nord-Larsen, T., Talbot, B., 2004. Assessment of forest-fuel resources in Denmark: technical and economic availability. Biomass & Bioenergy 27 (2), 97–109.

549

Raison, R.J., 2006. Opportunities and impediments to the expansion of forest bioenergy in Australia. Biomass & Bioenergy 30 (12), 1021–1024. Rosenberg, N.J., 2007. A Biomass Future for the North American Great Plains: Toward Sustainable Land Use and Mitigation of Greenhouse Warming. Advances in Global Change Research: Vol. 27. Springer. Rozakis, S., Soldatos, P.G., Kallivroussis, L., Nicolaou, I., 2001. Multiple criteria analysis of bio-energy projects: evaluation of bio-electricity production in Farsala Plain, Greece. Journal of Geographic Information and Decision Analysis 5 (1), 49–64. Sadras, V., 2004. Development of multi-purpose biomass industries for the South Australian Murray Mallee—Project Phase 1. CSIRO Land and Water Client Report. Sands, R.D., Leimbach, M., 2003. Modeling agriculture and land use in an integrated assessment framework. Climatic Change 56 (1), 185–210. Schneider, U.A., McCarl, B.A., 2003. Economic potential of biomass based fuels for greenhouse gas emission mitigation. Environmental and Resource Economics 24 (4), 291–312. Schneider, L.C., Kinzig, A.P., Larson, E.D., Solorzano, L.A., 2001. Method for Spatially Explicit Calculations of Potential Biomass Yields and Assessment of Land Availability for Biomass Energy Production in Northeastern Brazil. Agriculture, Ecosystems and Environment 84 (3), 207–226. Ska¨rba¨ck, E., Becht, P., 2005. Landscape perspective on energy forests. Biomass & Bioenergy 28 (2), 151–159. Steininger, K.W., Voraberger, H., 2003. Exploiting the medium term biomass energy potentials in Austria: a comparison of costs and macroeconomic impact. Environmental and Resource Economics 24 (4), 359–377. Sterner, T., 2003. Policy Instruments for Environmental and Natural Resource Management. Washington, Resources for the Future. Tolbert, V.R., Wright, L.L., 1998. Environmental enhancement of US biomass crop technologies: research results to date. Biomass & Bioenergy 15 (1), 93–100. Updegraff, K., Baughman, M.J., Taff, S.J., 2004. Environmental benefits of cropland conversion to hybrid poplar: economic and policy considerations. Biomass & Bioenergy 27 (5), 411–428. Vandenhove, H., Goor, F., O’Brien, S., Grebenkov, A., Timofeyev, S., 2002. Economic viability of short rotation coppice for energy production for reuse of caesium-contaminated land in Belarus. Biomass & Bioenergy 22 (6), 421–431. Van Ierland, E., Lansink, A.O., 2003. Editorial introduction: economics of sustainable energy and agriculture. Environmental and Resource Economics 24 (4), 287–289. Varela, M., Lechon, Y., Saez, R., 1999. Environmental and socioeconomic aspects in the strategic analysis of a biomass power plant integration. Biomass & Bioenergy 17 (5), 405–413. Varela, M., Saez, R., Audus, H., 2001. Large-scale economic integration of electricity from short-rotation woody crops. Solar Energy 70 (2), 95–107. Vitousek, P.M., Mooney, H.A., Lubchenco, J., Melillo, J.M., 1997. Human domination of earth’s ecosystems. Science 277 (5325), 494–499. Walsh, M.E., Ugarte, D.G.D., Shapouri, H., Slinsky, S.P., 2003. Bioenergy crop production in the United States—potential quantities, land use changes, and economic impacts on the agricultural sector. Environmental and Resource Economics 24 (4), 313–333. Ward, J., Trengove, G., 2005. Developing re-vegetation strategies by identifying biomass based enterprise opportunities in the mallee areas of South Australia: CSIRO Client Report for South Australian Department Water, Land, Biodiversity and Conservation. Folio S/04/1161. Ward, J., Bryan, B.A., Gale, G., Hobbs, T., 2005. Market-based instrument approaches to implementing priority revegetation in the South Australian Murray-Darling Basin. CSIRO Land and Water Client report. Williams, J., Saunders, D., 2005. Land use and ecosystems. In: Goldie, J., Douglas, B., Furnass, B. (Eds.), In Search of Sustainability. CSIRO Publishing, Victoria.