Risk analysis of possible impacts of climate changeon South Australian wheat production

Qunying Luo & William Bellotti & Martin Williams &Ian Cooper & Brett Bryan

Received: 21 May 2004 /Accepted: 6 September 2006 / Published online: 15 February 2007# Springer Science + Business Media B.V. 2007

Abstract This study presents a model-based risk assessment of wheat production underprojected climate change by 2080 in eight locations of South Australia. The vulnerability ofwheat production under future climate change was quantitatively evaluated via a riskanalysis in which the identification of critical yield thresholds applies. Research resultsshow that risk (conditional probability of not exceeding the critical yield thresholds)increased more or less across all locations under the most likely climate change. Wheatproduction in drier areas such as Minnipa, Orroroo and Wanbi will not be economicallyviable under the most likely climate change. Intensive studies on adaptation are nowrequired.

1 Introduction

Risk is defined by the US Presidential/Congressional Commission on Risk Assessment andRisk Management (USPCC RARM 1997) as the probability that a substance or situationwill produce harm under specified conditions. Risk is a combination of two factors: theprobability that an adverse event will occur and the consequences of the adverse event(USPCC RARM 1997). Risk analysis is the process of assessing these two factors. Riskmanagement/treatment is applied to reduce the consequences of adverse events identifiedby risk analysis. The enhanced greenhouse gas effect and its accompanying effect, climatechange, can be identified as environmental risk in the sense that the wheat production is

Climatic Change (2007) 85:89–101DOI 10.1007/s10584-006-9203-6

Q. Luo (*) :M. WilliamsDepartment of Geographical and Environmental Studies, University of Adelaide, Adelaide,South Australia 5005, Australiae-mail: qunying.luo@adelaide.edu.au

W. Bellotti : I. CooperSchool of Agriculture & Wine, University of Adelaide, Adelaide, South Australia 5371, Australia

B. BryanPolicy and Economic Research Unit, CSIRO Land and Water, Private Bag 2, Glen Osmond, Burnside,South Australia 5064, Australia

directly exposed to risk from these effects. Risk assessment encompasses an analysis phaseand an implementation phase: risk management/treatment. Collectively, risk management/treatment is the process of identifying, evaluating, selecting and implementing actions toreduce risk to ecosystems (USPCC RARM 1997).

Risk analysis and risk management/treatment have emerged recently in the field ofclimate change impact assessment and focus on the utility/treatment of ranges ofuncertainty in climate change impact assessment. Rather than being the end result, levelsof impact can be addressed in the initial stages of risk assessment. These levels of impactthen become the criteria against which risk can be evaluated in the light of systemuncertainties (Jones 2001). In the context of climate change impacts, these criteria arereferred to as thresholds – the point where a stimulus leads to a significant response (Parryet al. 1996). An impact threshold is a generic term for any threshold that can link anecological or socio-economic impact to environmental state(s) (Jones 2000, 2001; McInneset al. 2002). The aim of risk analysis is to quantify the relationship between impactthresholds and the uncertainty space created from the combination of key environmentalvariables under climate change. If the uncertainties surrounding projected climate changeand its effect on particular impacts can be treated, then the probability of thresholdsexceedance/non-exceedance can be calculated and the consequences of that exceedance/non-exceedance (i.e. risk) can be assessed (Jones 2001). Risk assessment, which utilisesprojected ranges of uncertainties, needs to be involved in the determination of impactthresholds and provide conditional probability of exceeding/not exceeding the impactthresholds. This information can then be used in a risk assessment to identify windows foradaptation, describing the timing and degree of adaptation needed to prevent ‘dangerous’climate change occurring for a particular activity (Jones 2000; McInnes et al. 2002). Thismethod will lay a sound basis for adopting better adaptation and mitigation strategies tocope with climate change.

Several prerequisites exist for conducting risk assessment (Jones 2001):

& Key environmental variables forcing an exposure unit at a particular location canbe identified and used as input for an impact model. An exposure unit is defined asthe sector, location or activity being assessed for impacts under climate change(Carter et al. 1994).

& Those variables can be expressed in terms of a projected range with high and lowends with a given probability distribution function. Present techniques allow rangesof climate change to be constructed, and allow the upper and lower limits ofquantifiable climate change to be estimated (Jones 2000; Luo et al. 2005a).

& An impact threshold forced by those key environmental variables can be quantifiedwith reference to their projected ranges.

& A conditional probability of that threshold being exceeded/not exceeded can becalculated on the basis of explicitly referenced assumptions linking keyenvironmental variables with the impact model.

& Adaptation and/or mitigation options can be assessed to reduce the exposure of thatthreshold to climate change.

Seven steps were involved in risk assessment of climate change impact (Jones 2001):

(1) Identify the key environmental variables affecting the exposure units being assessed.(2) Create scenarios and /or projected ranges for key environmental variables.(3) Carry out an impact analysis to assess the relationship between climate change and

impacts.

90 Climatic Change (2007) 85:89–101

(4) Identify the impact thresholds to be analysed for risk.(5) Carry out risk analysis.(6) Evaluate risk and identify feedbacks likely to result in autonomous adaptations.(7) Analyse proposed adaptations and recommend planned adaptation options.

There are several studies associated with steps of 1–3 in Australia. Howden et al. (1999)examined the potential impacts of global change on the Australian wheat cropping withatmospheric pCO2 fixed at 700 ppm for year 2100 based on CSIRO (1996) in which IPCCIS92a–f scenarios were applied. Reyenga et al. (2001) investigated the possible shift of theSouth Australia wheat belt under several climate change scenarios based on CSIRO (1996)with 1 t/ha of grain yield used as the criteria for boundary shift. Howden and Jones (2001)constructed the probability distribution of wheat productivity across the Australia wheatbelt by applying updated regional climate change scenarios (CSIRO 2001) and a range ofatmospheric pCO2 (IPCC 2000). Luo et al. (2005a) constructed probabilistic distributionsof regional climate changes for eight wheat production areas in South Australia based onIPCC (2000) and CSIRO (2001) with uncertainties from projection of greenhouse gasemission, projection of global warming and projection of regional climate changeincorporated. The generated probabilistic distributions of regional climate change (Luo etal. 2005a) were applied to the estimation of wheat yield in the corresponding locations (Luoet al. 2005b). Luo et al. (2005a,b) discussed steps 1–3 of risk assessment and we nowconsider steps 4 and 5 — identify the impact thresholds to be analysed for risk and carryout risk analysis. The purpose of this study is to evaluate the viability of wheat productionin South Australia from an economical perspective. The critical yield thresholds were firstlyidentified through economic analyses and then used as references for the calculation of theconditional probability of not exceeding the critical yield threshold based on the simulatedresults presented in Luo et al. (2005b).

2 Background and study sites

Agriculture contributes a substantial proportion of returns to South Australia’s economy.Agriculture, forestry and fishing yield 6.2% of gross product in South Australia in thefinancial year 2003–2004 (ABS 2004). The total value of agricultural production in SAwasestimated to be A$4,417.4 m in the financial year of 2001 (up 47.3% from $2,999.7 m in2000). Crops accounted for 75.7% ($3,343.6 m) of the total value of the State’s agriculturalproduction (ABS 2003). There are seven statistical divisions in South Australia. The mainagricultural enterprises and their contribution to the state’s total value of agriculturalproduction across the seven statistical divisions are shown in Table 1.

Eight locations (Cummins, Keith, Lameroo, Minnipa, Naracoorte, Orroroo, Roseworthy,Wanbi) from the South Australian wheat producing areas were chosen in this study. Theeight locations are representative of the major grain growing districts of South Australia andcover the full spectrum of grain production environments in terms of low and medium-highrainfall areas, the most common soils in the State. Average annual rainfall ranges from 305to 580 mm (Table 2). Figure 1 shows the geographical location of these eight sites.Different annual rainfall will lead to different yield thresholds and different profitability.Soils at the study locations vary greatly in chemical and physical properties, including theirplant available water capacity (PAWC). Based largely on soil texture, but also on chemicaland/or physical restrictions (e.g. boron, pH, high bulk density) that determine plant rooting-depth, PAWC varies widely.

Climatic Change (2007) 85:89–101 91

Future climate change scenarios of an increase in temperature and decrease in rainfall,especially during the winter growing season (CSIRO 2001; McInnes et al. 2002), will havesignificant implications for wheat production, regional/local economies and for thesustainability of the wheat industry in these regions.

3 Probability distribution of regional climate change

Local climate change (monthly temperature change in °C per degree of global warming andmonthly rainfall change in percentage change per degree of global warming) and informationdrawn from the IPCC Special Report on Emission Scenarios marker scenarios (SRES) (IPCC2000) for the year 2080 were used to construct probability distributions of regional climatechanges by using a Monte Carlo Random Sampling (MCRS) technique. The local climatechange information consists of downscaled outputs of eight General Circulation Models(GCMs) and one Regional Climate Model (RCM) obtained from CSIRO Marine andAtmospheric Research, Australia. Several factors are associated with the construction ofprobability distributions of regional climate changes such as atmospheric pCO2, totalforcing, climate sensitivity, global warming, and local climate change. The first step inderiving the regional climate change is to quantify the upper and lower limits for thesevariables among different greenhouse gas (GHG) emission scenarios and different GCMs/RCM. Based on these ranges MCRS was conducted with an assumption of uniformdistribution for independent variables and relationship application for dependent variables.The random samples of climate change for regional temperature, regional rainfall and CO2

Table 1 Types of agricultural enterprises and their contribution to the state’s total value of agriculturalproduction across seven statistical divisions (ABS 2003)

Statistical divisions Agricultural enterprises Proportion (%)*

Adelaide Horticulture and Viniculture 4.2Outer Adelaide Horticulture and Viniculture 13.8Yorke and Lower North Cereal crop and sheep 16.9Murray Lands Cereal and horticulture 27.2South East Cereal and viticulture 15.4Eyre Cereal crop (mainly wheat) 14.2Northern Cereal, sheep and cattle grazing 8.3

*Proportion to the state’s total value of agricultural production in 2001

Table 2 Coordinates and annual rainfall of study sites (Data source: http://www.bom.gov.au/climate/averages/tables/ca_sa_names.shtml)

Study sites Latitude Longitude Average annual rainfall (mm)

Wanbi −34.78 140.27 304Orroroo −32.74 138.61 342Minnipa −32.86 135.15 362Lameroo −35.33 140.52 388Cummins −34.27 135.73 431Roseworthy −34.53 138.69 440Keith −36.10 140.35 468Naracoorte −36.96 140.74 578

92 Climatic Change (2007) 85:89–101

change were grouped into classes and summarised. A distinct climate change class wascreated for unique combinations of CO2, rainfall and temperature change class breaks. Eachrandomly sampled combination was allocated to the nearest class and the total count ofcombinations occurring in each class was tabulated. Climate change classes were thenranked from the most likely to the least likely. The cumulative probability was calculated bysumming probabilities from the most likely to the least likely change class.

The cumulative probabilities of each climate change class were used to create bivariatecontour graphs (shaded area of Fig. 2). Each graph displays the likelihood of occurrence ofdifferent classes of climate change. Five contour classes represented by different shades ofgrey were defined for each graph and represent cumulative probability quintiles such thateach contour class contains 20% of the total number of random samples. The darkest shadeof grey represents the 20% most likely climate change classes, grading through to the whitearea representing the 20% least likely climate change classes. For more information on theconstruction of probability distribution of regional climate change, readers are directed toLuo et al. (2005a,b).

4 The APSIM-wheat model

The Agricultural Production System sIMulator (APSIM)-Wheat model was used in thisstudy to examine the sensitivity of wheat production systems to future climate change. TheAPSIM is an integration of several interactive modules including Biological Modules (CropModules, Soil Module, etc.), and other utility and application modules. The APSIM-Wheatmodel simulates the growth and development of a wheat crop in a daily time-step on anarea basis as a function of weather (temperature, rainfall and radiation), soil (soil water andsoil nitrogen), crop genetic coefficients and crop management information. The APSIM-Wheat module has been described in detail elsewhere (Keating et al. 2003; Luo 2003). The

Fig. 1 Location of study sites.Smaller dots indicate locationswith lower average annual rain-fall; larger dots denote locationswith higher average annualrainfall

Climatic Change (2007) 85:89–101 93

APSIM-Wheat model has been tested in the South Australian environment (Luo 2003;Yunusa et al. 2004).

Luo (2003) and Luo et al. (2005b) provided detailed information on soil water andnitrogen, cultivar genetic coefficients and the configuration and management details forsimulation runs across the eight locations. Table 3 shows climate change informationincluding the increase of pCO2 used by the APSIM-Wheat model. Five levels of rainfalland four levels of temperature were chosen based on the outcomes of Section 3 to perturbthe historical 100-year (1900–1999) daily climate data for each location. The rationale forselecting these levels for regional rainfall and temperature is (1) the upper and lower limitswere two of these levels; (2) Other levels of regional climate change should be as evenlydistributed as possible. The change in regional climate here is ‘mean’ climate change fromeight GCMs and one RCM.

26

32

3742

47

5358

63

2632

37

42475358

63

47

42

53

Lameroo

Temperature Change ( C)o

Rainfall Change (%)Temperature Change ( C)o

Rainfall Change (%)

CC

han

ge

(%)

O2

CO

C (

%)

2h

ang

e

The next most likely 20% climate changes of all climate changes

The most likely 20% climate changes of all climate changes

Risk response (contour line): the conditional probability of not exceeding the critical yield threshold is 42% under the most likely 20% climate changes

The least likely 20% climate changes of all climate changes

Fig. 2 Illustration of cumulative probability of climate changes (shaded area) and the conditionalprobability of not exceeding the critical yield thresholds (contour lines)

94 Climatic Change (2007) 85:89–101

The APSIM-Wheat model has been modified (Reyenga et al. 1999) to account for thephysiological effects of increased atmospheric pCO2. Four levels of atmospheric pCO2

based on IPCC (2000) for 2080 were used in this study. As a result, there are 81 simulationruns per location (80 climate change scenarios (5×4×4) and 1 baseline control). Thebaseline is defined as the current climate and atmospheric pCO2 (350 ppm).

5 Determination of yield thresholds

Yield is a very important economic indicator for the sustainability of crop production. Theyield thresholds for this study were determined through economic analysis. The study siteswere classified into two categories on the basis of average annual rainfall, namely: lowrainfall locations with annual rainfall < 400 mm and high rainfall locations with annualrainfall > 400 mm (Table 4).

Net profit (long term average return) is an economic indicator of a certain industry suchas the wheat industry. Net profit is derived from gross margin, net margin and land value.Gross margin (GM) of an enterprise is defined as the ‘output’ (revenue) minus the‘variable’ costs (Warren 1998). GM is associated with grain yield, grain price and variableexpenses. Net margin is the difference between total output and variable cost and any othercost that can be allocated to the enterprise, which is related to gross margin, interest,allowance for permanent labor and family labor, allowance for machinery overheads andallowance for other overhead (Giles 1987). Net profit is the margin left behind after allcosts have been accounted for including those of a genuinely overhead type (Giles 1987).Table 4 gives the typical values of some of these factors for each rainfall category. The“Constants” comprise the value shared by the two categories described at the bottom of thistable. Based on long-term analysis, the average return on agricultural land in SouthAustralia is close to 3% (ABARE 2002). Once other values are determined/assumed, in

Table 3 Change levels for rainfall, temperature and atmospheric pCO2 used by APSIM-wheat module

Locations Rainfall change (%) Temperaturechange (°C)

CO2 concentrationlevels (ppm)

Growing season Non-growing season

Cummins −30.6, −17.3,−16.5,10.5, 12.8

−27.8, −8.7, 25.4,18.0, 42.5

0.5, 2, 3, 4 527, 635, 687, 786

Keith −27.4, −24.0, −21.5,6.3, 3.0

−28.5, −3.9, 25.4,25.3, 36.4

1, 2, 3, 4 527, 635, 687, 786

Lameroo −23.4, −16.8, −14.0,−2.6, 7.1

−27.4, −12.4, 12.5,15.8, 44.7

1, 2, 3, 4 527, 635, 687, 786

Minnipa −32.1, −18.2, −16.7,−7.1, 16.4

−24.3, −8.4, 27.7,57.9, 56.5

1, 2, 3, 4 527, 635, 687, 786

Naracoorte −24.8, −14.5, −15.9,−0.1, −0.1

−22.5, −10.0, 16.9,7.2, 24.9

0.5, 2, 3, 4 527, 635, 687, 786

Orroroo −29.0, −20.9, −28.0,−6.5, 13.6

−26.7, −5.6, 57.5,49.3, 59.7

1, 2, 3, 4 527, 635, 687, 786

Roseworthy −28.6, −28.4, −12.3,−3.5, 10.8

−26.9, 10.0, 16.2,32.3, 46.3

1, 2, 3, 4 527, 635, 687, 786

Wanbi −27.5, −22.8, −18.8,−7.8, 8.1

−27.8, 2.8, 31.8, 37.5,41.5

1, 2, 3, 4 527, 635, 687, 786

*The four levels of atmospheric CO2 change were based on IPCC (2000) and applied to all of the locations.

Climatic Change (2007) 85:89–101 95

order to obtain this 3% return, there must be a critical yield threshold corresponding to thisreturn (below which, the wheat industry is not sustainable). Equations 1, 2, 3, and 4(Makeham and Malcolm 1993) are used to derive critical yield thresholds combined withthe information provided in Table 4. These equations operate on an annual basis.

The critical yield thresholds can be derived by substituting Eqs. 3 and 4 into Eq. 2 andby substituting Eq. 2 into Eq. 1. The calculated critical yield threshold is 1.87 t/ha for highrainfall areas and 0.99 t/ha for low rainfall areas (Table 4). Changes in yield due to rainfallor other reasons are the most likely and probably the greatest reason for change in grossmargin and hence profit.

LTAR ¼ NM=LV ¼ 3% ð1ÞWhere

LTAR long term average return (3%)NM net margin ($)LV land value ($ ha−1)

NM ¼ GM � IN � APFL� AMO� AOO ð2ÞWhere

GM gross margin ($)IN interest (%)APFL labor hour * labor priceAPFL allowance for permanent and family labor ($)AMO value of machinery * depreciation rate / farm sizeAMO allowance for machinery overheads ($ ha−1year−1)AOO total overheads / farm sizeAOO allowance for other overheads ($ ha−1)

Table 4 Specification of factors involved in critical yield thresholds calculation (data source: ABARE 2002;http://agsurf.abareconomics.com)

Locations Annualrainfall(mm)

Rainfallareas

Grainprice($/ha)

Value ofmachinery($)

Landvalue($/ha)

Variableexpenses($)

Critical yieldthresholds (t/ha)

Orroroo 342 low 170 50,000 300 114 0.99Wanbi 304Minnipa 362Lameroo 388Keith 468 high 170 100,000 2000 194 1.87Cummins 431Naracoorte 578Roseworthy 440

Constants: Interest Rate: 10%; Labour Hour: 1.2 hs/ha; Labour Price: $11/hr; Farm Size: 500 ha; Rate ofDepreciation on Machinery: 16%; Allowance for Other Overheads: $5,000; Long Term Average Return onAgricultural Land: 3%.

96 Climatic Change (2007) 85:89–101

GM ¼ CY � YP � VE ð3ÞWhere

CY critical yield (t ha−1)YP grain price ($ ha−1)VE variable expense ($)

IN ¼ interest rate � VE=2 ð4Þ

6 Results and discussions

Risk is defined here as the conditional probability of not exceeding the critical yieldthresholds. Higher conditional probability means a higher risk for wheat production. Forexample, if the conditional probability is 60% at a certain location, this means that wheatproduction in that location will be economically non-viable in 6 years out of 10. Aconditional probability of not exceeding the critical yield thresholds in at least 50% of yearsis regarded as a nonviable farm economy in this study. The probability of not exceeding thecritical yield thresholds for each location has been calculated following the study of Luo etal. (2005b) in which the possible wheat yield has been projected under the 80 climatechange combinations. Conditional probability is divided into five levels for the convenienceof quantifying the risk with 0∼20% conditional probability assigned as very low level, 20∼40% as low level, 40∼60% as medium level, 60∼80% as high level, 80∼100% as veryhigh level. The 0∼20% and 80∼100% conditional probability ranges comprise the 5%probability level to which Australian policy and industry respond.

The conditional probability of not exceeding the critical yield thresholds for thecombined effect of atmospheric factors on risks of wheat production is depicted in a riskresponse surface (lines of Figs. 2 and 3), combining the probabilistic climate changes(shaded area of Figs. 2 and 3) for the eight locations under study. Risk levels underbaseline, the most likely climate changes and the full range of climate changes (80) aresummarised in Table 5. It can be seen that different locations have different risk levels dueto the diversification of the wheat production environment under the current situation(Table 5). Our results show a very low risk of wheat production at Keith (14%) andNaracoorte (5%). There is low risk for wheat production in Cummins (34%), Lameroo(33%), Orroroo (37%) and Roseworthy (27%). Minnipa has a medium risk level (41%).The risk level for wheat production at Wanbi is 56%, which is greater than the 50% leveland is regarded as nonviable farm economy under current situation. A large range ofchanges (increase or decrease) in the conditional probability of not exceeding the criticalyield thresholds have been observed under the 80 climate changes across all locationsexcept Naracoorte where climate change has no dramatic influence on the viability of wheatproduction (Fig. 3 and Table 5). The thick line in each plane of Fig. 3 possesses the closestrisk level to that of baseline. It is possible that risk level can be reduced under somecombinations of changes in regional rainfall, temperature and atmospheric pCO2, but theprobability for this to occur is very low (conditional probability overlaid with white contourshaded area) and only takes a very small uncertainty space (lower right hand corner in theplane between temperature and rainfall change, upper right hand corner in the planebetween rainfall and atmospheric pCO2 change) (Fig. 3). Although the degree of change forrisk level across the 80 climate changes is very big, the change ranges for risk level under

Climatic Change (2007) 85:89–101 97

3853

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3124

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2153

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Lameroo Minnipa

51

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83

70

7670 63

57

5144 38

31

38

44

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6370

7683

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Naracoorte

9

11 13

1618

25222018

161311

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Orroroo

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Roseworthy

5852

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7064

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4641

35 29

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Wanbi

66

72

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888377

72 66

49 4460 55

4449

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6066

77 7283

88

77

Fig. 3 Conditional probability of not exceeding the yield thresholds (contour lines) under future climatechanges for each location. The thick contour line has the closest risk level to that of baseline

98 Climatic Change (2007) 85:89–101

the most likely climate changes are much smaller and show a consistent change trend: risklevel increased more or less across all the locations (Table 5). The three bivariate graphs aredisplayed in three dimensions to represent the interrelationships of CO2, rainfall andtemperature in climate change scenarios, thereby creating a virtual climate change impactvolume. However, each bivariate graph is essentially a projection of the volume onto thegraph wall. For example, the bivariate graph of Rainfall vs Temperature representsprobabilities for all values of CO2, rather than a slice of those probabilities occurring at theplanar intersection of the chart in the diagram. As a result of this method of visualizationyield contours may not intersect at the edges of the graph. This style does however, providean integrated way to visualise the complexity of wheat yield as it varies complexly withchanges in rainfall, temperature and CO2.

7 Conclusion

Risk analysis has been conducted in this study based on results from Luo et al. (2005b) toevaluate the viability of wheat production in South Australia by applying economicanalysis. Risks to wheat production generally show an increase trend under the 80 climatechanges (Fig. 3, Table 5). There are some possibilities that risk level can be decreased undersome combinations of changes in regional rainfall, temperature and pCO2, but theprobability of this occurring is very low and occupies very limited space in Fig. 3. Risklevel will increase under the most likely climate changes compared with the baseline(current situation) across all locations. According to the criterion of a nonviable farmeconomy defined in Section 6, wheat production at Wanbi is nonviable even under baselineconditions with a risk level of 56%. Wheat production is even worse under the most likelyclimate changes at this site for 2080 with risk level in the range of 64∼74%. Next to Wanbiare another two sites — Minnipa and Orroroo where wheat production is also economicallynonviable with risk levels greater than 50% under the most likely climate changes in 2080.

Table 5 Conditional probability (%) of not exceeding the critical yield thresholds under current and climatechange scenarios

Locations Baseline Most likely climate changes 1 80 climate changes2

Cummins 34 38∼47 10∼81Keith 14 29∼37 11∼63Lameroo 33 46∼47 21∼74Minnipa 41 51∼60 25∼89Naracoorte 5 8∼12 4∼27Orroroo 37 49∼64 24∼80Roseworthy 27 43∼48 17∼76Wanbi 56 64∼74 38∼94

1: The most likely climate changes— regional rainfall, temperature and atmospheric CO2 simultaneously fallwithin the 20% most likely classes

2: whole range of climate changes including five levels of regional rainfall changes, four levels of regionaltemperature change and four levels of atmospheric CO2 increase which resulted in 5×4×4=80 climatechanges

Climatic Change (2007) 85:89–101 99

Risks recognised in this study could bring significant changes to the South Australianwheat industry, economy, land use patterns and rural community activities. Urgent action isneeded to counteract these adverse effects resulting from future climate change. Aspecialised project is underway investigating possible adaptation strategies. Although theseresults were obtained from specific locations of South Australia, they may have widerimplications for wheat production in areas with similar environmental conditions.

The critical yield thresholds concept used in this study provides a convenient way toanalyse climate change risks exerted on the wheat industry. The critical yield thresholdsused is not intended to represent an actual threshold in the year 2080. There may be someunreasonable assumptions associated with the identification of critical yield thresholds inthis study. Farmers’ adaptation options and adaptive capacity, market fluctuations andagricultural technology levels including genetic adaptation and plant breeding will all affectthe level of critical yield thresholds. Stakeholders and investigators will need to worktogether to formulate criteria that become a common and agreed metric for risk analysis(Jones 2001). This should be strengthened in future risk analysis.

Acknowledgements We thank the three anonymous referees for their constructive suggestions and theAustralian Research Council (ARC) for financial support.

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