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Potential impact of climate change on wheat yield in South Australia
Agricultural and Forest Meteorology 132 (2005) 273–285 www.elsevier.com/locate/agrformet
Potential impact of climate change on wheat yield in South Australia Qunying Luo a,*, William Bellotti b, Martin Williams a, Brett Bryan c a
Department of Geographical and Environmental Studies, University of Adelaide, SA 5005, Australia b School of Agriculture & Wine, University of Adelaide, SA 5371, Australia c Policy and Economic Research Unit, CSIRO Land and Water, Private Bag 2, Glen Osmond SA 5064, Australia Received 22 December 2004; received in revised form 25 July 2005; accepted 8 August 2005
Abstract Refined and improved climate change scenarios have been applied in this study to quantify the possible impacts of future climate change on South Australian wheat yield with probability attached. This study used the APSIM-Wheat module and information drawn from the Special Report on Emission Scenarios (SRES) and nine climate models for 2080. A wheat yield response surface has been constructed within 80 climate change scenarios. The most likely wheat yield changes have been defined under combinations of changes in regional rainfall, regional temperature and atmospheric CO2 concentration (CO2). Median grain yield is projected to decrease across all locations from 13.5 to 32% under the most likely climate change scenarios. This has economic and social implications from local to national levels. # 2005 Elsevier B.V. All rights reserved. Keywords: Climate change; Scenario construction; APSIM-Wheat module; Most likely wheat yield
1. Introduction Research on the potential impacts of climate change on wheat yield has been pursued for more than a decade worldwide. A large number of studies were conducted by linking the downscaled output of general circulation models (GCMs) with dynamic wheat models (Brklacich and Stewart, 1995; Dele´colle et al., 1995; Mearns, 1995; Menzhulin et al., 1995; Seino, 1995; Tubiello et al., 1995; Smith et al., 1996; Howden et al., 1999a,b,c; Reyenga et al., 1999a,b; Luo et al., 2003). In quantifying the potential impacts of climate change on wheat yield, single scenarios or high and low end scenarios were used in most previous studies. Assessment results
* Corresponding author. Tel.: +61 8 8303 3860; fax: +61 8 8303 3772. E-mail address: [email protected] (Q. Luo). 0168-1923/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2005.08.003
derived from single scenarios are fairly precise, but are conditional on those single scenarios only, and are unlikely to be representative of other possible futures and are highly speculative. The outcomes are plausible, but contain no information as to their likelihood. There are no confidence limits as to the possibility of these outcomes, nor how the results fit into broader ranges of uncertainty and what those ranges of uncertainty may be. While appropriate for testing sensitivity and vulnerability of a particular system, this methodology is poorly suited for planning or policy purposes. A range of projections will always be more likely to encompass what will actually transpire than a single scenario. The resulting range of outcomes arising from the high and low ends of the range of regional climate change is often too large to be of real use for planning or policy purposes, although certain levels of probability can be attached to the regional climate change (Jones, 2000; IPCC, 2001). Assignment of probability to impact
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Table 1 Coordinates and annual rainfall of study sites Study sites
Latitude
Longitude
Altitude (m)
GSa rainfall (mm)
Annual average rainfall (mm)
Cummins Keith Lameroo Minnipa Naracoorte Orroroo Roseworthy Wanbi
348160 S 368060 S 358200 S 328520 S 368580 S 328440 S 348320 S 348470 S
1358440 E 1408210 E 1408310 E 1358090 E 1408440 E 1388370 E 1388410 E 1408160 E
68 29 99 168 58 418 68 77
321 315 251 226 397 210 292 186
431 468 388 326 578 342 440 304
a
GS, growing season (May–October inclusive).
outcomes is urgently required and will facilitate policy and decision-making in adapting to climate change. Very limited studies have probabilistic analysis on wheat yield impact resulting from climate change. Howden et al. (1999d) attempted to assign probability to impact outcomes across the ranges of projected regional rainfall change and temperature change with atmospheric CO2 concentration fixed at 700 ppm for year 2100, based on CSIRO (1996) in which IPCC IS92a–f scenarios were applied. However, the change range for atmospheric CO2 concentration under IPCC (2000) is now available and can be applied in current impact assessments. The existing methodological problems noted above, the recent release of the IPCC Special Report on Emission Scenarios (SRES) (IPCC, 2000) and the vulnerable agricultural production system of South Australia provided the impetus to assess comprehensively the potential impacts of climate change on wheat production. The aim of this study is to provide probabilistic impact outcomes across the full change ranges of rainfall, temperature and atmospheric CO2 concentration based on IPCC (2000). The originality of this work lies in using the updated greenhouse gas emission scenarios-IPCC SRES in projecting global warming, and thus in projecting regional temperature and regional rainfall. In addition, a range of CO2 concentration change was used rather than a fixed CO2 concentration change in the wheat model. We here present a pilot study in climate change impact assessment involving probabilistic analysis within the ranges of three atmospheric variables based on IPCC (2000). 2. Materials and methods 2.1. Study sites Wheat production in South Australia is a significant employer and major export earner. Future anthropogenic
warming and drying of the region during the austral winter is a potential threat to production, which is highly relevant for the regional and national economy. Eight localities (Cummins, Keith, Lameroo, Minnipa, Naracoorte, Orroroo, Roseworthy and Wanbi) across the South Australian wheat belt were chosen (Table 1 and Fig. 1). These eight sites are representative of each agricultural region. Minnipa and Cummins are representative of the Western agricultural region. Orroroo represents the Upper North agricultural region. Roseworthy belongs to the Central agricultural region; Lameroo and Wanbi are from the Murray Mallee agricultural region. Keith and Naracoorte are physically located in the Southeast agricultural area. These eight sites have different average annual rainfall (Table 1). Cummins, Keith, Naracoorte and Roseworthy have wetter climates with an annual rainfall of 430–580 mm. The other four sites are drier with annual rainfall ranging from 305 to 390 mm. 2.2. Generation of probabilistic climate change scenarios Future atmospheric CO2 concentration increase and global warming for 2080 were derived from four different narrative storylines/families (B1, B2, A1 and A2) about greenhouse gas (GHG) emission scenarios or three families and three groups of GHG emission scenarios within family A1 (A1B, A1T and A1F) and shown in Table 2 (IPCC, 2000). The bold figures are the upper limits and lower limits of these two components. Data on local climate change was derived from GCMs and regional climate models (RCMs) outputs. Downscaled outputs of nine climate models (Table 3) for local climate change per degree of global warming were obtained from CSIRO Atmospheric Research. The upper limits and lower limits for local temperature and local rainfall including growing season (May–October
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Fig. 1. Location of study sites. Smaller dots indicate drier sites, larger dots denote wetter sites.
Table 2 Atmospheric CO2 concentrations and global warming in year 2080 (IPCC, 2000)
CO2 concentration (ppmv) CO2 concentration change (%)
B1a
B2b
A1Bc
A2d
A1Fc
A1Tc
527
549
635
687
786
546
51
57
81
96
125
56
Global warming (8C) Lower limit 1.1 Middle 1.6 Upper limit 2.3 a
1.1 1.7 2.5
1.4 2.1 3.0
1.5 2.2 3.2
1.9 2.7 3.8
1.2 1.8 2.6
The B1 storyline and scenario family describes a convergent world with the same global population as in the A1 storyline. b The B2 storyline and scenario family describes a world in which the emphasis is on local solutions to economic, social and environmental sustainability. c The A1 storyline and scenario family describe a future world of very rapid economic growth, global population and the rapid introduction of new and more efficient technologies. A1B, a balanced mix of technologies and supply sources; A1F, fossil intensive and A1T, non-fossil energy sources. d The A2 storyline and scenario family describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities.
Table 3 GCMs/RCMsa details and their projection features for study sites considered GCMs/RCMs
Laboratories
Features
MK2 CGCM1
CSIRO Canadian Climate Centre Hadley Centre, UK Max Planck Max Planck
Rainfall decrease Rainfall decrease
HADCM2 ECHAM4/OPYC3 ECHAM3/LSG
Rainfall decrease Rainfall decrease Winter rainfall decrease, summer rainfall increase DARLAM 125 km CSIRO Winter rainfall decrease, summer rainfall increase DOE-PCM NCAR Rainfall decrease HADCM3 Hadley Centre, UK Rainfall decrease R15-a GFDL Winter rainfall increase, summer rainfall decrease a More information on these GCMs can be found at http://ipccddc.cru.uea.ac.uk. Further information on the RCM:DARLAM can be found at http://www.dar.csiro.au/earthsystems/darlam.html.
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rainfall change and 1/3 given to non-growing season rainfall change. The magnitude of the average weighting is based on the distribution of rainfall during the growing season and the non-growing season from the current climate condition. The random samples of climate change scenarios for regional temperature, regional rainfall and CO2 change were grouped into classes and summarised. Class breaks were created at every 10% change in CO2 (e.g. 70%, 80%, 90%, . . .), 5% change in rainfall (e.g. 10%, 5%, 0%, 5%, . . .) and 0.5 8C change in temperature (e.g. 1 8C, 1.5 8C, 2 8C, . . .). A distinct climate change scenario class was created for unique combinations of CO2, rainfall and temperature change class breaks. Each randomly sampled scenario was allocated to the nearest class and the total count of scenarios occurring in each class was tabulated. The probability for each class was calculated by dividing the number of randomly sampled scenarios in the class by the total number of scenarios (100,000). Scenario classes were then ranked according to the most likely to least likely. The cumulative probability was calculated by summing probabilities from the most likely to the least likely scenario class. The cumulative probabilities of each scenario class were used to create bivariate contour graphs for each of the eight selected localities across South Australia. Each graph displays the likelihood of occurrence of different climate change scenarios. For each locality, three contour graphs were created, CO2 versus rainfall, rainfall versus temperature and temperature versus CO2. Five contour classes represented by different shades of grey were defined for each graph and represent cumulative probability quintiles such that each contour class contains 20% of the total number of random samples. The darkest shade of grey is the 20% most likely climate change scenarios, grading through to the lightest shade
inclusive) and non-growing season (November–April inclusive) were derived as follows. First, downscaled monthly outputs of specific GCMs or RCMs were averaged (12 months for temperature, specific months for growing season rainfall and non-growing season rainfall as defined above). Then the upper limits and lower limits can be extracted among the nine GCMs or RCMs. Table 4 presents change ranges for atmospheric CO2 concentration, global warming, regional temperature and regional rainfall (including the growing season and non-growing season) used to create probabilistic climate change scenarios through Monte Carlo Random Sampling (MCRS) procedures. Once the upper limits and lower limits for atmospheric CO2 concentration change, global warming and local climate change were determined; MCRS was used to create probability distributions for climate change scenarios. Global warming and local temperature were uniformly randomly sampled 100,000 times and were then multiplied repeatedly, to obtain a non-uniform distribution of regional temperature change, peaking around an average value. Global warming was used to scale the possible ranges for growing season and nongrowing season rainfall change. The lower limit and upper limit for growing season and non-growing season rainfall change were multiplied by the upper bound of global warming (i.e. 3.8 8C) to ensure that values anomalous to that degree of warming were not sampled. In other words, the upper and lower limits of the growing and non-growing seasons were constrained by the upper limit of global warming to derive all possible outcomes of regional rainfall for these two seasons. The two altered ranges were then separately sampled and repeated 100,000 times. Samples from these two ranges were then averaged to get regional rainfall change based on average weight with 2/3 given to growing season
Table 4 Ranges for global warming, atmospheric CO2 concentration increase and local climate change for 2080 Localities
Cummins Keith Lameroo Minnipa Naracoorte Orroroo Roseworthy Wanbi
Local rainfall change (%)
Local temperature change (8C)
Global warming (8C)
Atmospheric CO2 concentration Change (%)
Growing season
Non-growing season
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
8.0 7.2 7.0 8.6 6.5 7.9 7.5 7.2
3.5 1.6 2.2 4.6 1.0 3.8 2.9 2.5
7.3 7.6 7.4 6.9 8.0 7.4 7.2 7.3
11.2 10.1 11.7 15.4 7.8 16.0 12.2 12.5
0.7 0.8 0.8 0.9 0.7 0.9 0.8 0.9
1.0 1.1 1.1 1.0 1.1 1.1 1.1 1.1
1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1
3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8
51 51 51 51 51 51 51 51
125 125 125 125 125 125 125 125
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representing the 20% least likely scenarios. The three graphs are displayed in three dimensions to represent the inter-relationships of CO2, rainfall and temperature in climate change scenarios, thereby creating a climate change volume. However, each bivariate graph is essentially a projection of the volume onto the graph wall. For example, the bivariate graph of rainfall versus temperature represents probabilities for all values of CO2, rather than a slice of those probabilities occurring at the planar intersection of the chart in the diagram.
coefficients and crop management information. The APSIM-Wheat module has been described in detail elsewhere (Keating et al., 2003; Luo, 2003). The performance of APSIM-Wheat in the Australian environment (Keating et al., 2003) and in the South Australian environment (Luo, 2003; Yunusa et al., 2004) has been evaluated. Fig. 2 is a flow chart of the data and program used in this study. The data used in this study are described in detail in Section 2.2 and the following subsections.
2.3. APSIM-Wheat module and its parameterisation
2.3.1. Historical climate data Maximum temperature, minimum temperature, solar radiation and rainfall are identified as key climatic variables, which are explicitly used within the APSIMWheat module. Two tiers of climate information were acquired for this study: historical climate data and climate change information (described in Section 2.2). Historical daily climate data including daily maximum temperature, minimum temperature, rainfall and solar radiation covering 1900–1999 were derived from the SILO Patched Point dataset (Bureau of Meteorology, 2004) and used by the APSIM-Wheat module. There are two functions for historical climate data. First, they were used to drive the wheat model to produce baseline wheat yields. Second, the historical weather data were
The APSIM-Wheat module (Version 2.0) was used to quantify the potential impact of future climate change on wheat yield in this study. APSIM stands for Agricultural Production Systems sIMulator. It is an integration of several interactive modules including biological modules (crop modules, soil module, etc.), and other utility and application modules. The wheat module is one of the crop modules within the APSIM system. It simulates the growth and development of a wheat crop in a daily time-step on an area basis as a function of weather (temperature, rainfall and radiation), soil (soil water and soil nitrogen), crop genetic
Fig. 2. Schematic diagram of data and tools used in this study. GCM, general circulation model and RCM, regional climate model.
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Table 5 Change levels for rainfall, temperature and atmospheric CO2 concentration used by APSIM-Wheat module Locations
Cummins Keith Lameroo Minnipa Naracoorte Orroroo Roseworthy Wanbi
Rainfall change (%) Growing season
Non-growing season
30.6, 27.4, 23.4, 32.1, 24.8, 29.0, 28.6, 27.5,
27.8, 28.5, 27.4, 24.3, 22.5, 26.7, 26.9, 27.8,
17.3, 24.0, 16.8, 18.2, 14.5, 20.9, 28.4, 22.8,
16.5,10.5, 12.8 21.5, 6.3, 3.0 14.0, 2.6, 7.1 16.7, 7.1, 16.4 15.9, 0.1, 0.1 28.0, 6.5, 13.6 12.3, 3.5, 10.8 18.8, 7.8, 8.1
8.7, 25.4, 18.0, 42.5 3.9, 25.4, 25.3, 36.4 12.4, 12.5, 15.8, 44.7 8.4, 27.7, 57.9, 56.5 10.0, 16.9, 7.2, 24.9 5.6, 57.5, 49.3, 59.7 10.0, 16.2, 32.3, 46.3 2.8, 31.8, 37.5, 41.5
perturbed by climate change information derived from Section 2.2 and used by the APSIM-Wheat module to project wheat yield for future time frames. To calculate wheat yields under the full range of climate change scenarios, several change levels of regional temperature, regional rainfall and atmospheric CO2 concentration (described in Section 2.2) were used in the APSIM-Wheat module. Five rainfall levels (including growing season rainfall and non-growing season rainfall), four temperature levels (annual temperature) and four CO2 concentration levels were chosen for running the APSIM-Wheat module covering the range of probabilistic climate change scenarios for each location (Table 5). Selection of these values is based on the following three criteria: Values should cover the whole range of climatic change. The upper and lower ends of the range were chosen as two levels of the inputs. Intermediate values should be as evenly distributed within the range as possible. Thus, 81 APSIM simulation runs were undertaken (80 climate change scenarios (five rainfall changes four
Temperature change (oC)
CO2 concentration levels (ppm)
0.5, 2, 3, 4 1, 2, 3, 4 1, 2, 3, 4 1, 2, 3, 4 0.5, 2, 3, 4 1, 2, 3, 4 1, 2, 3, 4 1, 2, 3, 4
527, 527, 527, 527, 527, 527, 527, 527,
635, 635, 635, 635, 635, 635, 635, 635,
687, 687, 687, 687, 687, 687, 687, 687,
786 786 786 786 786 786 786 786
temperature increases four CO2 concentration increases) and one baseline scenario: historical climate and current CO2 concentration) for each of the eight localities totalling 648 APSIM runs. Each run consisted of 100 years of daily climate data. The asymmetrical increase in maximum and minimum temperatures and the possible change in future climate variability attributed to greenhouse warming were not dealt with in developing climate input to APSIM-Wheat module. 2.3.2. Soil data One representative soil profile for each location was used in this study. A large number of soil water and soil nitrogen parameters are needed to run the APSIMWheat module. Plant available water capacity (PAWC) is a key determinant of crop productivity and is determined by lower and upper soil water contents for the depth of wheat roots. Soil nitrogen is the most important nutrient for crop growth and the availability of nitrogen to wheat plants is determined by soil factors (organic carbon, total nitrogen, residues and inorganic nitrogen) and fertiliser nitrogen. Table 6 gives detailed information about the PAWC, available soil water and inorganic nitrogen at reset for each soil profile. Fig. 3 shows soil and crop parameters, such as the drained
Table 6 PAWC and soil inorganic nitrogen for the eight soils used in this study Locations
Soil types
PAWC (mm)
Available soil water at reset (mm)
Soil inorganic nitrogen at reset (kg ha1)
Cummins Keith Lameroo Minnipa Naracoorte Orroroo Roseworthy Wanbi
Clay loam Loamy sand Fine sandy loam Sandy loam Sandy clay loam Sandy loam Loam Sandy loam
140 76 111 157 125 134 122 132
42 28 45 51 44 44 50 42
245 85 327 146 210 98 181 246
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Fig. 3. APSIM soil parameters used to define plant available water capacity (PAWC) and value of soil water reset for two contrasting soils: low PAWC (76 mm) at Keith and high PAWC (157mm) at Minnipa. SW represents soil water at reset. DUL stands for the drained upper limit of soil water. CLL represents crop lower limit.
upper limit of soil water and crop lower limit used in the APSIM-Wheat module for two contrasting soils at Keith and Minnipa. 2.3.3. Configuration and management details for simulations Two types of wheat cultivar were used in this study. One was Janz, an Australian Hard Wheat (AH) with grain protein content of 11.5%. The other cultivar was Excalibur, an Australian Soft Wheat (ASW) variety with grain protein content of 10%. Janz is a mid-late maturity variety, while Excalibur is an early maturity cultivar. The reason for including the two cultivars was to better match wheat cultivar maturity grading with average length of growing season. Early maturing varieties are normally higher yielding in low rainfall, short growing season environments. The genetic coefficients for the two cultivars are listed in Table 7. Soil nitrogen, soil water (Table 6) and residues (fababean) were reset on the first of March for each year of simulation run. The sowing rule was specified as follows: if cumulative rainfall within 3 days between 15 April and 15 June 20 mm (early start to season), then sow Janz; if cumulative rainfall within 3 days between 15 June and 15 August 15 mm (late start to season), then sow Excalibur. Sowing density was 200 plants m2, at a depth of 3 cm. Two fertiliser levels were applied at planting date: 40 kg ha1 NO3–N at medium–high rainfall locations and 20 kg ha1 NO3–N at lower rainfall locations at a depth of 5 cm. There is about 115 kg ha1 inorganic nitrogen for the top 50 cm of soil at Roseworthy. These nitrogen levels were intended to represent a situation where soil inorganic nitrogen was non-limiting in most years. The logic behind this was that the prime factor under study was
climate change rather than N fertiliser response. It is recognised that the optimum nitrogen fertiliser rate will vary from year to year depending primarily on variation in in-crop rainfall. This is true for the historical baseline scenario as well as for future climate change scenarios. Future research will investigate the interaction between climate change and N fertiliser strategies (both the amount and the timing of N fertiliser application). The physiological effects of increased atmospheric CO2 on wheat production were included in the simulations. Modifications have been made to the Wheat module through changes to radiation use efficiency (RUE), transpiration efficiency (TE) and to critical nitrogen concentration (CRC) based on experimental data (Reyenga et al., 1999a; Luo, 2003).
Table 7 Genetic coefficients for Janz and Excalibur (data source: Yunusa et al., 2004) Coefficient a
p1v : Sensitivity to vernalization p1db: Sensitivity to day length p5: Grain filling duration (8C day) grno: Grain number per head fillrate: Rate of grain filling (mg day1) Stem weightc (g) Phyllod (8C day) sla: Specific leaf area (mg cm2) a
Janz
Excalibur
1.0 2.0 640 34 2.5 1.65 95 185
1.5 2.0 703 27 3.5 2.4 95 180
Relative amount that development is slowed for each day of unfulfilled vernalization. b Relative amount that development is slowed when plants are grown in a photoperiod 1 h shorter than the optimum. c Non-stressed dry weight of a single stem and spike when elongation ceases. d Phyllochron interval, the interval in thermal time between successive leaf tip appearances.
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Fig. 4. Interpretation of probabilistic climate change scenarios and yield response.
The relative changes in median grain yield (lines of Fig. 5) were calculated based on the simulation of the APSIM-Wheat for 80 climate change scenarios covering the range of probable scenarios modelled above for each of the eight localities across South Australia. Note that the grain yield is presented as percentage change in the median grain yield for a given location compared to the median baseline grain yield for that location. The median grain yield is calculated from the simulated grain yield for 100 years under each of the 80 scenarios. Bivariate, line-based contour graphs were also created to represent yield for the three combinations of CO2 versus rainfall, rainfall versus temperature and temperature versus CO2 to enable a direct comparison of the likelihood of occurrence of the climate change scenarios with the potential impact on yield. These yield contours are also projections of the volume onto a plane and are superimposed upon the climate change graphs in the three-dimensional plots. Fig. 4 is an explanatory graph about the 3D plots. This enables the simultaneous direct interpretation of both the likelihood of occurrence of particular climate change scenarios and the impact on wheat yield. 3. Results The median grain yields under baseline for each location are shown in Table 8. The changes in median grain yield under the most likely climate change
scenarios are derived from Fig. 5 and summarised in Table 8. The yield change range or change trend in the horizontal plane (between temperature and rainfall change) and the vertical plane (rainfall and CO2 concentration change) are quite similar (Fig. 5). Rainfall dominated yield response in these two planes in the lower rainfall areas (Lameroo, Minnipa, Orroroo and Wanbi). Yield response in the medium rainfall area (Cummins, Keith and Roseworthy) is the result of interactive effects between rainfall and temperature change and between CO2 concentration and rainfall change. Yield was dominated by temperature in the plane between temperature and rainfall change and by CO2 concentration in the plane between rainfall and
Table 8 Median grain yields under baseline and most likely scenarios Locations
Cummins Keith Lameroo Minnipa Naracoorte Orroroo Roseworthy Wanbi
Baseline (kg ha1)
Most likely scenarios Change range (%)
Median yield (%)
3054 4108 1827 1321 5977 1357 3630 847
33 38 31 35 26 33 41 34
20 26 16 20 13.5 16.5 32 20.5
to to to to to to to to
7 14 1 5 1 0 23 7
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Fig. 5. Median yield response (%) under future climate change scenarios at the eight locations. Lines across the three planes are yield response within the 80 climate change scenarios. The thick line in each plane represents the yield threshold below which wheat production is not viable.
CO2 concentration change at the higher rainfall sites, such as at Naracoorte. Increase of temperature and CO2 concentration interactively exerted effects on median grain yield across all localities under study.
Median grain yield is positively correlated with the increase of rainfall and CO2 concentration, and negatively correlated with temperature increase across all locations. This finding is reflected in Fig. 6A–C, which
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Fig. 6. Relationship between change in median grain yield and change in environmental factors at Roseworthy: (A) relationship between change in median grain yield and change in rainfall; (B) relationship between change in median grain yield and change in temperature and (C) relationship between change in median grain yield and change in atmospheric CO2 concentration. Each diamond is for each simulation run (there are 80 simulation runs for each location).
show the relationship at Roseworthy of change in median grain yield to changes in rainfall, atmospheric CO2 concentration and temperature, respectively, under the 80 climate change scenarios. It can be seen that rainfall change is the main determinant in change of median grain yield with a correlation coefficient of 0.88. Temperature increase has much less impact on change of grain yield (with a correlation coefficient of 0.055) compared with that of rainfall. However, its effect is greater than that of the increase in atmospheric CO2 concentration with a correlation coefficient of 0.0089. It is very different in the higher rainfall area (Naracoorte). Temperature is the dominant factor in determining median grain yield change in this site. Rainfall has the least influence on median grain yield among the three factors.
Significant changes to median grain yield were projected in each location due to the large change range in local temperature, local rainfall and atmospheric CO2 concentration. Yields change from 88 to +131% across all locations. However, median grain yields present a decrease trend (32 to 13.5%) under the most likely climate change scenarios across all locations (Table 8). For example, at Roseworthy, median grain yield in 2080 is projected to be 32% less than the current historical median grain yield of 3630 kg ha1. A larger range in response of median yield has been noted for lower rainfall area (87 to +131%) compared with that of medium–high rainfall areas (88 to +77%) under the 80 climate change scenarios.
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4. Discussion The combined effects of changes in rainfall, temperature and atmospheric CO2 concentration on wheat yield have been reported in many studies worldwide. Mearns (1995) quantified the potential impacts of change in ‘mean’ climate and change in climate variability on wheat yields in the central Great Plains of the United Sates and found that wheat yield would increase from 44 to 82%. Seino (1995) estimated that wheat yield would change from 41 to +6.3% under climate change in Japan. Brklacich and Stewart (1995) projected that wheat yield would change from 40 to +60% in the Canadian Prairies. Tubiello et al. (1995) quantified the combined effects of climate change and increased atmospheric CO2 concentration on spring wheat (4 to +8% across management practices) in the Canadian wheat belt. Dele´colle et al. (1995) examined wheat yields potential under future climate change in France and found that wheat yield change would range from 30 to 7% under GISS, GFDL and UKMO transient and equilibrium climate change scenarios by using CERES-Wheat model. Menzhulin et al. (1995) projected that national wheat yield would change from 19 to +41% in Russia and the former Soviet Republics. Smith et al. (1996) used the CERES model to simulate winter wheat yield across Kazakhstan for the tenth decade and found that winter wheat yield increased 21 and 17%, respectively, under the transient and the equilibrium climate change scenarios of GFDL. Similar studies were conducted in Australia. Howden et al. (1999a) concluded that wheat yield would increase from 12 to 19% in northeast Queensland. Change of 2 to 14% in wheat yield has been projected in Howden et al. (1999b). A later study by Howden et al. (1999c) stated that wheat yield in South Australia will decrease under the A1-mid and B2mid scenarios based on IPCC SRES. Howden et al. (1999d) found that wheat yield will increase from 9 to +37% across the Australian wheat belt based on the IS92 GHG emission scenarios. Reyenga et al. (1999a) reported that wheat yield would change from 3 to 33% in southeast Queensland. Reyenga et al. (1999b) simulated that wheat yield would decrease between 10 and 35% under dry scenarios across the South Australian wheat belt. Luo et al. (2003) quantified the combined effects of climate change and increased atmospheric CO2 concentration on South Australian wheat production and found wheat yield changes from 2 to 40% using the CERES-Wheat model. The current study projected a change range in median grain yield of 87 to 131%. This change range is larger than previous
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studies for several reasons. First is the use of the latest IPCC SRES projections that include higher global warming and reductions in growing season rainfall especially in Mediterranean type environments such as South Australia. Second is the use of different climate change scenarios. Probabilistic scenarios have been used in this study across the full change range of possible rainfall, temperature and atmospheric CO2 concentration, rather than the single scenarios used in previous studies. Third, a different crop model or version was used which brought about different results. Different wheat models have different responses to the physiological effects of increased atmospheric CO2 concentration. Modifiers of 1.2 for radiation use efficiency and 1.37 for transpiration use efficiency were used in the CERES-Wheat model while modifiers of 1.17 for RUE and 1.06 for TE were adopted in the APSIM-Wheat module at atmospheric CO2 concentration of 550 ppm (Reyenga et al., 1999a; Luo, 2003; Hoogenboom, personal communication). The physiological effects of increased atmospheric CO2 concentration on wheat production need to be studied thoroughly under different environments and appropriately represented in the wheat models. Finally, different configuration of crop model probably will lead to different outcomes. Uncertainties in crop models also need to be quantified and managed according to the last two points in projecting crop production. Strong non-linear relationships between change in rainfall and change in median grain yield, between increase in CO2 concentration and change in median grain yield were found at Cummins, Naracoorte and Orroroo. This needs to be investigated further. 5. Key conclusions This study evaluated the possible wheat yield impacts from climate change including climate change and increase of atmospheric CO2 concentration under IPCC SRES in South Australia in 2080. Of the three variables (rainfall change, temperature change and increase of atmospheric CO2 concentration), rainfall change is by far the most influential factor in the medium to low rainfall areas. Temperature increase has some impact on change in median grain yield, but its effects are much less than that of rainfall alteration. Change in atmospheric CO2 concentration has the least impact on change in median grain yield (Fig. 6). In other words, the potential yield enhancing effect of increased CO2 concentration is insignificant compared to the overriding influence of decreased rainfall. However, in a higher rainfall area, such as Naracoorte, temperature
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increase is the main factor determining the change in the median grain yield. There is a big increase in the number of years when crops cannot be established according to our sowing rule when rainfall decreases. Change in temperature and atmospheric CO2 concentration have no effects on failed sowing years due to the sowing rules set in this study. The larger change range of grain yield (87 to +131%) in dry areas compared with that of in mid-high rainfall areas (88 to +77%) indicates that dry areas are more sensitive to climate change especially to rainfall increase as temperature change and CO2 concentration change are quite similar across the eight locations. The most likely median grain yield change (32 to 13.5%) (Table 8) for the eight locations under study indicates that adverse effects from future climate change are projected for wheat production in South Australia. An important implication flowing from this key result is that adaptation strategies should be put in place to minimise these adverse impacts. It is important to realize that the current study assumed no adaptive management strategies were put into practice in response to the projected climate change and this is clearly an unrealistic assumption. However, the study does provide a clear picture of the adverse impact of climate change on wheat production given no adaptive management strategies are put into practice. There are a number of limitations to this study. As mentioned above, no adaptation management was considered in this study. In reality, farmers would gradually adapt to climate change. The possible impacts of change in climate variability was not considered due to the unavailability of daily output of GCMs and the poor performance of GCMs in simulating the behavior of El Nin˜o Southern Oscillation (ENSO) events in our region, even though change in climate variability is more relevant to agricultural production than change in mean climate. This study was designed to look at the possible climate change impacts for the year 2080. Farmers may be more interested in short to medium climate change impacts such as a time frame of 2030. Our ongoing research is addressing these issues. Some advances have been made in this study. Probabilistic scenarios within the full range of uncertainties associated with changes in local rainfall, temperature and atmospheric CO2 concentration have been generated and applied to a wheat production system. This study is based on the latest greenhouse gas emission scenarios and GCM/RCM projections (IPCC, 2000; CSIRO, 2001). A greater degree of soil variability was considered than previous studies. The magnitude of relative contribution of change in rainfall, temperature
and atmospheric CO2 concentration to wheat yield in South Australia was quantified. This study has significant implications for future research directions and for the grain industry. Rigorous studies are needed in the area of adaptive management to minimise the risk of climate change. There is also a need to study the combined effects of change in climatic variability and change in mean climate. Climatic variability or extreme events may have fatal impacts on agro-ecosystems. Complex economic analyses are required to evaluate the liability of the wheat industry. Some current grain growing areas will not be viable under future climate change scenarios. Other areas may be viable, but profitability will be greatly reduced. These findings have important implications for farm enterprise combinations, land values, grain handling and marketing. Acknowledgements The authors would like to thank Dr. Roger Jones (Atmospheric Research, CSIRO) for the provision of local climate change information and of software for extracting those local climate change data. Thanks go to the Agricultural Production System Research Unit (APSRU) for distributing the APSIM-Wheat module and its technical support to this project. Two people need to be specially mentioned. One is Dr. Enli Wang who provided QL strong technical support in applying the APSIM-Wheat module to this study. The other is Dr. Holger Meinke who organized a workshop in which QL was trained to apply APSIM and provided partial financial support for that training. We also would like to record our thanks to David Maschmedt (Sustainable Resources, Primary Industries and Resources South Australia (PIRSA)) who provided us with large samples of soil profile data and helped us with the derivation of some soil parameters. We thank the Australian Research Council (ARC) for financial support. References Brklacich, M., Stewart, R.B., 1995. Impacts of climate change on wheat yield in the Canadian Prairies. In: Rosenzweig, C., Ritchie, J.T., Jones, J.W. (Eds.), Climate Change and Agriculture: Analysis of Potential International Impacts, No. 59. ASA Special Publication, Madison, pp. 147–161. Bureau of Meteorology, 2004. http://www.nrm.qld.gov.au/silo/ppd/ PPD_frameset.html. CSIRO, 1996. Climate Change Scenarios for the Australian Region. Climate Impact Group, CSIRO Division of Atmospheric Research, Melbourne, 8 pp. CSIRO, 2001. Climate Change Projections for Australia. Climate Impact Group, CSIRO Division of Atmospheric Research, Melbourne, 8 pp.
Q. Luo et al. / Agricultural and Forest Meteorology 132 (2005) 273–285 Dele´colle, R., Ruget, F., Ripoche, D., 1995. Possible effects of climate change on wheat and maize crops in France. In: Rosenzweig, C., Ritchie, J.T., Jones, J.W. (Eds.), Climate Change and Agriculture: Analysis of Potential International Impacts, No. 59. ASA Special Publication, Madison, pp. 241–257. Howden, S.M., Mckeon, G.M., Reyenga, P.J., Entel, M., Meinke, H., Flood, N., 1999a. Past and future competitiveness of wheat and beef cattle production in Emerald, NE Queensland. In: MODSIM99 Proceedings of the International Congress on Modelling and Simulation, Hamilton, New Zealand, December 6–9, 1997. Howden, S.M., Reyenga, P.J., Meinke, H., 1999b. Global Change Impacts on Australian Wheat Cropping: Studies on Hydrology, Fertiliser Management and Mixed Crop Rotations. Report to the Australian Greenhouse Office, CSIRO Wildlife and Ecology Working Paper 99/13, Canberra, p. 24. Howden, S.M., Reyenga, P.J. Meinke, H., McKeon, G.M., 1999c. Integrated Global Change Impact Assessment on Australian Terrestrial Ecosystems. Report to the Australian Greenhouse Office, CSIRO Wildlife and Ecology Working Paper 99/14, Canberra, p. 51. Howden, S.M., Reyenga, P.J., Meinke, H., 1999d. Global Change Impacts on Australian Wheat Cropping. Report to the Australian Greenhouse Office. CSIRO, Sustainable Ecosystem, Canberra. IPCC, 2000. Special Report: Emissions Scenarios. Summary for Policymakers. IPCC, Geneva, Switzerland. URL http:// www.ipcc.ch/. IPCC, 2001. Climate Change 2001: Impacts, Adaptation, and Vulnerability. Cambridge University Press. Jones, R.N., 2000. Managing uncertainty in climate change projections-issues for impact assessment. Climatic Change 45, 403–419. Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J.P., Silburn, M., Wang, E., Brown, S., Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L., Freebairn, D.M., Smith, C.J., 2003. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron 18 (3–4), 267–288. Luo, Q., 2003. Assessment of the potential impacts of climate change on South Australian wheat production. Unpublished Ph.D. thesis. The University of Adelaide.
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Luo, Q., Williams, M.A.J., Bellotti, W., Bryan, B., 2003. Quantitative and visual assessment of climate change impacts on South Australian wheat production. Agric. Syst. 77, 173–186. Mearns, L.O., 1995. Research issues in determining the effects of changing climate variability on crop yields. In: Rosenzweig, C., Ritchie, J.T., Jones, J.W. (Eds.), Climate Change and Agriculture: Analysis of Potential International Impacts, No. 59. ASA Special Publication, Madison, pp. 123–146. Menzhulin, G.V., Koval, L.A., Badenko, A.L., 1995. Potential impacts of global warming and carbon dioxide on wheat production in the commonwealth of independent states. In: Rosenzweig, C., Ritchie, J.T., Jones, J.W. (Eds.), Climate Change and Agriculture: Analysis of Potential International Impacts, No. 59. ASA Special Publication, Madison, pp. 275–292. Reyenga, P.J., Howden, S.M., Meinke, H., Mckeon, G.M., 1999a. Modelling global change impacts on wheat cropping in southeast Queesland, Australia. Environ. Model. Software 14, 297– 306. Reyenga, P.J., Howden, S.M., Meinke, H., Hall, W.B., 1999b. Global change impacts on wheat production along an environmental gradient in South Australia. In: MODSIM 99 Proceedings of the International Congress on Modelling and Simulation, Hamilton, New Zealand, December 6–9, 1997. Seino, H., 1995. Implication of climate change for crop production in Japan. In: Rosenzweig, C., Ritchie, J.T., Jones, J.W. (Eds.), Climate Change and Agriculture: Analysis of Potential International Impacts, No. 59. ASA Special Publication, Madison, pp. 293– 306. Smith, J.B., Huq, S., Lenhart, S., Mata, L.J., Nemesova, I., Toure, S., 1996. Vulnerability and Adaptation to Climate Change: Interim Results from the US Country Studies Program. Kluwer Academic Publishers, Dordrecht and Boston, p. 366. Tubiello, F.N., Rosenzweig, C., Volk, T., 1995. Interactions of CO2, temperature and management practices: simulations with a modified version of CERES-Wheat. Agric. Syst. 49, 135–152. Yunusa, I.A.M., Bellotti, W.D., Moore, A.D., Probert, M.E., Baldock, J.A., 2004. An exploratory evaluation of APSIM to simulate growth and yield processes for winter cereals in rotation systems in South Australia. Aust. J. Exp. Agric. 44, 787– 800.
Potential impact of climate change on wheat yield in South Australia Qunying Luo a,*, William Bellotti b, Martin Williams a, Brett Bryan c a
Department of Geographical and Environmental Studies, University of Adelaide, SA 5005, Australia b School of Agriculture & Wine, University of Adelaide, SA 5371, Australia c Policy and Economic Research Unit, CSIRO Land and Water, Private Bag 2, Glen Osmond SA 5064, Australia Received 22 December 2004; received in revised form 25 July 2005; accepted 8 August 2005
Abstract Refined and improved climate change scenarios have been applied in this study to quantify the possible impacts of future climate change on South Australian wheat yield with probability attached. This study used the APSIM-Wheat module and information drawn from the Special Report on Emission Scenarios (SRES) and nine climate models for 2080. A wheat yield response surface has been constructed within 80 climate change scenarios. The most likely wheat yield changes have been defined under combinations of changes in regional rainfall, regional temperature and atmospheric CO2 concentration (CO2). Median grain yield is projected to decrease across all locations from 13.5 to 32% under the most likely climate change scenarios. This has economic and social implications from local to national levels. # 2005 Elsevier B.V. All rights reserved. Keywords: Climate change; Scenario construction; APSIM-Wheat module; Most likely wheat yield
1. Introduction Research on the potential impacts of climate change on wheat yield has been pursued for more than a decade worldwide. A large number of studies were conducted by linking the downscaled output of general circulation models (GCMs) with dynamic wheat models (Brklacich and Stewart, 1995; Dele´colle et al., 1995; Mearns, 1995; Menzhulin et al., 1995; Seino, 1995; Tubiello et al., 1995; Smith et al., 1996; Howden et al., 1999a,b,c; Reyenga et al., 1999a,b; Luo et al., 2003). In quantifying the potential impacts of climate change on wheat yield, single scenarios or high and low end scenarios were used in most previous studies. Assessment results
* Corresponding author. Tel.: +61 8 8303 3860; fax: +61 8 8303 3772. E-mail address: [email protected] (Q. Luo). 0168-1923/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2005.08.003
derived from single scenarios are fairly precise, but are conditional on those single scenarios only, and are unlikely to be representative of other possible futures and are highly speculative. The outcomes are plausible, but contain no information as to their likelihood. There are no confidence limits as to the possibility of these outcomes, nor how the results fit into broader ranges of uncertainty and what those ranges of uncertainty may be. While appropriate for testing sensitivity and vulnerability of a particular system, this methodology is poorly suited for planning or policy purposes. A range of projections will always be more likely to encompass what will actually transpire than a single scenario. The resulting range of outcomes arising from the high and low ends of the range of regional climate change is often too large to be of real use for planning or policy purposes, although certain levels of probability can be attached to the regional climate change (Jones, 2000; IPCC, 2001). Assignment of probability to impact
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Table 1 Coordinates and annual rainfall of study sites Study sites
Latitude
Longitude
Altitude (m)
GSa rainfall (mm)
Annual average rainfall (mm)
Cummins Keith Lameroo Minnipa Naracoorte Orroroo Roseworthy Wanbi
348160 S 368060 S 358200 S 328520 S 368580 S 328440 S 348320 S 348470 S
1358440 E 1408210 E 1408310 E 1358090 E 1408440 E 1388370 E 1388410 E 1408160 E
68 29 99 168 58 418 68 77
321 315 251 226 397 210 292 186
431 468 388 326 578 342 440 304
a
GS, growing season (May–October inclusive).
outcomes is urgently required and will facilitate policy and decision-making in adapting to climate change. Very limited studies have probabilistic analysis on wheat yield impact resulting from climate change. Howden et al. (1999d) attempted to assign probability to impact outcomes across the ranges of projected regional rainfall change and temperature change with atmospheric CO2 concentration fixed at 700 ppm for year 2100, based on CSIRO (1996) in which IPCC IS92a–f scenarios were applied. However, the change range for atmospheric CO2 concentration under IPCC (2000) is now available and can be applied in current impact assessments. The existing methodological problems noted above, the recent release of the IPCC Special Report on Emission Scenarios (SRES) (IPCC, 2000) and the vulnerable agricultural production system of South Australia provided the impetus to assess comprehensively the potential impacts of climate change on wheat production. The aim of this study is to provide probabilistic impact outcomes across the full change ranges of rainfall, temperature and atmospheric CO2 concentration based on IPCC (2000). The originality of this work lies in using the updated greenhouse gas emission scenarios-IPCC SRES in projecting global warming, and thus in projecting regional temperature and regional rainfall. In addition, a range of CO2 concentration change was used rather than a fixed CO2 concentration change in the wheat model. We here present a pilot study in climate change impact assessment involving probabilistic analysis within the ranges of three atmospheric variables based on IPCC (2000). 2. Materials and methods 2.1. Study sites Wheat production in South Australia is a significant employer and major export earner. Future anthropogenic
warming and drying of the region during the austral winter is a potential threat to production, which is highly relevant for the regional and national economy. Eight localities (Cummins, Keith, Lameroo, Minnipa, Naracoorte, Orroroo, Roseworthy and Wanbi) across the South Australian wheat belt were chosen (Table 1 and Fig. 1). These eight sites are representative of each agricultural region. Minnipa and Cummins are representative of the Western agricultural region. Orroroo represents the Upper North agricultural region. Roseworthy belongs to the Central agricultural region; Lameroo and Wanbi are from the Murray Mallee agricultural region. Keith and Naracoorte are physically located in the Southeast agricultural area. These eight sites have different average annual rainfall (Table 1). Cummins, Keith, Naracoorte and Roseworthy have wetter climates with an annual rainfall of 430–580 mm. The other four sites are drier with annual rainfall ranging from 305 to 390 mm. 2.2. Generation of probabilistic climate change scenarios Future atmospheric CO2 concentration increase and global warming for 2080 were derived from four different narrative storylines/families (B1, B2, A1 and A2) about greenhouse gas (GHG) emission scenarios or three families and three groups of GHG emission scenarios within family A1 (A1B, A1T and A1F) and shown in Table 2 (IPCC, 2000). The bold figures are the upper limits and lower limits of these two components. Data on local climate change was derived from GCMs and regional climate models (RCMs) outputs. Downscaled outputs of nine climate models (Table 3) for local climate change per degree of global warming were obtained from CSIRO Atmospheric Research. The upper limits and lower limits for local temperature and local rainfall including growing season (May–October
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Fig. 1. Location of study sites. Smaller dots indicate drier sites, larger dots denote wetter sites.
Table 2 Atmospheric CO2 concentrations and global warming in year 2080 (IPCC, 2000)
CO2 concentration (ppmv) CO2 concentration change (%)
B1a
B2b
A1Bc
A2d
A1Fc
A1Tc
527
549
635
687
786
546
51
57
81
96
125
56
Global warming (8C) Lower limit 1.1 Middle 1.6 Upper limit 2.3 a
1.1 1.7 2.5
1.4 2.1 3.0
1.5 2.2 3.2
1.9 2.7 3.8
1.2 1.8 2.6
The B1 storyline and scenario family describes a convergent world with the same global population as in the A1 storyline. b The B2 storyline and scenario family describes a world in which the emphasis is on local solutions to economic, social and environmental sustainability. c The A1 storyline and scenario family describe a future world of very rapid economic growth, global population and the rapid introduction of new and more efficient technologies. A1B, a balanced mix of technologies and supply sources; A1F, fossil intensive and A1T, non-fossil energy sources. d The A2 storyline and scenario family describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities.
Table 3 GCMs/RCMsa details and their projection features for study sites considered GCMs/RCMs
Laboratories
Features
MK2 CGCM1
CSIRO Canadian Climate Centre Hadley Centre, UK Max Planck Max Planck
Rainfall decrease Rainfall decrease
HADCM2 ECHAM4/OPYC3 ECHAM3/LSG
Rainfall decrease Rainfall decrease Winter rainfall decrease, summer rainfall increase DARLAM 125 km CSIRO Winter rainfall decrease, summer rainfall increase DOE-PCM NCAR Rainfall decrease HADCM3 Hadley Centre, UK Rainfall decrease R15-a GFDL Winter rainfall increase, summer rainfall decrease a More information on these GCMs can be found at http://ipccddc.cru.uea.ac.uk. Further information on the RCM:DARLAM can be found at http://www.dar.csiro.au/earthsystems/darlam.html.
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rainfall change and 1/3 given to non-growing season rainfall change. The magnitude of the average weighting is based on the distribution of rainfall during the growing season and the non-growing season from the current climate condition. The random samples of climate change scenarios for regional temperature, regional rainfall and CO2 change were grouped into classes and summarised. Class breaks were created at every 10% change in CO2 (e.g. 70%, 80%, 90%, . . .), 5% change in rainfall (e.g. 10%, 5%, 0%, 5%, . . .) and 0.5 8C change in temperature (e.g. 1 8C, 1.5 8C, 2 8C, . . .). A distinct climate change scenario class was created for unique combinations of CO2, rainfall and temperature change class breaks. Each randomly sampled scenario was allocated to the nearest class and the total count of scenarios occurring in each class was tabulated. The probability for each class was calculated by dividing the number of randomly sampled scenarios in the class by the total number of scenarios (100,000). Scenario classes were then ranked according to the most likely to least likely. The cumulative probability was calculated by summing probabilities from the most likely to the least likely scenario class. The cumulative probabilities of each scenario class were used to create bivariate contour graphs for each of the eight selected localities across South Australia. Each graph displays the likelihood of occurrence of different climate change scenarios. For each locality, three contour graphs were created, CO2 versus rainfall, rainfall versus temperature and temperature versus CO2. Five contour classes represented by different shades of grey were defined for each graph and represent cumulative probability quintiles such that each contour class contains 20% of the total number of random samples. The darkest shade of grey is the 20% most likely climate change scenarios, grading through to the lightest shade
inclusive) and non-growing season (November–April inclusive) were derived as follows. First, downscaled monthly outputs of specific GCMs or RCMs were averaged (12 months for temperature, specific months for growing season rainfall and non-growing season rainfall as defined above). Then the upper limits and lower limits can be extracted among the nine GCMs or RCMs. Table 4 presents change ranges for atmospheric CO2 concentration, global warming, regional temperature and regional rainfall (including the growing season and non-growing season) used to create probabilistic climate change scenarios through Monte Carlo Random Sampling (MCRS) procedures. Once the upper limits and lower limits for atmospheric CO2 concentration change, global warming and local climate change were determined; MCRS was used to create probability distributions for climate change scenarios. Global warming and local temperature were uniformly randomly sampled 100,000 times and were then multiplied repeatedly, to obtain a non-uniform distribution of regional temperature change, peaking around an average value. Global warming was used to scale the possible ranges for growing season and nongrowing season rainfall change. The lower limit and upper limit for growing season and non-growing season rainfall change were multiplied by the upper bound of global warming (i.e. 3.8 8C) to ensure that values anomalous to that degree of warming were not sampled. In other words, the upper and lower limits of the growing and non-growing seasons were constrained by the upper limit of global warming to derive all possible outcomes of regional rainfall for these two seasons. The two altered ranges were then separately sampled and repeated 100,000 times. Samples from these two ranges were then averaged to get regional rainfall change based on average weight with 2/3 given to growing season
Table 4 Ranges for global warming, atmospheric CO2 concentration increase and local climate change for 2080 Localities
Cummins Keith Lameroo Minnipa Naracoorte Orroroo Roseworthy Wanbi
Local rainfall change (%)
Local temperature change (8C)
Global warming (8C)
Atmospheric CO2 concentration Change (%)
Growing season
Non-growing season
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
8.0 7.2 7.0 8.6 6.5 7.9 7.5 7.2
3.5 1.6 2.2 4.6 1.0 3.8 2.9 2.5
7.3 7.6 7.4 6.9 8.0 7.4 7.2 7.3
11.2 10.1 11.7 15.4 7.8 16.0 12.2 12.5
0.7 0.8 0.8 0.9 0.7 0.9 0.8 0.9
1.0 1.1 1.1 1.0 1.1 1.1 1.1 1.1
1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1
3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8
51 51 51 51 51 51 51 51
125 125 125 125 125 125 125 125
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representing the 20% least likely scenarios. The three graphs are displayed in three dimensions to represent the inter-relationships of CO2, rainfall and temperature in climate change scenarios, thereby creating a climate change volume. However, each bivariate graph is essentially a projection of the volume onto the graph wall. For example, the bivariate graph of rainfall versus temperature represents probabilities for all values of CO2, rather than a slice of those probabilities occurring at the planar intersection of the chart in the diagram.
coefficients and crop management information. The APSIM-Wheat module has been described in detail elsewhere (Keating et al., 2003; Luo, 2003). The performance of APSIM-Wheat in the Australian environment (Keating et al., 2003) and in the South Australian environment (Luo, 2003; Yunusa et al., 2004) has been evaluated. Fig. 2 is a flow chart of the data and program used in this study. The data used in this study are described in detail in Section 2.2 and the following subsections.
2.3. APSIM-Wheat module and its parameterisation
2.3.1. Historical climate data Maximum temperature, minimum temperature, solar radiation and rainfall are identified as key climatic variables, which are explicitly used within the APSIMWheat module. Two tiers of climate information were acquired for this study: historical climate data and climate change information (described in Section 2.2). Historical daily climate data including daily maximum temperature, minimum temperature, rainfall and solar radiation covering 1900–1999 were derived from the SILO Patched Point dataset (Bureau of Meteorology, 2004) and used by the APSIM-Wheat module. There are two functions for historical climate data. First, they were used to drive the wheat model to produce baseline wheat yields. Second, the historical weather data were
The APSIM-Wheat module (Version 2.0) was used to quantify the potential impact of future climate change on wheat yield in this study. APSIM stands for Agricultural Production Systems sIMulator. It is an integration of several interactive modules including biological modules (crop modules, soil module, etc.), and other utility and application modules. The wheat module is one of the crop modules within the APSIM system. It simulates the growth and development of a wheat crop in a daily time-step on an area basis as a function of weather (temperature, rainfall and radiation), soil (soil water and soil nitrogen), crop genetic
Fig. 2. Schematic diagram of data and tools used in this study. GCM, general circulation model and RCM, regional climate model.
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Table 5 Change levels for rainfall, temperature and atmospheric CO2 concentration used by APSIM-Wheat module Locations
Cummins Keith Lameroo Minnipa Naracoorte Orroroo Roseworthy Wanbi
Rainfall change (%) Growing season
Non-growing season
30.6, 27.4, 23.4, 32.1, 24.8, 29.0, 28.6, 27.5,
27.8, 28.5, 27.4, 24.3, 22.5, 26.7, 26.9, 27.8,
17.3, 24.0, 16.8, 18.2, 14.5, 20.9, 28.4, 22.8,
16.5,10.5, 12.8 21.5, 6.3, 3.0 14.0, 2.6, 7.1 16.7, 7.1, 16.4 15.9, 0.1, 0.1 28.0, 6.5, 13.6 12.3, 3.5, 10.8 18.8, 7.8, 8.1
8.7, 25.4, 18.0, 42.5 3.9, 25.4, 25.3, 36.4 12.4, 12.5, 15.8, 44.7 8.4, 27.7, 57.9, 56.5 10.0, 16.9, 7.2, 24.9 5.6, 57.5, 49.3, 59.7 10.0, 16.2, 32.3, 46.3 2.8, 31.8, 37.5, 41.5
perturbed by climate change information derived from Section 2.2 and used by the APSIM-Wheat module to project wheat yield for future time frames. To calculate wheat yields under the full range of climate change scenarios, several change levels of regional temperature, regional rainfall and atmospheric CO2 concentration (described in Section 2.2) were used in the APSIM-Wheat module. Five rainfall levels (including growing season rainfall and non-growing season rainfall), four temperature levels (annual temperature) and four CO2 concentration levels were chosen for running the APSIM-Wheat module covering the range of probabilistic climate change scenarios for each location (Table 5). Selection of these values is based on the following three criteria: Values should cover the whole range of climatic change. The upper and lower ends of the range were chosen as two levels of the inputs. Intermediate values should be as evenly distributed within the range as possible. Thus, 81 APSIM simulation runs were undertaken (80 climate change scenarios (five rainfall changes four
Temperature change (oC)
CO2 concentration levels (ppm)
0.5, 2, 3, 4 1, 2, 3, 4 1, 2, 3, 4 1, 2, 3, 4 0.5, 2, 3, 4 1, 2, 3, 4 1, 2, 3, 4 1, 2, 3, 4
527, 527, 527, 527, 527, 527, 527, 527,
635, 635, 635, 635, 635, 635, 635, 635,
687, 687, 687, 687, 687, 687, 687, 687,
786 786 786 786 786 786 786 786
temperature increases four CO2 concentration increases) and one baseline scenario: historical climate and current CO2 concentration) for each of the eight localities totalling 648 APSIM runs. Each run consisted of 100 years of daily climate data. The asymmetrical increase in maximum and minimum temperatures and the possible change in future climate variability attributed to greenhouse warming were not dealt with in developing climate input to APSIM-Wheat module. 2.3.2. Soil data One representative soil profile for each location was used in this study. A large number of soil water and soil nitrogen parameters are needed to run the APSIMWheat module. Plant available water capacity (PAWC) is a key determinant of crop productivity and is determined by lower and upper soil water contents for the depth of wheat roots. Soil nitrogen is the most important nutrient for crop growth and the availability of nitrogen to wheat plants is determined by soil factors (organic carbon, total nitrogen, residues and inorganic nitrogen) and fertiliser nitrogen. Table 6 gives detailed information about the PAWC, available soil water and inorganic nitrogen at reset for each soil profile. Fig. 3 shows soil and crop parameters, such as the drained
Table 6 PAWC and soil inorganic nitrogen for the eight soils used in this study Locations
Soil types
PAWC (mm)
Available soil water at reset (mm)
Soil inorganic nitrogen at reset (kg ha1)
Cummins Keith Lameroo Minnipa Naracoorte Orroroo Roseworthy Wanbi
Clay loam Loamy sand Fine sandy loam Sandy loam Sandy clay loam Sandy loam Loam Sandy loam
140 76 111 157 125 134 122 132
42 28 45 51 44 44 50 42
245 85 327 146 210 98 181 246
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Fig. 3. APSIM soil parameters used to define plant available water capacity (PAWC) and value of soil water reset for two contrasting soils: low PAWC (76 mm) at Keith and high PAWC (157mm) at Minnipa. SW represents soil water at reset. DUL stands for the drained upper limit of soil water. CLL represents crop lower limit.
upper limit of soil water and crop lower limit used in the APSIM-Wheat module for two contrasting soils at Keith and Minnipa. 2.3.3. Configuration and management details for simulations Two types of wheat cultivar were used in this study. One was Janz, an Australian Hard Wheat (AH) with grain protein content of 11.5%. The other cultivar was Excalibur, an Australian Soft Wheat (ASW) variety with grain protein content of 10%. Janz is a mid-late maturity variety, while Excalibur is an early maturity cultivar. The reason for including the two cultivars was to better match wheat cultivar maturity grading with average length of growing season. Early maturing varieties are normally higher yielding in low rainfall, short growing season environments. The genetic coefficients for the two cultivars are listed in Table 7. Soil nitrogen, soil water (Table 6) and residues (fababean) were reset on the first of March for each year of simulation run. The sowing rule was specified as follows: if cumulative rainfall within 3 days between 15 April and 15 June 20 mm (early start to season), then sow Janz; if cumulative rainfall within 3 days between 15 June and 15 August 15 mm (late start to season), then sow Excalibur. Sowing density was 200 plants m2, at a depth of 3 cm. Two fertiliser levels were applied at planting date: 40 kg ha1 NO3–N at medium–high rainfall locations and 20 kg ha1 NO3–N at lower rainfall locations at a depth of 5 cm. There is about 115 kg ha1 inorganic nitrogen for the top 50 cm of soil at Roseworthy. These nitrogen levels were intended to represent a situation where soil inorganic nitrogen was non-limiting in most years. The logic behind this was that the prime factor under study was
climate change rather than N fertiliser response. It is recognised that the optimum nitrogen fertiliser rate will vary from year to year depending primarily on variation in in-crop rainfall. This is true for the historical baseline scenario as well as for future climate change scenarios. Future research will investigate the interaction between climate change and N fertiliser strategies (both the amount and the timing of N fertiliser application). The physiological effects of increased atmospheric CO2 on wheat production were included in the simulations. Modifications have been made to the Wheat module through changes to radiation use efficiency (RUE), transpiration efficiency (TE) and to critical nitrogen concentration (CRC) based on experimental data (Reyenga et al., 1999a; Luo, 2003).
Table 7 Genetic coefficients for Janz and Excalibur (data source: Yunusa et al., 2004) Coefficient a
p1v : Sensitivity to vernalization p1db: Sensitivity to day length p5: Grain filling duration (8C day) grno: Grain number per head fillrate: Rate of grain filling (mg day1) Stem weightc (g) Phyllod (8C day) sla: Specific leaf area (mg cm2) a
Janz
Excalibur
1.0 2.0 640 34 2.5 1.65 95 185
1.5 2.0 703 27 3.5 2.4 95 180
Relative amount that development is slowed for each day of unfulfilled vernalization. b Relative amount that development is slowed when plants are grown in a photoperiod 1 h shorter than the optimum. c Non-stressed dry weight of a single stem and spike when elongation ceases. d Phyllochron interval, the interval in thermal time between successive leaf tip appearances.
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Fig. 4. Interpretation of probabilistic climate change scenarios and yield response.
The relative changes in median grain yield (lines of Fig. 5) were calculated based on the simulation of the APSIM-Wheat for 80 climate change scenarios covering the range of probable scenarios modelled above for each of the eight localities across South Australia. Note that the grain yield is presented as percentage change in the median grain yield for a given location compared to the median baseline grain yield for that location. The median grain yield is calculated from the simulated grain yield for 100 years under each of the 80 scenarios. Bivariate, line-based contour graphs were also created to represent yield for the three combinations of CO2 versus rainfall, rainfall versus temperature and temperature versus CO2 to enable a direct comparison of the likelihood of occurrence of the climate change scenarios with the potential impact on yield. These yield contours are also projections of the volume onto a plane and are superimposed upon the climate change graphs in the three-dimensional plots. Fig. 4 is an explanatory graph about the 3D plots. This enables the simultaneous direct interpretation of both the likelihood of occurrence of particular climate change scenarios and the impact on wheat yield. 3. Results The median grain yields under baseline for each location are shown in Table 8. The changes in median grain yield under the most likely climate change
scenarios are derived from Fig. 5 and summarised in Table 8. The yield change range or change trend in the horizontal plane (between temperature and rainfall change) and the vertical plane (rainfall and CO2 concentration change) are quite similar (Fig. 5). Rainfall dominated yield response in these two planes in the lower rainfall areas (Lameroo, Minnipa, Orroroo and Wanbi). Yield response in the medium rainfall area (Cummins, Keith and Roseworthy) is the result of interactive effects between rainfall and temperature change and between CO2 concentration and rainfall change. Yield was dominated by temperature in the plane between temperature and rainfall change and by CO2 concentration in the plane between rainfall and
Table 8 Median grain yields under baseline and most likely scenarios Locations
Cummins Keith Lameroo Minnipa Naracoorte Orroroo Roseworthy Wanbi
Baseline (kg ha1)
Most likely scenarios Change range (%)
Median yield (%)
3054 4108 1827 1321 5977 1357 3630 847
33 38 31 35 26 33 41 34
20 26 16 20 13.5 16.5 32 20.5
to to to to to to to to
7 14 1 5 1 0 23 7
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Fig. 5. Median yield response (%) under future climate change scenarios at the eight locations. Lines across the three planes are yield response within the 80 climate change scenarios. The thick line in each plane represents the yield threshold below which wheat production is not viable.
CO2 concentration change at the higher rainfall sites, such as at Naracoorte. Increase of temperature and CO2 concentration interactively exerted effects on median grain yield across all localities under study.
Median grain yield is positively correlated with the increase of rainfall and CO2 concentration, and negatively correlated with temperature increase across all locations. This finding is reflected in Fig. 6A–C, which
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Fig. 6. Relationship between change in median grain yield and change in environmental factors at Roseworthy: (A) relationship between change in median grain yield and change in rainfall; (B) relationship between change in median grain yield and change in temperature and (C) relationship between change in median grain yield and change in atmospheric CO2 concentration. Each diamond is for each simulation run (there are 80 simulation runs for each location).
show the relationship at Roseworthy of change in median grain yield to changes in rainfall, atmospheric CO2 concentration and temperature, respectively, under the 80 climate change scenarios. It can be seen that rainfall change is the main determinant in change of median grain yield with a correlation coefficient of 0.88. Temperature increase has much less impact on change of grain yield (with a correlation coefficient of 0.055) compared with that of rainfall. However, its effect is greater than that of the increase in atmospheric CO2 concentration with a correlation coefficient of 0.0089. It is very different in the higher rainfall area (Naracoorte). Temperature is the dominant factor in determining median grain yield change in this site. Rainfall has the least influence on median grain yield among the three factors.
Significant changes to median grain yield were projected in each location due to the large change range in local temperature, local rainfall and atmospheric CO2 concentration. Yields change from 88 to +131% across all locations. However, median grain yields present a decrease trend (32 to 13.5%) under the most likely climate change scenarios across all locations (Table 8). For example, at Roseworthy, median grain yield in 2080 is projected to be 32% less than the current historical median grain yield of 3630 kg ha1. A larger range in response of median yield has been noted for lower rainfall area (87 to +131%) compared with that of medium–high rainfall areas (88 to +77%) under the 80 climate change scenarios.
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4. Discussion The combined effects of changes in rainfall, temperature and atmospheric CO2 concentration on wheat yield have been reported in many studies worldwide. Mearns (1995) quantified the potential impacts of change in ‘mean’ climate and change in climate variability on wheat yields in the central Great Plains of the United Sates and found that wheat yield would increase from 44 to 82%. Seino (1995) estimated that wheat yield would change from 41 to +6.3% under climate change in Japan. Brklacich and Stewart (1995) projected that wheat yield would change from 40 to +60% in the Canadian Prairies. Tubiello et al. (1995) quantified the combined effects of climate change and increased atmospheric CO2 concentration on spring wheat (4 to +8% across management practices) in the Canadian wheat belt. Dele´colle et al. (1995) examined wheat yields potential under future climate change in France and found that wheat yield change would range from 30 to 7% under GISS, GFDL and UKMO transient and equilibrium climate change scenarios by using CERES-Wheat model. Menzhulin et al. (1995) projected that national wheat yield would change from 19 to +41% in Russia and the former Soviet Republics. Smith et al. (1996) used the CERES model to simulate winter wheat yield across Kazakhstan for the tenth decade and found that winter wheat yield increased 21 and 17%, respectively, under the transient and the equilibrium climate change scenarios of GFDL. Similar studies were conducted in Australia. Howden et al. (1999a) concluded that wheat yield would increase from 12 to 19% in northeast Queensland. Change of 2 to 14% in wheat yield has been projected in Howden et al. (1999b). A later study by Howden et al. (1999c) stated that wheat yield in South Australia will decrease under the A1-mid and B2mid scenarios based on IPCC SRES. Howden et al. (1999d) found that wheat yield will increase from 9 to +37% across the Australian wheat belt based on the IS92 GHG emission scenarios. Reyenga et al. (1999a) reported that wheat yield would change from 3 to 33% in southeast Queensland. Reyenga et al. (1999b) simulated that wheat yield would decrease between 10 and 35% under dry scenarios across the South Australian wheat belt. Luo et al. (2003) quantified the combined effects of climate change and increased atmospheric CO2 concentration on South Australian wheat production and found wheat yield changes from 2 to 40% using the CERES-Wheat model. The current study projected a change range in median grain yield of 87 to 131%. This change range is larger than previous
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studies for several reasons. First is the use of the latest IPCC SRES projections that include higher global warming and reductions in growing season rainfall especially in Mediterranean type environments such as South Australia. Second is the use of different climate change scenarios. Probabilistic scenarios have been used in this study across the full change range of possible rainfall, temperature and atmospheric CO2 concentration, rather than the single scenarios used in previous studies. Third, a different crop model or version was used which brought about different results. Different wheat models have different responses to the physiological effects of increased atmospheric CO2 concentration. Modifiers of 1.2 for radiation use efficiency and 1.37 for transpiration use efficiency were used in the CERES-Wheat model while modifiers of 1.17 for RUE and 1.06 for TE were adopted in the APSIM-Wheat module at atmospheric CO2 concentration of 550 ppm (Reyenga et al., 1999a; Luo, 2003; Hoogenboom, personal communication). The physiological effects of increased atmospheric CO2 concentration on wheat production need to be studied thoroughly under different environments and appropriately represented in the wheat models. Finally, different configuration of crop model probably will lead to different outcomes. Uncertainties in crop models also need to be quantified and managed according to the last two points in projecting crop production. Strong non-linear relationships between change in rainfall and change in median grain yield, between increase in CO2 concentration and change in median grain yield were found at Cummins, Naracoorte and Orroroo. This needs to be investigated further. 5. Key conclusions This study evaluated the possible wheat yield impacts from climate change including climate change and increase of atmospheric CO2 concentration under IPCC SRES in South Australia in 2080. Of the three variables (rainfall change, temperature change and increase of atmospheric CO2 concentration), rainfall change is by far the most influential factor in the medium to low rainfall areas. Temperature increase has some impact on change in median grain yield, but its effects are much less than that of rainfall alteration. Change in atmospheric CO2 concentration has the least impact on change in median grain yield (Fig. 6). In other words, the potential yield enhancing effect of increased CO2 concentration is insignificant compared to the overriding influence of decreased rainfall. However, in a higher rainfall area, such as Naracoorte, temperature
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increase is the main factor determining the change in the median grain yield. There is a big increase in the number of years when crops cannot be established according to our sowing rule when rainfall decreases. Change in temperature and atmospheric CO2 concentration have no effects on failed sowing years due to the sowing rules set in this study. The larger change range of grain yield (87 to +131%) in dry areas compared with that of in mid-high rainfall areas (88 to +77%) indicates that dry areas are more sensitive to climate change especially to rainfall increase as temperature change and CO2 concentration change are quite similar across the eight locations. The most likely median grain yield change (32 to 13.5%) (Table 8) for the eight locations under study indicates that adverse effects from future climate change are projected for wheat production in South Australia. An important implication flowing from this key result is that adaptation strategies should be put in place to minimise these adverse impacts. It is important to realize that the current study assumed no adaptive management strategies were put into practice in response to the projected climate change and this is clearly an unrealistic assumption. However, the study does provide a clear picture of the adverse impact of climate change on wheat production given no adaptive management strategies are put into practice. There are a number of limitations to this study. As mentioned above, no adaptation management was considered in this study. In reality, farmers would gradually adapt to climate change. The possible impacts of change in climate variability was not considered due to the unavailability of daily output of GCMs and the poor performance of GCMs in simulating the behavior of El Nin˜o Southern Oscillation (ENSO) events in our region, even though change in climate variability is more relevant to agricultural production than change in mean climate. This study was designed to look at the possible climate change impacts for the year 2080. Farmers may be more interested in short to medium climate change impacts such as a time frame of 2030. Our ongoing research is addressing these issues. Some advances have been made in this study. Probabilistic scenarios within the full range of uncertainties associated with changes in local rainfall, temperature and atmospheric CO2 concentration have been generated and applied to a wheat production system. This study is based on the latest greenhouse gas emission scenarios and GCM/RCM projections (IPCC, 2000; CSIRO, 2001). A greater degree of soil variability was considered than previous studies. The magnitude of relative contribution of change in rainfall, temperature
and atmospheric CO2 concentration to wheat yield in South Australia was quantified. This study has significant implications for future research directions and for the grain industry. Rigorous studies are needed in the area of adaptive management to minimise the risk of climate change. There is also a need to study the combined effects of change in climatic variability and change in mean climate. Climatic variability or extreme events may have fatal impacts on agro-ecosystems. Complex economic analyses are required to evaluate the liability of the wheat industry. Some current grain growing areas will not be viable under future climate change scenarios. Other areas may be viable, but profitability will be greatly reduced. These findings have important implications for farm enterprise combinations, land values, grain handling and marketing. Acknowledgements The authors would like to thank Dr. Roger Jones (Atmospheric Research, CSIRO) for the provision of local climate change information and of software for extracting those local climate change data. Thanks go to the Agricultural Production System Research Unit (APSRU) for distributing the APSIM-Wheat module and its technical support to this project. Two people need to be specially mentioned. One is Dr. Enli Wang who provided QL strong technical support in applying the APSIM-Wheat module to this study. The other is Dr. Holger Meinke who organized a workshop in which QL was trained to apply APSIM and provided partial financial support for that training. We also would like to record our thanks to David Maschmedt (Sustainable Resources, Primary Industries and Resources South Australia (PIRSA)) who provided us with large samples of soil profile data and helped us with the derivation of some soil parameters. We thank the Australian Research Council (ARC) for financial support. References Brklacich, M., Stewart, R.B., 1995. Impacts of climate change on wheat yield in the Canadian Prairies. In: Rosenzweig, C., Ritchie, J.T., Jones, J.W. (Eds.), Climate Change and Agriculture: Analysis of Potential International Impacts, No. 59. ASA Special Publication, Madison, pp. 147–161. Bureau of Meteorology, 2004. http://www.nrm.qld.gov.au/silo/ppd/ PPD_frameset.html. CSIRO, 1996. Climate Change Scenarios for the Australian Region. Climate Impact Group, CSIRO Division of Atmospheric Research, Melbourne, 8 pp. CSIRO, 2001. Climate Change Projections for Australia. Climate Impact Group, CSIRO Division of Atmospheric Research, Melbourne, 8 pp.
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