Download 1 - Terranova

Climate risk matrix for the cropping and grazing industries in the Central
Slopes NRM region
David Cobon and Dave McRae, ICACS, USQ, Toowoomba
Summary
Climate change is likely to provide significant challenges for the region’s agricultural industries and
communities. Regional bodies and industries therefore need to know the location, nature and extent of
potential risks and what, if any adaptation strategies are likely to be effective. This study used the most
recent climate change projection data (for 2030, best estimate) for the region to develop climate change risk
and vulnerability matrices using information gathered from published research and expert opinion.
We found that the risks to the cropping (wheat and sorghum) and grazing industries to 2030 from
anthropogenic climate change can largely be managed by primary producers (broad acre farmers and
graziers) continuing to implement best management practice and managing the risks already posed by
climate variability. This includes recognising that the existing climate variability of the region poses more
immediate challenges to the ongoing viability and sustainability of primary industries than any anticipated
changes to the regions climate to 2030. For example, extremes of climate such as prolonged drought, late
frosts and consecutive hot days already pose significant risk to agricultural profitability and sustainability.
A recent crop modelling study showed that a warmer and drier climate in 2030 may 1) have little effect on
annual crop yield but there were differences between GCM’s and regions and 2) decrease the number of
planting opportunities in dryland broad-acre cropping systems which may result in lower production over
the longer term.
We recommend that future work consider the likely impacts of climate change to at least 2050, that it
includes assessments which consider physical conditions at the local scale (e.g. soil water holding capacity,
soil fertility, local climate), and that it focuses on ensuring that primary producers have the capability and
adaptation strategies needed to best manage existing and future climate risk.
Background
Agriculture (particularly cropping) is a significant land use across the Central Slopes. Agricultural productivity
is affected by climate change through higher temperatures; changes in the amount, intensity, seasonality
and variability of rainfall; and changes in the frequency and/or intensity of extreme events such as droughts,
bushfires and floods.
Climate change poses significant challenges for the region’s agricultural industries. This project aims to
articulate the specific risks to wheat, sorghum and mixed cropping using the Climate Risk Management
Matrix approach. This approach was developed by Cobon et al. (2009) for the grazing industry and adapted
by Deuter et al. (2014) for horticulture to capture existing information and knowledge to provide a basis for
future discussions on managing climate risk with primary producers, policy makers and resource managers.
Industry and regional bodies need to know which areas within the Central Slopes are likely to be suitable for
growing current, and potential new crops under the future climate projected for the region. This includes
identifying the production changes and/or adaptation interventions that will have the greatest potential to
improve on-farm profitability and sustainability.
This work is a first pass assessment to identify the sources of risk and adaptation strategies from the
literature and expert opinion. It was undertaken as a desktop review. Regional NRM bodies may use it to
Central Slopes NRM – Impacts and adaptation to climate change
Page 1
support landholder assumptions and build shared knowledge and agree to a way forward. It may also
identify critical gaps where further investments in research would be beneficial.
With the Central Slopes covering cropping lands from Dalby to Dubbo and recognising changes in climate,
soil characteristics and topography across that region, local information, knowledge and context should be
considered where possible when developing any adaptation strategies.
As a result of the potential climate changes there may be some areas where land uses other than cropping
may need to be pursued. Sequestration opportunities in these areas could be flagged for consideration.
Primary producers are already accustomed to dealing with a highly variable climate. However, new risk
management and adaptation strategies will gain importance as the climate changes. This project will assist
with the identification and evaluation of these strategies.
Aims
For climate change conditions in 2030,
1) Identify the key risks and adaptation strategies for cropping, particularly wheat, sorghum and mixed
enterprises by completing a review of literature and assembling some opinions from experts.
2) Compile climate risk matrices for wheat, sorghum and grazing enterprises that are relevant to the
region.
Current climate trends
The warming trend on the Central Slopes has resulted in a rise in temperature of 0.9 to 1.8oC since 1950
(Table 1). Overall, the Central Slopes has recorded a reduction in rainfall of approximately 15mm/decade
with the greatest decline being in the eastern and southern region. This represents a drop in the average
annual rainfall of 90mm when compared to 1950. The only exception was in one pocket west of Moree
where the annual rainfall has increased slightly since 1950 (5mm/decade or 30mm overall).
Table 1: Historical Climate Trends (Interpreted and summarised from BOM 2011)
Variable
Maximum Temperature
(°C)
Minimum Temperature
(°C)
Mean Temperature
(°C)
Pan Evaporation
(mm)
Rainfall
(mm)
Number of Hot Days
Warm Spell Duration
Trend Since
(year)
Change per decade
Annual
Summer
Winter
1950
+0.10 to +0.30
(north)
0.0 to +0.20
+0.15 to +0.40
(north)
1950
+0.15 to +0.30
+0.15 to +0.40
+0.05 to +0.30
1950
+0.15 to +0.30
+0.10 to +0.30
+0.10 to +0.30
1970
-5 to NSC
-2.5 to NSC
-2.5
1950
+5 to -15 (east and
south)
+5 to -5 (east)
+5 (south-west) to
-10 (east)
1970
+2.5 days
1970
+5 days
NSC – No significant change | Unknown Growing Season Length | Pan Evaporation = the amount of water evaporated from an open
pan per day | Hot Days = annual count of days with maximum temperature >35°C | Warm Spell Duration = Annual count of days with
at least 4 consecutive days when daily maximum temperature >90th percentile | Unknown Growing Season Length = Annual (01 July to
30 June) count between first span of 6 or more days with daily mean temperature >15°C and first span of 6 or more days with daily
mean temperature <15°C
Central Slopes NRM – Impacts and adaptation to climate change
Page 2
Climate in 2030
Uncertainty – climate projections and biological processes
The most recent comprehensive best estimate projections for Australia in 2030 indicate a rise in average
temperature of 0.7–1.2oC, little change in annual rainfall in the far north and decreases of 2–5% elsewhere,
and 2% increased potential evapotranspiration (CSIRO and Bureau of Meteorology 2007). There is
uncertainty associated with these projections due to differences between models and uncertainty in the
likely green house emissions in future years.
Gaps in our understanding of some biological process (e.g. relationship between CO2 level and vegetation
growth) and problems associated with accurately representing complex systems in modelling limit our
capacity to understand the ‘real impacts and most effective adaptive responses’ of climate change (McKeon
et al. 2009).
Despite the lack of accurate future climate details and understanding of some biological processes,
adaptation needs to occur now because past emissions of greenhouse gases have already committed
Australia to further warming (Solomon et al. 2007) and these emissions are continuing to increase (Raupach
et al. 2007). Risk assessment allows informed decisions to be made even where knowledge is limited.
Climate projections
The best estimate climate projections for 2030 on the Central Slopes under the high (RCP85) emissions
scenario show an increase in temperature of 1.1oC, little or no change in rainfall (-2 to +2%) , a 3% rise in
potential evaporation, little or no change in relative humidity and a 2% rise in wind speed (Table 2).
Table 2: Historical means for the period 1971-2000 (Eastern Downs) and climate projections for 2030 under
the high (RCP85) emissions scenario relative to the model base period of 1986-2005
Variable
Annual
Summer
Autumn
Winter
Spring
Historical means
18.3
24.1
18.9
11.7
18.8
Temperature
Projections for
+1.1
+1.0
+1.0
+1.0
+1.1
(°C)
2030
+0.7 to +1.5 +0.4 to +1.7 +0.5 to +1.5 +0.7 to +1.4 +0.6 to +1.9
Historical means
694
262
149
99
178
Rainfall
Projections for
-1%
+2%
-2%
-2%
-1%
(mm)
2030
-13% to +8% -12% to +23% -17% to +14% -27% to +15% -23% to +12%
Potential
Evaporation*
(mm)
Historical mean
1737
Projections for
2030
Relative
Humidity
Projections for
2030
Wind Speed*
Projections for
2030
+3%
+2% to +5%
-0.8%
-2.8% to
+1.6%
+2%
0% to +5%
Historical means from 1971-2000 | Projections for 2030
(30-year period centred on 2030)
Best Estimate | Range of Change
For more information on the region, including projections for 2050 and
2070, please refer to ‘Climate change in the Eastern Downs Region’
(http://www.ehp.qld.gov.au/climatechange/pdf/regionsummaryeasterndowns.pdf)
Additional climate projections
 Global carbon dioxide concentration (CO2) is rapidly increasing. In 2011, carbon dioxide levels were at
390 ppm, well above the natural historical range from the last 80,000 years of 172 ppm to 300 ppm
(CSIRO and BOM 2012). Carbon dioxide levels are projected to be 450 ppm by 2030, 600 ppm by 2050
and 975 ppm by 2070 (high emissions scenario) (IPCC 2011).
 Queensland can expect longer dry periods interrupted by more intense rainfall events.
 Drought is projected to be as much as 20-40% more frequent by 2030 over much of Australia
(Mpelasoka et al. 2008).
Central Slopes NRM – Impacts and adaptation to climate change
Page 3
Impacts and adaptation
Wheat and Sorghum
Climate change adaptation options for broad-acre mixed farms are relatively well documented. Options
include diversification of crop varieties, species change, shifting planting windows, 'new' adaptive farming
and tillage methods (no/low tillage, controlled traffic systems, row configurations, moisture conservation
etc.) proactive pest and disease management and taking advantage of seasonal climate risk forecasts
(Howden et al., 2006; Kingwell et al., 2013; Kokic et al., 2005). The majority of these options are what could
be considered best management practice and encouraged at an industry level regardless of anthropogenic
climate change scenarios.
The primary focus of any farm, industry or investment decision to respond to climate change should be to
improve farm profitability (Kingwell et al., 2013). This will have a positive flow on effect in improving
adaptive capacity. However, to date there appears little evidence in the literature that adaption options
have been considered in terms of whole farm profit (John et al., Rodriguez et al., 2011).
A modelling study of the Central Slopes using APSIM (Power 2014 - unpublished) showed projected climate
change to 2030 to have little overall effect on either annual wheat or sorghum yield compared to historical
levels. In general, the CO2 fertilisation effects negated the negative effects of less rainfall and higher
temperatures. However, there were differences between models (5 CMIP5 models were tested), regions,
soil water holding capacity and in-crop soil moisture levels. A key finding was that those cropping zones with
soils of lower fertility and water holding capacity were likely to be the most vulnerable to future climate risk.
Positive impacts were found with lower deep drainage, lower runoff, and lower soil loss.
The significant detrimental finding was that over the longer-term there was less planting opportunities for
dryland cropping systems. This had the flow-on effect of reducing overall production for both wheat and
sorghum crops. The agronomic parameters to initiate crop planting for wheat and sorghum were that 1)
rainfall over 7 days must be greater than 25mm and that 2) stored soil water must be greater than 100mm.
The implication of this finding is that a risk and vulnerability assessment needs undertaking for each location
modelled (19 locations were examined).
Grazing
The risk assessment reported here for 2030 (Appendix 7) showed a small positive impact on pasture growth,
little change in pasture quality, a high risk of increase in animal pests and disease and low to medium risk of
decreases in production and profitability. More droughts and increase in temperature (mainly in winter)
leading to an increase in animal pests and disease were the major causes of down-side risk.
Generally there is already capacity in the grazing industry of the region to adapt to the climate change
impacts of 2030 (Appendix 8). In most cases the use of best management practices and judicious
management of climate variability should overcome any detrimental impacts from climate change. However,
producers have less capacity to adapt to the longer-term impacts that droughts produce and this is where
they appear to be most vulnerable. Warmer winter temperatures will also provide the opportunity of pests
from the north to over-winter and complete life-cycles in the south so awareness and monitoring, rapid
responses and border surveillance will be important in stopping the spread of pests and disease.
Human Well-Being
The variable and changing climate of the region will have both direct and indirect impacts on health, location
and living arrangements (TCI 2011, Hughes and McMichael 2011, NCCARF 2011a). Direct effects of extremes
Central Slopes NRM – Impacts and adaptation to climate change
Page 4
of weather include injury and death during floods, and heat stress during heatwaves. Indirect effects of
extremes of weather include an increase in the:

number of bushfires due to extreme heat and aridity;

number of mosquito-borne, water-borne and food-borne diseases;

number of infectious and contagious diseases with an increase in the number of injuries; and

incidence of disease from microbial food poisoning with an increase in temperature.
Increases in extreme events can lead to increased pressure on health systems, including an increased
demand for health professionals, ambulance and hospital workers. Severe weather events can destroy
places and disrupt livelihoods and communities leading to long-term mental health effects. According to
Bonanno et al. 2010, a significant part of the community, as many as one in five, will suffer the debilitating
effects of extreme stress, emotional injury and despair. Evidence is beginning to emerge that drought and
heatwaves lead to higher rates (by about 8%) of self-harm and suicide (Doherty and Clayton 2011).
Those most vulnerable to extremes of weather and climate include children, the elderly, Indigenous
communities and people with pre-existing diseases and disabilities.
Communities and individuals are capable of adapting relatively well to climate change and extreme climate
events through strategies including:

Adapting existing buildings and planning any new infrastructure to take into account likely climate
impacts and extreme events such as flooding;

Implementing control measures to reduce the impact of bushfires, heatwaves, mosquitoes, water-borne
and food-borne diseases, infectious and contagious diseases and injuries;

Continuingto provide information on the expected effects of a changing climate;

Developing workplace agreements on how to identify and manage when weather and climate events are
affecting working conditions;

Developing social and community support networks;

Encouraging regional councils and government department to develop and provide information on social
and health support programs.
Biodiversity
The Brigalow Belt (BB) and New England Tablelands (NET) bioregions are present within the Central Slopes
region. The BB is very rich in species, including large numbers of plants and animals with small ranges. This
bioregion has endemic and near-endemic eucalypt, wattle and invertebrate species. The outstanding feature
of the NET is the Granite Belt, which is an elevated rocky plateau that has long served as a refuge for plants
and animals. The NET is the coldest bioregion in Queensland and is the northern limit for large numbers of
animals and plants with temperature distributions, including the Superb Lyrebird (Menura novaehollandiae),
Common Wombat (Vombatus ursinus), Snow Gum (Eucalyptus panciflora) and the Juniper Grevillea
(Grevillea juniperina). Several species are confined within Queensland in the Girraween National Park,
including the Common Wombat, Bell’s Turtle, Alpine Water Skink, New England Tree Frog, New England
Crayfish and various plants.
Potential impacts in the Brigalow Belt and New England Tableland include (Low 2011):
 Increased tree mortality of Brigalow and Cypress due to severe drought in the BB; ,
 Change in native pasture structure due to Buffel grass invasion (of particular concern within the BB),
displacing groundcover plants and significantly increasing fire risk;
 Increased fire intensity and size may threaten shrubs and smaller plants within the NET. For example, a
geebung species, endangered in New South Wales, recently disappeared from its one known site in
Girraween, Queensland, after a hot fire;
Central Slopes NRM – Impacts and adaptation to climate change
Page 5

Populations of mammals confined to the region, including the common wombat, Northern hairy nosed
wombat and bridled nailtail wallaby may be threatened by rising temperatures and severe drought
followed by intense fires.
Adaptation strategies for the impacts of extreme climate (drought, flood, heatwaves) on biodiversity include
(Low 2011):
 More pro-active fire management;
 Identification and control of targeted weeds and invasive pasture grasses such as buffel grass and guinea
grass;
 Control of pests and feral animals including goats, pigs and horses to reduce losses and protect rare plants;
and
 Identification and protection of key refugia habitats.
References
Bonanno, GA, Brewin, CR, Kaniasty, K and La Greca, AM 2010, Weighing the Costs of Disaster: Consequences,
Risk, and Resilience in Individuals, Families, and Communities, Psychological Science in the Public Interest,
vol. 11, pp. 1-49
CSIRO and Bureau of Meteorology (2007). Climate Change in Australia. Technical Report. (Eds. K. Pearce, P.
Holper, M. Hopkins, W. Bouma, P. Whetton, K. Hennessy and S. Power) p. 148. (CSIRO Division of Marine
and Atmospheric Research: Melbourne)
Deuter, PL, Carey, D, Zull, A, NcGrath, C 2014, Vulnerability and Adaptive Capacity of the Granite Belt Tomato
Industry to Future Increases in Temperature. International society for Hort science, in press
Doherty, TJ and Clayton, S 2011, The Psychological Impacts of Global Climate Change, American Psychologist,
vol. 66, pp. 265-76
Finch, VA, Bennett, IL and Holmes, CR 1984, Coat colour in cattle - effect on thermal balance, behaviour and
growth, and relationship with coat type, Journal of Agriculture Science, vol. 102, pp. 141-47
Howden, M., Ash, A., Barlow, S., Booth, T., Charles, S., Cechet, B., Crimp, S., Gifford, R., Hennessey, K., Jones,
R., Kirschbaum, M., McKeon, G., Meinke, H., Park, S., Sutherst, B., Webb, L. and Whetton, P. (2006) An
overview of the adaptive capacity of the Australian agricultural sector to climate change; Options, costs
and benefits, CSIRO, Canberra.
Hughes, L and McMichael, T 2011, The Critical Decade: Climate change and health, Climate Commission,
Commonwealth of Australia (Department of Climate Change and Energy Efficiency),
<http://climatecommission.gov.au/wp-content/uploads/111129_FINAL-FOR-WEB.pef>
Intergovernmental Panel on Climate Change (IPCC) 2011, Carbon Dioxide: Projected emissions and
concentrations, http://www.ipcc-data.org/ddc_co2.html
John, M., Pannell, D. and Kingwell, R. (2005) Climate change and the economics of farm management in the
face of land degradation: Dryland salinity in Western Australia. Canadian Journal of Agricultural
Economics 53, 443-459.
Kingwell, R, Anderton, L, Islam, N, Xayavong, V, Wardell-Johnson, A, Feldman, D, Speijers, J 2013 Broadacre
farmers adapting to a changing climate, National Climate Change Adaptation Research Facility, Gold Coast
Kokic, P., Heaney, A., Pechey, L., Crimp, S. and Fisher, B. (2005) Climate change: predicting the impacts on
agriculture: a case study. Australian Commodities 12(1), 161-170.
Low, T 2011, Climate Change and Terrestrial Biodiversity in Queensland, Department of Environment and
Resource Management, Queensland Government, Brisbane
McKeon, G.M., Stone, G.S., Syktus, J.I., Carter, J.O., Flood, N., Fraser, G.W., Ahrens, D.G., Bruget, D.N., Crimp,
S.J., Cowley, R.A., Johnston, P.W., Stokes, C.J., Cobon, D.H., Ryan, J.G., Day, K.A., and Howden, S.M. (2009)
Climate change impacts on rangeland livestock carrying capacity : a review. The Rangeland Journal
(submitted).
Mpelasoka, F, Hennessy, K, Jones, R and Bates, B 2008, Comparison of suitable drought indices for climate
change impacts assessment over Australia towards resource management, International Journal of
Climatology, vol. 28, pp. 1283-92
Central Slopes NRM – Impacts and adaptation to climate change
Page 6
National Climate Change Adaptation Research Facility (NCCARF) 2011a, Climate change impacts factsheet: 8.
Human Health, http://www.nccarf.edu.au/sites/default/files/8.Health-Impacts.pdf
Raupach, M.R., Marland, G., Ciaia, P., Le Quere, C., Canadell, J.G., Klepper, G. and Field, C.B. (2007). In
‘Proceedings of the National Academy of Sciences of the United States of America’ 104, 10288-10293
Rodriguez, D., de Voil, P., Power, B., H. Cox, Crimp, S. and Meinke, H (2011) The intrinsic plasticity of farm
businesses and their resilience to change. An Australian example. Field Crops Research 124 (2), 157-170.
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M. and Miller, H.L. (2007).
Climate Change 2007: The Physical Science Basis (Cambridge University Press: Cambridge, United
Kingdom)
The Climate Institute (TCI) 2011, A Climate of Suffering: the real cost of living with inaction on climate
change (Melbourne and Sydney), The Climate Institute
Appendix
Appendix 1. GCM simulated changes in a range of climate variables for the 2020 to 2039 (2030) and 2080 to
2099 (2090) periods relative to the 1986 to 2005 period. The table gives the 10th, 50th and 90th percentile
change, as projected by the CMIP5 model archive (10th to 90th percentile range given within brackets).
Results are given for the low (RCP2.6), intermediate (RCP4.5) and high (RCP8.5) emission scenarios on annual
and seasonal resolution. The colouring indicate CMIP5 model agreement, as detailed in the legend given in
Appendix 2 (CSIRO 2014 – unpublished data)
Variable
Temperature
Mean (°C)
Season
2030, RCP45
2030, RCP85
2090, RCP26
2090, RCP45
Annual
1 (0.6 to 1.3)
1.1 (0.7 to 1.5)
1.1 (0.6 to 1.8)
2.1 (1.4 to 2.7)
DJF
1 (0.5 to 1.5)
1 (0.4 to 1.7)
1.3 (0.5 to 2)
2.2 (1.3 to 3.1)
MAM
0.9 (0.6 to 1.3)
1 (0.5 to 1.5)
1.1 (0.7 to 1.6)
1.9 (1.4 to 2.7)
JJA
0.9 (0.5 to 1.2)
1 (0.7 to 1.4)
0.9 (0.6 to 1.5)
1.9 (1.3 to 2.6)
SON
1 (0.5 to 1.4)
1.1 (0.6 to 1.9)
1 (0.4 to 2)
2.2 (1.3 to 3.3)
Annual
1.1 (0.6 to 1.4)
1.2 (0.5 to 1.6)
1.3 (0.6 to 1.9)
2.2 (1.5 to 3)
Temperature
Maximum
(°C)
Temperature
Minimum (°C)
Precipitation
(%)
Solar
radiation
DJF
0.9 (0.5 to 1.7)
1.1 (0.4 to 1.8)
1.5 (0.5 to 2.3)
2.3 (1.5 to 3.2)
MAM
1 (0.5 to 1.4)
1.1 (0.4 to 1.5)
1.1 (0.6 to 1.9)
2.1 (1.2 to 2.8)
JJA
1.1 (0.6 to 1.5)
1.1 (0.6 to 1.7)
1 (0.6 to 1.6)
2.2 (1.2 to 3)
SON
1.1 (0.5 to 1.7)
1.2 (0.5 to 2.1)
1.2 (0.4 to 2.2)
2.3 (1.3 to 3.7)
Annual
1 (0.6 to 1.1)
1 (0.7 to 1.4)
1 (0.6 to 1.6)
1.9 (1.3 to 2.7)
DJF
1 (0.7 to 1.4)
1 (0.6 to 1.6)
1.3 (0.4 to 1.8)
2.2 (1.2 to 3.2)
MAM
0.9 (0.6 to 1.3)
1 (0.5 to 1.5)
0.9 (0.4 to 1.7)
1.8 (1.2 to 2.7)
JJA
0.8 (0.3 to 1.1)
0.9 (0.6 to 1.3)
0.8 (0.4 to 1.4)
1.7 (1.1 to 2.4)
SON
1 (0.6 to 1.3)
1.1 (0.6 to 1.6)
1.1 (0.4 to 1.7)
2.1 (1.2 to 2.9)
Annual
-2 (-11 to 7)
-1 (-13 to 8)
-3 (-18 to 8)
-4 (-16 to 6)
DJF
1 (-9 to 16)
2 (-12 to 23)
-5 (-23 to 13)
0 (-14 to 17)
MAM
-5 (-22 to 19)
-2 (-17 to 14)
-10 (-26 to 17)
-4 (-28 to 23)
JJA
-3 (-20 to 11)
-2 (-27 to 15)
-4 (-24 to 11)
-10 (-24 to 9)
SON
-2 (-18 to 12)
-1 (-23 to 12)
-1 (-25 to 12)
-8 (-26 to 12)
Annual
0.5 (-0.7 to 1.8)
0.6 (-0.7 to 2.1)
1.7 (0.3 to 3.9)
1.3 (-0.3 to 2.6)
DJF
0 (-1.6 to 2.4)
0 (-2 to 2.1)
2.7 (-0.3 to 4.4)
0.8 (-1.8 to 3)
MAM
0.4 (-1.4 to 3.4)
0.5 (-1.8 to 3.5)
2.1 (-1.5 to 5.6)
0.9 (-1.4 to 5.2)
(%)
Central Slopes NRM – Impacts and adaptation to climate change
Page 7
Relative
humidity
JJA
1.2 (-0.5 to 4.1)
1.4 (-0.8 to 4.6)
1.7 (0.1 to 4.8)
2.1 (-0.3 to 6.5)
SON
0.6 (-1.1 to 1.7)
0.2 (-1.7 to 3)
0.7 (-1.1 to 4.3)
1.4 (-0.8 to 2.6)
Annual
-0.6 (-2.5 to 0.9)
-0.8 (-2.8 to 1.6)
-1.2 (-4.2 to 0.7)
-1.6 (-4.1 to -0.3)
DJF
-0.4 (-2.7 to 1.7)
-0.1 (-3 to 1.3)
-1.3 (-6.8 to 0.9)
-1.2 (-4.7 to 1.4)
MAM
-0.5 (-4.1 to 1.5)
-0.6 (-2.8 to 1.8)
-2 (-5.5 to 1.5)
-1.9 (-6.3 to 0.9)
JJA
-0.7 (-2.9 to 0.7)
-1 (-3.7 to 1.6)
-1.3 (-3.3 to 1.1)
-2 (-5.6 to 0.5)
SON
-0.6 (-4 to 1.5)
-0.7 (-4.4 to 1.9)
-0.3 (-5.8 to 1.9)
-1.7 (-6.7 to 0.8)
Annual
3.3 (1.6 to 4.8)
3.6 (1.8 to 5.8)
4.2 (2.3 to 6.9)
6.8 (4.2 to 10.8)
(absolute, %)
Evapotranspiration
(%)
Soil moisture
(Budyko)
(%)
DJF
2.8 (1.3 to 6.2)
3.1 (1.2 to 6.6)
5.3 (2 to 7.7)
7.1 (4.3 to 11.5)
MAM
3.2 (1 to 7.2)
4.7 (2.3 to 7.2)
5.3 (2.3 to 7.7)
7.8 (5 to 12.5)
JJA
3.7 (0.5 to 8)
4.5 (1.1 to 7.4)
4.3 (1.5 to 6.8)
7.7 (4.4 to 13.5)
SON
3 (0.8 to 4.6)
2.2 (0.5 to 4.9)
2.5 (0.2 to 5.5)
5.1 (1.5 to 9.4)
Annual
-1.3 (-7.6 to 1.2)
-1.5 (-7.8 to 3.2)
NA
-3.2 (-11.2 to 0.5)
DJF
-0.6 (-5.5 to 4)
-1.5 (-6.2 to 3.2)
-3 (-8.9 to 0.6)
MAM
+0.1 (-10.7 to 3.7)
-1.1 (-10.7 to 4.9)
-2.6 (-11.9 to 1.5)
JJA
-1.8 (-8.5 to 3.7)
-3.2 (-14.8 to 2.3)
-2.3 (-13.3 to 2.5)
SON
-0.8 (-9.8 to 1.7)
-1.6 (-10 to 3.7)
-2 (-11.1 to 0.3)
Appendix 2. Colouring legend to Appendix 1, where high model agreement refer to about 8 out of 10 chance
of being correct and medium agreement refer to about 5 out of 10 chance of being correct. ‘Substantial’
being a change outside the 10 to 90 percentile range of the model’s natural variability (CSIRO – unpublished
data)
High model agreement on substantial increase
Medium model agreement on substantial increase
High model agreement on increase
Medium model agreement on increase
High model agreement on little change
Medium model agreement on little change
High model agreement on substantial decrease
Medium model agreement on substantial decrease
High model agreement on decrease
Medium model agreement on decrease
Appendix 3. Impacts and the level of impact risk associated with climate change for wheat grown in the
Central Slopes – see attached xls
Appendix 4. Adaptation responses and vulnerability to climate change for wheat grown on the Central
Slopes – see attached xls
Appendix 5. Impacts and the level of impact risk associated with climate change for sorghum grown in the
Central Slopes – see attached xls
Central Slopes NRM – Impacts and adaptation to climate change
Page 8
Appendix 6. Adaptation responses and vulnerability to climate change for sorghum grown on the Central
Slopes – see attached xls
Appendix 7. Impacts and the level of impact risk associated with climate change for grazing in the Central
Slopes – see attached xls
Appendix 8. Adaptation responses and vulnerability to climate change for grazing on the Central Slopes –
see attached xls
Appendix 9. References used in Appendices 3-8
1. Stokes, C and Howden, M 2010, Adapting agriculture to climate change: preparing Australian agriculture,
forestry and fisheries for the future, CSIRO
2. Tubiello, FN, Donatelli, M, Rosenzweig, C, Stockle, CO 2000, Effects of climate change and elevated CO2 on
cropping systems: model predictions at two Italian locations. European Jounrnal of Agronomy 13, 179189
3. Cobon, DH and Terwijn, MJ 2012, Impacts and adaptation strategies for a variable and changing climate in
the EASTERN DOWNS REGION. Department of Science, Information Technology, Innovation and the Arts.
Toowoomba, Queensland, Australia – unpublished
4. Hayman, PT, Alston, CL 1999, A survey of farmer practices and attitudes to nitrogen management in the
northern New South Wales grain belt. Australian Journal of Experimental Agriculture 39, 51-63
5. Van Ittersum, MK, Howden, SM, Asseng S 2003, Sensitivity of productivity and deep drainage of wheat
cropping systems in a Mediterranean environment to changes in CO2 temperature and precipitation.
Agriculture Ecosystems and Environment 97, 255-273
6. Potgieter, A, Meinke, H and Doherty, A 2004, Likely impact of climate on wheat and sorghum production
in central Queensland, 4th International Crop Science Congress, Brisbane
7. ClimateQ Workshops 2012, Expert opinion from workshops held with producers, industry, educators and
manufacturers. Department of Environment and Resource Management, Toowoomba
8. Richards, RA, 2002, Current and emerging environmental challenges in Australian agriculture – the role
ogfplant breeding. Australian Journal of Agricultural Research 53, 881-892
9. McKeon, GM, Howden, SM, Abel, NOJ, King, JM 1993, Climate change: adapting tropical and subtropical
grasslands. Proceedings of the 17th International Grassland Congress, Palmerston North, New Zealand, pp.
1181-90
10. Hammer, GL, Holzworth, DP, Stone, R 1996 The value of skill in seasonal forecasting to wheat crop
management in a region with high climate variability. Australian Journal of Agricultural Research 47, 717737
11. Rogers, GS, Gras, PW, Batey, IL, Milham, PJ, Payne, L, Conroy, JP 1998, The influence of atmospheric CO2
concentration on the protein, starch and mixing properties of wheat flour. Australian Journal of Plant
Physiology 25, 387-393
12. Cobon, D, Stone, G, Carter, J, Scanlan, J, Toombs, N, Zhang, X, Willcocks, J and McKeon, G 2009, The
Climate Change Risk Management Matrix for the grazing industry of northern Australia, The Rangeland
Journal, vol. 31, pp.31-49 http://www.longpaddock.qld.gov.au/about/publications/index.html
13. Anwar et.al, (2007) Climate change impact on rainfed wheat in south-eastern Australia, Field Crops
Research 104 (2007) 139-147
14. Nutall et al., (2012) Heat waves and wheat growth under a future climate, Australian Society of
Agronomy, Australian Agronomy Conference, 2012 16th AAC
http://regional.org.au/au/asa/2012/climate-change/8085_nuttalljg.htm
15. Teixeira et.al., (2013) Global hot-spots of heat stress on agricultural crops due to climate change,
Agricultural and Forest Meteorology 170 (2013) 206–215
16. ASSENG, S., FOSTER, I. and TURNER, N. C. (2011), The impact of temperature variability on wheat yields.
Global Change Biology, 17: 997–1012
Central Slopes NRM – Impacts and adaptation to climate change
Page 9
17. Chenu, K. and Chapman, S. (2012) Drought experienced by Australian wheat: current and future trends,
Australian Society of Agronomy, Australian Agronomy Conference, 2012 16th AAC,
http://regional.org.au/au/asa/2012/climate-change/8304_chenu.htm#TopOfPage
18. CSIRO, Australian Bureau of Meterology (2007). 'Climate Change in Australia'. Technical Report.
http://climatechangeinaustralia.gov.au/
19. Chakraborty S, Datta S (2003). How will plant pathogens adapt to host plant resistence at elevated CO2
under a changing climate? New Phytologist 159, 733-742
20. Asseng S, Bar-Tal A, Bowden JW et al. (2002) Simulation of grain protein content with APSIM-Nwheat.
European Journal of Agronomy, 16, 25–42.
21. Noor Hasniza M. Z., Wilkes M.A, Uthayakumaran S. and Copeland L, (2014) Growth Environment
Influences Grain Protein Composition and Dough Functional Properties in Three Australian Wheat
Cultivars, Cereal Chemistry Journal, March/April 2014, 91, 2 , Page 169
22. Asseng S. and Pannell D.J., Adapting dryland agriculture to climate change:Farming implications and
research and developmentneeds in Western Australia,Climatic Change (2013) 118:167–181
23. Vadez et.al., Adaptation of grain legumes to climate change: a review, Agron. Sustain. Dev. (2012) 32:31–
44
24. Garrett K.A., et.al., (2014) Climate Change and Plant Pathogen Invasions, in (eds) Ziska L.H. and Dukes J.S.,
Invasive Species and Global Climate Change, Volume 4 of CABI Invasives Series, CABI, 2014
25. Potgieter A. et.al., (2013) Spatial imapct of projected changes in rainfall and temperature on wheat yields
in Australia, Climatic Change, 117:163-179
26. Hunt J. R., Kirkegaard J. A. (2011) Re-evaluating the contribution of summer fallow rain to wheat yield
in southern Australia. Crop and Pasture Science 62, 915–929.
27. Hayman Peter, Rickards Lauren, Eckard Richard, Lemerle Deirdre (2012) Climate change through the
farming systems lens: challenges and opportunities for farming in Australia. Crop and Pasture Science 63,
203–214."
28. Rayner D.P., (2007) Wind run changes: the dominent factor affecting pan evaporation trends in Australia.
Journal of Climate20, 3379-3394.
29. New South Wales Climate Impact Profile Technical Report - Potential impacts of climate change on soils
(2011), NSW Government, Office of Environment and Heritage, Department of Premier and Cabinet
30. Kingwell, R, Anderton, L, Islam, N, Xayavong, V, Wardell-Johnson, A, Feldman, D, Speijers, J 2013
Broadacre farmers adapting to a changing climate, National Climate Change Adaptation Research Facility,
Gold Coast, pp. 171.
31. Howden, M. and Jones, R. (2004) Risk assessment of climate change impacts on Australia’s wheat
industry. In Proceedings for the 4th International Crop Science Congress, Brisbane, Australia, 26
September – 1 October 2004. Available at: www.cropscience.org.au
32. Bange M.P., Constable, G.A., McRae D., and Roth G. (2009) ‘Cotton’, In C. Stokes and M. Howden (eds),
'Adapting agriculture to climate change: Preparing Australian agriculture, forestry and fisheries for the
future' (CSIRO Publishing, Melbourne, Australia).
33. McRae, D., Bange, M. and Roth, G. (2007) Climate Change in Cotton Catchment Communities – A Scoping
Study, Cotton Catchment Communities CRC, for the Australian Greenhouse Office, Department of
Environment and Heritage, Canberra.
34. Howden, M., Ash, A., Barlow, S., Booth, T., Charles, S., Cechet, B., Crimp, S., Gifford, R., Hennessey, K.,
Jones, R., Kirschbaum, M., McKeon, G., Meinke, H., Park, S., Sutherst, B., Webb, L. and Whetton, P. (2006)
An overview of the adaptive capacity of the Australian agricultural sector to climate change; Options,
costs and benefits, CSIRO, Canberra.
35. Cobon, DH and Terwijn, MJ 2012, Impacts and adaptation strategies for a variable and changing climate
in the EASTERN DOWNS REGION. Department of Science, Information Technology, Innovation and the
Arts. Toowoomba, Queensland, Australia – unpublished
36. White, N, Sutherst, R, Hall, W and Whish-Wilson, P 2003, The vulnerability of the Australian beef industry
to the impacts of the cattle tick (Boophilus microplus) under climate change, Climatic Change, vol. 61, pp.
157-90
Central Slopes NRM – Impacts and adaptation to climate change
Page 10
37. Howden, SM, Hall, WB and Bruget, D 1999, Heat stress and beef cattle in Australian rangelands: recent
trends and climate change, People and Rangelands, Building the Future, Proceedings of the 6th
International Rangelands Conference, pp. 43-44
38. Wylie, P., (2008) Managing sorghum for high yields, Grains Research and Development Corporation.
39. Hatfield, J. L., Boote, K. J., Kimball, B. A., Ziska, L. H., Izaurralde, R. C., Ort, D., Thomson, A. M., Wolfe, D.,
Climate Impacts on Agriculture: Implications for Crop Production Agron. J. 2011. 103:351–370.
40. Leakey, A.D.B. (2009) Rising atmospheric carbon dioxide concentration and the future of C4 crops for
food and fuel, Proc Biol Sci. Jul 7, 2009; 276(1666): 2333–2343.
41. http://www.daff.qld.gov.au/plants/field-crops-and-pastures/broadacre-field-crops/sorghum/diseasemanagement
42. van Oosterom, E.J. and Hammer, G.L. (2008). Determination of grain number in sorghum. Field Crops
Research, 108: 259-268
43. Power, B. (2014) Modelling the impacts of climate change to 2030 on wheat, sorghum and mixed farming
systems in the Central Slopes NRM region – in preparation.
Central Slopes NRM – Impacts and adaptation to climate change
Page 11