Download Global Change Impacts on Australian Rangelands

Global Change Impacts
on
Australian Rangelands
S.M. Howden, G.M. McKeon
and P.J. Reyenga
Working Paper Series 99/09
Report to the
Australian Greenhouse Office
1999
CSIRO Wildlife and Ecology, GPO Box 284, Canberra ACT 2601
AUSTRALIA
Disclaimer
The views expressed are not necessarily the views of the Commonwealth, and the
Commonwealth does not accept responsibility in respect of any information or advice given
in relation to or as a consequence of anything contained herein.
The information contained in this report is provided for the purpose of general research and
policy development and should not be relied upon for the purpose of a particular matter.
Legal advice should be obtained before any action is taken on the basis of any material in this
report. CSIRO does not assume liability of any kind whatsoever resulting from any person’s
use or reliance upon the contents of this report.
Contributing Authors and Affiliations
S.M. Howden
CSIRO Division of Wildlife and Ecology, GPO Box 284, Canberra, ACT 2601,
G.M. McKeon, W.B. Hall, D. Bruget, J.O. Carter, K.A. Day, A. Beswick,and N. Flood
Climate Impacts and Grazing Systems, Queensland Department of Natural Resources, 80 Meiers Rd,
Indooroopilly, Queensland, 4068.
P.J. Reyenga
Bureau of Rural Sciences, PO Box E11, Kingston, ACT, 2604, Australia
J.L. Moore
NERC Centre for Population Biology, Imperial College, Berkshire SL5 7PY UK
H. Meinke
Agricultural Production Systems Research Unit, DPI/DNR/CSIRO, P.O. Box 102, Toowoomba, Qld, 4350
J.R. Turnpenny
Department of Physiology and Environmental Science, University of Nottingham, Leicestershire, LE12 5RD,
UK.
L. Walker and A.J. Ash
CSIRO Tropical Agriculture, PMB Aitkenvale, Qld 4814
J.P. Conroy and 0. Ghannoum
University of Western Sydney, Locked Bag 1, Richmond NSW 2753
J.C. Scanlan,
Robert Wicks Pest Animal Research Centre, PO Box 318, Toowoomba. Qld. 4350, Australia
M. Entel
Meteorology CRC, Monash University, Wellington Road, Clayton, Vic 3168
ii
Executive Summary
• Rangelands are complex systems with the dominant influence of climate affecting strong
interactions between plant growth, livestock and human management with additional local
controls through factors such as soils and landforms. Consequently, assessments of global
change (increased CO2 levels and climate change) impacts and adaptation options are
difficult, needing multi-component studies. We have focussed on key drivers from a
biophysical viewpoint limiting our assessment of economic and social issues to farm-level
economics and management. A later study will deal with broader social, economic, tenure
and policy issues within the Western Division of New South Wales.
• Doubling of CO2 concentrations by itself is likely to have substantially positive impacts on
rangeland forage production and sustainability in many regions of Australia. This occurs
largely because increased CO2 concentrations make plants more water-efficient resulting in
potentially more growth in water limited environments such as rangelands. The added
growth would be particularly pronounced in dry years and in dry regions, resulting in
damping of variability in livestock production giving substantially increased farm returns.
• The likely increase in grass growth will potentially improve ground cover, thus reducing
runoff and soil erosion rates. Safe carrying capacities of livestock are thus likely to
increase (0 to 34% assuming no climate change)
• The response to CO2 is likely to be limited in sites where soil nutrition is limiting. This
will particularly affect the higher rainfall rangelands and those with either poor or
degraded soils.
• High CO2 levels are likely to significantly increase (+20%) the deep drainage component
under pastures which may increase the risks and rates of salinisation in areas where there is
a potential hazard. The increase in drainage is particularly likely in sites with poor soil
nutrient status and in areas with strongly seasonal rainfall patterns.
• If climate change occurs in combination with CO2 rises, the above results will differ. There
will remain a sensitivity to rainfall with substantial reductions in rainfall still reducing
productivity although in some cases (-10% rainfall) this will just be balanced out by the
positive CO2 effects giving pasture productivities little changed from the present. Greater
reductions in rainfall (~20%) will reduce plant productivity by about 15%, liveweight gain
by 12% and substantially increase variability of stocking rates compared with the present thus having strongly negative impacts on farm income. These assessments do not
explicitly take into account increased frequency of El Niño events - an increasing concern.
It is important to also note that the latest climate change scenarios suggest strong drying
trends.
• If rainfall increases along with CO2 levels, there will be some increase in pasture
production but this will be limited by nutrient availability more frequently than at present.
This will be more of an issue in the higher rainfall regions than in the more arid areas. If
warming occurs as well, there could be enhanced nitrogen mineralisation which could
partly offset such impacts. The deep soil drainage component is likely to substantially
increase (+50% in the site investigated) under high rainfall and high CO2 scenarios giving
rise to greatly increased salinity risk and higher rates of salinisation.
iii
• Increases in CO2 in combination with warmer conditions are likely to lengthen the pasture
growing season in many regions (subject to directional change in rainfall) particularly in
autumn, resulting in increased liveweight gain potential by livestock. However this is
likely to be offset by two factors: 1) forage quality is likely to decrease due to both
decreased nutrient contents of leaves with high CO2 and decreased digestibility of C4
grasses with increases in temperature, and 2) there is likely to be significantly increased
frequency of heat stress days in livestock. The second issue may be able to be addressed by
a combination of breeding strategies and improved animal husbandry but there remains a
likelihood of lower productivities across northern Australia. There has been no attempt to
combine in a formal way these different factors - the modelling capacity does not currently
exist in Australia.
• There have been assessments in the literature that increased CO2 concentrations would
result in almost total global replacement of C4 grasses (which are currently the major group
in all but the southern regions of Australia) by C3 grasses. If true, such a change would
have immense implications for productivity, biodiversity and ecosystem function across
Australia’s rangelands. We considered a further assessment was needed using an approach
that incorporated the strong climatic components that demonstrably affect grass species
distributions in Australia. Our assessment is that doubling of CO2 concentrations will
result in only limited changes in the distributions of C3 and C4 grasses and that such
changes will be moderated by any temperature change (eg small southern movement if
temperature warms).
• Increases in CO2 have the potential to result in greater storage of carbon in the rangeland
region studied (7-17%) provided that fire is kept out of the pastures to the greatest extent
possible. However, increased pasture growth is likely to increase burning opportunities
substantially and, if these opportunities were used, this could reduce carbon stores and
increase net greenhouse gas emissions. Climate changes in addition to CO2 increase is
likely to result in only moderate variations to rates of carbon storage although the driest
scenarios are likely to slow this down somewhat (4-25%) compared with historical rates.
However, with grazing under existing burning practices, all CO2 and climate change
scenarios increased the already positive net emissions from the system (by up to a factor of
ten) due to increased fuel loads and increased stocking rates. Under most management
regimes, there are strong trade-offs in terms of carbon storage (or net emissions) and
livestock productivity on the same unit of land (ie. it is possible to have high C storage and
low livestock productivity OR vice versa but not high values for both). This doesn’t
however, preclude effective integration by using a mosaic of landcover - a strategy already
used by some graziers to provide drought reserves.
• Under some global change scenarios, increased landuse competition from cropping may
occur as crop yields are simulated to increase more than animal productivity in those sites
investigated. This will tend to reduce the size of the grazing industries, but perhaps more
importantly in the regions affected, result in the higher productivity land-units being
allocated to cropping rather than grazing. This could reduce flexibility in meeting new
market specifications for livestock. There are also major considerations relating to
biodiversity conservation and the potential emission of large quantities of greenhouse
gases from land clearing and cultivation.
• The global change impacts on farm income addressed above, whilst significant, are
expected to be less than current variation in income arising through changes in livestock
iv
prices or through different management strategies. Global change impacts are expected to
be generally greater than impacts arising from changing policies and fuel costs.
• There remain several methodological and other issues that need attention. At a
fundamental level, the representation of climate change itself can vary the results. Whilst
we have adopted approaches that appear to be fairly robust, further research is needed into
downscaling climate change data. There remains a lack of integration across the various
components of grazing systems with improvements needed in soil nutrient dynamics,
livestock production and mortality, landscape-level processes, CO2 responses and treegrass interactions. Entire regional plant communities remain largely unassessed (eg the
southern chenopod shrublands and mallee communities). Potentially major issues such as
the implications of global change for biodiversity conservation remain unaddressed let
alone integration of biodiversity with sustainable agriculture. Integration with social,
policy and tenure issues is being attempted within the project through the Western
Division of NSW study, however, the resources available for that work are a bare fraction
of those needed for a comprehensive analysis.
• The studies presented here have generally incorporated some degree of autonomous
adaptation to global change, however, more research is needed into explicit adaptation
strategies and how these strategies and global change interact to alter greenhouse gas
emissions or sinks - thus bringing together research into impacts, adaptation and
mitigation. A related concept is to attempt to close the gap between climate variability
studies (which tend to address the past and assume that risk as time-independent) and
climate change studies (which address the far future and implicitly assume risk is timedependent). Adaptation studies which assess existing trends in climate and projections of
future risk in the one to three decade timescale have the potential to merge these two
powerful streams of scientific activity and at the same time provide policy-makers and
agricultural managers with information pertinent to decisions which fit into shorter,
perhaps more tractable timeframes (eg those relating to the Kyoto Protocol).
v
Contents
Executive Summary
iii
Introduction
1
1.
Summary of collaborative studies
3
2.
Impacts on native pastures in south-east Queensland, Australia.
8
3.
Impacts on fire dynamics in the mulga woodlands of south-west Queensland.
23
4.
Potential impacts on C3-C4 grass distributions in eastern Australian rangelands
33
5.
Climate change impacts on heat stress and water requirements of cattle in Australia
38
6.
Past and future competitiveness of wheat and beef cattle production in Emerald, NE
Queensland.
52
vi
Introduction
The project proposal ‘Global change impacts on Australian Terrestrial Ecosystems’ required
delivery on six main themes plus several subsidiary ones. Rangelands is one of the six main
themes. There is a large array of work needed to comprehensively address global change
impacts on complex systems such as rangelands (Fig. 1). The demands of such a
comprehensive analysis was significantly at variance with the relatively small resources
available in the project for this theme. For the rangelands component of the project, to achieve
the project goals there was a need to:
.
.
.
assess existing work on global change impacts on Australian rangelands
identify components which have not been effectively addressed to date and which are
critical for understanding possible impacts
focus on analysis of the above components through direct work and via collaboration
with other projects to add value to those so that they address issues of concern here.
An excellent synthesis of likely impacts of global change on Australian rangelands was made
in 1994 (Stafford Smith et al. 1994) through a workshop involving many of the key
Australian and international researchers. Previous work had largely focussed on climate
change impacts on forage production (eg McKeon et al. 1988) and on adaptations to such
change (eg McKeon et al. 1993). Consequently, the synthesis identified many shortcomings
in terms of our knowledge of likely impacts including:
.
.
.
.
.
.
.
‘CO2 fertilisation’ impacts
sustainability implications
landuse change
heat tolerance of livestock
analyses of species changes and boundary changes,
woody weed and fire management
carbon sequestration
Issues such as impacts on tourism, mining industry, urban areas and conservation
management identified in the workshop proceedings were beyond the scope of this study.
Since the workshop, three major projects have been initiated in relation to global change and
Australian rangelands (apart from this study). These are ‘Evaluation of the impact of climate
change on northern Australian grazing industries’ (QDNR and RIRDC), ‘Landuse change in
Northern Australia’ (CSIRO and LWRRDC) and ‘Learning from History’ (QDNR and
AGO). The focus of these projects was identifying likely climate change impacts as an input
into developing more sustainable grazing systems. Through contributions of this project, they
were expanded to include impacts of CO2 change (Chapter 2), thus addressing one of the key
gaps identified in the workshop synthesis. The inclusion of CO2 into these analyses is critical
as this factor is the most probable global change likely over the next century and because of
its pervasive impacts on the functioning of ecosystems. The main investigative path of these
studies was to look at impacts of global change on plant production through animal
production to farm economics. Thus they address several major elements of Fig. 1. However,
key elements not addressed include the direct impact of temperature increases on animals,
changes in floristics of grasslands (eg alteration of abundance and distribution of C3 and C4
grasses), the changing impacts of fire on system dynamics, carbon stores and emissions and
1
potential competition with other landuses. This report addresses these gaps in knowledge
(Chapters 3 to 6) after a review of the results of the collaborative projects (Chapter 1).
Region 3 .....
Region 2 .....
Global
atmosphere
and climate
Region 1 by landuse - grazing (cattle, sheep etc),
conservation, tourism, Aboriginal use etc
Water use efficiency
Soil Moisture
CO2
Regional climate
El Nino
Soil C levels
Foliage quality
Plant production
(cover, standing dry
matter,
growing season,
woody weeds)
(reduced by high CO2
and temperature)
CH4
N2O
Animal intake
Animal
production
Heat stress
Stock
management
Other
climate
CO2
CH4
Farm
economics
Biodiversity
Fire
management
Emissions
policy
CO2
CH4
N2O
+
Figure 1:
2
CO2
source
or
sink
Species 3
Species 2
Species 1
Rainfall
Temperature
Runoff
Erosion
Drainage
+
Social stability
Rural policy (climate change adaptation,
sustainable resources, farms and
societies, biodiversity conservation,
education, infrastructure, tenure systems)
Conceptual diagram of rangelands systems in relation to global change.
1.
Summary of collaborative studies
Representation of climate change
Pasture growth models require daily climate data and so trials were made using different
representations of climate change (McKeon et al. 1998b). These included assessments with
actual data, combinations of actual values and calculated variables, the output of stochastic
weather generators parameterised with either actual data or data from GCM output.
Generally, there were small differences (<10%) between climate change representations of
hydrological, biological and managerial effects when averaged across sites although there was
some variability in response between sites particularly in the managerial variables (eg
stocking rates, burning opportunities). This suggests that the most simple representation of
climate change (eg addition of 2oC to daily temperatures or proportionate change in daily
rainfall) is suitable for evaluations where the likely change is greater than 10%. This result
contrasts with similar studies of wheat production systems (Howden et al. 1999) where the
representation of climate resulted in gross differences in mean yields due to the sensitivity of
planting rules to the timing and amounts of rain. Howden et al. (1999) found that downscaled
GCM data or data derived from stochastic weather generators were less suitable for the task
of climate change impacts than simple changes due to their inability to generate an adequate
baseline simulation. Further development of these capabilities is needed.
Preliminary investigations of CO2 increase
A scoping analysis of the effects of doubling of CO2 to 700ppm was performed for six
locations (Gayndah, Charters Towers, Charleville, Julia Creek, Alice Springs, Kidman
Springs) across northern Australia (Howden et al. 1998). The impacts varied considerably
depending on the variable investigated and the location. Pasture growth averaged across the
locations was simulated to increase by 15% whilst grass transpiration decreased by 13%. The
increased grass cover decreased runoff (12%) which resulted in enhanced soil moisture status
which both increased the drainage component (27%) and the length of the growing season
(5%). Increased pasture growth over a longer season resulted in increased livestock
production (23%), increased opportunities for control burns (33%) and reduced variability of
stock numbers (24%). Nitrogen limitations in wet years and in higher productivity sites
limited various responses (eg pasture growth, burning opportunities) whilst enhancing others
(eg drainage).
When increased temperatures and changes in rainfall based on the CSIRO Climate Change
Scenarios were evaluated in combination with increased CO2 concentrations, there was
generally a small net change from the current situation for northern and north-eastern
Australia (0-13% increase in pasture growth) but quite variable impacts for more southern,
arid locations (13 to 100% increase).
Statewide analyses of impacts of global change on ‘safe’ carrying capacity were made for
Queensland (Hall et al. 1998). Baseline case models were based on property data and expert
opinion. These models were re-run for a factorial combination of rainfall (+10%), temperature
(no change or 3oC increase) and CO2 changes (350ppm or 700ppm). These impacts varied
3
considerably across the State depending on whether moisture, temperature or nutrients were
the limiting factors. Without the effect of doubled CO2, warmer temperatures and rainfall
changes resulted in -35 to +70% changes in safe carrying capacity. When CO2 was doubled,
these changes were -12 to +115% across scenarios and locations. When aggregated to a
whole-of-State level the combined scenarios resulted in changes of +3% to +45% in carrying
capacity.
The results of the above and later studies on CO2 response are based on small-scale,
physiologically-based studies often without any management-related treatments. There is
growing evidence that such responses are not a complete guide to possible impacts of
increased CO2 in the complex, semi-natural Australian rangelands. For example, recent work
demonstrates that important Australian C4 grasses may have responses to defoliation which
interact with CO2 concentration and that this interaction varies with species (Walker et al.
1999). Field-based experiments such as Free Air CO2 Experiments (FACE) or open-topped
chambers are needed with treatments such as clipping and burning to investigate these
unexpected responses. An industry-supported proposal for such an experiment has been
provided to the AGO (Ash et al. 1999).
Assessment of CO2 and climate changes in relation to price, policy and
productivity changes
Stafford Smith et al. (1999a,b,c) have undertaken an integrated modelling analysis of
regional enterprises to simulate how the profitability of a variety of management strategies in
grazing systems across northern Australia changes with external drivers including CO2
increase and climate change. The regions addressed are around Charters Towers (NE Qld),
Alice Springs (central Australia), the Sturt Plateau/Victoria River District (NT) and the
Kimberleys (WA). In each case, intensive collaborative work with property managers had
been undertaken to develop a picture of the main management strategies and tactics used in
the region, including through accessing detailed property economic records. They used this
information in massive combinatorial simulations to attempt to derive simple validated ‘rulesof-thumb’ guidelines for future policy development. Management strategies and tactics (eg
trading vs constant stocking vs target pasture utilisation), various CO2 responses and various
climate changes were run for site-specific combinations of three levels of pasture response or
perennial grass composition, effective production area (+10% and +20% of normal year
prices and +20% and +40% of dry year prices), three levels of tree basal area, and with or
without policy-related costs associated with transport, the number of livestock, numbers of
animals sold or the value of sales.
From the huge array of results, a general assessment is that increased economic productivity
favours increased stocking rates and higher cash flows regardless of the cause of the increased
productivity (eg a ‘better’ climate, CO2 ‘fertilisation’ or higher prices) however, whilst
positive climate and CO2 effects do not necessarily place increased pressure on the resource
base, changes due to prices or policies may lead to reduced resilience and this change can be
quantified. Generally, prices and choice of management strategies had greater effects than
climate change and CO2 increase. Policy options generally had small direct effects but could
affect choice of management strategy. For Central Australia, climate change, even with
modest reductions in rainfall, resulted in higher production which favoured higher stocking
rates, improving economic output by $2-9 per animal equivalent (AE) per year, as well as,
4
reducing risks of degradation. This occurred as warming enabled additional growth in cooler
seasons. CO2 impacts were not evaluated due to uncertainty of response on these annual
pastures but are likely to be positive. For north-east Queensland the climate change scenarios
by themselves changed cash flows by +$6/AE/year, whilst increases in CO2 may increase
cash flows by $9/AE/year for no significant change in resource impact. In contrast, climate
changes are likely to reduce cash flows in the Northern Territory VRD region by -$3 to $9/AE/year whilst CO2 increase may increase returns by $8 to$9/AE/year. In both regions,
climate and CO2 effects were approximately additive (ie a negative climate impact and an
equal positive CO2 impact cancel each other out). Results for the Kimberleys were not
available at the time of going to press.
It is important in all the above studies to note that they have not fully incorporated possible
changes in climate that would occur if there were significant changes in the frequency and/or
intensity of El Niño. There is growing concern that El Niño will become more frequent with
global warming (Meehl and Washington 1996, Wilson and Hunt 1997, Timmerman et al.
1999) and suggestions that the increase in El Niño events over recent decades may be related
to the observed global warming over this century (Cai and Whetton in prep). Even without El
Niño-induced drought, the most recent scenarios of global change from synthesis of many
global climate model results suggests substantial reductions in rainfall across Australia
(Hulme and Sheard 1999). Mid-range scenarios for the year 2080 suggest 0 to 30% reduction
in the rangelands (presumably somewhat drier for the year 2100) and no scenarios suggest
significant increase in rainfall. Temperature changes suggested for 2080 are in the range of
2.5 to 3.4oC for mid-range scenarios.
References
Ash, A.J, Walker, L., Holtum, J., Howden, S.M., and McKeon, G.M. (1999) Climate change
and biological carbon sinks: a role for Australia’s rangelands. Proposal to the Australian
Greenhouse Office.
Cai, W. and Whetton, P.H. (in prep) Evidence for a time-varying pattern of greenhouse
warming in the Pacific Ocean. CSIRO Atmospheric Research, 15pp.
Hall, W.B., McKeon, G.M., Carter, J.O., Day, K.A., Howden, S.M. & Scanlan, J.C (1998)
Climate change in Queenslands grazing lands: II. An assessment of the impact on animal
production from native pastures. Rangeland Journal 20: 177-205.
Howden, S.M., P.J. Reyenga and H. Meinke, (1999) Global Change Impacts on Australian
Wheat Cropping. Report to the Australian Greenhouse Office. CSIRO Wildlife and Ecology,
Canberra. pp 121
Howden, S.M., Walker, L., McKeon, G.M., Hall, W.B., Ghannoum, O., Day, K.A., Conroy,
J.P., Carter, J.O. & Ash, A.J. (1998) Simulation of changes in CO2 and climate on native
pasture growth. pp141-184. In: Evaluation of the impact of climate change on northern
Australian grazing industries. Final Report for the Rural Industries Research and
Development Corporation. RIRDC, Canberra, Australia.
5
Hulme, M. and Sheard, N. (1999) Climate change scenarios for Australia. Climate Research
Unit, Norwich, UK. 6pp. http://www.cru.uea.ac.uk/~mikeh/research/australia.pdf
McKeon, G.M., Carter, J.O., Day, K.A., Hall, W.B. and Howden, S.M. (1998a) Evaluation of
the impact of climate change on northern Australian grazing industries. Final Report for the
Rural Industries Research and Development Corporation. RIRDC, Canberra, Australia. pp
287
McKeon, G.M., Charles, S.P., Bates B.C., and Hall, W.B. (1998b) Methods for evaluating
climate change impacts on pasture growth. pp 113-140. In: Evaluation of the impact of
climate change on northern Australian grazing industries. Final Report for the Rural
Industries Research and Development Corporation. RIRDC, Canberra, Australia.
McKeon, G.M., Howden, S.M., Abel, N.O.J. & King, J.M. (1993) Climate change: adapting
tropical and subtropical grasslands. Proc. XVII Int. Grasslands Congress, Palmerston North,
New Zealand, 1181-1190.
McKeon, G.M., S.M. Howden, D.M. Silburn, J.O. Carter, J.F. Clewett, G.L. Hammer, P.W.
Johnstone, P.L. Lloyd, J.J. Mott, B. Walker, E.J. Weston & J.R. Wilcocks (1988) The effect
of climatic change on crop and pastoral production in Queensland, In Greenhouse. Planning
for Climate Change, (ed.) Pearman, G.I., CSIRO pp. 546-563.
Meehl, G.A. and Washington, W.M. (1996) El Niño-like climate change in a model with
increased atmospheric CO2 concentrations. Nature, 382:56-60.
Stafford Smith, D.M., Campbell, B., Steffen, W. and Archer, S. (1994) State-of-the-Science
Assessment of the likely impacts of global change on the Australian rangelands. GCTE
Working Document No. 14. Canberra, Australia. pp72.
Stafford Smith, D.M., Buxton, R., Breen, J., McKeon, G.M., Ash, A.J., Howden, S.M. and
Hobbs, T.J. (1999a) Land Use Change in Northern Australia - The impacts of markets, policy
and climate change. Regional Report No. 1 - Charters Towers. Report to the Rural Industries
Research and Development Corporation and Environment Australia (in prep.)
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Use Change in Northern Australia - The impacts of markets, policy and climate change.
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Industries Research and Development Corporation and Environment Australia (in prep.)
Stafford Smith, D.M., Buxton, R., Breen, J., McKeon, G.M., and Hobbs, T.J. (1999c) Land
Use Change in Northern Australia - The impacts of markets, policy and climate change.
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Development Corporation and Environment Australia (in prep.)
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6
Walker, L., Ash, A.J., and Brown, J., (1999) Response of C4 perennial pasture grasses to
elevated CO2 and clipping. In: D. Eldridge & D. Freudenberger (eds) Proceedings of the VI
International Rangeland Congress, pp 262-263, Townsville, Australia.
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7
2.
Impacts on native pastures in south-east Queensland,
Australia.
S.M. Howden, G.M. McKeon, L. Walker, J.O. Carter, J.P. Conroy, K.A. Day, W.B. Hall,
A.J. Ash and 0. Ghannoum,
1.
Introduction
Since pre-industrial times, atmospheric CO2 concentrations have increased 28% from 280
parts per million volume (ppm) in 1800 to 358 ppm in 1994 (Houghton et al. 1996) mainly as
a result of human activities such as burning fossil fuels and landuse change. Further increases
in atmospheric CO2 concentrations will occur with predictions for the year 2100 ranging from
about 480 ppm to over 800 ppm depending on the economic, resource use and population
scenarios used (Houghton et al. 1996). Increasing atmospheric concentrations of CO2 are
likely to have significant impacts on plant production and through this on livestock
production and resource sustainability. This impact will be through the ‘CO2 fertilisation
effect’ where increased CO2 concentrations enhance plant growth as well as through climate
changes. This study investigates these potential impacts on grazing in south-east Queensland
as part of a larger study looking at global change impacts on Australian agriculture.
The response of plants with C3 photosynthetic pathways (some grasses, most forbs, trees and
shrubs) to increasing CO2 has been extensively reviewed (e.g. Kimball et al. 1993, Poorter
1993). Controlled environment studies generally demonstrate both increased photosynthetic
rates (about 20-30%) and reduced stomatal conductance although there is variation in the
degree of response and some exceptions have been reported (Poorter 1993). These changes
result in increased biomass accumulation through both enhanced assimilate supply and
increased water use efficiency. However, there is strong interaction with other variables such
as temperature, soil moisture and soil nutrient availability (Kimball et al. 1993). Hence, it is
uncertain whether the potential enhancement will occur, particularly given the low nutrient
status of natural ecosystems in northern Australian grazing lands (McKeon et al. 1990).
The majority of tropical Australian grazing lands have C4 grasses as the dominant component
of understorey vegetation (Hattersley 1983, Hattersley and Watson 1992). The mulga lands
are an important exception with C3 grass species dominating in some management situations.
The impact of increased CO2 concentrations on C4 grasses is less well documented than for C3
plants. Poorter (1993) reviewed existing experimental data and found an average increase of
28% in dry matter production for C4 species (compared with 71% for C3 species) with
doubled CO2. This increase was due to improved water use efficiency as there was no
significant difference in assimilation rates. However, some recent studies have shown
moderate increases in photosynthetic rates in response to increasing CO2 (Hunt et al. 1996,
Morgan et al. 1994) whilst others have shown none (Ghannoum et al. 1997, Nie et al. 1992a,
Kirkham et al. 1991), or even reduced photosynthetic rates in moist conditions (Nie et al.
1992b). The increased photosynthesis reported with enhanced CO2 under water limiting
conditions (Nie et al. 1992b, Nie et al. 1993) may be due to wetter soil profiles from more
conservative water use.
8
The improvement in water use efficiency under high atmospheric CO2 levels is likely to be
the most significant effect on C4 pasture grasses. Increases in water use efficiency in such
situations occur as a result of reduced stomatal conductance reducing moisture loss while the
increased atmospheric CO2 levels maintain internal CO2 concentrations and thus
photosynthesis. Increases in water use efficiency of up to 36% have been found in field
conditions (Ham et al. 1996, Owensby et al. 1993, Nie et al. 1992b, Kirkham et al. 1991,
Morgan et al. 1994, Read and Morgan 1996) with these effects being reduced in wet years
(e.g. Knapp et al. 1993). However, in some circumstances, the increase in leaf temperature
caused by reduced transpiration (Morison and Gifford 1984, Kirkham et al. 1991, Nie et al.
1992b, Ham et al. 1996) may feedback to increase water use as suggested by Hunt et al.
(1996). Several field studies have recorded higher soil moisture contents in elevated CO2
treatment plots (e.g. Nie et al. 1992b, Nie et al. 1993). These findings are potentially
important in northern Australian grazing lands as they suggest the possibility of more
conservative soil moisture use by pastures and thus an increase in the number of ‘green days’,
and hence animal liveweight gain (McCown 1981). In subtropical Queensland the benefit of
more conservative water use could be enhanced if the trend of increasing autumn minimum
temperatures continues (McKeon and Howden 1993).
The longevity of enhanced growth responses to increased CO2 concentrations has been raised
as an important issue. There remains some uncertainty as to the mechanisms involved in this
‘acclimation’ process and the degree to which it may occur in the field. Hunt et al. (1996) and
Morgan et al. (1994) studying intact grassland communities and Ghannoum et al. (1997) with
glasshouse trials have found evidence of photosynthetic acclimation but report that the
stomatal responses appeared to be more stable than the photosynthetic responses. Long-term
acclimation (as evidenced by reductions in stomatal density over periods of decades) may
result in an improved response under drought conditions (Woodward 1987).
Improved resilience to drought conditions under conditions of high CO2 may also result
through reducing the effects of increasing vapour pressure deficit (VPD). For example,
Seneweera et al. (1998) studying the C4 grass Panicum coloratum found that enhanced levels
of CO2 offset the impacts of high VPD and low soil water by maintaining higher leaf water
potentials. Similar results have been found in other studies (e.g. Nie et al. 1992b, 1993).
The above processes suggest potential increases in plant production in Australian tropical
grazing lands with increasing CO2 concentrations. Increased grass production provides the
opportunity to reduce soil erosion by increasing plant cover and to increase feed availability
for livestock. However, increased CO2 may decrease plant nitrogen concentrations (e.g. Hunt
et al. 1996) which may reduce liveweight gains in those situations where dietary nitrogen
limits feed intake and thus animal growth (Hendricksen et al. 1982). However, Hungate et al.
(1997) found no effect of increased CO2 on nitrogen concentration in cool season rangeland
annuals. Hence, there remains a considerable need for additional research on the issue of
nitrogen dynamics and animal nutrition.
Enhanced plant growth under elevated CO2 levels may result in alterations to the relative
competitive abilities of species in tropical grasslands. For example, Polley et al. (1994)
suggest that increased CO2 levels may have been associated with increased shrub invasions in
US savannas during this century due to increased competitive abilities of C3 shrubs over the
C4 grasses. This issue is particularly pertinent in Queensland given the existing issue of
managing woody weeds and regrowth from cleared areas. However, Archer et al. (1995) and
Burrows (1995) have shown that management effects such as frequency of pasture burning
9
are likely to dominate the ecosystem response and that climate and CO2 effects are likely to
be of secondary importance.
The possibility of changes in the ratio of C3 and C4 grass species has also been raised (Carter
and Peterson 1983). However, Owensby et al. (1993) and Nie et al. (1992c) found no
evidence that increased CO2 concentrations significantly changed grass species composition
with community structure providing a strong buffering capacity. Nevertheless, there remains
the possibility that changes in partitioning to roots and shoots (Rogers et al. 1994) or changes
in seedhead production (Ghannoum et al. 1997) may result in long term changes. Changes in
response to defoliation regimes may also affect community composition although Wilsey et
al. (1994) found no effect of elevated CO2 on regrowth rates or allocation patterns in the one
African shortgrass savanna species (Sporobolus kentrophyllus) that they studied.
A major problem facing the grazing industry is to make reasonable predictions of how
increases in CO2 and climate change will interact. The challenge is to extrapolate the
physiological knowledge gained on a limited number of species under artificial growing
conditions to the complex semi-natural ecosystem of native pastures in northern Australia.
Our approach is to use a currently operational model of pasture and animal production
(GRASP: McKeon et al. 1990, Carter et al. 1996, Day et al. 1997, Littleboy and McKeon
1997) and examine to what extent it can be modified to represent the CO2 effects reviewed
above. Such an approach builds a logical and repeatable pathway to the future allowing the
implications of new research to be rapidly evaluated.
2.
Simulating CO2 effects
2.1
Description of GRASP
GRASP is a model simulating the above-ground yield of a sward dominated by perennial
native grasses. GRASP includes a four layer soil water balance and a plant growth model
which calculates the processes of run-off, infiltration, drainage, soil evaporation, tree and
grass transpiration, pasture growth, consumption and decay, nitrogen uptake, pasture
management effects (i.e. stocking rate and pasture burning) and plant density (i.e. perennial
grass basal cover). GRASP calculates pasture growth as a function of grass transpiration,
radiation interception, temperature, VPD, nitrogen availability and regrowth potential. The
model has been parameterised for over 40 native pasture communities in Queensland and has
been derived from the results of the last 50 years of field experimentation and grazier
experience. Descriptions of the model development include Rickert and McKeon (1982),
McKeon et al. (1982), Hendricksen et al. (1982) and McKeon et al. (1990). A full description
of each equation is given in Littleboy and McKeon (1997) and evaluation, calibration and
validation are described in Carter et al. (1996) and Day et al. (1997).
In this preliminary analysis we have yet to consider the effects of CO2 increase and climate
change on the other flows of dry matter (detachment, decomposition and consumption) in the
grazing system. However, given the generality of parameters for these other processes across
a wide range of climates (Day et al. 1997) it is reasonable to concentrate initially on plant
growth.
10
The modelling approach used in GRASP has both benefits and limitations in simulating CO2
effects on plant production. The benefits are that the model has been parameterised for a wide
range of native pasture communities, soils and locations, and is operational in simulating
grazing trials, grazing properties and statewide drought alerts. However, GRASP does not
simulate: individual species/varieties in the sward; root growth; phenological development;
nor the partitioning of net-photosynthate between roots and shoots in perennial plants which
varies with species, phenology, grazing history and soil water. Given the lack of data for
native pastures, GRASP has been parameterised, calibrated and validated with only aboveground sward yields.
2.2
CO2 effects on growth, water use and nitrogen parameters in GRASP
To simulate pasture growth under enhanced CO2 levels, there is a need to determine how
processes and several parameters in GRASP are likely to change. The key parameters are
those relating to radiation interception, transpiration and nitrogen dynamics. In this study we
do not consider acclimation and hence are concentrating on potential responses derived from
growth studies.
The evidence reviewed previously suggests that radiation use efficiency (RUE) does not
increase in the long term in C4 grasses. Theoretical models (e.g. Chen et al. 1993) suggest an
increase in net photosynthesis of 4-10% for a doubling of CO2. For a doubling of CO2, we
assume an increase of RUE by 5%. However, we note that Walker et al. (unpublished data)
have measured substantial increases (13-60%) in photosynthetic rate for two native C4 grasses
in the first year at elevated CO2 under glasshouse conditions.
As native pastures are frequently burnt and heavily grazed, regrowth response is an important
parameter. The experiment of Walker et al. (unpublished data) provides data specifically on
the response of two northern Australian tussock grasses to increased CO2 under weekly
defoliation. Doubling CO2 increased regrowth rate by 10% on average. Other studies on
young plants and theoretical analysis support this order of magnitude response (Agren 1994)
and hence a 10% change has been used in this simulation study.
Decreases in stomatal conductance for C4 and C3 species with increasing CO2 are well
documented. Reviews of available data suggest that doubling of CO2 concentrations increases
water use efficiency, calculated over the seasonal growth period of 100-180 days, by an
average 30 - 40%. Similarly, instantaneous measurements of transpiration efficiency (TE: µ
mol CO2 s-1 per mmol H2O m2 s -1) show that large increases are possible (30-150% for a
doubling of CO2). Despite these increases in instantaneous TE, most studies show that total
seasonal water use does not change substantially with increasing CO2, probably due to
increased leaf area. Hence we expect GRASP to simulate little change in seasonal water use,
but increased green cover and increased seasonal transpiration efficiency expressed as plant
growth per mm of seasonal transpiration. Based on the measurements for tropical grasses
(Walker et al. unpublished data) and reviewed data we use here a 40% increase in TE for
doubled CO2. Transpiration is reduced in GRASP for doubling of CO2 by changing the
relationship between green yield (DM kg/ha) and the ratio of potential transpiration to
potential evapo-transpiration (ET). Thus changes in the TE parameter are linked to changes in
parameters describing the above relationship. However, the relationship between green yield
and proportion of radiation intercepted is not expected to change.
11
Modelling studies (e.g. Sellers et al. 1996) and experimental data referred to earlier suggest
that the above effect of reducing daily transpiration with increasing CO2 could increase land
and canopy ‘surface’ temperature above the increase that would result from just the CO2
effect on radiative forcing. In GRASP the effect of reducing transpiration on leaf surface
temperature could be represented by re-calculating daily temperature, vapour pressure deficit
and potential evapo-transpiration (e.g. Class A pan) which have been input from the daily
climate file. To determine the value of such an approach the effect of transpiration on
ambient temperatures and VPD was examined using a spatial version of GRASP (Carter et al.
1996) simulating soil water in Queensland’s grazing lands over the last 40 years. For one
month (January) years with similar solar radiation conditions (21-24 MJ/m2/day) were used to
establish multiple regression relationships between monthly averages of solar radiation and
VPD. The relationship changed with simulated monthly evapo-transpiration (calculated from
soil evaporation and grass transpiration). From this analysis it was calculated that a reduction
in monthly evapo-transpiration of 20% (e.g. from 60mm to 48mm) results in a 0.4oC increase
of average temperature and maximum temperature; 1.2 hPa increase in VPD; and 0.4mm
increase in potential ET (i.e. Class A pan). However, even in January in the middle of the
‘wet’ season, monthly rainfall is well below potential ET, and hence it is unlikely that
changing daily TE and daily transpiration as indicated above will actually change monthly ET
and the driving climate variables at a monthly or seasonal time scale. Sensitivity studies
indicated small effects of the above CO2 changes on seasonal ET. Thus we do not currently
represent any effect of changing CO2 on daily climatic inputs.
GRASP includes relationships between senescence (death of green material) and frost and
soil water. Of particular importance is the relationship between maximum possible green
cover and soil water availability. Measurement of leaf area of maize under increasing water
stress showed that about 50% higher leaf area could be maintained under high CO2 (Gifford
1988) at a given soil moisture level. This was similar to the change in stomatal conductance,
and hence we assume in GRASP that the green cover able to be supported for a given level of
soil water changes in proportion to transpiration efficiency, i.e. 40% increase in this case.
A nitrogen mineralisation index is calculated in GRASP as a function of surface (0-10cm) soil
moisture and air temperature using a similar approach to Parton et al. (1988). Although we
suggest that the index represents the climatic potential for nitrogen (N) mineralisation, we
have not used it in the following simulation studies to change nitrogen availability for the
following reasons. The calculated mineralisation index was compared with measured nitrogen
yield for over 100 years x site combinations of regularly burnt or mown exclosures across
Queensland. The results indicated a general plateau of N yield of about 20-25 kg N/ha for
undisturbed native pastures (Day et al. 1997). This plateau of total seasonal N uptake was in
some cases reached by the middle of the growing season. Even if subsequent soil moisture
conditions were apparently favourable for mineralisation, no further uptake occurred (e.g.
Norman 1963). However, increases in soil moisture under enhanced CO2 appear to have
increased nitrogen mineralisation in mediterranean ecosystems (Hungate et al. 1997) and
increased temperatures are also likely to increase mineralisation rates (Parton et al.1988).
Thus, the incorporation of a soil carbon:nitrogen model into GRASP may be needed before
these effects can be adequately simulated and at this stage the parameter ‘maximum aboveground N uptake per year’ is not changed with increasing CO2.
In GRASP, the rate of N uptake, before maximum uptake is reached, is parameterised as kg
N/ha per 100 mm of transpiration (3-10 kg N/ha per 100 mm) depending on location and
species. Reduced transpiration with increased CO2 will reduce rate of N uptake in this case.
12
However, reduced uptake and increased mineralisation conditions are likely to lead to
increased N concentrations in soil water, and hence increased rate of uptake per mm of
transpiration. Sensitivity studies showed that a 20% increase in this parameter for doubled
CO2 resulted in approximately the same rate of N uptake as occurred in ambient conditions
(e.g. Hungate et al. 1997).
GRASP uses a critical nitrogen content in dry matter (%N) to calculate a nitrogen index used
to limit radiation use efficiency and regrowth. C4 grasses are able to continue above-ground
growth with concentrations of nitrogen as low as 0.4 to 0.8 % N in dry matter with variation
relating to differing leaf/stem ratios. Any changes in leaf/stem partitioning with enhanced
CO2 levels will thus influence plant critical nitrogen levels as well as affecting potential dry
matter production through changes in nitrogen use efficiency. However, data for northern
Australia native grasses (L. Walker et al. unpublished data) do not indicate substantial
changes in proportion of stem and in other tropical grasses the effects are inconsistent, with
both decreases (Seneweera et al. 1998) and increases (Ghannoum et al. 1997) observed.
Reductions in the concentrations of Rubisco in leaves under high CO2 levels have been
frequently measured and there is some experimental evidence (e.g. Rogers et al. 1996) that
this reduces critical leaf nitrogen contents in crop plants although there is no evidence of the
same effect in tropical grasses. Considering the above, no change is made in critical nitrogen
concentrations with CO2.
The availability of young green leaf in native pastures is one of the major driving variables of
steer liveweight gain (Poppi et al. 1981, Ash et al. 1982) through the effects of diet selection
for leaf on dietary nitrogen concentration (Hendricksen et al. 1982). Several approaches to
simulating animal (sheep and cattle) liveweight gain have been evaluated with GRASP
ranging from nitrogen and energy balance systems (Hendricksen et al. 1982, Hall 1996) to
annual climatic indices (McCown 1981, Hall 1996). Simple seasonal or annual climatic
indices have so far proved more successful than mechanistic models in accounting for the
observed spatial and temporal variation (Hall 1996) and this approach has been used here to
simulate the effect of CO2 change on annual liveweight gain through its effects on soil water
and temperature. However, possible animal production effects of changes in pasture
composition (Henderson et al. 1994) in response to climate change and CO2 increase are yet
to be considered.
The above review has examined in detail the necessary changes to parameters in GRASP to
represent the effects of doubled atmospheric CO2. Confirmation of many of the changes is
dependent on current research on response of native grasses and trees to CO2. It is vital that
research on those species important to the productivity of Australia’s grazing lands is
continued to be supported so that prediction of impact of future changes can be made.
Day et al. (1997) have parameterised GRASP for several sites in south-east Queensland.
Parameter values for the typical black speargrass communities in south-east Queensland for
the major driving relationships (as described above) and their changes under enhanced CO2
levels are shown in Table 1. The model simulates pasture growth reasonably effectively over
the current range of year-to-year climatic variability (Figure 1) although testing of the model
under extreme climatic conditions is not yet complete.
Table 1 Parameter values changed in GRASP for current (1 x CO2) and doubled CO2 (2 x
CO2) scenarios for C4 native pastures in south-east Queensland.
13
GRASP parameters
Potential regrowth (kg/ha/day)
Potential regrowth /unit grass basal cover
Transpiration efficiency (kg/ha/mm @ 20hPa)
Green yield at which potential transpiration is 50% of potential
ET
Rate of N uptake (kg N/ha per 100 mm transpiration)
Radiation use efficiency (kg/ha per MJ/m2)
1xCO2
15.0
3.5
13.5
1000
2 xCO2
16.5
3.85
18.9
1400
6.0
12
7.2
12.6
Simulated yield (kg/ha)
5000
y = 1.0168x
R2 = 0.7828
4000
3000
2000
1000
0
0
1000
2000
3000
4000
5000
Observed yield (kg/ha)
Figure 1 Observed and simulated yield (i.e. peak standing pasture dry matter) for a native C4
pasture sward at ‘Ronnies Paddock’ site, Brian Pastures Research Station, south-east
Queensland for 1987-96 with Gayndah daily rainfall and other climate variables.
3.
Scenario definition
GRASP was used with the above parameter changes to explore possible outcomes from a
range of global change scenarios for the location of Gayndah in south-east Queensland. The
CO2 and climate scenarios were;
1) current climate + CO2 (350 ppm);
2) current climate + doubled CO2 (700 ppm);
3) temperature increase (+2.76oC) + 700 ppm CO2;
4) reduced rainfall (-24% summer, -12% winter) + temp. change + 700 ppm CO2; and
5) increased rainfall (+12% winter) + temp. change + 700 ppm CO2.
These scenarios are named ‘Baseline’, ‘CO2’, ‘Warm’, ‘Warm/Dry’ and ‘Warm/Wet’
respectively. These scenarios are drawn from the CSIRO 1996 scenarios as described in
Reyenga et al. (1997) and were used to modify the historical climate record from 1957 to
1996. The grazing management strategies adopted were to: 1) adjust stocking rates in June of
each year to use 30% of standing pasture dry matter over the next six months; and 2) burning
of a third of the pasture in years when standing pasture dry matter exceeded 1200 kg/ha.
These strategies resulted in an implicit adaptation of overall stocking rate and burning
practice to changes in pasture growth.
4.
Results
Doubling of CO2 concentration was simulated to have significant impacts on soil hydrology,
plant growth and animal production. Mean seasonal transpiration efficiency was increased by
14
about 10% as a result of both reductions in transpiration (-2%) and increases in growth (8%).
Reduced transpiration and runoff enhanced soil moisture status, increasing through-drainage
by 20% (Table 2). The growth response to enhanced CO2 was greatest during dry years,
showing up to 65% increases when compared with current CO2 levels (Figure 2 and Table 3).
Runoff was decreased due to increased mean dry matter levels and higher minimum cover
(61%, Table 4). Increased dry matter levels and small increases in growing days (1%) resulted
in both increased animal production per hectare (10%) and a markedly reduced coefficient of
variation of stocking rates (-27%) (Table 3). However, growth was nitrogen-limited in these
simulations with seasonal nitrogen uptake reaching its maximum in several years and this
possibly restricted expression of climatic variability. The 6% increase in the climate-derived
mineralisation index and increased dry matter flow suggest that nitrogen supply could change
under these scenarios and hence there is a need for a more detailed analysis of nitrogen
mineralisation in future studies.
Table 2 Mean annual transpiration (mm), runoff (mm) and drainage (mm) for the baseline
conditions and mean % change from baseline for the four scenarios.
Scenario
Baseline
CO2
Warm
Warm/Dry
Warm/Wet
Transpiration
399
-2
0
-28
+4
Runoff
19
-21
-11
-53
-11
Drainage
20
+20
+20
-65
+50
Growth response
(2CO2/1CO2)
2
1.5
1
0.5
0
0
200
400
600
800
Rainfall (mm)
1000
1200
Figure 2. The simulated growth response to enhanced CO2 (ratio of 2CO2/1CO2) with rainfall
for a C4 native pasture in south-east Queensland. Each point represents one year
from 1959 to 1996.
Table 3 Mean pasture growth (kg/ha/year), liveweight gain (kg/ha/year), the coefficient of
variation (%) of the stocking rate, and simulated wheat yields (g/m2) from Reyenga
et al. 1997 for the baseline conditions and mean % change from baseline for the four
scenarios.
Scenario
Baseline
CO2
Warm
Warm/Dry
Growth
4516
+8
+7
-13
LWG/ha
75.4
+11
+19
-12
CV of SR
36.1
-27
-10
+40
Wheat yield
206
+35
+15
0
15
Warm/Wet
+7
+20
-13
+26
Table 4 Mean percentage of days of the year with growth index greater that 0.05, minimum
standing dry matter and mean mineralisation index for the baseline conditions and
mean % change from baseline for four scenarios.
Scenario
Baseline
CO2
Warm
Warm/Dry
Warm/Wet
%GI days
81
+1
+13
+9
+13
SDM min.
324
+61
-13
-15
+71
Mineralisation
62
+6
+27
+8
+31
The Warm scenario, with both increased CO2 and temperature, tended to remove some of the
effects of the CO2 change alone resulting in smaller increases in transpiration efficiency (7%)
and drainage. However, compared with the baseline results, simulated animal production
increased markedly (19%) due to increases in the number of growing days in the cooler
months (13%). Similarly, the 27% increase in mineralisation index suggests that additional
nitrogen could become available under such conditions, reinforcing the need to better
simulate processes affecting nitrogen availability.
The two scenarios with altered rainfall in addition to the CO2 and temperature changes
showed varying responses. The Warm/Dry scenario showed a 21% increase in seasonal
transpiration efficiency and significantly reduced runoff and drainage compared with the
baseline. Under this global change scenario, the positive effects of CO2 and temperature on
animal production are negated. The Warm/Wet scenario shows small increases in seasonal
transpiration efficiency (3%) but retains most of the positive response to the CO2 and
temperature change. Drainage is increased substantially (50%) compared with the baseline
conditions.
5.
Discussion
This simulation study has certain caveats which have important implications for
interpretation. The major purpose of the study was to evaluate how an empirical operational
pasture model such as GRASP could be modified to reasonably simulate the effects of CO2
and climate change scenarios. The CO2 effects have been derived from recent growth
chamber and/or glasshouse studies on tropical grasses. Thus, this study represents our first
attempt to link ecosystem modelling to physiological studies. Similarly, the studies with
climate change scenarios represent our first attempt to evaluate alternative representations of
climate change and their interaction with doubled CO2. However, it must be remembered that
we have evaluated only an ‘average’ native pasture without trees and the climate change data
are small samples (38 years) of possible climate change representations. Nevertheless, the
simulation results using the above representation of CO2 effects highlight :
1) the beneficial effects on plant growth of doubling CO2 in dry years and the mean increase
in soil moisture observed experimentally;
16
2) the effects on plant growth are likely to lead to increased animal production through
increased pasture productivity, length of growing season, reduced variability and greater
opportunities for pasture burning;
3) erosion may be reduced due to lower runoff and increased ground cover but increased
drainage may pose problems where there are salinity and waterlogging risks; and
4) nitrogen limitations on plant growth may restrict the beneficial impacts of increased CO2.
This study suggests that empirical models such as GRASP can be plausibly modified to
include the effects of CO2 as measured in growth chambers and glasshouses once
measurements for relevant species and appropriate conditions (low nutrient availability, high
VPD, and frequent water stress) are available. However, the parameter changes made to
represent the effects of doubled CO2 were derived from short term (i.e. seasonal) growth
chamber experiments. Longer term studies using open-topped chambers or Free Air Carbondioxide Experiments are required to determine whether these effects are permanent responses
to elevated CO2. Nevertheless, the above preliminary simulation analysis indicated that the
potential beneficial effects are worthy of further research.
The simulations are limited by lack of knowledge of the impact of CO2 on additional
processes operating in complex semi-natural ecosystems such as grazed native pastures. For
example, the effects of climate change and management on nutrient cycles, especially
nitrogen and phosphorus, will play a large role in limiting or amplifying the effects of CO2
increase. Furthermore, the interactions of individual plant species or ecotype attributes with
increased CO2 are yet to be considered.
The simulated liveweight gain per hectare provides only an indication of change in animal
production. Graziers have a large number of possible adaptation options given the available
variation in animal type (sheep, cattle), product (wool, calves, steers, cows), breed (e.g. Bos
indicus, Bos taurus) and pasture management. Furthermore, several important issues in
animal production are yet to be considered: the nutritional effects of digestibility, leaf/stem
ratio, species composition, availability of browse, landscape driven redistribution of runoff,
soil mineral limitations such as phosphorus, and compensatory gain. In addition, Howden and
Turnpenny (1997) suggest that under the global warming scenario used here, the frequency of
heat stress days will increase markedly (by about 140%) in cattle in this location in south-east
Queensland. Despite these caveats we consider that the most important impacts of CO2 and
climate change will be on plant growth which drives the animal productivity of the grazing
system (Day et al. 1997) through quantity, quality, seasonality and year-to-year variation.
Explicit modelling of the whole soil-plant-animal production-management system is now
required to examine to what extent changes in plant growth and variability translate into
changes in economic performance (i.e. Campbell et al. 1997). In this study we have simulated
a likely grazier response to changes in plant growth by burning a third of the pasture
whenever yields exceeded 1200kg/ha; and adjusting animal numbers each year to eat a fixed
proportion of the standing dry matter in June (i.e. end of the growing season) over the next 12
months. This type of approach allows calculation of the effects of the likely adaptation to
climate change and CO2 increase in relation to these particular management decisions. When
combined with an emissions budget approach (Moore et al. 1997) this will enable assessment
of both mitigation options and the potential feedbacks of global change into atmospheric
composition.
17
Global change impacts on production and economic performance of the grazing industry will
not happen in isolation from other agricultural pursuits. In the Gayndah region of south-east
Queensland, cropping is an alternative landuse on some land units. Reyenga et al. (1997) have
assessed the impacts of the same global change scenarios used here on wheat cropping for this
location thus enabling some preliminary assessment of the impacts of global change on
landuse. However, choice of landuse will be affected by the relative profitability of different
production systems such as dairying, extensive grazing, forage cropping for finishing cattle,
and grain production. Prices received for products can be strongly affected by overseas
demand and hence are subject to the impact of global climate and other changes. In the
absence of a global economic analysis the results of Reyenga et al. and the current study can
be compared only in terms of relative change from the current baseline. Such a comparison
(Table 3) suggests greater relative advantages to wheat production for all scenarios with the
exception of the Warm scenario. Full integration of these landuse change effects would
require: 1) spatial modelling of the respective systems with a socio-economic component; 2)
consideration of pests and diseases; and 3) calculation of fluxes of greenhouse gases to
determine feedback effects on the global changes.
6.
Conclusion
The major results of this simulation study were the demonstration that:
1) a simple empirical and operational model of pasture growth was able to be modified, with
stated caveats, to include current experimental results on the response of C4 native pastures to
increased CO2 ;
2) the evaluation of the potential benefit of increased CO2 especially under warmer, wetter
conditions will require careful attention to the effects on nitrogen availability; and
3) the impact on increased CO2 and climate change on grazing management strategies such as
changes in annual stocking rate, animal production and frequency of pasture burning can be
evaluated providing a basis for comparison with alternative land uses.
7.
References
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18
Carter, D.R. and Peterson, K.M. (1983) Effects of a CO2-enriched atmosphere on the growth and
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22
3.
Impacts on fire dynamics in the mulga woodlands of
south-west Queensland.
Howden, S.M., Moore, J.L., McKeon, G.M., Reyenga, P.J., Carter, J.O. and Scanlan, J.C.
1.
Introduction
Increases in the density of shrubs and trees (woody weeds) in grasslands in semi-arid
Australia is currently a major threat to sustainable agricultural production due to the
reductions in grass growth and hence livestock performance that occur (eg Burrows et al.
1990). However, whilst these invasions reduce grass production and hence wool cuts, they
can also result in substantial increases in carbon stores which may become a new source of
income for these regions in the eventuality that carbon emission trading arises from the Kyoto
Protocol. This may require adaptation of grazing and burning regimes in these regions
depending on the management goals. A further complication is that the dynamics of woody
weed invasions may themselves be changed by increases in atmospheric CO2 levels and by
possible changes in climate. We investigate responses of the mulga (Acacia aneura)
woodland ecosystems in south-west Queensland under scenarios of CO2 and climate change
in the context of adaptation for differing management goals. We use an existing model of the
dynamics of these woodlands (Moore et al. 1997) adapted to simulate responses to varying
atmospheric CO2 levels.
2.
Methods
The model of Moore et al. (1997) is an extension of GRASP (McKeon et al. 1990). GRASP is
a model simulating the above-ground yield of a sward dominated by perennial native grasses.
GRASP includes a four layer soil water balance and a plant growth model which calculates
the processes of run-off, infiltration, drainage, soil evaporation, tree and grass transpiration,
pasture growth, consumption and decay, nitrogen uptake, pasture management effects (i.e.
stocking rate and pasture burning) and plant density (i.e. perennial grass basal cover). GRASP
calculates pasture growth as a function of grass transpiration, radiation interception,
temperature, VPD, nitrogen availability and regrowth potential. Evaluation, calibration and
validation of the model are described in Day et al. (1997).
The density of trees in GRASP, however, is set by the user within each simulation. The model
of Moore et al. (1997) adds modules which simulate the population dynamics and growth of
mulga in response to changes in environment and management (stocking rate, burning, tree
clearing). It also has an emissions module which tracks the flow of carbon and other
emissions through the plant and animal components of the ecosystem although it doesn’t
currently model the soil carbon components. We have modified this model to simulate
responses to high atmospheric CO2 concentrations within both the grass sward and the mulga
components.
The general impacts of increased CO2 on C3 grasses are understood in terms of photosynthetic
and stomatal responses and changes in nitrogen nutrition (eg Wand et al. 1999). Mulga lands
in good condition have a large component of C3 grasses and we assume for these simulations
23
that they dominate the sward, acknowledging that many pastures have substantial C4
components. The CO2 response of the grass component was modified following Howden
(1999a,b) by changing the transpiration efficiency, radiation use efficiency and nitrogen
efficiency of the grasses. In contrast, the responses of woody plants to increased CO2 appear
to be more variable than the C3 grass responses with the meta-analysis by Curtis et al. (1998)
showing relatively consistent photosynthetic responses (averaging +16% for woody plants in
nutrient-stressed conditions) but quite variable stomatal conductance responses (mean -11%
but not significant). We thus have used two sensitivity scenarios which vary the response of
mulga to high CO2. These are a low response where daily transpiration per unit tree basal area
decreases by 11% but where the water-use efficiency increases proportionately, and a high
response where transpiration per unit basal area decreases as before but there is an additional
increase in water-use efficiency of 16%.
We apply this model to a range of feasible global change scenarios which reflect the different
uncertainties. Increases in atmospheric CO2 concentration are highly likely due to existing
economic and population development pathways and energy and landuse patterns. In the midrange IPCC scenarios, atmospheric CO2 concentrations are likely to double to about 700ppm
around the year 2100 (Houghton et al. 1996). Consequent increases in global temperature are
consistently forecast whilst changes in rainfall at regional level have considerable uncertainty.
We thus construct a cascading hierarchy of scenarios (Table 1). The temperature and rainfall
changes in the Hot, Wet and Dry scenarios are implemented by using the historical climate
record for Charleville for the period 1885-1995 but adding 3oC equally to maximum,
minimum and dewpoint temperature (Hennessy et al. 1999), whilst rainfall changes are
implemented as + 10% multipliers on daily rainfall (Howden 1999a). To ensure consistency
between all files, pan evaporation was recalculated using a regression derived from the period
1980 to 1995 when the Class A pan measurements were most reliable (Eq’n 1). VP and VPD
were recalculated for the scenarios with changing temperature using the equations in McKeon
et al. (1998).
Table 1: Changes in carbon dioxide levels, temperature and rainfall for each global change
scenario
Scenario
CO2 (ppm)
Baseline
CO2
Hot
Wet
Dry
350
700
700
700
700
Temperature
(oC)
0
0
+3
+3
+3
Epan = -1.338 + (0.177 *Rad) + (0.238 * VPD) r2 = 0.920
Rainfall (%)
0
0
0
+10
-10
[Eq’n 1]
The simulations were run using the site data and parameterisations from Moore et al. (1997).
The initial mulga cohort was altered to give a more even age distribution (200stems/ha each at
0.25m and 1m height, 50 stems/ha at 2m, 30 stems/ha at 5m and 5 stems/ha at 10m) and a
total basal area of 0.475m2/ha. Treatments were a factorial combination of grazing by fire
with two grazing treatments [ungrazed (U) and grazed (G) with sheep stocking rate set for
24
20% utilisation based on pasture availability at 1 June: McKeon et al. 1990] and three fire
treatments [never burnt (N), burnt every 6 years (F) and burnt annually (A). Burning occurs at
the end of the dry season, if a threshold biomass of 1000kg DM/ha is present]. These
treatments were simulated for both the global change and baseline scenarios. All scenarios
apart from the Baseline were simulated with both the Low and High CO2 response by the
mulga.
Data are generally presented as means of the 110 year simulation. Net emissions are
expressed in terms of carbon-dioxide equivalent emissions (CO2-e) by using Global Warming
Potentials to adjust for the different radiative forcing and lifetimes of gases such as methane
and nitrous oxide (Houghton et al. 1996). The difference between final and inital carbon
stores was calculated to give the change in carbon store over the simulation.
3.
Results
There was a substantial trade-off between wool production and net emissions with four
clusters of results being dominated by grazing and burning treatment (Fig 1). Variations in
global change scenario and/or mulga CO2 response had lesser impacts than the management.
The substantial variation of results within a treatment suggests that there are feasible global
change scenarios which result in both high wool production and low net emissions/high
average carbon store.
2.0
Grazed and burnt
Wool (kg/ha)
1.5
Grazed and unburnt
1.0
0.5
Ungrazed and unburnt
0.0
-400
-300
-200
Ungrazed and burnt
-100
0
100
Net Emissions (kg CO 2-e/ha/year)
Figure 1: Wool production (kg/ha) and net emissions (kg CO2-e/ha/year) for all treatments
and scenarios.
Doubling CO2 and climate change increased average net emissions for all treatments except
UN with GN remaining similar to the Baseline simulation (Table 2). Average net emissions
were negative (ie a sink) for GN (-140kg CO2-e/ha/year) and not influenced much by global
change scenario. The sink size was doubled in the UN treatments (-250 for the Dry scenario
to -340 kg CO2-e/ha/year for the Wet scenario). Climate change in addition to CO2 increase
had minor effects on net emissions for UA, UF, GA and GF treatments. All changes in net
25
Table 2: Average net emissions, average carbon store, change in carbon store, average wool
production, number of burns and grass production for each management treatment and global
change scenario.
Treatment
Average Net
Emissions
Average C
Store
Change in
C Store
(kg/ha)
Wool
Burns
Grass
Growth
(kg CO2-e
/ha/yr)
(kg/ha)
(kg/ha/yr)
(no.)
(kg DM/ha)
Ungrazed - Annual Burn (UA)
Baseline
11
3436
CO2
30/29
2854/2939
Hot
30/29
2555/2669
Wet
31/29
2735/2858
Dry
22/19
2726/2836
294
-525/-446
-644/-539
-504/-396
-336/-111
0
0
0
0
0
33
51/51
53/53
55/56
44/44
758
1086/1078
1068/1054
1163/1157
919/909
Ungrazed – Burnt every 6 years (UF)
Baseline
-36
5913
CO2
-12/-18
5405/5697
Hot
-13/-18
5891/6132
Wet
-13/-18
5672/5871
Dry
-9/-13
5488/5775
3285
2138/2536
2228/2503
2289/2603
1798/2102
0
0
0
0
0
11
17/17
15/15
17/17
15/15
622
971/949
901/886
1028/1013
808/789
Ungrazed - Never Burnt (UN)
Baseline
-273
11726
CO2
-300/-332
12947/13807
Hot
-286/-316
12434/13255
Wet
-315/-343
13347/14101
Dry
-249/-280
11258/12117
17314
19176/21074
18303/20097
20084/21758
16021/17857
0
0
0
0
0
0
0
0
0
0
428
601/574
583/556
626/602
544/518
Grazed - Annual Burn (GA)
Baseline
39
CO2
56/56
Hot
53/52
Wet
59/52
Dry
49/49
3586
2939/3003
2658/2741
2698/2788
2693/2765
-56
-677/-685
-618/-614
-830/-315
-649/-648
1.18
1.39/1.38
0.99/0.98
1.13/1.12
0.89/0.88
23
43/44
45/46
51/48
39/39
719
1038/1032
1022/1016
1133/1150
891/886
Grazed - Burnt every 6 years
Baseline
3
CO2
31/25
Hot
28/28
Wet
34/33
Dry
16/14
(GF)
5721
5450/5778
5489/5644
5604/5820
5452/5619
2233
1427/1805
1466/1508
1391/1472
1909/1988
1.1
1.66/1.62
1.16/1.15
1.38/1.36
0.98/0.97
10
15/13
14/14
15/15
11/11
588
885/864
848/839
946/941
731/721
10912
11247/11626
11157/11541
11499/11830
10618/11033
0.9
1.25/1.21
0.87/0.84
0.98/0.93
0.78/0.74
0
0
0
0
0
435
605/587
588/571
639/622
537/519
Grazed - Never Burnt (GN)
Baseline
-146
10079
CO2
-141/-148
10820/11278
Hot
-141/-148
10543/10998
Wet
-144/-150
11068/11468
Dry
-135/-143
9863/10357
26
emissions were closely linked to differences in average carbon store with decreases in average
carbon store from the Baseline simulations for all treatments except UN with marginal
changes in GN depending on the climate change scenario. However, carbon store trajectories
differed significantly between treatments (Figure 2) with some treatments (UA, GA) having a
carbon store at the end of the 110 year simulation less than that at the start. Only the UN
treatments showed continuing carbon storage at the end of the simulation (Table 2) with
significant variation with scenario in the increase in storage from +17000 kgC/ha (Dry) to
+21000 kgC/ha (Wet). The GN treatments also maintained carbon storage for a long period
into the simulation.
22000
20000
UN
18000
Carbon Store (kg/ha)
16000
GN
14000
12000
10000
8000
GF
6000
4000
2000
GA
0
1885 1895 1905 1915 1925 1935 1945 1955 1965 1975 1985 1995
Year
Figure 2: System carbon stores (kg/ha) over the 110-year historical simulations for selected
treatments under the Baseline scenario.
Grass growth was substantially stimulated by the doubling of CO2 (mean 44%) with the
response being mediated by both grazing/burning treatment and the response of the mulga to
CO2 (Table 3). The grass response was least in the treatments that were never burnt (+34 to
+40%) and greatest in those frequently burnt (+47 to +56%). High mulga CO2 response
decreased the grass CO2 response from 40 to 34% in the UN treatment. There was no effect in
some treatments (UA and GA).
Table 3: Response (% change from baseline) of grass production to CO2 under the different
management scenarios
Treatment
Ungrazed – Annual
Ungrazed – 6 yearly
Ungrazed – Never
Grazed – Annual
Grazed – 6 yearly
Grazed – Never
Grass Response
Low
High
43
42
56
53
40
34
44
44
50
47
39
35
The number of burning events (Table 2) in the Baseline simulations were 10 (GF), 23 (GA),
11 (UF) and 33 (UA). Doubling CO2 increased these markedly (14, 43, 17 and 51
respectively). The Hot and the Dry climate change scenarios reduced the number of fire
events but they always remained above baseline values.
27
Wool production varied by 30% for the Baseline scenario with values of 1.18, 1.1 and 0.9
kg/ha/year for the GA, GF and GN treatments (Table 2). Doubling of CO2 increased
production markedly especially for the GF(50%) and GN (38%) treatments. However, when
climate change was simulated in conjunction with CO2 increase, wool production was
reduced in all scenarios sometimes to below the Baseline values (eg 30% reduction in the Dry
scenario of GA). The differences between the two CO2 responses for the mulga component
was small for the average carbon store, average net emissions and the other variables (eg
wool production, burning events).
4.
Discussion
These simulations suggest that this system can be managed to act as either a net source or a
net sink of greenhouse gases under current climate and CO2 and under a range of global
change scenarios. The key component in determining source or sink status is the management
of the woody mulga. Excluding both burning and grazing is the most effective means of
increasing carbon stores and hence reducing net emissions although we note that permanent
exclusion of fire is probably not a practical option. Under a zero-grazing scenario, this land
would no longer be used for agricultural production. If grazing is undertaken with various fire
management regimes, there remains a significant trade-off between wool production and
increasing carbon stores although there are combinations of management regimes such as
excluding fire with light grazing which, on average can provide both. However, it was only in
the simulations where no fires or grazing occurred that there a continuing increment of carbon
throughout the 110 year simulation. Under other management regimes, initial increases in
carbon stores are followed by reductions as the initial mulga cohorts die out. The model is of
course a simplification of real management practices and there are opportunities which
generally exclude grazing but use the mulga and the associated chenopods as drought reserve
(eg Lauder and Freudenberger 1999) which could make multiple uses feasible.
The issue of whether carbon sequestration is an alternative landuse to grazing is currently
hotly debated. The purchase cost of land, maintenance costs (eg firebreaks and fencing),
income foregone, security of store, the costs, accuracy and precision of monitoring and
verification, transaction costs and other possible associated benefits and costs (eg
biodiversity) all need to be addressed. The low cost of land and current low returns from wool
production from the rangelands in south-west Queensland suggests that this region may be
suitable to develop a mosaic of landuses which incorporates carbon storage as one goal
provided that some of the above cost issues can be addressed (eg reduce monitoring costs).
The simulations in Moore et al. (1997) using this model suggest that both the dynamics of
storage and total carbon storage are influenced by the initial mulga stand structure. This needs
further investigation as it may have an influence on the viability of carbon storage as a
landuse.
The effects of increases in CO2 on ecosystem carbon stores were unexpected. Mean growth
responses from the literature for doubling of CO2 are 44% for C3 grasses (Wand et al. 1999)
and 16% for woody species in nutrient stressed conditions (Curtis and Wang 1998). In these
simulations when ungrazed and unburnt, this resulted in a 10-17% increase in mean carbon
storage with the variation due to the different mulga CO2 responses investigated. This is
consistent with the physiological potential of 16%. When grazed but still unburnt, this
28
changed to 7-11% increase in mean carbon store: considerably lower than the potential.
However, under all treatments in which burning occurred, doubling CO2 resulted in a smaller
mean carbon store and less carbon stored over the simulation. This occurred because the
substantial increases in grass growth with doubling of CO2 (34 to 56%) enabled more fire
events to occur and this affected the mulga populations, killing off the establishing cohorts
needed to ensure continued carbon accumulation. The mean effect of increased CO2 on grass
growth across all treatments is 44%. This is identical with the mean effect of doubling CO2
for C3 grasses found in the meta-analysis of Wand et al. (1999) suggesting that this result may
be applicable in other ecosystems where fire has a similar function.
The effect of changing the CO2 response of the mulga had surprisingly little impact on the
emissions dynamics or other aspects of the ecosystems simulated such as fire frequency or
wool production. The high mulga CO2 response slightly reduced the grass CO2 response by
increasing competition. Further investigation of the response of woody plants under increased
CO2 is needed as we have not explored the full array of possible responses. For example, a
scenario with no change in water use from stomatal conductance change but a growth
increase due photosynthetic response would result in increased competition with grasses.
There remain, however, several sources of uncertainty in these analyses apart from those we
explored by using scenarios. For example, the potential burning frequency of 23% when
grazed (baseline GA treatment) appears more frequent than seen on actual properties. This
difference probably reflects maximum possible fire frequency with biological constraints as
opposed to current cultural practice. The frequently burnt scenario (every 6 years) is probably
more representative of current pasture management in the region with 10 burning events in
the historical record. Burning frequency is very sensitive to woody plant density. In a study of
the effects of Eremophilla gilesii on potential fire frequency at Charleville, Carter and
Johnston (1986) showed that with grazing at 20% utilisation an increase in E. gilesii canopy
cover from 7.5% to 10% reduced the frequency of 1000kg yields from 36% to 12%. When
average grass fuel load is close to the threshold required for fire one could expect high model
sensitivity. The ungrazed/annual burn treatment gave results consistent with those of Johnston
and Carter (1986). In the simulations, we also retain domestic stock on the paddock at all
times whereas graziers may spell their paddocks occasionally. Mulga is very sensitive to
grazing (eg Brown 1985) and these spelling periods appear to be vital in allowing the saplings
to grow to a height where they are no longer susceptible to grazing and burning. For these
reasons, these simulations cannot be considered to provide a hindcast trajectory of mulga
spread even though as discussed in Moore et al. (1997), they simulate the correct gross
response observed in various management regimes. In addition, different plant communities
are likely to provide very different interactions between grazing and fire management,
particularly those with unpalatable shrub species. Further studies are needed on such systems.
We do not simulate changes in soil carbon which may be important under some treatments
and scenarios. These lands were likely to have been burnt very regularly as part of aboriginal
land management practices resulting in the possibility of a relatively large pool of elemental
carbon (‘charcoal’). This pool can be as large as 30% of the total soil carbon pool (Skjemstad
et al. 1996). Removal of burning from the system as simulated in some of these treatments
could result in long-term reduction in the ‘charcoal’ pool which may offset part of the
increase in biomass carbon store. The global change scenarios could also alter both carbon
inputs through increasing growth and carbon outputs through increasing decomposition rates
with increased temperature and, in some scenarios, increased soil moisture. The balance
29
between these factors needs evaluating through a model such as CENTURY (Parton et al.
1988).
5.
Summary
•
The possibility of trading greenhouse gas emission permits as a result of the Kyoto
Protocol has spurred interest in developing land-based sinks for greenhouse gases.
Extensive grazing lands which have the potential to develop substantial woody biomass
are one obvious candidate for such activities.
•
However, any such activity needs to take into account the possible impacts on existing
grazing and the possible impacts on fire frequency of continuing CO2 buildup in the
atmosphere and resultant climate change. We use simulation models to investigate these
issues in south-west Queensland.
•
These simulations suggest that this system can be managed to act as either a net source or
a net sink of greenhouse gases under current climate and CO2 and under a range of global
change scenarios.
•
The key component in determining source or sink status is the management of the woody
mulga. Excluding both burning and grazing is the most effective means of permanently
increasing carbon stores and hence reducing net emissions. There are combinations of
management regimes such as excluding fire with light grazing which, on average, allow
productive grazing but transient carbon storage.
•
The effects of increased CO2 on ecosystem carbon stores were unexpected. Carbon stores
increased (7-17%) with doubling of CO2 only in those simulations where burning did not
occur, but decreased when burnt. This occurred because the substantial increases in grass
growth with doubling of CO2 (34 to 56%) enabled more fires, killing off the establishing
cohorts needed to ensure continued carbon accumulation.
•
On average doubling CO2 increased grass growth by 44%, this is identical with mean
literature values suggesting that this result may be applicable in other ecosystems where
fire has a similar function.
•
A sensitivity analysis of the CO2 response of mulga showed only minor impacts. We
discuss additional uncertainties and shortcomings.
•
Another report in this series will document for Australia’s forests the likely changes in
fire regimes with global change, the impacts these changes may have and adaptation
options that could be developed to counter these changes and impacts.
6.
References
Brown, R.F., (1985) The growth and survival of young mulga (Acacia aneura F. Muell) trees
under different levels of grazing. Australian Rangeland Journal, 7:143-48.
30
Burrows, W.H., Carter, J.O., Scanlan J.C. and Anderson, E.R. (1990) Management of
savannas for livestock production in north-east Australia: contrasts across the tree-grass
continuum, Journal of Biogeography, 17:503-512.
Carter, J.O. and Johnston, P.J. (1986) Modelling expected frequencies of fuel loads for fire at
Charleville in Western Queensland. In: Proceedings Third Queensland Fire Research
Workshop, Gatton, 53-67.
Curtis, P.S., and Wang, X. (1998) A meta-analysis of elevated CO2 effects on woody plant
mass, and physiology, Oecologia, 113(3):299-313.
Day, K.A., McKeon G.M. and Carter, J.O. (1997) Evaluating the risk of pasture and land
degradation in native pastures in Queensland. Final Project Report for RIRDC project
DAQ124A.
Hennessy, K.J., Whetton, P.H., Katzfey, J.J., McGregor, J.L., Jones, R.N., Page, C.M. and
Nguyen, K.C. (1998) Fine Resolution Climate Change Scenarios for New South Wales.
Annual Report 1997-98. CSIRO Atmospheric Research.
Howden, S.M., McKeon, G.M., Walker, L., Carter, J.O., Conroy, J.P, Day, K.A., Hall, W.B.,
Ash A.J. and Ghannoum, O. (1999a) Global change impacts on native pastures in south-east
Queensland, Australia, Environmental Modelling and Software, 14, 307-316.
Howden, S.M., McKeon, G.M., Carter, J.O. and Beswick, A. (1999b) Potential global change
impacts on C3-C4 grass distribution in eastern Australian rangelands. In Proceedings of the VI
International Rangeland Congress, pp 41-43, Townsville, Australia.
Houghton, J.T., Meira Filho, L.G., Callander, B.A., Harris, N., Kattenberg, A. and Maskell
K.M. (eds), (1996) Climate Change 1995. The Science of Climate Change, IPCC.
Johnston, P.J. and Carter, J.O. (1986) The role of fire in production systems in western
Queensland: a simulation approach. In: Proceedings Third Queensland Fire Workshop, Gatton,
161-172.
Lauder, A. and Freudenburger, D. (1999) Sustaining a grazing enterprise in south-western
Queensland, Australia, In Proceedings of the VI International Rangeland Congress, pp 10371039, Townsville, Australia.
McKeon, G.M., Day, K.A., Howden, S.M., Mott, J.J., Orr, D.M., Scattini, W.J. and Weston,
E.J. (1990) Northern Australian savannas: management for pastoral production, Journal of
Biogeography, 17:355-72.
McKeon, G.M., Hall, W.B., Crimp, S.J., Howden, S.M., Stone, R.C., Jones, D.A. (1998)
Climate change in Queensland’s grazing lands. I. Approaches and climatic trends, Rangeland
Journal 20:151-176.
Moore, J.L., Howden, S. M., McKeon, G.M., Carter, J.O. and Scanlan, J.C. (1997) A method
to evaluate greenhouse gas emissions from sheep grazed rangelands in south west
Queensland. In Modsim ’97 International Congress on Modelling and Simulation
31
Proceedings, 8-11 December, University of Tasmania, Hobart (Eds McDonald, D.A. and
McAleer, M.). Modelling and Simulation Society of Australia, Canberra. pp137-142.
Parton, W.J., Stewart, J.W.B. and Cole, C.V. (1988) Dynamics of C,N,P and S in grassland
soils: a model. Biogeochemistry, 5:109-131.
Skjemstad J.O., Clarke, P. Taylor, J.A., Oades, J.M. and McClure, S.G. (1996) The chemistry
and nature of protected carbon in soil. Australian Journal of Soil Research, 34:251-271.
Wand, S.J.E., Midgley, G.F., Jones, M.H. and Curtis, P.S. (1999) Response of wild C4 and C3
grass (Poaceae) species to elevated atmospheric CO2 concentration: a meta-analytic test of
current theories and perceptions, Global Change Biology, 5:723-741.
32
4.
Potential impacts on C3-C4 grass distributions in
eastern Australian rangelands
S.M. Howden, G.M. McKeon, J.O. Carter and A. Beswick
1.
Introduction
In the rangelands of eastern Australia, livestock productivity is influenced by the proportion
of C3 and C4 grasses in the sward (Wilson and Minson 1980). The proportion of C4 grasses
(measured as % of native grass species within regional floristic lists) declines strongly with
declining spring and summer temperatures (Hattersley 1983). Temperature and other climatic
elements are likely to change in the future due to ongoing human activities such as the
burning of fossil fuels. Henderson et al. (1994) suggested that global warming of 4oC may
result in the 50% C4 isoline moving southwards by about 250km. The latitude where the
frequency of C4 species is 90% may move southwards by up to 500km in eastern Australia.
Changes in CO2 concentrations affect water use efficiency of grasses and, in the case of C3
species, photosynthetic rates and nitrogen use efficiency (eg Lutze and Gifford 1999) and
these could also be expected to impact on the relative distributions of C3 and C4 species.
Ehleringer et al. (1997) have assessed implications of altering CO2 on the ‘temperature
crossover point’ for quantum yields of C3 and C4 plants based on a simple physiological
photosynthesis model and measurements. Their approach indicates a crossover point of about
21oC (mean growing season temperature) for current CO2 levels for NADP-ME C4 subtypes
(the dominant subtype in eastern Australia) with this corresponding to observed change in
biomass from C3 to C4 grasses in the US (Epstein et al. 1997). However, this approach
suggests that this ‘crossover point’ will increase to about 35oC when CO2 rises to 700 ppm (eg
about year 2100) implying that the majority of Australia would become dominated by C3
grasses even considering maximum likely temperature increases.
Thus in assessing the likely impacts of global change on rangeland vegetation there is a need
to reconcile these opposing hypotheses derived from expected temperature and CO2 change.
Furthermore, the analyses to date for Australia have focussed on the relative frequency of C3
and C4 species not the likely pasture production which is both the basis of the grazing
industries in these regions and ecosystem dominance. However, in Australia there is no
comprehensive database of pasture production that would allow such an analysis. We use
instead a pasture growth model validated for tropical C4 pastures (GRASP: Day et al. 1997)
parameterised to represent three generic functional groups (cool climate C3, warm climate C3
and C4 grasses) to develop a relationship between floristic representation and C3 and C4
functional group biomass production for eastern Australian rangelands. We then evaluate
global change scenarios.
2.
Methods
Growing season (Sept-April) temperatures were calculated from historical data (1958 to
1988) for 31 sites distributed across the eastern Australian rangelands (Fig 1). GRASP was
parameterised using mean values from the predominantly C4 pastures across northern
33
Australia (160 site-by-year sets of pasture growth measurements). Whilst there are
considerable data to support C4 parameterisations, there are few equivalent data for native C3
grasslands. The temperature response functions of the cool and warm C3 groups were altered
to optima of 15-25oC and 18-30oC respectively. The minimum nitrogen content (ie the N%
where nitrogen dilution stops growth) was assumed to be 0.88% compared with 0.68% for C4
grasses. We did not change transpiration efficiency nor potential regrowth rates of C3 groups
due to lack of information. Average annual biomass for each functional group was simulated
using climate data for 1958 to 1988. The C3 functional group with greatest simulated biomass
at each site was then used to calculate a growth index (C4/C3 biomass) which was regressed
against observed %C4 frequency interpolated from Hattersley (1983).
N
0
km
500
Figure 1. The eastern Australian rangelands showing the line where growth index equals 1
for the current climate and CO2 levels (solid line), the mulga lands which are the
northernmost extent of grasslands with a significant C3 component in eastern
Australia (shaded area) and the existing 50% C4 isoline from Hattersley (1983)
(dashed line). The transect in Table 1 is represented by the dotted line with
included sites marked (fi) and other sites marked ( ).
34
The simulation studies were a factorial combination of current CO2/doubled CO2 (700ppm)
and temperature change of 0 or 3oC applied to both maximum, minimum and dewpoint
temperatures. Modifications to GRASP to simulate doubled CO2 response for C4 grasses and
for C3 grass transpiration followed Howden et al. (1999) and change in radiation use
efficiency and leaf critical nitrogen content for C3 species based on Reyenga et al. (1999).
The impact of CO2 increase on minimum nitrogen contents has been rarely studied and hence
uncertain.. Wheat and some cultivated grasses show no discernible change in this parameter
(eg Conroy and Hocking 1993) but a recent study of a native C3 grass indicated a substantial
decline with doubled CO2 (Lutze and Gifford 1999). We thus ran the doubled CO2 scenarios
with this value both set to current levels (0.88%) and reduced by 25% so that it was the same
as the C4 grasses.
3.
Results
Mean growing season (Sept-April) temperature was 20.5oC at the sites where 50% of grasses
were C4 which is similar to the crossover point based on quantum yields. The location of the
isoline where the growth index equals 1 corresponds closely with the northernmost extent in
Queensland of the rangelands with a significant C3 component (Fig 1). However, the position
of this line is likely to change with C3 parameterisation as more data become available. The
percent C4 species in the native flora was related curvilinearly with the growth index (G)
C4%=95.59(1-e-2.918G), R2=0.84, P<0.0001).
Southwards movements of the two isolines for growth index=1 and 50% C4 frequency
occurred under the 2CO2 (~150km), 2CO2+3oC (~250km) and 3oC (~100km) scenarios and
there was a corresponding increase in C4% in the southern regions especially under the
scenarios with temperature increase (Table 1). In contrast, in the scenarios in which increased
CO2 was assumed to reduce the minimum nitrogen content, there was a northwards movement
of the growth index isoline (~250-300km) but the movement of the 50% C4 isoline varied
with scenario, being slightly (~100km) northward under the 2CO2 scenario but substantially
southward under the 2CO2+3oC scenario.
Table 1
Growth index values for seven sites across the transect (Figure 1) for scenarios
with factorial combination of +- CO2 increase and + 3oC increase. The two
scenarios with CO2 change are repeated but with the C3 functional types minimum
N content reduced from 0.88% to 0.68% (LN). Simulated % frequency of C4
values are in parentheses.
Site
CO2
2CO2
Horsham
Balranald
Bourke
Cunnamulla
Charleville
Roma
Emerald
0.23 (46)
0.33 (59)
0.50 (73)
0.54 (76)
0.87 (88)
1.07 (91)
1.27 (93)
0.27 (52)
0.35 (61)
0.54 (76)
0.60 (79)
0.96 (90)
1.16 (92)
1.27 (93)
2CO2 LN
0.21 (44)
0.27 (52)
0.42 (67)
0.47 (72)
0.77 (86)
0.90 (89)
0.99 (90)
+3oC
0.41 (67)
0.43 (69)
0.68 (82)
0.66 (82)
0.94 (89)
1.10 (92)
1.28 (93)
2CO2 +3oC 2CO2 +3oC
LN
0.48 (72)
0.37 (63)
0.48 (72)
0.37 (63)
0.67 (82)
0.55 (76)
0.72 (84)
0.57 (78)
1.01 (91)
0.81 (87)
1.18 (93)
0.91 (89)
1.28 (93)
1.01 (91)
35
4.
Discussion
The 50% C4 isoline occurs close to where it is predicted on the basis of relative quantum
yields for current CO2 concentrations. However, the quantum yield approach does not
incorporate effects caused by seasonality of rainfall nor nitrogen dynamics which we
hypothesise result in significant C3 representation in communities occurring far north of this
point. We used a simulation approach to incorporate these factors in calculating a growth
index. The observed northern boundary of where C3 grasses contribute substantially to native
pasture production corresponds to a line with a growth index of 1, suggesting a bioclimatic
component to current distributions. Major differences in soil-species-grazing management
associations occur in this region and hence possible bioclimatic effects may be overridden by
these non-climatic factors. Improvement of C3 parameterisation is required before further
direct comparison of growth of functional types can be made.
Global change scenarios suggest possible changes to this simulated ‘boundary’ and to
frequency of C3/C4 functional group representation south of this. Temperature increases of
3oC, particularly under the doubled CO2 scenario, generally moved southward both the line
where the growth index equals 1 and the 50% C4 line consistent with the changes presented
in Henderson et al. (1994). The effects of CO2 increases alone were dependent upon the
parameterisation of nitrogen dynamics in the grasses. If no change was made to the minimum
nitrogen content (eg Conroy and Hocking 1993), then these lines moved southwards whilst if
minimum nitrogen content was reduced (eg Lutze and Gifford 1999) then the lines moved
northwards substantially. This occurred because increased N dilution enabled expression of
the enhanced photosynthetic and water use efficiency by C3 groups under higher levels of
CO2. Under the doubled CO2 and temperature increase scenario with reduced minimum
nitrogen content, there was both a small movement north of the growth index line but a
southwards movement of the 50% C4 isoline. These uncertainties suggest improved
understanding is needed of the nitrogen dynamics of native grasses under global change,
particularly in quantifying the minimum nitrogen content which is critical to biomass
accumulation in these infertile semi-arid rangelands. However, none of the doubled CO2
scenarios studies resulted in the major changes in distributions suggested by the quantum
yield analysis (Ehleringer et al. 1996).
5.
Acknowledgements
We would like to thank Dr Peter Johnston for discussions on the distribution of grasses in
Queensland
6.
References
Conroy, J. and Hocking, P.J. (1993) Nitrogen nutrition of C3 plants at elevated atmospheric
CO2 concentrations. Physiologia Plantarum, 89:570-576.
Day, K.A., McKeon, G.M. and Carter, J.O. (1997) Evaluating the risks of pasture and land
degradation in native pasture in Queensland. Final report for Rural Industries Research and
Development Corporation. Project DAQ124A.
36
Ehleringer, J.R., Cerling, T.E. & Helliker, B.R. (1997) C4 photosynthesis, atmospheric CO2,
and climate. Oecologia, 112, 285-299.
Epstein, H.E., Lauenroth, W.K., Burke, I.C. and Coffin, D.P. (1997) Productivity patterns of
C3 and C4 functional types in the US Great Plains. Ecology 78, 722-731.
Hattersley, P.W. (1983) The distribution of C3 and C4 grasses in Australia in relation to
climate. Oecologia, 57, 113-128.
Henderson, S., Hattersley, P., von Caemmerer, S. & Osmond, C.B. (1994) Are C4 pathway
plants threatened by global climatic change ? In: Schultz, E-D., Caldwell, M. (eds)
Ecophysiology of photosynthesis. Ecological studies, Vol 100, pp 529-549. Springer-Verlag,
New York.
Howden, S.M., McKeon, G.M., Walker, L., Carter, J.O., Conroy, J.P., Day, K.A., Hall W.B.,
Ash, A.J.,& Ghannoum, 0. (1999). Global change impacts on native pastures in south-east
Queensland, Australia. Environmental Modelling and Software, 14:307-316.
Lutze, J.L. & Gifford, R.M. (1999) Nitrogen accumulation and distribution in Danthonia
richardsonii swards in response to CO2 and nitrogen supply over four years of growth. Global
Change Biology, (in press)
Reyenga, P.J., Howden, S.M., Meinke, H. and McKeon, G.M. (1999) Modelling global
change impacts on wheat cropping in south-east Queensland, Australia. Environmental
Modelling and Software, 14:297-306.
Wilson, J.R. & Minson, D.J. (1980) Prospects for improving the digestibility and intake of
tropical grasses. Tropical Grasslands, 14: 253-259.
37
5.
Climate change impacts on heat stress and water
requirements of cattle in Australia
S.M. Howden, J.R. Turnpenny W.B. Hall and D. Bruget
1.
Introduction
Heat stress is a significant issue for the productivity of livestock grazing in tropical areas of
Australia (e.g. Petty et al. 1998) and these stresses are likely to increase with prospects of
global warming due to the accelerating emission of greenhouse gases (e.g. Houghton et al.
1996). Heat stress in livestock has been reported to decrease conception rates and increase
foetal and postnatal mortalities (Finch 1983; Berman 1991), impair spermatogenesis
(Entwhistle 1992), increase urinary nitrogen loss (O’Kelly 1988) and increase susceptibility
to a range of parasitic and non-parasitic diseases (e.g. Finch 1983). Heat stress has also been
found to decrease growth rates (e.g. Frisch 1981, Hahn 1985) although there appears to be
strong compensatory growth responses when animals are returned to less stressful conditions
following drought (G. McKeon unpublished data). High heat loads also result in greater water
requirements for livestock (e.g. King 1983). Lack of availability of water due to the large
distances from water sources that tropical animals sometimes have to forage, can reduce
production directly through reductions in metabolic rates and feed intake (e.g. Utley 1970),
and indirectly by reducing the area grazed through restriction of distance travelled from
watering points (e.g. Noble 1975).
Heat stress in cattle has been analysed using two main approaches. Firstly, through the use of
relationships such as the Temperature-Humidity Index (THI; Johnson et al. 1963) which
relates stress to both daily maximum temperature and dewpoint temperature. This
relationship, originally derived for dairy cattle with high intake and metabolic rates, has been
shown to be a robust predictor of heat stress in cattle, being related to reduced liveweight gain
in beef cattle (Petty et al. 1998), milk production in dairy cattle (Hahn and Oosburn 1969),
conception rates (Hahn 1981) and mortality rates (Hahn 1985) and distribution of beef cattle
varieties in Africa (King 1983). The THI has also been used operationally for heat stress
assessment in dairy cattle in South Africa (Du Preez et al. 1990) and the USA (Hahn and
Mader 1997). The THI has minimal input requirements and has been used in a variety of
environments, suggesting it is suitable for the broad scale assessment of issues such as climate
change impacts. However, King (1993) and Finch (1983) suggest that effective evaluation of
the implications of heat stress for beef cattle in extensive grazing systems requires additional
measurements of net radiation load and convection. Furthermore, the THI approach does not
explicitly incorporate important factors such as coat colour which influence heat loads and
thus provides little information on how to direct selection programs to enhance heat loss
mechanisms (Finch 1983). An alternative approach is to explicitly model fluxes of water and
energy from animals using a physically-based approach such as that in Turnpenny et al.
(1997). However, this approach requires considerable parameterisation information and
intensive data input (i.e. hourly meteorological variables) which are often unavailable.
The second approach uses physically-based models to simulate evaporation from the skin
surface of livestock using an energy balance model of the animal. This provides the
38
possibility of modelling water requirements of livestock by using a mass balance approach
which accounts for other water gains (e.g. in feed) and losses (e.g. urine).
The aim of this study was to compare the two approaches to analysing heat stress on
livestock, to develop an approach for modelling water requirements of livestock in the
Australian tropics, and to investigate how both heat stress and water requirements are likely
to be affected under plausible climate change scenarios.
2.
Heat stress model descriptions
The THI is calculated as:
THI =
Tmax + 0.36Tdewpoint + 41.2
where Tmax is daily maximum dry bulb temperature (oC) and Tdewpoint is dewpoint temperature
(oC)
A series of mathematical models have been developed to predict the metabolic rate and
occurrence of thermal stress in different livestock species (Turnpenny 1997). These models
calculate heat loss from model animals using hourly meteorological data as input and assess
the degree of thermal stress under different combinations of weather conditions. The models
were designed to be applicable to as wide a range of conditions as possible, and as such have
been based on animal physiology and the physics of heat transfer rather than empirical
relationships.
The model for beef cattle was developed as a system of cylinders with rounded ends, and
incorporates three layers - the underlying tissue, the coat and the external environment. By
specifying the thermal environment, metabolic rate and weight of the animal, the temperatures
of the layer interfaces are calculated by solving the energy balance at each interface. This
allows the heat loss from the animal to be calculated. Physiological responses to heat and cold
stress, including sweating and varying blood flow to the peripheries have been parameterised.
Further details of the general energy balance model are in Turnpenny et al. (1997), and a
more in-depth discussion of considerations for outdoor animals, such as shade and shelter
requirements can be found in Turnpenny (1997). The beef cattle model is described briefly in
Parsons et al. (submitted). The model requires hourly data for the environmental inputs which
we generated by downscaling daily climate data using the approach of Turnpenny (1997).
Briefly, hourly values of the following variables: cloud cover fraction, direct and diffuse
components of solar radiation, air temperature, radiant temperature of the sky, ground
temperature and precipitation were calculated from daily observations of temperature,
rainfall, vapour pressure and incident solar radiation. Solar radiation is generated by
comparing the top of the atmosphere total radiation with the measured total, and deducing an
atmospheric transmittance which is related to cloud cover fraction. The direct and diffuse
components for each hour are then calculated empirically. Air temperature is a sine function
between the maximum and minimum, with the maximum occurring at 2 pm, and the
minimum at 2 am. Sky temperature is an empirical function of cloud cover and air
temperature while ground temperature is found by solving a simple energy balance. Vapour
pressure is assumed constant over the day. Wind speed data were not available, so a typical
39
value of 2 m/s was used for all times. Precipitation was distributed using a triangular function
centred on midday.
Brahman (Bos indicus) cattle are now widely used throughout the Australian tropics. Their
efficient sweating responses providing superior thermoregulatory ability over those of Bos
taurus breeds (Finch et al. 1982) for which the heat loss model was originally parameterised.
The model was re-parameterised for Brahman cattle using information in Finch et al.
(1982,1984). The simulations reported here were for a 425 kg animal. Key parameter changes
for these simulations were thermoneutral heat production of 600W, coat albedo 0.5 and 15
mm coat depth on the body and 3 mm on head and legs.
3.
Water requirement model
Cattle have a very efficient sweating mechanism which increases heat loss when the
conditions are too hot for the metabolic heat production and heat gain from the environment
to be dissipated by sensible heat loss alone. Under hot conditions, a 500 kg cow can lose up to
one litre per hour through sweating alone (Thompson 1973, Webster 1974). The thermal
balance model described above calculates the evaporative water flux (E) from the skin given
the environmental conditions. This flux can then be combined with estimates of water gain
from metabolic water production (M), water in the feed (F) and water in the urine (U) and
faeces (S) in a mass balance approach to calculate water intake:
Water intake (l/day) = E + U + S - M - F
F depends on the feed intake per unit time, and the water content of the forage which can vary
from 0% to more than 80% of dry matter (DM) depending on the growing conditions and
species (Wood et al. 1996). In this study, the water content of the forage was assumed to be a
constant 35% with an intake of 12 kg DM/day (for the 425 kg animal modelled here). M was
modelled as a linear function of the total metabolic heat production (600 W for a 425 kg
animal) following Brown and Lynch (1972). The fraction of water intake lost as urine and
faeces remained constant at 32% and 35% respectively following the results of Colditz and
Kellaway (1972) for Brahman cattle in tropical Australia under a range of temperature
conditions. Additional water is needed if there is a high salt concentration in the diet,
however, this is generally not a major issue in tropical Australia unlike the saltbush (Atriplex
spp.) shrublands of temperate Australia.
4.
Single site modelling study
The two approaches for calculating heat stress were tested for one site (Gayndah, Qld.,25.7oS,
151.8oE) in subtropical Australia for the forty years 1957-96. Daily climate data for maximum
and minimum air temperatures, rainfall, evaporation, total solar radiation on a horizontal
surface and vapour pressure (HPa) were retrieved from climate surfaces (Carter et al. 1996)
and then downscaled to hourly data for the physically-based approach.
Dewpoint was calculated following McKeon et al. (1998) as:
40
Tdewpoint
=
(237.3*ln(VP/6.107))/(17.269-ln(VP/6.107))
To enable comparison with the THI, a daily heat stress index (HSI) was constructed from the
physically-based model by averaging the calculated ratio between hourly evaporative flux
density needed to maintain homeostasis and the maximum evaporative flux for the five hours
from 11am to 3pm. Daily modelled water intake was calculated using the mass-balance
approach described earlier for the years 1957 (driest year of the record - 220mm) and 1959
(wettest year - 945mm). These years were chosen as they represent the extremes available in
this 40 year record. The THI was compared with the stress index from the physically-based
model for all years and the calculated water intake was compared with both indices.
Thermal stress is likely to occur in beef cattle when THI exceeds a threshold value of 79-80
(Hahn and Mader 1997, D. Mayer pers. comm.) and days where this occurred are
subsequently termed ‘heat stress days’. Regression analysis was used to determine if there
was any trend of change in the frequency of heat stress days during the past 40 years.
A climate change scenario (2xCO2) was constructed based on the Australian 1996 CSIRO
scenarios (http:/www.dar.csiro.au/pub/programs/climod/cm4.htm) for a doubling of
atmospheric CO2 concentration for the mid-range emissions scenarios and mid-range climate
sensitivities. This suggested a 2.76oC increase in temperatures in this location happening in
around the year 2100. The historical temperature record was modified by increasing both the
daily maximum and minimum temperatures and then recalculating the hourly data. The
climate change scenarios also suggest rainfall changes (summer -24% to 0% and winter -12%
to +12%), however these were not included in these simulations as they are likely to have
minor secondary impacts compared with the temperature and associated changes.
5.
National modelling study
Daily climate data for maximum air temperatures and vapour pressure (VP; HPa) were
retrieved from climate surfaces (Carter et al. 1996) to give values on a 5km grid across
Australia for the period 1957 to 1997. Dewpoint was calculated as before.
We recorded the frequency of days (%) above the THI threshold of 80 for each year to
produce a map of mean frequencies across the historical period. For each grid cell, the
frequency of heat stress days was regressed against year from 1957 to 1997 to determine if
there was a trend over time. A climate change scenario was constructed as before and this
suggested a general 2.7oC increase in temperatures for the year 2100. We have not varied this
by region as this would have resulted in geographic discontinuities in model response. The
historical temperature record was modified by increasing both the daily maximum and
dewpoint temperatures by 2.7oC. The frequency of heat stress days using the climate change
scenario record was then recalculated and mapped.
6.
Results
41
Site-based analysis
Simulated daily water requirements were highly correlated with daily mean temperatures for
both the wettest and driest years on the climate record used for Gayndah (e.g. Figure 1). There
was no significant difference between the regressions (p <0.001) for the two years with a
pooled regression being:
Water requirement (l/day) = 1.30 Tmean - 1.59
Simulated water requirements using this regression were 20.5, 29.6, 40.0 and 47.8 litres/day
for 17oC, 24oC, 32oC and 38oC respectively. Measured values of water requirements for
Brahman cattle in controlled temperature rooms are 30, 36, 38 and 44-49 litres/day for the
above temperatures (Colditz and Kellaway 1972, O’Kelly and Reich 1981) indicating good
agreement at temperatures of 32oC or higher.
Water requirements (l/day)
40
35
y = 1.2895x - 1.4125
R2 = 0.9812
30
25
20
15
10
5
0
0
10
20
o
Temperature ( C)
30
40
Figure 1. Variation of simulated water requirements (l/day) with daily mean temperature (oC)
for Gayndah for the year 1957.
THI
There were strong linear relationships between the daily THI values and the HSI (Figure 2:
r2= 0.84) and between simulated water requirements and THI (Figure 3: r2 = 0.96). The
relationship between water requirements and the HSI (Figure 4) was less strong (r2 = 0.74)
with significant variation due to other factors such as solar radiation, evaporation and vapour
pressure.
100
90
80
70
60
50
40
30
20
10
0
y = 53.696x + 34.958
R2 = 0.8369
0
0.2
0.4
0.6
0.8
Stress Index
1
1.2
Figure 2: Relationship between daily THI and the daily stress index from the physicallybased model.
42
Water requirements (l/day)
45
40
35
30
25
20
y = 50.583x - 11.37
R2 = 0.7382
15
10
5
0
0
0.5
1
Stress Index
1.5
Water requirements (l/day)
Figure 3: Relationship between simulated daily water requirements and the daily stress
index from the physically-based model.
45
40
y = 0.9803x - 47.116
R2 = 0.9553
35
30
25
20
15
10
5
0
0
20
40
60
80
100
THI
Figure 4: Relationship between simulated daily water requirements and daily THI.
The incidence of days that exceeded a THI threshold value of 80 has increased significantly
(P < 0.01) during the past 40 years (Figure 5) with the regression being:
Frequency (days/year) = 0.7028*year - 1331 (r2= 0.17)
Inspection of the data showed that high frequencies of days exceeding the threshold occurred
in drought years.
Frequency of THI stress
(days/year)
120
100
y = 0.7028x - 1330.6
R2 = 0.169
80
60
40
20
0
1950
1960
1970
1980
1990
2000
Year
Figure 5: Annual frequency of days when THI greater than 80 for the years 1957-1996.
Median water requirements were increased by about 13% under the climate change scenario
compared with current conditions (Figure 6) whilst this increase was only 7% for the HSI
43
(Figure 7) and 5% for the THI (Figure 8). However, in the climate change scenario there were
92 days where the HSI exceeded a value of 1 whereas there were no days which this occurred
in the current climate scenario. Heat stress index values greater than unity mean that the cattle
can no longer thermoregulate via sweating alone and that they need to either start to pant or
adopt behavioural changes (i.e. seek shade or stand head-on to the sun) to avoid hyperthermia.
In contrast the highest HSI value for the climate record 1957-1997 was 0.92.
Probability of exceedence (%)
The THI threshold value of 80 was exceeded on only 16% of days under the current climate.
Under the climate schange scenario this occurred on 38% of days.
100
90
80
70
60
50
40
30
20
10
0
1xCO2
2xCO2
0
10
20
30
40
Water requirements (l/day)
50
Probability of exceedence (%)
Figure 6: Probabilities of exceedence of water requirements (l/day) under the current climate
and with the climate change scenario.
100
90
80
70
60
50
40
30
20
10
0
1xCO2
2xCO2
0
0.5
1
1.5
Heat Stress Index
Probability of exceedence (%)
Figure 7: Probabilities of exceedence of the heat stress index under the current climate and
with the climate change scenario.
100
90
80
70
60
50
40
30
20
10
0
1xCO2
2xCO2
0
20
40
60
80
100
THI
Figure 8: Probabilities of exceedence of the THI under the current climate and with the
climate change scenario.
National analysis
44
The current mean frequency of heat stress days decreases substantially north (70-80% of
days) to south (0-10%; Figure 9) with a broad band of low heat stress incidence along the
southern and eastern coastlines. Under the climate change scenario, there is a marked increase
in mean frequency of heat stress days in most locations with an increase of 10-20 percentage
points occurring across most of the Australian rangelands and with greater increases (about
30 percentage points) in the Northern Territory and northern Queensland (Figure 10).
Figure 9: Average frequency of heat stress days 1957-1998
Figure 10: Average frequency of heat stress days: 2.7oC climate change scenario
45
The frequency of days in which THI exceeded the threshold value of 80 has increased
significantly since 1957 for large areas of northern, central and eastern Australia (Figure 11).
However, in a large part of West Australia and southeast Australia there has been little or no
increase with declines in some regions.
Figure 11: Change in frequency of heat stress days (%/year) 1957-1998
7.
Discussion
Mean frequencies of heat stress days currently vary markedly across Australia with
frequencies of 70-80% occurring at the top of the Northern Territory and 0-10% in a broad
band across coastal southern and southeast Australia. Under the climate change scenario used
here, the pattern of heat stress shifts generally southwards with frequencies of 90-100%
experienced in the top of the Northern Territory and western Cape York and the band where
frequencies are 0-10% shrinks markedly to encompass small areas in southern Western
Australia and southeast Australia. Generally there is an increase of about 10 to 20 percentage
points in heat stress frequencies although in some locations, such as Arnhem Land in the
Northern Territory, the increase is greater.
The incidence of heat stress as measured by the frequency of days with THI greater than 80
has increased significantly over the 40 year record at both the Gayndah site and more
generally over large areas of Australia although there are also regions where there has been
no change through to a small decline. This increase is consistent with recorded increases in
maximum temperatures (e.g. Wright et al. 1996) and relative humidity (McKeon et al. 1998)
and also the incidence of drought as drought years appear to be also associated with high
stress frequencies. The changes experienced at Gayndah over the past 40 years
(approximately 60% increase over the period) are proportionately similar to those suggested
over the next 100 years in the climate change scenario (138% increase compared with the
mean of the 1957-97 period).
We found a strong correspondence between the empirical THI and the stress index
constructed from the physically-based model using the ratio of energy loss from evaporative
46
transfer needed to maintain homeostasis against the maximum possible loss under those
environmental conditions. This suggests that studies of heat stress in cattle using the more
empirical THI model remain useful indicators of animal stress even though they use much
lower levels of data input. However, the physically-based model used here provides an
additional capability to investigate specific situations where the data is available.
The physically-based model simulates water requirements of Brahman cattle effectively at
temperatures at or above 32oC but tends to underpredict them at lower temperatures when
compared with measured water needs in controlled environment rooms (Colditz and Kellaway
1972, O’Kelly and Reich 1981). However, total heat loads in such rooms are not necessarily
equivalent to those experienced in the field at the same nominal temperature (e.g. Finch
1983). Finch suggests that the heat exchanges are approximately equivalent in the two
environments in the range of 35 to 45oC, suggesting that the model is performing well but that
further testing is required at lower temperatures.
Strong linear relationships were found between modelled water requirements and both
temperature and THI as we would expect. This suggests that relationships similar to those of
King (1983) between water requirements and readily available climate data could be
developed for broad scale studies. In contrast, the relationship between water requirements
and the stress index derived from the physically-based model was less robust with factors
such as solar radiation, precipitation and vapour pressure adding variability.
Simulated water requirements at Gayndah increased significantly (~13%) under the climate
change scenario used here when compared with current conditions suggesting that any
overgrazing near watering points is likely to be exacerbated under global change. Hence, the
smaller median increases in the two indices (5% for THI and 7% for the stress index)
understates the potential significance of the climate change on animal stress. Additionally, in
the current climate, the animal temperature regulation was achieved by sweating alone on all
days, with the maximum ratio of evaporative flux density being 0.92. However, under climate
change, the value of this index exceeded unity on 92 days in the record suggesting that
physiological and behavioral change would be needed by animals to avoid hyperthermia. As
found more generally, climate change at Gayndah is likely to increase frequency of heat stress
days with the current frequency of 16% of days more than doubling (to 38%) under the
climate change scenario used here.
These results suggest that past selection for cattle lines with effective thermoregulatory
control (e.g. Hammond et al. 1996) are likely to need to be continued into the future if
livestock productivity is to be maintained across northern Australia. However, Finch et al.
(1982) found that thermoregulatory control was negatively correlated with metabolic rate
suggesting that it may be difficult to combine the desirable traits of adaptation to high
temperature environments with high production potential in cattle, although there are
possibilities for selection of coat colour and characteristics that may provide some
opportunities for selection of heat tolerance Finch et al. 1984). A physically-based model
such as that used here could be useful in determining the significance of different sets of
thermoregulatory mechanisms for selection programs. There may also be a need on a
continuing basis to proactively research and implement management strategies to offset
increased heat stress (e.g. Hahn and Mader 1997, Blackshaw and Blackshaw 1994)
The current work provides a simple assessment of the drinking requirements of cattle in
tropical environments. However, some of the assumptions could be improved, for example,
47
the parameterisations of metabolic water production and water from feed intake. The
inclusion of wind run data is also needed to determine the effect this variable may have.
Variations in feed intake and feed quality which occur in the field could be accounted for by
linking the model to existing or new diet selection or feed intake models. The hourly weather
data could be improved, either by using measured hourly data or using more complicated
statistical techniques to downscale variables like rain. At present, the rainfall scheme may
lessen the impact of high solar radiation in the middle of the day or the combination of high
winds and rainfall at night. Finally, more data on the physiology of tropical animals such as
properties of the coat and tissue insulation are needed to allow a full model of tropical cattle
to be built.
8.
Acknowledgements
We would like to thank the Queensland Department of Natural Resources for supplying the
daily weather data and Dr Bob Hunter (CSIRO Animal Production) for background reading
materials on the topic. Thanks also to Prof. Ian Noble for discussions and ideas on the water
balance models, to Penny Reyenga and Drs Mikhail Entel and Barney Foran for comments on
the manuscript and to the UK Ministry of Agriculture, Nottingham University and the Royal
Meteorological Society for funding for travel by JT. This work was partly funded by the
climate research program of Environment Australia and the Climate Variability in Agriculture
Program administered by the Land and Water Resources Research and Development
Corporation.
9.
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51
6.
Past and future competitiveness of wheat and beef
cattle production in Emerald, NE Queensland.
Howden, S.M., McKeon, G.M., Reyenga, P.J., Entel, M. Meinke, H. and Flood, N.
1.
Introduction
Managing climate variability is a key requirement for Australian livestock and cropping
industries (eg Meinke and Hammer 1995). Farm management and government policies
require analyses based on probabilistic information (eg Stafford Smith and McKeon 1999).
These analyses typically use climate information from a 40 to 100 year record. However,
recent evaluations suggest that there is long-term variability in our climate record (eg Isdale et
al. 1986; Power et al. 1999) and this along with the possible emergence of trends related to
climate change and increased atmospheric concentrations of carbon dioxide (CO2) raise issues
about the applicability of such evaluations (McKeon et al. 1998). Furthermore, such
evaluations typically focus on one industry in isolation whereas shifts between industries can
be a rational response to climate variations. For example, the peanut industry further south in
Queensland expanded considerably in response to favourable climatic conditions followed by
retraction when these conditions reverted (Meinke and Hammer 1995). There is thus a general
expectation that agricultural industries at the climatic margin will be most affected by changes
in climate means and variability and are thus appropriate locations for study of long-term
industry viability.
Emerald, north-east Queensland (23o 34’ S, 148o 11’ E) is at the northern margin of the wheat
cropping region of Australia. The Emerald region was previously used predominantly for
grazing beef cattle, however, cropping has developed in importance over the past 30 years
(Figure 1). The trends in climate documented by McKeon et al. (1998) suggest that it is
possible that the relative suitability of cropping versus grazing in Emerald is an artifact of
recent climate. In addition, increasing concentrations of CO2 in the atmosphere may have had
an impact on yields. Expectations of further changes in climate and CO2 concentration
suggest that relative productivity of the grazing and cropping landuses will change in the
future (Howden et al. 1999b). The implications for industry and policy will be different if
change in cropping area is due to long-term variability or to climate change. We use
simulation models of grazing systems (GRASP) and wheat cropping systems (I_WHEAT) to
assess the relative biological productivities of these two landuses over the last 108 years using
a daily climate record and recorded CO2 concentrations. We compare these results with a set
of global change scenarios consisting of combinations of CO2 and climate change.
2.
Methods
2.1
GRASP description
GRASP (McKeon et al. 1990) is a model simulating the above-ground yield of a sward
dominated by perennial native grasses. A full description of each equation is given in
Littleboy and McKeon (1997). Evaluation, calibration and validation are described in Day et
al. (1997) including for the Dichanthium pastures where cropping occurs in Emerald. GRASP
52
Area (ha)
was run with a responsive stocking strategy aiming to use 20% of the standing biomass
present at the 1st June.
45000
40000
35000
30000
25000
20000
15000
10000
5000
0
1960
1970
1980
1990
2000
Year
Figure 1: Area of wheat cropping in the Emerald Statistical Local Area
The changes to GRASP to incorporate CO2 effects are extensions of those described in
Howden et al. (1999a). Parameterisations of GRASP were largely made through
experimentation around 1990 when CO2 levels were about 355ppm and all parameter changes
were scaled against this CO2 concentration. Parameters relating to transpiration were altered
linearly with CO2 concentration between values of 280ppm (pre-industrial levels) and
700ppm (doubled current levels) using the relationships in Howden et al. (1999a). Similarly
radiation use efficiency (RUE) was varied linearly to increase by 5% with CO2 concentration
of 710ppm and reduced linearly by 8% at 280ppm.
2.2
I_WHEAT description
I_WHEAT is a wheat crop module of the APSIM modelling system which simulates soil
moisture, soil carbon:nitrogen dynamics and residue management. A full description of
I_WHEAT and model testing is given in Meinke et al. (1998). Changes to I_WHEAT to
simulate variable CO2 environments are documented in Reyenga et al. (1999a). Management
and soil parameterisations are as in Howden et al. (1999b). I_WHEAT adequately simulates
yield from statistical areas (Howden et al. 1999b).
Regressions of dewpoint as a function of maximum and minimum temperature (Eq’n 1) were
calculated from the post-1957 Emerald record, and pan evaporation was calculated as a
function of maximum and minimum temperature and solar radiation (Eq’n 2) from the 19751994 period when Emerald pan values were more likely to be accurate. These relationships
were then used to calculate pan evaporation and dewpoint consistently over the whole climate
file. GRASP uses dewpoint, assumed to be reached at minimum daily temperature, to
calculate vapour pressure deficit. Simulations with measured humidity and pan evaporation
against the reconstructed values showed strong correspondence (within 5%) and no difference
in average values over 40 years (data not shown).
Dewpoint (oC) = 1.45 - 0.0448Tmax + 0.884Tmin (r2=0.727, n=13000)
(Eq’n 1)
Pan evaporation (mm/day) = -3.60 + 0.126Tmax + 0.101Tmin + 0.219Srad (r2=0.77, n=7304)
(Eq’n 2)
53
The long-term CO2 record was constructed from the ice-core data of Etheridge et al. (1998)
and more recently from the direct atmospheric measurements at Mauna Loa (Keeling and
Whorf 1998). Linear interpolation was used for values for years in which no measurement
occurred.
2.3
Climate change and CO2 scenarios
Mid-range scenarios of atmospheric CO2 concentrations suggest an increase from the current
levels of 364ppm to about 700ppm by the year 2100 (Houghton et al. 1996). This is expected
to result in global warming as well as uncertain changes to rainfall characteristics. Global
change scenarios were constructed with 1) 700ppm CO2 but with historical climate, 2)
700ppm but with temperature increases of 3oC, 3) both CO2 and temperature increase but with
10% rainfall decrease and 4) both 700ppm CO2 and temperature increase but with 10%
rainfall increase. The climate change scenarios were implemented as in Howden et al. (1999a)
with pan evaporation recalculated as described previously.
2.4
Data analysis
Wheat yields, grass yields and LWG and their coefficients of variation (CV) are presented as
running means to allow ready comparison over periods of different lengths. In all cases the
mean presented is that of the preceding number of years (eg a running mean of 20 years for
1950 is the mean for the period of 1931 to 1950).
To compare relative biological productivities of the cropping and grazing industries we
calculated two annual indices by dividing wheat yield by grass production and wheat yield by
LWG/ha.
We also compare El Niño events and values of the Interdecadal Pacific Oscillation (IPO), an
index of decadal and multi-decadal climatic variability derived from the principal component
score of the third empirical orthogonal function (EOF3) of a near-global sea-surface
temperature analysis, (Power et al. 1999) with the above results.
3.
Results
The 5-year running mean of wheat yield has been above the mean since the 1960s and was the
highest on record during the 1980s but declined towards the overall mean over the past few
years (Figure 2). The lowest 5-year running mean yield occurred in 1905 but was similar to
several values in the 1930s. The lowest 20-year mean occurred in the late 1930s to 1940s with
the greatest value occurring over the last few decades.
For grass yield, the lowest 10 and 20-year means were found in the 1930s to 1940s, whilst the
highest on record were experienced over the last few decades declining to the mean over the
past few years (Figure 3). The lowest 5-year mean value was in 1996 although this was again
similar to values in the 1930s to 1940s.
54
3500
4000
(a)
3000
3500
(a)
2500
3000
2000
2500
1500
1000
2000
3500
500
(b)
4000
1500
3000
(b)
3500
2500
3000
2000
2500
1500
1000
500
3500
(c)
3000
2000
1500
4000
3500
(c)
2500
3000
2000
2500
1500
1000
2000
500
(d)
1900
1920
1940
1960
1980
0 .7 5
0 .7 0
0 .6 5
0 .6 0
0 .5 5
0 .5 0
0 .4 5
0 .4 0
0 .3 5
2000
Figure 2: Wheat yield (kg/ha) running
means for (a) 5, (b) 10, (c) 20 year periods
and (d) 20 year running mean coefficient of
variation for Emerald. The bar on the top
right indicates the range of mean yields for
the year 2100 global change scenarios.
0 .6
1500
(d)
0 .5
0 .4
0 .3
0 .2
1900
1920
1940
1960
1980
0 .1
2000
Figure 3: Grass production (kg/ha) running
means for (a) 5, (b) 10, (c) 20 year periods
and (d) 20 year running mean coefficient of
variation for Emerald. The bar on the top
right indicates the range of mean
production for the year 2100 global change
scenarios.
40
35
(a)
30
25
20
15
40
(b)
40
35
0 .9
35
0 .8
30
0 .7
25
0 .6
20
0 .5
15
0 .4
(c)
(a)
100
0 .3
(b)
90
30
80
25
70
20
60
50
0 .5
15
(d)
0 .4
1900
1920
1940
1960
1980
40
2000
0 .3
0 .2
1900
1920
1940
1960
1980
0 .1
2000
Figure 4: LWG (kg/ha/year) running
means for (a) 5, (b) 10, (c) 20 year periods
and (d) 20 year running mean coefficient of
variation for Emerald. The bar indicate the
range of mean LWG for the year 2100
global change scenarios.
Figure 5: Ratio of (a) wheat yield (kg/ha)
to grass production (kg/ha) and (b) wheat
yields to LWG/ha for Emerald. Bars
indicate the range of mean ratio for the
global change scenarios
55
For LWG, the lowest 10 and 20-year running mean values were found in the 1930-40s and
highest values over the past decades, although they have been declining over the last several
years with the 5-year mean in 1997 being the lowest on record (Figure 4).
The 10-year running mean of the ratio of grain:grass production has been above the overall
mean since the 1970’s and over the past few years has been the equal highest on record. The
ratio of grain:LWG has been above the overall mean over the past two decades but was below
the overall mean from the 1950s to the 70s (Figure 5).
The coefficient of variation (CV; 20 year running mean) varied by a factor of 1.8 over the
period for wheat and 3 for grass yields but a factor of 4 for LWG. The CV for wheat declined
sharply following 1976 and has remained the lowest on record since then. For grass
production and LWG, there was a marked decline in CV from the mid-1950s and has
remained low except for LWG which has increased to the mean over the last decade. The
periods around the 1940s had high variability for all production systems.
El Niño and La Niña events had a marked effect on wheat and grass production but smaller
effects on LWG (Table 1). EOF3 values show peaks around 1900, the 1930-1940 period and
1980 onwards.
Table 1. Effect of average SOI values for June-November on annual wheat yield (kg/ha),
grass growth (kg/ha) and LWG (kg/ha) for 1890-1998.
Wheat yield
Grass growth
LWG
SOI<-5
1130
2313
25.8
-5<SOI<5
1680
3172
29.4
SOI>5
2100
3573
29.5
Increases in CO2 levels over the past 108 years were simulated to increase wheat production
by about 8% and grass production by about 5% with most of this change occurring since the
1960s (data not shown).
The CO2 and climate change scenarios gave a range of wheat yields from +12 to +29%, grass
yields of +2 to +17% and LWG of +5 to +57% above the means of those experienced since
the 1890s using recorded climate and CO2 concentrations. These values straddle the 20-year
mean for the most recent decades for wheat and grass production but are higher for LWG
(Figure 2,3,4). Doubling of CO2 concentration without climate changes increased the ratios of
wheat:grass production and wheat:LWG by 27 and 17% respectively. Changes in CO2 in
conjunction with climate changes resulted in little change in this ratio against the Baseline for
grass production (except for the Wet scenario with +9%) but around 20% decreases for LWG.
4.
Discussion
The recent expansion of wheat cropping in the Emerald region appears to have been a
response to a changing environment (both climate and CO2 changes). The period of major
expansion from 1980 to the early 1990s had by far the greatest mean wheat yields and the
56
lowest variability of yield in the past 108-years. Furthermore, increased yields associated with
progressive reductions in frost frequency over this period may underestimate this simulated
change (Stone et al. 1996). The relativity of wheat production against grass growth was also
the highest on record whilst the ratio of wheat production against LWG was also above
average suggesting that the timing of change from grazing to cropping was not just a result of
historical development paths or technological change but rather a rational response to a
change in the relative productivities and risks of the respective industries. Meinke and
Hammer (1995) also have suggested that the peanut industry expansion in south-east
Queensland in the 1960s and 1970s was driven by similar climate variations and there are
other examples in Australia’s history (eg those relating to the Goyder Line in South Australia;
Reyenga et al 1999b). Increases in CO2 probably had a small but positive effect (less than
8%) impact on wheat yields.
This suggested response of the cropping industry to growing conditions raises the issue of
future stability of the industry in this location. If this favourable ‘window’ for cropping is a
result of long-term variability, then return to more average conditions is inevitable and
cropping will decline. If the ‘window’ is due to climate change, then some continuation of
wheat cropping appears likely provided the incidence of El Niño events doesn’t increase.
Simulations of future potential yields under climate change and CO2 increase suggest wheat
yields that are 12 to 29% above the 108-year average but similar to those experienced in the
high-yielding period in the 1980s. In contrast, in most other wheat cropping areas in
Australia, increases over recent yields are likely (Howden et al. 1999b) the relative lack of
response being due the likelihood of supra-optimal temperature conditions at Emerald. The
historical correspondence of periods of low yield with El Niño events is cause for some
concern as there has been a change in the frequency of El Niño events over past decades that
may be related to climate change (Cai and Whetton 1999) and the incidence of El Niño events
may increase with future global warming (eg Timmerman et al. 1999).
The likely expansion or contraction of the wheat industry compared with grazing landuses
depended on the future scenario used. Under doubled CO2 without climate changes, the
relative productivity of wheat compared with grazing may increase by around 20% suggesting
expansion may be possible. Whereas if warmer temperatures occur as well, there may be a
20% reduction of the ratio of wheat yields against liveweight gain but little change against
grass production. The increase in LWG was due to increases in the growing season of the
native grasses with increases in minimum temperatures and rainfall (Hall et al. 1998).
However, GRASP doesn’t simulate the effects of increased heat stress on livestock and this
may restrict LWG in these regions leading to lower animal productivities than indicated
(Howden et al. 1999c). Nevertheless, even small reductions in relative productivities
compared with the 108-year mean suggests contraction of the wheat industry may be likely.
Decadal and interdecadal climatic variability, such as evident in the IPO record (Power et al.
1999) manifests itself in corresponding variability of production. In the early part of the
record (1890 to the 1950’s) there was a correspondence between peaks in the index values
and periods when productivity was low. However, over the past four decades, the relationship
appears to be breaking down as the IPO values are high but productivity is also high. Further
statistical analyses of the relationship between El Niño, the IPO and wheat yields is currently
being undertaken (Meinke unpub. data). Inspection of the simulated production data show
two distinct points of change: the 1950s when variability in grazing system productivity
declined and the mid 1970s when variability in wheat yields declined. The latter change
appears to be related to changes in ocean circulations and temperatures from 1976 onwards
57
(eg Zhang et al. 1998) which appear to have resulted in changes in autumn minimum
temperatures and other climate variables in Queensland (McKeon et al. 1998).
These results have considerable implications for policymakers dealing with drought issues.
The low variability and high productivity of the past few decades are unique in the 108-year
record for both grazing and cropping systems. This is likely to have biased the expectations of
producers. Return to more normal levels of productivity and variability over the last six years
has resulted in claims for ‘Exceptional Circumstances’ drought support. This study suggests
that this has not been an unusually poor period if a decadal view is taken. If a 5-year view is
adopted, then on average, the last several years have been the poorest on record for the
grazing industry. The worst production periods simulated in the record differ depending on
the production element being addressed (ie wheat yield, grass production or LWG ) and with
the duration being assessed (ie 5, 10 and 20-year windows) as noted by Stafford Smith and
McKeon (1999). There exists a challenge to ensure that industry and government policymaking effectively uses such information.
5.
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