Transcript S1-Meinke.pps

sustainable agricultural systems
Actionable climate
knowledge – from
analysis to synthesis
Experiences from 20 years of applied
climate risk research in Australia
Holger Meinke, Rohan Nelson, Roger
Stone, Selvaraju, Aline de Holanda,
Walter Baethgen
Why focus on case studies from
Australia?
 has long been at the forefront of
applied climate research
 often regarded (rightly or wrongly) as a
role model for the creation and
maintenance of ‘actionable climate
knowledge’
 has one of the most variable climates
in the world
Why focus on case studies from
Australia?
 has strong ENSO impact and vulnerable
sectors with considerable scope to
improve risk management
 climate change already a reality and
not just a scenario
 public policy focus on self-reliance,
resilience and societal benefits
 involves many agencies and many
stakeholders (farmers, agribusiness,
policy makers)
Climate knowledge vs climate
forecasting
 Climate knowledge is more that ENSO
and more than just forecasting.
 Climate knowledge is the intelligent use
of climate information. This includes
knowledge about climate variability,
climate change AND climate forecasting
used such that it enhances resilience by
increasing profits and reducing
economic/environmental risks.
sustainable agricultural systems
Risk management
 The systematic process of identifying,
analysing and responding to risk. It
includes maximising the probability and
consequences of positive and adverse
events.
(Guide to the Project Management Body of Knowledge)
 ‘It is our competitive advantage that we
show courage after carefully deliberating
our actions. Others, in contrast, are
courageous from ignorance but hesitant
upon reflection’.
(Pericles’ Funeral Oration, 431 AD; Thucydides 2, 40, 3)
sustainable agricultural systems
Risks arise from variability
Australian farmers are excellent risk
managers. They run successful businesses
within the world’s most variable climate
and without subsidies.
…it seems that the 21st century
has a good chance of becoming
‘the climate century’, a century
in which climate-related
concerns will occupy significant
attention of the next generations
of policy makers…
Mickey Glantz, 2003
sustainable agricultural systems
Sources of variability
 Temporal and spatial
weather (hail, frost); climate (at a range of temporal
scales); soils (at a range of spatial scales); economic
conditions (inputs, commodity prices); management
 External and internal
either beyond manager’s control or consequence of
management
sustainable agricultural systems
Example of Decision Types
Key Stakeholder
Frequency
Logistics (eg. scheduling of planting / harvest
operations)
Farm Manager
MJO, months
Crop type, weather derivatives, insurance, herd
management, irrigation scheduling, marketing
Farm Manager,
Agribusiness
ENSO, season
Crop sequence, fallow management, stocking
rates, water allocation, insurance
Farm Manager,
Agribusiness, Policy
Season to
interannual
Crop industry (grain or cotton; native versus
improved pastures), rural versus off-farm
investments
Business Manager,
Agribusiness, Policy
Decadal
(~ 10 yr)
Agricultural industry (eg. crops, pastures,
forestry, horticulture), investments
Agribusiness, Policy
Multi-decadal
(10 – 20 yrs)
Landuse, community impact and adaptation of
current systems
Policy
Climate change
???
Three important steps to create
climate knowledge
1.
2.
3.
understanding rainfall (climate)
variability (physical measure)
understanding production variability
(bio-physical measure)
understanding farm income variability
(economic measure)
The first step: understanding rainfall
JJA rainfall for Dalby, Queensland
The first step: understanding rainfall
JJA rainfall for
Dalby,
Queensland
How good is the forecast?
Skill vs Discriminatory Ability
S quantifies agreement
between observed and
predicted values
DA represents the additional
knowledge about future
states arising from the
forecast system over and
above the total variability of
the prognostic variable
Forcast skill and discriminatory ability, Dalby, Qld
1
LEPS p-values
KW p-values
p-value
0.8
0.6
0.4
0.2
0
JFM FMA MAM AMJ
MJJ
JJA
JAS ASO SON OND NDJ
3-monthly period
DJF
The first step:
understanding
rainfall
Discriminatory
Ability of the 5phase SOI
forecast system
as quantified by
KW p-values (KW
is a measure of
shift in
distributions)
sustainable agricultural systems
The second step:
understanding production impacts
Simulation models for better risk management
 how do they work?
 are based on our component knowledge
of science
 integrate many sources of variability
 account for management options
 what can they do?
 benchmark, assess and quantify potential,
attainable, economically optimal and
achieved yield or income
 overcome issues related to moral hazards
and ground truthing
sustainable agricultural systems
WhopperCropper
for on-farm decision
making
Yield (kg/ha)
5000
4000
3000
2000
1000
Wheat
120
Wheat
190
Sorghum
120
Sorghum
190
Crop & PAWC (mm)
WhopperCropper training and
distribution is now through
Nutrient Management Systems.
www.apsru.gov.au/apsru/products/whopper
sustainable agricultural systems
Gross Margin (100$ per ha)
GM($ per ha)
SOI effect on gross margins
500
450
Wheat, Dalby, 150mm, 2/3 full, 15 June
sowing, April/May SOI phase
400
350
300
Negative SOI Phase
Positive SOI Phase
250
200
150
100
50
0
0N
0N
Negative
25N
25N
Negative
50N
100N
50N
Negative 100N
Negative
0N
0N
Positive
25N
25N
Positive
AppliedN & SOI
Phase
Applied Nitrogen
and
SOI Phase
50N
50N
Positive
100N
100N
Positive
sustainable agricultural systems
Gross Margin (100$ per ha)
GM($ per ha)
SOI effect on gross margins
500
450
Wheat, Dalby, 150mm, 2/3 full, 15 June
sowing, April/May SOI phase
400
350
300
Negative SOI Phase
Positive SOI Phase
250
200
150
100
50
0
0N
0N
Negative
25N
25N
Negative
50N
100N
50N
Negative 100N
Negative
0N
0N
Positive
25N
25N
Positive
AppliedN & SOI
Phase
Applied Nitrogen
and
SOI Phase
50N
50N
Positive
100N
100N
Positive
sustainable agricultural systems
Using field/farm scale models
 Tactical risk management
(which crop to grow when and how)
 Optimising resource use
(how much water / nitrogen to use when and where)
 Estimating crop value
(benchmarking, forward selling, insurance)
 Determine enterprise mix
(rotation planning)
sustainable agricultural systems
Regional Commodity Models (RCM)
Predicted sorghum
shire yield for the
2004/2005
season, ranked
relative to all
years (1901-2003)
July 2001
July 2002
Legend:
0-10%
10-20%
20-30%
30-40%
40-50%
50-60%
60-70%
70-80%
80-90%
90-100%
No data
NT
Legend:
0-10%
10-20%
20-30%
30-40%
40-50%
50-60%
60-70%
70-80%
80-90%
90-100%
No data
NT
#
Emerald
WA
WA
Rom a
#
Dalby
#
Goondi wi ndi
#
SA
SA
NSW
(a)
VIC
TAS
NSW
(b)
VIC
TAS
Probabilities of exceeding long-term median wheat yields
for every wheat producing shire (= district) in Australia
issued in July 2001 and July 2002, respectively.
Chance of exceeding median pasture
growth for NSW, April to June 2005
sustainable agricultural systems
Using regional models
 Marketing decisions
(hedging, contract negotiations, logistics)
 Value chain issues
(quality fluctuations, export vs domestic use,
milling operations)
 Anticipating resource use
(water allocations, nitrogen or seed demand,
storage capacity)
0.5
0.0
?
-0.5
-1.0
1.0
0.5
0.0
?
-0.5
-1.0
1992
1985
1978
1971
1964
1957
1950
1943
1936
1929
1922
1915
1908
1901
-1.5
1998
1995
1992
1989
1986
1983
1980
1977
1974
1971
1968
1965
1962
1959
-1.5
5-year running mean - Wentworth, 1884 to 1998
1894
Standard Deviations from the mean
1.0
1956
Simulated Wheat Yield 1890+
1.5
5-year running mean - Wentworth, 1950 to 1998
1950
When is a drought
a drought?
1.5
1953
Simulated Wheat Yield 1950+
Standard Deviations from the mean
sustainable agricultural systems
sustainable agricultural systems
Using models for public and
private policy decisions
 When is a drought a drought
(Exceptional circumstances, drought relief,
structural adjustments etc).
 Investment / disinvestment
(portfolio balance; cotton, grain or pastures)
 Structural adjustment
(diversification, industry mix eg. sugar industry)
The policy relevance gap
1.
2.
3.
no policy mechanisms for influencing
rainfall (step 1),
few policy options to affect crop or
pasture yields (step 2),
but strong community demand for
policies to anticipate and moderate
the effects of climate variability on
farm incomes (step 3).
The third step ( ‘the big stumble’):
making science relevant
Drought
“The defining feature of drought is its
impact on human activity – it is essentially
socially constructed.
It is about the mismatch between the
availability of water and the uses to which
human communities wish to put it.”
Linda Courtenay Botterill 2003
Exposure to risk does not equal
vulnerability
Climate is often ‘important but not
urgent’
 Many problems are the result of
applying narrow, specialised knowledge
to complex systems
 Modern science has been described as
‘islands of understanding in oceans of
ignorance’
 Scientists and practitioners need to
work together to produce trustworthy
knowledge that combines scientific
excellence with social relevance
Hayman (2001); Lowe (2001)
The multiple dimensions of vulnerability
Human
Carney, 1998; Ellis, 2000
Financial
Social
Exposure to
risk does not
equal
vulnerability
Physical
Natural
Vulnerability of Australian agriculture:
Exposure vs Coping Capacity
10% (most extreme)
10 to 25% (extreme)
below 25% (least extreme)
(Nelson et al. 2005)
Vulnerability includes
 exposure to climate risk
 exposure to other sources of risk
 capacity of rural households to cope
with risk
Why is coping capacity so
important?
 Farming systems have evolved to
effectively manage the risks of farming
in a highly variable climates – without
science intervention.
 While climate synthesis tools might
have contributed to the development of
more effective on-farm risk
management, there is little or no
connection to policy.
Why is coping capacity so
important?
 Greater diversity of income sources
facilitates substitution between activities
and assets in response to shocks such as
drought.
 Policies that enhance diversity of farm
income include investment in production,
transport and marketing infrastructure,
education and training, regional
development, and policies that impact on
the cost and availability of rural credit.
Why is coping capacity so
important?
 We need to distinguish the effects of
climate from other sources of income
risk.
 Without a capacity to distinguish
between sources of income variability,
policies directed toward reducing the
impact of climate risk may
inadvertently reduce incentives to
better manage other sources of risk.
A tool for bridging the policy
relevance gap
 The Agricultural Farm Income Risk
Model (AgFIRM) combines regional,
biophysical models of Australian crop
and pasture yield with an econometric
model of farm incomes.
 AgFIRM simulates regional impacts of
climate variability on farm incomes.
2002/3
2002-03
Forecasting
farm
incomes
2001/2
2001-02
1982/3
Probability of
exceeding median
farm income
1982-83
(Nelson et al. 2005)
2002-03
2001-02
Better
drought
assistance
Probability of 1-in-20
worst farm income
1982-83
1982-83
(Nelson et al. 2005)
Tools for bridging the policy
relevance gap
 Policies aimed at increasing the
capacity of rural communities to cope
with climate risk need to be informed
by measures of the multiple socioeconomic dimensions of resilience.
 Current emphasis on rainfall and
production variability only informs
policy makers of the exposure to
drought, for which there is no policy
solution.
Public versus private policy
development
 Risk managers must decide which risks
should be retained and managed
adaptively and which risks should be
shared through risk sharing contracts.
 It requires financial markets to device
and price risk sharing contracts in a
manner that create benefits for all
stakeholders involved, a process that
has only just begun in Australia.
sustainable agricultural systems
Real options, insurance and
other financial products
retained risks
shared risks
Farm
Reinsurer
Community
Weather/
climate
derivatives
Business
Insurer
Financial
Derivatives
courtesy of Greg Hertzler, Uni of WA
Climate knowledge or seasonal
rainfall forecasting?
 Applied climate knowledge is generated by
synthesising scientific insights across
disciplinary boundaries, often through the
use of models and always jointly with
stakeholders.
 Climate risk management in rural
industries is not solely the responsibility of
farmers. Likewise, it is not the role of
Governments to absorb these risks.
 Risk managers, policy makers and private
sector companies all play important roles
in this process.
The case for institutional
realignment
 Rainfall and production are not what
policy makers are interested in. They
are interested in the social and
economic wellbeing of rural
communities.
 Analytical support for drought policy
that focuses on exposure to climate
risk is largely irrelevant  climate
variability cannot be altered by policy
in the short term.
Failures and risks
 The artificial division of climate
variability and climate change gets in
the way of better decision making.
 The focus of the climate change
community on mitigation bears the
danger of overlooking some obvious
and immediate adaptation strategies
that should from part of any sound
climate risk management approach.
Failures and risks
 A problem rather than a disciplinary
focus will require some scientists to
stop doing what comes naturally
(addressing simple issues such as
rainfall variability, with increasingly
complex analytical tools).
 Instead, they need to take a broader
perspective to addresses not only
exposure to risk, but also the people’s
ability to cope and the system’s ability
to bounce back after times of stress
(resilience)
Other impediments
 institutional and disciplinary
fragmentation prevails
 difficult to ‘gain simplicity on the far
side of complexity’
 R&D funding agencies reluctant to
resource genuinely multi-disciplinary,
cross institutional projects
Some suggestions
 public / private partnership models
need to be explored further in order to
‘mainstream’ climate risk management
 public / private policy concerns need to
be explicitly addressed
 communicate climate risk management
knowledge through functional, existing
communication networks of farmers
and other landholders
First key lesson from several
decades of experience
 Climate knowledge needs to deliver true
societal benefits.
 We need to expand the systems
boundaries and fully explore the scientific
and socio-economic tensions and
interactions - the system is bigger than
most of us thought.
 We need to include the socio-economic
dimensions important to rural
communities and policy makers, but
without abandoning science.
 We need to achieve true integration of
disciplinary knowledge, rather than
focusing on certain aspects of the system
at the exclusion of others.
Second key lesson from several
decades of experience




True integration without abandoning
science takes real resourcing.
The capacity to think and act
beyond disciplinary boundaries is
rare and difficult to nurture in the
established institutional context.
Existing institutional arrangements
often act as a disincentive to true
integration.
Strong leadership is required to
induce cultural change in established
institutional arrangements.
sustainable agricultural systems
Modelling for a purpose
Climate
Warning
Adapt
TEMPORAL
now
field
Mitigate
farm
enterprise
future
SPATIAL
catchment region state
ECONOMICAL
business industry sector
sustainable agricultural systems
Modelling for a purpose
Value of adaptation to the grain industry
PROBABILITY
0.25
0.2
“Increased efficiencies
have outweighed all
expenditure involved.
The costs of tackling
climate change are
clearly lower than
many feared. This is a
manageable problem.”
0.15
Lord Browne, CEO of BP,
announcing that BP had
reached it’s target of reduce
carbon emissions to 10%
below 1990 levels eight years
ahead of schedule
0.1
0.05
0
Values in Millions
The Economist, 9 Oct 2004
Failures and risks
Why do institutional arrangements need to be realigned
in order to implement advances in climate risk
management policy?
1.
Rainfall and production are not what policy makers
are ultimately interested in. They are interested in
the social and economic wellbeing of rural
communities. There should be a natural evolution
from analytical support at certain scales to synthesis
tools that integrate the analysis of rainfall right
through production, farm incomes and sustainability
indicators. So far, institutional and funding structures
have largely prevented this from happening in
Australia, and probably anywhere else.
Policy options for managing climate
variability
 income smoothing and price stabilisation
 emergency relief
 undermines self reliance
 enhanced diversity of income sources
 investment in
 infrastructure
 human and social capital
 outsourcing risk
 enhances self reliance
(Nelson et al. 2005)
Public versus private policy
development
 Underpinned and informed by
quantitative systems analysis, such
policy development should go hand-inhand with the establishment of novel
financial risk management tools such
as ‘real options’ (a right, but not the
obligation, to take action).
 Real options are property rights created
by investments.
The case for institutional
realignment
 Institutional and funding structures
have largely prevented this from
happening in Australia, and probably
anywhere else.
 There should be a natural evolution
from analytical support at certain
scales to synthesis tools that integrate
the analysis of climate right through
production, farm incomes and
sustainability indicators.
Failures and risks
 Climate science and agricultural
systems science has to become more
policy relevant.
 To some extent this has happened with
climate change research.
 Not so with climate variability research
that must also inform policy
development to assist stakeholders to
better cope and adapt.