sustainable agricultural systems

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.


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)


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


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


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. understanding rainfall (climate) variability (physical measure)

2. understanding production variability (bio-physical measure)

3. understanding farm income variability (economic measure)

The first step: understanding rainfall

JJA rainfall for Dalby, Queensland

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

0

0.2

0.4

0.6

0.8

1

JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ DJF

3-monthly period

p-v

alu

e

LEPS p-values KW p-values

Discriminatory Ability of the 5-phase SOI forecast system as quantified by KW p-values (KW is a measure of shift in distributions)



The second step: understanding production impactsSimulation 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

Manager

Report

Soil pH

Soilwater

Soil N

Erosion

Surface Residue

APSI

M

Crop CMaiz

Wheat

Crop CCrop B

Cowpea

Soil P

Arbitrator

ClimateManager

Report

Soil pH

Soilwater

Soil N

Erosion

Surface Residue

APSI

M

Crop CMaiz

Wheat

Crop CMaiz

Wheat

Crop CCrop B

Cowpea

Crop CCrop B

Cowpea

Soil PSoil P

Arbitrator

Climate

WhopperCropper for on-farm decision making

WhopperCropper training and distribution is now through Nutrient Management Systems.

Crop & PAWC (mm)

Wheat120

Wheat190

Sorghum120

Sorghum190

Yie

ld (

kg/h

a)

5000

4000

3000

2000

1000


www.apsru.gov.au/apsru/products/whopper

SOI effect on gross margins

AppliedN & SOI Phase

0NNegative

25NNegative

50NNegative

100NNegative

0NPositive

25NPositive

50NPositive

100NPositive

GM

($ p

er

ha

)

500

450

400

350

300

250

200

150

100

50

0

Positive SOI Phase

Wheat, Dalby, 150mm, 2/3 full, 15 Wheat, Dalby, 150mm, 2/3 full, 15 June sowing, April/May SOI phaseJune sowing, April/May SOI phase

Applied Nitrogen and SOI Phase

0N 25N 50N 100N 0N 25N 50N 100N

Negative SOI Phase

Gro

ss M

arg

in (

100$ p

er

ha)

5

4

3

2

1

0



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)

Regional Commodity Models (RCM)


Predicted sorghum shire yield for the 2004/2005 season, ranked relative to all years (1901-2003)

(a) (b)

Probabilities of exceeding long-term median wheat yields for every wheat producing shire (= district) in Australia issued in July 2001 and July 2002, respectively.

July 2001 July 2002

WA

NT

SA

NSW

VIC

TAS

Legend:0-10%10-20%20-30%30-40%40-50%50-60%60-70%70-80%80-90%90-100%No data

#

#

#

#WA

NT

SA

NSW

VIC

TAS

Roma

Dalby

Emerald

Goondiwindi

Legend:0-10%10-20%20-30%30-40%40-50%50-60%60-70%70-80%80-90%90-100%No data

Chance of exceeding median pasture growth for NSW, April to June 2005


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)

5-year running mean - Wentworth, 1950 to 1998

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

19

50

19

53

19

56

19

59

19

62

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65

19

68

19

71

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74

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77

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80

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83

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86

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89

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92

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95

19

98

Sta

nd

ard

De

via

tio

ns

fro

m t

he

me

an

Simulated Wheat Yield 1950+

??

Simulated Wheat Yield 1890+ 5-year running mean - Wentworth, 1884 to 1998

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

1894

1901

1908

1915

1922

1929

1936

1943

1950

1957

1964

1971

1978

1985

1992

Stan

dard

Dev

iatio

ns fr

om th

e m

ean

??

When is a drought a drought?



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. no policy mechanisms for influencing rainfall (step 1),

2. few policy options to affect crop or pasture yields (step 2),

3. but strong community demand for policies to anticipate and moderate the effects of climate variability on farm incomes (step 3).

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

The third step ( ‘the big stumble’): making science relevant

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

Carney, 1998; Ellis, 2000

Human

Social

NaturalPhysical

Financial

Exposure to risk does not equal vulnerability

Vulnerability of Australian agriculture: Exposure vs Coping Capacity

(Nelson et al. 2005)

10% (most extreme)

10 to 25% (extreme)

below 25% (least extreme)

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.


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.


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.

2002-03 2001-02

1982-83(Nelson et al. 2005)

Forecasting farm incomesProbability of exceeding median farm income

2002/3 2001/2

1982/3

1982-83

2002-03 2001-02

1982-83 (Nelson et al. 2005)

Better drought assistance Probability of 1-in-20 worst farm income

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 socio-economic 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.

shared risks

Farm

Community

Business

Insurer

Reinsurer

Weather/climate

derivatives

Financial Derivative

s

Real options, insurance and other financial products

courtesy of Greg Hertzler, Uni of WA


retained risks

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.

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.

Second key lesson from several decades of experience

Modelling for a purpose

TEMPORALnow future

SPATIALfield farm catchment region state

ECONOMICALenterprise business industry sector

Adapt Mitigat

e

Climate Warning


Modelling for a purpose“Increased efficiencies have outweighed all expenditure involved. The costs of tackling climate change are clearly lower than many feared. This is a manageable problem.”

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

The Economist, 9 Oct 2004

Value of adaptation to the grain industry

0

0.05

0.1

0.15

0.2

0.25

014

028

042

056

070

084

098

011

2012

60

Values in Millions

PR

OB

AB

ILIT

Y


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-in-hand 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.

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.

The case for institutional realignment

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.

sustainable agricultural systems

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