Managing Climate Variability · capability used for justifying and/or accounting for decision and action. 3. Flexible simulator for system analysis via a consultant 4. Flexible simulator

1www.managingclimate.gov.au

Managing Climate VariabilityAustralia’s Climate – already the most variable, changing rapidly and predicted to be more variable

Adaptation – happens on farm now! Its all about continuous improvement in practices and climate risk management.

Managing Climate Variability – 7 year R&D Strategy 2008 -2014 and high benefit : cost

Responding to Needs – questions & criteria - a 4 x 4 matrix


Managing Climate VariabilityFarmer funded via levies

Must be relevant

Listen – Refine - Define – Design

Present the Value Proposition

Innovate – Interact - Implement

Legacy

Flow variability of major world rivers.Ratio – High Flow : Low Flow

Brazil Amazon 1.3Switzerland Rhine China Yangtze Sudan White Nile USA Potomac South Africa Orange Australia Murray Australia Hunter

Australia Darling

The notion of “average rainfall” has always been dubious for Australia, both agriculturally & ecologically. The real story is in the variance, not the mean, and how the system (whether farming system or ecosystem) responds to extreme events. (National Land & Water Resources Audit, 1999)

Flow variability of major world rivers.Brazil Amazon 1.3Switzerland Rhine 1.9China Yangtze 2Sudan White Nile 2.4USA Potomac 4South Africa Orange 17Australia Murray Australia Hunter Australia Darling The notion of “average rainfall” has always been dubious for Australia, both agriculturally & ecologically. The real story is in the variance, not the mean, and how the system (whether farming system or ecosystem) responds to extreme events. (National Land & Water Resources Audit, 1999)

Flow variability of major world rivers.Brazil Amazon 1.3Switzerland Rhine 1.9China Yangtze 2Sudan White Nile 2.4USA Potomac 4South Africa Orange 17Australia Murray 30Australia Hunter 54Australia Darling 4700The notion of “average rainfall” has always been dubious for Australia, both agriculturally & ecologically. The real story is in the variance, not the mean, and how the system (whether farming system or ecosystem) responds to extreme events. (National Land & Water Resources Audit, 1999)

Australia’s Rangelands - Rainfall variability

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Australia – most variable climate, other than Antarctica; averages and analogue years are not useful concepts!

McKeon et al (2004)


Dates of first and last frosts - EmeraldFirst and last days of frost at Emerald

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Meinke et al (2007)

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Central thesis for adaptation – agriculture will respond to climate change through adaptation to our already & increasingly variable climate – within and by season or at most to 10 year investment timeframes

2070 climate change projections - the impetus to participate in mitigation


Key Questions - Forecasting“just tell me when its going to rain”…..heatwave / frost /

wind / etc….

Key Concepts –1. systems approach – climate and enterprise2. dynamical modelling + statistical + ensembles + multi-

models & expertise 3. intra-institutional interfaces 4. weather = climate, blurring the distinctions


ForecastingGlobal Circulation Models - a major research challenge as we recognise the dynamic nature of our climate; - already providing increased certainty and local relevance in forecasts;- our investments focus on where improved skill will benefit agriculture- teleconnections and initialisation

Multi-Week Forecasting- breaking down the barriers of weather & climate - fostering “in crop” climate risk management

Relevance- ensuring forecasts and products that meet agriculture’s needs- extremes and reanalysis


Climate Drivers & Synoptic FeaturesExample for Qld –Drivers of: ENSO, MonsoonMadden-Julian Oscillation

Synoptic features of:Trade windsCyclones & DepressionsMonsoon conditionsInland troughsEast Coast / Cut Off LowsCloud BandsFrontal Changes


Improving Model Skill – sea surface temperature prediction

SST biases at November for forecasts initialized on 1st July from (a) POAMA-2.0, (b) POAMA2.1A (c) POAMA-2.1F. SST biases are SST climatology differences between model and observation. (BOM, 2009)


One Example – south west WA

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100 For Katanning and Nyabing – 75% correct forecast, whereas for Mullawa, no skill

Using wheat price @ $300 and N @ $2/kg this translates for all 27 years into $30/ha benefit at Katanning & $90/ha at Nyabing [Asseng et al, in prep]% correct forecast in May for wheat

season rainfall above or below Median


Forecasting, Agriculture & Risk Management– Beneficial risk management using seasonal forecasts together with a

partial hedge with weather derivatives requires forecasts only marginally better than climatology. (Stern & Dawson, 2004)

– “risk assessment is an accepted aspect of all economic activity these days, and so the uncertainty associated with a seasonal climate forecast is an additional risk to be included in the overall decision-making process for a user sector…..Optimal value is derived when there is good communication between the climate and user communities, so that the nature and uncertainties of seasonal forecasts are understood and taken into account in a particular application”. (Manton et al, 2006)

We never expect 100% correct forecasts. Many of the benefits for agriculture will accrue @ about 70% correct…..and advances are significant


Key Questions - Attributes“yes, but what does that mean on farm”…..runoff / soil

moisture / sediment transport / nutrient leaching chemical uptake etc-

Key Concepts –

1. sequences, especially extremes for profitability

2. real time initialisation with real people involvement!

3. integrators of climate components – eg soil moisture

4. sustainability & practice implications


Soil – Climate - Water

Tipping Points for Horticulture – 50% private sector funded [Woolworths], temperature change impacts on key commodities

Frost –grains and grapes

Heat Stress and Cereals – southern Australia

Sugar Practices - Great Barrier Reef


Key Questions - Applications“how can I optimise?”…..fertiliser / chemicals for

pest / variety / irrigation etc-

Key Concepts –1. decision tools with metadata

2. discussion support with learning outcomes

3. integration with multiple factors….if significant

4. right questions…useful answers


C rystal B rook rainfall

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fallow-rain 170m min-season rain 234mm

Rainfall for Crystal Brook, SA 1889 - 2005

www.yieldprophet.com.au

Seasonal climate forecasting

In-season (canopy) management

S oil w a te r in Octobe r

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Tools for monitoring system status

CSIRO.

Learning from History of Deecision Support

If men could learn from history,What lessons it would teach us!But passion and party blind our eyes,And the light which experience gives usIs a lantern on the stern,Which shines only on the waves behind us.

- Samuel Taylor Coleridge

All courtesy of Dr Zvi Hochman

MCV project integrating cereal tools

CSIRO.

Operations Research to Decision Support Systems

• Little (1970) advanced the DSS idea as a solution to the implementation problem

The meeting of the manager and the model required a model that is:• Simple and robust, • easy to control, • adaptive, • as complete as possible, and • easy to communicate with

• Ackoff (1979) re-articulated the problem“The principal benefit of planning comes from engaging in it…It is better to plan for oneself, no matter how badly, than to be planned for by others no matter how well”

• Checkland (1983)Operation Research deals with the logic of the situation but ignores the divergence between textbook OR and what practitioners actually do.

CSIRO.

Farmers tend not to use Decision Support Systems

• “Although there are occasional successes, the fact is that many, if not most, decision support systems that are developed are never used.”(Ascough and Deer-Ascough 1994)

• Unwarranted escalation of agricultural DSS is based on a category mistake; confusion of process models for professional research with DSSs as a guide to practical action (Cox, 1996)

• DSS has few success stories, without adequate understanding of failures researchers will continue to naively repeat earlier mistakes (McCown et al. 2002)

• Despite relatively high levels of computer ownership, the use of DSS for routine decision making has been disappointing… To repeat the DSS model without reflection is at best an unnecessarily long journey, at worst a cul-de-sac (Hayman 2004)

CSIRO.

The technological response to failure: DSS are a good idea not sufficiently well developed

• Need better user interfaces• Need to develop more realistic/accurate models• Need to account for the fact that farmers have multiple

objectives• Economic• Social• Cultural

• Need to work at whole farm or catchment level

• This type of “solution” has invariably produced the next impressive DSS to be ignored by farmers!

CSIRO.

An alternative view

• Ackoff (1979) in considering the history Operations Research (from which DSS emerged) cautioned that “Unless the researcher is involved in and concerned with implementation we shall succeed only in amassing technical success and practical failures”

• DSS are a bad idea driven by a technology push or an outdated linear Technology Transfer model. (e.g. Rolling 1988)

• DSS are ignored by managers because they don’t fit with how managers make decisions (Sage 1991, Clancy 1997)

CSIRO.

1. ‘Calculator’ for a specific system feature of novel management value

2. DSS for technical ‘best practice’ with record-keeping capability used for justifying and/or accounting for decision and action.

3. Flexible simulator for system analysis via a consultant

4. Flexible simulator to enable a learning environment for farmers

From 9 case studies: Forms in which models may have some future for intervention in farm decision making

McCown (2002) Changing systems for supporting farmers’decisions: Problems, paradigms, and prospects

spare farmer’s thinking

justify farmer’s thinking

farmer’s delegation of thinking

facilitate farmer’s thinking

CSIRO.

Features of Yield Prophet®

• Yield Prophet emerged from an action research program. The product continues to evolve in response to user feedback as well as to scientists’ ideas

• Reports are often used as a focus for discussions between managers and consultants, with scientists sometimes asked to discuss unexpected results

• Users can specify a simulation to a specific paddock

• Available for instant use when needed at decision points as determined by the user

• Used for monitoring and post-decisional support

• There is a wide range of simulations that can be flexibly specified through a range of available report types and crops

• Simulation results can be validated at harvest


Key Questions - Knowledge“look we know each season varies – just make it so I can get the

information I need”…..weather = climate / attributes / tools / understanding

Key concepts –

1. availability

2. multiple learning styles, questions & applications

3. learning & understanding = adoption

4. innovation made easier

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Water and the Land – BOM interface for Seasonal Forecasting

Pages present an integrated suite of information for people involved in primary production, natural resource management, industry, trade and commerce. www.bom.gov.au/watl



Quick QuizWhere would you put the $?

1. Forecasting

2. Soil, climate & water attributes

3. Tools for Agriculture

4. Knowledge & adoption

Commissioned and output focussed R&D to maximise benefit:cost ratio on farm


Australia’s FarmersInvestment Focus

1. Forecasting 55 – 65%

2. Soil, climate & water attributes 15%

3. Tools for Agriculture 10 – 15%

4. Knowledge & adoption 15%

Commissioned and output focussed R&D to maximise benefit:cost ratio on farm


Discussion

Source: Australian Government – Bureau of Meteorology

Discussion

Documents

Managing Climate Variability · capability used for justifying and/or accounting for decision and action. 3. Flexible simulator for system analysis via a consultant 4. Flexible simulator