Innovation, at the Boundary Professor Holger Meinke Director & - - PowerPoint PPT Presentation

TIA – a joint venture Understanding Agriculture & Food Systems Presentation prepared for the Australian Farm Institute’s Conference, Australian Agricultural Innovations Systems at the Crossroads, May 2013

The future role of government in the Australian agricultural innovation system

Innovation, at the Boundary

Professor Holger Meinke

Director & Head of School Tasmanian Institute of Agriculture (TIA)

Installing the Midlands Water Scheme Distribution Pipeline at Tunbridge

(R. Grant, ABC)

Tasmania’s Deputy Premier Bryan Green and Federal Member for Bass Geoff Lyons opening the expanded Winnaleah Irrigation Scheme in Tasmania's North East which doubles the amount of reliable water available to local farmers.

Tas Govt

For innovation to occurred, societies and communities must show

• a readiness to be disrupted
• a fundamental inquisitiveness
• a degree of cultural aspiration

Chericbaker/Flickr

Rice farmers inspecting their collectively managed rice field in Phu Tho Province, Vietnam.

New industrial complex under development in the near distance

Vietnam now exports 43 million t of rice annually – 10% of global production

Graduation at the Temple of Literature, Hanoi

… the process that translates knowledge into economic growth and social well- being. … encompasses a series

• f scientific, technological,
• rganisational, financial

and commercial activities.

… are networks of organizations, enterprises, and individuals focused on bringing new products, new processes, and new forms of

• rganisation into economic use, together with

the institutions and policies that affect their behaviour and performance

World Bank, 2006

Innovation systems …

… encompasses a series of scientific, technological, organisational, financial & commercial activities.

Matching scientific knowledge with the nature of the problem

Cause/effect Research goals Decision making

Level I Clear, deterministic Disciplinary, reductionist to provide solutions to clearly defined goals Control, solve well defined problems with

• bvious outcomes

Level II Level III

Derived from Allenby & Sarewitz (2010) – The Techno-Human Condition

Improving water use efficiency on-farm through the introduction of a higher yielding, more efficient variety

Matching scientific knowledge with the nature of the problem

Cause/effect Research goals Decision making

Level I Clear, deterministic Disciplinary, reductionist to provide solutions to clearly defined goals Control, solve well defined problems with

• bvious outcomes

Level II Complex, emergent Interdisciplinary, systems to create

• ptions for addressing

contended goals Adaptive, seek consensus

• n ways to manage

reasonably well understood situations Level III

Derived from Allenby & Sarewitz (2010) – The Techno-Human Condition

Improving water use efficiency on-farm through the introduction of a higher yielding, more efficient variety Optimising the use of limited water resources across enterprises of a farming operation

Matching scientific knowledge with the nature of the problem

Cause/effect Research goals Decision making

Level I Clear, deterministic Disciplinary, reductionist to provide solutions to clearly defined goals Control, solve well defined problems with

• bvious outcomes

Level II Complex, emergent Interdisciplinary, systems to create

• ptions for addressing

contended goals Adaptive, seek consensus

• n ways to manage

reasonably well understood situations Level III Unforeseeable shifts with no definable boundaries Design governance systems capable of consensus on ill-defined and highly contended goals Seek consensus on issues, & robust institutional responses (ways of

• rganising) to anticipate

future conditions despite persistent uncertainty

Derived from Allenby & Sarewitz (2010) – The Techno-Human Condition

Improving water use efficiency on-farm through the introduction of a higher yielding, more efficient variety Optimising the use of limited water resources across enterprises of a farming operation Introducing irrigation on a large scale as a transformational technology that will alter not

• nly farming operations, but the fabric of rural

communities in a region

Matching scientific knowledge with the nature of the problem

Cause/effect Research goals Decision making

Level I Clear, deterministic Disciplinary, reductionist to provide solutions to clearly defined goals Control, solve well defined problems with

• bvious outcomes

Level II Complex, emergent Interdisciplinary, systems to create

• ptions for addressing

contended goals Adaptive, seek consensus

• n ways to manage

reasonably well understood situations Level III Unforeseeable shifts with no definable boundaries Design governance systems capable of consensus on ill-defined and highly contended goals Seek consensus on issues, & robust institutional responses (ways of

• rganising) to anticipate

future conditions despite persistent uncertainty

Derived from Allenby & Sarewitz (2010) – The Techno-Human Condition

When can science compel policy action?

Value consensus Uncertainty

Low High Low

Science used to select between well defined options Information may reduce ambiguity about what the options are, but does not help to select between them Science used to select preferred

• ption

There is clear agreement about what the options are, why action should be taken and what the best course of action is

High

Science used to justify divergent approaches More science leads to increasing number of contended options, paralysing decision processes. Strong commitment to act, but not sure how. Policy may decide to act ahead of science with varying consequences. Science can be used to build governance systems for managing societal change. Source: Pielke (2007) – The Honest Broker, Ch2

Key roles for organisations involved in innovation systems

• Boundary work: Creating institutional arrangements that

strongly engage science with society and policy;

• Governing outcomes: Empowering outcome-focused

institutions through adaptive governance processes that value impact;

• Earn a ‘social license’: Encouraging critical thinking and

debates about public goods, values and market failure; science is important, but not sufficient;

• PAR as an important, policy-relevant process: Designing

innovations systems that include robust approach to participatory planning and development;

• Effective decision-making: Acknowledging that ‘perfect

knowledge’ is unobtainable – and unnecessary. Chance, ignorance and indeterminacy cannot be eliminated, but must be taken into account

Thank you