Elliott, h. evolution of systems thinking (for NARS)
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Transcript of Elliott, h. evolution of systems thinking (for NARS)
Evolution of Systems Thinking Towards Agricultural Innovation
SystemsHoward Elliott
April 7, 2008
Objective of Presentation
• Argue that both systems thinking and innovation approaches are needed
• Provide a brief introduction to “systems thinking” and to “innovation”
• Trace the evolution of agricultural research and development approaches leading towards AIS
• Discussion of insights from success and failure• Challenges to AIS
Why Agricultural Innovation Systems (AIS)?
• We need both systems thinking and innovation thinking to solve complex problems and deal with uncertainty arising from dynamic complexity“ The adaptive possibility of societies is the
main source allowing them to survive in the long term and to innovate of themselves and to produce originality”
Source: Nicolis and Prigogine (1989)
A System Defined
• A system is defined as a “set of parts coordinated to achieve a common objective”.
• It is defined first and foremost by its: – Objectives (and its performance measures)
• It is then defined by its:– Environment: its fixed constraints– Resources– Components: activities, goals, performance– Management or means of coordination
1. Modeling a System in Equilibrium
Environment
Objective
Resources
2. System During Process of Change
• Internal processes are important
• Tools are needed to understand the internal change process
Propositions: Systems and Change
• Hierarchy of Systems and Sub-systems
• “Open” or “Closed”
• Change is “overwhelmingly incremental” and “path- dependent”
• “Best fit” not “best practice”
Table 1. System Coherence Across Functions and Levels
Level Goal Participation Information Analysis Concern
President
Cabinet
Ministry
Private Sector
Farmers
AKIS Components
Program Leaders
Bench Scientists
Innovation1. Business (Drucker)
• The application of knowledge, whether new or re-discovered, in a way that creates a new dimension of performance
2. Economic (Schumpeter): • A new good or new quality of a good• A new method of production• Opening of a new market• Conquest of a new source of supply• A new organization of the industry
3. Network theory:• a new element that lowers the cost of transactions among
actors, nodes or elements in the network
• Note: Importance of Knowledge and Information
Table 2. EVOLUTION OF CONTEXT AND SYSTEMS
1970s 1980s 1990s 2000+
Context Instability Adjustment Liberalization Globalization
Goal Stabilize Get budget right
Get prices right
Open systems
Paradigm Green Revolution
NRM Poverty and Environment
Growth and MDGs
Driver Science Policies Institutions Systems
Focus Research Research, T&VNARS
AKIS AIS
Capacity Consolidation AKIS NGOs AIS
Policy Environment
External:
Donors and Investors
Initial Structural Conditions
External:
S&T,
Emerging Technologies
FARMER
TT/PSEDN
RES
Source: Adapted from Elliott 1987
Fig. 3 EVOLUTION OF KEY COMPONENTS AND RELATIONSHIPS IN AN AIS
Institutions
AIS is a convergence of several related perspectives
1. Policy and institutional:• Ministries NARIs NARS AKIS AIS
2. Scientific and technical:• Farming systems INRM IAR4D• Breeding Emerging Technologies
3. Learning and change:• Farmer First FFS Convergence of
Sciences
Lessons from Historical Examples
• AIS framework useful for retrospective review of experience:
• Previous paradigms were not “simplistic” but often a reasonable reflection of the time and the problem
• Analytical tools are improving
• AIS demands “best fit, not best practice”:
Table 3: Potential Case Studies for AIS Diagnosis by System Level
Initiative Elements to Study for Potential lessons
Continental SPAAR Base Centers and Regional Programs
FARA SSA-CP
IAR4D framework; Benchmark sites
Sub-regional SRO (ASARECA)
ASARECA StrategyChoice of SSA-CP benchmark sites in innovation framework (Lake Kivu);
CG-RPCA Innovation nodes (“Flagships”) at boundaries of organizations’ core activities.
National NAPPs Designing support for National Agricultural Productivity Projects in an AIS framework
NAADS, ATIRI Different national approaches in different contexts
District or Local
Enterprise zones
Decentralization and specialization around promising enterprise;
Convergence of Sciences
Engage small farmers around windows of opportunity; experiment, learn
Challenges1. Phase 1: Use in Diagnostic Way:
• Predict from experience (Spielman)• Explore diversity of approaches (Hall)• Define opportunities for learning (COS)
2. Phase 2: Use in Design mode: “best fit” for components
• Market liberalization and non-market solutions (Dorward and Poulton)
• Priority to staples versus niche exports (ASARECA)• Simultaneous determination of policy, institutions and technology• Ensure system coherence across levels and functions• Ensure that science is not lost as a driver of innovation
3. Phase 3: Develop a “self-aware” system that can improve itself
The Super-Challenge
Design an AIS for African populations condemned to be part of Collier’s
“Bottom Billion”