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”