The Second Science with Africa Conference

Evolution of Agricultural Biotechnology Innovation: Lessons from

governance of Kenya’s biotechnology sector

Ann Kingiri PhD

Research Fellow- DFID Research into use (RIU) program

www.researchintouse.com

Agriculture mainstay of Africa/Kenya economy Meeting MDGs revolve around agriculture & improved

productivity Many options e.g. New/emerging innovations like new life

sciences e.g. agricultural biotechnology innovations But deployment of biotech innovations is not smooth…..has

governance issues (different for different countries but there are certain evolutions trends in Africa that are similar).

The governance aspects bring about the social & policy processes rarely given attention in entrepreneurship endeavors.

INTRODUCTION

Kenya’s Biotech sector & governance issues

Technological & policy milestones 6 trials approved since early 1990’s Biotech policy in 2006 Biosafety Act in 2009 & upcoming regulations

Evident co-evolution process (which is good and where a no. of lessons can be drawn for productive innovation and entrepreneurship) Technological growth Increased capacities (human, institutional, policy etc to harness

knowledge) Technology & policy shaping each other (The Biotech innovation

system has shaped the governance trajectory and vice versa)

Features of Kenya’s agric biotech innov. Sys

Is young and evolving (most activities are in the R &D stage); is dynamic; multi-actor; political; diverse knowledge nodes linked to the many linkages (technological, policy, regulatory, market, individual actors etc) .

Evolution process (from S&T to meaningful innovation) has been slow. No product so far has been commercialized.

Policy innovations bounded up within the overall biotech innovation system.

There is a clear gap between technological, policy and institutional development which collectively should connect innovation to entrepreneurship.

Clearly, the innovation component of the STI unit is weak.

Lessons & sustainability issues

top-down; less user-oriented approach modern biotechnology initiatives in Kenya heavily donor influenced. They often lack the demand driven approach implying that the

immediate needs of the farmers may not have been considered.

Inadequate and unbalanced institutional and technical capacity Biotech initiatives are mainly in public research institutes and

academic institutes have been slow to take up this science (citing the high investments needed in GE science).

Many researchers agree that it is an uphill task translating the basic academic initiatives/R & D) to innovations or economic gains (where other users can benefit).

Lessons & sustainability issues con’td

Unsustainable funding for research and regulatory process Reduced government funding leading to increased PPPs and

donor driven policy processes.

Contestations and lack of consensus on policy and strategy Politics; some actors marginalized (mainly public/farmers);

process largely driven by scientific actors. No product has been commercialized to benefit users – a

factor blamed on lack of regulatory policies but is this the case or there is more to this stalemate?

What can we learn from the inevitable technological & policy interface in order to translate research into

products? The link between science, technology, innovation

and entrepreneurship is not simplistic and linear, and the dynamics involved need to be looked into holistically; technological aspects that form the broader S & T capacity are

important, but also important are the dynamics embedded in the social & policy processes.

The innovation systems approach provides insights on how this interface can be enhanced productively;

Defining Agricultural Innovation Systems

a “network of organizations, enterprises and individuals focused on bringing new products, new processes, and new forms of organization into economic use, together with the institutions and policies that affect their behavior and performance (Hall, 2006; World Bank, 2007).

The IS concept embraces not only the science suppliers but the totality of and interactions of actors involved in innovation as well.

It extends beyond the creation of knowledge to encompass the factors affecting demand for and use of knowledge in novel and useful ways.” (World Bank, 2007:vi).

Project ideas: Moving beyond R & D: how to translate biotech research into products in

Africa The traditional linear approach to innovation has been challenged.

It is now time to move from building S & T capacity towards building innovation capacity.

Building innovation capacity targets the way a system operates rather than science and technology capacity (cf Hall, 2005).

This approach has been recommended for operationalisation in developing countries (World Bank, 2007; OECD, 1997), and particularly in biotechnology innovation systems (Hall, 2005).

RIU: research into putting research into use (An example of a program that has adopted innovation systems

& innovation capacity building approach)

Researchers have always gradually improved they way they do research as a way of making it more useful.

But while technical learning has been structured and legitimate, institutional and policy learning has been an ad hoc process or an after thought.

The RIU is an explicit attempt to systematically learn how to put research into use and to communicate that to policy practice audiences.

Central Research Team (UK)Research design and execution and synthesis and communication of lessons

4 RIU experiments

Best Bets• Sleeping

sickness eradication

• Client oriented breeding companies and capacities

Asia Innovation challenge fund• Value chain

cluster• Natural resource

management cluster

• (Client orientated breeding cluster)

Africa country programmes on innovation capacity development.

Innovation development fund.Social venture capital fund for Africa

Independent impact assessment exercise

National and regional research and innovation policies and practices

International agricultural research for development policies and practices. Principally DFID

Conclusion: Key messages

There are social, institutional and policy dimensions of technical change that are equally important as the technical dimensions and these need to be understood rigorously.

Understanding innovation as emerging from complex systems puts learning at the centre stage from operational to policy levels.

Suggests that technical research and policy research should be combined.

• If you want to know more about RIU program, please visit www.researchintouse.com or www.innovationstudies.com

• You can also talk to me later

THANK YOU FOR LISTENING