This paper argues that efforts to measure the impact of agricultural research have largely failed to improve developmental impacts, because commonly used assessment methods fail to provide research managers with critical institutionallessons for improving innovation. Approaches such as measuring rates of return to investment, while providing politically expedient evidence of the value of public research, offer little help in understanding the complex processes and institutional factors that gave rise to the relative success of different research initiatives. The reasons behind this problem relate to the disciplinary and conceptual conventions that underpin both impact assessment methods and agricultural research policy generally. The former is based on linear assumptions about the relationship between research inputs and economic outputs. Methodological problems aside, economic analysis of this type is ill equipped to give insights into the evolution of agricultural systems.||This paper proposes a systems model of innovation as a complementary framework to understanding agricultural innovation (technical and economic change) in its wider institutional context. In this systems conceptualization, innovation performance and impact is viewed as resulting from the existence and ability of coalitions, or systems of public and private research and non-research institutions, to interact, create, transfer, and apply economically useful knowledge. In these systems, innovations are derived from evolutionary combinations of technical and institutional change. Case studies from India and Africa are used to demonstrate how this conceptualization of the innovation process could be used to evaluate research and technology programs and inform planning and research management.||The paper concludes by recognizing that international and national research efforts have some way to go before better integration with a wider set of innovation system actors can be achieved. However, there is increasing evidence that developmental impacts from investments in agricultural science could be improved if policy was informed by the institutional lessons provided by an innovations systems perspective. Adopting the evolutionary institutional learning processes that innovation systems thinking identifies as critically important could be part of a complementary assessment approach to improve planning and research management practices. This would assist policy to address the efficiency of the research process rather than, as at present, only measuring its outputs.

English

0310|AGRIS 0301|AL-Economics Program|R01PROCE

Juan Carlos Mendieta

CIMMYT Publications Collection