Published in ILRI Discussion Paper No. 3: Targeting and Innovation, January 1, 2007.
NOTE: At the time of publication, the author Charles F. Nicholson was not yet affiliated with Cal Poly.
Acceleration of economic, technological, social, and environmental change challenge decision-makers of various kinds to learn at increasing rates, and at the same time, the complexity of the dynamic systems in which we live is growing (Sterman 2000). In agriculture and international development contexts, there are often significant delays in the development and implementation of technologies and policies, and agriculture-based livelihood systems are in constant and sometimes rapid evolution. In order to make technologies and policies better match the future state of these systems, it is necessary to better understand the likely evolution of agricultural systems. The goal of these efforts should be to improve our understanding about which technologies and policies will be relevant for the state of the future systems so that work can begin on them now. In essence, researchers, policymakers and donors need an improved understanding of general behavioural tendencies for target systems 5 to 10 years hence. Moreover, modelling can be used to assess the impact of specific interventions over a relevant time horizon. Many modelling approaches are available that allow greater consideration of dynamic system characteristics, technology and policy options. These approaches have the potential to allow more dynamic, comprehensive and consistent ex ante evaluation of specific interventions, which in turn are one element in the specification of research priorities.
Agribusiness | Agricultural and Resource Economics | Business
Copyright © 2007 International Livestock Research Institute, Nairobi, Kenya
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