So far, numerous studies have exhibited Silicon Valley and other thriving innovation ecosystems by distinguishing special characteristics in which their survival rely on sustaining activities that convert them to specific regions. These regions provide ready-made grounds for networking to be innovative. Meantime, it is struggling for innovations to be transformed into measurable economic results if players encounter a weak network of collaborative relationships in the ecosystem.
This paper presents an overview of current opportunities and challenges facing efforts to increase the impact of rural and agricultural extension. The starting point for this analysis is in recognition that the days when agricultural extension was synonymous with the work of public sector agencies are over.
This presentation on a FAO and GFRAS study, on occasion of the Global Conference on Agricultural Research for Development (GCARD) held in March 2010 in Montpellier, deals with the core challenges to extension and the relationship between research and extension
Ce document présente la position de l’Organisation des Nations Unies pour l’alimentation et l’agriculture (FAO) et du Forum mondial pour le conseil rural (GFRAS) sur la place actuelle des services de vulgarisation et de conseil agricole et sur les chemins qu’elle devra suivre à l’avenir. Les résultats présentés dans le document sont destinés à mieux situer la vulgarisation compte tenu de l’avenir de la recherche agricole en faveur du développement.
Despite the concept's widespread popularity, the terminology surrounding missions can come across as convoluted. This is understandable, given that the term - which denotes ambitious, time bound, cross-sectoral and measurable policy objectives to address grand societal challenges such as climate change mitigation, biosphere restoration or tackling health inequities - has proven to be both deceptively intricate and remarkably versatile.
Rather than merely supporting R&D and strengthening innovation systems, the focus of innovation policy is currently shifting towards addressing societal challenges by transforming socio-economic systems. A particular trend within the emerging era of transformative innovation policy is the pursuit of challenge-based innovation missions, such as achieving a 50 % circular economy by 2030. By formulating clear and ambitious societal goals, policy makers are aiming to steer the directionality and adoption of innovation.
This paper reflects on the experience of the Research Into Use (RIU) projects in Asia. It reconfirms much of what has been known for many years about the way innovation takes place and finds that many of the shortcomings of RIU in Asia were precisely because lessons from previous research on agricultural innovation were “not put into use” in the programme’s implementation. However, the experience provides three important lessons for donors and governments to make use of agricultural research: (i) Promoting research into use requires enabling innovation.
This paper reflects on the experience of the Research Into Use (RIU) projects in Asia. It reconfirms much of what has been known for many years about the way innovation takes place and finds that many of the shortcomings of RIU in Asia were precisely because lessons from previous research on agricultural innovation were “not put into use” in the programme’s implementation. However, the experience provides three important lessons for donors and governments to make use of agricultural research: (i) Promoting research into use requires enabling innovation.
This shift in thinking will require major shifts in policy, research, and investment. But where should these investments go? What foundations should be strengthened? Which gaps need filling? What’s working? What’s not?
In order to answer these questions in an informed way, we need to examine the evidence that exists and identify areas where more research is needed.
But this is easier said than done.
The evidence base on agri-food systems is growing exponentially. The CoSAI-commissioned study, Mining the Gaps, applied artificial intelligence to mine more than 1.2 million publications for data, creating a clearer picture of what research has been conducted on small-scale farming and post-production systems from 2000 to the present, and where evidence gaps exist.