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.
The study used Havos AI machine learning models to extract information from each publication based on a series of modular questions. Graphical maps of the data provide policymakers and funders with a more nuanced view of the information available, which can help them to prioritize and coordinate international funding and research efforts.
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...
Dans le contexte de la RAD, le renforcement des capacités est vu comme un processus de développement continu, et non comme une activité de formation ponctuelle. Ce processus améliore les interactions, instaure la confiance et crée des synergies entre des...
This brief illustrates the different forms of knowledge, and the ways to create and manage it.
The organisation of sector and multi-stakeholder consultations was an integral part of the first phase of the PAEPARD II programme, covering the period 2009–2013. These consultations contributed to the overall objective of the programme, the reorientation of scientific and technical...
L’organisation de consultations sectorielles et multiacteurs a fait partie intégrante de la première phase du programme PAEPARD II, qui a couvert la période 2009-2013. Ces consultations se sont inscrites dans l’objectif général du programme, soit la réorientation de la collaboration...