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...
This policy brief consolidates lessons learned from an in-depth literature review on small-scale farmer (SSF) innovation systems and a two-day expert consultation on the same topic, hosted in Geneva by Quaker United Nations Office (QUNO) in May 2015. This review...
The lessons and recommendations outlined in this paper were captured at a PAEPARD Capitalization Workshop with all partners, held in Cotonou, Benin, on 2-6 October 2017. The workshop was key to the overall evaluation of PAEPARD II, as it encouraged...
Les leçons et les recommandations mises en avant dans cette publication sont issues d’un atelier de capitalisation de PAEPARD qui a réuni tous les partenaires à Cotonou, au Bénin, du 2 au 6 octobre 2017. Cet atelier a joué un...
The nature of the issues around which Agricultural Research for Development (ARD) partnerships are formed requires a different way of conceptualizing and thinking to that commonly found in many agricultural professionals. This brief clarifies the components of a system of...