Although agricultural innovation systems (AIS) have recently received considerable attention in academic and development circles, links between an AIS's regional specifications and structural-functional analysis have been neglected. This paper aims to understand how regional and structural dimensions determine systemic problems and blocking mechanisms that, in turn, hinder a regional AIS's function.
Agriculture Innovation System (AIS) thinking and approaches are largely perceived as a sine-qua-non for the design and implementation of effective and sustainable agriculture development programmes. AIS has gained popularity in the agriculture innovation literature and has been embedded in policy documents of agriculture sector institutions in many countries. However, there is much less evidence of AIS thinking influencing the behaviours of research and extension institutions and staff ‘on the ground’.
This paper details the analytical framework used for developing a nested understanding of systemic innovation capacity in an AIS. The paper then introduces the two case studies, along with the data and methods of analysis, followed by a presentation of the results as timelines of configurations of capabilities at different levels of the AIS.
This reports highlights social learning as an essential aspect in dealing with the complexities around climate change and uncertainty in food production. It is not just about adapting to these complexities, it is actually about implementing tranformative changes.
TECA is an FAO online platform for the exchange and sharing of agricultural technologies and practices for smallholder farmers and producers. The platform facilitates the transformation process in rural areas by making relevant and innovative technologies available to farmers in the field. In doing so, TECA further enhances the access to knowledge of smallholder producers in rural areas increasing their capacity to innovate and contribute to achieving the Sustainable Development Goals (SDGs).
This book documents the proof of the Integrated Agricultural Research for Development (IAR4D) Concept that was developed by the Forum for Agricultural Research for Development in Africa (FARA). The IAR4D concept forms the basis for the Sub Saharan Africa Challenge Program (SSA CP) which is the only CGIAR Challenge Program that was limited to only one region in the world.
As a key pillar of the Ugandan economy, the agriculture sector is a critical driver of economic growth and poverty alleviation. Uganda's agricultural sector is dominated by smallholders with low levels of productivity. The agriculture sector is highly exposed to co-variant risks, which include weather, biological, infrastructure (post-harvest loss), price, and market risks. This plethora of risks suppresses appetite for investment in the sector. Despite the sector's contribution to the economy, farmers' access to finance remains a major constraint.
This new Africa Region Sustainable Development Series aims to focus international attention on a range of topics, spur debate, and use robust, evidence-based, informed approaches to advance policy dialogue and policy-making. This new Series synthesizes a large body of work from disparate sources, and uses simple language to convey the findings in an easily-digestible format. Ultimately, we want to seed solutions that can help accelerate the fight to end poverty in Africa.
Ce document présente une série d'initiatives visées á renforcer les capacités de la société civile, par exemple en promouvant la participation des citoyens dans les processus de prise de décisions, ou en améliorant la gestion des ressources et l’accès à l’éducation pour les enfants.
L’une des avancées les plus importantes dans le domaine de l’observation de la terre est la découverte des indices spectraux, ils ont notamment prouvé leur efficacité dans la caractérisation des surfaces agricoles, mais ils sont généralement définis de manière empirique. Cette étude basée sur l’intelligence artificielle et le traitement du signal, propose une méthode pour trouver un indice optimal. Et porte sur l’analyse d’images issues d’une caméra multi-spectrale, utilisée dans un contexte agricole pour l’acquisition en champ proche de végétation.