This paper analyses intermediary organisations in developing economy agricultural clusters. The paper critically engages with a growing narrative in studies of intermediaries that have stressed the ownership structure of intermediaries as a key driver for enabling knowledge transfer, inter-firm learning and upgrading of small producers in clusters. Two case studies of Latin American clusters are presented and discussed.
The agricultural innovation system can be strengthened by increasing the learning capacity of research and field organisations. Participatory methods were developed to study three dimensions of the capacity of such organisations in Nicaragua to access and analyse information, highly correlated to learning capacity – the individual routines of their professionals, the formal procedures of the organisation and the organisation's use of collaborative projects to strengthen core operations.
This paper examines different practical methods for stakeholders to analyse power dynamics in multi-stakeholders processes (MSPs), taking into account the ambiguous and uncertain nature of complex adaptive systems. It reflects on an action learning programme which focused on 12 cases in Africa and Asia put forward by 6 Dutch development non-governmental organizations (NGOs).
The agrarian system Analysis and Diagnosis is used for this study, the goal of which was to provide a corpus of basic knowledge and elements of reflection necessary for the understanding the Niayes farming systems dynamics in Senegal, West Africa. Such holistic work has never been done before for this small region that provides the majority of vegetables in the area, thanks to its microclimate and access to fresh water in an arid country.
Innovation is considered as one of the key drivers for a competitive and sustainable agriculture and the European Commission highlights the importance of tailoring innovation support to farmers’ needs, especially in European Rural Development Policy (reg EU 1305/2013). The scientific literature offers a wide panorama of tools and methods for the analysis of innovation in agriculture but the lack of data on the state of innovation in the farms hampers such studies. A possibility to partially overcome this limit is the use of data collected by the Farm Accountancy Data Network (FADN).
Farmers Training Center (FTC)-based farmer training is an emerging extension strategy geared towards human capital development through need-based, hands-on practical training in order to facilitate agricultural transformation and rural livelihood improvement. Although FTCs were established and made functional in the Tigray National Regional State and Alamata Woreda no systematic assessment of the relevance and effectiveness of the training were made.
A value chain study on sweet potato was conducted in 11 districts of Malawi across all the three regions to analyze and identify bottlenecks and inherent opportunities for possible investments for upgrading and development of the value chain. The study applied both quantitative and qualitative methods to collect primary data from 94 farmers belonging to 7 farmer groups using Focus Group Discussions (FGDs), 14 traders and 16 key informants comprising policy makers, NGO representatives and scientists from both local and international research institutions.
This research project aims to build ACP capacity to better understand the strengths and weaknesses of the local science, technology and innovation system in the agricultural sector.
Research for development (R4D) projects increasingly engage in multi-stakeholder innovation platforms (IPs) asan innovation methodology, but there is limited knowledge of how the IP methodology spreads from one contextto another. That is, how experimentation with an IP approach in one context leads to it being succesfully re-plicated in other contexts.
Georeferenced data are a key factor in many decision-making systems. However, their interpretation is user and context dependent so that, for each situation, data analysts have to interpret them, a time-consuming task. One approach to alleviate this task, is the use of semantic annotations to store the produced information. Annotating data is however hard to perform and prone to errors, especially when executed manually. This difficulty increases with the amount of data to annotate.