At an average above 6.0 percent per year over the past two decades, Uganda' s growth rate was impressive by all standards. In parallel, poverty declined significantly, not only in urban areas, but also to some extent within the rural areas. This combination was possible because the key drivers of growth were labor-intensive services sectors, some of which are agriculture based. In fact, Uganda's growth process has reduced overall poverty faster than what has been observed in many other developing countries.

This paper builds on experiences from the Research Into Use programme in South Asia that tried to up-scale promising research outputs into wider use. The experience suggests that while facilitating access to technology is important in putting research into use, it has value only when it is bundled together with other innovation-management tasks such as: developing networks, organising producers, communicating research needs, mediating conflicts, facilitating access to inputs and output services, convening innovation platforms, and advocating for policy change and other negotiated changes in

The latest comprehensive research agenda in the Journal of Agricultural Education and Extension was published in 2012 (Faure, Desjeux, and Gasselin 2012), and since then there have been quite some developments in terms of biophysical, ecological, climatological, social, political and economic trends that impact farming and the transformation of agriculture and food systems at large as well as new potentially disruptive technologies.

Research, extension, and advisory services are some of the most knowledge-intensive elements of agricultural innovation systems. They are also among the heaviest users of information communication technologies (ICTs). This module introduces ICT developments in the wider innovation and knowledge systems as well as explores drivers of ICT use in research and extension.

This presentation argues the need of green growth in agriculture, analyzes features of the innovation systems and ends with some policies practices. The presentation has been prepared for "Innovation and Modernising the Rural Economy", OECD’s 8th Rural Development Policy Conference, 3-5 October 2012 (Krasnoyarsk, Russian Federation).

 Conflicts of interests have been hypothesized when agricultural advisory services are connected to agri-input businesses. However, these have not been examined using large sets of advisory service and grower data. We provide quantitative insights into dependencies between service, crop production, sustainability and the level of agri-input business-linkage of extension workers. This study analyzed 34,000+ prescription forms (recommendations) issued to growers in China, as well as grower interview data.

Increasing attention is being given to evaluating the impact of advisory services in terms of their effectiveness in providing farmers with knowledge and networks for innovation as well as understanding the factors that influence this effectiveness (Prager et al, 2017). The demand and uptake of advisory services is one factor and Klerkx et al (2017) comment on the variation in farmers’ demand and the influences of variables such as farm size, asset status and education as well as stability or turbulence in the regulatory environment.

Research, extension, and advisory services are some of the most knowledge-intensive elements of agricultural innovation systems. They are also among the heaviest users of information communication technologies (ICTs). This module introduces ICT developments in the wider innovation and knowledge systems as well as explores drivers of ICT use in research and extension

The aim of this document is to produce a state-of-the-art of the academic literature in order to identify theories and concepts available for: a) describing the structure, the dynamics and the functioning of agricultural advisory services; b) understanding how these services are embedded into national Agricultural Knowledge and Innovation Systems (AKIS), and into various agricultural and rural policies across the European Union (EU) countries; c) providing some conceptual elements to support the methodology for an inventory of agricultural advisory services in EU 27 countries (WP3 of the PR

Agricultural information is transferred through social interactions; therefore, ties to agricultural informants and network structures within farmers’ local neighborhoods determine their information-gathering abilities. This paper uses a spatial autoregressive model that takes account of spatial autocorrelation to examine such network connections, including friendship networks and advice networks, upon farmers’ knowledge-gathering abilities during formal agricultural training.