Multi-actors networks are increasingly used by farmers to link between them and to be interactively connected with other partners, such as advisory organizations, local governments, universities, and non-farm organizations. Given the importance assigned to the agricultural innovation by EU resorting to the networking between the research chain actors and the farmers, a strong focus on enhancing the creation of learning and innovation networks is expected.
A challenge for researchers and other developers of new technologies in agriculture is to find ways of communicating their results and recommendations. This challenge is particularly acute in regions in which farmers have limited access to education and where illiteracy is widespread, such as in the rural areas of Mali. One approach that shows potential, yet remains largely unused by extension services, is the dissemination of educational video on mobile phones with video and Bluetooth technology, which are widespread in the region.
The private sector’s presence in agricultural advisory services worldwide has been on the increase for over three decades. This trend has also been observed in the Mantaro Valley (Peru), in a context of dairy family farming. The objective of the communication is to analyse the modalities of advisory services privatization and assess the consequences of this privatization for the farmers and their livestock systems. Data were collected through input suppliers, different types of advisers and producers interviews.
The Guyana School of Agriculture (GSA) was established in the year 1963 by former President Dr. Cheddi Jagan. It became a state corporation in 1964 offering the Diploma in Agriculture and the Certificate in Agriculture Programmes, and graduated the first batch of 15 students in 1966. Agricultural diversification has become an important mechanism for economic growth by providing opportunities that facilitate technological advancement and demand driven government policy.
In this paper, presented at the 12th European IFSA Symposium (Workshop: "Generating spaces for innovation in agricultural and rural development") in 2016, the authors assess the integration of new entrants to small-scale farming into agricultural knowledge and innovation systems (AKIS), in four study sites located on Europe’s periphery (Bulgaria, Poland, Portugal, and the United Kingdom).
While privatization of extension has received considerable attention with respect to implications for public and private good, less consideration has been given to structural and relational implications for knowledge sharing.
More than 25 years after the first implementation of Farmer Field Schools (FFS), there is a rich corpus of evidence that participation in FFS improves farmers’ knowledge, skills, and competencies. On the other hand, several studies converge to show that FFS, by strengthening group action, have the potential to build-up social capital among participants and, thereafter, within local communities.
The paper, prepared for the "High Level Policy Dialogue on Investment in Agricultural Research for Sustainable Development in Asia and the Pacific" (Bangkok Thailand; 8-9 December 2015), presents the Common Framework on Capacity Development for Agricultural Innovation Systems (CDAIS).The framework is a core component of the Action Plan of the TAP, a G20 Initiative, aiming to increase coherence and effectiveness of capacity development for agricultural innovation that lead to sustainable change and impact at scale.
The problems of agricultural development for small and medium enterprises (SMEs) are considered. The features of modeling business processes in agriculture are analyzed. A financial decision support system is proposed to increase sustainability and reduce risks in the development of agricultural SMEs. The software modules are based on TEO-INVEST.
For an intelligent agricultural robot to reliably operate on a large-scale farm, it is crucial to accurately estimate its pose. In large outdoor environments, 3D LiDAR is a preferred sensor. Urban and agricultural scenarios are characteristically different, where the latter contains many poorly defined objects such as grass and trees with leaves that will generate noisy sensor signals. While state-of-the-art methods of state estimation using LiDAR, such as LiDAR odometry and mapping (LOAM), work well in urban scenarios, they will fail in the agricultural domain.