This tool was designed to assess innovation capacities, identify strengths and weaknesses and monitor capacity changes over time. The scoring tool makes it clear which functional capacities are going to be needed to promote, lead or successfully participate in innovation processes. The tool evaluates capacities on the basis of 21 indicators (each of which is graded on a scale from 0 (low capacity) to 3 (high capacity), and build on the key innovation capacities identified in the capacity needs assessment.
This tool enables participants to become cognisant of the functional capacities discovered through the capacity scoring questionnaire, and test the limits of these capacities through simulations or role-playing (e.g. problem-solving, collaboration, information sharing, and engagement). The simulation game leads to an intuitive understanding of innovation capacities and of the importance of the enabling environment, helping participants to learn about the significance of these capacities.
Social Network Analysis is a practical and useful tool that looks at the linkages present in an innovation network and monitors their development over time. It maps the relationships between the stakeholders in the network and highlights the changes between them.
The timeline tool is generally put to use when stakeholders embark upon the self-assessment phase of their innovation partnership. Stakeholders are asked to recall moments they feel were significant for the partnership, from its beginning to the present and to reflect upon how the partnership has evolved since it began.
In the AgriSpin project (2015-2017) fifteen organisations involved in innovation support tried to understand better how each of them made a difference in helping farmers to innovate. In principle, each partner organisation hosted a Cross Visits of 3 – 4 days, to present a number of interesting innovation cases in which it was involved. The visiting team, composed of colleagues from other partner organisations, interviewed key actors in each case, and gave feedback about pearls, puzzlings and proposals in these innovation processes.
In this chapter the authors compute measures of total factor productivity (TFP) growth for developing countries and then contrast TFP growth with technological capital indexes. In developing these indexes, the authors incorporate schooling capital to yield two new indexes: Invention-Innovation Capital and Technology Mastery. They find that TFP performance is strongly related to technological capital and that technological capital is required for TFP and cost reduction growth.
In this paper, presented at the 8th European IFSA Symposium ( Workshop 6: "Change in knowledge systems and extension services: Role of the new actors") in 2008, the authors discuss a conceptual framework that understands innovation processes as the outcome of collaborative networks where information is exchanged and learning processes happen. They argue that technical and economic factors used to analyse drivers and barriers alone are not sufficient to understand innovation processes.
Intermediary actors have been proposed as key catalysts that speed up change towards more sustainable socio-technical systems. Research on this topic has gradually gained traction since 2009, but has been complicated by the inconsistency regarding what intermediaries are in the context of such transitions and which activities they focus on, or should focus on. This study briefly elaborates on the conceptual foundations of the studies of intermediaries in transitions, and how intermediaries have been connected to different transition theories.
3D Move To See (3DMTS) is a mutli-perspective visual servoing method for unstructured and occluded environments, like that encountered in robotic crop harvesting. This paper presents a deep learning method, Deep-3DMTS for creating a single-perspective approach for 3DMTS through the use of a Convolutional Neural Network (CNN). The novel method is developed and validated via simulation against the standard 3DMTS approach.
This training manual was prepared under the EU-funded project Capacity Development for Agricultural Innovation Systems (CDAIS), a global partnership (Agrinatura, FAO and 8 pilot countries) that aims to strengthen the capacity of countries and key stakeholders to innovate in complex agricultural systems, thereby achieving improved rural livelihoods.