This paper is aimed at raising the discussion on frameworks and practices to analyse and support of innovation processes of operational groups in rural development policy. The analysis highlights an increasing interest of the current evaluation and research practices on interactive innovation processes, collaborative learning and capacity development both at individual, collective and systems levels. Particularly, transformative-oriented frameworks have been developed in view of supporting capacity development in innovation systems
In this paper its argued that when flexibly applied and adapted to capture dynamics typical in systems innovation projects, the Log Frame Approach (LFA) ( and logical frameworks have considerable utility to support evaluation for both learning and accountability, and for identifying and addressing institutional logics, which leads to system innovation.
The study relies on the activities performed within EU funded Horizon 2020 project, AgriSpin (www.agrispin.eu), specifically for the case of Cilento Bio-district in Campania region, Italy. The methodology is centred on the “cross-visit method” developed within the AgriSpin Project, based on direct observation, interviews with relevant actors and analysis of grey literature.
This paper explores the potential of Actor Network Theory (ANT) in understanding how the process of interaction and translation between human and non-human actors contribute to the development, adoption and diffusion of science-based innovations linked to the transition to organic farming. The study relies on two case studies, the French Camargue case covering a range of technical and social innovations, and the case from Bulgaria focusing on the development of a technical and product innovation, i.e. a veterinary product for organic beekeeping.
This paper is an inquiry into the process of setting up a national, multi-stakeholder project collaboration aimed at stimulating the role of the private sector in the Australian agricultural extension and innovation systems.
Primary Innovation is a five year collaborative initiative demonstrating and evaluating co-innovation, a systemic approach to innovation addressing complex problems, in five ‘innovation projects’ (active case studies) in different agricultural industries. In defining the elements of co-innovation, Primary Innovation has emphasised nine principles which guide activity in the innovation projects.
This paper is part of the H2020 project AgriLink “Agricultural Knowledge: Linking farmers, advisors and researchers to boost innovation”. It presents and develops the concept of ‘microAKIS’, i.e. the micro knowledge- and innovation-system that farmers personally assemble to manage their agricultural practices and ensure sustainability.
It is adressed in this paper opportunities of Q Methodology for empirical agricultural innovation studies. In the systems perspective on innovation, multi-actor innovation networks are seen as a key strategy to successful innovation. Given the several types of actors involved, the scientific and policy literature points at the need for ‘innovation brokers’ to build capacity for collective innovation and prevent innovation network failures.
As part of the EU funded AgriSpin project (www.agrispin.eu), which aimed at “creating space for innovations” in agriculture across Europe, this contribution addresses the above mentioned knowledge gaps by a. elaborating a generic typology appropriate to capture the variety of ISS, b. structuring selected innovations along the degree of technological change and coordination levels, and c.
This study identifies systemic problems in the New Zealand Agricultural Innovation System (AIS) that affect the ability of participants in the agricultural sectors to co-develop technologies. We integrate structural and functional streams of innovation system enquiry, gathering data through 30 semi-structured interviews with individuals in Government, industry and research. Interviews explored perceptions of the influence of actors, interactions, institutions, infrastructure, and market structure on the effectiveness of AIS functions.