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.

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 describes a process for stimulating this engagement to develop a shared understanding of systemic problems, challenge prevalent institutional logics, and identify individual and collective actions that change agents might undertake to stimulate system innovation. To achieve this the process included (i) multiple actors from the agricultural innovation systems, (ii) steps to prompt reflexivity to challenge underlying institutional logics, (iii) an iterative process of practical experimentation to challenge current practices, and (iv) actions to encourage generative collaboration.

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.

In this article it is analysed the results of applying a co-innovation approach to five research projects in the New Zealand primary sector. The projects varied in depth and breadth of stakeholder engagement, availability of ready-made solutions, and prevalence of interests and conflicts. The projects show how and why co-innovation approaches in some cases contributed to a shared understanding of complex problems. Our results confirm the context-specificity of co-innovation practices

This paper details the analytical framework used for developing a nested understanding of systemic innovation capacity in an AIS. The paper then introduces the two case studies, along with the data and methods of analysis, followed by a presentation of the results as timelines of configurations of capabilities at different levels of the AIS.

Agriculture Innovation System (AIS) thinking and approaches are largely perceived as a sine-qua-non for the design and implementation of effective and sustainable agriculture development programmes. AIS has gained popularity in the agriculture innovation literature and has been embedded in policy documents of agriculture sector institutions in many countries. However, there is much less evidence of AIS thinking influencing the behaviours of research and extension institutions and staff ‘on the ground’.

Digital agriculture is likely to transform productive processes both on- and off- farm, as well as the broader social and institutional context using digital technologies. It is largely unknown how agricultural knowledge providing organisations, such as advisors and science organisations, understand and respond to digital agriculture. The concept of ‘organisational identity’ is used to describe both initial understandings of, and emerging responses, to digital agriculture, which together show how organisations ‘digi-grasp’, i.e.

The shift to industrial agriculture in Europe brought along a range of environmental and social externalities. This led policy makers, researchers and civil servants to consider and explore the potential of diversified farming systems (DFS) to address current problems in agriculture. However, because of multiple obstacles adoption of these DFS by farmers is not obvious. In this study we investigate the case of agroforestry (AF) systems in Flanders, where a government incentive scheme initiated in 2011, did not lead to the expected uptake of AF systems by farmers.

The framework is designed to assess resilience to specific challenges (specified resilience) as well as a farming system's capacity to deal with the unknown, uncertainty and surprise (general resilience). The framework provides a heuristic to analyze system properties, challenges (shocks, long-term stresses), indicators to measure the performance of system functions, resilience capacities and resilience-enhancing attributes. Capacities and attributes refer to adaptive cycle processes of agricultural practices, farm demographics, governance and risk management.