Situate within new institutionalism literature, this paper builds a complex system model of institutional analysis for adaptive governance. This model combines Young’s institutional environmental analysis method, elements of subsequent environmental governance projects models, and ideas of multiple institutional levels and drivers. By applying the model, policy instruments are identified that build agricultural producer livelihoods improving their adaptive capacity to respond to climate change and drought.
This paper presents and discusses a diagnostic framework to identify institutional processes in the creation of public-private partnerships (PPPs) for agricultural innovation. The diagnostic framework proposed here combines a conceptualisation of institutions with a conceptualisation of technology. We argue that a performative notion of institutions provides a better tool for institutional diagnostics than the common understanding of institutions as ‘rules of the game’.
This presentation was given for the SEARCA Forum-workshop on Platforms, Rural Advisory Services, and Knowledge Management: Towards Inclusive and Sustainable Agricultural and Rural Development, Los Banos, 17-19 May 2016. It briefed innovation, innovation systems and multistakeholder processes (innovation platforms and learning alliances).
This paper examines different practical methods for stakeholders to analyse power dynamics in multi-stakeholders processes (MSPs), taking into account the ambiguous and uncertain nature of complex adaptive systems. It reflects on an action learning programme which focused on 12 cases in Africa and Asia put forward by 6 Dutch development non-governmental organizations (NGOs).
Innovation is considered as one of the key drivers for a competitive and sustainable agriculture and the European Commission highlights the importance of tailoring innovation support to farmers’ needs, especially in European Rural Development Policy (reg EU 1305/2013). The scientific literature offers a wide panorama of tools and methods for the analysis of innovation in agriculture but the lack of data on the state of innovation in the farms hampers such studies. A possibility to partially overcome this limit is the use of data collected by the Farm Accountancy Data Network (FADN).
In creating a usable Information System (IS), the quality of information is crucial for making the right decisions. Although, many Information Quality (IQ) features have been identified in a broader context, only certain IQ features would become applicable for each domain from the usability perspective. This study focuses on a theoretical analysis to identify the IQ features which would be significant to produce a usable agricultural information system with respect to the developing countries.
Various researchers and policy analysts have made empirical studies of innovation systems in order to understand their current structure and trace their dynamics. However, policy makers often experience difficulties in extracting practical guidelines from studies of this kind. In this paper, we operationalize our previous work on a functional approach to analyzing innovation system dynamics into a practical scheme of analysis for policy makers. The scheme is based on previous literature and our own experience in developing and applying functional thinking.
Research for development (R4D) projects increasingly engage in multi-stakeholder innovation platforms (IPs) asan innovation methodology, but there is limited knowledge of how the IP methodology spreads from one contextto another. That is, how experimentation with an IP approach in one context leads to it being succesfully re-plicated in other contexts.
This data article contains annotation data characterizing Multi Criteria Assessment (MCA) Methods proposed in the agri-food sector by researchers from INRA, Europe's largest agricultural research institute (INRA, https://institut.inra.fr/en). MCA can be used to assess and compare agricultural and food systems, and support multi-actor decision making and design of innovative systems for crop production, animal production and processing of agricultural products.
Se analizan los efectos de las interacciones, directas e indirectas, entre agricultores y otros actores relevantes en el intercambio de información y conocimiento para la innovación agrícola. Los datos se obtuvieron al preguntar a 120 agricultores «¿de quién aprende y/o a quién recurre para obtener información o conocimiento de cuestiones técnicas y productivas en torno a su unidad de producción?». Se emplean indicadores del análisis de redes sociales para proponer lineamientos que permitan catalizar la innovación agrícola.