The concept of technology adoption (along with its companions, diffusion and scaling) is commonly used to design development interventions, to frame impact evaluations and to inform decision-making about new investments in development-oriented agricultural research. However, adoption simplifies and mischaracterises what happens during processes of technological change. In all but the very simplest cases, it is likely to be inadequate to capture the complex reconfiguration of social and technical components of a technological practice or system.
The concept of technology adoption (along with its companions, diffusion and scaling) is commonly used to design development interventions, to frame impact evaluations and to inform decision-making about new investments in development-oriented agricultural research. However, adoption simplifies and mischaracterises what happens during processes of technological change. In all but the very simplest cases, it is likely to be inadequate to capture the complex reconfiguration of social and technical components of a technological practice or system.
Rather than merely supporting R&D and strengthening innovation systems, the focus of innovation policy is currently shifting towards addressing societal challenges by transforming socio-economic systems. A particular trend within the emerging era of transformative innovation policy is the pursuit of challenge-based innovation missions, such as achieving a 50 % circular economy by 2030. By formulating clear and ambitious societal goals, policy makers are aiming to steer the directionality and adoption of innovation.
Individuals from a diverse range of backgrounds are increasingly engaging in research and development in the field of artificial intelligence (AI). The main activities, although still nascent, are coalescing around three core activities: innovation, policy, and capacity building. Within agriculture, which is the focus of this paper, AI is working with converging technologies, particularly data optimization, to add value along the entire agricultural value chain, including procurement, farm automation, and market access.
Establishing food security remains a global challenge; it is thus a specific objective of the United Nations Sustainable Development Goals for 2030. Successfully delivering productive and sustainable agricultural systems worldwide will form the foundations for overcoming this challenge. Smart agriculture is often perceived as one key enabler when considering the twin objectives of eliminating world hunger and undernourishment. The practical realization, deployment, and adoption of smart agricultural systems remain distant due to a confluence of technological, social, and economic factors.
This article proposes ways to use programme theory for evaluating aspects of programmes that are complicated or complex. It argues that there are useful distinctions to be drawn between aspects that are complicated and those that are complex, and provides examples of programme theory evaluations that have usefully represented and address both of these.
This article applies a historical analysis of the progressive development and complexity of Malawi’s diary innovation system through phased emphasis on technological, organizational and institutional development to illustrate the centrality of smallholder dairy farmers in the innovation system. A social network analysis is applied to assess the influence of smallholder farmers on other actors. The existence and growth of the diary innovation system in Malawi is founded on the resilience of smallholder dairy farmers to produce milk.
Recently, Agricultural Knowledge and Innovation Systems (AKISs) have gained considerable attention in scientific and political forums in the European Union (EU). AKIS is considered a key concept in identifying, analysing and assessing the various actors in the agricultural sector as well as their communication and interaction for innovation processes. Using qualitative expert interviews and organizational mapping, the features of national AKISs were investigated in selected EU member states (Belgium, France, Ireland, Germany, Portugal and the UK).
Consumer concerns are leading to changes in China’s food markets and demands for higher quality food. In this article, we explore the role of farmer cooperatives in China in linking farmers with high-quality food markets. We consider food quality a social construct and farmer cooperatives key players in the ‘quality battlefield’. Using a case study approach, we investigate the everyday practices of three farmer cooperatives.
This paper analyses intermediary organisations in developing economy agricultural clusters. The paper critically engages with a growing narrative in studies of intermediaries that have stressed the ownership structure of intermediaries as a key driver for enabling knowledge transfer, inter-firm learning and upgrading of small producers in clusters. Two case studies of Latin American clusters are presented and discussed.