This paper draws lessons from selected country experiences of adaptation and innovation in pursuit of food security goals.
There are divergent views on what capacity development might mean in relation to agricultural biotechnology. The core of this debate is whether this should involve the development of human capital and research infrastructure, or whether it should encompass a wider range of activities which also include developing the capacity to use knowledge productively. This paper uses the innovation systems concept to shed light on this discussion, arguing that it is innovation capacity rather than science and technology capacity that has to be developed.
We present a model for research and development (R&D) investment in food innovations based on new plant engineering techniques (NPETs) and traditional hybridization methods. The framework combines uncertain and costly food innovation with consumers' willingness to pay (WTP) for the new food. The framework is applied with elicited WTP of French and US consumers for new improved apples. NPETs may be socially beneficial under full information and when the probability of success under NPETs is relatively high. Otherwise, the traditional hybridization is socially optimal.
Agricultural communication to mitigate climate change enables information dissemination of both scientific knowledge (SCK) and indigenous knowledge (IDK) for practical farming. This research analyzed knowledge utilization and conducted community-based participatory communication to propose a practical agricultural communication framework for climate mitigation. Based on a qualitative method of data collection in Phichit province, the key findings showed that SCK and IDK can be mutually utilized to enhance the good relationship among the people and for the people with nature.
An analysis of the impact of simulation modelling in three diverse crop-livestock improvement projects in Agricultural Research for Development (AR4D) reveals benefits across a range of aspects including identification of objectives, design and implementation of experimental programs, effectiveness of participatory research with smallholder farmers, implementation of system change and scaling-out of results. In planning change, farmers must consider complex interactions within both biophysical and socioeconomic aspects of their crop and animal production activities.
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.
Based on eleven case studies from Africa, Asia, and Latin America, this report describes farmer-led research findings and their dissemination, and analyzes available evidence on the impact of farmer-led approaches to agricultural research and development on rural livelihoods, local capacity to innovate and adapt, and influence on governmental institutions of agricultural research and development.
The Guidance Note on Operationalization provides a brief recap of the conceptual underpinnings and principles of the TAP Common Framework as well as a more detailed guide to operationalization of the proposed dual pathways approach. It offers also a strategy for monitoring and evaluation as well as a toolbox of select tools that may be useful at the different stages of the CD for AIS cycle.