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.
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.
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 reviews the approaches proposed by SCARDA to address capacity strengthening for research management, how implementation took place and the lessons learned from the implementation activities. It begins with an overview of the intended project outputs and approach to capacity strengthening, followed by the implementation processes as undertaken in each sub-regional organisation and finishes with the lessons learned.
The poor performance of agriculture in sub-Saharan Africa is known to be largely due to the lack of effective and client- responsive agricultural research and development that could generate appropriate technologies and innovations to stimulate the agricultural development process. As a contribution to address this challenge, the Forum for Agricultural Research in Africa (FARA), with support from the United Kingdom’s Department for International Development (DFID), developed a project for Strengthening Capacity for Agricultural Research and Development in Africa (SCARDA).
This paper traces the evolution of the innovation systems framework within the agricultural sector in Sub-Saharan Africa, and presents a conceptual framework for agricultural innovation systems. The difference between innovation ecology/ecosystems and intervention-based innovations systems is highlighted, given that these two concepts are used at different levels in promoting and sustaining agricultural innovations.
The paper sets out the general concepts and principles of the Agricultural Innovation Systems approach, and its application to agricultural research and development, particularly in sub-Saharan Africa. It is intended for those interested in applying new approaches to research with farmers, NGOs and the private sector that lead to developmental outcomes.
This synthesis report presents the outputs of the workshop organised by CTA at its headquarters in Wageningen, The Netherlands, 15-17 July 2008. The outputs are presented in two main parts, each corresponding to one of the workshop objectives, and ends with a section on the way forward as suggested by the workshop participants. It also includes a first attempt to come to a consolidated generic framework on AIS performance indicators, based on the outputs of the different working groups.
This briefing considers the status, systems, instruments and institutions underpinning agricultural innovation and research for development (ARD) globally and in the ACP countires. It puts a strong emphasis on lessons learnt and opportunities for successful agricultural innovation, based on broad range of interventions at different levels of the agricultural value chain. It analyses participatory innovation processes to find more efficient and effective modes of agricultural research and technology development benefiting farmers and rural communities.