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.
Grants for agricultural innovation are common but grant funds specifically targeted to smallholder farmers remain relatively rare. Nevertheless, they are receiving increasing recognition as a promising venue for agricultural innovation. They stimulate smallholders to experiment with improved practices, to become proactive and to engage with research and extension providers. The systematic review covered three modalities of disbursing these grants to smallholder farmers and their organisations: vouchers, competitive grants and farmer-led innovation support funds.
Drawing on studies from Africa, Asia and South America, this book provides empirical evidence and conceptual explorations of the gendered dimensions of food security. It investigates how food security and gender inequity are conceptualized within interventions, assesses the impacts and outcomes of gender-responsive programs on food security and gender equity, and addresses diverse approaches to gender research and practice that range from descriptive and analytical to strategic and transformative.
Social learning processes can be the basis of a method of agricultural innovation that involves expert and empirical knowledge. In this sense, the objective of this study was to determine the effectiveness and sustainability of an innovation process, understood as social learning, in a group of small farmers in the southern highlands of Peru. Innovative proposals and its permanence three years after the process finished were evaluated. It was observed that innovation processes generated are maintained over time; however, new innovations are not subsequently generated
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.
The question of how agricultural research can best be used for developmental purposes is a topic of some debate in developmental circles. The idea that this is simply a question of better transfer of ideas from research to farmers has been largely discredited. Agricultural innovation is a process that takes a multitude of different forms, and, within this process, agricultural research and expertise are mobilised at different points in time for different purposes. This paper uses two key analytical principles in order to find how research is actually put into use.
This paper presents an overview of current opportunities and challenges facing efforts to increase the impact of rural and agricultural extension. The starting point for this analysis is in recognition that the days when agricultural extension was synonymous with the work of public sector agencies are over.
This paper has been prepared under the guidelines provided by the TAP Secretariat at the FAO, as a contribution to the G20 initiative TAP, which includes near 40 partners and is facilitated by FAO. Its purpose is to provide a Regional synthesis report on capacity needs assessment for agricultural innovation, with capacity gaps identified and analyzed, including recommendations to strengthen agricultural innovation systems (AIS) and draft policy recommendations to address the capacity gaps.