The co-creation and sharing of knowledge among different types of actors with complementary expertise is known as the Multi-Actor Approach (MAA). This paper presents how Horizon2020 Thematic-Networks (TNs) deal with the MAA and put forward best practices during the different project phases, based on the results of a desktop study, interviews, surveys and expert workshops. The study shows that not all types of actors are equally involved in TN consortia and participatory activities, meaning TNs might be not sufficiently demand-driven and the uptake of the results is not optimal.
The government of Rwanda is promoting agricultural intensification focused on the production of a small number of targeted commodities as a central strategy to pursue the joint policy goals of economic growth, food security and livelihood development. The dominant approach to increase the productive capacity of the land, crops and animal resources has been through large-scale land consolidation, soil fertility management, and the intensive use of biotechnology and external inputs.
Even prior to COVID, there was a considerable push for food system transformation to achieve better nutrition and health as well as environmental and climate change outcomes. Recent years have seen a large number of high visibility and influential publications on food system transformation. Literature is emerging questioning the utility and scope of these analyses, particularly in terms of trade-offs among multiple objectives.
Assessing or understanding the agriculture innovation system (AIS) is an essential step to better understand the needs, new skills and functions needed by the actors and the system. To accelerate the uptake of innovation and progress towards eradicating poverty, there is an urgent need for well-coordinated, demand-driven, and market-oriented information, knowledge, technologies and services.
Agricultural Internet of Things (IoT) has brought new changes to agricultural production. It not only increases agricultural output but can also effectively improve the quality of agricultural products, reduce labor costs, increase farmers' income, and truly realize agricultural modernization and intelligence. This paper systematically summarizes the research status of agricultural IoT. Firstly, the current situation of agricultural IoT is illustrated and its system architecture is summarized. Then, the five key technologies of agricultural IoT are discussed in detail.
Droughts are causing severe damages to tropical countries worldwide. Although water abundant, their resilience to water shortages during dry periods is often low. As there is little knowledge about tropical drought characteristics, reliable methodologies to evaluate drought risk in data scarce tropical regions are needed.
The need to urgently transition food systems to net-zero, nature-positive that can nourish all people, leaving no one behind is more critical than ever. The COVID-19 pandemic has furthered deepened complex challenges we already face from hunger and nutrition, climate and nature, and societal inequity. Innovation offers a profound opportunity to achieve these transitions and help unlock challenges across food systems.
Facilitation has proved crucial for enabling the interaction of Agricultural Innovation System (AIS) actors to address the target and to innovate. This “Guide on training of facilitators of multi-actor agricultural innovation platform” is aimed at serving facilitators when multi-actor agricultural innovation platforms (MAIPs) are organized. Since MAIPs are still an emerging concept, there are not many cases to refer to.
Digitization in agriculture is rapidly advancing further on. New technologies and solutions were developed and get invented which ease farmers’ daily life, help them and their partners to gain knowledge about farming processes and environmental interrelations. This knowledge leads to better decisions and contributes to increased farm productivity, resource efficiency, and environmental health. Along with numerous advantages, some negative aspects and dependencies risk seamless workflow of agricultural production.
This article extends social science research on big data and data platforms through a focus on agriculture, which has received relatively less attention than other sectors like health. In this paper, I use a responsible innovation framework to move attention to the social and ethical dimensions of big data “upstream,” to decision-making in the very selection of agricultural data and the building of its infrastructures.