Chile is a leading country in food exports, where beekeeping plays a fundamental role with more than 1 300 000 hives supporting food production through pollination. Good practices must be addressed from generation to generation of beekeepers to promote the healthy and active hives for the provision of systemic pollination services.
Local stakeholders and agricultural producers in Latin America have limited access to agroclimatic information and, when they do gain access to it, they have difficulty translating it into understandable and actionable knowledge. While climate services are recognized as contributing to bridging the gap between the generation of climate information and its use by stakeholders, their provision and use in Latin America still represents critical challenge.
Chile es un país líder en la exportación de alimentos, en donde la apicultura juega un rol fundamental y cuenta con más de 1 300 000 colmenas para apoyar la producción de alimentos a través de la polinización. Las buenas prácticas deben ser abordadas de generación en generación de apicultores para favorecer el mantenimiento de colmenas sanas y activas para la presentación de servicios sistémicos de polinización.
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.