This training manual, which is based on a methodology developed by FAO’s Research and Extension Unit (OINR), presents a training course on assessing AIS consisting of eight modules.
Agrifood system transformation to achieve the Sustainable Development Goals requires increased attention to developing, adapting and diffusing impactful science, technology and innovation (STI). Current levels and patterns of STI uptake are inadequate to facilitate needed agrifood system transformations, especially in today's low- and middle-income countries.
Equipping agricultural extension and advisory services with nutrition knowledge, competencies and skills is essential to promote nutrition-sensitive agriculture. This report presents the results of an assessment of capacity within agricultural extension and advisory services, undertaken in Telangana State, India, with the global capacity needs assessment (GCNA) methodology developed by FAO and GFRAS. The methodology is available online at https://doi.org/10.4060/cb2069en
The purpose of this methodological guide is to outline how to conduct an empirical assessment of the current landscape of women’s small-scale fishery (SSF) organizations. Applying this methodology will enhance understanding of where women are organized in SSFs (both geographically and within the value chain); what their present organizational characteristics and capacities are; and their primary needs.
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 Digital Innovation Strategy (DIS) of the Regional Office for Africa (RAF) of FAO has been prepared to respond to critical challenges facing inclusive and sustainable agrifood system transformation in sub-Saharan Africa. It is enshrined in the new Strategic framework 2022–2030 that aims to accelerate the "transformation to more efficient, inclusive, resilient and sustainable agri-food systems for better production, better nutrition, a better environment and a better life, leaving no one behind".
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.