In the face of the climate emergency, around 140 countries, which emit close to 90% of the global greenhouse gas emissions, are planning to reduce their emissions to as close to zero as possible (known as net zero) in the upcoming decades. Around a third of these are low- and middle-income countries (LMICs), the countries most affected by climate change. So how can countries in the Global South achieve a socially-just transition? One key element is innovation, and potentially mission-oriented innovation.
The OECD Mission Action Lab critically examines the practice of missions and mission-oriented policies as well as their suitability to different problem contexts. Addressing complex challenges comes first, methods come second. The Lab is, thus, not promoting "missions" as the one and only instrument to address complex problems or societal transitions. Rather, we aim to understand when and how mission as an approach to public policies is useful and, sometimes more importantly, when it is not.
This study draws on social-psychology in an attempt to identify the various motivations for technology adoption (TA), including both economic and non-economic, and to gain insights into how and why Brazilian innovative beef farmers make decisions about whether or not to adopt particular technologies.
This article departs from the assumption that the challenge of putting the Farm to Fork Strategy (F2F) into action stems from the broader challenge of attaining cross-sectoral policy integration. Policy integration has been part of the EU's policy approach for a long time and has predominantly been achieved in the form of environmental policy integration (EPI). However, the scope of the F2F extends beyond EPI, as it includes the integration of climate-related concerns into sectoral policies, for instance.
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.
Since 2017, in line with COAG’s recommendation, the Research and Extension Unit engaged in the development of a participatory AIS assessment framework including a customizable toolbox for countries with a totally new capacity development perspective. The assessment framework is meant for actors of the national agricultural innovation systems, i.e.
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
Climate change is threatening development gains and intensifying global inequities—putting peace and important gains in human well-being at risk.
Extension and advisory services (EAS) play a key role in facilitating innovation for sustainable agricultural development. To strengthen this role, appropriate investment and conducive policies are needed in EAS, guided by evidence. It is therefore essential to examine EAS characteristics and performance in the context of modern, pluralistic and increasingly digital EAS systems. In response to this need, the Food and Agriculture Organization of the United Nations (FAO) has developed guidelines and instruments for the systematic assessment of national EAS systems.
Extension and advisory services (EAS) play a key role in facilitating innovation processes, empowering marginalized groups through capacity development, and linking farmers with markets. EAS are increasingly provided by a range of actors and funded from diverse sources. With the broadened scope of EAS and the growing complexity of the system, the quantitative performance indicators used in the past (for example related to investment, staffing or productivity) are no longer adequate to assess the performance of EAS systems.