Innovation portfolio management enables not only commercial actors but also public sector organisations to systematically manage and prioritise innovation activities according to concurrent and diverse purposes and priorities. It is a core component of a comprehensive approach to innovation management and a condition to assess the social return of investment across an entire portfolio. The OECD Observatory of Public Sector Innovation (OPSI) has worked in this space for a number of years.
Familiar mixed dairy sheep farm is the most widespread system in the Mediterranean basin, in Latin America and in developing countries (85%). There is a strong lack of technological adoption in packages of feeding and land use in small-scale farms. To increase competitiveness, it would be of great interest to deepen the knowledge of how innovation was selected, adopted, and spread. The objective of this research was to select strategic feeding and land use technologies in familiar mixed dairy sheep systems and later assess dairy sheep farms in Spain.
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
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.
Extension and advisory services (EAS) play a key role in facilitating innovation processes, empowering marginalized groups through capacity development, and linking farmers with markets. Advisory services 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 (e.g. related to investment, staffing or productivity) are not adequate anymore to understand whether the system is well-functioning.
The potential beneficial and harmful social impacts generated by the introduction of novel technologies, in general, and those concerning nutrient recovery and the improvement of nutrient efficiency in agriculture, in particular, have received little attention, as shown in the literature. This study investigated the current social impacts of agricultural practices in Belgium, Germany and Spain, and the potential social impacts of novel technologies introduced in agriculture to reduce nutrient losses.
The digital transformation in agriculture introduces new challenges in terms of data, knowledge and technology adoption due to critical interoperability issues, and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.
The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach.