The European small ruminants (i.e. sheep and goats) farming sector (ESRS) provides economic, social and environmental benefits to society, but is also one of the most vulnerable livestock sectors in Europe. This sector has diverse livestock species, breeds, production systems and products, which makes difficult to have a clear vision of its challenges through using conventional analyses. A multi-stakeholder and multi-step approach, including 90 surveys, was used to identify and assess the main challenges for the sustainability of the ESRS to prioritize actions.
This article starts by describing the evolution of innovation in agricultural research and cooperation for development, including an historical overview of agricultural research for development from green revolution to the re-discover of traditional knowledge. Then the authors analyze participation in innovation processes and make a comparison of innovation systems and platforms targeting the agri-food sector in developing countries. A particular focus is reserved to the European regional networks and to the experience of the USAID Middle East Water and Livelihoods Initiative.
This book discusses innovation problems and opportunities for family farming in the different regions of the American continent, as well as the role of hemispheric, regional and national agrifood research systems. Likewise, it provides a description of the main innovation actions and projects promoted by IICA, and the main success cases over recent years.
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.
This study identifies entry points for innovation for sustainable intensification of agricultural systems. An agricultural innovation systems approach is used to provide a holistic image of (relations between) constraints faced by different stakeholder groups, the dimensions and causes of these constraints, and intervention levels, timeframes and types of innovations needed. The authors aim at showing that constraints for sustainable intensification of agricultural systems are mainly of economic and institutional nature.
Using Nepal as a case, this paper illustrates how farmers and their supporting institutions are evolving and co-producing climate sensitive technologies on demand. Drawing upon the hypothesis of induced innovation, the authors examine the extent to which resource endowments have influenced the evolution of technological and institutional innovations in Nepal’s agricultural research and development. This study reveals that Nepal has developed a novel multilevel institutional partnership, including collaboration with farmers and other non-governmental organizations in recent years.
There is increasing evidence that public organizations dedicated exclusively to research and development (R&D) in agribusiness need systematic management tools to incorporate the uncertainties and complexities of technological and nontechnological factors of external environments in its long-term strategic plans. The major issues are: What will be the agribusiness science and technology (S&T) needs be in the future? How to prepare in order to meet these needs?
Innovation platforms are fast becoming part of the mantra of agricultural research and development projects and programs with an innovation objective.
The Colombian Ministry of Agriculture Colombia, an international research center and a national farmers’ organization developed a data-driven agricultural program that: (i) compiles information from multiple sources; (ii) interprets that data; and (iii) presents the knowledge to farmers through the local advisory services. Data was collected from multiple sources, including small-scale farmers. Machine learning algorithms combined with expert opinion defined how variation in weather, soils and management practices interact and affect maize yield of small-scale farmers.