Given the diversity and context-specificity of innovation systems approaches, in March 2007 the World Bank organized a workshop in which about 80 experts (representing donor agencies, development and related agencies, academia, and the World Bank) took stock of recent experiences with innovation systems in agriculture and reconsidered strategies for their future development. This paper summarizes the workshop findings and uses them to develop and discuss key issues in applying the innovation systems concept. The workshop’s recommendations, including next steps for the wider
Dairy farmers in the northern regions of New Zealand expressed widespread dissatisfaction with the performance and persistence of their pastures following drought conditions in 2007/08. Farmers were becoming disillusioned with the practice of renewing pasture as a means to introduce modern perennial ryegrass cultivars in their paddocks. This paper describes the formation and operation of an innovation network, consisting of private and public sector actors, that was formed in 2010 to improve the quality and consistency of advice provided to farmers.
The latest turmoil of production and price volatility in the global food sector has put agriculture back to the top of the development agenda. Population growth, changing consumer preferences, bioenergy demand and climate change are some of the huge challenges for agricultural production today and in the future. In the last decades, productivity has been constantly improved through the introduction of improved crop varieties and the greater use of mechanization, irrigation, chemical fertilizer and pesticides.
Natural resource management practices, such as the System of Rice Intensification (SRI), have been proposed to tackle agricultural challenges such as decreasing productivity growth and environmental degradation. Yet, the benefits of system technologies for farmers are often debated. Impacts seem to be context-specific, which is especially relevant in the small farm sector with its large degree of agroecological and socioeconomic heterogeneity. This was not always considered in previous research.
This publication comprises 24 full papers/abstracts presented at the “High Level Policy Dialogue on Investment in Agricultural Research for Sustainable Development in Asia and the Pacific” (Bangkok, 8-9 December 2015).
This paper explores the potential of Actor Network Theory (ANT) in understanding how the process of interaction and translation between human and non-human actors contribute to the development, adoption and diffusion of science-based innovations linked to the transition to organic farming. The study relies on two case studies, the French Camargue case covering a range of technical and social innovations, and the case from Bulgaria focusing on the development of a technical and product innovation, i.e. a veterinary product for organic beekeeping.
In this paper the High Nature Value (HNV) livestock farming systems are defined. These systems are found mainly in marginal areas where physical factors, and in some cases social factors, have prevented intensification of land-use. NV-LINK is a Horizon2020 project that seeks to improve the socio-economic and environmental sustainability of HNV farming in 10 Learning Areas, and more widely across the EU, by promoting innovation.
Agricultural machinery manufacturers historically referred to the intermediate players for selling, maintenance, customer service and/or training of equipment appear to interact with farmers and end-users. Intermediate players have therefore faced the burden to master the technology, in constant evolution, and the associated training needs at the interface between sophisticated equipment and the end-user and its sociological characteristics (age, education, background, etc.).
L’une des avancées les plus importantes dans le domaine de l’observation de la terre est la découverte des indices spectraux, ils ont notamment prouvé leur efficacité dans la caractérisation des surfaces agricoles, mais ils sont généralement définis de manière empirique. Cette étude basée sur l’intelligence artificielle et le traitement du signal, propose une méthode pour trouver un indice optimal. Et porte sur l’analyse d’images issues d’une caméra multi-spectrale, utilisée dans un contexte agricole pour l’acquisition en champ proche de végétation.