Since 2004, the Institutional Knowledge Sharing (IKS) Project, managed by CIAT, has focused on scaling up project activities in CGIAR Centers and Programs, with the aim of mainstreaming knowledge sharing (KS) principles and tools. The overall objective is to contribute to organizational development, and improve CGIAR effectiveness by promoting collaborative learning and innovation, and supporting effective use of KS approaches and tools throughout the CGIAR.
Peri-urban areas, at the interface between urban and rural, link rural livelihoods with the urban lifestyles that put multiple pressures on peri-urban ecosystems. This poses huge challenges for the health and livelihoods of an increasing number of disenfranchised, poor and marginalised citizens, and for sustainable urban development.
The CGIAR Research Program on Livestock and Fish aims to increase the productivity of small-scale livestock and fish systems in sustainable ways, making meat, milk and fish more available and affordable across the developing world. This project analyzed the current gender capacities against desired future gender capacities of the Livestock and Fish partners and subsequently designed tailor-made capacity development interventions.
De manera particular, el establecimiento y desarrollo de mecanismos de aprendizaje entre actores tiene un alto potencial para generar conocimientos acumulativos y compartidos acerca de lo que funciona, lo que no funciona y los factores que influyen en el éxito o fracaso de diferentes iniciativas de intervención en tiempos y espacios diversos. Por esto, las alianzas de aprendizaje son un mecanismo con potencial para mejorar la efectividad de las intervenciones externas.
The government of Ethiopia gives great attention to agriculture and rural development for the country’s economy development. Dairy development is one of the components of agricultural development. To improve dairy production in certain locality, dairy producers should able to access and use appropriate knowledge for the particular problem at the right time. This research was conducted to assess agricultural knowledge management system and its challenges and opportunities of knowledge management processes in Bure district.
The IPMS project proposes to ‘contribute to improved agricultural productivity and production through market-oriented agricultural development, as a means for achieving improved and sustainable livelihoods for the rural population’ in Ethiopia. To accomplish this goal the project supports development and (action) research on innovative technologies, processes and institutional arrangements in three focus areas i.e.
Ethiopian needs to achieve accelerated agricultural development along a sustainable commercialization path to alleviate poverty and ensure overall national development. In this regard, sustainable commercial of smallholder dairying provides a viable and growing opportunity; with deliberate, appropriate and sustained policy support. A recent empirical analysis concludes however, that Ethiopian smallholder dairy sub-sector has not been able to take-off despite decades of development interventions.
This guide is developed by synthesizing successful field experiences within a project known as Improving Productivity & Market Success of Ethiopian Farmers Project or IPMS Project. The main purpose of the IPMS project was to test methods, approaches, and processes that can help transform Ethiopian smallholder agriculture from subsistence to a market‐oriented agriculture.
The Feed the Future Asia Innovative Farmers Activity (AIFA) is a regional project working to facilitate the scaling of critical agricultural technologies through regional partnership and technology transfer. The project works with a range of agricultural technology stakeholders on a regional basis (private sector, research institutions, governments, networks, etc.) to increase food security, reduce poverty, and improve environmental sustainability by facilitating agricultural innovation and technology diffusion in the Asia region.
Georeferenced data are a key factor in many decision-making systems. However, their interpretation is user and context dependent so that, for each situation, data analysts have to interpret them, a time-consuming task. One approach to alleviate this task, is the use of semantic annotations to store the produced information. Annotating data is however hard to perform and prone to errors, especially when executed manually. This difficulty increases with the amount of data to annotate.