This background note for the development of an AIS Investment Sourcebook provides a menu of tools and guidance to invest in agricultural innovation in different contexts. The content is drawn on tested good practice examples and innovative approaches with emphasis on lessons learned, benefits and impacts, implementation issues, and replicability
In line with the government of Mozambique’s strategies, this document proposes an innovative model with high promise to develop value-adding market led post-harvest processing enterprises and to transform the post harvest-processing sector in Mozambique, while creating sustainable jobs and increasing incomes. The challenge is to ensure coordination across value chains to guarantee that the right conditions are in place for making the Agribusiness Innovation Center (AIC) a success.
Tanzania has tremendous potential to support a thriving agribusiness sector. Agriculture is diverse and extensive, employing more than 80 percent of the population, and contributing about 28 percent of Gross Domestic Product, or GDP and 30 percent of export earnings. A wide range of agricultural commodities are produced in Tanzania, including fiber (sisal, cotton), beverages (coffee, tea), sugar, grains (a diverse range of cereals and legumes), horticulture (temperate and tropical fruits, vegetables and flowers) and edible oils.
This flyer is about the AgriFood chain toolkit, which has been launched in 2013 by the CGIAR programme on Policies, institutions and markets.The AgriFood chain toolkit acts as a clearing house and learning platform – using the power of information and communication technologies to bring together people and resources.
Communication is a crucial part of facilitating the process of innovation within an innovation platform. It comprises a broad range of practices and approaches which include information management, publishing, use of information and communication technologies, communication for development, knowledge sharing and knowledge management. Its goal is not just to produce or disseminate more information, but rather to use communication processes to power changes identified by the platform.
The presentation was given at the ILRI Policy, Trade and Value Chains Program (May–November 2014) Seminar, ILRI Nairobi, 21 November 2014. It included the introduction of Dairy Development Forum, background and purpose, literature review, methodology, results and discussions, and conclusions.
On 15 November 2012, as part of the IFAD East and Southern Africa regional meeting in Addis Ababa, ILRI was asked to convene and facilitate a 1 hour session on ways that CGIAR and IFAD could collaborate. The session drew on contributions from different CGIAR centres; it involved speakers from ILRI, IWMI and ICARDA. It provided a very good, but short, opportunity to make connections between some CGIAR staff and IFAD and project staff; several individual follow up conversations were triggered.
The presentation (www.slideshare.net/ILRI/cgiar-and-ifad-sharing-and-scaling-up-innovations) reflected on current collaboration experiences between IFAD and the CGIAR, it introduced the ‘renewed’ research for development focus of the CGIAR and its multi-center Research Programs and it explored ideas for future collaboration.
Strengthening the capacity of farmer training centers (FTCs) in Ethiopia and enhancing FTC‐based training and knowledge services is important to leverage and optimize potential contributions of FTCs to facilitating market‐led and knowledge‐based agricultural transformation.
The CGIAR research program on livestock and fish aims to sustainably increase the productivity of small-scale livestock and fish systems so as to increase the availability and affordability of meat, milk and fish for poor consumers across the developing world. The purpose of this document is to lay out a Monitoring, Evaluation and Learning (MEL) Framework for the program. The Framework provides a concise narrative of why the M&E system is important, how it operates, what kinds of data it will collect and who is responsible for data collection and analysis.