In this report, food distribution is analysed within the context of food systems in Tanzania. This study looks at entry points for further studies of food system issues within the country that will affect progress towards the achievement of Sustainable Development Goal (SDG) 2. Both qualitative and quantitative methods are used, first to map and conceptualize the complexity of the food system in Tanzania, and then to quantify the likely impacts of scenarios of action and inaction.
This PROLINNOVA report to the 3rd GFAR Programme-Committee meeting is composed of two parts.
The past 1 entitles ‘ PROLINNOVA genesis and growth’ describes historical background and
PROLINOVA in general while the part 2 entitles ‘2007 accomplishments’ narrates specific
accomplishments of PROLINNOVA during the period January-November 2007 . Further, the annex 1
lists contact addresses.
Development education, it combines various methodologies of education to promoting knowledge, so that agriculture sector needs development education to revive productivity through agriculture. ICT (Information communication technology) help to provide knowledge to the door step of farmers.
This study introduces a framework for managing information flow in innovation systems. An organisation's capacity to receive information, to share it with others and to learn from it is assumed to be the key factor that shapes the flow patterns and, hence, the performance of the innovation system concerned. The framework is applied to characterise the information structure underlying the agricultural innovation system of Azerbaijan and to develop an information strategy for the system to accelerate the information flow.
Innovation platforms are equitable, dynamic spaces designed to bring heterogeneous actors together to exchange knowledge and take action to solve a common problem. Although innovation platforms are being set up to attain collectively defined development objectives, there are limited methods and tools available using quantitative data to evaluate whether they are effective.
This decision guide is intended to help extension professionals and their organizations make informed decisions about which extension method and approach to use for providing information, technologies and services to rural producers and to facilitate interactions and knowledge flow. Expected users include field-based rural advisors, extension managers and programme planners.
This publication describes the activities carried out in the tripartite event ‘Transforming Nutrition-Sensitive Value Chain Development in the Pacific Islands.” It was implemented by the Technical Centre for Agricultural and Rural Cooperation (CTA) in collaboration with MORDI Tonga Trust, the International Fund for Agricultural Development (IFAD) and the Pacific Islands Private Sector Organisation (PIPSO). The document starts discussing the main events and field trips that were realized after talk about the lessons learned and in the end brings some case studies and sucess stories.
This chapter proposes a network-based framework to analyze and evaluate participatory and evidence-based policy processes. Four network based performance indicators are derived by incorporating a network model of political belief formation into a political bargaining model of the Baron–Grossmann–Helpman type. The application of our approach to the CAADP reform in Malawi delivers the following results: (i) beyond incentive problems, i.e.
ICT-driven digital tools to support smallholder farmers are arguably inevitable for agricultural development, and they are gradually evolving with promising outlook. Yet, the development and delivery of these tools to target users are often fraught with non-trivial, and sometimes unanticipated, contextual realities that can make or mar their adoption and sustainability. This article unfolds the experiential learnings from a digital innovation project focusing on surveillance and control of a major banana disease in East Africa which is being piloted in Rwanda.
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.