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.
There have been repeated calls for a ‘new professionalism’ for carrying out agricultural research for development since the 1990s. At the centre of these calls is a recognition that for agricultural research to support the capacities required to face global patterns of change and their implications on rural livelihoods, requires a more systemic, learning focused and reflexive practice that bridges epistemologies and methodologies.
Relying entirely on survey information and personal exchanges with over 70 scientists from within the CGIAR network, this working paper attempts to achieve a better understanding of the scope of social learning related efforts undertaken in CGIAR and main issues of relevance to more current efforts, such as that planned by the CGIAR program on Climate Change Agriculture and Food Security (CCAFS). A wide range of methods was identified, where groups of people learn in order to jointly arrive at solutions to pressing food security problems.
Local extension agents can benefit from the simple procedures in developing irrigation calendars for other irrigated crops. This study gives important lesson for local and regional decision makers, on their endeavour to increase the productivity of small scale irrigated agriculture. This paper is organized as follows: Section 2 describes the study area, practical irrigation schedule development method, alternative irrigation schedules and data collection and analysis methods. Section 3 presents the results.
Here, it is described a new participatory protocol for assessing the climate-smartness of agricultural interventions in smallholder practices. This identifies farm-level indicators (and indices) for the food security and adaptation pillars of CSA. It also supports the participatory scoring of indicators, enabling baseline and future assessments of climate-smartness to be made. The protocol was tested among 72 farmers implementing a variety of CSA interventions in the climate-smart village of Lushoto, Tanzania.
This practitioner’s guide, a companion volume to The Innovation Paradox picks up where the previous report left off. It aims to help policy makers in developing countries better formulate innovation policies. It does so by providing a rigorous typology of innovation policy instruments, including evidence of impact—and more importantly, the critical conditions in terms of institutional capabilities to successfully implement these policy instruments in developing countries.
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.
While there is a lot of literature from a natural or technical sciences perspective on different forms of digitalization in agriculture (big data, internet of things, augmented reality, robotics, sensors, 3D printing, system integration, ubiquitous connectivity, artificial intelligence, digital twins, and blockchain among others), social science researchers have recently started investigating different aspects of digital agriculture in relation to farm production systems, value chains and food systems. This has led to a burgeoning but scattered social science body of literature.
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.
This article focus on studying brand experience under the background of IOT through data selecting and analysis , try to make a service design plan according to the design-driven branding innovation. The study take a local fruit brand as study object named “Taozhiyuan” , not only focus on logo or package but try to establish a co-design platform which all the stakeholders and take part in . This platform is based on the system supported by the Wuxi PeachWell IOT Technology Co. Ltd