This paper presents a case study of the Honey Bee Network’s decentralized model for collecting, verifying and disseminating grassroots innovations and provides a roadmap for its replication in Africa. The Honey Bee Network brings together governmental and non‐governmental institutions, members of academia, scholars and a large number of volunteers.
This paper analyzes the integration of coffee producers into potential sustainability-oriented market segments. The analysis is presented through the lens of value chains by mapping the different coffee chains present in a predominantly coffee growing region, Kodagu, district, India as well as the governance structures that influence interactions and strategies for upgrading
Agricultural Technology Management Agency (ATMA) is a single-window institutional arrangement for technology and information dissemination at the district level and an attempt was made to assess the dairy extension system in the context of ATMA in Guntur district of Andhra Pradesh during 2016. The study revealed that along with organized dairy extension services, ATMA is an important alternative to provide extension services to the dairy sector as animal husbandry sector is an existing allied sector for the ATMA.
This chapter examines the current state of agricultural extension reforms and their linkages to the agricultural research system reforms in India and identifies the policy options and strategic priorities for making it relevant, responsive, and efficient. It explores how the National Agriculture Research Systems (NARS) responded with its own set of reforms that were sought to increase its relevance and its linkages to the extension system reforms.
This paper comparatively analyzes the structure of agricultural policy development networks that connect organizations working on agricultural development, climate change and food security in fourteen smallholder farming communities across East Africa, West Africa and South Asia.
In this paper, is first described the design and development process of a modular ICT application system called GeoFarmer. Geofarmer was designed to provide a means by which farmers can communicate their experiences, both positive and negative, with each other and with experts and consequently better manage their crops and farms. We designed GeoFarmer in a collaborative, incremental and iterative process in which user needs and preferences were paramount.
This work has largely focused on the developed world, yet the majority of people and future economic growth lies in the developing world. Further, most research examines micro data on consumers or firms, limiting what is known regarding the role of macro factors on diffusion, such as social systems. Addressing these limitations, this research provides the first high-level insights into how green building adoption is occurring in developing countries.
This study presents a framework of climate smart agriculture (CSA) priority setting methodology for identifying and developing portfolios of options based on local stakeholders' responses to CSA technologies. The methodology uses a participatory prioritization framework which is widely used in the development sector This study has modified the existing participatory framework to indicator based prioritization of CSA technologies.
This study examines the effectiveness of mobile as a novel approach for providing targeted and equitable agri-advisory services to farmers at scale. A cross-sectional survey of farmers registered on CABI's Direct2Farm (D2F) user database was undertaken using a combination of telephone interviews, household survey and focus group discussions covering six states in India. Was used mixed method approach that utilized both quantitative and qualitative data collection methods.
The objective of the study was to identify a viable trade-off between low data requirements and useful household-specific prioritizations of advisory messages. At three sites in Ethiopia, Kenya, and Tanzania independently, we collected experimental preference rankings from smallholder farmers for receiving information about different agricultural and livelihood practices. At each site, was identified socio-economic household variables that improved model-based predictions of individual farmers’ information preferences.