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.
This Final report identifies best-fit practices, and makes recommendations on how to target women advisory service providers in capacity development programmes.
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.
The Farmer Field School (FFS) approach has been very successful and witnessed a strong expansion in many areas beyond crop production. Notwithstanding this success, the adoption of FFS in national extension often remains problematic and FFS activities have often been implemented in the margin of national institutions with strong reliance on donor funding. The creation of an enabling environment for institutional support is essential for expanding the effort, improving quality, and strengthening impact and continuity of the FFSs.
Several posters have been created on the occasion of the 5th TAP Partners Assembly (Laos, 20-22 September 2017) to show recent activities and achievements in the eight pilot countries of the CDAIS project.
The level of agricultural productivity in Sub-Saharan Africa remains far below the global average. This is partly due to the scarce use of production- and process-enhancing technologies. This study aims to explore the driving forces and effects of adopting innovative agricultural technologies in food value chains (FVC). These enhancing FVC technologies are referred to as upgrading strategies (UPS) and are designed to improve specific aspects of crop production, postharvest processing, market interaction, and consumption.
WhatsApp sends real-time messages and is one of the world’s most popular communication applications in the 21st century. The present study highlights and examines the domestication of WhatsApp among farmers. An exhaustive whatsapp user list was prepared from each village and 3 farmers were selected with the help of systematic sampling technique. Thus, 90 farmers from the 3 blocks of the district using whatsapp were purposively selected and grouped for sending the messages of agricultural aspects. The study was conducted at Krishi Vigyan Kendra, Neemuch during 2016-17.
This report reviews the evidence of impact of capacity strengthening on agricultural research for development (AR4D) in developing countries. The study was commissioned by DFID as part of the documentation process of the project Strengthening Capacity for Agricultural Research for Development in Africa (SCARDA).
The purpose of the paper, using a comprehensive innovation systems failure framework, is to assess the performance of agrifood innovation systems of Scotland and the Netherlands, through analysis of the key innovation actors (organisations, networks or influential individuals), and their key functions (research provider, intermediary etc), and those mechanisms that either facilitate or hinder the operation of the IS (known as inducing and blocking mechanisms, respectively).
This paper examines the level of heterogeneity of member countries of the Organisation for Economic Co-operation and Development (OECD), regarding their potential and performance as Agricultural Sectoral Innovation Systems (ASIS). The main objective is the classification of the ASIS in an OECD context; based on a series of indicators that correspond to their productivity, competitiveness, social, economic and institutional conditions, as well as their capacities and innovation results.