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.
Technological innovations have driven economic development and improvement in living conditions throughout history. However, the majority of smallholder farmers in sub‐Saharan Africa have seldom adopted or used science‐based technological innovations. Consequently, several scholars have been persistently questioning the effectiveness of intervention models in smallholder agriculture.
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.
Powerpoint presentation on Global Partnership on Developing Innovation Capacities in Agriculture.
The Guide to Effective Collaborative Action is built on the foundation of 10 years' experience in transforming food and agricultural commodity systems by UNDP's Green Commodities Programme. It is broadening the application from support to commodity production to the transformation of food systems. The four building blocks of putting systems change into practice, integrated with backbone support and essential practices for stakeholder actions, provide a framework for Changing Systems through Collaborative Action.
The IFAD-NUS project, implemented over the course of a decade in two phases, represents the first UN-supported global effort on neglected and underutilized species (NUS). This initiative, deployed and tested a holistic and innovative value chain framework using multi-stakeholder, participatory, inter-disciplinary, pro-poor gender- and nutrition-sensitive approaches.
During May 2010 the International Centre for Tropical Agriculture (CIAT) hosted two events related to knowledge management (KM): The Knowledge Share Fair for Latin America and the Caribbean, funded by the Food and Agriculture Organization of the United Nations (FAO), and a regional meeting of the Knowledge Management for Development (KM4Dev) community. The Fair was attended by 200 professionals from more than 70 organizations and 18 countries and showcased more than 40 experiences related to KM in agriculture, development and food security.
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.
Fall Armyworm (Spodoptera frugiperda), or FAW, is an insect native to tropical and subtropical regions of the Americas. In the absence of natural controls or good management, it can cause significant damage to crops. It prefers maize, although it can feed on more than 80 additional species of crops including rice, sorghum, millet, sugarcane, vegetable crops and cotton.
The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach.