African agriculture is currently at a crossroads, at which persistent food shortages are compounded by threats from climate change. But, as this book argues, Africa can feed itself in a generation and help contribute to global food security. To achieve this Africa has to define agriculture as a force in economic growth by: advancing scientific and technological research; investing in infrastructure; fostering higher technical training; and creating regional markets.
The European Innovation Partnership for agricultural productivity and sustainability (EIP-AGRI), which can be perceived as a platform based on interaction among farmers, researchers, and advisors/extensionists, represents a useful tool for a better understanding of applied innovation processes.
Multi-actors networks are increasingly used by farmers to link between them and to be interactively connected with other partners, such as advisory organizations, local governments, universities, and non-farm organizations. Given the importance assigned to the agricultural innovation by EU resorting to the networking between the research chain actors and the farmers, a strong focus on enhancing the creation of learning and innovation networks is expected.
This article starts by describing the evolution of innovation in agricultural research and cooperation for development, including an historical overview of agricultural research for development from green revolution to the re-discover of traditional knowledge. Then the authors analyze participation in innovation processes and make a comparison of innovation systems and platforms targeting the agri-food sector in developing countries. A particular focus is reserved to the European regional networks and to the experience of the USAID Middle East Water and Livelihoods Initiative.
This document sets out how EU Research and Innovation (R&I) policy contributes to the major global challenge of ensuring food and nutrition security (FNS). It is a first step in the further development of a more coherent approach to European R&I which aims at mobilising resources and stakeholders to set out aligned R&I agendas in response to recent international political drivers such as the Sustainable Development Goals and the COP 21 climate commitments.
This report presents the main results of the EU-funded IN-SIGHT project ‘Strengthening Innovation Processes for Growth and Development’. The authors sketched out a conceptual framework and knowledge base for a more effective European policy on innovation in agriculture and rural areas. Both conceptual framework and knowledge base are consistent with the new European agenda for agricultural and rural policy and sensitive to the diversity of the European agricultural and rural systems.
In the AgriSpin project (2015-2017) fifteen organisations involved in innovation support tried to understand better how each of them made a difference in helping farmers to innovate. In principle, each partner organisation hosted a Cross Visits of 3 – 4 days, to present a number of interesting innovation cases in which it was involved. The visiting team, composed of colleagues from other partner organisations, interviewed key actors in each case, and gave feedback about pearls, puzzlings and proposals in these innovation processes.
This paper describes a novel approach to create a collaborative space for grassland innovations contributing to profitability of European grassland farms while preserving environmental benefits. Innovative modes of collaboration between practice and science are enabled by an international thematic network across eight European member states.
The European Union (EU) promotes collaboration across functions and borders in its funded innovation projects, which are seen as complex collaboration to co-create knowledge. This requires the engagement of multiple stakeholders throughout the duration of the project. To probe complexity in EU-funded innovation projects the research question is: How does complexity affect the co-creation of knowledge in innovation projects, according to project participants?
Crop surface models (CSMs) representing plant height above ground level are a useful tool for monitoring in-field crop growth variability and enabling precision agriculture applications. A semiautomated system for generating CSMs was implemented. It combines an Android application running on a set of smart cameras for image acquisition and transmission and a set of Python scripts automating the structure-from-motion (SfM) software package Agisoft Photoscan and ArcGIS. Only ground-control-point (GCP) marking was performed manually.