Given the diversity and context-specificity of innovation systems approaches, in March 2007 the World Bank organized a workshop in which about 80 experts (representing donor agencies, development and related agencies, academia, and the World Bank) took stock of recent experiences with innovation systems in agriculture and reconsidered strategies for their future development. This paper summarizes the workshop findings and uses them to develop and discuss key issues in applying the innovation systems concept. The workshop’s recommendations, including next steps for the wider
The paper aims to identify barriers to the development of Learning and Innovation Networks for sustainable agriculture (LINSA). In such networks, social learning processes take place, and knowledge about sustainable agriculture is co-produced by connecting between the different frames and social worlds of the stakeholders with the help of boundary objects. Studying such processes at the interface between different knowledge spheres of research, policy and practice requires a specific methodology.
In this paper, presented at the 8th European IFSA Symposium ( Workshop 6: "Change in knowledge systems and extension services: Role of the new actors") in 2008, the authors discuss a conceptual framework that understands innovation processes as the outcome of collaborative networks where information is exchanged and learning processes happen. They argue that technical and economic factors used to analyse drivers and barriers alone are not sufficient to understand innovation processes.
Le Tuy, province de l'Ouest du Burkina Faso est une région soudanienne à forte pression démographique et pastorale où se posent avec acuité des problèmes de fertilité des sols. Face à la dégradation des ressources naturelles, opter vivre dans son milieu natal et s'assurer une bonne production agropastorale nécessite de la part des acteurs des actions concertées. Le projet Fertipartenaires aide les producteurs de cette province à se concerter, à réfléchir à leurs problèmes, proposer et expérimenter des solutions et les évaluer afin d'améliorer leur sécurité alimentaire.
The paper, prepared for the "High Level Policy Dialogue on Investment in Agricultural Research for Sustainable Development in Asia and the Pacific" (Bangkok Thailand; 8-9 December 2015), presents the Common Framework on Capacity Development for Agricultural Innovation Systems (CDAIS).The framework is a core component of the Action Plan of the TAP, a G20 Initiative, aiming to increase coherence and effectiveness of capacity development for agricultural innovation that lead to sustainable change and impact at scale.
The problems of agricultural development for small and medium enterprises (SMEs) are considered. The features of modeling business processes in agriculture are analyzed. A financial decision support system is proposed to increase sustainability and reduce risks in the development of agricultural SMEs. The software modules are based on TEO-INVEST.
For an intelligent agricultural robot to reliably operate on a large-scale farm, it is crucial to accurately estimate its pose. In large outdoor environments, 3D LiDAR is a preferred sensor. Urban and agricultural scenarios are characteristically different, where the latter contains many poorly defined objects such as grass and trees with leaves that will generate noisy sensor signals. While state-of-the-art methods of state estimation using LiDAR, such as LiDAR odometry and mapping (LOAM), work well in urban scenarios, they will fail in the agricultural domain.
It is difficult to establish the precise mathematical model of agricultural wheeled robots with differential drive for path tracking control, due to characteristics of nonlinear, strong coupling and multivariable. Here, path tracking control is studied for agricultural wheeled robot with differential drive based on sliding mode variable structure. Firstly, the motion model of agricultural wheeled robots with differential drive is established and control goal is determined for path tracking. Then, sliding mode variable structure is applied to design the controller.
This paper presents Thorvald II, a modular, highly re-configurable, all-weather mobile robot intended for applications in the agricultural domain. Researchers working with mobile agricultural robots tend to work in a wide variety of environments such as open fields, greenhouses, and polytunnels. Until now agricultural robots have been designed to operate in only one type of environment, with no or limited possibilities for customization.
3D Move To See (3DMTS) is a mutli-perspective visual servoing method for unstructured and occluded environments, like that encountered in robotic crop harvesting. This paper presents a deep learning method, Deep-3DMTS for creating a single-perspective approach for 3DMTS through the use of a Convolutional Neural Network (CNN). The novel method is developed and validated via simulation against the standard 3DMTS approach.