The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Weeds cause crop yield loss due to competition, interfere with agricultural activities and reduce grain quality due to seed contamination. Among the numerous methods for weed control, the use of herbicides is the most common practice. Nowadays, the optimization of herbicide application is pursued to reduce the environmental impact, delay the appearance of herbicide-resistant weed populations, and improve the cost/benefit ratio of the agronomic business. This work proposes an operational planning model, aimed at calculating the optimal application times of herbicides in no-tillage systems within a growing season in order to maximize the economic benefit of the activity while rationalizing the intensity of the applications with respect to expert-knowledge-based recommendations. The model can decide on herbicide applications on a daily basis, consistent with timing of agricultural activities, and provides an explicit quantification of the environmental impact as an external cost. The proposed approach was tested on a winter wheat (Triticum aestivum)-wild oat (Avena fatua) system, typical of the semiarid region of Argentina. In all the studied scenarios at least two pre-sowing applications of non-selective herbicides were required to effectively control early emerging weed seedlings. Additional pre-sowing and post-emergence applications were also advised in cases when competitive pressure was significant. (C) 2013 Elsevier Ltd. All rights reserved.
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