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
A decision_support system (DSS) is an interactive software-based system used to help decision-makers compile useful information from a combination of raw data, documents, and personal knowledge; to identify and solve problems; and to make an optimized decision. The DSS architecture consists of the database (or knowledge base), the model (i.e., the decision context and user criteria), and the user interface. The main advantages of using a DSS include examination of multiple alternatives, better understanding Of the processes, identification of unpredicted situations, enhanced communication, cost effectiveness, and better use of data and resources. The application DSS in agriculture and environment has been rapidly increased in the past decade, which allows rapid assessment of agricultural production systems around the world and decision-making at both farm and district levels, though constraints exist for successful adoption of this technology in agriculture. One of the important applications of DSS in agriculture is water management at both field and district levels. Agriculture is facing more severe and growing competition with other sectors for freshwater. The water resources are becoming increasingly insufficient to meet the demand in developing countries and their quality is declining due to pollution and inadequate management. Irrigation is an effective means to enhance crop productions, but water needs to be supplied accurately, taking into account its availability, crop requirement and land size, irrigation systems, and crop productivity and feasibility. This chapter attempts to present the state-of-art principles, design, and application of DSS in agriculture, particularly irrigation practices, and to identify emerging approaches and future direction of research in this field.
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