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
Using indicators and models for an ecosystem approach to fisheries and aquaculture management: the anchovy fishery and Pacific oyster culture in Chile: case studies
This study illustrate the use of indicators and models to support the Ecosystem Approach to Fisheries and Aquaculture management using two case studies in Chile: prediction of environmental variability effects upon anchovy (Engraulis ringens) fishery of northern Chile and prediction of suitable sites and carrying capacity of Pacific oyster (Crassostrea gigas) culture using FARM and geographic information system (GIS) models in the Valdivia estuary. A three stage approach was applied: Stage 1 considers spatio-temporal ecosystem indicators (fisheries, aquaculture, environmental, and regulatory), Stage 2 uses statistical relationships between indicators, GIS, and other simulation models (e.g., artificial neural networks and FARM) of environment-resources interaction, and Stage 3 is the analysis and validation of models outputs. The methodology illustrates how indicators and models may be used to assist decision-makers in developing an ecosystem approach to fisheries and aquaculture. The application of these approaches provides an integrative methodology for abundance prediction of anchovy and site selection for shellfish aquaculture, despite limitations in the available data.
Inappropriate format for Document type, expected simple value but got array, please use list format