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
Prototype Geographic Information System for Agricultural Water Quality Management Using CropSyst
A prototype raster geographic information system (GIS) coupled with a decision_support submodel for agricultural nitrogen nonpoint source pollution analysis is presented herein. This analysis is an extension of the one-dimensional approach used to simulate and analyze farm production systems and their impact on the environment. The farm system was rasterized into an aggregation of spatial units with homogeneous physical and management characteristics. A crop model to simulate the farm and the environmental response to management alternatives was integrated with this prototype GIS. The system coordinates the running of the crop model on each homogeneous unit and results are passed to a maximum expected utility decision analysis submodel. Only the crop yield and chemical leaching are considered in the decision model. Based on the utility of these two parameters and the probability of realization, the management alternative that potentially leads to the highest yield and lowest nitrogen leaching will be recommended. This prototype model was evaluated with field data from a controlled lettuce production with intense nitrogen application in Arizona. The results achieved compared well with the actual field data including the best management practice recommended for this farm production system.
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