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
The challenges and opportunities when integrating animal models into grazing system models for evaluating productivity and environmental impact
The intensification of livestock production has highlighted the importance of balancing production and the environmental impact in grazing systems. With the advent of more distributed computing power we have seen more complex models being developed, capable of simulating most aspects of a livestock production system. Where the modelling objective includes prediction of both productivity and environmental impacts, it is imperative to include appropriate consideration of the grazing animal in the simulation. This raises numerous challenges with respect to environmental impact modelling, including explicit treatment of nutrients in dung and urine, the prediction of grazing behaviour, dry matter intake and associated enteric methane loss. This paper discusses these challenges and opportunities when integrating animal models into grazing system models for evaluating productivity and environmental impact.
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