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
Accurate prediction of daily milk production is a crucial aspect of the dairy industry. During the past decades, although many models using various data analytic techniques have been proposed in literature to address the milk prediction problem, these models have yet to be widely applied in daily operations. Dairy producers need to predict milk yield at individual cow and group level. Given the increasing amount of milk production information collected every year, difficulty also arises from analyzing big data. To address challenges in dairy supply chains and help dairy producers, especially small-scale producers, make use of data analytics in milk supply decision-making, a targeted effort to develop a feasible and cost-effective tool, Milk Yield Prediction and Analysis Tool (PAT), is launched. This tool allows dairy producers to use various prediction models to discover insight into milk production and forecast future milk yield at both the individual cow and the group level. This paper provides a detailed discussion on the design of this tool and demonstrates how big data analytics can be applied in a cost-effective manner.
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