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
Industrial livestock husbandry is one of the most important industries in the field of providing nutrients. Nowadays, man), branches of the science are applied in this industry, like genetic which is employed for improving the race of cattle. This science tries to transport good features from current generation to the next. Many researchers have reported that there is a meaningful correlation between facial type (physical form) and production. So, type judging is one of the best ways for evaluating useful features. This assessment contains those features that have maximum correlation for producing milk. Fulfilling of the form related to type judging, named Unified Score Card, needs very much experiences and skills. A judge (human expert) does this uncertainly with regarding to his experiences and skills. In this paper, possibility of developing of an expert system for replacing human expert is investigated. Also, the knowledge extraction methods are described. Fuzzy logic is used for dealing with uncertainty. Finally, the knowledge representation methods are discussed and fuzzy rule base is proposed for representing this knowledge.
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