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
Risk mapping of bovine hypodermosis using geographical information system (GIS) in cattle of subtropical region, Pakistan
Introduction: Hypodermosis is an ectoparasitic disease of cattle caused by Hypoderma lineatum and Hypoderma bovis. It is an important health problem of cattle, leading to considerable economic losses. There are various factors that are involved in the spread of this disease such as herd size, location, temperature, humidity, and precipitation. Methodology: Blood samples from 112 herds were collected to determine the presence of Hypoderma spp. infestation. For these herds, size and location were determined; temperature, humidity, and precipitation data were obtained from meteorological stations; and topographic features were obtained from existing maps and through field work. A regression analysis was then used to generate a risk factor analysis profile for hypodermosis and geographic information system (GIS) was used to map the risks. Results: The GIS map developed showed the degree of infestation in different geographical locations at district and village levels. Cluster analysis demonstrated that hypodermosis prevalence varied within zones and across zones. The regression analysis showed that the temperature in the months of January, February, March, August, and November, and the precipitation in September and October had significant results (p < 0.05) when all the risks factors were analyzed. Conclusions: It is concluded that different ecological factors have an important impact on the intensity and infestation rate of hypodermosis across the globe. The present study might be used to control and eradicate the hypodermosis across the globe.
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