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
Using community observations to predict the occurrence of malleefowl (Leipoa ocellata) in the Western Australian wheatbelt
The Malleefowl is a ground-dwelling bird species that has declined in distribution and abundance in Australia since European settlement. These declines have been exacerbated in the Western Australian wheatbelt by the extensive clearing of native vegetation for agricultural development. A wealth of opportunistic, presence-only data exists for this species but absence data required for distribution modelling is lacking. This situation is typical of many species distribution datasets. We sought to establish the distribution of malleefowl within the Western Australian wheatbelt (210000 km(2)) and their choice of habitat within this broad region. We supplemented a large presence-only dataset of malleefowl sightings with absence data derived from a bird atlas scheme and used these data to effectively predict the distribution of the species for the wheatbelt using a combined GAM/GLM approach. Both datasets were derived largely from community sightings. The distribution of malleefowl within the Western Australian wheatbelt was associated with landscapes that had lower rainfall, greater amounts of mallee and shrubland that occur as large remnants, and, lighter soil surface textures. This study illustrates how community knowledge, coupled with solid ecological understanding, can play a key role in developing the knowledge base to inform conservation and management of species in agricultural landscapes. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.
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