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
APPLICATIONS OF FACTOR ANALYSIS AND GEOGRAPHICAL INFORMATION SYSTEMS FOR PRECISION AGRICULTURE OVER ALLUVIAL LANDS
In this study, soil samples were taken from 32 different locations from two different soil depths (0-30 and 30-60 cm) and 16 physical and chemical soil properties were assessed through factor analysis (FA) and geographical information systems (GIS). FA revealed 5 factors for the surface soil depth (0-30 cm) explaining 79.58% of total variation in data set. These factors were entitled as factor 1 "soil water holding capacity", factor 2 "microelement availability", factor 3 "organic matter", factor 4 "soil reaction" and factor 5 "soil salinity". FA for the subsurface depth (30-60 cm) revealed 4 factors explaining 75.14% of total variation in data set. These factors were entitled as factor 1 "nutrient availability soil reaction relationship", factor 2 "soil water holding capacity", factor 3 "organic matter" and factor 4 "soil salinity". A total of nine spatial distribution maps were prepared for these factors by using the factor scores obtained from FA for both soil depths. Significant similarities were observed in both factor components and spatial distribution patterns of both soil depths. It was concluded that FA with various soil properties used as multiple variables might reveal significant hidden information about soil properties and yield highly valuable outcomes for the management and planning of precise agricultural practices.
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