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
A method for predicting the timing of winter rains is presented, making no assumptions about the functional form of any relationships that may exist. Ideas built on classification and regression trees and machine learning are used to develop robust predictive rules. These methods are applied in a case study to predict the timing of winter rain in five farming towns in the southwest of Western Australia. The variables used to construct the model are mean monthly sea Surface temperatures (SSTs) over a 72-cell grid in the Indian Ocean, Perth monthly mean sea level pressure (MSLP), and monthly values of the Southern Oscillation index (SOI). A predictive model is constructed from data over the period 1949-99. This model correctly classifies the onset of the winter rains approximately 80% of the time with SST variables proving to be the most important in deriving the predictions. Further analysis indicates a change point in the mid-1970s, a well-known phenomenon in the region. The prediction rates are significantly worse after 1975. Furthermore. the important region of the Indian Ocean, in terms of SSTs for prediction, moves from the Tropics down toward the Southern Ocean after this date.
Inappropriate format for Document type, expected simple value but got array, please use list format