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
Soil water distribution and variation are helpful in predicting and understanding various hydrologic processes, including weather changes, rainfall/runoff generation and irrigation scheduling. Soil water content prediction is essential to the development of advanced agriculture information systems. In this paper, we apply support vector machines to soil water content predictions and compare the results to other time series prediction methods in purple hilly area. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that support vector machine will perform well for time series analysis. Predictions exhibit good agreement with actual soil water content measurements. Compared with other predictors, our results show that the SVMs predictors perform better for soil water forecasting than ANN models. We demonstrate the feasibility of applying SVMs to soil water content forecasting and prove that SVMs are applicable and perform well for soil water content data analysis.
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