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
Modular structure of web-based decision_support systems for integrated pest management. A review
Sustainable pest management implies less pesticide use and replacement by safe control alternatives. This requires decision_support for rational pest management. However, in practice, successful decision making is dependent upon the availability of integrated, high-quality information. Computer-aided forecasting and related decision_support systems make pest control more sustainable by avoiding unwanted consequences of pesticide applications. Here, I review integrated pest management for web-based decision_support systems. The major points are the following: (1) Principles of integrated pest management are compatible with sustainable agriculture. (2) Pest models serve as basis of decision making because they offer means to predict the exact time of pest phenological development and initiate management actions. Most models are climate driven. (3) New hardware technology has permitted the registration of automatically recorded climatic data. This data can be combined with pest models through logical operations and forecasting algorithms to develop a software of pest management. (4) Dynamic web interfaces can serve as decision_support systems providing the user with real-time pest warnings and recommendations for management actions. (5) Ontology web programing and semantic knowledge representations provide a way to classify and describe agrodata to facilitate information sharing and data exploitation over distributed systems. (6) Most available pest management data is published on static web pages and, thus, cannot be classified as decision_support systems. Some web-based decision_support systems provide user-interactive content and real-time pest forecasts and management support.
Inappropriate format for Document type, expected simple value but got array, please use list format