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
For millennia farmers have continually improved their crop management and production practices through their observations and experience. More recently modern science and research methods based on controlled experiments became the most visible instrument of technological change in agriculture, nevertheless farmers continued to develop and implement new technologies based on their own observations made under commercial conditions. Modern information technology and social organization of producers make it possible to use operational research, which is based on the observation and analysis of operations so as to improve them, to manage crops better. The article describes two cases, coffee and sugarcane, in which observation of the results obtained by farmers, with the natural variation in the environment and the distinct management practices they apply can be used to determine site specific crop management practices. The basis of the methodology is to (a) obtain data from a series of cropping events that characterizes the conditions under which each crop is grown, how it is managed and how it performs under commercial conditions (data capture), (b) to manage and analyze the data in centralized databases (data management and analysis) and (c) make the information derived from the data analysis available to growers so that they can use it to make better informed decisions (interpretation). All aspects of the methodology depend on the social organization of the growers and the supply chain of which they form a part, and hence social organization is an integral part of the methodology. The processes of characterization of the growing conditions, including both environmental and management parameters, the establishment of databases, the data analysis and interpretation, and mechanisms of interacting with producers are described with emphasis on the importance of social organization and farmers' groups. Examples are given of how this approach can be used to better understand the crop response to variation in the environment and management, and how this can be used by farmers to improve productivity and quality in two contrasting crops. The paper demonstrates that operational research can be used to evaluate farmers' experiences and to share that knowledge amongst them so as to improve their production practices in the context of their particular environment. It is suggested that the operation research approach is particularly effective in heterogeneous landscapes with perennial crops that have not been the subject of intense research. Furthermore operational research is effective in determining the crop response to variables that are not readily studied in small plots and in determining optimal combinations of multiple variables. Producers believe in the results obtained as there are none of the problems of scaling up from experimental plots to commercial conditions. It is proposed that the approaches described can readily be applied to other crop species. (C) 2011 Elsevier Ltd. All rights reserved.
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