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
Today's world is in safe of secure environment but really we are in need of healthy food and chemical-free environment which is free from pollution and harmful pesticides. This paper is to project the aspects of geographical information system(GIS) in agricultural field for effective yield and efficient use of water for crops and it helps as a guide to the non-technical person(farmer) who suffer in making good productivity due to missing of seasonal rainfall. In terms of effective yield the requirement based on climatic condition, soil type and rainfall. Additionally an important factor is in terms of managing water, hereby we concentrate on evapotranspiration and it is evaluated by penman-monteith method. This method is best of ET calculation. Linear Regression Analysis is evaluated between estimated ET and observed ET. The knowledge discovered from the large dataset and conglomeration of GIS helps in precise management of agricultural crops with good yield in organic farming.
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