e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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.

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Title

An end-to-end assessment of extreme weather impacts on food security

en
Abstract

Both governments and the private sector urgently require better estimates of the likely incidence of extreme weather events(1), their impacts on food crop production and the potential consequent social and economic losses(2). Current assessments of climate change impacts on agriculture mostly focus on average crop yield vulnerability(3) to climate and adaptation scenarios(4,5). Also, although new-generation climate models have improved and there has been an exponential increase in available data(6), the uncertainties in their projections over years and decades, and at regional and local scale, have not decreased(7,8). We need to understand and quantify the non-stationary, annual and decadal climate impacts using simple and communicable risk metrics(9) that will help public and private stakeholders manage the hazards to food security. Here we present an 'end-to-end' methodological construct based on weather indices and machine learning that integrates current understanding of the various interacting systems of climate, crops and the economy to determine short-to long-term risk estimates of crop production loss, in different climate and adaptation scenarios. For provinces north and south of the Yangtze River in China, we have found that risk profiles for crop yields that translate climate into economic variability follow marked regional patterns, shaped by drivers of continental-scale climate. We conclude that to be cost-effective, region-specific policies have to be tailored to optimally combine different categories of risk management instruments.

en
Year
2015
en
Country
  • GB
  • FR
  • US
Organization
  • Imperial_Coll_London (UK)
  • Univ_Calif_Los_Angeles (US)
  • ENS_Paris (FR)
  • World_Bank (US)
Data keywords
  • machine learning
en
Agriculture keywords
  • agriculture
en
Data topic
  • big data
  • modeling
en
SO
NATURE CLIMATE CHANGE
Document type

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Institutions 10 co-publis
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    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.