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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Identifying climatic limitations to grain maize yield potentials using a suitability evaluation approach


Climate plays a fundamental role in agriculture. The quantity and quality of crop yield can be affected by water stress, heat stress or frost or by pests and diseases. As climate conditions change, suitability zones for the cultivation of specific crops may shift. For planners and land managers it is important to understand such changes in order to develop short- and long-term adaptation strategies for resource and development planning. In this paper, we present a flexible and comprehensive, rule-based approach for evaluating crop-specific climate suitability. Climate indices are calculated over dynamically estimated phenological phases, and factor suitability functions are defined to relate these phase-specific climate indices to suitability values. The dynamic consideration of crop phenology allows to assess effects of climate-induced shifts in phenological development. To complement the knowledge-based definition of factor suitability functions with empirical data, we introduce an automated procedure for refining this definition within knowledge-based bounds based on observed climate and yield data. The approach was tested and applied for grain maize (Zea mays L) production in Switzerland. Comparison with independent yield data showed a good agreement with crop-specific climatic suitability, both in terms of temporal and spatial variability. Analysis of phase-specific suitability limitations at three representative sites revealed that suitability during the period 1981-2011 was mostly limited by sub-optimum temperatures and radiation during early and late phases, whereas drought was only an important limitation in exceptionally dry years (e.g. 2003 and 2010). An analysis of constant changes in temperature and precipitation indicated that grain maize suitability at a selected site on the western Central Plateau was more sensitive to temperature changes than to changes in precipitation. This suggests that this crop could benefit from an increase in temperature, not only through shifting towards the temperature optimum, but also by avoiding drought stress through accelerated plant development. However, these positive effects could be counteracted by negative effects of shortened phenology (i.e. reduced biomass accumulation) and heat stress. These results show that the approach offers diverse possibilities for applications, including climate impacts studies, and serves to provide information for the planning of crop adaptation strategies. (C) 2012 Elsevier B.V. All rights reserved.

  • CH
  • Agroscope (CH)
Data keywords
  • knowledge
  • knowledge based
Agriculture keywords
  • agriculture
Data topic
  • information systems
  • semantics
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

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.