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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From ideotypes to genotypes: approaches to adapt wheat phenology to climate change


Introduction: Simulations using crop models can assist in designing ideotypes for current and future agricultural conditions. This approach has been often in recent years to identify avenues for adapting wheat to climate change. However, this approach has rarely been used to guide commercial breeding programs. We hypothesize that the lack of link between models and the available tools for breeding, i.e. available genetic variability and selection methods. Materials and methods: - We use a modified ARCWHEAT2 phenology model and future climate data from the ARPEGE global circulation model to identify targets for future phenologies - We genotyped over 400 French cultivars for known phenology genes and confronted the genetic make-up of these varieties to their success in France over the past 25 years - We developed a methodology to link model parameters to underlying marker data. We tested the performance of the methodology against circa 60 varieties Results: - Earlier phenology may be an avenue for stress avoidance in the future. - Current photoperiod sensitivity of early cultivars already poses problems in terms of adaptation, as exemplified by the interaction between Ppd-D1 and Vrn-A3 We show that a gene-based model can be used to predict wheat phenology without a significant loss in predictive performance. Discussion : Analyzing current phenology genes of existing cultivars and their adaptation allowed us to identify a limit to past breeding efforts in obtaining early cultivars. This requires that a more knowledge based approach be taken. Gene-based modelling of phenology is possible on a collection of elite, adapted varieties and provides the tools for constructing genotypes with specific allelic combinations leading to more appropriate constructions of earliness. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

  • FR
  • ARVALIS_Inst_Vegetal (FR)
Data keywords
  • knowledge
  • knowledge based
Agriculture keywords
  • agriculture
Data topic
  • modeling
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.