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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Agricultural modelling: a possible road map


In this contribution a view is given of the current state of agricultural modelling in the UK. I begin with the ontogeny of this group. A brief description of modelling is presented to define the conceptual framework. The importance of modelling objectives is stressed. The possible significance of the avalanche of data now coming from the high-through-put techniques in the '-omics' research areas (genomics, proteomics and metabolomics) is discussed. The 103 contributions given at these meetings since 1998 are categorized in terms of topic, type of model, and type of research. The results are considered. At the topic (submodel) level, there are few biological themes where improved submodels would not be appreciated, although some submodels do seem to be 'good enough'. In animals as well as plants, better treatments for development and allocation might be singled out for attention. Although most work is now mechanistic, there is little basic work at submodel level; applied work may suffer from this imbalance. Few contributions on forest models, at any level, have been presented in this forum. Crop modelling especially might benefit from greater interaction with forest modelling, as might forest modelling from crop modelling. There have been no mechanistic contributions at the ecosystem level. It seems likely that, taking a 20-year view, mechanistic ecosystem models of engincering strength will be much needed. The lack of competence in this area, in the UK and worldwide, is due, in part, to the level of maturity of this area of science, but this is possibly accentuated by fragmentation of effort and the current vogue for short-termism. There is now a broad consensus that a model is de rigeur for any agricultural or ecological research programme which aims to take a firm grasp of the responses of such systems, which can be complex. Mechanistic models are required to provide the understanding needed for intelligent and flexible management, given the near certainty of changing environmental and economic conditions. The -omics data avalanche increases the importance of connecting to the molecular level, but does not change matters qualitatively. Large mechanistic models, of animals, crops and ecosystems, present two challenges, neither of which is being adequately addressed: first the sheer scale of the model and the scope of the science which must be represented poses organizational problems; second, making the connections from the higher levels down to the molecular level of the protein and gene is hampered by lack of work at intermediate (often physiological) levels.

  • GB
  • NERC_Nat_Env_Res_Council (UK)
Data keywords
  • agricultural model
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.