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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A metamodelling approach to estimate global N2O emissions from agricultural soils


Aim Modelling complex environmental and ecological processes over large geographic areas is challenging, particularly when basic research and model development for such processes has historically been at the local scale. Moving from local toward global analysis brings up numerous issues related to data processing, aggregation, tradeoffs between model quality and data quality, and prioritization of data collection and/or compilation efforts. We studied these issues in the context of modelling emissions of N2O (a potent greenhouse gas) from agricultural soils. Location Global. Methods We developed metamodels of the DeNitrification-DeComposition (DNDC) model, a mechanistic model that simulates greenhouse gas emissions from agricultural soils, to estimate global N2O emissions from maize and wheat fields. We ran DNDC for a diverse sample of global climate and soil types, and fitted the model output as a function of (sometimes simplified) model input variables, using the random forest machine learning algorithm. We used the metamodels to estimate global N2O emissions from maize and wheat at a very high spatial resolution (c. 1 km(2)) and examined the effects of different approaches of using soil data as well as the effects of spatial aggregation of soil and climate data. Results The average coefficient of determination (R-2) between holdout data (DNDC output not used to construct the metamodel) and metamodel predictions was 0.97 for maize and 0.91 for wheat. The metamodels were sensitive to soil properties, particularly to soil organic carbon content. Global emission estimates with the metamodel were highly sensitive to the spatial aggregation and other forms of generalization of soil data, but much less so to aggregation of climate data. Main conclusions Using a simplified metamodel with data of high spatial resolution could produce results that are more accurate than those obtained with a full mechanistic model and lower-resolution data.

  • US
  • Univ_Calif_Davis (US)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • modeling
Document type

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Institutions 10 co-publis
  • Univ_Calif_Davis (US)
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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.