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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Modeling water outflow from tile-drained agricultural fields


The estimation of the pollution risk of surface and ground water with plant protection products applied on fields depends highly on the reliable prediction of the water outflows over (surface runoff) and through (discharge through sub-surface drainage systems) the soil. In previous studies, water movement through the soil has been simulated mainly using physically-based models. The most frequently used models for predicting soil water movement are MACRO, HYDRUS-1D/2D and Root Zone Water Quality Model. However, these models are difficult to apply to a small portion of land due to the information required about the soil and climate, which are difficult to obtain for each plot separately. In this paper, we focus on improving the performance and applicability of water outflow modeling by using a modeling approach based on machine learning techniques. It allows us to overcome the major drawbacks of physically-based models e.g., the complexity and difficulty of obtaining the information necessary for the calibration and the validation, by learning models from data collected from experimental fields that are representative for a wider area (region). We evaluate the proposed approach on data obtained from the La Jailliere experimental site, located in Western France. This experimental site represents one of the ten scenarios contained in the MACRO system. Our study focuses on two types of water outflows: discharge through sub-surface drainage systems and surface runoff. The results show that the proposed modeling approach successfully extracts knowledge from the collected data, avoiding the need to provide the information for calibration and validation of physically-based models. In addition, we compare the overall performance of the learned models with the performance of existing models MACRO and RZWQM. The comparison shows overall improvement in the prediction of discharge through sub-surface drainage systems, and partial improvement in the prediction of the surface runoff, in years with intensive rainfall. (C) 2014 Elsevier B.V. All rights reserved.

  • SI
  • FR
  • ARVALIS_Inst_Vegetal (FR)
Data keywords
  • machine learning
  • knowledge
Agriculture keywords
  • agriculture
Data topic
  • big data
  • 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.