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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Artificial neural network models of the rumen fermentation pattern in dairy cattle


The objectives of this study were: (1) to predict the rumen fermentation pattern from milk fatty acids using a machine learning technique, i.e. artificial neural networks (ANN) combined with feature selection and (2) to compare the prediction accuracy of the resulting model to that of a statistical multi-linear regression model, based on odd and branched chain milk fatty acids. Data were collected from 10 experiments with rumen fistulated dairy cows, resulting in a dataset of 138 observations. Feature selection was based on correlation and principal component analysis, and background physiological knowledge. Different ANN architectures and training algorithms were assessed. The evaluation of the model performance, based on the test dataset, showed a root mean square prediction error, expressed relative to the observed mean, of 2.65%, 7.67% and 7.61% of the observed mean for acetate, propionate and butyrate, respectively. Compared to a multi-linear regression model, the ANN revealed not to perform significantly better. However, the results confirm that milk fatty acids have great potential to predict molar proportions of individual volatile fatty acids in the rumen. (C) 2007 Elsevier B.V. All rights reserved.

  • BE
  • Ghent_Univ (BE)
Data keywords
  • knowledge
  • machine learning
Agriculture keywords
  • cattle
Data topic
  • modeling
  • sensors
Document type

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

Institutions 10 co-publis
  • Ghent_Univ (BE)
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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.