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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Using inductive learning to assess compound feed production in cooperative poultry farms


Production scheduling is one of the most important functions in a production company. As a consequence, in recent decades various methods have been proposed for the modeling and solution of particular scheduling problems. In this context, a special case is that of centralized feed manufacturing plants supplying animal food in a cooperative poultry environment. In this paper, we present the SP4 system, an integrated software environment that combines a statistical method (used to calculate the previous consumption data, mortality indices and feed delivery types), a machine learning method (M5P and IBk models used to calculate the total amount of feed consumed by type) and an ad hoc algorithm which makes flexible orders for compound feed production forecasting. The data used for this study was provided by a leading Spanish Company (Coren Cooperative) specialized in animal feed production and delivery. Raw data (from the years 2007 and 2008) was built from client orders, company production logs, information about the number of animals at different farms and truck trips to the clients. To ensure that the developed system is able to reproduce acceptable results for the unseeable future, we have evaluated various aggregate measures to forecast error (MSE, MAE, MAPE, ME) during the validation of the models. The results reveal that the proposed system performed well, being able to track the dynamic non-linear trend and seasonality, as well as the numerous interactions between correlated variables. (C) 2011 Elsevier Ltd. All rights reserved.

  • ES
  • Univ_Vigo_UVIGO (ES)
Data keywords
  • machine learning
Agriculture keywords
  • farm
Data topic
  • modeling
Document type

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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.