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.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
In the present study, the detection of cows with artificial insemination (AI) difficulties using selected statistical and machine learning methods is presented. Cows were divided into two classes: those that conceived after one or two services ("GOOD") and those that required more than two services per conception ("POOR"). The best performance was exhibited by one of the artificial neural networks (ANN) and the multivariate adaptive regression spline (MARS) method (AIC, BIC, RMS and accuracy): whereas logistic regression (LR) and classification functions (CF) were of somewhat lower quality. The detection of cows with AI difficulties, performed on the basis of the test set comprising new instances, showed that the ANN and MARS were more precise in comparison with the statistical methods. Sensitivity and specificity were over 85% for the perceptron with two hidden layers (MLP2) and MARS and approximately 80% or lower for LR and CF. From among variables determining the AI category, the average calving interval and cow body condition index were the most important. Other significant variables were lactation number, pregnancy length, sex of calf from previous calving and cow age. The prognoses obtained using ANN and MARS can be used for the appropriate preparation of cows for AI. (C) 2010 Elsevier B.V. All rights reserved.
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