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
Using machine learning procedures to ascertain the influence of beef carcass profiles on carcass conformation scores
In this study, a total of 163 young-bull carcasses belonging to seven Spanish native beef cattle breeds showing substantial carcass variation were photographed in order to obtain digital assessments of carcass dimensions and profiles. This dataset was then analysed using machine learning (ML) methodologies to ascertain the influence of carcass profiles on the grade obtained using the SEUROP system. To achieve this goal, carcasses were obtained using the same standard feeding regime and classified homogeneous conditions in order to avoid non-linear behaviour in grading performance. Carcass weight affects grading to a large extent and the classification error obtained when this attribute was included in the training sets was consistently lower than when it was not. However, carcass profile information was considered non-relevant by the ML algorithm in earlier stages of the analysis. Furthermore.. when carcass weight was taken into account, the ML algorithm used only easy-to-measure attributes to clone the classifiers decisions. Here we confirm the possibility of designing a more objective and easy-to-interpret system to classify the most common types of carcass in the territory of the EU using only a few single attributes that are easily obtained in an industrial environment. (c) 2005 Elsevier Ltd. All rights reserved.
- Univ_Oviedo_UNIOVI (ES)
- SERIDA_Reg_Serv_Agro_Food_Res_&_Dev (ES)
- CITA_Aragon_Agrifood_Res_&_Technol_Ctr_Aragon (ES)
- Univ_Zaragoza_UNIZAR (ES)
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