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
The extreme effects of heat stress can cause losses exceeding 5% of all the cattle on feed in a single feedlot. These losses can be devastating to a localized area of feedlot producers. Animal heat stress is a result of the combination of three different components: environmental conditions, animal susceptibility, and management. This article describes the development of a model to predict individual animal susceptibility to heat stress. The model utilizes a hierarchical knowledge-based fuzzy inference system with 11 animal characteristics (color, sex, species, temperament, hair thickness, previous exposure to hot conditions, age, condition score, previous cases of pneumonia, previous other health issues, and current health) to predict susceptibility to heat stress. For model validation, a team of experts was asked to assess the susceptibility to heat stress of ten hypothetical animals. The output of the model was tested against the experts' opinions. The validation equation had a slope of 1.038 +/- 0.047 with an intercept of -0.063 +/- 0.028 and an R-2 value of 0.88. The opinions of the experts were also compared to the model output by comparing the stress susceptibility class of each of the ten hypothetical animals. The model and the experts agreed perfectly on seven of the ten animals. Further, the model prediction and the experts' opinions deviated by no more than one class on the remaining three animals. This exercise revealed that there was agreement between the model output and the experts' opinions.
Inappropriate format for Document type, expected simple value but got array, please use list format