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
Behavioral and physiological changes around estrus events identified using multiple automated monitoring technologies
This study included 2 objectives. The first objective was to describe estrus-related changes in parameters automatically recorded by the CowManager SensOor (Agis Automatisering, Harmelen, the Netherlands), DVM bolus (DVM Systems LLC, Greeley, CO), HR Tag (SCR Engineers Ltd., Netanya, Israel), IceQube (IceRobotics Ltd., Edinburgh, UK), and Track a Cow (Animart Inc., Beaver Dam, WI). This objective was accomplished using 35 cows in 3 groups between January and June 2013 at the University of Kentucky Cold-stream Dairy. We used a modified Ovsynch with G7G protocol to partially synchronize ovulation, ending after the last PGF(2 alpha), injection (d 0) to allow estrus expression. Visual observation for standing estrus was conducted for four 30-min periods at 0330, 1000, 1430, and 2200 h on d 2, 3, 4, and 5. Eighteen of the 35 cows stood to be mounted at least once during the observation period. These cows were used to compare differences between the 6 h before and after the first standing event (estrus) and the 2 wk preceding that period (nonestrus) for all technology parameters. Differences between estrus and nonestims were observed for CowManager SensOor minutes feeding per hour, minutes of high ear activity per hour, and minutes ruminating per hour; twice daily DVM bolus reticulorumen temperature; HR Tag neck activity per 2 h and minutes ruminating per 2 h; IceQube lying bouts per hour, minutes lying per hour, and number of steps per hour; and Track a Cow leg activity per hour and minutes lying per hour. No difference between estrus and nonestrus was observed for CowManager SensOor ear surface temperature per hour. The second objective of this study was to explore the estrus detection potential of machine-learning techniques using automatically collected data. Three machine-learning techniques (random forest, linear discriminant analysis, and neural network) were applied to automatically collected parameter data from the 18 cows observed in standing estrus. Machine learning accuracy for all technologies ranged from 91.0 to 100.0%. When we compared visual observation with progesterone profiles of all 32 cows, we found 65.6% accuracy. Based on these results, machine-learning techniques have potential to be applied to automatically collected technology data for estrus detection.
Inappropriate format for Document type, expected simple value but got array, please use list format