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
Data-driven Low-Complexity Nitrate Loss Model utilizing Sensor Information - Towards Collaborative Farm Management with Wireless Sensor Networks
Excessive or poorly timed application of irrigation and fertilizers, coupled with the inherent inefficiency of nutrient uptake by crops result in nutrient fluxes into the water system. The ability to predict nutrient-rich discharges, in real time, can be very valuable to enable reuse mechanisms within farm systems. Wireless Sensor Networks (WSNs) offer an opportunity to monitor environmental systems with unprecedented temporal and spatial resolution. As part of our previous work, we proposed a novel framework (WQMCM) to combine increasingly common local farm-scale sensor networks across a catchment to learn and predict (using predictive models) the impact of catchment events on their downstream environments, allowing dynamic decision. Existing models use complex parameters which are difficult to extract and this, coupled with constraints on network nodes (battery life, computing power etc., availability of sensors) makes it necessary to develop simplified models for deployment within the networks. The paper investigates data-driven model for predicting daily total oxidized nitrate (TON) fluxes by seeking simplification in model parameters and using only a yearlong training data set. Data from a catchment in Ireland is used for training the model. Model simplification is investigated by abstracting details from an existing nitrate loss model. By using M5 decision tree model on the training samples of the proposed parameters, results give R2 as 0.92 and RRMSE as 0.26. The proposed novel model gives better results with fewer samples and simple parameters when compared to the traditional model. This shows promise for enabling real time nutrient control and management within the collaborative networked farm system.
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