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
Due to the increasing demands of a growing world population, farmers are tasked with increasing productivity without increasing resource use. This can be achieved by deploying resources in a more efficient manner and by ensuring operations will produce maximum results. However when contemplating the problem of scheduling multiple machines, executing multiple operations at multiple fields, it is a cumbersome task to find an optimal solution without the aid of a decision_support tool. A field's readiness, in terms of its trafficability and workability, is an important factor to consider when scheduling operations. Executing operations on an unready field can lead to additional operational costs, such as drying grain harvested at an unready moisture level or removing subsoil compaction caused by machinery driving on unready soils. To ensure a schedule is actionable, the fields' readiness must be accounted for by the scheduling process, further increasing the complexity of the problem to be solved. In this paper, a novel scheduling algorithm is presented which creates individual machine work plans for multiple machines to execute multiple consecutive operations at multiple fields, accounting for the field's readiness for the specified operation. Two optimisation algorithms are utilised to find near optimal solutions of predefined scenarios, a standard Tabu Search and a modified Tabu Search, producing optimised work schedules. The functionality of the optimisation algorithms are tested both for a real world scenario and a large set of numerical examples. The results of the optimisation methods are assessed by their computational time and their relative error compared to the optimal solution, where available. The combination of the presented scheduling algorithm and optimisation algorithms could be used either as a stand-alone tool for farm managers and agricultural contractors as part of their logistic planning, or as a component of an overall Farm Management Information System. (C) 2015 Elsevier B.V. All rights reserved.
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