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
AUTOMATIC CALIBRATION OF A HYDROLOGIC MODEL FOR SIMULATING GROUNDWATER TABLE FLUCTUATIONS ON FARMS IN THE EVERGLADES AGRICULTURAL AREA OF SOUTH FLORIDA
The Shuffled Complex EvolutionUniversal Algorithm (SCE-UA) is an automatic calibration algorithm that has shown success in finding a globally optimum objective function with more efficiency than other methods. We incorporated the SCE-UA into our novel modeling environment, utilizing an ontology-based simulation (OntoSim-Sugarcane) framework adapted to analyze groundwater table (WT) fluctuations and drainage practices on four farm basins in the Everglades Agricultural Area of south Florida. Utilizing two water years (WY96-97) of farm WT fluctuations observed at a portion (< 16 ha) of each farm basin, two parameterslateral hydraulic conductivities of soil profile and vertical hydraulic conductivity of underlying limestonewere automatically calibrated. Regardless of farms, the best parameter sets that minimize the objective function of daily root mean square error could be found after 1500 simulation runs. The quality of matching simulated to observed values of farm WT were further assessed by the Nash-Sutcliffe efficiency coefficient (NSE). The NSE ranged from 0.38 to 0.75 (calibration period, WY96-97) and 0.10 to 0.76 (validation period, WY98-99) on all four farms. These results indicate that this coupling strengthens the capability of OntoSim-Sugarcane to model hydrology by objectively finding the best parameter sets. Copyright (c) 2014 John Wiley & Sons, Ltd.
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