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
Using precision farming technology to quantify yield effects attributed to weed competition and herbicide application
Field experiments using precision farming technology and Geographic Information Systems, following a so-called Precision Experimental Design, were conducted in maize, winter barley and winter wheat and compared with two randomised plot experiments in maize to quantify yield effects attributed to weed competition and weed control. Fields were divided into cells, and weed densities for all weed species, soil conductivity and grain yield were measured in each cell. Untreated plots and herbicide treatments against grass weeds or broad-leaved weeds were included in all three experiments. Chenopodium album, Polygonum spp. and Echinochloa crus-galli were the dominating weed species in maize. Stellaria media, Veronica hederifolia, Matricaria chamomilla, Alopecurus myosuroides and Galium aparine were the most abundant weed species in the winter barley and winter wheat fields. All species were distributed heterogeneously within the fields with densities ranging from 0 to more than 200 plants m-2. In the Precision Experimental Design, it was found that grass-weed competition and herbicide application had a significant effect on grain yield, using a linear mixed model with spatial correlation structure to determine the effects of groups of weed species, soil variability and herbicide application on grain yield separately. When a conventional plot experiment was set up in the same field, no statistically significant grain yield difference between the treatments was found. The results highlight the benefits of Precision Experimental Design for studying weedcrop competition. Data can be used to calculate yield loss functions for groups of weed species and to create a decision_support system for site-specific weed control.
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