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
The use of cover crops generates a number of agro-ecological benefits for sustainable row-crop agriculture. However, their performance across agricultural fields is often highly spatially variable and there is insufficient information on factors affecting this variability and on tools to manage it. Topography is one of the main factors affecting spatial patterns of plant growth in the American Midwest. Digital elevation models are readily available for deriving topographic attributes; also sensor digital data can be used to indirectly assess cover crop biomass. However, processing procedures for identifying the proper scale of topographic and biomass representations are not well defined. The objectives of this study are to examine how relationships between cover crop biomass, assessed using the normalized difference vegetation index (NDVI), and topography depend on the neighborhood size used for deriving topographic attributes and creating NDVI maps; and identify the optimal neighborhood size for correlation and regression analyses. Slope, relative elevation and the potential solar radiation index were the variables that contributed the most to explaining variability in NDVI for raw data. However, other topographic attributes became significant predictors of NDVI at larger neighborhood sizes. We demonstrated that neighborhood size greatly affects some topographic attributes, i.e. curvature, flow accumulation, flow length and the wetness index; and changing the neighborhood size in both topography and NDVI considerably changes the strength of the prediction performance in multiple regression models. We studied six neighborhood sizes from 1 to 40 m and the original raw data. On average, across all studied fields the best performance of multiple regression, as determined by the adjusted-R-2, was obtained at neighborhood sizes 20 and 40 m. Parameters of semivariogram models for terrain slope, such as the spatial autocorrelation range and the nugget/sill ratio, were found to be good indicators of prediction performance and optimum neighborhood size for filtering the raw data. The results demonstrate that topographic effects on growth and biomass production of cover crops are most pronounced at certain spatial scales, and topographic model predictions will be most accurate when used at the optimal scales. (C) 2012 Elsevier B.V. All rights reserved.
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