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
Modelling the stump biomass of stands for energy production using a harvester data management system
The value and volumes of industrial stump wood fuel supply are increasing. In 2006, the consumption of stump wood for energy generation in Finland was equivalent to more than 900 GWh. Accurate estimates of the below-ground biomass of trees are important when estimating the potential use of stumps as bioenergy. sources. In this study the biomass model by Marklund {(1988). Biomassafunktioner for tall, gran och bjork i Sverige. [Biomass Functions for Pine, Spruce and Birch in Sweden], Rep. No 45, Department of Forest Survey, Swedish University of Agricultural Sciences (in Swedish)} was tested at the operational level of stump wood procurement and a calibrated biomass model was developed to predict the stump biomass of local stands. Regression analysis was applied to best fit model using forest harvester data as the source of local information. The independent variables of the best fit model were the cumulative sum of biomass estimates from Marklund, and the cumulative sum of the square of the diameter at 30% relative height. in tests of goodness of fit for the linear relationships, better reliability of the estimates obtained with the model was presented as a root mean square error (RMSE) of the stand stump biomass. The adjusted R 2 value obtained was 60% which was higher than the adjusted R 2 values of the stump biomass model (52%) obtained by Marklund. Thus, the data management system can be used to produce more accurate information for planning and business management of energy production. (c) 2008 IAgrE. Published by Elsevier Ltd. All rights reserved.
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