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
Influence of emission-factor uncertainty and farm-characteristic variability in LCA estimates of environmental impacts of French dairy farms
Life Cycle Assessment (LCA) is a useful framework for environmental assessment; however, the reliability of LCA results suffers from many sources of uncertainty and variability. Now that systematic uncertainty analysis in LCA is recommended, it can be useful to revisit past LCA studies to see whether inclusion of uncertainty (or additional types of it) changes interpretation of their results. In this study, we added uncertainty in 67 emission factors (EFs) to the variability in farm characteristics of 47 French dairy farms analyzed in a previous LCA study (van der Werf et al., 2009). We propagated uncertainty in EFs with Monte-Carlo simulation to estimate contributions of uncertainty and variability to uncertainty in potential climate change, acidification, and eutrophication impacts. For individual farms, uncertainty in emission factors added uncertainty to the farms formerly deterministic impacts (coefficients of variation of 2-7% for climate change, 4-11% for acidification, and 2-46% for eutrophication). By farm type (conventional vs. organic), the addition of uncertainty in EFs increased uncertainty in impacts. Although uncertainty in emission factors contributed less to impact uncertainty than variability in farm characteristics did, it did add enough to potentially change decisions about whether differences in certain impacts between farm types were significant, depending upon the significance level and functional unit chosen. Variance-based sensitivity analysis identified emission source categories whose uncertainty contributed most to the uncertainty in impacts: manure deposited in pasture for climate change, cattle housing and manure storage for acidification, and leachate for eutrophication. Although larger uncertainties in potential impacts decrease apparent differences between the systems or scenarios studied, considering more than one type of uncertainty provides decision makers with a more complete and realistic assessment of the state of knowledge. Based on the degree of uncertainty in impacts, they can decide which location on impact intervals (e.g., mean, lower limit, upper limit) is best suited for decisions in a given system. Future studies should explore additional methods to combine multiple sources of uncertainty in LCA and express their relative influences on potential impacts. (C) 2014 Elsevier Ltd. All rights reserved.
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