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Roggero, P.P.; Matthews, R. |
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Strategies for engagement on adaptation and mitigation with national and EU policy makers and with the agro--food chain sector (Update) |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C6.3 |
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This report is grounded on the hypotheses, methodologies and approaches for stakeholder mapping designed during the early stages of MACSUR and described in the previous report1. It describes the kind of activities conducted by the WPC6-3-4 MACSUR team and the emerging design of activities for the second phase of MACSUR (2015-2017). The designed process of strategic stakeholder mapping was implemented by some of the teams involved in the task and through hub initiatives. Key actions were the (i) development of suitable intermediary objects to engage with stakeholders, through the regional pilot case studies, (ii) the design and implementation of key events (we report here the case of the Agroscenari event at the case study scale, the national event between the MACSUR Italian partnership with Italian policy makers held in Rome in July 2014, the international stakeholder events at the MACSUR mid term meeting in Sassari (April 2014), and the one held in Bruxelles on 6 May 2015) and (iii) the process of stakeholder and stakeholding mapping at the case study scale. Results indicate that when dealing with high level stakeholders (e.g. institutional or large agro-food enterprises), occasional stakeholder events will only serve as opportunity for showcasing and possibly for a data collection useful for researchers, with almost no impact on the ongoing social learning process sought by the designed activities. At the case study scale, instead, the long term and ongoing activities can generate new spaces for mutual learning and knowledge hybridization, through a variety of mediating objects emerging from the continuous interactions. The lesson learned is that the engagement of high level stakeholders can be effective insofar they are somehow involved in the interactions with stakeholders at the case study scale, as this can provide a key experience leading to a change in understanding about the nature of the issues that can ultimately result into a change in practice. These results will be the basis for the design of new strategies for engaging EU policy makers and large agro-food energy representatives in the second phase of MACSUR. No Label |
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2107 |
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Roggero, P.P. |
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Managing Agricultural Greenhouse Gases Network (MAGGnet): Exploring Greenhouse Gas Mitigation Potential of Cropland Management Practices |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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9 C6 - |
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Sp9-8 |
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Global Research Alliance on Agricultural Greenhouse Gases Established: December 2009, United Nations Climate Change Conference, Copenhagen, Denmark•Purpose: Facilitate research, development and extension of technologies and practices that will help deliver ways to grow more food (and more climate-resilient food systems) without growing greenhouse gas emissions.•Current Membership: 46 countries (Europe, Americas, Asia Pacific, Africa) |
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4840 |
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Roggero, P.P. |
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Title |
Strategies for engagement on adaptation and mitigation with national and EU policy makers and with the agro-food chain sector |
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2013 |
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FACCE MACSUR Reports |
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2 |
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D-C6.3 |
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A process for the strategic mapping of national and EU policy makers to be engaged in an interactive and iterative process of learning was designed, based on literature review and specific experience of some participants. In this first intermediate version, we propose a stakeholder mapping process design which will ideally lead to setting the boundaries of context-sensitive systems of interest for pilot actions or interdisciplinary case studies. The mapping exercise will be tested by participants No Label |
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2242 |
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Rivington, M.; Wallach, D. |
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Quantified Evidence of Error Propagation |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C4.2.3 |
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Error propagation within models is an issue that requires a structured approach involving the testing of individual equations and evaluation of the consequences of error creation from imperfect equation and model structure on estimates of interest made by a model. This report briefly covers some of the key issues in error propagation and sets out several concepts, across a range of complexity, that may be used to organise an investigation into error propagation. No Label |
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MA @ admin @ |
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2102 |
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Rivington, M.; Wallach, D. |
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Information to support input data quality and model improvement |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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6 |
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D-C4.2.4 |
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Data quality is a key factor in determining the quality of model estimates and hence a models’ overall utility. Good models run with poor quality explanatory variables and parameters will produce meaningless estimates. Many models are now well developed and have been shown to perform well where and when good quality data is available. Hence a major limitation now to further use of models in new locations and applications is likely to be the availability of good quality data. Improvements in the quality of data may be seen as the starting point of further model improvement, in that better data itself will lead to more accurate model estimates (i.e. through better calibration), and it will facilitate reduction of model residual error by enabling refinements to model equations. This report sets out why data quality is important as well as the basis for additional investment in improving data quality. No Label |
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MA @ admin @ |
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2103 |
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