Abstract: Stakeholder engagement in research projects can take a number of forms according to the scope of the project and the purpose of the interaction. L4.2. has focused on comparing different approaches to stakeholder engagement in collaborative projects. This report presents a synthesis of the experiences and lessons learnt through the stakeholder engagement activities of LiveM researchers within MACSUR, within an Italian (Oristano) case study, and within the SOLID (Sustainable, Organic and Low Input Dairying) project. An overview of these examples, and some of the lessons drawn from them, can also be found in the MACSUR paper on stakeholder engagement methods being developed by researchers from all three MACSUR themes (Koenig et al. under production). The first part of this report describes the stakeholder engagement strategy within the SOLID project. Stakeholder engagement methods are analysed through observations of activities and using semi-structured interviews with researchers and stakeholders. Two aspects of the SOLID approach are described – the stakeholder panel and the Future Dairying workshop. Transcripts of the workshop and the contribution of the stakeholder panel to the SOLID annual meeting in Helsinki are included (Appendices 1 and 2), as a contribution to the analysis of workshop outcomes being undertaken within the SOLID project. As part of a wider suite of stakeholder engagement activities, the SOLID stakeholder panel provided an example of how ongoing oversight of scientific outputs and direction by stakeholders can be effective in identifying weaknesses in approach and communication, and in suggesting relevant and effective directions for research activities. The stakeholder workshop demonstrated a useful structure for the exploration of stakeholder concerns, their view of ideal states and their solutions for reaching them. Low participation levels demonstrated the need to understand the motivations that drive stakeholders to engage in such projects, and highlighted the value of developing long-term relationships between stakeholders and researchers that allow scientific research to become an accepted part of practical problem-solving. The second part of the report describes stakeholder engagement activities carried out in the context of one of the MACSUR regional pilot studies (Oristanese case study in Sardinia, Italy). The Oristanese case study demonstrates the potentialities and constraints of participatory methodologies in relation to the different categories of stakeholder involved. It highlights the importance of creating new spaces for dialogue between farmers, researchers and policy makers in order to promote the generation of “hybrid knowledge” (Nguyen et al. 2013) for the emergence of more sustainable and longer-lasting strategies to adapt to CC. This would require the promotion of open knowledge generation platforms where multiple stakeholders are encouraged to participate and make their views heard. These approaches are designed in order to overcome the misalignment between scientists’ suggestions and policy implementation. In the third part of the report, the outcomes of a “learning event” held in Sassari (MACSUR mid-term meeting) with decision makers from different EU countries, are discussed. Finally, some reflections are presented on the importance of involving local stakeholders and decision makers in research projects, of sharing views and knowledge between scientists and stakeholders, and on the pros and cons of different methodologies at the different scales of stakeholder engagement, drawing on all three examples of practice. The research approach analysed includes two important components, which are represented by “transdisciplinarity” (to be included in the macro area of “scientific knowledge”) and “local knowledge”, as fundamental elements to fill the Science and Policy Gap. No Label

Abstract: The study ROTATIONEFFECT aims to compare the output of different models simulating field data sets with multi-year crop rotations including different treatments.Within the first Step (1a2a) data sets (comprising a total of 301 crop growth seasons) for 5 locations in Europe were distributed to 15 interested modeller groups.For this step only minimal information for calibration were provided to the modellers. In total 15 modelling teams sent their “uncalibrated” results as single-year calculations and/or continuous calculations of rotation depending on the capability of the model. Results have been evaluated and the paper submitted (European Journal of Agronomy).Now, within the 2nd step (1b2b) modellers were provided with more information on the crop for the calibration of models. Again, results of calibrated runs were collected.6 models were capable to run the rotations as continuous runs and another set of 6 models provided single year simulations.A first overview of the improvement of predictions due to calibration has been produced. Result files have been uploaded to the web platform for CropM results at Aarhus University (Work package C2 – data management). No Label

Abstract: Agreeing that increased future global food demand will have to be met by production intensification rather than land use expansion (e.g. Hertel, 2011), scientists have moved to empirically analyse the causes for differences between potentially attainable yields and actually realized yields – the yield gap (e.g. van Ittersum et al., 2013, Neumann et al., 2010). In the long run, we aim at disentangling the effects of biophysical, economic and political impacts and farmers’ response to them on crop yields by analysing yield gaps at regional scale in the European Union. Apart from generally improving our understanding of yield gaps and their drivers in the EU, our analysis will contribute to the integration of economic and biophysical models at a later stage of our research. As a first step towards an advanced yield gap analysis, the current paper will give an overview of yield developments in the EU27. The overview will be based on regional yield trend and yield variability estimates derived from socioeconomic panel data from the Farm Accountancy Data Network (FADN). The analysis will continue and extend the work of Ewert et al. (2005) and Reidsma et al. (2009) in terms of drawing on single farm instead of country level/farm type data, including the new EU member states and most recent years (until 2011). The EU-wide analysis of yield trends and variability will serve as a basis for the later analysis of yield gaps. No Label