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König, H.J.; Helming, K.; Seddaiu, G.; Kipling, R.; Köchy, M.; Graversgaard, M.; van den Pol-van Dasselaar, A.; Nguyen, T.P.L.; Quaranta, G.; Salvia, R.; Sieber, S.; Ithes, S.; Kjeldsen, C.; Turner, K.G.; Dalgaard, T.; Roggero, P.P. |
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Stakeholder participation in agricultural research: Who should be involved, why, and how? |
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Research in sustainable agricultural management requires appropriate participatory processes and tools enabling efficient dialogue and cooperation to allow researchers and stakeholders to co-produce knowledge. Research approaches that encourage stakeholder participation are in high demand because they allow a better understanding of human-nature interactions and interdependencies between actors. Participatory approaches also support multiple goals of agricultural management: improved productivity, food security, climate change adaptation, environmental conservation, rural development and policy decision making. Approaches to stakeholder engagement in the field of agricultural management research are manifold. Therefore, selecting the “right” approach depends on the specific purpose and contextualized issues at stake. We analyzed ten stakeholder approaches and propose a new framework with which to identify and select appropriate approaches for stakeholder engagement. The framework consists of three components: whom to engage (i.e., stakeholder type and mandate), why to engage (i.e., research purpose: consult, inform, collaborate), and how to engage (i.e., different methodological approaches). We identified different stakeholder groups (who?): farmers, agricultural actors, land users, and policymakers; different purposes (why?): facilitate engagement process, inform stakeholders, and obtain stakeholder perceptions; and different types of engagement methods (how?): participatory field experiments, desk simulations, interviews, panel discussions and different types of workshops. The framework was applied to arrange these approaches, organize them to improve understanding of their main strengths, weaknesses and supports for identifying and selecting an appropriate approach. We conclude that understanding the different facets of available approaches is crucial for selecting an appropriate stakeholder engagement approach. ; |
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MA @ admin @ |
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2564 |
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Roggero, P.P. |
| 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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MA @ admin @ |
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2242 |
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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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MA @ admin @ |
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2107 |
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Dono, G.; Cortignani, R.; Doro, L.; Roggero, P.P. |
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The adaptation of farm and awareness of ongoing climate change (CC) |
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2014 |
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Farm planning is based on awareness of climate variability, here assumed to depend on experience gained over the years, and to generate expectations on climatic variables. Expectations are based on probability distributions (pdfs) estimated on climate data and used to generate managing choices by means of Discrete Stochastic Programming. The model simulates the income losses in case farmers do not recognize the ongoing CC, and continue to plan assuming climate stability. In particular, the use of resources in 2010 is simulated based on the pdfs of the early 2000s, despite CC has changed the probabilities of the various states of nature. The model, calibrated with Positive Mathematical Programming, generates a 0.9% income increase when is allowed to adapt to 2010 climate pdfs. The model is also calibrated according to pdfs of 2010, i.e. recognizing CC: in this case income falls of 0.7% when farmers are simulated to use their soil mistakenly based of the 2000 pdfs. Given the short period of CC, the differences represent an appreciable error that farmers may be already committing. Properly specifying with the CC at local level can help building farmers’ awareness on it, and to properly manage their resources, recovering profitability. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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MA @ admin @ |
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5131 |
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Grosz, B.; Dechow, R.; Gebbert, S.; Hoffmann, H.; Zhao, G.; Constantin, J.; Raynal, H.; Wallach, D.; Coucheney, E.; Lewan, E.; Eckersten, H.; Specka, X.; Kersebaum, K.-C.; Nendel, C.; Kuhnert, M.; Yeluripati, J.; Haas, E.; Teixeira, E.; Bindi, M.; Trombi, G.; Moriondo, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Tao, F.; Roetter, R.; Kassie, B.; Cammarano, D.; Asseng, S.; Weihermueller, L.; Siebert, S.; Gaiser, T.; Ewert, F. |
| Title |
The implication of input data aggregation on up-scaling soil organic carbon changes |
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Journal Article |
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2017 |
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Environmental Modelling & Software |
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Env. Model. Softw. |
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96 |
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361-377 |
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Biogeochemical model; Data aggregation; Up-scaling error; Soil organic carbon; DIFFERENT SPATIAL SCALES; NITROUS-OXIDE EMISSIONS; MODELING SYSTEM; DATA; RESOLUTION; CROP MODELS; CLIMATE; LONG; PRODUCTIVITY; CROPLANDS; DAYCENT |
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In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. (C)2017 Elsevier Ltd. All rights reserved. |
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2017-09-14 |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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5176 |
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