Wallach, D., & Rivington, M. (2013). Development of a common set of methods and protocols for assessing and communicating uncertainties (Vol. 2).
Abstract: This reports sets out an outline approach to create definitions of uncertainty and how it might be classified. This is not a prescriptive approach rather it should be seen as a starting point from which further development can be made by consensus with CropM partners and across MACSUR Themes. We propose both a numerical quantification of uncertainty and text based classification scheme. The rational is to be able to both establish the terms and definitions in quantifying the impact of uncertainty on model estimates and have a scheme to enable identification of connectivity between types and sources of uncertainty. The aim is to establish a common set of terms and structure within which they operate that can be used to guide work within CropM. No Label
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Höglind, M., & the partners of LiveM task L1.3. (2017). Bringing together grassland and farm scale modelling. Part 1. Characterizing grasslands in farm scale modelling (Vol. 10).
Abstract: This report provides an overview of how grasslands are represented in six different farmscale models represented in MACSUR. A survey was conducted, followed by a workshop in which modellers discussed the results of the survey, and identified research challenges and knowledge gaps. The workshop was attended by grassland as well as livestock specialists. The investigated models differed largely with respect to how grasslands were represented, e.g. as regards weather and management factors accounted for, spatial and temporal resolution, and output variables. All models had grassland modules that simulate DM yield and herbage N content (or crude protein (CP) content = N content x 6.25). Many models also simulate P content, whereas only one simulate K content. About half of the model simulate herbage energy value and/or herbage fibre content and fibre and/or dry matter digestibility. Critical input data required from grassland models to simulate ruminant productivity and GHG emissions at farm scale was identified by the workshop participants. The different types of input data required were ranked in order of importance as regards their influence on important system outputs. For simulation of ruminant productivity and GHG emissions, herbage DM yield was ranked as the most important input variable from grassland models, followed by CP content together with at least one variable describing herbage fibre characteristics. These findings suggest that work on improving the ability of the current grassland models with respect to simulation of fibre/energy should be prioritized in farm-scale modelling aiming at quantifying livestock production and GHG emissions under different management regimes and climate conditions. More work is also needed on model evaluation, a task that has not been prioritized yet for some models.
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Roggero, P. P., & Matthews, R. (2015). Strategies for engagement on adaptation and mitigation with national and EU policy makers and with the agro--food chain sector (Update) (Vol. 6).
Abstract: 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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Hutchings, N. (2017). Farm-scale model linkage for ruminant systems (Vol. 10).
Abstract: This report describes the findings of the first workshop and associated actions of task L1.4. The findings detailed below, along with the outputs of a second workshop (L1.4-D2) are currently being synthesized into an article for submission as a peer reviewed paper. The work presented here addresses the scientific/conceptual issues related to model linkage.
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Bellocchi, G., Ma, S., Köchy, M., & Braunmiller, K. (2013). Identified grassland-livestock production systems and related models (Vol. 2).
Abstract: This report describes grassland-livestock production systems, as selected for model-basedstudies. A list of grassland models was identified for evaluation against such datasets(WP2) and application at reference farm (WP3) and regions (WP4) across Europe and peri-European countries. No Label
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