Ruiz-Ramos, M., Ferrise, R., Rodríguez, A., Lorite, I. J., Bindi, M., Carter, T. R., et al. (2017). Applying adaptation response surfaces for managing wheat under perturbed climate and elevated CO2 in a Mediterranean environment (Vol. 1ß).
Abstract: This study developed Adaptation Response Surfaces and applied them to a study case in North East Spain on winter crops adaptation, using rainfed winter wheat as reference crop. Crop responses to perturbed temperature, precipitation and CO2 were simulated by an ensemble of crop models. A set of combined changes on cultivars (on vernalisation requirements and phenology) and management (on sowing date and irrigation) were considered as adaptation options and simulated by the crop model ensemble. The discussion focused on two main issues: 1) the recommended adaptation options for different soil types and perturbation levels, and 2) the need of applying our current knowledge (AOCK) when building a crop model ensemble. The study has been published Agricultural Systems (Available online 25 January 2017, https://doi.org/10.1016/j.agsy.2017.01.009 ), and the text below consists on extracts from that paper.
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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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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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Ruiu, L. M., Maurizi, S., Sassu, S., Seddaiu, G., Zuin, O., Blackmore, C., et al. (2017). Re-Staging La Rasgioni: lessons learned from transforming a traditional form of conflict resolution to engage stakeholders in agricultural water governance. Water, 9(4), 297.
Abstract: This paper presents an informal process inspired by a public practice of conflict mediation used until a few decades ago in Gallura (NE Sardinia, Italy), named La Rasgioni (The Reason). The aim is twofold: (i) to introduce an innovative method that translates the complexity of water-related conflicts into a “dialogical tool”, aimed at enhancing social learning by adopting theatrical techniques; and (ii) to report the outcomes that emerged from the application of this method in Arborea, the main dairy cattle district and the only nitrate-vulnerable zone in Sardinia, to mediate contrasting positions between local entrepreneurs and representatives of the relevant institutions. We discuss our results in the light of four pillars, adopted as research lenses in the International research Project CADWAGO (Climate Change Adaptation and Water Governance), which consider the specific “social–ecological” components of the Arborea system, climate change adaptability in water governance institutions and organizations, systemic governance (relational) practices, and governance learning. The combination of the four CADWAGO pillars and La Rasgioni created an innovative dialogical space that enabled stakeholders and researchers to collectively identify barriers and opportunities for effective governance practices. Potential wider implications and applications of La Rasgioni process are also discussed in the paper.
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Janssen, S. (2017). Open data journal as a publishing and data sharing mechanism (Vol. 10).
Abstract: This deliverable lays out the work as done as part of MACSUR CropM on data publishing, with the focus on improving data sharing and discovery and have shared data curation for future use. As part of the first phase MACSUR, The Open Data Journal for Agricultural Research (www.odjar.org) was started and documented in Deliverable C2.2 as part of Crop M. Odjar.org mainly focuses on long term data archival and citation of data sets, as input and outputs to the modelling work, as part of MACSUR, lead by Wageningen UR This deliverable is a short update on the process of creating such a data journal by demonstrating a set of articles published through the journal, some of which are based on MACSUR results, as well as related networks. The deliverable does not further explain what the journal is, as this is part of the previous deliverable.
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