Marton, T. (2016). Assessing the impact of agro-climatic factors and farm characteristics on the yield variation of the Norwegian fruit sector (Vol. 9 C6 -).
Abstract: Main drivers of ag. yields:–Technology–R&D (new hybrids etc.)–Weather–Etc.•Common sense and anecdotal observations (remember the Tromsø presentation) revealed extreme events tended to impact wide geographic areas•This was called the «systemic» nature of agriculture No semi-aggregation farm-level•Not the boring corn, maize, wheat fruits•No OLS-like Pearson correlation or functional form approach for conditioning spatial correlations on weather SDM•Finally, if we are smart enough to set the explanatory proxies in a meaningful way presumably we can make the distinction between the effects of, say draught and extreme heat.•And much more in policy relevance
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Dono, G. (2015). Awareness of climate change for adaptation of the farm sector (Vol. 4).
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Braunmiller, K., & Köchy, M. (2013). Background information on Shared Socioeconomic Pathways for use in MACSUR case studies (Vol. 2).
Abstract: This document is intended to aid in the development of regional Representative Agricultural Pathways in Europe for use in MACSUR case studies, especially the regional pilot studies. We present overviews of existing characterisations of RCPs, SSPs, SPAs, RAPs and more detailed descriptions of the scenarios and assumptions relevant for MACSUR. 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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Saetnan, E. R. (2015). Capacity building strategy (Vol. 7).
Abstract: Introduction Raising the capacity of established researchers Capacity for cross-theme collaboration Short “Master Classes” Raising the capacity of early career researchers PhD/ECR training courses Training integrative and international modellers through a Marie Curie ITN Raising the capacity of our stakeholders MACSUR input to the Advanced Training Partnership (ATP)
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