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Höglind, M.; the partners of LiveM task L1.3 |
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Bringing together grassland and farm scale modelling. Part 1. Characterizing grasslands in farm scale modelling |
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2017 |
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FACCE MACSUR Reports |
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10 |
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L1.3-D |
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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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LiveM |
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MA @ admin @ |
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4957 |
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Braunmiller, K.; Köchy, M. |
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Background information on Shared Socioeconomic Pathways for use in MACSUR case studies |
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2013 |
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FACCE MACSUR Reports |
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2 |
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R-H2.1 |
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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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MA @ admin @ |
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2238 |
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Dono, G. |
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Awareness of climate change for adaptation of the farm sector |
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2015 |
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FACCE MACSUR Reports |
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4 |
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SP4-5 |
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TradeM International Workshop 2014 »Economics of integrated assessment approaches for agriculture and the food sector«, 25–27 November 2014, Hurdalsjø, Norway |
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MA @ admin @ |
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2195 |
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Marton, T. |
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Assessing the impact of agro-climatic factors and farm characteristics on the yield variation of the Norwegian fruit sector |
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2016 |
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FACCE MACSUR Reports |
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9 C6 - |
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Sp9-5 |
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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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MA @ admin @ |
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4857 |
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Bodin, P. |
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Assessing modelling approaches for simulating the effect of high temperature stress on yield |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-7 |
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High temperature events can have a large negative effect on crop yields, and the effects of these events are strongly dependent on not only the maximum temperature but also on the length and timing of these heat stress events. In future climate the likelihood of these types of events are expected to increase and thus make it crucial to be able to correctly assess not only the effect of changes in mean temperature but also the effect of changes in climate extremes. Crop models are often employed to predict yield responses to a changing climate, and traditionally they have not included the effect of heat stress events. In recent years more and more models have come to include the effect of high temperature stress on crop yield. Here we implement three of these approaches (APSIM, GAEZ and CERES-Wheat) into the Crop-DGVM: LPJ-GUESS and results from an initial sensitivity analysis are presented. Results show a large difference in year to year variability in simulated yield for the different approaches, and also on differences in sensitivity in relation to temperature change. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2122 |
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