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Köchy, M. |
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FACCE MACSUR Kickoff-Meeting |
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2013 |
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FACCE MACSUR Reports |
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1 |
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M-H3.1.0 |
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The FACCE MACSUR kickoff meeting took place 15-16 October 2012 in Berlin, Germany. The date for the meeting was moved from July to October to accommodate the participation of most institutions involved in MACSUR in a sufficiently large meeting place. One hundred and forty-five persons participated in the event. The meeting was organized as a workshop to allow for expanded time for interaction among the participants of the 73 involved institutions. The keynote lectures by Tim Benton (Global Food Security and University of Leeds) and Tim Carter (Finnish Environment Institute – SYKE) set the background by describing the need for assessing future impacts on food security and how to deal with the uncertainty associated with data, models, and projections. In parallel sessions participants discussed the organization of the work in the project and in each Theme, common approaches to answering the questions, selection of scenarios, involvement of stakeholders, and how the results will be presented. The results of the four cross-cutting workshops are documented in separate reports (M-H3.3.1 through M-H3.3.4) and serve as the starting point for more detailed planning over the following months.A post-hoc survey filled in by 75 attendees showed that the workshop had answered many organizational issues. But since the project has only started, many more issues must be discussed and clarified in the coming months, the survey showed. Overall, two thirds of the participants were satisfied with the organization oft he workshop. No Label |
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MA @ admin @ |
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2248 |
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Bojar, W. |
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Title |
Factsheets of the models |
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2013 |
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FACCE MACSUR Reports |
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1 |
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D-T1.1 |
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The exploration of adaptation and mitigation measures in the context of global challenges like climate change, food security and expected demographic boom is an field of research of growing importance. Over the last decades many research groups have been developing economic-trade models to analyse consequences on farm welfare, market supply and trade, some of them also address food security and other global concerns. There are many different ways to tackle these issues and the specific advantages and limitations of alternative modelling strategies are not yet well understood. The objective of the WP1 T1.1 task within TradeM theme of MACSUR is to use the results of a survey on trade and economic models of MACSUR Consortium partners to show which topics are currently addressed in the different models, which methods are used and how well these tools are prepared for an integration with other models like climate, crop and livestock models. This work was co-financed by NCBiR, Contract no. FACCE JPI/04/2012 – P100 PARTNER No Label |
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MA @ admin @ |
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2261 |
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Coucheney, E. |
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Sensitivity of crop water and N stress to soil input data in regional cropyield simulations and the implications for data aggregation effects: a case study with the COUP-model |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-13 |
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The effects of aggregating soil input data on modelling crop yields at regional scale have been explored within the MACSUR- Crop M – WP3 scaling exercise for an ensemble of crop models 1. The models were run for the North Rhine-Westphalia region in Germany with an average climate time-series (30 years) and soil data at resolution 1 km to 100 km. Aggregation effects showed substantial differences between the models 1. This could be linked to differences in model structure and concepts and to different procedures for the parameterization of soil properties. A further analysis of the sensitivity of the outputs to key soil properties, for each ‘model – method of parameterization’, could help in understanding differences observed within the model ensemble. In this study, we explored the relationship between winter wheat yields, water and N-stress indexes and simple key-soil properties, based on the COUP-model 2 simulations. Soils were grouped into classes according to selected parameters (i.e. soil depth, soil texture and soil organic content). Preliminary results show that some of those soil classes are clearly associated with high water and / or N-stress and lower yields or with high inter-annual variation of the yield. As such they represent key factors explaining the spatial pattern of the simulated yield at the different resolutions. In addition we identified differences in the fractional area of those soil classes between high and low spatial resolutions (‘inherent errors’ due to data aggregation). How this may influence soil data aggregation effects on simulated yields will be further analyzed. 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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2128 |
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Bannink, A. |
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Trade-offs of dietary N-reducing dietary measures on enteric methane emission and P excretion in lactating cows |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-2 |
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The dairy sector may expand by over 2% per annum with expiration of the milk quota system in countries with a major and intensive dairy sector. Such expansion will increase pressure to further reduce on-farm nitrogenous emission per unit of milk produced even more. A straightforward N-reducing measure is the manipulation of the cow diet resulting in a lower excretion of ammoniacal N excreted with urine in particular. However, dietary N-reducing measures also affect enteric methane emissions and P excretion. For an integral evaluation of the consequences of N-reducing dietary measures on on-farm emissions, the trade-offs between N emissions and P and methane emissions at the cow level need to be taken into account. Therefore, a simulation study was performed to simulate the consequence of various N-reducing and/or P-reducing dietary measures (altered grassland management, grass silage replaced by low-N feeds, increased concentrate allowance) on enteric methane emission and on N and P excretion. Results indicate a large scattering, but there was a trend of higher methane emissions with lower N excretion was significant. Specific measures had a synergistic effect on emissions such as the exchange of maize for grass silage. The present detailed model evaluations may aid in quantifying the extent of trade-offs between various types of emissions at the cow level, but also prove to be relevant when evaluating consequences of management options taken at the farm scale. 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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2117 |
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Author |
Pasqui, M. |
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Evaluation of future diurnal variability and projected changes in extremes of precipitation and temperature and their impacts on crop production over regional case studies (e.g. Agroscenari case studies) |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C4.3.3 |
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The daily weather of the four decades were used as input to EPIC simulation model to test the effects on crop yield, crop evapotranspiration, number of days with water and nitrogen stress in the silage maize -Italian ryegrass irrigated cropping systems in the Oristanese case study area.The monthly DTR (diurnal temperature range) pattern predicted for the FC (future climate, 2020-2030) indicates that spring and summer months are the most sensitive to DTR increase. The increase ryegrass yield simulated by EPIC under FC was interpreted as the positive effects on increased temperature on the winter-spring grass growth rates. The decreased production of maize was attributed to a shortening of the crop cycle, which reduced the intercepted radiation. The simulations run to assess the pure effect of DTR shift indicated almost no effects on crop yield but significant effects on crop evapotranspiration, whose increase observed under FC was largely associated to DTR, particularly in maize. The stochastic generation of daily weather with WXGEN indicates a sufficient accuracy for average DTR patterns and the central part of the daily DTR distribution, while the range of absolute values increased substantially, in relation to the increased probability of extremes in one century vs one decade.(Abstract supplied by the publisher) No Label |
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MA @ admin @ |
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2106 |
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