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Biewald, A.; Rolinski, S.; Lotze-Campen, H.; Schmitz, C. |
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Global valuation of agricultural, virtual blue water trade measured on a local scale |
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2012 |
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Conference paper presented at the 10th Annual meeting of the International Water Resource Economics Consortium, Stockholm, Sweden, 2012-08-27 to 2012-08-28 |
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MA @ admin @ |
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2323 |
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Ebrahimi, E.; Manschadi, A.M.; Neugschwandtner, R.W.; Eitzinger, J.; Thaler, S.; Kaul, H.-P. |
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Assessing the impact of climate change on crop management in winter wheat – a case study for Eastern Austria |
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2016 |
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Journal of Agricultural Science |
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J. Agric. Sci. |
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154 |
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07 |
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1153-1170 |
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Climate change is expected to affect optimum agricultural management practices for autumn-sown wheat, especially those related to sowing date and nitrogen (N) fertilization. To assess the direction and quantity of these changes for an important production region in eastern Austria, the agricultural production systems simulator was parameterized, evaluated and subsequently used to predict yield production and grain protein content under current and future conditions. Besides a baseline climate (BL, 1981–2010), climate change scenarios for the period 2035–65 were derived from three Global Circulation Models (GCMs), namely CGMR, IPCM4 and MPEH5, with two emission scenarios, A1B and B1. Crop management scenarios included a combination of three sowing dates (20 September, 20 October, 20 November) with four N fertilizer application rates (60, 120, 160, 200 kg/ha). Each management scenario was run for 100 years of stochastically generated daily weather data. The model satisfactorily simulated productivity as well as water and N use of autumn- and spring-sown wheat crops grown under different N supply levels in the 2010/11 and 2011/12 experimental seasons. Simulated wheat yields under climate change scenarios varied substantially among the three GCMs. While wheat yields for the CGMR model increased slightly above the BL scenario, under IPCM4 projections they were reduced by 29 and 32% with low or high emissions, respectively. Wheat protein appears to increase with highest increments in the climate scenarios causing the largest reductions in grain yield (IPCM4 and MPEH-A1B). Under future climatic conditions, maximum wheat yields were predicted for early sowing (September 20) with 160 kg N/ha applied at earlier dates than the current practice. |
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0021-8596 |
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TradeM |
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4723 |
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Köchy, M. |
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Hub website and administrative tasks |
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2013 |
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FACCE MACSUR Reports |
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1 |
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D-H3.1 |
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The Hub management has set up and is administering an interactive web site, organized the kickoff-workshop, developed the consortium agreement with 70 partners, developed logos and templates for use by members, published four newsletters, organized seven Project Steering Committee meetings and interacted with the FACCE JPI secretariat. No Label |
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MA @ admin @ |
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2266 |
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Köchy, M. |
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Strategy for the dissemination of outputs at national, EU, and global levels |
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2013 |
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FACCE MACSUR Reports |
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1 |
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D-H2.4 |
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Dissemination of FACCE MACSUR will operate in a tiered manner to the scientific community, funding agencies, decision makers in policy and agri-food businesses, and the general public. Efforts by the MACSUR hub level are targeted at the supra-national level, while individual institutions target regional and national stakeholders.The dissemination approaches will include multiple and various methods of information distribution including• scientific papers and presentations,• conferences, congresses, workshops, and courses targeted to specific stakeholders,• a fully developed and interactive website,• regular newsletters,• flyers,• alignment of methodologies and protocols with other research networks or projects. Dissemination is both passive (web site, publications) and active (flyers, presentations, workshops held jointly with other projects or organizations, Twitter). No Label |
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MA @ admin @ |
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2265 |
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Braunmiller, K.; Köchy, M. |
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Grassland datasets |
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2013 |
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FACCE MACSUR Reports |
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1 |
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D-L1.3 |
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In the MACSUR project, there are several grassland models in use that were designed for and adjusted with data from different climatic regions. To be able to run these modelsfor a wide geographical range, there is a need to validate and calibrate them on the same basis.Therefore, a high-quality dataset is needed, which includes a wide range of climatic conditions, management systems and other variables.Through this search 23 grassland related institutes from eleven countries were found and contacted, where 12 of them responded to the request. Nine institutes from cooler (e.g. Finland) and warmer regions (e.g. Israel) are now willing to provide their experimental data. One contributor is even planning to join the project bringing its own grassland model.These new grassland datasets cover in addition to already available ones (Fig. 1) a wide range of climatic regions for a substantiated calibration and validation of the models. Data supplied by the institutes have been checked for internal consistency and cast into a common format. The data have been passed on to WP L2 (Model intercomparison on climate change in relation to livestock and grassland). No Label |
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MA @ admin @ |
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2258 |
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