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Ruiz-Ramos, M.; Ferrise, R.; Rodríguez, A.; Lorite, I.J.; Bindi, M.; Carter, T.R.; Fronzek, S.; Palosuo, T.; Pirttioja, N.; Baranowski, P.; Buis, S.; Cammarano, D.; Chen, Y.; Dumont, B.; Ewert, F.; Gaiser, T.; Hlavinka, P.; Hoffmann, H.; Höhn, J.G.; Jurecka, F.; Kersebaum, H.-C.; Krzyszczak, J.; Lana, M.; Mechiche-Alami, A.; Minet, J.; Montesino, M.; Nendel, C.; Porter, J.R.; Ruget, F.; Semenov, M.A.; Steinmetz, Z.; Stratonovitch, P.; Supit, I.; Tao, F.; Trnka, M.; de Wit, A.; Rötter, R.P. |
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Applying adaptation response surfaces for managing wheat under perturbed climate and elevated CO2 in a Mediterranean environment |
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2017 |
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FACCE MACSUR Reports |
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1ß |
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C4.4-D |
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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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CropM |
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4955 |
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Hlavinka, P.; Olesen, J.E.; Kersebaum, K.-C.; Trnka, M.; Pohankova, E.; Stella, T.; Ferrise, R.; Moriondo, M.; Hoogenbom, G.; Shelia, V.; Nendel, C.; Wimmerová, M.; Topaj, A.; Medvedev, S.; Ventrella, D.; Ruiz-Ramos, M.; Rodríguez Sánchez, A.; Takáč, J.; Patil, R.H.; Öztürk, I.; Hoffmann, M.; Gobin, A.; Rötter, R.P. |
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Modelling long term effects of cropping and managements systems on soil organic matter, C/N dynamics and crop growth |
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2017 |
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FACCE MACSUR Reports |
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10 |
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C1.3-D |
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While simulation of cropping systems over a few years might reflect well the short term effects of management and cultivation, long term effects on soil properties and their consequences for crop growth and matter fluxes are not captured. Especially the effect on soil carbon sequestration/depletion is addressed by this task. Simulations of an ensemble of crop models are performed as transient runs over a period of 120 year using observed weather from three stations in Czech Republic (1961-2010) and transient long time climate change scenarios (2011-2080) from five GCM of the CMIP5 ensemble to assess the effect of different cropping and management systems on carbon sequestration, matter fluxes and crop production in an integrative way. Two cropping systems are regarded comprising two times winter wheat, silage maize, spring barley and oilseed rape. Crop rotations differ regarding their organic input from crop residues, nitrogen fertilization and implementation of catch crops. Models are applied for two soil types with different water holding capacity. Cultivation and nutrient management is adapted using management rules related to weather and soil conditions. Data of phenology and crop yield from the region of the regarded crops were provided to calibrate the models for crops of the rotations. Twelve models were calibrated in this first step. For the transient long term runs results of four models were submitted so far. Outputs are crop yields, nitrogen uptake, soil water and mineral nitrogen contents, as well as water and nitrogen fluxes to the atmosphere and groundwater. Changes in the carbon stocks and the consequences for nitrogen mineralisation, N fertilization and emissions also considered. |
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XC |
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MA @ admin @ |
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4976 |
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Bellocchi, G., B.; Brilli, L.; Ferrise, R.; Dibari, C.; Bindi, M. |
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Model comparison and improvement: Links established with other consortia |
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2017 |
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FACCE MACSUR Reports |
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10 |
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XC1.3-D |
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XC1 has established links to other research activities and consortia on model comparison and improvement. They include the global initiatives AgMIP (http://www.agmip.org ) and GRA (http://www.globalresearchalliance.org), and the EU-FP7 project MODEXTREME (http://modextreme.org ). These links have allowed sharing and communication of recent results and methods, and have created opportunities for future research calls. |
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MA @ admin @ |
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4941 |
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Reidsma, P.; Janssen, S.; Jansen, J.; van Ittersum, M. |
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On the development and use of farm models for policy impact assessment in the European Union – A review |
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2017 |
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Agricultural Systems |
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Agric. Syst. |
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159 |
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111-125 |
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• Evidence use in EU Impact Assessment reports is limited. • Many scientific studies used farm models for assessment of policies in the EU. • Scientific challenges include understanding farmer decision-making and interactions. • Model codes and data should be published, including evaluation. • Stronger science-policy interaction is required. Farm models are potentially relevant tools for policy impact assessment. Governments and international organizations use impact assessment (IA) as an ex-ante policy process and procedure to evaluate impacts of policy options as part of the introduction of new policies. IA is increasingly used. This paper reviews both the use of farm models in such policy IAs in the European Commission, and the development and use of farm models for policy IA by the scientific community over the past decade. A systematic review was performed, based on 202 studies from the period 2007–2015 and results were discussed in a science-policy workshop. Based on the literature review and the workshop, this paper describes progress in the development of farm models, challenges in their use in policy processes and a research and cooperation agenda. We conclude that main issues for a research agenda include: 1) better understanding of farmer decision-making and effects of the social milieu, with increased focus on the interactions between farmers and other actors, the link to the value chain, and farm structural change; 2) thorough and consistent model evaluation and model comparison, with increased attention for model sensitivity and uncertainty, and 3) the organization of a network of farm modellers. In addition, the agenda for science-policy cooperation emphasizes the need for: 4) synthesizing research evidence into systematic reviews as an institutional element in the existing science-policy-interfaces for agricultural systems, 5) improved and timely data collection, allowing to assess heterogeneity in farm objectives, management and indicators, and 6) stronger science-policy interaction, moving from a research-driven to a user-driven approach. |
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LiveM, ftnotmacsur |
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MA @ admin @ |
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5179 |
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Popp, A.; Calvin, K.; Fujimori, S.; Havlik, P.; Humpenöder, F.; Stehfest, E.; Bodirsky, B.L.; Dietrich, J.P.; Doelmann, J.C.; Gusti, M.; Hasegawa, T.; Kyle, P.; Obersteiner, M.; Tabeau, A.; Takahashi, K.; Valin, H.; Waldhoff, S.; Weindl, I.; Wise, M.; Kriegler, E.; Lotze-Campen, H.; Fricko, O.; Riahi, K.; Vuuren, D.P. van |
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Land-use futures in the shared socio-economic pathways |
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Journal Article |
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2017 |
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Global Environmental Change |
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Glob. Environ. Change |
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42 |
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331-345 |
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Scenarios; Land use; Emissions; Mitigation; Food prices; Integrated assessment; SSP |
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• Narratives for the Shared Socio-Economic Pathways (SSPs) focusing on the land sector are presented. • Integrated Assessment Models have been applied for the SSPs to assess potential future developments for land use, greenhouse gas emissions, food provision and prices. • Model results reflect the general storylines of the SSPs and indicate a broad range of potential land-use futures. • SSP-based land use pathways aim at supporting future climate research, climate impact analysis, biodiversity research and sustainability science. Abstract In the future, the land system will be facing new intersecting challenges. While food demand, especially for resource-intensive livestock based commodities, is expected to increase, the terrestrial system has large potentials for climate change mitigation through improved agricultural management, providing biomass for bioenergy, and conserving or even enhancing carbon stocks of ecosystems. However, uncertainties in future socio-economic land use drivers may result in very different land-use dynamics and consequences for land-based ecosystem services. This is the first study with a systematic interpretation of the Shared Socio-Economic Pathways (SSPs) in terms of possible land-use changes and their consequences for the agricultural system, food provision and prices as well as greenhouse gas emissions. Therefore, five alternative Integrated Assessment Models with distinctive land-use modules have been used for the translation of the SSP narratives into quantitative projections. The model results reflect the general storylines of the SSPs and indicate a broad range of potential land-use futures with global agricultural land of 4900 mio ha in 2005 decreasing by 743 mio ha until 2100 at the lower (SSP1) and increasing by 1080 mio ha (SSP3) at the upper end. Greenhouse gas emissions from land use and land use change, as a direct outcome of these diverse land-use dynamics, and agricultural production systems differ strongly across SSPs (e.g. cumulative land use change emissions between 2005 and 2100 range from −54 to 402 Gt CO2). The inclusion of land-based mitigation efforts, particularly those in the most ambitious mitigation scenarios, further broadens the range of potential land futures and can strongly affect greenhouse gas dynamics and food prices. In general, it can be concluded that low demand for agricultural commodities, rapid growth in agricultural productivity and globalized trade, all most pronounced in a SSP1 world, have the potential to enhance the extent of natural ecosystems, lead to lowest greenhouse gas emissions from the land system and decrease food prices over time. The SSP-based land use pathways presented in this paper aim at supporting future climate research and provide the basis for further regional integrated assessments, biodiversity research and climate impact analysis. |
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TradeM, ftnotmacsur |
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MA @ admin @ |
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5006 |
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