Kersebaum, Kurt Christian

[["dc.bibliographiccitation.firstpage","141"],["dc.bibliographiccitation.journal","Climate Research"],["dc.bibliographiccitation.lastpage","157"],["dc.bibliographiccitation.volume","65"],["dc.contributor.author","Zhao, H.-G."],["dc.contributor.author","Hoffmann, Holger"],["dc.contributor.author","van Bussel, Lenny G. J."],["dc.contributor.author","Enders, Andreas"],["dc.contributor.author","Specka, Xenia"],["dc.contributor.author","Sosa, C."],["dc.contributor.author","Yeluripati, J."],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Constantin, Julie"],["dc.contributor.author","Raynal, Helene"],["dc.contributor.author","Teixeira, Edmar"],["dc.contributor.author","Grosz, B."],["dc.contributor.author","Doro, Luca"],["dc.contributor.author","Zhao, Zhigan"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Kiese, Ralf"],["dc.contributor.author","Eckersten, Henrik"],["dc.contributor.author","Haas, Edwin"],["dc.contributor.author","Vanuytrecht, E."],["dc.contributor.author","Wang, Enli"],["dc.contributor.author","Kuhnert, Matthias"],["dc.contributor.author","Trombi, Giacomo"],["dc.contributor.author","Moriondo, Marco"],["dc.contributor.author","Bindi, Marco"],["dc.contributor.author","Lewan, Elisabet"],["dc.contributor.author","Bach, M."],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Rötter, Reimund Paul"],["dc.contributor.author","Roggero, Pier Paolo"],["dc.contributor.author","Wallach, Daniel"],["dc.contributor.author","Cammarano, Davide"],["dc.contributor.author","Asseng, Senthold"],["dc.contributor.author","Krauss, G."],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Ewert, Frank"],["dc.date.accessioned","2017-09-07T11:47:54Z"],["dc.date.available","2017-09-07T11:47:54Z"],["dc.date.issued","2015"],["dc.description.abstract","We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Δ), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the Δ, especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area."],["dc.identifier.doi","10.3354/cr01301"],["dc.identifier.gro","3149393"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6064"],["dc.language.iso","en"],["dc.notes.intern","Roetter Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","0936-577X"],["dc.title","Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.peerReviewed","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","260"],["dc.bibliographiccitation.journal","Agricultural Systems"],["dc.bibliographiccitation.lastpage","274"],["dc.bibliographiccitation.volume","159"],["dc.contributor.author","Ruiz-Ramos, Margarita"],["dc.contributor.author","Ferrise, R."],["dc.contributor.author","Rodríguez, A."],["dc.contributor.author","Lorite, I. J."],["dc.contributor.author","Bindi, Marco"],["dc.contributor.author","Carter, T. R."],["dc.contributor.author","Fronzek, S."],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Pirttioja, N."],["dc.contributor.author","Baranowski, P."],["dc.contributor.author","Buis, S."],["dc.contributor.author","Cammarano, Davide"],["dc.contributor.author","Chen, Y."],["dc.contributor.author","Dumont, B."],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Hlavinka, Petr"],["dc.contributor.author","Hoffmann, Holger"],["dc.contributor.author","Höhn, Jukka G."],["dc.contributor.author","Jurecka, F."],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Krzyszczak, J."],["dc.contributor.author","Lana, Marcos"],["dc.contributor.author","Mechiche-Alami, A."],["dc.contributor.author","Minet, J."],["dc.contributor.author","Montesino, M."],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Porter, J. R."],["dc.contributor.author","Ruget, F."],["dc.contributor.author","Semenov, Mikhail A."],["dc.contributor.author","Steinmetz, Z."],["dc.contributor.author","Stratonovitch, Pierre"],["dc.contributor.author","Sun, Jian"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Trnka, Mirek"],["dc.contributor.author","Wit, Allard de"],["dc.contributor.author","Rötter, Reimund P."],["dc.date.accessioned","2017-09-07T11:47:57Z"],["dc.date.available","2017-09-07T11:47:57Z"],["dc.date.issued","2018"],["dc.description.abstract","Adaptation of crops to climate change has to be addressed locally due to the variability of soil, climate and the specific socio-economic settings influencing farm management decisions. Adaptation of rainfed cropping systems in the Mediterranean is especially challenging due to the projected decline in precipitation in the coming decades, which will increase the risk of droughts. Methods that can help explore uncertainties in climate projections and crop modelling, such as impact response surfaces (IRSs) and ensemble modelling, can then be valuable for identifying effective adaptations. Here, an ensemble of 17 crop models was used to simulate a total of 54 adaptation options for rainfed winter wheat (Triticum aestivum) at Lleida (NE Spain). To support the ensemble building, an ex post quality check of model simulations based on several criteria was performed. Those criteria were based on the “According to Our Current Knowledge” (AOCK) concept, which has been formalized here. Adaptations were based on changes in cultivars and management regarding phenology, vernalization, sowing date and irrigation. The effects of adaptation options under changed precipitation (P), temperature (T), [CO2] and soil type were analysed by constructing response surfaces, which we termed, in accordance with their specific purpose, adaptation response surfaces (ARSs). These were created to assess the effect of adaptations through a range of plausible P, T and [CO2] perturbations. The results indicated that impacts of altered climate were predominantly negative. No single adaptation was capable of overcoming the detrimental effect of the complex interactions imposed by the P, T and [CO2] perturbations except for supplementary irrigation (sI), which reduced the potential impacts under most of the perturbations. Yet, a combination of adaptations for dealing with climate change demonstrated that effective adaptation is possible at Lleida. Combinations based on a cultivar without vernalization requirements showed good and wide adaptation potential. Few combined adaptation options performed well under rainfed conditions. However, a single sI was sufficient to develop a high adaptation potential, including options mainly based on spring wheat, current cycle duration and early sowing date. Depending on local environment (e.g. soil type), many of these adaptations can maintain current yield levels under moderate changes in T and P, and some also under strong changes. We conclude that ARSs can offer a useful tool for supporting planning of field level adaptation under conditions of high uncertainty."],["dc.identifier.doi","10.1016/j.agsy.2017.01.009"],["dc.identifier.gro","3149417"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6091"],["dc.language.iso","en"],["dc.notes.intern","Roetter Crossref Import"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","chake"],["dc.relation.issn","0308-521X"],["dc.title","Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1291"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Global Change Biology"],["dc.bibliographiccitation.lastpage","1307"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Rötter, Reimund P."],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Hernández Díaz-Ambrona, Carlos Gregorio"],["dc.contributor.author","Mínguez, M. Inés"],["dc.contributor.author","Semenov, Mikhail A."],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Specka, Xenia"],["dc.contributor.author","Hoffmann, Holger"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Dambreville, Anaelle"],["dc.contributor.author","Martre, Pierre"],["dc.contributor.author","Rodríguez, Lucía"],["dc.contributor.author","Ruiz-Ramos, Margarita"],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Höhn, Jukka G."],["dc.contributor.author","Salo, Tapio"],["dc.contributor.author","Ferrise, Roberto"],["dc.contributor.author","Bindi, Marco"],["dc.contributor.author","Cammarano, Davide"],["dc.contributor.author","Schulman, Alan H."],["dc.date.accessioned","2018-02-12T10:48:38Z"],["dc.date.available","2018-02-12T10:48:38Z"],["dc.date.issued","2018"],["dc.description.abstract","Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple‐ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple‐ensemble probabilistic assessment, the median of simulated yield change was −4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple‐ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources."],["dc.identifier.doi","10.1111/gcb.14019"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/12151"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.doi","10.1111/gcb.14019"],["dc.title","Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","18"],["dc.bibliographiccitation.lastpage","19"],["dc.contributor.author","Pirttioja, Nina"],["dc.contributor.author","Fronzek, Stefan"],["dc.contributor.author","Bindi, Marco"],["dc.contributor.author","Carter, Timothy R."],["dc.contributor.author","Hoffmann, Holger"],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Ruiz-Ramos, Margarita"],["dc.contributor.author","Trnka, Miroslav"],["dc.contributor.author","Acutis, Marco"],["dc.contributor.author","Asseng, Senthold"],["dc.contributor.author","Baranowski, Piotr"],["dc.contributor.author","Basso, Bruno"],["dc.contributor.author","Bodin, Per"],["dc.contributor.author","Buis, Samuel"],["dc.contributor.author","Cammarano, Davide"],["dc.contributor.author","Deligios, Paola"],["dc.contributor.author","Destain, Marie-France"],["dc.contributor.author","Doro, Luca"],["dc.contributor.author","Dumont, Benjamin"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Ferrise, Roberto"],["dc.contributor.author","François, Louis"],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Hlavinka, Petr"],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Kollas, Chris"],["dc.contributor.author","Krzyszczak, Jaromir"],["dc.contributor.author","Torres, Ignacio Lorite"],["dc.contributor.author","Minet, Julien"],["dc.contributor.author","Mínguez, M. Inés"],["dc.contributor.author","Montesino, Manuel"],["dc.contributor.author","Moriondo, Marco"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Öztürk, Isik"],["dc.contributor.author","Perego, Alessia"],["dc.contributor.author","Ruget, Françoise"],["dc.contributor.author","Rodríguez, Alfredo"],["dc.contributor.author","Sanna, Mattia"],["dc.contributor.author","Semenov, Mikhail A."],["dc.contributor.author","Slawinski, Cezary"],["dc.contributor.author","Stratonovitch, Pierre"],["dc.contributor.author","Supit, Iwan"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Wu, Lianhai"],["dc.contributor.author","Rötter, Reimund P."],["dc.date.accessioned","2018-06-11T14:02:07Z"],["dc.date.available","2018-06-11T14:02:07Z"],["dc.date.issued","2014"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15015"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.conference","FACCE MACSUR CropM International Symposium and Workshop \"Modelling climate change impacts on crop production for food security\""],["dc.relation.eventend","2014-02-12"],["dc.relation.eventlocation","Oslo"],["dc.relation.eventstart","2014-02-10"],["dc.relation.ispartof","Proceedings of the FACCE MACSUR CropM International Symposium and Workshop \"Modelling climate change impacts on crop production for food security\""],["dc.title","Examining wheat yield sensitivity to temperature and precipitation changes for a large ensemble of crop models using impact response surfaces"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","53"],["dc.bibliographiccitation.journal","Climate Research"],["dc.bibliographiccitation.lastpage","69"],["dc.bibliographiccitation.volume","65"],["dc.contributor.author","Hoffmann, Holger"],["dc.contributor.author","Zhao, G."],["dc.contributor.author","van Bussel, Lenny G. J."],["dc.contributor.author","Enders, Andreas"],["dc.contributor.author","Specka, Xenia"],["dc.contributor.author","Sosa, C."],["dc.contributor.author","Yeluripati, J."],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Constantin, Julie"],["dc.contributor.author","Raynal, Helene"],["dc.contributor.author","Teixeira, Edmar"],["dc.contributor.author","Grosz, B."],["dc.contributor.author","Doro, Luca"],["dc.contributor.author","Zhao, Zhigan"],["dc.contributor.author","Wang, Enli"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Haas, Edwin"],["dc.contributor.author","Kiese, Ralf"],["dc.contributor.author","Klatt, S."],["dc.contributor.author","Eckersten, Henrik"],["dc.contributor.author","Vanuytrecht, E."],["dc.contributor.author","Kuhnert, Matthias"],["dc.contributor.author","Lewan, Elisabet"],["dc.contributor.author","Rötter, Reimund Paul"],["dc.contributor.author","Roggero, Pier Paolo"],["dc.contributor.author","Wallach, Daniel"],["dc.contributor.author","Cammarano, Davide"],["dc.contributor.author","Asseng, Senthold"],["dc.contributor.author","Krauss, G."],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Ewert, Frank"],["dc.date.accessioned","2017-09-07T11:47:53Z"],["dc.date.available","2017-09-07T11:47:53Z"],["dc.date.issued","2015"],["dc.description.abstract","Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha-1, whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization."],["dc.identifier.doi","10.3354/cr01326"],["dc.identifier.gro","3149398"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6070"],["dc.language.iso","en"],["dc.notes.intern","Roetter Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","0936-577X"],["dc.title","Variability of effects of spatial climate data aggregation on regional yield simulation by crop models"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.peerReviewed","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","5072"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Global Change Biology"],["dc.bibliographiccitation.lastpage","5083"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Wallach, Daniel"],["dc.contributor.author","Martre, Pierre"],["dc.contributor.author","Liu, Bing"],["dc.contributor.author","Asseng, Senthold"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Thorburn, Peter J."],["dc.contributor.author","van Ittersum, Martin"],["dc.contributor.author","Aggarwal, Pramod K."],["dc.contributor.author","Ahmed, Mukhtar"],["dc.contributor.author","Basso, Bruno"],["dc.contributor.author","Biernath, Christian"],["dc.contributor.author","Cammarano, Davide"],["dc.contributor.author","Challinor, Andrew J."],["dc.contributor.author","De Sanctis, Giacomo"],["dc.contributor.author","Dumont, Benjamin"],["dc.contributor.author","Eyshi Rezaei, Ehsan"],["dc.contributor.author","Fereres, Elias"],["dc.contributor.author","Fitzgerald, Glenn J."],["dc.contributor.author","Gao, Y."],["dc.contributor.author","Garcia-Vila, Margarita"],["dc.contributor.author","Gayler, Sebastian"],["dc.contributor.author","Girousse, Christine"],["dc.contributor.author","Hoogenboom, Gerrit"],["dc.contributor.author","Horan, Heidi"],["dc.contributor.author","Izaurralde, Roberto C."],["dc.contributor.author","Jones, Curtis D."],["dc.contributor.author","Kassie, Belay T."],["dc.contributor.author","Kersebaum, Kurt C."],["dc.contributor.author","Klein, Christian"],["dc.contributor.author","Koehler, Ann-Kristin"],["dc.contributor.author","Maiorano, Andrea"],["dc.contributor.author","Minoli, Sara"],["dc.contributor.author","Müller, Christoph"],["dc.contributor.author","Naresh Kumar, Soora"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","O'Leary, Garry J."],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Priesack, Eckart"],["dc.contributor.author","Ripoche, Dominique"],["dc.contributor.author","Rötter, Reimund P."],["dc.contributor.author","Semenov, Mikhail A."],["dc.contributor.author","Stöckle, Claudio"],["dc.contributor.author","Stratonovitch, Pierre"],["dc.contributor.author","Streck, Thilo"],["dc.contributor.author","Supit, Iwan"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Wolf, Joost"],["dc.contributor.author","Zhang, Zhao"],["dc.date.accessioned","2020-12-10T18:28:42Z"],["dc.date.available","2020-12-10T18:28:42Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1111/gcb.14411"],["dc.identifier.issn","1354-1013"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76386"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Multimodel ensembles improve predictions of crop-environment-management interactions"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","261"],["dc.bibliographiccitation.lastpage","277"],["dc.contributor.author","Rötter, Reimund P."],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","van Bussel, Lenny G. J."],["dc.contributor.author","Zhao, Gang"],["dc.contributor.author","Hoffmann, Holger"],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Specka, Xenia"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Sosa, Carmen"],["dc.contributor.author","Lewan, Elisabet"],["dc.contributor.author","Yeluripati, Jagadeesh"],["dc.contributor.author","Kuhnert, Matthias"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Constantin, Julie"],["dc.contributor.author","Raynal, Helene"],["dc.contributor.author","Wallach, Daniel"],["dc.contributor.author","Teixeira, Edmar"],["dc.contributor.author","Grosz, Balázs"],["dc.contributor.author","Bach, Michaela"],["dc.contributor.author","Doro, Luca"],["dc.contributor.author","Roggero, Pier Paolo"],["dc.contributor.author","Zhao, Zhigan"],["dc.contributor.author","Wang, Enli"],["dc.contributor.author","Kiese, Ralf"],["dc.contributor.author","Haas, Edwin"],["dc.contributor.author","Eckersten, Henrik"],["dc.contributor.author","Trombi, Giacomo"],["dc.contributor.author","Bindi, Marco"],["dc.contributor.author","Klein, Christian"],["dc.contributor.author","Biernath, Christian"],["dc.contributor.author","Heinlein, Florian"],["dc.contributor.author","Priesack, Eckart"],["dc.contributor.author","Cammarano, Davide"],["dc.contributor.author","Asseng, Senthold"],["dc.contributor.author","Elliott, Joshua"],["dc.contributor.author","Glotter, Michael"],["dc.contributor.author","Basso, Bruno"],["dc.contributor.author","Baigorria, Guillermo A."],["dc.contributor.author","Romero, Consuelo C."],["dc.contributor.author","Moriondo, Marco"],["dc.contributor.editor","Rosenzweig, Cynthia"],["dc.contributor.editor","Hillel, Daniel"],["dc.date.accessioned","2018-05-25T15:49:21Z"],["dc.date.available","2018-05-25T15:49:21Z"],["dc.date.issued","2015"],["dc.identifier.doi","10.1142/9781783265640_0010"],["dc.identifier.uri","http://hdl.handle.net/2/14753"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.publisher","Imperial College Press"],["dc.publisher.place","London"],["dc.relation.crisseries","ICP Series on Climate Change Impacts, Adaptation, and Mitigation"],["dc.relation.isbn","978-1-78326-563-3"],["dc.relation.ispartof","Handbook of Climate Change and Agroecosystems The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments"],["dc.relation.ispartofseries","ICP Series on Climate Change Impacts, Adaptation, and Mitigation; 3"],["dc.title","Uncertainties in Scaling-Up Crop Models for Large-Area Climate Change Impact Assessments"],["dc.type","book_chapter"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","209"],["dc.bibliographiccitation.journal","Agricultural Systems"],["dc.bibliographiccitation.lastpage","224"],["dc.bibliographiccitation.volume","159"],["dc.contributor.author","Fronzek, Stefan"],["dc.contributor.author","Pirttioja, Nina"],["dc.contributor.author","Carter, Timothy R."],["dc.contributor.author","Bindi, Marco"],["dc.contributor.author","Hoffmann, Holger"],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Ruiz-Ramos, Margarita"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Trnka, Miroslav"],["dc.contributor.author","Acutis, Marco"],["dc.contributor.author","Asseng, Senthold"],["dc.contributor.author","Baranowski, Piotr"],["dc.contributor.author","Basso, Bruno"],["dc.contributor.author","Bodin, Per"],["dc.contributor.author","Buis, Samuel"],["dc.contributor.author","Cammarano, Davide"],["dc.contributor.author","Deligios, Paola"],["dc.contributor.author","Destain, Marie-France"],["dc.contributor.author","Dumont, Benjamin"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Ferrise, Roberto"],["dc.contributor.author","François, Louis"],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Hlavinka, Petr"],["dc.contributor.author","Jacquemin, Ingrid"],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Kollas, Chris"],["dc.contributor.author","Krzyszczak, Jaromir"],["dc.contributor.author","Lorite, Ignacio J."],["dc.contributor.author","Minet, Julien"],["dc.contributor.author","Minguez, M. Ines"],["dc.contributor.author","Montesino, Manuel"],["dc.contributor.author","Moriondo, Marco"],["dc.contributor.author","Müller, Christoph"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Öztürk, Isik"],["dc.contributor.author","Perego, Alessia"],["dc.contributor.author","Rodríguez, Alfredo"],["dc.contributor.author","Ruane, Alex C."],["dc.contributor.author","Ruget, Françoise"],["dc.contributor.author","Sanna, Mattia"],["dc.contributor.author","Semenov, Mikhail A."],["dc.contributor.author","Slawinski, Cezary"],["dc.contributor.author","Stratonovitch, Pierre"],["dc.contributor.author","Supit, Iwan"],["dc.contributor.author","Waha, Katharina"],["dc.contributor.author","Wang, Enli"],["dc.contributor.author","Wu, Lianhai"],["dc.contributor.author","Zhao, Zhigan"],["dc.contributor.author","Rötter, Reimund P."],["dc.date.accessioned","2018-02-12T10:43:57Z"],["dc.date.available","2018-02-12T10:43:57Z"],["dc.date.issued","2018"],["dc.description.abstract","Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities."],["dc.identifier.doi","10.1016/j.agsy.2017.08.004"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/12148"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.title","Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","361"],["dc.bibliographiccitation.journal","Environmental Modelling & Software"],["dc.bibliographiccitation.lastpage","377"],["dc.bibliographiccitation.volume","96"],["dc.contributor.author","Grosz, Balázs"],["dc.contributor.author","Gebbert, Sören"],["dc.contributor.author","Dechow, Rene"],["dc.contributor.author","Zhao, Gang"],["dc.contributor.author","Hoffmann, Holger"],["dc.contributor.author","Constantin, Julie"],["dc.contributor.author","Raynal, Helene"],["dc.contributor.author","Wallach, Daniel"],["dc.contributor.author","Coucheney, Elsa"],["dc.contributor.author","Lewan, Elisabet"],["dc.contributor.author","Eckersten, Henrik"],["dc.contributor.author","Specka, Xenia"],["dc.contributor.author","Kersebaum, Kurt-Christian"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Kuhnert, Matthias"],["dc.contributor.author","Yeluripati, Jagadeesh"],["dc.contributor.author","Haas, Edwin"],["dc.contributor.author","Teixeira, Edmar"],["dc.contributor.author","Bindi, Marco"],["dc.contributor.author","Trombi, Giacomo"],["dc.contributor.author","Moriondo, Marco"],["dc.contributor.author","Doro, Luca"],["dc.contributor.author","Roggero, Pier Paolo"],["dc.contributor.author","Zhao, Zhigan"],["dc.contributor.author","Wang, Enli"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Rötter, Reimund P."],["dc.contributor.author","Kassie, Belay"],["dc.contributor.author","Cammarano, Davide"],["dc.contributor.author","Asseng, Senthold"],["dc.contributor.author","Weihermüller, Lutz"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Ewert, Frank"],["dc.date.accessioned","2018-02-12T10:49:55Z"],["dc.date.available","2018-02-12T10:49:55Z"],["dc.date.issued","2017"],["dc.description.abstract","In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low."],["dc.identifier.doi","10.1016/j.envsoft.2017.06.046"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/12153"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.title","The implication of input data aggregation on up-scaling soil organic carbon changes"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","911"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Global Change Biology"],["dc.bibliographiccitation.lastpage","925"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Martre, Pierre"],["dc.contributor.author","Wallach, Daniel"],["dc.contributor.author","Asseng, Senthold"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Jones, James W."],["dc.contributor.author","Rötter, Reimund Paul"],["dc.contributor.author","Boote, Kenneth J."],["dc.contributor.author","Ruane, Alex C."],["dc.contributor.author","Thorburn, Peter J."],["dc.contributor.author","Cammarano, Davide"],["dc.contributor.author","Hatfield, Jerry L."],["dc.contributor.author","Rosenzweig, Cynthia"],["dc.contributor.author","Aggarwal, Pramod K."],["dc.contributor.author","Angulo, Carlos"],["dc.contributor.author","Basso, Bruno"],["dc.contributor.author","Bertuzzi, Patrick"],["dc.contributor.author","Biernath, Christian"],["dc.contributor.author","Brisson, Nadine"],["dc.contributor.author","Challinor, Andrew J."],["dc.contributor.author","Doltra, Jordi"],["dc.contributor.author","Gayler, Sebastian"],["dc.contributor.author","Goldberg, Richard"],["dc.contributor.author","Grant, Robert F."],["dc.contributor.author","Heng, Lee"],["dc.contributor.author","Hooker, Josh"],["dc.contributor.author","Hunt, Leslie A."],["dc.contributor.author","Ingwersen, Joachim"],["dc.contributor.author","Izaurralde, Roberto C."],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Müller, Christoph"],["dc.contributor.author","Naresh Kumar, Soora"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","O'Leary, G."],["dc.contributor.author","Olesen, Jørgen E."],["dc.contributor.author","Osborne, Tom M."],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Priesack, Eckart"],["dc.contributor.author","Ripoche, Dominique"],["dc.contributor.author","Semenov, Mikhail A."],["dc.contributor.author","Shcherbak, Iurii"],["dc.contributor.author","Steduto, Pasquale"],["dc.contributor.author","Stöckle, Claudio O."],["dc.contributor.author","Stratonovitch, Pierre"],["dc.contributor.author","Streck, Thilo"],["dc.contributor.author","Supit, Iwan"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Travasso, Maria"],["dc.contributor.author","Waha, Katharina"],["dc.contributor.author","White, Jeffrey W."],["dc.contributor.author","Wolf, Joost"],["dc.date.accessioned","2017-09-07T11:47:52Z"],["dc.date.available","2017-09-07T11:47:52Z"],["dc.date.issued","2015"],["dc.description.abstract","Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24–38% for the different end‐of‐season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in‐season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e‐mean) or median (e‐median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e‐median ranked first in simulating measured GY and third in GPC. The error of e‐mean and e‐median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models."],["dc.identifier.doi","10.1111/gcb.12768"],["dc.identifier.gro","3149394"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6066"],["dc.language.iso","en"],["dc.notes.intern","Roetter Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1354-1013"],["dc.title","Multimodel ensembles of wheat growth: many models are better than one"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]