Rötter, Reimund Paul

[["dc.bibliographiccitation.firstpage","287"],["dc.bibliographiccitation.journal","Environmental Modelling & Software"],["dc.bibliographiccitation.lastpage","303"],["dc.bibliographiccitation.volume","72"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Rötter, Reimund Paul"],["dc.contributor.author","Bindi, Marco"],["dc.contributor.author","Webber, Heidi"],["dc.contributor.author","Trnka, Mirek"],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Olesen, Jørgen E."],["dc.contributor.author","Van Ittersum, Martin K."],["dc.contributor.author","Janssen, S."],["dc.contributor.author","Rivington, M."],["dc.contributor.author","Semenov, Mikhail A."],["dc.contributor.author","Wallach, Daniel"],["dc.contributor.author","Porter, J.R."],["dc.contributor.author","Stewart, D."],["dc.contributor.author","Verhagen, J."],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Roggero, Pier Paolo"],["dc.contributor.author","Bartošová, L."],["dc.contributor.author","Asseng, Senthold"],["dc.date.accessioned","2017-09-07T11:47:54Z"],["dc.date.available","2017-09-07T11:47:54Z"],["dc.date.issued","2015"],["dc.description.abstract","The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches."],["dc.identifier.doi","10.1016/j.envsoft.2014.12.003"],["dc.identifier.gro","3149400"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6072"],["dc.language.iso","en"],["dc.notes.intern","Roetter Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1364-8152"],["dc.title","Crop modelling for integrated assessment of risk to food production from climate change"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","555"],["dc.bibliographiccitation.lastpage","560"],["dc.contributor.author","Rötter, Reimund P."],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Bindi, Marco"],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Olesen, Jørgen E."],["dc.contributor.author","Trnka, Miroslav"],["dc.contributor.author","Van Ittersum, Martin K."],["dc.contributor.author","Rinvinton, M."],["dc.contributor.author","Janssen, S."],["dc.contributor.author","Semenov, Mikhail A."],["dc.contributor.author","Wallach, Daniel"],["dc.contributor.author","Porter, J. R."],["dc.contributor.author","Stewart, D."],["dc.contributor.author","Verhagen, Jan"],["dc.contributor.author","Angulo, Carlos"],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Martre, Pierre"],["dc.contributor.author","de Wit, Allard"],["dc.date.accessioned","2018-06-12T13:17:02Z"],["dc.date.available","2018-06-12T13:17:02Z"],["dc.date.issued","2013"],["dc.description.abstract","Modelling European Agriculture with Climate Change for Food Security (MACSUR) is a knowledge hub exploiting and improving data, methods and modelling tools for a detailed climate change risk assessment. The hub comprises 73 interacting agricultural (crop, livestock, trade) scientific and modelling research groups from 16 European countries and Israel. The crop modelling (CropM) component of MACSUR concentrates on overcoming weaknesses in crop modelling approaches and tools with specific attention to exploiting data on important European field crops, crop rotations and farming systems, and the modelling of diverse (mitigative) adaptation options. CropM outputs are scaled up to farm, regional and (supra-) national level as required for concerted integrated studies on the European agri-food sector and its contribution to global food security under climate change. The specific objectives of CropM are: (i) to conduct crop model intercomparisons to detect deficiencies, (ii) compile data in support of model improvements, (iii) advance scaling methods and model linkages, (iv) improve climate scenario data and impact uncertainty analysis, (v) build research capacity in these areas, and (vi) combine new knowledge and tools with those from livestock and trade modellers to allow interdisciplinary studies and interaction with a diverse range of stakeholders for climate change impact assessments. We identify requirements for improving model simulations, e.g. concerning impacts of heat and drought stress as well as intense rainfall and warm winters on crop yield. We show possibilities for enhancing methods of linking models and data of different resolutions. Finally, we give examples of how to improve quantification and reporting of crop impact uncertainties including the contribution from various sources (i.e. emission scenarios, climate modelling, downscaling of climate model data and crop impact modelling itself)."],["dc.identifier.doi","10.2312/pik.2013.001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15035"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.publisher","Potsdam Institute for Climate Impact Research"],["dc.publisher.place","Potsdam"],["dc.relation.conference","Impacts World 2013, International Conference on Climate Change Effects"],["dc.relation.eventend","2013-05-30"],["dc.relation.eventlocation","Postdam, Germany"],["dc.relation.eventstart","2013-05-27"],["dc.relation.ispartof","Conference Proceedings of Impacts World 2013, International Conference on Climate Change Effects"],["dc.title","Challenges for agro-ecosystem modelling in climate change risk assessment for major European crops and farming systems"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","103"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","European Journal of Agronomy"],["dc.bibliographiccitation.lastpage","114"],["dc.bibliographiccitation.volume","35"],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Angulo, Carlos"],["dc.contributor.author","Hlavinka, Petr"],["dc.contributor.author","Moriondo, Marco"],["dc.contributor.author","Olesen, Jørgen E."],["dc.contributor.author","Patil, Ravi H."],["dc.contributor.author","Ruget, Françoise"],["dc.contributor.author","Rumbaur, Christian"],["dc.contributor.author","Takáč, Jozef"],["dc.contributor.author","Trnka, Miroslav"],["dc.contributor.author","Bindi, Marco"],["dc.contributor.author","Çaldağ, Barış"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Ferrise, Roberto"],["dc.contributor.author","Mirschel, Wilfried"],["dc.contributor.author","Şaylan, Levent"],["dc.contributor.author","Šiška, Bernard"],["dc.contributor.author","Rötter, Reimund"],["dc.date.accessioned","2018-05-20T12:32:55Z"],["dc.date.available","2018-05-20T12:32:55Z"],["dc.date.issued","2011"],["dc.description.abstract","We compared the performance of eight widely used, easily accessible and well-documented crop growth simulation models (APES, CROPSYST, DAISY, DSSAT, FASSET, HERMES, STICS and WOFOST) for winter wheat (Triticum aestivum L.) during 49 growing seasons at eight sites in northwestern, Central and southeastern Europe. The aim was to examine how different process-based crop models perform at the field scale when provided with a limited set of information for model calibration and simulation, reflecting the typical use of models for large-scale applications, and to present the uncertainties related to this type of model application. Data used in the simulations consisted of daily weather statistics, information on soil properties, information on crop phenology for each cultivar, and basic crop and soil management information. Our results showed that none of the models perfectly reproduced recorded observations at all sites and in all years, and none could unequivocally be labelled robust and accurate in terms of yield prediction across different environments and crop cultivars with only minimum calibration. The best performance regarding yield estimation was for DAISY and DSSAT, for which the RMSE values were lowest (1428 and 1603 kg ha−1) and the index of agreement (0.71 and 0.74) highest. CROPSYST systematically underestimated yields (MBE – 1186 kg ha−1), whereas HERMES, STICS and WOFOST clearly overestimated them (MBE 1174, 1272 and 1213 kg ha−1, respectively). APES, DAISY, HERMES, STICS and WOFOST furnished high total above-ground biomass estimates, whereas CROPSYST, DSSAT and FASSET provided low total above-ground estimates. Consequently, DSSAT and FASSET produced very high harvest index values, followed by HERMES and WOFOST. APES and DAISY, on the other hand, returned low harvest index values. In spite of phenological observations being provided, the calibration results for wheat phenology, i.e. estimated dates of anthesis and maturity, were surprisingly variable, with the largest RMSE for anthesis being generated by APES (20.2 days) and for maturity by HERMES (12.6). The wide range of grain yield estimates provided by the models for all sites and years reflects substantial uncertainties in model estimates achieved with only minimum calibration. Mean predictions from the eight models, on the other hand, were in good agreement with measured data. This applies to both results across all sites and seasons as well as to prediction of observed yield variability at single sites – a very important finding that supports the use of multi-model estimates rather than reliance on single models."],["dc.identifier.doi","10.1016/j.eja.2011.05.001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/14688"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.title","Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["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","138"],["dc.bibliographiccitation.lastpage","142"],["dc.contributor.author","Rötter, Reimund P."],["dc.contributor.author","Lehtonen, Heikki Sakari"],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Salo, Tapio"],["dc.contributor.author","Helin, Janne Antero"],["dc.contributor.author","Kahiluoto, Helena"],["dc.contributor.author","Aakkula, Jyrki Juhani"],["dc.contributor.author","Granlund, K."],["dc.contributor.author","Rankinen, Katri"],["dc.contributor.author","Carter, Timothy R."],["dc.contributor.editor","Eitzinger, Josef"],["dc.contributor.editor","Kubu, Gerhard"],["dc.date.accessioned","2018-06-13T15:11:19Z"],["dc.date.available","2018-06-13T15:11:19Z"],["dc.date.issued","2010"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15045"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.publisher","Universität für Bodenkultur"],["dc.publisher.place","Vienna"],["dc.relation.conference","Impact of Climate Change and Adaptation in Agriculture International Symposium,"],["dc.relation.eventend","2009-06-23"],["dc.relation.eventlocation","Vienna"],["dc.relation.eventstart","2009-06-22"],["dc.relation.ispartof","Impact of Climate Change and Adaptation in Agriculture International Symposium, Vienna, 22-23 June 2009"],["dc.relation.issn","1994-4179"],["dc.title","A modelling framework for assessing adaptive management options of Finnish agricultural systems to climate change"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","221"],["dc.bibliographiccitation.journal","Climate Research"],["dc.bibliographiccitation.lastpage","236"],["dc.bibliographiccitation.volume","65"],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Rötter, Reimund P."],["dc.contributor.author","Salo, Tapio"],["dc.contributor.author","Peltonen-Sainio, Pirjo"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Lehtonen, Heikki"],["dc.date.accessioned","2017-09-07T11:47:57Z"],["dc.date.available","2017-09-07T11:47:57Z"],["dc.date.issued","2015"],["dc.description.abstract","In this study, the WOFOST crop simulation model was used together with comprehensive empirical databases on barley Hordeum vulgare L. to study the contributions of different yield-determining and -limiting factors to observed trends of barley yield in Finland from 1988 to 2008. Simulations were performed at 3 study sites representing different agro-ecological zones, and compared with the data from experimental sites and that reported by local farmers. Yield gaps between simulated potential yields and farmers’ yields and their trends were assessed. Positive observed yield trends of Finnish barley mostly resulted from the development and usage of new, high-yielding cultivars. Simulated trends in climatic potential and water-limited potential yields of individual cultivars showed a slight declining trend. Yield gaps showed an increasing trend in 2 out of 3 study areas. Since the mid-1990s, a major reason for this has been the lack of market and policy incentives favouring crop management decisions, i.e. annual fertilisation, soil maintenance, drainage and crop rotation decisions, aiming for higher yields. The study indicates potential options for increasing or maintaining barley yields in the future. The breeding of new climate-resilient cultivars is the primary option. However, this needs to work alongside overall adjustments to farm management and must be supported by financial incentives for farmers to increase yields."],["dc.identifier.doi","10.3354/cr01317"],["dc.identifier.gro","3149415"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6089"],["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","Effects of climate and historical adaptation measures on barley yield trends in Finland"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["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","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","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"]]