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","142"],["dc.bibliographiccitation.journal","Field Crops Research"],["dc.bibliographiccitation.lastpage","156"],["dc.bibliographiccitation.volume","221"],["dc.contributor.author","Rötter, R.P."],["dc.contributor.author","Appiah, M."],["dc.contributor.author","Fichtler, E."],["dc.contributor.author","Kersebaum, K.C."],["dc.contributor.author","Trnka, M."],["dc.contributor.author","Hoffmann, M.P"],["dc.date.accessioned","2020-12-10T14:24:02Z"],["dc.date.available","2020-12-10T14:24:02Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1016/j.fcr.2018.02.023"],["dc.identifier.issn","0378-4290"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72112"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Linking modelling and experimentation to better capture crop impacts of agroclimatic extremes—A review"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["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","87"],["dc.bibliographiccitation.journal","Climate Research"],["dc.bibliographiccitation.lastpage","105"],["dc.bibliographiccitation.volume","65"],["dc.contributor.author","Pirttioja, N."],["dc.contributor.author","Carter, T. R."],["dc.contributor.author","Fronzek, S."],["dc.contributor.author","Bindi, M."],["dc.contributor.author","Hoffmann, H."],["dc.contributor.author","Palosuo, T."],["dc.contributor.author","Ruiz-Ramos, M."],["dc.contributor.author","Tao, F."],["dc.contributor.author","Trnka, M."],["dc.contributor.author","Acutis, M."],["dc.contributor.author","Asseng, S."],["dc.contributor.author","Baranowski, P."],["dc.contributor.author","Basso, B."],["dc.contributor.author","Bodin, P."],["dc.contributor.author","Buis, S."],["dc.contributor.author","Cammarano, D."],["dc.contributor.author","Deligios, P."],["dc.contributor.author","Destain, M. F."],["dc.contributor.author","Dumont, B."],["dc.contributor.author","Ewert, F."],["dc.contributor.author","Ferrise, R."],["dc.contributor.author","François, L."],["dc.contributor.author","Gaiser, T."],["dc.contributor.author","Hlavinka, P."],["dc.contributor.author","Jacquemin, I."],["dc.contributor.author","Kersebaum, K. C."],["dc.contributor.author","Kollas, C."],["dc.contributor.author","Krzyszczak, J."],["dc.contributor.author","Lorite, I. J."],["dc.contributor.author","Minet, J."],["dc.contributor.author","Mínguez, M. I."],["dc.contributor.author","Montesino, M."],["dc.contributor.author","Moriondo, M."],["dc.contributor.author","Müller, C."],["dc.contributor.author","Nendel, C."],["dc.contributor.author","Öztürk, I."],["dc.contributor.author","Perego, A."],["dc.contributor.author","Rodríguez, A."],["dc.contributor.author","Ruane, A. C."],["dc.contributor.author","Ruget, F."],["dc.contributor.author","Sanna, M."],["dc.contributor.author","Semenov, M. A."],["dc.contributor.author","Slawinski, C."],["dc.contributor.author","Stratonovitch, P."],["dc.contributor.author","Supit, I."],["dc.contributor.author","Waha, K."],["dc.contributor.author","Wang, E."],["dc.contributor.author","Wu, L."],["dc.contributor.author","Zhao, Z."],["dc.contributor.author","Rötter, R. P."],["dc.date.accessioned","2017-09-07T11:47:54Z"],["dc.date.available","2017-09-07T11:47:54Z"],["dc.date.issued","2015"],["dc.description.abstract","This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development."],["dc.identifier.doi","10.3354/cr01322"],["dc.identifier.gro","3149390"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6061"],["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","Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["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.journal","European Journal of Agronomy"],["dc.bibliographiccitation.volume","93"],["dc.contributor.author","Durand, Jean-Louis"],["dc.contributor.author","Delusca, Kenel"],["dc.contributor.author","Boote, Ken"],["dc.contributor.author","Lizaso, Jon"],["dc.contributor.author","Manderscheid, Remy"],["dc.contributor.author","Weigel, Hans-Joachim"],["dc.contributor.author","Ruane, Alex C."],["dc.contributor.author","Rosenzweig, Cynthia"],["dc.contributor.author","Jones, Jim"],["dc.contributor.author","Ahuja, Laj"],["dc.contributor.author","Anapalli, Saseendran"],["dc.contributor.author","Basso, Bruno"],["dc.contributor.author","Baron, Christian"],["dc.contributor.author","Bertuzzi, Patrick"],["dc.contributor.author","Biernath, Christian"],["dc.contributor.author","Deryng, Delphine"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Gayler, Sebastian"],["dc.contributor.author","Heinlein, Florian"],["dc.contributor.author","Kersebaum, Kurt Christian"],["dc.contributor.author","Kim, Soo-Hyung"],["dc.contributor.author","Müller, Christoph"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Olioso, Albert"],["dc.contributor.author","Priesack, Eckart"],["dc.contributor.author","Villegas, Julian Ramirez"],["dc.contributor.author","Ripoche, Dominique"],["dc.contributor.author","Rötter, Reimund Paul"],["dc.contributor.author","Seidel, Sabine I."],["dc.contributor.author","Srivastava, Amit"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Timlin, Dennis"],["dc.contributor.author","Twine, Tracy"],["dc.contributor.author","Wang, Enli"],["dc.contributor.author","Webber, Heidi"],["dc.contributor.author","Zhao, Zhigan"],["dc.date.accessioned","2017-09-07T11:47:54Z"],["dc.date.available","2017-09-07T11:47:54Z"],["dc.date.issued","2017"],["dc.description.abstract","This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thünen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 models was less than 1 Mg ha−1. However under water deficit in one of the two years, the models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase."],["dc.identifier.doi","10.1016/j.eja.2017.01.002"],["dc.identifier.gro","3149399"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6071"],["dc.language.iso","en"],["dc.notes.intern","Roetter Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1161-0301"],["dc.title","How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield?"],["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"]]