Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Global wheat production with 1.5 and 2.0°C above pre‐industrial warming
Liu, B. ; Martre, P. ; Ewert, F. ; Porter, J.R. ; Challinor, A.J. ; Muller, G. ; Ruane, A.C. ; Waha, K. ; Thorburn, Peter J. ; Aggarwal, P.K. ; Ahmed, M. ; Balkovic, Juraj ; Basso, B. ; Biernath, C. ; Bindi, M. ; Cammarano, D. ; Sanctis, Giacomo De; Dumont, B. ; Espadafor, M. ; Eyshi Rezaei, Ehsan ; Ferrise, Roberto ; Garcia-Vila, M. ; Gayler, S. ; Gao, Y. ; Horan, H. ; Hoogenboom, G. ; Izaurralde, Roberto C. ; Jones, C.D. ; Kassie, Belay T. ; Kersebaum, K.C. ; Klein, C. ; Koehler, A.K. ; Maiorano, Andrea ; Minoli, Sara ; Montesino San Martin, M. ; Kumar, S.N. ; Nendel, C. ; O'Leary, G.J. ; Palosuo, T. ; Priesack, E. ; Ripoche, D. ; Rötter, R.P. ; Semenov, M.A. ; Stockle, Claudio ; Streck, T. ; Supit, I. ; Tao, F. ; Velde, M. van der; Wallach, D. ; Wang, E. ; Webber, H. ; Wolf, J. ; Xiao, L. ; Zhang, Z. ; Zhao, Z. ; Zhu, Y. ; Asseng, S. - \ 2019
Global Change Biology 25 (2019)4. - ISSN 1354-1013 - p. 1428 - 1444.
Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5°C and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5 °C scenario and -2.4% to 10.5% under the 2.0 °C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer -India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production are therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.
Climate change impact and adaptation for wheat protein
Asseng, Senthold ; Martre, Pierre ; Maiorano, Andrea ; Rötter, Reimund P. ; O’Leary, Garry J. ; Fitzgerald, Glenn J. ; Girousse, Christine ; Motzo, Rosella ; Giunta, Francesco ; Babar, M.A. ; Reynolds, Matthew P. ; Kheir, Ahmed M.S. ; Thorburn, Peter J. ; Waha, Katharina ; Ruane, Alex C. ; Aggarwal, Pramod K. ; Ahmed, Mukhtar ; Balkovič, Juraj ; Basso, Bruno ; Biernath, Christian ; Bindi, Marco ; Cammarano, Davide ; Challinor, Andrew J. ; Sanctis, Giacomo De; Dumont, Benjamin ; Eyshi Rezaei, Ehsan ; Fereres, Elias ; Ferrise, Roberto ; Garcia-Vila, Margarita ; Gayler, Sebastian ; Gao, Yujing ; Horan, Heidi ; Hoogenboom, Gerrit ; Izaurralde, R.C. ; Jabloun, Mohamed ; Jones, Curtis D. ; Kassie, Belay T. ; Kersebaum, Kurt Christian ; Klein, Christian ; Koehler, Ann Kristin ; Liu, Bing ; Minoli, Sara ; Montesino San Martin, Manuel ; Müller, Christoph ; Naresh Kumar, Soora ; Supit, Iwan ; Tao, Fulu ; Wolf, Joost ; Zhang, Zhao ; Ewert, Frank - \ 2019
Global Change Biology 25 (2019)1. - ISSN 1354-1013 - p. 155 - 173.
climate change adaptation - climate change impact - food security - grain protein - wheat

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low-rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations
Rodríguez, A. ; Ruiz-Ramos, M. ; Palosuo, T. ; Carter, T.R. ; Fronzek, S. ; Lorite, I.J. ; Ferrise, R. ; Pirttioja, N. ; Bindi, M. ; Baranowski, P. ; Buis, S. ; Cammarano, D. ; Chen, Y. ; Dumont, B. ; Ewert, F. ; Gaiser, T. ; Hlavinka, P. ; Hoffmann, H. ; Höhn, J.G. ; Jurecka, F. ; Kersebaum, K.C. ; Krzyszczak, J. ; Lana, M. ; Mechiche-Alami, A. ; Minet, J. ; Montesino, M. ; Nendel, C. ; Porter, J.R. ; Ruget, F. ; Semenov, M.A. ; Steinmetz, Z. ; Stratonovitch, P. ; Supit, I. ; Tao, F. ; Trnka, M. ; Wit, A. de; Rötter, R.P. - \ 2019
Agricultural and Forest Meteorology 264 (2019). - ISSN 0168-1923 - p. 351 - 362.
Climate change - Decision support - Outcome confidence - Response surface - Uncertainty - Wheat adaptation

Climate change is expected to severely affect cropping systems and food production in many parts of the world unless local adaptation can ameliorate these impacts. Ensembles of crop simulation models can be useful tools for assessing if proposed adaptation options are capable of achieving target yields, whilst also quantifying the share of uncertainty in the simulated crop impact resulting from the crop models themselves. Although some studies have analysed the influence of ensemble size on model outcomes, the effect of ensemble composition has not yet been properly appraised. Moreover, results and derived recommendations typically rely on averaged ensemble simulation results without accounting sufficiently for the spread of model outcomes. Therefore, we developed an Ensemble Outcome Agreement (EOA) index, which analyses the effect of changes in composition and size of a multi-model ensemble (MME) to evaluate the level of agreement between MME outcomes with respect to a given hypothesis (e.g. that adaptation measures result in positive crop responses). We analysed the recommendations of a previous study performed with an ensemble of 17 crop models and testing 54 adaptation options for rainfed winter wheat (Triticum aestivum L.) at Lleida (NE Spain) under perturbed conditions of temperature, precipitation and atmospheric CO2 concentration. Our results confirmed that most adaptations recommended in the previous study have a positive effect. However, we also showed that some options did not remain recommendable in specific conditions if different ensembles were considered. Using EOA, we were able to identify the adaptation options for which there is high confidence in their effectiveness at enhancing yields, even under severe climate perturbations. These include substituting spring wheat for winter wheat combined with earlier sowing dates and standard or longer duration cultivars, or introducing supplementary irrigation, the latter increasing EOA values in all cases. There is low confidence in recovering yields to baseline levels, although this target could be attained for some adaptation options under moderate climate perturbations. Recommendations derived from such robust results may provide crucial information for stakeholders seeking to implement adaptation measures.

A Summary of Research Activities from the AgMIP Potato Crop Modeling Intercomparison Pilot
Fleisher, D.H. ; Condori, B. ; Quiroz, R. ; Alva, A. ; Asseng, S. ; Barreda, Carolina ; Berghuijs, H.N.C. ; Bindi, M. ; Boote, K.J. ; Craigon, J. ; Fangmeier, A. ; Ferrise, Roberto ; Franke, A.C. ; Gayler, S. ; Govindakrishnan, P.M. ; Harahagazwe, Dieudonne ; Hoogenboom, G. ; Kremer, P. ; Kroes, J. ; Naresh Kumar, S. ; Merante, Paolo ; Nendel, C. ; Olesen, J.E. ; Parker, P.S. ; Pleijel, H. ; Raes, Dirk ; Raymundo, Rubi ; Reidsma, P. ; Ruana, A. ; Silva, J.V. ; Stella, T. ; Stockle, Claudio ; Supit, I. ; Evert, F.K. van; Vandermeiren, K. ; Vanuytrecht, Eline ; Vorne, V. ; Wolf, J. ; Woli, Prem - \ 2018
Activity-1 of the potato crop model intercomparison pilot was recently completed and focused on quantifying multi-model uncertainty to climate responses when using common data sets from low-and high-input management sites. Median model ensemble response outperformed any single model in terms of replicatingobserved yield across all sites. Uncertainty among models averaged 15% higher for low-versus high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by carbon dioxide (C), but increased as much as 41 and 23% for yield and ET respectively as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Activity-2 research is on-going and tests the capability of multiple models to mimic effects of elevated C concentration on potato yields measured at eight different locations in Europe. A subset from observed OTC and FACE data was used to initially calibrate the models. This research will also evaluate the stability of the models’ calibration with respect to changes in geographic location, as the same variety was used in all locations. This presentation will summarize the Activity-1 results and discuss the current status of Activity-2 investigations.
Simulation of soil organic carbon effects on long-term winter wheat (Triticum aestivum) production under varying fertilizer inputs
Ghaley, Bhim B. ; Wösten, Henk ; Olesen, Jørgen E. ; Schelde, Kirsten ; Baby, Sanmohan ; Karki, Yubaraj K. ; Børgesen, Christen D. ; Smith, Pete ; Yeluripati, Jagadeesh ; Ferrise, Roberto ; Bindi, Marco ; Kuikman, Peter ; Lesschen, Jan Peter ; Porter, John R. - \ 2018
Frontiers in Plant Science 9 (2018). - ISSN 1664-462X
Crop productivity - DAISY model - Grain yield - Long-term experiment - Nitrogen - Pedotransfer functions - Plant available water

Soil organic carbon (SOC) has a vital role to enhance agricultural productivity and for mitigation of climate change. To quantify SOC effects on productivity, process models serve as a robust tool to keep track of multiple plant and soil factors and their interactions affecting SOC dynamics. We used soil-plant-atmospheric model viz. DAISY, to assess effects of SOC on nitrogen (N) supply and plant available water (PAW) under varying N fertilizer rates in winter wheat (Triticum aestivum) in Denmark. The study objective was assessment of SOC effects on winter wheat grain and aboveground biomass accumulation at three SOC levels (low: 0.7% SOC; reference: 1.3% SOC; and high: 2% SOC) with five nitrogen rates (0–200 kg N ha−1) and PAW at low, reference, and high SOC levels. The three SOC levels had significant effects on grain yields and aboveground biomass accumulation at only 0–100 kg N ha−1 and the SOC effects decreased with increasing N rates until no effects at 150–200 kg N ha−1. PAW had significant positive correlation with SOC content, with high SOC retaining higher PAW compared to low and reference SOC. The mean PAW and SOC correlation was given by PAW% = 1.0073 × SOC% + 15.641. For the 0.7–2% SOC range, the PAW increase was small with no significant effects on grain yields and aboveground biomass accumulation. The higher winter wheat grain and aboveground biomass was attributed to higher N supply in N deficient wheat production system. Our study suggested that building SOC enhances agronomic productivity at only 0–100 kg N ha−1. Maintenance of SOC stock will require regular replenishment of SOC, to compensate for the mineralization process degrading SOC over time. Hence, management can maximize realization of SOC benefits by building up SOC and maintaining N rates in the range 0–100 kg N ha−1, to reduce the off-farm N losses depending on the environmental zones, land use and the production system.

Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
Fronzek, Stefan ; Pirttioja, Nina ; Carter, Timothy R. ; Bindi, Marco ; Hoffmann, Holger ; Palosuo, Taru ; Ruiz-Ramos, Margarita ; Tao, Fulu ; Trnka, Miroslav ; Acutis, Marco ; Asseng, Senthold ; Baranowski, Piotr ; Basso, Bruno ; Bodin, Per ; Buis, Samuel ; Cammarano, Davide ; Deligios, Paola ; Destain, Marie France ; Dumont, Benjamin ; Ewert, Frank ; Ferrise, Roberto ; François, Louis ; Gaiser, Thomas ; Hlavinka, Petr ; Jacquemin, Ingrid ; Kersebaum, Kurt Christian ; Kollas, Chris ; Krzyszczak, Jaromir ; Lorite, Ignacio J. ; Minet, Julien ; Minguez, M.I. ; Montesino, Manuel ; Moriondo, Marco ; Müller, Christoph ; Nendel, Claas ; Öztürk, Isik ; Perego, Alessia ; Rodríguez, Alfredo ; Ruane, Alex C. ; Ruget, Françoise ; Sanna, Mattia ; Semenov, Mikhail A. ; Slawinski, Cezary ; Stratonovitch, Pierre ; Supit, Iwan ; Waha, Katharina ; Wang, Enli ; Wu, Lianhai ; Zhao, Zhigan ; Rötter, Reimund P. - \ 2018
Agricultural Systems 159 (2018). - ISSN 0308-521X - p. 209 - 224.
Classification - Climate change - Crop model - Ensemble - Sensitivity analysis - Wheat

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.

Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment
Ruiz-Ramos, M. ; Ferrise, R. ; Rodríguez, A. ; Lorite, I.J. ; Bindi, M. ; Carter, T.R. ; Fronzek, S. ; Palosuo, T. ; Pirttioja, N. ; Baranowski, P. ; Buis, S. ; Cammarano, D. ; Chen, Y. ; Dumont, B. ; Ewert, F. ; Gaiser, T. ; Hlavinka, P. ; Hoffmann, H. ; Höhn, J.G. ; Jurecka, F. ; Kersebaum, K.C. ; Krzyszczak, J. ; Lana, M. ; Mechiche-Alami, A. ; Minet, J. ; Montesino, M. ; Nendel, C. ; Porter, J.R. ; Ruget, F. ; Semenov, M.A. ; Steinmetz, Z. ; Stratonovitch, P. ; Supit, I. ; Tao, F. ; Trnka, M. ; Wit, A. De; Rötter, R.P. - \ 2018
Agricultural Systems 159 (2018). - ISSN 0308-521X - p. 260 - 274.
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.
A potato model intercomparison across varying climates and productivity levels
Fleisher, David H. ; Condori, Bruno ; Quiroz, Roberto ; Alva, Ashok ; Asseng, Senthold ; Barreda, Carolina ; Bindi, Marco ; Boote, Kenneth J. ; Ferrise, Roberto ; Franke, Angelinus C. ; Govindakrishnan, Panamanna M. ; Harahagazwe, Dieudonne ; Hoogenboom, Gerrit ; Naresh Kumar, Soora ; Merante, Paolo ; Nendel, Claas ; Olesen, Jorgen E. ; Parker, Phillip S. ; Raes, Dirk ; Raymundo, Rubi ; Ruane, Alex C. ; Stockle, Claudio ; Supit, Iwan ; Vanuytrecht, Eline ; Wolf, Joost ; Woli, Prem - \ 2017
Global Change Biology 23 (2017)3. - ISSN 1354-1013 - p. 1258 - 1281.
A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.
Adopting soil organic carbon management practices in soils of varying quality : Implications and perspectives in Europe
Merante, Paolo ; Dibari, Camilla ; Ferrise, Roberto ; Sánchez, Berta ; Iglesias, Ana ; Lesschen, Jan Peter ; Kuikman, Peter ; Yeluripati, Jagadeesh ; Smith, Pete ; Bindi, Marco - \ 2017
Soil & Tillage Research 165 (2017). - ISSN 0167-1987 - p. 95 - 106.
European soils - SOC management practices - Soil stability

Soil organic carbon (SOC) content can greatly affect soil quality by determining and maintaining important soil physical conditions, properties and soil functions. Management practices that maintain or enhance SOC affect soil quality and may favour the capacity of soils to sequester further organic carbon. Nevertheless, the effectiveness of these measures depends upon both the soil characteristics and the current SOC content. This study defines an indicator of soil potential stability (n-potential) allowing the most effective practices in terms of soil stability and capacity to store organic carbon to be selected. By relating the clay content to SOC content, the n-potential indicates the “potential” presence of non-complexed clay (NCC) in soils, enabling the soil stability and its capacity to store carbon (C) to be inferred. In this work, we classify soils of European regions based on five n-potential categories (i.e. >20; 15–20; 10–15; 5–10;

Assessing uncertainties of water footprints using an ensemble of crop growth models on winter wheat
Kersebaum, Kurt Christian ; Kroes, Joop ; Gobin, Anne ; Takáč, Jozef ; Hlavinka, Petr ; Trnka, Miroslav ; Ventrella, Domenico ; Giglio, Luisa ; Ferrise, Roberto ; Moriondo, Marco ; Marta, Anna Dalla ; Luo, Qunying ; Eitzinger, Josef ; Mirschel, Wilfried ; Weigel, Hans Joachim ; Manderscheid, Remy ; Hoffmann, Munir ; Nejedlik, Pavol ; Iqbal, Muhammad Anjum ; Hösch, Johannes - \ 2016
Water 8 (2016)12. - ISSN 2073-4441
Crop yield - Model ensemble - Uncertainty - Water footprint - Wheat

Crop productivity and water consumption form the basis to calculate the water footprint (WF) of a specific crop. Under current climate conditions, calculated evapotranspiration is related to observed crop yields to calculate WF. The assessment of WF under future climate conditions requires the simulation of crop yields adding further uncertainty. To assess the uncertainty of model based assessments of WF, an ensemble of crop models was applied to data from five field experiments across Europe. Only limited data were provided for a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Up to eight models were applied for wheat. The coefficient of variation for the simulated actual evapotranspiration between models was in the range of 13%-19%, which was higher than the inter-annual variability. Simulated yields showed a higher variability between models in the range of 17%-39%. Models responded differently to elevated CO2 in a FACE (Free-Air Carbon Dioxide Enrichment) experiment, especially regarding the reduction of water consumption. The variability of calculated WF between models was in the range of 15%-49%. Yield predictions contributed more to this variance than the estimation of water consumption. Transpiration accounts on average for 51%-68% of the total actual evapotranspiration.

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