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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    Using farmer’s field data and crop modelling to benchmark resource use efficiencies of arable crops in The Netherlands
    Silva, J.V. ; Tenreiro, T.R. ; Spätjens, L. ; Anten, N.P.R. ; Ittersum, M.K. van; Reidsma, P. - \ 2020
    In: iCROPM 2020 Book of Abstracts: Second international Crop Modelling Symposium. - Montpellier : CIRAD/INRA - p. 203 - 204.
    Winter wheat development and growth in The Netherlands: Using a detailed field trial to parametrize and improve
    Silva, J.V. ; Wit, A.J.W. de; Rijk, H.C.A. ; Supit, I. ; Reidsma, P. ; Ittersum, M.K. van - \ 2020
    In: iCROPM 2020 Book of Abstracts: Second international Crop Modelling Symposium. - CIRAD/INRA - p. 317 - 318.
    The effect of potato cultivar differences on parameters in WOFOST
    Den, T. ten; Wiel, I. van de; Wit, A. De; Evert, F. van; Ittersum, M.K. van; Reidsma, P. - \ 2020
    In: iCROPM 2020 Book of Abstracts: Second international Crop Modelling Symposium. - Montpellier : CIRAD/INRA - p. 319 - 320.
    Modelling food security : Bridging the gap between the micro and the macro scale
    Müller, Birgit ; Hoffmann, Falk ; Heckelei, Thomas ; Müller, Christoph ; Hertel, Thomas W. ; Polhill, J.G. ; Wijk, Mark van; Achterbosch, Thom ; Alexander, Peter ; Brown, Calum ; Kreuer, David ; Ewert, Frank ; Ge, Jiaqi ; Millington, James D.A. ; Seppelt, Ralf ; Verburg, Peter H. ; Webber, Heidi - \ 2020
    Global environmental change : human and policy dimensions 63 (2020). - ISSN 0959-3780
    Agent-based models - Crop models - Economic equilibrium models - Food security - Land use - Model integration - Multi-scale interactions - Social-ecological feedbacks

    Achieving food and nutrition security for all in a changing and globalized world remains a critical challenge of utmost importance. The development of solutions benefits from insights derived from modelling and simulating the complex interactions of the agri-food system, which range from global to household scales and transcend disciplinary boundaries. A wide range of models based on various methodologies (from food trade equilibrium to agent-based) seek to integrate direct and indirect drivers of change in land use, environment and socio-economic conditions at different scales. However, modelling such interaction poses fundamental challenges, especially for representing non-linear dynamics and adaptive behaviours. We identify key pieces of the fragmented landscape of food security modelling, and organize achievements and gaps into different contextual domains of food security (production, trade, and consumption) at different spatial scales. Building on in-depth reflection on three core issues of food security – volatility, technology, and transformation – we identify methodological challenges and promising strategies for advancement. We emphasize particular requirements related to the multifaceted and multiscale nature of food security. They include the explicit representation of transient dynamics to allow for path dependency and irreversible consequences, and of household heterogeneity to incorporate inequality issues. To illustrate ways forward we provide good practice examples using meta-modelling techniques, non-equilibrium approaches and behavioural-based modelling endeavours. We argue that further integration of different model types is required to better account for both multi-level agency and cross-scale feedbacks within the food system.

    Towards a multiscale crop modelling framework for climate change adaptation assessment
    Peng, Bin ; Guan, Kaiyu ; Tang, Jinyun ; Ainsworth, Elizabeth A. ; Asseng, Senthold ; Bernacchi, Carl J. ; Cooper, Mark ; Delucia, Evan H. ; Elliott, Joshua W. ; Ewert, Frank ; Grant, Robert F. ; Gustafson, David I. ; Hammer, Graeme L. ; Jin, Zhenong ; Jones, James W. ; Kimm, Hyungsuk ; Lawrence, David M. ; Li, Yan ; Lombardozzi, Danica L. ; Marshall-Colon, Amy ; Messina, Carlos D. ; Ort, Donald R. ; Schnable, James C. ; Vallejos, C.E. ; Wu, Alex ; Yin, Xinyou ; Zhou, Wang - \ 2020
    Nature Plants 6 (2020)4. - ISSN 2055-026X - p. 338 - 348.

    Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

    Data from: Implications of shared predation for space use in two sympatric leporids
    Weterings, M.J.A. ; Ewert, Sophie P. ; Peereboom, Jeffrey N. ; Kuipers, Henry J. ; Kuijper, Dries P.J. ; Prins, H.H.T. ; Jansen, P.A. ; Langevelde, F. van; Wieren, S.E. van - \ 2019
    Wageningen University & Research
    alternative prey - habitat characteristics - habitat riskiness - residence time - space race - vegetation structure - Lepus europaeus - Oryctolagus cuniculus - Vulpes vulpes
    Spatial variation in habitat riskiness has a major influence on the predator–prey space race. However, the outcome of this race can be modulated if prey shares enemies with fellow prey (i.e., another prey species). Sharing of natural enemies may result in apparent competition, and its implications for prey space use remain poorly studied. Our objective was to test how prey species spend time among habitats that differ in riskiness, and how shared predation modulates the space use by prey species. We studied a one‐predator, two‐prey system in a coastal dune landscape in the Netherlands with the European hare (Lepus europaeus) and European rabbit (Oryctolagus cuniculus) as sympatric prey species and red fox (Vulpes vulpes) as their main predator. The fine‐scale space use by each species was quantified using camera traps. We quantified residence time as an index of space use. Hares and rabbits spent time differently among habitats that differ in riskiness. Space use by predators and habitat riskiness affected space use by hares more strongly than space use by rabbits. Residence time of hare was shorter in habitats in which the predator was efficient in searching or capturing prey species. However, hares spent more time in edge habitat when foxes were present, even though foxes are considered ambush predators. Shared predation affected the predator–prey space race for hares positively, and more strongly than the predator–prey space race for rabbits, which were not affected. Shared predation reversed the predator–prey space race between foxes and hares, whereas shared predation possibly also released a negative association and promoted a positive association between our two sympatric prey species. Habitat riskiness, species presence, and prey species’ escape mode and foraging mode (i.e., central‐place vs. noncentral‐place forager) affected the prey space race under shared predation.
    Implications of shared predation for space use in two sympatric leporids
    Weterings, Martijn J.A. ; Ewert, Sophie P. ; Peereboom, Jeffrey N. ; Kuipers, Henry J. ; Kuijper, Dries P.J. ; Prins, Herbert H.T. ; Jansen, Patrick A. ; Langevelde, Frank van; Wieren, Sipke E. van - \ 2019
    Ecology and Evolution 9 (2019)6. - ISSN 2045-7758 - p. 3457 - 3469.
    Spatial variation in habitat riskiness has a major influence on the predator–prey space race. However, the outcome of this race can be modulated if prey shares enemies with fellow prey (i.e., another prey species). Sharing of natural enemies may result in apparent competition, and its implications for prey space use remain poorly studied. Our objective was to test how prey species spend time among habitats that differ in riskiness, and how shared predation modulates the space use by prey species. We studied a one‐predator, two‐prey system in a coastal dune landscape in the Netherlands with the European hare (Lepus europaeus) and European rabbit (Oryctolagus cuniculus) as sympatric prey species and red fox (Vulpes vulpes) as their main predator. The fine‐scale space use by each species was quantified using camera traps. We quantified residence time as an index of space use. Hares and rabbits spent time differently among habitats that differ in riskiness. Space use by predators and habitat riskiness affected space use by hares more strongly than space use by rabbits. Residence time of hare was shorter in habitats in which the predator was efficient in searching or capturing prey species. However, hares spent more time in edge habitat when foxes were present, even though foxes are considered ambush predators. Shared predation affected the predator–prey space race for hares positively, and more strongly than the predator–prey space race for rabbits, which were not affected. Shared predation reversed the predator–prey space race between foxes and hares, whereas shared predation possibly also released a negative association and promoted a positive association between our two sympatric prey species. Habitat riskiness, species presence, and prey species’ escape mode and foraging mode (i.e., central‐place vs. noncentral‐place forager) affected the prey space race under shared predation.
    Australian wheat beats the heat
    Giller, Ken E. ; Ewert, Frank - \ 2019
    Nature Climate Change 9 (2019)3. - ISSN 1758-678X - p. 189 - 190.

    Collaborative research utilizing field trials and whole farm crop simulation enables adaptation of Australian wheat crop practices. Novel varieties sown earlier enable a longer growing season, which facilitates wheat yield increases despite an increasingly challenging climate.

    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.

    Multimodel ensembles improve predictions of crop–environment–management interactions
    Wallach, Daniel ; Martre, Pierre ; Liu, Bing ; Asseng, Senthold ; Ewert, Frank ; Thorburn, Peter J. ; Ittersum, Martin van; Aggarwal, Pramod K. ; Ahmed, Mukhtar ; Basso, Bruno ; Biernath, Christian ; Cammarano, Davide ; Challinor, Andrew J. ; Sanctis, Giacomo De; Dumont, Benjamin ; Eyshi Rezaei, Ehsan ; Fereres, Elias ; Fitzgerald, Glenn J. ; Gao, Y. ; Garcia-Vila, Margarita ; Gayler, Sebastian ; Girousse, Christine ; Hoogenboom, Gerrit ; Horan, Heidi ; Izaurralde, Roberto C. ; Jones, Curtis D. ; Kassie, Belay T. ; Kersebaum, Christian C. ; Klein, Christian ; Koehler, Ann Kristin ; Maiorano, Andrea ; Minoli, Sara ; Müller, Christoph ; Naresh Kumar, Soora ; Nendel, Claas ; O'Leary, Garry J. ; Palosuo, Taru ; Priesack, Eckart ; Ripoche, Dominique ; Rötter, Reimund P. ; Semenov, Mikhail A. ; Stöckle, Claudio ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Wolf, Joost ; Zhang, Zhao - \ 2018
    Global Change Biology 24 (2018)11. - ISSN 1354-1013 - p. 5072 - 5083.
    climate change impact - crop models - ensemble mean - ensemble median - multimodel ensemble - prediction

    A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2–6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

    The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations
    Martre, Pierre ; Kimball, Bruce A. ; Ottman, Michael J. ; Wall, Gerard W. ; White, Jeffrey W. ; Asseng, Senthold ; Ewert, Frank ; Cammarano, Davide ; Maiorano, Andrea ; Aggarwal, Pramod K. ; Anothai, Jakarat ; Basso, Bruno ; Biernath, Christian ; Challinor, Andrew J. ; Sanctis, Giacomo De; Doltra, Jordi ; Dumont, Benjamin ; Fereres, Elias ; Garcia-Vila, Margarita ; Gayler, Sebastian ; Hoogenboom, Gerrit ; Hunt, Leslie A. ; Izaurralde, Roberto C. ; Jabloun, Mohamed ; Jones, Curtis D. ; Kassie, Belay T. ; Kersebaum, Kurt C. ; Koehler, Ann-Kristin ; Müller, Christoph ; Kumar, Soora Naresh ; Liu, Bing ; Lobell, David B. ; Nendel, Claas ; O'Leary, Garry ; Olesen, Jørgen E. ; Palosuo, Taru ; Priesack, Eckart ; Rezaei, Ehsan Eyshi ; Ripoche, Dominique ; Rötter, Reimund P. ; Semenov, Mikhail A. ; Stöckle, Claudio ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Thorburn, Peter ; Waha, Katharina ; Wang, Enli ; Wolf, Joost ; Zhao, Zhigan ; Zhu, Yan - \ 2018
    ODjAR : open data journal for agricultural research 4 (2018). - ISSN 2352-6378 - p. 28 - 34.
    The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.
    Data from the Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations
    Martre, Pierre ; Kimball, Bruce A. ; Ottman, Michael J. ; Wall, Gerard W. ; White, Jeffrey W. ; Asseng, Senthold ; Ewert, Frank ; Cammarano, Davide ; Maiorano, Andrea ; Aggarwal, Pramod K. ; Supit, I. ; Wolf, J. - \ 2018
    Wageningen University & Research
    wheat - heat stress - field experimental data - simulations
    The dataset reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.
    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.
    The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations
    Martre, P. ; Kimball, B.A. ; Ottman, M.J. ; Wall, G.W. ; White, J. ; Asseng, S. ; Ewert, F. ; Cammarano, D. ; Maiorano, Andrea ; Supit, I. - \ 2017
    Harvard Dataverse
    wheat - field experimental data - heat stress - crop model simulations - AgMIP - Hot Serial Cereal
    The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.
    The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations
    Martre, P. ; Reynolds, M.P. ; Asseng, S. ; Ewert, F. ; Alderman, P.D. ; Cammarano, D. ; Maiorano, Andrea ; Ruane, A.C. ; Aggarwal, P.K. ; Anothai, J. ; Supit, I. ; Wolf, J. - \ 2017
    ODjAR : open data journal for agricultural research 3 (2017). - ISSN 2352-6378 - 6 p.
    The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. All data are available via DOI 10.7910/DVN/ECSFZG.
    Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions
    Wang, Enli ; Martre, Pierre ; Zhao, Zhigan ; Ewert, Frank ; Maiorano, Andrea ; Rötter, Reimund P. ; Kimball, Bruce A. ; Ottman, Michael J. ; Wall, Gerard W. ; White, Jeffrey W. ; Reynolds, Matthew P. ; Alderman, Phillip D. ; Aggarwal, Pramod K. ; Anothai, Jakarat ; Basso, Bruno ; Biernath, Christian ; Cammarano, Davide ; Challinor, Andrew J. ; Sanctis, Giacomo De; Doltra, Jordi ; Dumont, Benjamin ; Fereres, Elias ; Garcia-Vila, Margarita ; Gayler, Sebastian ; Hoogenboom, Gerrit ; Hunt, Leslie A. ; Izaurralde, Roberto C. ; Jabloun, Mohamed ; Jones, Curtis D. ; Kersebaum, Kurt C. ; Koehler, Ann-Kristin ; Liu, Leilei ; Müller, Christoph ; Kumar, Soora Naresh ; Nendel, Claas ; O’Leary, Garry ; Olesen, Jørgen E. ; Palosuo, Taru ; Priesack, Eckart ; Rezaei, Ehsan Eyshi ; Ripoche, Dominique ; Ruane, Alex C. ; Semenov, Mikhail A. ; Shcherbak, Iurii ; Stöckle, Claudio ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Thorburn, Peter ; Waha, Katharina ; Wallach, Daniel ; Wang, Zhimin ; Wolf, Joost ; Zhu, Yan ; Asseng, Senthold - \ 2017
    Nature Plants 3 (2017)10. - ISSN 2055-026X - p. 833 - 833.
    The uncertainty of crop yield projections is reduced by improved temperature response functions
    Wang, Enli ; Martre, Pierre ; Zhao, Zhigan ; Ewert, Frank ; Maiorano, Andrea ; Rötter, Reimund P. ; Kimball, Bruce A. ; Ottman, Michael J. ; Wall, Gerard W. ; White, Jeffrey W. ; Reynolds, Matthew P. ; Alderman, Phillip D. ; Aggarwal, Pramod K. ; Anothai, Jakarat ; Basso, Bruno ; Biernath, Christian ; Cammarano, Davide ; Challinor, Andrew J. ; Sanctis, Giacomo De; Doltra, Jordi ; Fereres, Elias ; Garcia-Vila, Margarita ; Gayler, Sebastian ; Hoogenboom, Gerrit ; Hunt, Leslie A. ; Izaurralde, Roberto C. ; Jabloun, Mohamed ; Jones, Curtis D. ; Kersebaum, Kurt Christian ; Koehler, Ann Kristin ; Liu, Leilei ; Müller, Christoph ; Naresh Kumar, Soora ; Nendel, Claas ; O'Leary, Garry ; Olesen, Jørgen E. ; Palosuo, Taru ; Priesack, Eckart ; Eyshi Rezaei, Ehsan ; Ripoche, Dominique ; Ruane, Alex C. ; Semenov, Mikhail A. ; Shcherbak, Iurii ; Stöckle, Claudio O. ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Thorburn, Peter J. ; Waha, Katharina ; Wallach, Daniel ; Wang, Zhimin ; Wolf, Joost ; Zhu, Yan ; Asseng, Senthold ; Dumont, Benjamin - \ 2017
    Nature Plants 3 (2017). - ISSN 2055-026X
    Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.
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