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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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
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
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.
Climate change impacts on crop yields, land use and environment in response to crop sowing dates and thermal time requirements
Zimmermann, Andrea ; Webber, Heidi ; Zhao, Gang ; Ewert, Frank ; Kros, Hans ; Wolf, Joost ; Britz, Wolfgang ; Vries, Wim de - \ 2017
Agricultural Systems 157 (2017). - ISSN 0308-521X - p. 81 - 92.
Climate change - Crop management - Europe - Integrated assessment
Impacts of climate change on European agricultural production, land use and the environment depend on its impact on crop yields. However, many impact studies assume that crop management remains unchanged in future scenarios, while farmers may adapt their sowing dates and cultivar thermal time requirements to minimize yield losses or realize yield gains. The main objective of this study was to investigate the sensitivity of climate change impacts on European crop yields, land use, production and environmental variables to adaptations in crops sowing dates and varieties' thermal time requirements. A crop, economic and environmental model were coupled in an integrated assessment modelling approach for six important crops, for 27 countries of the European Union (EU27) to assess results of three SRES climate change scenarios to 2050. Crop yields under climate change were simulated considering three different management cases; (i) no change in crop management from baseline conditions (NoAd), (ii) adaptation of sowing date and thermal time requirements to give highest yields to 2050 (Opt) and (iii) a more conservative adaptation of sowing date and thermal time requirements (Act). Averaged across EU27, relative changes in water-limited crop yields due to climate change and increased CO2 varied between − 6 and + 21% considering NoAd management, whereas impacts with Opt management varied between + 12 and + 53%, and those under Act management between − 2 and + 27%. However, relative yield increases under climate change increased to + 17 and + 51% when technology progress was also considered. Importantly, the sensitivity to crop management assumptions of land use, production and environmental impacts were less pronounced than for crop yields due to the influence of corresponding market, farm resource and land allocation adjustments along the model chain acting via economic optimization of yields. We conclude that assumptions about crop sowing dates and thermal time requirements affect impact variables but to a different extent and generally decreasing for variables affected by economic drivers.
Recent advances in integrated assessments of climate change impacts on European agriculture
Webber, Heidi ; Reidsma, P. ; Ewert, Frank - \ 2017
In: Book of abstracts. - - p. 38 - 38.
The broad EU public expects agriculture to improve global food security, protect the environment and sustain rural communities and landscapes. Agricultural policy makers must additionally consider resource scarcity and degradation, loss of biodiversity, climate change adaptation and, increasingly, mitigation. Integrated assessment modelling (IAM) can simultaneously consider key
agricultural drivers and the main economic and environmental outcomes in identifying opportunities and balancing trade-offs for EU agriculture in the future. In this review of recent and on-going European scale IAM studies, results are synthesized to quantify the range of uncertainty for key impact variables. Explicit attention is given to the drivers (climate change, socio-economic scenarios, technological) and adaptations considered, their relative importance
across impact variables, feedbacks and cross-scale linkages. Crop management adaptations, widely demonstrated in regional studies, were found to have a large effect on crop yields as simulated with crop models, with relatively less influence on simulated economic variables. The few studies to simultaneously consider climate change and technological development, found yield trends offset yield losses due to climate change and be more important than adaptation.
The MACSUR Coordinate Global and Regional Assessment (CGRA) seeks to explicitly model yields trends with crop models, partnering with the Global Yield Gap Atlas (GYGA) to understand the relative contribution of management and breeding to past trends. Examples of heat and drought risk analysis with crop models are presented, though their consideration in economic studies
remains limited. Finally, opportunities are identified for cross-scale analysis and assessment within MACSUR.
Modeling crops from genotype to phenotype in a changing climate
Martre, Pierre ; Yin, Xinyou ; Ewert, Frank - \ 2017
Field Crops Research 202 (2017). - ISSN 0378-4290 - p. 1 - 4.
Climate change is exerting daunting challenges to world agriculture. Several studies have shown that modern crop cultivars are not well adapted to the recent climate changes (Brisson et al., 2010; Oury et al., 2012). Crop models are potentially able to capture crop genotype-to-phenotype relationships. They are hence a helpful tool to identify and assess the effectiveness of improved crop traits and to support the efficiency of plant breeding programs (Messina et al., 2009; Martre et al., 2014; Hammer et al., 2016). A recent compilation of studies (Yin and Struik, 2016) described some of the progress in combining crop modelling and genetics but it also recognized that current crop models need upgrading. This special issue aims to contribute to the further development of this research area with a particular focus on crops grown under a changing climate. It presents results of a workshop of the Wheat team (https://www. agmip.org/s/wheat/) of The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) and of the Expert Working Group on Wheat Plant and Crop Modeling of the Wheat Initiative (http://www.wheatinitiative.org/activities/ expert-working-groups/wheat-plant-and-crop-modelling) in 2014 at INRA Clermont-Ferrand, France, and includes also invited papers. The contributions cover three main areas. 1. Improving crop growth models to account for climate change impacts Several recent papers (e.g. Rötter et al., 2011; Boote et al., 2013; Ewert et al., 2015), have discussed the need for an upgrading of current crop models to efficiently project the growth of crops under conditions that will more often be encountered in future climates, with higher temperature, more frequent heat spells, more severe and longer periods of drought, or flooding. To improve crop models , an important step are model inter-comparison studies, which requires a level of international coordination that has only recently been made possible by AgMIP, and in Europe by the project on Modelling European Agriculture with Climate Change for Food Security of the Joint Research Programming Initiative on Agriculture, Food Security and Climate Change (http://macsur.eu/). Two papers in this special issue report results from such concerted research actions. Maiorano et al. (2017) present the first concerted crop model improvement study following up on recent wheat model intercomparison exercises (Asseng et al., 2013; Asseng et al., 2015; Martre et al., 2015; Cammarano et al., 2016), where 15 models were improved for predicting the impact of high temperature on wheat crop growth and yield. The authors show that the improvement of individual model skills translates into a reduced uncertainty of the multi-model ensemble prediction and in a reduction by half of the number of models which are needed in a multi-model ensemble to stay within acceptable uncertainty range. Heat stress signals are in the form of absolute temperature thresholds above which the formation of reproductive sink (Alghabari et al., 2014; Prasad and Djanaguiraman, 2014) or leaf senescence (Zhao et al., 2007) could be adversely affected. Under warm conditions the air and surface temperatures can differ by more than 10 • C (Siebert et al., 2014) and heat stress effect on sink formation or leaf senescence are likely determined by tissue or canopy temperature (Eyshi Rezaei et al., 2015). It may thus be more important to consider the organ or canopy temperature as the driving variable of crops to heat stress rather than air temperature as typically used by most crop models so far. But how good are current crop models to predict canopy temperature? What is the importance of considering canopy temperature to simulate the impact of heat stress on grain yield formation? These questions were addressed by Webber et al. (2017), who evaluated nine wheat crop models that implement different mechanistic or empirical algorithms to calculate canopy temperature. The models varied widely in their ability to simulate measured canopy temperature and in general had better predictions of yield when calculated canopy temperature was used to model the effect of heat stress on processes determining grain yield but the improvement was relatively small. Several studies have previously suggested that the level of sink limitation of yield formation may significantly increase under future climate. Shi et al. (2017) studied the effect of high night temperature on rice yield formation under different levels of nitrogen supply. They used a novel modeling approach to quantify source–sink relationships during the grain filling period and showed that increased nitrogen application does not alleviate the impact of high night temperature on source-sink interactions across cultivars and seasons (water supply). However, genetic differences in the tolerance to high night temperature appeared to be related to genetic differences in sink size. Transparent evaluation of crop models with detailed field studies is an important aspect of model improvement that helps to set the limits of the conditions under which a given model can be used with enough confidence and to identify aspects where improvements are needed. Asseng et al. (2017) used field experiments with modified sink–source relationships to explore how the Nwheat crop model respond to such modifications. The authors show that their model is able to reproduce several experimental treatments with reduced source (e.g. crop shading) or sink (e.g. ear halving) but they also identify deficiencies in simulating grain set and final grain size, point to areas http://dx. where Nwheat, and most likely other wheat and cereal models, need improvement. 2. Modeling the response of genotypes to the environment More than two decades ago researchers have started to emphasize the potential role that crop models could play for crop improvement (Shorter et al., 1991). Since then important milestones have been reached with the use of crop models to characterize breeding and production environments (e.g. Chenu et al., 2013; Harrison et al., 2014), the development of QTL-based models (e.g. Quilot et al., 2005; Yin et al., 2005; Zheng et al., 2013), and more recently the link of crop models with genomic prediction models (e.g. Heslot et al., 2014; Technow et al., 2015). Several articles in this special issue contribute to the advancement of genotype to phenotype modelling. Most underrepresented so far are studies that aim to link crop system models with understandings of gene action at the molecular level. In this special issue Chew et al. (2017) present such an effort in the model species Arabidopsis thaliana and discuss avenues to further extend on cross-disciplinary work for crop species. The type of models presented by Chew and coworkers represent a new step in integrating knowledge across biological scales with significant potential to contribute to crop improvement. Uptmoor et al. (2017) combined a genome-wide prediction model with a process-based flowering time model to predict the heading time of the progeny of a barley population in independent environments. Rice, like several other cereal species, shows large adaptive phenotypic plasticity enabling yield stability across environments. Such plasticity is often observed between tiller production and pan-icle size. In their paper Kumar et al. (2017) report on the plasticity of organ size and number of 12 high-yielding rice genotypes of high and low tillering plant types. They show that most of the observed genetic variations in morphological and yield component traits can be predicted by the rice model SAMARA and discuss the possibility offered by such a type of process-based models to predict the response of new ideotypes to changing environments and crop management practices. Gouache et al. (2017) used a modified version of the phenology module of the wheat model ARCWHEAT parametrized for hundreds of genotypes to analyze the possibilities to adapt wheat to future growing conditions in France. Their results suggest that the beginning of stem extension can be advanced by several weeks without significant risk of frost damage and that photoperiod insensitive PpdD1 and spring type Vrn3 allele combinations are undesirable. The authors discuss the need, in addition to crop modeling, to use available knowledge in crop physiology and of the allelic variability at the loci underpinning important traits in gene pools to implement breeding ideotypes in commercial improvement programs. Global simulation studies usually do not consider the adaptation of cultivars to regional or sub-regional climate, soil properties, and crop management practices, which limits our capacity to analyze the impacts of growing conditions on food security. Gbegbelegbe et al. (2017) calibrated the wheat crop model CROPSIM-CERES for modern high-yielding cultivars adapted to the 17 CIMMYT wheat mega environments. Their results show that the use of ex-ante calibrated region-specific cultivars improves significantly the model skills for predicting grain yield at country level. This study is an important step to reduce the uncertainty of the projections of regional and global wheat production to enable advanced studies on food security to address questions related to the impact of genetic improvement and agricultural technology with climate change. Comprehensive model testing is often a neglected aspect to identify model strengths and deficiencies. Raymundo et al. (2017) present a comprehensive field testing of the SUBSTOR-potato model with experiments carried out in 19 countries. They show that while tuber yield is in general well simulated, for different potato species and cultivars, the response of the model to elevated atmospheric CO 2 concentration and high temperature needs to be improved before it can be used to project the impact of climate change and discussed how its skills can be enhanced. 3. Modelling the impact of climate change on grains and seeds quality Studies of the impact of climate change on food security issues have essentially focused on crop production, while nutritional and functional quality aspects of grains have received limited attention (Müller et al., 2014). This is at least partly due to the limited capability of most crop models which are typically restricted to predicting average grain size and protein or oil concentration (Martre et al., 2011). In this special issue Nuttall et al. (2017) review the current knowledge of the response on wheat grain functional properties to high temperature and elevated atmospheric CO 2 concentration. The authors also discuss how the capability of wheat crop models to consider quality aspects can be advanced and provide a conceptual framework to model the size distribution of gluten proteins and grain distribution using a single spike approach. The rise in global temperature is also a concern for sunflower quality. High oleic sunflower hybrids are increasingly grown worldwide as their higher content in unsaturated fatty acids compared with traditional hybrids increases the storage life of oil and because of the potent hypocholesterolemic effect of unsaturated fatty acids. However, the percentage of oleic acids in the oil decreases under warmer night temperatures (Aguirrezábal et al., 2014). Here, Angeloni et al. (2017) explored the response of sunflower phenol-ogy and seed oleic acid percentage to temperature for high oleic hybrids of sunflower grown in a network of field experiments in the argentine sunflower growing regions. The authors used these information to calibrate a sunflower model and to project the impact of a future global warming scenario on oil oleic percentage at different sites with different sowing dates. Their results provide important information to optimize the conditions to phenotype for high oleic percentage in both controlled and conditions field, and identifies traits on which breeders should focus to improve oil quality in the future. In summary, this special issue presents promising advances in modeling the improvement of crop species under climate change and future growing conditions. They represent significant efforts to reduce model uncertainty to improve confidence in using crop models in climate change impact studies to support the breeding of genotypes better adapted to future growing conditions. Much more research will be required and we hope that the publication of this special issue will catalyze more activities in this emerging research area.
Disentangle mechanisms of nitrogen and water availability on soybean yields
Kroes, J.G. ; Groenendijk, P. ; Supit, I. ; Wit, A.J.W. de; Abelleyra, D. d'; Vero, S.R. - \ 2016
In: Crop modelling for agriculture and food security under global change: abstracts. - Berlin : - p. 294 - 295.
Similar estimates of temperature impacts on global wheat yield by three independent methods
Liu, Bing ; Asseng, Senthold ; Müller, Christoph ; Ewert, Frank ; Elliott, Joshua ; Lobell, David B. ; Martre, Pierre ; Ruane, Alex C. ; Wallach, Daniel ; Jones, James W. ; Supit, Iwan ; Wolf, Joost - \ 2016
Nature Climate Change 6 (2016)12. - ISSN 1758-678X - p. 1130 - 1136.

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO 2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify 'method uncertainty' in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

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